Indoor residual spraying with SumiShield WG was found effective, operationally feasible and safe, and it is effective for up to 6 months.

Background Previous conventional models for the prediction of diabetes could be updated by incorporating the increasing amount of health data available and new risk prediction methodology. Objective We aimed to develop a substantially improved diabetes risk prediction model using sophisticated machine-learning algorithms based on a large retrospective population cohort of over 230,000 people who were enrolled in the study during 2006-2017. Methods We collected demographic, medical, behavioral, and incidence data for type 2 diabetes mellitus (T2DM) in over 236,684 diabetes-free participants recruited from the 45 and Up Study. We predicted and compared the risk of diabetes onset in these participants at 3, 5, 7, and 10 years based on three machine-learning approaches and the conventional regression model. Results Overall, 6.05% (14,313/236,684) of the participants developed T2DM during an average 8.8-year follow-up period. The 10-year diabetes incidence in men was 8.30% (8.08%-8.49%), which was significantly higher (odds ratio 1.37, 95% CI 1.32-1.41) than that in women at 6.20% (6.00%-6.40%). The incidence of T2DM was doubled in individuals with obesity (men: 17.78% [17.05%-18.43%]; women: 14.59% [13.99%-15.17%]) compared with that of nonobese individuals. The gradient boosting machine model showed the best performance among the four models (area under the curve of 79% in 3-year prediction and 75% in 10-year prediction). All machine-learning models predicted BMI as the most significant factor contributing to diabetes onset, which explained 12%-50% of the variance in the prediction of diabetes. The model predicted that if BMI in obese and overweight participants could be hypothetically reduced to a healthy range, the 10-year probability of diabetes onset would be significantly reduced from 8.3% to 2.8% (P<.001). Conclusions A one-time self-reported survey can accurately predict the risk of diabetes using a machine-learning approach. Achieving a healthy BMI can significantly reduce the risk of developing T2DM.

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Introduction The COVID‐19 pandemic is driving unprecedented changes in healthcare services worldwide. This study aimed to quantify the impact of the first wave of the COVID‐19 pandemic on diagnostic imaging services in Australia using an interrupted time series model. Methods Monthly data were extracted from the Australian Medicare Benefits Schedule for all diagnostic imaging services performed between January 2016 and December 2019. Holt‐Winters forecasting models were developed for total imaging services as well as for each imaging modality. The models were used to predict monthly data between January 2020 and June 2020 with a 95% confidence interval ( P < 0.05). Absolute and percentage residual differences (RD) between observed and predicted services for this time period were calculated. Results There were statistically significant reductions in total imaging services performed in March 2020 (RD: −332260, −13.1%, 95% CI: −17.5% to −8.4%), April 2020 (RD: −716957, −32.4%, 95% CI: −36.2% to −28.1%) and May 2020 (RD: −571634, −21.4%, 95% CI: −25.1% to −17.3%). Nuclear medicine and CT services were relatively less impacted than general radiography, ultrasound, and MRI services. There was also a statistically significant increase in nuclear medicine and CT services performed in June 2020 compared to predicted values. Conclusions During the first wave of COVID‐19 in Australia, there was a significant reduction in total diagnostic imaging services, with variable impacts on different imaging modalities. These findings may have significant public health implications and can be used to inform evidence‐based strategies in the recovery phase of the pandemic.