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Estimating HIV incidence from surveillance data indicates a second wave of infections in Brazil.

ABSTRACT: Emerging evidence suggests that HIV incidence rates in Brazil, particularly among men, may be rising. Here we use Brazil's integrated health systems data to develop a mathematical model, reproducing the complex surveillance systems and providing estimates of HIV incidence, number of people living with HIV (PLHIV), reporting rates and ART initiation rates. An age-structured deterministic model with a flexible spline was used to describe the natural history of HIV along with reporting and treatment rates. Individual-level surveillance data for 1,077,295 cases (HIV/AIDS diagnoses, ART dispensations, CD4 counts and HIV/AIDS-related deaths) were used to calibrate the model using Bayesian inference. The results showed a second wave of infections occurring after 2001 and 56,000 (95% Credible Interval 43,000-71,000) new infections in 2015, 37,000 (95% CrI 28,000-54,000) infections in men and 16,000 (95% CrI 10,000-23,000) in women. The estimated number of PLHIV by end-2015 was 838,000 (95% CrI 675,000-1,083,000), with 80% (95% CrI 62-98%) of those individuals reported to the Ministry of Health. Women were more likely to be diagnosed and reported than men; 86.8% of infected women had been reported compared with 75.7% of men. Likewise, ART initiation rates for women were higher than those for men. The second wave contradicts previous estimates of HIV incidence trends in Brazil and there were persistent differences in the rates of accessing care between men and women. Nevertheless, the Brazilian HIV program has achieved high rates of detection and treatment, making considerable progress over the past ten years.

SUBMITTER: Mangal TD

PROVIDER: S-EPMC6543066 | biostudies-other | 2019 Jun

REPOSITORIES: biostudies-other

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Estimating HIV incidence from surveillance data indicates a second wave of infections in Brazil.

Mangal Tara D TD Pascom Ana Roberta Pati ARP Vesga Juan F JF Meireles Mariana Veloso MV Benzaken Adele Schwartz AS Hallett Timothy B TB

Epidemics 20190207

Emerging evidence suggests that HIV incidence rates in Brazil, particularly among men, may be rising. Here we use Brazil's integrated health systems data to develop a mathematical model, reproducing the complex surveillance systems and providing estimates of HIV incidence, number of people living with HIV (PLHIV), reporting rates and ART initiation rates. An age-structured deterministic model with a flexible spline was used to describe the natural history of HIV along with reporting and treatmen ...[more]

PMID: 30772250

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Project description:BackgroundAccurate measures of malaria incidence are essential to track progress and target high-risk populations. While health management information system (HMIS) data provide counts of malaria cases, quantifying the denominator for incidence using these data is challenging because catchment areas and care-seeking behaviours are not well defined. This study's aim was to estimate malaria incidence using HMIS data by adjusting the population denominator accounting for travel time to the health facility.MethodsOutpatient data from two public health facilities in Uganda (Kihihi and Nagongera) over a 3-year period (2011-2014) were used to model the relationship between travel time from patient village of residence (available for each individual) to the facility and the relative probability of attendance using Poisson generalized additive models. Outputs from the model were used to generate a weighted population denominator for each health facility and estimate malaria incidence. Among children aged 6 months to 11 years, monthly HMIS-derived incidence estimates, with and without population denominators weighted by probability of attendance, were compared with gold standard measures of malaria incidence measured in prospective cohorts.ResultsA total of 48,898 outpatient visits were recorded across the two sites over the study period. HMIS incidence correlated with cohort incidence over time at both study sites (correlation in Kihihi = 0.64, p < 0.001; correlation in Nagongera = 0.34, p = 0.045). HMIS incidence measures with denominators unweighted by probability of attendance underestimated cohort incidence aggregated over the 3 years in Kihihi (0.5 cases per person-year (PPY) vs 1.7 cases PPY) and Nagongera (0.3 cases PPY vs 3.0 cases PPY). HMIS incidence measures with denominators weighted by probability of attendance were closer to cohort incidence, but remained underestimates (1.1 cases PPY in Kihihi and 1.4 cases PPY in Nagongera).ConclusionsAlthough malaria incidence measured using HMIS underestimated incidence measured in cohorts, even when adjusting for probability of attendance, HMIS surveillance data are a promising and scalable source for tracking relative changes in malaria incidence over time, particularly when the population denominator can be estimated by incorporating information on village of residence.

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Estimating HIV incidence from surveillance data indicates a second wave of infections in Brazil.

Publications

Estimating HIV incidence from surveillance data indicates a second wave of infections in Brazil.

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