Abstract
The Joint United Nations Programme on HIV/AIDS-supported Spectrum software package is used by most countries worldwide to monitor the HIV epidemic. In Spectrum, HIV incidence trends among adults (aged 15-49 years) are derived by either fitting to seroprevalence surveillance and survey data or generating curves consistent with case surveillance and vital registration data, such as historical trends in the number of newly diagnosed infections or AIDS-related deaths. This article describes development and application of the case surveillance and vital registration (CSAVR) tool for the 2019 estimate round. Incidence in CSAVR is either estimated directly using single logistic, double logistic, or spline functions, or indirectly via the 'r-logistic' model, which represents the (log-transformed) per-capita transmission rate using a logistic function. The propensity to get diagnosed is assumed to be monotonic, following a Gamma cumulative distribution function and proportional to mortality as a function of time since infection. Model parameters are estimated from a combination of historical surveillance data on newly reported HIV cases, mean CD4 at HIV diagnosis and estimates of AIDS-related deaths from vital registration systems. Bayesian calibration is used to identify the best fitting incidence trend and uncertainty bounds. We used CSAVR to estimate HIV incidence, number of new diagnoses, mean CD4 at diagnosis and the proportion undiagnosed in 31 European, Latin American, Middle Eastern, and Asian-Pacific countries. The spline model appeared to provide the best fit in most countries (45%), followed by the r-logistic (25%), double logistic (25%), and single logistic models. The proportion of HIV-positive people who knew their status increased from about 0.31 [interquartile range (IQR): 0.10-0.45] in 1990 to about 0.77 (IQR: 0.50-0.89) in 2017. The mean CD4 at diagnosis appeared to be stable, at around 410 cells/μl (IQR: 224-567) in 1990 and 373 cells/μl (IQR: 174-475) by 2017. Robust case surveillance and vital registration data are routinely available in many middle-income and high-income countries while HIV seroprevalence surveillance and survey data may be scarce. In these countries, CSAVR offers a simpler, improved approach to estimating and projecting trends in both HIV incidence and knowledge of HIV status.
Highlights
− (mdt(t, v, w) + μ(t)) Idt(t, s, v, w) where λ is the incidence function; μ is the background mortality; δ is the diagnosis rate; mun is the mortality among HIV infected and undiagnosed; mdu is the mortality among infected, diagnosed and untreated; η is the treatment initiation among diagnosed individuals; and mdt is the mortality rate among infected, diagnosed and treated individuals
This shows that, in order to tract infection, diagnosis and treatment initiation, a minimum of 10 compartment should be considered by age or risk group
− (λ(t) + μ(t)) S(t) λ(t)S(t) − mun(t) + δ(t) + μ(t) Iun(t) δ(t)Iun(t) − (mun(t) + η(t) + μ(t)) Idu(t) η(t)Idu(t) − (mdt(t) + μ(t)) Idt(t) where the symbolput on top of population type, indicates that a sum was taken over all the possible infection time, diagnosis time and/or treatment initiation; and the the overall rates in the populations of interest
Summary
We consider a birth cohort in a population in which HIV infection is spreading over time. We obtain the CD4 trajectory as a function of age at infection using Spectrum progression rates as follows. In order to obtain the trajectory as a function of time only, we integrate that function over the CD4 distribution at infection, using Spectrum assumption regarding that distribution; i.e. f·(t, a, w, c0)πa−t+w,c0 , c0 =1 where πa−t+w,c0 is the probability that an individual who became infected at age a − t + w had CD4 count in the category c0 at infection. In order to obtain mortality rates as a function of time and age at infection for undiagnosed individuals, one can follow the the CD4 trajectory and assign the mortality rate that corresponds to the CD4 category. Diagnosed individuals who do not meet treatment eligibility intiate treatment at a rate proportional to HIV related mortality; i.e., if μ0 is HIV related mortality at treatment initiation and μc is mortality rate of noneligible individuals in the CD4 category c, treatment itiation rate for these individuals is μc μ0 eǫ 1+eǫ
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