Varying Vaccination Rates Among Patients Seeking Care for Acute Respiratory Illness:A Systematic Review and Meta-analysis

Background: Complications following influenza infection are a major cause of morbidity and mortality, and the Centers for Disease Control Advisory Committee on Immunization Practices recommends universal annual vaccination. However, vaccination rates have remained significantly lower than the Department of Health and Human Services goal. The aim of this work was to assess the vaccination rate among patients who present to health care providers with influenza-like illness and identify groups with lower vaccination rates. Methods: We performed a systematic search of the PubMed and EMBASE databases with a time frame of January 1, 2010, to March 1, 2019 and focused on the vaccination rate among patients seeking care for acute respiratory illness in the United States. A random effects meta-analysis was performed to estimate the pooled seasonal influenza vaccination rate, and we used a time trend analysis to identify differences in annual vaccination over time. Results: The overall pooled influenza vaccination rate was 48.61% (whites: 50.87%; blacks: 36.05%; Hispanics: 41.45%). There was no significant difference among gender groups (men: 46.43%; women: 50.11%). Interestingly, the vaccination rate varied by age group and was significantly higher among adults aged >65 (78.04%) and significantly lower among children 9-17 years old (36.45%). Finally, we found a significant upward time trend in the overall influenza vaccination rate among whites (coef. = .0107; P = .027). Conclusions: In conclusion, because of the significantly lower influenza vaccination rates in black and Hispanic communities, societal initiatives and community outreach programs should focus on these populations and on children and adolescents aged 9-17 years

Pre-operative Prediction of Difficult Laparoscopic Cholecystectomy

Introduction: Laparoscopic cholecystectomy is one of the most common operation performed. Though LC have become safer and easier at times it can be difficult. Difficult cases can result in prolonged operative time, bleeding, bile spillage, conversion to open technique and bile duct injury resulting in unplanned prolonged hospital stay, increase in estimated cost to the patients and for the surgeon it leads to increased stress during operation and time pressure to complete the operative list. . Identification of difficult cases has potential advantages for surgeons, patients and their relatives. We aim to develop and validate a scoring system to predict difficult LC preoperatively. Methods: Prospective study. History, physical examination, abdominal ultrasound and biochemical parameters were included to develop a scoring system. Hundred patients undergoing LC were included and preoperative scores were calculated preoperatively to predict difficult LC which was compared with operative assessment. Results: Sensitivity and specificity of the preoperative scoring for difficult case was 53.8 % and 89.2 % respectively with PPV of 63.64 % and NPV of 84.62%. Only three parameters (history of acute cholecystitis, gall bladder wall thickness and contracted gall bladder) were statistically significant to predict difficult LC individually. Area under ROC curve was 0.779 (95 % CI, 0.657-0.883). Conclusions: Preoperative scoring system can be used to predict difficult LC. Surgeons can plan operation based on predicted difficulty. Patients and relatives can be counselled preoperatively for the possibility of difficult operation, prolonged hospital stay and increased cost in predicted difficult case. Keywords: difficult cholecystectomy; laparoscopic cholecystectomy; symptomatic cholelithiasis

Modelling CO2 and CH4 emissions from drained peatlands with grass cultivation by the BASGRA-BGC model

202

Pre-operative Prediction of Dif cult Laparoscopic Cholecystectomy

Introduction: Laparoscopic cholecystectomy is one of the most common operation performed.Though LC have become safer and easier at times it can be dif cult. Dif cult cases can result in prolonged operative time, bleeding, bile spillage, conversion to open technique and bile duct injury resulting in unplanned prolonged hospital stay, increase in estimated cost to the patients and for the surgeon it leads to increased stress during operation and time pressure to complete the operative list. Identication of dif cult cases has potential advantages for surgeons, patients and their relatives. We aim to develop and validate a scoring system to predict dif cult LC preoperatively. Methods: Prospective study. History, physical examination, abdominal ultrasound and biochemical parameters were included to develop a scoring system. Hundred patients undergoing LC were included and preoperative scores were calculated preoperatively to predict dif cult LC which was compared with operative assessment. Results: Sensitivity and speci city of the preoperative scoring for dif cult case was 53.8 % and 89.2 % respectively with PPV of 63.64 % and NPV of 84.62%. Only three parameters (history of acute cholecystitis, gall bladder wall thickness and contracted gall bladder) were statistically signi cant to predict dif cult LC individually. Area under ROC curve was 0.779 (95 % CI, 0.657-0.883).  Conclusions: Preoperative scoring system can be used to predict dif cult LC. Surgeons can plan operation based on predicted difculty. Patients and relatives can be counselled preoperatively for the possibility of dif cult operation, prolonged hospital stay and increased cost in predicted difcult case. Keywords: Dif cult cholecystectomy; Laparoscopic cholecystectomy; Symptomatic cholelithiasi

Modelling CO2 and CH4 emissions from drained peatlands with grass cultivation by the BASGRA-BGC model

Cultivated peatlands under drainage practices contribute significant carbon losses from agricultural sector in the Nordic countries. In this research, we developed the BASGRA-BGC model coupled with hydrological, soil carbon decomposition and methane modules to simulate the dynamic of water table level (WTL), carbon dioxide (CO2) and methane (CH4) emissions for cultivated peatlands. The field measurements from four experimental sites in Finland, Denmark and Norway were used to validate the predictive skills of this novel model under different WTL management practices, climatic conditions and soil properties. Compared with daily observations, the model performed well in terms of RMSE (Root Mean Square Error; 0.06–0.11 m, 1.22–2.43 gC/m2/day, and 0.002–0.330 kgC/ha/day for WTL, CO2 and CH4, respectively), NRMSE (Normalized Root Mean Square Error; 10.3–18.3%, 13.0–18.6%, 15.3–21.9%) and Pearson's r (Pearson correlation coefficient; 0.60–0.91, 0.76–0.88, 0.33–0.80). The daily/seasonal variabilities were therefore captured and the aggregated results corresponded well with annual estimations. We further provided an example on the model's potential use in improving the WTL management to mitigate CO2 and CH4 emissions while maintaining grass production. At all study sites, the simulated WTLs and carbon decomposition rates showed a significant negative correlation. Therefore, controlling WTL could effectively reduce carbon losses. However, given the highly diverse carbon decomposition rates within individual WTLs, adding indi-cators (e.g. soil moisture and peat quality) would improve our capacity to assess the effectiveness of specificmitigation practices such as WTL control and rewetting

Modelling CO₂ and CH₄ emissions from drained peatlands with grass cultivation by the BASGRA-BGC model

Abstract Cultivated peatlands under drainage practices contribute significant carbon losses from agricultural sector in the Nordic countries. In this research, we developed the BASGRA-BGC model coupled with hydrological, soil carbon decomposition and methane modules to simulate the dynamic of water table level (WTL), carbon dioxide (CO₂) and methane (CH₄) emissions for cultivated peatlands. The field measurements from four experimental sites in Finland, Denmark and Norway were used to validate the predictive skills of this novel model under different WTL management practices, climatic conditions and soil properties. Compared with daily observations, the model performed well in terms of RMSE (Root Mean Square Error; 0.06–0.11 m, 1.22–2.43 gC/m²/day, and 0.002–0.330 kgC/ha/day for WTL, CO₂ and CH₄, respectively), NRMSE (Normalized Root Mean Square Error; 10.3–18.3%, 13.0–18.6%, 15.3–21.9%) and Pearson's r (Pearson correlation coefficient; 0.60–0.91, 0.76–0.88, 0.33–0.80). The daily/seasonal variabilities were therefore captured and the aggregated results corresponded well with annual estimations. We further provided an example on the model's potential use in improving the WTL management to mitigate CO₂ and CH₄ emissions while maintaining grass production. At all study sites, the simulated WTLs and carbon decomposition rates showed a significant negative correlation. Therefore, controlling WTL could effectively reduce carbon losses. However, given the highly diverse carbon decomposition rates within individual WTLs, adding indicators (e.g. soil moisture and peat quality) would improve our capacity to assess the effectiveness of specific mitigation practices such as WTL control and rewetting