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

Odds-ratios (OR) of HCV infection associated with exposure to iatrogenic procedures, based on a previously published meta-analysis [18].

The 15 procedure types in the IMMHoTHep data (second column) are aggregated into 8 of the 10 procedure groups defined in the meta-analysis and sorted from higher to lower risk. No procedures from the remaining 2 groups defined in the meta-analysis (dental care and transplantation) were observed in the IMMHoTHep data.</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

Panel of ward characteristics for each ward in the surgery hospital.

(A) HCV prevalence in each ward with their associated 95% confidence intervals. (B) Average number of procedures per patient. Procedure types are represented from the high-risk ones to the low-risk ones (from left to right). (C) Boxplots of average ward-specific risk of HCV infection, coloured according to the number of patients visiting these wards. Mean values are represented by purple diamond dots. Three wards are not represented because no patients underwent invasive procedures within them.</p

Sensitivity analysis for the cut-off value of the risk considered in the logistic regression.

Sensitivity analysis for the cut-off value of the risk considered in the logistic regression.</p

Panel of ward characteristics for each ward in the internal medicine hospital.

(A) HCV prevalence in each ward with their associated 95% confidence intervals. (B) Average number of procedures per patient. Procedure types are represented from the high-risk ones to the low-risk ones (from left to right). (C) Boxplots of average ward-specific risk of HCV infection, coloured according to the number of patients visiting these wards. Mean values are represented by purple diamond dots.</p

Inclusivity in global research.

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

Chronograms for each of the four clusters of patients identified after sequence analysis.

Dotted lines represent the average length of stay for each group of patients.</p