Characteristics associated with recent HIV testing among men in univariate and multivariate analysis (N = 3438).

<p>OR Odds Ratio; CI Confidence Interval.</p>1<p>Adjusted for age, region of residence and all other variables associated with recent HIV testing with a <i>p</i>-value<20% in univariate analysis.</p

Characteristics associated with recent HIV testing among women in multivariate analysis (N = 3882), according to whether they reported having been proposed an HIV test as part of antenatal care in the past two years or not.

<p>OR Odds Ratio; CI Confidence Interval.</p>1<p>Adjusted for age, region of residence and all other variables associated with recent HIV testing with a <i>p</i>-value<20% in univariate analysis.</p

Seasonal patterns in YF reports and covariates by geographical region.

<p>Left axis (left to right): the mean monthly probability of YF reports and the number of YF reports. Right axis (left to right): monthly means of EVI, temperature suitability index, rainfall and the interaction of temperature suitability and rainfall. Colours of axes indicate covariate. See Fig 1 in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0006284#pntd.0006284.s004" target="_blank">S4 Text</a> for classifications of different regions of Africa.</p

Seasonal model monthly predictions.

<p>Model predictions of the monthly YF report probabilities across Africa from the seasonal model. The AUC was 0.81 (0.78; 0.84).</p

Annual covariates and report predictions.

<p>A) Presence/absence of YF reports (1971–2015) that fit inclusion criteria, B) model predictions from the annual model showing the probability of YF report from 1971–2015, C) mean annual EVI, D) mean annual rainfall, E) mean annual temperature suitability index and F) mean annual interaction of temperature suitability and rainfall.</p

Daily temperature dependent morality and biting rate over a range of temperatures.

<p>A) Daily temperature dependent mortality, triangles indicate data from Christophers (1960)[<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0006284#pntd.0006284.ref036" target="_blank">36</a>] and squares data from Yang et al., (2009)[<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0006284#pntd.0006284.ref035" target="_blank">35</a>]. The lines depict the linear relationship between temperature and mortality below 41°C (green) and above 41°C (red). B) Daily biting rate against temperature from Martens (1998)[<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0006284#pntd.0006284.ref044" target="_blank">44</a>]. Black dots show the data and the red line the polynomial fit.</p

Average effect of simulated intervention on the overall risk of HCV infection during hospitalization.

Labels under bars correspond to the proportion of concerned patients for a given intervention for the four sub-scenarios considered in the analysis (Comparison groups A, B, C and D). As proportions of patients for the ward-focused scenario were chosen based on the number of cumulative patients in these wards, they were not exactly equal to the proportions given for patient-based scenarios.</p

Point Biserial Correlation (PBC) for 1 to 20 clusters.

PBC was very similar for 3, 4 and 5 partitions. Therefore, we chose to build 4 clusters of patients (vertical dashed line). (PNG)</p

Distributions of the procedure–specific risks of HCV infection in case of contaminated equipment.

Distributions of the procedure–specific risks of HCV infection in case of contaminated equipment.</p

Mode calculation.

When compliance with infection control recommendations is non-optimal, hospitals may play an important role in hepatitis C (HCV) transmission. However, few studies have analyzed the nosocomial HCV acquisition risk based on detailed empirical data. Here, we used data from a prospective cohort study conducted on 500 patients in the Ain Shams hospital (Cairo, Egypt) in 2017 with the objective of identifying (i) high-risk patient profiles and (ii) transmission hotspots within the hospital. Data included information on patient HCV status upon admission, their trajectories between wards and the invasive procedures they underwent. We first performed a sequence analysis to identify different hospitalization profiles. Second, we estimated each patient’s individual risk of HCV acquisition based on ward-specific prevalence and procedures undergone, and risk hotspots by computing ward-level risks. Then, using a beta regression model, we evaluated upon-admission factors linked to HCV acquisition risk and built a score estimating the risk of HCV infection during hospitalization based on these factors. Finally, we assessed and compared ward-focused and patient-focused HCV control strategies. The sequence analysis based on patient trajectories allowed us to identify four distinct patient trajectory profiles. The risk of HCV infection was greater in the internal medicine department, compared to the surgery department (0·188% [0·142%-0·235%] vs. 0·043%, CI 95%: [0·036%-0·050%]), with risk hotspots in the geriatric, tropical medicine and intensive-care wards. Upon-admission risk predictors included source of admission, age, reason for hospitalization, and medical history. Interventions focused on the most at-risk patients were most effective to reduce HCV infection risk. Our results might help reduce the risk of HCV acquisition during hospitalization in Egypt by targeting enhanced control measures to ward-level transmission hotspots and to at-risk patients identified upon admission.</div