What is the impact of simulation-based training for paediatric procedures on patient outcomes, cost and latent safety threats?

BACKGROUND: Simulation-based training (SBT) provides a safe space for medical trainees to experience realistic scenarios. SBT has been found to improve trainee performance in paediatric procedures. However, limited evidence exists regarding its effects on higher-level outcomes. This scoping review aims to identify studies that investigate the impact of SBT for procedural skills on T3 (patient outcomes) and T4 level outcomes (latent safety threats [LSTs], and hospital level costs) in paediatrics. METHODS: Full-text articles were included if they focused on medical trainees, used simulation training for paediatric procedures and reported T3/T4 level outcomes. Six databases were searched from January 2011 to September 2022. Search strategies were developed with the assistance of a librarian. Three independent reviewers performed pilot screenings before title/abstract and full-text screenings. A data extraction sheet was created to gather information on interventions, outcomes, research design, and other study characteristics. FINDINGS: After title/abstract screening of 4,076 sources, 50 were included for full-text review, with 15 articles selected for data extraction. Four were randomised control studies (RCTs), fourteen focused on T3 level outcomes including mortality rates, and one measured LSTs. There were no studies reporting cost-related data. Three of the studies focused on bag-and-mask ventilation, and eight mentioned the use of mannequins. DISCUSSION: We highlight the potential effectiveness of simulation-based training of paediatric procedural skills in improving patient outcomes, such as reduced mortality rates and incidence of illness/injury. CONCLUSION: Though the quality of research designs was low, researchers used different simulation modalities and outcome measures and showed a positive impact of SBT(e.g., decreased mortality rates).

Ranking Canadian waste management system efficiencies using three waste performance indicators.

Three waste management system (WMS) efficiency indicators are adopted to systematically assess WMS efficiency in Canada from 1998 to 2016. The study objectives are to examine the temporal changes in waste diversion activities and rank the performance of the jurisdictions using a qualitative analytical framework. Increasing Waste Management Output Index (WMOI) trends were identified in all jurisdictions, and more government subsidiaries and incentive packages are recommended. With the exception of Nova Scotia, statistically significant decreasing diversion gross domestic product (DGDP) ratio trends are observed. It appears that the increases in GDP from Sector 562 were not contributing to waste diversion. On average, Canada spent about $225/tonne of waste handled during the study period. Current spending per tonne handled (CuPT) trends are decreasing, with S ranging from + 5.15 to + 7.67. It appears that WMSs in Saskatchewan and Alberta are more efficient. The results suggest that the use of diversion rate alone to evaluate WMS may be misleading. The findings help the waste community to better understand the trade-offs between various waste management alternatives. The proposed qualitative framework utilizing comparative rankings is applicable elsewhere and can be a useful decision support tool for policy-makers.

Evolution of COVID-19 municipal solid waste disposal behaviors using epidemiology-based periods defined by World Health Organization guidelines.

This study aims to identify the effects of continued COVID-19 transmission on waste management trends in a Canadian capital city, using pandemic periods defined from epidemiology and the WHO guidelines. Trends are detected using both regression and Mann-Kendall tests. The proposed analytical method is jurisdictionally comparable and does not rely on administrative measures. A reduction of 190.30 tonnes/week in average residential waste collection is observed in the Group II period. COVID-19 virulence negatively correlated with residential waste generation. Data variability in average collection rates during the Group II period increased (SD=228.73 tonnes/week). A slightly lower COVID-19 induced Waste Disposal Variability (CWDW) of 0.63 was observed in the Group II period. Increasing residential waste collection trends during Group II are observed from both regression (b = +1.6) and the MK test (z = +5.0). Both trend analyses reveal a decreasing CWDV trend during the Group I period, indicating higher diversion activities. Decreasing CWDV trends are also observed during the Group II period, probably due to the implementation of new waste programs. The use of pandemic periods derived from epidemiology helps us to better understand the effect of COVID-19 on waste generation and disposal behaviors, allowing us to better compare results in regions with different socio-economic affluences.

Integrating Geographic Information System network analysis and nighttime light satellite imagery to optimize landfill regionalization on a regional level.

More than half of financial resources allocated for municipal solid waste management are typically spent on waste collection and transportation. An optimized landfill siting and waste collection system can save fuel costs, reduce collection truck emissions, and provide higher accessibility with lower traffic impacts. In this study, a data-driven analytical framework is developed to optimize population coverage by landfills using network analysis and satellite imagery. Two scenarios, SC1 and SC2, with different truck travel times were used to simulate generation-site-disposal-site distances in three Canadian provinces. Under status quo conditions, Landfill Regionalization Index (LFRI) ranging from 0 to 2 population centers per landfill in all three jurisdictions. LFRI consistently improved after optimization, with average LFRI ranging from 1.3 to 2.0 population centers per landfill. Lower average truck travel times and better coverage of the population centers are generally observed in the optimized systems. The proposed analytical method is found effective in improving landfill regionalization. Under SC1 and SC2, LFRI percentages of improvement ranging from 58.3% to 64.5% and 22.7% to 59.4%, respectively. Separation distance between the generation and disposal sites and truck capacity appear not a decisive factor in the optimization process. The proposed optimization framework is generally applicable to regions with different geographical and demographical attributes, and is particularly applicable in rural regions with sparsely located population centers.

Development and application of an analytical framework for mapping probable illegal dumping sites using nighttime light imagery and various remote sensing indices.

Illegal dump sites (IDS) pose significant risks to human and the environment and are a pressing issue worldwide. Due to their secretive nature, the detection of IDS is costly and ineffective. In this study, an analytical framework was developed to detect probable IDSs in rural and remote areas using nighttime light (NTL) as a proxy for populated areas. An IDS probability map is produced by aggregation of Landsat-8 and Suomi NPP satellite imagery, multiple-criteria decision-making analysis, and classification tools. Six variables are considered, including modified soil adjusted index, land surface temperature, NTL, highway length, railway length, and the number of landfills. Vulnerability of the inhabitants on reserve lands was assessed using three sample regions. The method appears effective in reducing potential IDSs. Only about 7% of the 31,285 km2 study area are identified as probable IDS, being classified as "very high" and "high". Landfills without permit are found more effective in lowering IDS occurrence. Spatial distributions of reserve lands and the maturity of highways network nearby may be more important than the length of railways when assessing the inhabitant vulnerability due to IDS. Highway length is the most decisive factor on IDS probability among all classes, with membership grades ranging from 0.99 to 0.55. Land surface temperature appears less effective for the identification of smaller scale IDS. NTL is more prominent on IDS probability in the "very high" class, with a membership grade of 0.80. The finding suggests that populated areas represented by NTL is a priori of IDS.

Hospital waste generation during the first wave of COVID-19 pandemic: a case study in Delhi.

In this study, the hospital waste generation rates and compositions in Delhi were examined temporally and spatially during the first COVID-19 wave of April 2020. A total of 11 representative hospitals located in five districts were considered. The pre-COVID hospital waste generation rates were relatively consistent among the districts, ranging from 15 to 23 tonne/month. It is found that the number of hospital beds per capita may not be a significant factor in the hospital waste quantity. Strong seasonal variations were not observed. All districts experienced a drastic decrease in generation rates during the 1-month lockdown. The average rates during the COVID period ranged from 12 to 24 tonne/month. Bio-contaminated and disposable medical product wastes were the most common waste in Delhi's hospitals, representing 70-80% by weight. The changes in waste composition were however not spatially consistent. The lockdown appeared to have had a higher impact on hospital waste generation rate than on waste composition. The findings are important as the design and operation of a waste management system are sensitive to both waste quantity and quality. Waste records at source helped to minimize waste data uncertainties and allowed a closer examination of generation trends.

The Effects of Risk Factors on One- and Five-Year Survival of Patients with Gastric Cancer in Isfahan in 2016.

Background: Gastric cancer (GC) is a frequent and multifactorial malignancy worldwide. The aim of this study was to investigate the relationship between some risk factors of GC and the 1-year or 5-year survival rates in newly diagnosed patients in Isfahan in 2016. Methods: We included 274 newly diagnosed patients in this survival analysis from a database of 484 GC cases. We used a checklist to collect information. To inform about missed data, we call the patients or their families in non-survived cases. We evaluated each patient's age, sex, body mass index (BMI), education, salt, salty foods, and red meat consumption. In addition, we asked patients about the intake of fresh fruits and vegetables, tobacco smoking, opium usage, and alcohol consumption. We surveyed the patient's job, physical activity, Helicobacter pylori infection, family history of GC, history of gastric surgery, and survival status after 1 or 5 years. Variables were evaluated between survived and dead patients and compared for means and frequencies using the independent samples t-test or Mann-Whitney, or chi-square test. The univariate relationship of each risk factor, with 1- and 5-year survival, was examined by the log-rank test and the Kaplan-Meyer method and their multivariate relationship with Cox regression. Results: 1- and 5-year survived patients were younger than dead patients with GC (P<0.001; HR for 1-year survival: 1.014, 95% CI: 0.997 to 1.030; HR for 5-year survival: 1.005, 95% CI: 0.994 to 1.017), and had more frequent higher educational levels (P<0.05; HR for 1-year survival: 1.887, 95% CI: 1.046 to 3.406; HR for 5-year survival: 1.482, 95% CI: 0.987 to 2.223). The death rate after 5 years was higher in men than in women (P=0.009; HR: 1.009, 95% CI: 0.593 to 1.717) and depended on the job status of the patients (P=0.021). The other studied variables were not significantly different between 1- or 5-year survived and dead patients. Conclusion: GC development depends on genomic changes, environmental factors, and lifestyle status. But all risk factors that play a role in its development are not notable for a patient's survival. We suggest that risk factors for these patients' survival become elucidated in future studies. It helps to gather the necessary pieces of evidence for the enhancement of survival in patients with GC.

The psychological effects of COVID-19 spread in young Canadian adults.

BACKGROUND AND AIM: The novel coronavirus disease 2019 (COVID-19) not only threatens physical health but also psychological well-being. As a result of the pandemic, increased mental health burdens have been recorded in young adults around the world. We sought to understand the association of stressors related to the COVID-19 pandemic with symptoms of psychological and emotional distress in young Canadian adults. METHOD: Questionnaire respondents were asked about the extent to which they were personally impacted by COVID-19, and symptoms related to depression, anxiety, post-traumatic disorder, and emotional distress. RESULTS: Of 84 respondents, most identified as female (74%; 62/84). Overall, 61% (51/84) reported experiencing symptoms of psychological distress related to depression, anxiety, or post-traumatic stress disorder (PTSD); specifically, 43% (36/84) reported anxiety-related symptoms, 33% (28/84) reported depression-related symptoms, and 6% (5/84) reported PTSD-related symptoms. Individuals with family in settings high risk for COVID-19 infection and individuals who received government aid with a reduction in income were 4.30-fold and 2.80-fold more likely, respectively, to experience symptoms of psychological distress (95% CI 1.31-14.14; p = .013 and 95% CI 1.08-7.25; p = .038, respectively). Visits to social media daily to hourly for COVID-19 related news resulted in a 3.24-fold increase in the likelihood of experiencing depression-related symptoms (95% CI 1.26-8.35; p = .020). CONCLUSION: We demonstrate a necessity to focus on alleviating the mental health burdens and contributing stressors within young Canadian adults. Though the mental health burdens are currently exacerbated by the effects of the COVID-19 pandemic, they may persist after the pandemic ends if left unaddressed.

Modeling of municipal waste disposal rates during COVID-19 using separated waste fraction models.

Municipal waste disposal behaviors in Regina, the capital city of Saskatchewan, Canada have significantly changed during the COVID-19 pandemic. About 7.5 year of waste disposal data at the Regina landfill was collected, verified, and consolidated. Four modeling approaches were examined to predict total waste disposal at the Regina landfill during the COVID-19 period, including (i) continuous total (Baseline), (ii) continuous fraction, (iii) truncated total, and (iv) truncated fraction. A single feature input recurrent neural network model was adopted for each approach. It is hypothesized that waste quantity modeling using different waste fractions and separate time series can better capture disposal behaviors of residents during the lockdown. Compared to the baseline approach, the use of waste fractions in modeling improves both result accuracy and precision. In general, the use of continuous time series over-predicted total waste disposal, especially when actual disposal rates were less than 50 t/day. Compared to the baseline approach, mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE) were reduced. The R value increased from 0.63 to 0.79. Comparing to the baseline, the truncated total and the truncated fraction approaches better captured the total waste disposal behaviors during the COVID-19 period, probably due to the periodicity of the weeklong data set. For both approaches, MAE and MAPE were lower than 70 and 22%, respectively. The model performance of the truncated fraction appears the best, with an MAPE of 19.8% and R value of 0.92. Results suggest the uses of waste fractions and separated time series are beneficial, especially if the input set is heavily skewed.

Thermal heterogeneity in the proximity of municipal solid waste landfills on forest and agricultural lands.

Information on the spatial extent of potential impact areas near disposal sites is vital to the development of a sustainable natural resource management policy. Eight Canadian landfills of various sizes and shapes in different climatic conditions are studied to quantify the spatial extent of their bio-thermal zone. Land surface temperature (LST) and normalized difference vegetation index (NDVI) are examined with respect to different Land Use Land Cover (LULC) classes. Within 1500 m of the sites, LST ranged from 18.3 °C to 29.5 °C and 21.3 °C-29.7 °C for forest land and agricultural land, respectively. Linear regression shows a decreasing LST trend in forest land for five out of seven landfills. A similar trend, however, is not observed for agricultural land. Both the magnitude and the variability of LST are higher in agricultural land. The size of the bio-thermal zone is sensitive to the respective LULC class. The approximate bio-thermal zones for forest class and agricultural classes are about 170 ± 90 m and 180 ± 90 m from the landfill perimeter, respectively. For the forest class, NDVI was negatively correlated with LST at six out of seven Canadian landfills, and stronger relationships are observed in the agricultural class. NDVI data has a considerably larger spread and is less consistent than LST. LST data appears more appropriate for identifying landfill bio-thermal zones. A subtle difference in LST is observed among six LULC classes, averaging from 23.9 °C to 27.4 °C. Geometric shape makes no observable difference in LST in this study; however, larger landfill footprint appears to have higher LST.

Siting and ranking municipal landfill sites in regional scale using nighttime satellite imagery.

In 2016, about 24.9 million tonnes of solid waste were disposed of in Canadian landfills, where landfill technology is a common choice. This study aims to develop a data-driven GIS-based method that considers spatial, environmental, and economic constraints using study regions derived from night time light data for a 40 km buffer around Regina, Saskatchewan, Canada. Unlike other similar studies, this site suitability study assumes no political or administrative boundaries as inputs. Road network stands as the most decisive factor that accounts for 0.239 of entire weight, followed by protective areas with a total weight of 0.220. The regions that ranked the best for siting new landfills were generally located far from predominant water resources and protected areas, but are in the vicinity of major road networks, but are also far from urbanized regions. The sensitivity analysis showed that, overall, road network and protected areas are the most essential layers in this analysis. For the environmental group, protected areas and water resources are major layers. For the economic group, road network and surface temperature are the most important. The method presented in this study can easily accommodate other data sets based on importance in any given area.

The Lake Urmia environmental disaster in Iran: A look at aerosol pollution.

Lake Urmia (LU) once was the second largest hypersaline lake in the world, covering up to 6000km2, but has undergone catastrophic desiccation in recent years resulting in loss of 90% of its area and extensive coverage by playas and marshlands that represent a source of salt and dust. This study examines daily Aerosol Optical Depth (AOD) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) between 2001 and 2015 over northwestern Iran, which encompasses LU. Intriguingly, salt emissions from the LU surface associated with ongoing desiccation do not drive the study region's AOD profile, whereas pollution transported from other regions and emissions around LU are more important. Signatures of increasing local crustal emissions are most evident outside of the peak dust season (January, February, and October) and on the periphery of LU. AOD has generally increased in the latter half of the study period with the onset of the AOD ramp-up starting a month earlier in the spring season when comparing 2009-2015 versus earlier years. Results indicate that suppression of emissions on the LU border is critical as the combined area of salt and salty soil bodies around LU have increased by two orders of magnitude in the past two decades, and disturbing these areas via activities such as grazing and salt harvesting on the lake surface can have more detrimental impacts on regional pollution as compared to benefits. These results have important implications for public health, climate, the hydrological cycle, and pollution control efforts.