172. Impact of COVID-19 Pandemic on Healthcare-associated Infections (HAIs) in a Large Network of Hospitals

Background The COVID-19 pandemic had a considerable impact on US healthcare systems, straining hospital resources, staff, and operations. Our objective was to evaluate the impact of COVID-19 pandemic on incidence and trends of healthcare-associated infections (HAIs) in a network of hospitals. Methods This was a retrospective review of central-line-associated bloodstream infections (CLABSIs), catheter-associated urinary tract infections (CAUTIs), C. difficile infections (CDI), and ventilator-associated events (VAE) in 51 hospitals from 2018 to 2021. Descriptive statistics were reported as mean hospital-level monthly incidence rates (IR) and compared using Poisson regression GEE models with period as the only covariate. Segmented regression (SR) analysis was performed to estimate changes in monthly IR of CAUTIs, CLABSIs and CDI in the baseline period (01/2018 – 02/2020) and the Pandemic period (03/2020 – 03/2021). SR model was not appropriate for VAE based on the plot. All models were constructed using SAS v.9.4 (SAS Institute, Cary NC). Results Compared to the baseline period, CLABSIs increased significantly by 50% from 0.6 to 0.9/ 1000 catheter days (P< 0. 001). In contrast, no significant changes were identified for CAUTI (P=0.87). Similar trends were seen in SR models for CLABSI and CAUTI (Figures 1, 2 and Table 1). While overall CDIs decreased significantly from 3.5 to 2.5/10,000 patient days in the pandemic period (P< 0.001), SR model showed increasing pandemic trend change (Figure 3). VAEs increased > 700% from 6.9 to 59.7/1000 ventilator days (P=0.15), but displayed considerable variation during the pandemic period (Figure 4). Compared to baseline period, there was a significant increase in central line days (647 vs 677, P=0.02), ventilator days (156 vs 215, P< 0.001), but no change in urinary catheter days (675 vs 686, P=0.32) during the pandemic period. Figure 1: Segmented Regression model showing baseline and pandemic period trends of CLABSI Figure 2: Segmented Regression model showing baseline and pandemic period trends of CAUTI Figure 3: Segmented Regression model showing baseline and pandemic period trends of C. difficile (HO-CDI) infections Conclusion The COVID-19 pandemic was associated with substantial increases in CLABSIs and VAEs, no change in CAUTIs, and an increasing trend in CDI incidence. These variations in trends of different HAIs are likely due, in part, to unique characteristics of the underlying infection, resource shortages, staffing concerns, increased device use, changes in testing practices, and the limitations of surveillance definitions. Figure 4: Trend of Ventilator-Associated Events (VAE) in the baseline and pandemic period (Segmented Regression model not appropriate) Disclosures Sonali D. Advani, MBBS, MPH, Nothing to disclose David J. Weber, MD, MPH, Merck (Individual(s) Involved: Self): Consultant; PDI (Individual(s) Involved: Self): Consultant; Pfizer (Individual(s) Involved: Self): Consultant; Sanofi (Individual(s) Involved: Self): Consultant; UVinnovators (Individual(s) Involved: Self): Consultant

The impact of an additional year of education on myopia: an interrupted time series in the UK Biobank.

Introduction: In the past decade, the minimal school leaving age has been raised twice. Previous studies have found evidence for a link between this type of policy and myopia. We aim to use the 1972 raising of school leaving age to estimate the effect of the raising of school leaving age in 2013 and 2015. Methods: We use a segmented regression model to conduct an instrumental time series analyses of the effect of years of education on myopia using the 1972 raising of school leaving age. To recover the effect of a one-year change, we use the effect of the change on years of education and reflective error in an instrumental variables analysis. Results: We found evidence for a 0.60 (SE = 0.10) increase in years of education and, after adjusting for probability of having missing data and sex, a -0.14d (SE = 0.03) for refractive error. Instrumental variables analyse implies a -0.24 d/year (SE = 0.05) change in refractive error for each additional year in education. Conclusion: Our results triangulate the findings of pervious quasi-experimental methods on the effect of years of education on myopia and imply that each raising of school leaving age in the 2010s should be expected to a lead to -0.07 d/yr change in refractive error in the UK population.

Inseminating dose for the artificial fertilization of Brycon amazonicus (Teleostei: Characidae)

Summary Knowledge of the sperm–oocyte ratio in fish fertilization serves as the basis for studies on artificial reproduction and gamete manipulation. The aim of this study was to determine the minimum insemination dose for Brycon amazonicus oocyte fertilization. Female and male gametes were used and tested with the following doses of spermatozoa oocyte–1 ml–1: 10,000, 20,000, 40,000, 60,000 and 80,000 (in triplicate). Fertilization rates were calculated and estimated from the regression equation by applying the segmented regression model ‘Linear Response Plateau’ to determine the appropriate proportion of gametes. Based on the equation Ŷ = 14.3415 + 0.0007836X, the fertilization rate increased up to 63.34% as it reached a plateau with a proportion of 62,524 spermatozoa oocyte–1 ml–1, which is the minimum insemination dose recommended for artificial insemination of the species.

BayesSMILES: Bayesian Segmentation Modeling for Longitudinal Epidemiological Studies

Coronavirus disease 2019 (COVID-19) is a pandemic. To characterize the disease transmissibility, we propose a Bayesian change point detection model using daily actively infectious cases. Our model is built upon a Bayesian Poisson segmented regression model that can 1) capture the epidemiological dynamics under the changing conditions caused by external or internal factors; 2) provide uncertainty estimates of both the number and locations of change points; 3) adjust any explanatory time-varying covariates. Our model can be used to evaluate public health interventions, identify latent events associated with spreading rates, and yield better short-term forecasts.

Time Series Analysis of Cutaneous Leishmaniasis in Sabzevar Northeastern Iran Using Segmented Regression Model

Background and aims: Cutaneous leishmaniasis (CL) is one of the most important parasitic diseases in the world. Sabzevar city is endemic area for CL in the north east of Iran. The aim of this study was to evaluate the time distribution of cutaneous leishmaniasis (CL) in Sabzevar County using the segmented regression model. Methods: This ecological study used the existing data related to the rural districts of Sabzevar County that were obtained from the Health Deputy of this county during 2011-2017. In addition, the segmented regression model was applied to evaluate the time trend of CLs. Finally, Joinpoint software was used for time series analysis. Results: A total of 1912 CL cases occurred in Sabzevar County from 2011 to 2017, with an incidence rate of 93.61 per 100000. The highest and lowest observed incidence rates were in 2011 (25 per 10000 persons) and 2015 (3.24 per 10000 persons), respectively. Based on the results, the annual incidence of CL in the intended region decreased and the annual percent change was equal to -22.40. Further, the time series analysis using segmented regression by rural districts showed a change point in the trend of the incidence of leishmaniasis in three rural districts (Pain Joveyn and Joghatai in 2014 and Qasabeh-ye Sharqi in 2013). In other words, the trend was different before and after the change point in the mentioned districts. Conclusion: In general, the results indicated that interventional, preventive, and therapeutic measures for breaking the chain of CL transmission in Sabzevar have been desirable in recent years. Eventually, it is suggested that further time-series studies be conducted at the level of the month or a longer interval in order to better evaluate the period effect and secular trend.

Exploring Change in Trend of Homicide Incidence Rate in Iran from 2006 to 2016: Applying Segmented Regression Model

Background: Murder is one of the public health problems. According to the WHO reports, murder is fourth leading cause of death among young people. The aim of this study was applying joint point regression model to study trend of homicide mortality in Iran, 2006-2016. Study design: A cross-sectional panel (pseudo-panel) study. Methods: Homicide data during 2006 to 2016 were extracted from Iranian legal medicine organization. Trends of homicide incidence were summarized by annual percent change (APC) and average annual percent change (AAPC) using non-linear segmented regression model. Results: Totally, 26918 homicide cases occurred during the period from 2006 to 2016. The highest and lowest frequency was related to the 15-29 yr (46.5%) and 0-4 yr (1.5%) age groups, respectively. The homicide incidence rate of the country in 2016 was 2.81 per 100,000. The four provinces of Sistan & Baluchistan, Khuzestan, Kerman and Ilam had the highest incidence rate in 2016, respectively. During the study period, the incidence rate of homicide in Iran and men have been significantly decreased (APC: -2.8% (95% CI: -3.9, -1.7) and -3.2% (95% CI: - 4.5, -1.8) respectively (P<0.001)). Conclusion: The pattern of homicide rate has a downward trend in the country. Moreover, the varying observed trends in some provinces can be due to the variability in mental, geographical, socio-economic and cultural conditions in each region.