Journal of AIDS & Clinical Research

ISSN: 2155-6113

Open Access

Results from a Method for Estimating HIV Incidence Based on the First Cd4 Count among Treatment-Na???¯ve Cases: Brazil, 2004-2013


Célia Landmann Szwarcwald, Paulo Roberto Borges de Souza Júnior, Ana Roberta Pati Pascom and Orlando da Costa Ferreira Júnior

Background: This paper introduces a method to estimate HIV incidence in Brazil using surveillance data. The interest is to estimate the annual lag (time from infection to reporting) distribution among incident cases in a given year with observations arising from a right truncated version of the distribution. Methods: For each treatment-naïve HIV case aged 15 years and over reported from 2004 to 2015 we estimated the time since infection based on a statistical model that relates the first CD4 count to time since infection. Under the assumption the lag distribution is expressed by a logistic probability distribution, we estimated HIV incidence as the upper limiting value of the logistic function. Since this approach requires at least eight observations per year, to estimate HIV incidence in recent years (2009-2013), we used linear regression models to estimate the missing observations for these years due to truncation in 2015. Using this approach, HIV incidence was estimated from 2004 to 2013, separately for males and females. Results: In 2013, HIV incidence among people aged 15 years and over was estimated to be 44827 (95% CI 41143-47987), 32459 men (95% CI: 29775-34642) and 12368 women (95% CI: 11368-13345). Results from 2004- 2013 have shown an increase among men and a slow decrease among women. The estimated proportion of cases reported less than one year after infection increased from 24.6% to 35.2%, among men, and from 35.1% to 45.8%, among women. For men who became infected from 2004 to 2013, 35.6% of the cases were not reported by year 2015 and among women, 23.2%. Conclusion: The delay between HIV infection and diagnosis is of concern. Designing interventions to motivate testing is essential, especially among most-at-risk groups, as the faster HIV infected cases are linked to care, the faster the HIV incidence curve will turn downward.


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