The citrus gall wasp (CGW), Bruchophagus fellis (Girault) (Hymenoptera: Eurytomidae), is a serious pest of citrus in southern Australia. Severe infestations can result in yield loss and a reduction in fruit size. Several parasitoid species attack CGW, with Megastigmus brevivalvus (Girault) (Hymenoptera: Torymidae) (MBV) being the dominant species. Both CGW and MBV are univoltine and adult wasps emerge from galls in the spring. Median emergence of MBV lags behind that of CGW by 2-3 weeks. We modelled the effect of spray timing for foliar-applied, non-systemic insecticides on the relative exposures of CGW and MBV based on their emergence distributions and estimated the optimal spray timing that maximises the control of CGW while minimising the impact on MBV. Three temporal emergence distribution scenarios for the two species and 28 periods of residual insecticide activity were considered in the investigation. The results showed that the optimal spray timing was always before the peak emergence of CGW. Depending on the residual activity of the insecticide, the optimal spray date pre-dated the peak emergence of CGW by 1-24 days, with longer lead times for insecticides with longer residual activity. By contrast, the optimal spray date varied little with the different emergence scenarios, suggesting that the results apply over a wide range of locations and seasons. A single insecticide application at the optimal timing may not guarantee satisfactory control of CGW. When the residual activity of the insecticide is short, multiple applications with a combined residual activity of 16 days are needed to ensure insecticide contact with ≥ 90% of all CGW that emerge in a season.
Leading up to August 2020, COVID-19 has spread to almost every country in the world, causing millions of infected and hundreds of thousands of deaths. In this paper, we first verify the assumption that clinical variables could have time-varying effects on COVID-19 outcomes. Then, we develop a temporal stratification approach to make daily predictions on patients' outcome at the end of hospital stay. Training data is segmented by the remaining length of stay, which is a proxy for the patient's overall condition. Based on this, a sequence of predictive models are built, one for each time segment. Thanks to the publicly shared data, we were able to build and evaluate prototype models. Preliminary experiments show 0.98 AUROC, 0.91 F1 score and 0.97 AUPR on continuous deterioration prediction, encouraging further development of the model as well as validations on different datasets. We also verify the key assumption which motivates our method. Clinical variables could have time-varying effects on COVID-19 outcomes. That is to say, the feature importance of a variable in the predictive model varies at different disease stages.
It is widely believed that timely follow-up decreases hospital readmissions; however, the literature evaluating time to follow-up is limited. The authors conducted a retrospective analysis of patients discharged from a tertiary care academic medical center and evaluated the relationship between outpatient follow-up appointments made and 30-day unplanned readmissions. Of 1044 patients discharged home, 518 (49.6%) patients had scheduled follow-up ≤14 days after discharge, 52 (4.9%) patients were scheduled ≥15 days after discharge, and 474 (45.4%) had no scheduled follow-up. There was no statistical difference in 30-day readmissions between patients with follow-up within 14 days and those with follow-up 15 days or longer from discharge (P = .36) or between patients with follow-up within 14 days and those without scheduled follow-up (P = .75). The timing of postdischarge follow-up did not affect readmissions. Further research is needed to determine such factors and to prospectively study time to outpatient follow-up after discharge and the decrease in readmission rates.
Objectives To determine the incidence and 1‐year outcomes of an elderly population with perioperative atrial arrhythmia (PAA) within 7 days of hip fracture surgery. Design Retrospective cohort study. Setting The Rochester Epidemiology Project (REP). Participants Elderly adults consecutive undergoing hip fracture repair from 1988 to 2002 in Olmsted County, Minnesota (N = 1,088, mean age 84.0 ± 7.4, 80.2% female). Measurements Baseline clinical variables were analyzed in relation to survival using Cox proportional hazards methods for comparison. Results Sixty‐one participants (5.6%) developed PAA within the first 7 days. During 1 year of follow‐up, 239 (22%) participants died. PAA was associated with greater mortality (45% vs 21%; hazard ratio (HR) = 2.8, 95% confidence interval (CI) = 1.9–4.2). Other mortality risk factors were male sex (HR = 2.0, 95% CI = 1.5–2.6), congestive heart failure (HR = 2.1, 95% CI = 1.7–2.8), chronic renal insufficiency (HR = 2.0, 95% CI = 1.5–2.8), dementia (HR = 2.9, 95% CI = 2.2–3.7), and American Society of Anesthesiologists risk Class III, IV, or V (HR = 3.3, 95% CI = 1.9–5.9). Conclusion Elderly adults undergoing hip fracture surgery who develop PAA within 7 days have significantly higher 1‐year mortality than those who do not. Further studies are indicated to determine whether prevention of PAA will reduce mortality in this population.
