Estimating HIV Incidence, Time to Diagnosis, and the Undiagnosed HIV Epidemic Using Routine Surveillance Data.

BACKGROUND Estimates of the size of the undiagnosed HIV-infected population are important to understand the HIV epidemic and to plan interventions, including "test-and-treat" strategies. METHODS We developed a multi-state back-calculation model to estimate HIV incidence, time between infection and diagnosis, and the undiagnosed population by CD4 count strata, using surveillance data on new HIV and AIDS diagnoses. The HIV incidence curve was modelled using cubic splines. The model was tested on simulated data and applied to surveillance data on men who have sex with men in The Netherlands. RESULTS The number of HIV infections could be estimated accurately using simulated data, with most values within the 95% confidence intervals of model predictions. When applying the model to Dutch surveillance data, 15,400 (95% confidence interval [CI] = 15,000, 16,000) men who have sex with men were estimated to have been infected between 1980 and 2011. HIV incidence showed a bimodal distribution, with peaks around 1985 and 2005 and a decline in recent years. Mean time to diagnosis was 6.1 (95% CI = 5.8, 6.4) years between 1984 and 1995 and decreased to 2.6 (2.3, 3.0) years in 2011. By the end of 2011, 11,500 (11,000, 12,000) men who have sex with men in The Netherlands were estimated to be living with HIV, of whom 1,750 (1,450, 2,200) were still undiagnosed. Of the undiagnosed men who have sex with men, 29% (22, 37) were infected for less than 1 year, and 16% (13, 20) for more than 5 years. CONCLUSIONS This multi-state back-calculation model will be useful to estimate HIV incidence, time to diagnosis, and the undiagnosed HIV epidemic based on routine surveillance data.

Impact of the Ebola epidemic on general and HIV care in Macenta, Forest Guinea, 2014.

OBJECTIVE The current Ebola epidemic massively affected the Macenta district in Forest Guinea. We aimed at investigating its impact on general and HIV care at the only HIV care facility in the district. DESIGN Prospective observational single-facility study. METHODS Routinely collected data on use of general hospital services and HIV care were linked to Ebola surveillance data published by the Guinea Ministry of Health. In addition, we compared retention among HIV-infected patients enrolled into care in the first semesters of 2013 and 2014. RESULTS Throughout 2014, service offer was continuous and unaltered at the facility. During the main epidemic period (August-December 2014), compared with the same period of 2013, there were important reductions in attendance at the primary care outpatient clinic (-40%), in HIV tests done (-46%), in new diagnoses of tuberculosis (-53%) and in patients enrolled into HIV care (-47%). There was a smaller reduction in attendance at the HIV follow-up clinic (-11%). Kaplan-Meier estimates of retention were similar among the patients enrolled into care in 2014 and 2013. In a multivariable Cox regression analysis, the year of enrolment was not associated with attrition (hazard ratio 1.02; 95% confidence interval: 0.72-1.43). CONCLUSION The Ebola epidemic resulted in an important decrease in utilization of the facility despite unaltered service offer. Effects on care of HIV-positive patients enrolled prior to the epidemic were limited. HIV care in such circumstances is challenging, but not impossible.

Genetic stability of Schmallenberg virus in vivo during an epidemic, and in vitro, when passaged in the highly susceptible porcine SK-6 cell line

Schmallenberg virus (SBV), an arthropod-borne orthobunyavirus was first detected in 2011 in cattle suffering from diarrhea and fever. The most severe impact of an SBV infection is the induction of malformations in newborns and abortions. Between 2011 and 2013 SBV spread throughout Europe in an unprecedented epidemic wave. SBV contains a tripartite genome consisting of the three negative-sense RNA segments L, M, and S. The virus is usually isolated from clinical samples by inoculation of KC (insect) or BHK-21 (mammalian) cells. Several virus passages are required to allow adaptation of SBV to cells in vitro. In the present study, the porcine SK-6 cell line was used for isolation and passaging of SBV. SK-6 cells proved to be more sensitive to SBV infection and allowed to produce higher titers more rapidly as in BHK-21 cells after just one passage. No adaptation was required. In order to determine the in vivo genetic stability of SBV during an epidemic spread of the virus the nucleotide sequence of the genome from seven SBV field isolates collected in summer 2012 in Switzerland was determined and compared to other SBV sequences available in GenBank. A total of 101 mutations, mostly transitions randomly dispersed along the L and M segment were found when the Swiss isolates were compared to the first SBV isolated late 2011 in Germany. However, when these mutations were studied in detail, a previously described hypervariable region in the M segment was identified. The S segment was completely conserved among all sequenced SBV isolates. To assess the in vitro genetic stability of SBV, three isolates were passage 10 times in SK-6 cells and sequenced before and after passaging. Between two and five nt exchanges per genome were found. This low in vitro mutation rate further demonstrates the suitability of SK-6 cells for SBV propagation.

LOGITCPRPLOT: Stata module to graph component-plus-residual plot for logistic regression

logitcprplot can be used after logistic regression for graphing a component-plus-residual plot (a.k.a. partial residual plot) for a given predictor, including a lowess, local polynomial, restricted cubic spline, fractional polynomial, penalized spline, regression spline, running line, or adaptive variable span running line smooth

RRLOGIT: Stata module to estimate logistic regression for randomized response data

rrlogit fits a maximum-likelihood logistic regression for randomized response data.