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Any view or opinion expressed in any Material is the view or opinion of the person who posts such view or opinion. You may only use this website for lawful purposes.
Accordingly you may only post Material that you have the right to do so. You may not use the website for any unlawful purpose, including without limitation, to upload, post, download or otherwise use any Material that you do not have the copyright owners permission to so upload, post, download or otherwise use, or that would result in you being in breach of these terms and conditions. Healthcare-associated infection HAI : Infection patients can get while receiving medical treatment in hospitals, outpatient clinics, nursing homes, and other facilities where people receive care.
Surgical site infection SSI : When germs get into an area where surgery is or was performed, patients can get a surgical site infection. Sometimes these infections involve only the skin. Other SSIs can involve tissues under the skin, organs, or implanted material an object or material inserted or grafted into the body, such as prosthetic joints. Antibiotic stewardship : Coordinated efforts and programs to improve the use of antimicrobials in healthcare settings to ensure that hospitalized patients receive the right antibiotic, at the right dose, at the right time, and for the right duration.
Antibiotic resistance : Antibiotic resistance is the result of bacteria changing in ways that reduce or eliminate the effectiveness of antibiotics.
Carbapenem-resistant Enterobacterales CRE infections : A family of germs that is difficult to treat because they have high levels of resistance to antibiotics. CRE infections are most seen in people with exposure to healthcare settings, like hospitals and long-term care facilities. Hand hygiene : The practice of cleaning hands to prevent the spread of disease-causing germs.
Healthcare personnel influenza vaccination : Influenza, or the flu, is a mild to severe respiratory illness caused by a virus. The contagious illness can easily spread from person to person, including from healthcare workers to patients. Vaccination is the best way to prevent getting and spreading the flu. Long-term care facilities LTCF : Nursing homes, skilled nursing facilities, and assisted living facilities collectively known as long-term care facilities provide a variety of services, both medical and personal care, to people who are unable to manage independently in the community.
Multidrug-resistant organism MDRO infections : An infection caused by germs that are resistant to multiple classes of antimicrobials. In some cases, the germs have become so resistant that no available antibiotics are effective against them. Prevention collaboratives : Prevention projects that consist of multiple healthcare facilities within a state or region to target an infection as a team, implement prevention strategies, share experiences between facilities, measure progress as a group, and provide feedback to clinicians and staff.
The TAP Strategy targets healthcare facilities and specific units within facilities with a disproportionate burden of HAIs to identify and address infection prevention gaps. An infection may occur if germs enter a patient through the tube, such as pneumonia or bacterial infections in the blood. In this report , the number of predicted infections is an estimate based on infections reported to NHSN during the national baseline for all facility types.
The SIR is not calculated when the number of predicted infections is less than 1. In this report , the SIRs compare the observed number of infections reported to the National Healthcare Safety Network NHSN during to the predicted number of infections based on the referent period, adjusting for key risk factors. The SUR is not calculated when the number of predicted device days is less than 1.
In this report , the SURs compare the observed number of device days reported to the National Healthcare Safety Network NHSN during to the predicted number of device days based on the referent period, adjusting for key risk factors. This generally involves an assessment to ensure all relevant infections were captured in the system. It may also involve checking the accuracy, or quality, of the submitted data. For example, some states only validate data from one facility while other states validate more widely.
States that validate data or use advanced tools to detect HAIs may find and report more infections than states that do not validate. In this report , state validation efforts are specified and classified into two categories for each HAI type: data checked for quality and additional in-depth data review.
This does not involve reviewing medical records. In this report , the following criteria were used to assign credit to states that performed data quality checks:. The auditing process may identify more HAIs in a hospital than originally reported.
As such, states that perform data audits may have a higher SIR when compared to states that do not perform data audits. Skip directly to site content Skip directly to page options Skip directly to A-Z link. Healthcare-associated Infections. Section Navigation. Facebook Twitter LinkedIn Syndicate. Minus Related Pages. Nationally, among acute care hospitals, the annual highlights in this report include:.
Note: The initial set of acute care hospital targets and metrics included a measure on SCIP processes. Develop and improve products. List of Partners vendors. An error term is a residual variable produced by a statistical or mathematical model, which is created when the model does not fully represent the actual relationship between the independent variables and the dependent variables.
As a result of this incomplete relationship, the error term is the amount at which the equation may differ during empirical analysis. An error term represents the margin of error within a statistical model; it refers to the sum of the deviations within the regression line , which provides an explanation for the difference between the theoretical value of the model and the actual observed results.
The regression line is used as a point of analysis when attempting to determine the correlation between one independent variable and one dependent variable. An error term essentially means that the model is not completely accurate and results in differing results during real-world applications.
For example, assume there is a multiple linear regression function that takes the following form:. When the actual Y differs from the expected or predicted Y in the model during an empirical test, then the error term does not equal 0, which means there are other factors that influence Y.
In instances where the price is exactly what was anticipated at a particular time, the price will fall on the trend line and the error term will be zero. Points that do not fall directly on the trend line exhibit the fact that the dependent variable, in this case, the price, is influenced by more than just the independent variable, representing the passage of time.
The error term stands for any influence being exerted on the price variable, such as changes in market sentiment. The two data points with the greatest distance from the trend line should be an equal distance from the trend line, representing the largest margin of error. If a model is heteroskedastic , a common problem in interpreting statistical models correctly, it refers to a condition in which the variance of the error term in a regression model varies widely.
Linear regression is a form of analysis that relates to current trends experienced by a particular security or index by providing a relationship between a dependent and independent variables, such as the price of a security and the passage of time, resulting in a trend line that can be used as a predictive model.
A linear regression exhibits less delay than that experienced with a moving average , as the line is fit to the data points instead of based on the averages within the data. Therefore, we propose to perform a smoothing average of data e. In the event of a big data dump, we also need to make adjustment to the data and distribute the cases over time. These adjustment to public databases would not only improve model handling but also be valuable for our interpretation and application.
First, we model the observed incidence cases using similar ideas as appeared in Cori et al. Assuming a Poisson distribution for the daily incidence number, and a gamma distribution for the series interval, we are able to estimate the parameter i.
The detailed description of our methods can be found in S1 Appendix. Some basic assumptions are necessary for using our methods. In order to determine the value of the effective reproduction number R e , we made the assumption that R e has a prior gamma distribution with a shape parameter of 1 and a scale parameter of 5, similar to Cori et al. These hyper-parameters are generally fixed in our model and in our projection.
We first demonstrate how to use our methods for predicting COVID cases in Texas, a large and diverse state in the US with a population size of approximately 29 million. We emphasize the importance of understanding how the case reports can be influenced by administrative issues, and the need to adjust our model accordingly.
For example, on September 21, there was a reported 14, cases for Harris county due to processing of backlogged data on that day. This artificial spike would influence the estimate of R e , and consequently, the prediction going forward. Therefore, we reassigned those cases from Harris county according to the following rule: We first imputed the number of cases on that day using the average number of cases in the past seven days.
Then we evenly spread the extra cases over the previous 31 days including that index day of September The modified series would be treated as the observed series in our subsequent modeling analysis.
Another modification we made was to smooth the data series. Due to the high variability of the daily cases, and the fact that there was often a delay in reporting especially during the weekends, we smoothed the data using the following algorithm, similar to Sun et al. The smoothed data series were the data we used for generating our prediction models. During the month of April, the case counts were kept very low due to a statewide Shelter-in-Place order that was enacted by the Governor.
Beginning May 1, Texas started phased reopening process, with many restrictions lifted in early June, right after the Memorial Day holiday. The daily incidence cases began to increase dramatically after Memorial Day weekend, and continued throughout June, reaching a peak daily incidence of about 13, in early July.
A statewide mask mandate was implemented on July 3, , and a couple of weeks after that, we started to see a downward trend in the incidence cases. Unfortunately, the trend reversed starting in early September, with cases increasing again and a reproduction number above 1. The uptick was possibly due to Labor Day weekend gatherings and widespread reopening of in-person options for schools and colleges for the Fall semester.
The epidemic was then kept under control for a while until Mid-October, when COVID cases started to increase dramatically both statewide and nationwide. For illustration purposes, we applied our prediction method at four equally spaced time points that were two months apart: April 15, June 15, August 15, and October The predicted daily cases and cumulative cases, together with their prediction intervals for the next three weeks are shown in Figs 2 and 3 separately.
The shaded areas indicate prediction intervals. As expected, our predictions performed differently at different times. On April 15, our forecast assuming constant transmission rate matched the observed data very well.
Secondarily, we chose to test the applicability of our model to a smaller geographic region within Texas. We applied our method to predicting the number of cases for the Brazos Valley BV , a group of seven counties in Texas i. This area is approximately miles from both Austin and Houston and has a younger population than Texas as a whole. Several healthcare entities and a public health authority in the BV needed timely and accurate forecasts to support planning for local COVID cases.
The BV incidence cases and the estimated reproduction number R e t using day intervals are presented in Fig 4. The trend for BV was influenced by the local context so it did not always follow the trend in Texas. Therefore, we chose to use day intervals for our modeling approach, but we also provided results using 7-day intervals in S4 — S6 Figs for additional information.
All other parameters were the same as appeared in the state model, and we made predictions on the same days as we did for the state model. The predicted daily incidence cases and cumulative incidence cases for BV are shown in Figs 5 and 6 separately. On April 15, our prediction assuming the same transmission rate sustained agreed well with the observed cases. On June 15, when the transmission rate increased rapidly, the prediction upper bounds followed approximately the observed curve.
Our forecast based on past history did not capture the increased case numbers at the end of August when school started, since we had an influx of cases due to thousands of students moving to Brazos county from all over Texas. Starting October 15, although past trend suggested increasing incidence cases, the observed data matched more closely with the prediction lower bounds.
Our model and method produced reasonably accurate results when the R e value is distributed similarly in the future as it is in the past. Large deviations from the predicted results can imply that a change in policy or some other factors have occurred that have dramatically altered the R e value over time. We have proposed a method that generates predictions for the number of COVID infectious disease cases in the future, based on what estimates of R e are like at the current time.
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