scholarly journals Predicting asymptomatic SARS-CoV-2 infection rates of inpatients: a time series analysis

2021 ◽  
pp. 1-11
Author(s):  
Frida Rivera ◽  
Kwang Woo Ahn ◽  
L. Silvia Munoz-Price
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Health System ◽  
System Level ◽  
County Level ◽  
Infection Rates ◽  
Series Analysis ◽  
Asymptomatic Patients

Abstract Asymptomatic SARS-CoV-2 infections are often difficult to identify as widespread surveillance has not been the norm. Using time-series analysis, we examined if COVID-19-rates at the county-level could predict positivity rates among asymptomatic-patients at a large Health System. Asymptomatic-positivity rates at the system-level and county-level COVID-19-rates failed to show an association.

scholarly journals Predicting SARS-CoV-2 Asymptomatic Infection Rate of Inpatients: A Time Series Analysis

2021 ◽  
Vol 1 (S1) ◽  
pp. s51-s52
Author(s):  
Frida Rivera ◽  
Kwang Woo Ahn ◽  
L. Silvia Munoz-Price
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Health System ◽  
Autocorrelation Function ◽  
Infection Rate ◽  
Asymptomatic Infection ◽  
County Level ◽  
Academic Health ◽  
Milwaukee County ◽  
Asymptomatic Patients

Background: Asymptomatic SARS-CoV-2 infections play a crucial role in viral transmission. However, they are often difficult to identify given that widespread surveillance has not been the norm. We sought to determine whether COVID-19 rates reported at the county level could predict the positivity rates for SARS-CoV-2 among asymptomatic patients tested in a large academic health system. Methods: This observational study was conducted from April 23, 2020, to December 10, 2020, at Froedtert Health (FH) system, the largest academic health system in Wisconsin. On April 23, 2020, FH implemented SARS-CoV-2 surveillance among all consecutive admissions not suspected of COVID-19, all patients scheduled for elective procedures and deliveries, and all asymptomatic patients with known exposures. Samples were processed by the FH laboratory using molecular methods (RT-PCR). To obtain the daily number of newly confirmed COVID-19 cases in Milwaukee County, we accessed the Wisconsin Department of Health Services publicly available COVID-19 database. For the purpose of this study, COVID-19 rates were defined as the percentage of positive tests among all daily tests performed at the county level, while SARS-CoV-2 positivity rates were the percentage of positive tests among all daily surveillance tests performed at FH among asymptomatic patients. The association between COVID-19 rates in Milwaukee County and asymptomatic rates at FH were assessed using an autoregressive moving average time series analysis. To examine the association between these rates, we fitted a seventh-order autoregression for the residuals based on autocorrelation function and partial autocorrelation function plots of the residuals from linear regression. Results: From April 23, 2020, to December 10, 2020, there were 2,347 new asymptomatic infections detected at FH and 75,196 new COVID-19 cases reported in Milwaukee County. Figure 1 shows the time-series plot of asymptomatic SARS-CoV-2 positivity rates at FH and Figure 2 shows COVID-19 rates in Milwaukee County. As the COVID-19 rate in Milwaukee County increased by 1 unit, the asymptomatic infection rate in FH decreased by 0.024 unit (95% CI, −0.053 to 0.004; P = .095) after accounting for autocorrelation over time. Thus, there was no association between these rates. Conclusions: The positivity rates among asymptomatic patients at a large medical center were not predicted by the positivity rate at the county level. This finding suggests that the epidemiology at a county level may be determined by pockets in the population who may not interact, and thus not affect, the positivity rates among asymptomatic patients served by a hospital system within the county.Funding: NoDisclosures: None

Effects of a refined evidence-based toolkit and mentored implementation on medication reconciliation at 18 hospitals: results of the MARQUIS2 study

2021 ◽  
pp. bmjqs-2020-012709
Author(s):  
Jeffrey L Schnipper ◽  
Harry Reyes Nieva ◽  
Meghan Mallouk ◽  
Amanda Mixon ◽  
Stephanie Rennke ◽  
...  
Keyword(s):  
Time Series ◽  
Quality Improvement ◽  
Best Practices ◽  
Time Series Analysis ◽  
Medication Reconciliation ◽  
System Level ◽  
Medication Discrepancies ◽  
Patient Level ◽  
Series Analysis ◽  
Over Time

BackgroundThe first Multicenter Medication Reconciliation Quality Improvement (QI) Study (MARQUIS1) demonstrated that mentored implementation of a medication reconciliation best practices toolkit decreased total unintentional medication discrepancies in five hospitals, but results varied by site. The objective of this study was to determine the effects of a refined toolkit on a larger group of hospitals.MethodsWe conducted a pragmatic quality improvement study (MARQUIS2) at 18 North American hospitals or hospital systems from 2016 to 2018. Incorporating lessons learnt from MARQUIS1, we implemented a refined toolkit, offering 17 system-level and 6 patient-level interventions. One of eight physician mentors coached each site via monthly calls and performed one to two site visits. The primary outcome was number of unintentional medication discrepancies in admission or discharge orders per patient. Time series analysis used multivariable Poisson regression.ResultsA total of 4947 patients were sampled, including 1229 patients preimplementation and 3718 patients postimplementation. Both the number of system-level interventions adopted per site and the proportion of patients receiving patient-level interventions increased over time. During the intervention, patients experienced a steady decline in their medication discrepancy rate from 2.85 discrepancies per patient to 0.98 discrepancies per patient. An interrupted time series analysis of the 17 sites with sufficient data for analysis showed the intervention was associated with a 5% relative decrease in discrepancies per month over baseline temporal trends (adjusted incidence rate ratio: 0.95, 95% CI 0.93 to 0.97, p<0.001). Receipt of patient-level interventions was associated with decreased discrepancy rates, and these associations increased over time as sites adopted more system-level interventions.ConclusionA multicentre medication reconciliation QI initiative using mentored implementation of a refined best practices toolkit, including patient-level and system-level interventions, was associated with a substantial decrease in unintentional medication discrepancies over time. Future efforts should focus on sustainability and spread.

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