Multicenter study of device-associated infection rates in hospitals of Mongolia: Findings of the International Nosocomial Infection Control Consortium (INICC)

Methods: A device-associated health care-associated infection prospective surveillance study in 3 adult intensive care units (ICUs) from 3 hospitals using the U.S. Centers for Disease Control and Prevention (CDC) and National Healthcare Safety Network (NHSN) definitions and INICC methods. Results: We documented 467 ICU patients for 2,133 bed days. The central line-associated bloodstream infection (CLABSI) rate was 19.7 per 1,000 central line days, the ventilator-associated pneumonia (VAP) rate was 43.7 per 1,000 mechanical ventilator days, and the catheter-associated urinary tract infection (CAUTI) rate was 15.7 per 1,000 urinary catheter days; all of the rates are higher than the INICC rates (CLABSI: 4.9; VAP: 16.5; and CAUTI: 5.3) and CDC-NHSN rates (CLABSI: 0.8; VAP: 1.1; and CAUTI: 1.3). Device use ratios were also higher than the CDC-NHSN and INICC ratios, except for the mechanical ventilator device use ratio, which was lower than the INICC ratio. Resistance of Staphylococcus aureus to oxacillin was 100%. Extra length of stay was 15.1 days for patients with CLABSI, 7.8 days for patients with VAP, and 8.2 days for patients with CAUTI. Extra crude mortality in the ICUs was 18.6% for CLABSI, 17.1% for VAP, and 5.1% for CAUTI. Conclusion: Device-associated health care-associated infection rates and most device use ratios in our Mongolian hospitals' ICUs are higher than the CDC-NSHN and INICC rates

REDUCING THE BURDEN OF DISEASE THROUGH TOBACCO TAXES IN MONGOLIA: A health impact analysis using a dynamic public health model.

BACKGROUND/OBJECTIVES: Smoking is the leading risk factor for many chronic diseases. The quantitative analysis of potential health gains from reduced smoking is important for establishing priorities in Mongolia’s health policy. This study quantifies the effect of tobacco-tax increases on future smoking prevalence and the associated smoking-related burden of disease in Mongolia. METHODS: The dynamic model for health impact assessment (DYNAMO-HIA) tool was used. The most recent data were used as input for evaluating tobacco-taxation scenarios. Demographic data were taken from the Mongolian Statistical Information Services. Smoking data came from a representative population-based STEPS survey, and smoking-related disease data were obtained from the health-information database of Mongolia’s National Health Center. Simulation was used to evaluate various levels of one-time price increases on tobacco products (25% and 75%) in Mongolia. Conservative interpretation suggests that the population will eventually adjust to the higher tobacco price and return to baseline smoking behaviors. RESULTS: Over a three-year period, smoking prevalence would be reduced by 1.2% points, corresponding to almost 40 thousand smokers at the population level for a price increase of 75%, compared to the baseline scenario. Projected health benefits of this scenario suggest that more than 137 thousand quality adjusted of life years would be gained by avoiding smoking-related diseases within a population of three million over a 30-year period. DISCUSSION: Prevention through effective tobacco-control policy could yield considerable gains in population health in Mongolia. Compared to current policy, tax increases must be higher to have a significant effect on population health. IMPLICATIONS: Tobacco taxation is an effective policy for reducing the harm of tobacco smoking, while benefiting population health in countries where the tobacco epidemic is still in an early stage. Smoking prevalence and smoking behaviors in these countries differ from those in Western countries. Reducing the uptake of smoking among young people could be a particularly worthwhile benefit of tobacco-tax increases

Clinical Risk Factor Prediction for Second Primary Skin Cancer: A Hospital-Based Cancer Registry Study

This study aimed to develop a risk-prediction model for second primary skin cancer (SPSC) survivors. We identified the clinical characteristics of SPSC and created awareness for physicians screening high-risk patients among skin cancer survivors. Using data from the 1248 skin cancer survivors extracted from five cancer registries, we benchmarked a random forest algorithm against MLP, C4.5, AdaBoost, and bagging algorithms for several metrics. Additionally, in this study, we leveraged the synthetic minority over-sampling technique (SMOTE) for the issue of the imbalanced dataset, cost-sensitive learning for risk assessment, and SHAP for the analysis of feature importance. The proposed random forest outperformed the other models, with an accuracy of 90.2%, a recall rate of 95.2%, a precision rate of 86.6%, and an F1 value of 90.7% in the SPSC category based on 10-fold cross-validation on a balanced dataset. Our results suggest that the four features, i.e., age, stage, gender, and involvement of regional lymph nodes, which significantly affect the output of the prediction model, need to be considered in the analysis of the next causal effect. In addition to causal analysis of specific primary sites, these clinical features allow further investigation of secondary cancers among skin cancer survivors

Clinical Risk Factor Prediction for Second Primary Skin Cancer: A Hospital-Based Cancer Registry Study

This study aimed to develop a risk-prediction model for second primary skin cancer (SPSC) survivors. We identified the clinical characteristics of SPSC and created awareness for physicians screening high-risk patients among skin cancer survivors. Using data from the 1248 skin cancer survivors extracted from five cancer registries, we benchmarked a random forest algorithm against MLP, C4.5, AdaBoost, and bagging algorithms for several metrics. Additionally, in this study, we leveraged the synthetic minority over-sampling technique (SMOTE) for the issue of the imbalanced dataset, cost-sensitive learning for risk assessment, and SHAP for the analysis of feature importance. The proposed random forest outperformed the other models, with an accuracy of 90.2%, a recall rate of 95.2%, a precision rate of 86.6%, and an F1 value of 90.7% in the SPSC category based on 10-fold cross-validation on a balanced dataset. Our results suggest that the four features, i.e., age, stage, gender, and involvement of regional lymph nodes, which significantly affect the output of the prediction model, need to be considered in the analysis of the next causal effect. In addition to causal analysis of specific primary sites, these clinical features allow further investigation of secondary cancers among skin cancer survivors

Multicenter study of device-associated infection rates in hospitals of Mongolia: Findings of the International Nosocomial Infection Control Consortium (INICC)

Background To report the results of the International Nosocomial Infection Control Consortium (INICC) multicenter study conducted in Mongolia from September 2013-March 2015. Methods A device-associated health care-associated infection prospective surveillance study in 3 adult intensive care units (ICUs) from 3 hospitals using the U.S. Centers for Disease Control and Prevention (CDC) and National Healthcare Safety Network (NHSN) definitions and INICC methods. Results We documented 467 ICU patients for 2,133 bed days. The central line-associated bloodstream infection (CLABSI) rate was 19.7 per 1,000 central line days, the ventilator-associated pneumonia (VAP) rate was 43.7 per 1,000 mechanical ventilator days, and the catheter-associated urinary tract infection (CAUTI) rate was 15.7 per 1,000 urinary catheter days; all of the rates are higher than the INICC rates (CLABSI: 4.9; VAP: 16.5; and CAUTI: 5.3) and CDC-NHSN rates (CLABSI: 0.8; VAP: 1.1; and CAUTI: 1.3). Device use ratios were also higher than the CDC-NHSN and INICC ratios, except for the mechanical ventilator device use ratio, which was lower than the INICC ratio. Resistance of Staphylococcus aureus to oxacillin was 100%. Extra length of stay was 15.1 days for patients with CLABSI, 7.8 days for patients with VAP, and 8.2 days for patients with CAUTI. Extra crude mortality in the ICUs was 18.6% for CLABSI, 17.1% for VAP, and 5.1% for CAUTI. Conclusion Device-associated health care-associated infection rates and most device use ratios in our Mongolian hospitals' ICUs are higher than the CDC-NSHN and INICC rates.Fil: Ider, Bat-Erdene. Intermed Hospital; MongoliaFil: Baatar, Otgon. State Central First Hospital; MongoliaFil: Rosenthal, Victor Daniel. International Nosocomial Infection Control Consortium; ArgentinaFil: Khuderchuluun, Chuluunchimeg. State Central First Hospital; MongoliaFil: Baasanjav, Battsetseg. State Central First Hospital; MongoliaFil: Donkhim, Chuluunbaatar. State Central First Hospital; MongoliaFil: Batsuur, Byambadorj. State Central First Hospital; MongoliaFil: Jambiimolom, Munhzul. Second State Central Hospital; MongoliaFil: Purevdorj, Suvd-Erdene. Second State Central Hospital; MongoliaFil: Tsogtbaatar, Uyanga. Second State Central Hospital; MongoliaFil: Sodnomdarjaa, Baigalmaa. Second State Central Hospital; MongoliaFil: Gendaram, Bayasgalan. Second State Central Hospital; MongoliaFil: Mendsaikhan, Naranpurev. Intermed Hospital; MongoliaFil: Begzjav, Tsolmon. Intermed Hospital; MongoliaFil: Narankhuu, Batsaikhan. Intermed Hospital; MongoliaFil: Ariungerel, Bat-Erdene. Intermed Hospital; MongoliaFil: Tumendemberel, Bolormaa. Intermed Hospital; MongoliaFil: Orellano, Pablo Wenceslao. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional; Argentina. International Nosocomial Infection Control Consortium; Argentin