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Periodic testing and estimation of STD-HIV association

Masse, B.; Guibord, P.; Boily, M.-C.; Alary, M.

2019-07-16 hiv aids
10.1101/19002345 medRxiv
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BackgroundThe validity of measures used in follow-up studies to estimate the magnitude of the HIV-STD association will be the focus of this paper. A recent simulation study by Boily et al [1] based on a model of HIV and STD transmission showed that the relative risk (RR), estimated by the hazard rate ratio (HRR) obtained by the Cox model had poor validity, either in absence or in presence of a real association between HIV and STD. The HRR tends to underestimate the true magnitude of a non-null association. These results were obtained from simulated follow-up studies where HIV was periodicaly tested every three months and every month for the STD. Aims and MethodsThis paper extends the above results by investigating the impact of using different periodic testing intervals on the validity of HRR estimates. Issues regarding the definition of exposure to STDs in this context are explored. A stochastic model for the transmission of HIV and other STDs is used to simulate follow-up studies with different periodic testing intervals. HRR estimates obtained with the Cox model with a time-dependent STD exposure covariate are compared to the true magnitude of the HIV-STD association. In addition, real data are reanalysed using the STD exposure definition described in this paper. The data from Laga et al [2] are used for this purpose. Results(1) Simulated data: independently of the magnitude of the true association, we observed a greater reduction of the bias when increasing the frequency of HIV testing than that of the STD testing. (2) Real data: The STD exposure definition can create substantial differences in the estimation of the HIV-STD association. Laga et al [2] have found a HRR of 2.5 (1.1 - 6.4) for the association between HIV and genital ulcer disease compared to an estimate of 3.5 (1.5 - 8.3) with our improved definition of exposure. ConclusionsResults on the simulated data have an important impact on the design of field studies. For instance when choosing between two designs; one where both HIV and STD are screened every 3 months versus one where HIV and STD are screened every 3 months and monthly, respectively. The latter design is more expensive and involves more complicated logistics. Furthermore, this increment in cost may not be justified considering the relatively small gain in terms of validity and variability.

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