Incentivizing hospital infection control

Healthcare-associated infections (HAIs) pose a significant burden to patient safety. Institutions can implement hospital infection control (HIC) measures to reduce the impact of HAIs. Since patients can carry pathogens between institutions, there is an economic incentive for hospitals to free ride on the HIC investments of other facilities. Subsidies for infection control by public health authorities could encourage regional spending on HIC. We develop coupled mathematical models of epidemiology and hospital behavior in a game-theoretic framework to investigate how hospitals may change spending behavior in response to subsidies. We demonstrate that under a limited budget, a dollar-for-dollar matching grant outperforms both a fixed-amount subsidy and a subsidy on uninfected patients in reducing the number of HAIs in a single institution. Additionally, when multiple hospitals serve a community, funding priority should go to the hospital with a lower transmission rate. Overall, subsidies incentivize HIC spending and reduce the overall prevalence of HAIs.

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Coronavirus (COVID-19) Outbreak Prediction Using Epidemiological Models of Richards Gompertz Logistic Ratkowsky and SIRD

10.1101/2020.11.29.20240580 ◽

2020 ◽

Author(s):

Ahmad Sedaghat ◽

Seyed Amir Abbas Oloomi ◽

Mahdi Ashtian Malayer ◽

Nima Rezaei ◽

Amir Mosavi

Keyword(s):

Transmission Rate ◽

Pearson Correlation ◽

Coefficient Of Determination ◽

Pharmacological Interventions ◽

Health Authorities ◽

Public Health Authorities ◽

Lower Transmission ◽

Near Future ◽

Second Wave ◽

Second Peak

AbstractOn 30 July 2020, a total number of 301,530 diagnosed COVID-19 cases were reported in Iran, with 261,200 recovered and 16,569 dead. The COVID-19 pandemic started with 2 patients in Qom city in Iran on 20 February 2020. Accurate prediction of the end of the COVID-19 pandemic and the total number of populations affected is challenging. In this study, several widely used models, including Richards, Gompertz, Logistic, Ratkowsky, and SIRD models, are used to project dynamics of the COVID-19 pandemic in the future of Iran by fitting the present and the past clinical data. Iran is the only country facing a second wave of COVID-19 infections, which makes its data difficult to analyze. The present study’s main contribution is to forecast the near-future of COVID-19 trends to allow non-pharmacological interventions (NPI) by public health authorities and/or government policymakers. We have divided the COVID-19 pandemic in Iran into two waves, Wave I, from February 20, 2020 to May 4, 2020, and Wave II from May 5, 2020, to the present. Two statistical methods, i.e., Pearson correlation coefficient (R) and the coefficient of determination (R2), are used to assess the accuracy of studied models. Results for Wave I Logistic, Ratkowsky, and SIRD models have correctly fitted COVID-19 data in Iran. SIRD model has fitted the first peak of infection very closely on April 6, 2020, with 34,447 cases (The actual peak day was April 7, 2020, with 30,387 active infected patients) with the re-production number R0=3.95. Results of Wave II indicate that the SIRD model has precisely fitted with the second peak of infection, which was on June 20, 2020, with 19,088 active infected cases compared with the actual peak day on June 21, 2020, with 17,644 cases. In Wave II, the re-production number R0=1.45 is reduced, indicating a lower transmission rate. We aimed to provide even a rough project future trends of COVID-19 in Iran for NPI decisions. Between 180,000 to 250,000 infected cases and a death toll of between 6,000 to 65,000 cases are expected in Wave II of COVID-19 in Iran. There is currently no analytical method to project more waves of COVID-19 beyond Wave II.

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Coronavirus (COVID-19) Outbreak Prediction Using Epidemiological Models of Richards Gompertz Logistic Ratkowsky and SIRD

10.31219/osf.io/c7twb ◽

2020 ◽

Author(s):

Ahmad Sedaghat ◽

Seyed Amir Abbas Oloomi ◽

Ashtian Malayer ◽

Nima Rezaei ◽

Amir Mosavi

Keyword(s):

Transmission Rate ◽

Pearson Correlation ◽

Coefficient Of Determination ◽

Pharmacological Interventions ◽

Health Authorities ◽

Public Health Authorities ◽

Lower Transmission ◽

Near Future ◽

Second Wave ◽

Second Peak

On 30 July 2020, a total number of 301,530 diagnosed COVID-19 cases were reported in Iran, with 261,200 recovered and 16,569 dead. The COVID-19 pandemic started with 2 patients in Qom city in Iran on 20 February 2020. Accurate prediction of the end of the COVID-19 pandemic and the total number of populations affected is challenging. In this study, several widely used models, including Richards, Gompertz, Logistic, Ratkowsky, and SIRD models, are used to project dynamics of the COVID-19 pandemic in the future of Iran by fitting the present and the past clinical data. Iran is the only country facing a second wave of COVID-19 infections, which makes its data difficult to analyze. The present study's main contribution is to forecast the near-future of COVID-19 trends to allow non-pharmacological interventions (NPI) by public health authorities and/or government policymakers. We have divided the COVID-19 pandemic in Iran into two waves, Wave I, from February 20, 2020 to May 4, 2020, and Wave II from May 5, 2020, to the present. Two statistical methods, i.e., Pearson correlation coefficient (R) and the coefficient of determination (R2), are used to assess the accuracy of studied models. Results for Wave I Logistic, Ratkowsky, and SIRD models have correctly fitted COVID-19 data in Iran. SIRD model has fitted the first peak of infection very closely on April 6, 2020, with 34,447 cases (The actual peak day was April 7, 2020, with 30,387 active infected patients) with the re-production number R0=3.95. Results of Wave II indicate that the SIRD model has precisely fitted with the second peak of infection, which was on June 20, 2020, with 19,088 active infected cases compared with the actual peak day on June 21, 2020, with 17,644 cases. In Wave II, the re-production number R0=1.45 is reduced, indicating a lower transmission rate. We aimed to provide even a rough project future trends of COVID-19 in Iran for NPI decisions. Between 180,000 to 250,000 infected cases and a death toll of between 6,000 to 65,000 cases are expected in Wave II of COVID-19 in Iran. There is currently no analytical method to project more waves of COVID-19 beyond Wave II.

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Hospital Infection Control Requirements Upgraded

Pediatric News ◽

10.1016/s0031-398x(08)70496-x ◽

2008 ◽

Vol 42 (10) ◽

pp. 16-17

Author(s):

MARY ELLEN SCHNEIDER

Keyword(s):

Infection Control ◽

Hospital Infection ◽

Hospital Infection Control

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Hospital infection control best practice: Five essential elements to successfully minimize healthcare-associated COVID-19

Infection Prevention in Practice ◽

10.1016/j.infpip.2020.100110 ◽

2020 ◽

pp. 100110

Author(s):

N.H. Chan ◽

S.Y. Lee ◽

N.H.Y. Cheng ◽

H.Y. Wong ◽

W.K. Lo ◽

...

Keyword(s):

Infection Control ◽

Best Practice ◽

Essential Elements ◽

Hospital Infection ◽

Hospital Infection Control ◽

Healthcare Associated

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The funding and organization of infection control in NHS hospital trusts: a study of infection control professionals' views

Health Services Management Research ◽

10.1258/095148403321591393 ◽

2003 ◽

Vol 16 (2) ◽

pp. 71-84 ◽

Cited By ~ 2

Author(s):

B. Croxson ◽

P. Allen ◽

J. A. Roberts ◽

K. Archibald ◽

S. Crawshaw ◽

...

Keyword(s):

Infection Control ◽

Nationwide Survey ◽

Organizational Structures ◽

Hospital Infection ◽

Hospital Acquired Infection ◽

Hospital Infection Control ◽

Complex Picture ◽

Ward Level ◽

Almost All ◽

Hospital Acquired

The problems associated with hospital-acquired infection have been causing increasing concern in England in recent years. This paper reports the results of a nationwide survey of hospital infection control professionals' views concerning the organizational structures used to manage and obtain funding for control of infection. A complex picture with significant variation between hospitals emerges. Although government policy dictates that specific funding for hospital infection control is formally made available, it is not always the case that infection control professionals have adequate resources to undertake their roles. In some cases this reflects the failure of hospitals' infection control budgetary mechanisms; in others it reflects the effects of decentralizing budgets to directorate or ward level. Some use was made of informal mechanisms either to supplement or to substitute for the formal ones. But almost all infection control professionals still believed they were constrained in their ability to protect the hospital population from the risk of infectious disease. It is clear that recent government announcements that increased effort will be made to support local structures and thereby improve the control of hospital acquired infection are to be welcomed.

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907. Technology and Medicine: Prediction of Surgical Site Infection in Clean Surgeries using Artificial Neural Networks

Open Forum Infectious Diseases ◽

10.1093/ofid/ofaa439.1095 ◽

2020 ◽

Vol 7 (Supplement_1) ◽

pp. S487-S487

Author(s):

Flávio Henrique Batista de Souza ◽

Braulio Roberto Gonçalves Marinho Couto ◽

Felipe Leandro Andrade da Conceição ◽

Gabriel Henrique Silvestre da Silva ◽

Igor Gonçalves Dias ◽

...

Keyword(s):

Statistical Analysis ◽

Data Collection ◽

Infection Control ◽

Surgical Site Infection ◽

Predictive Power ◽

Hospital Infection ◽

Automated System ◽

Site Infection ◽

Average Hospital ◽

Hospital Infection Control

Abstract Background In Belo Horizonte, a city with 3,000,000 inhabitants, a survey was performed in six hospitals, between July 2016 and June 2018, about surgical site infection (SSI) in patients undergoing clean surgery procedures. The main objective is to statistically evaluate such incidences and enable an analysis of the SSI predictive power, through MLP (Multilayer Perceptron) pattern recognition algorithms. Methods Through the Hospital Infection Control Committees (CCIH) of the hospitals, a data collection on SSI was carried out through the software SACIH - Automated System for Hospital Infection Control. So, three procedures were performed: a treatment of the collected database for use of intact samples; a statistical analysis on the profile of the collected hospitals; an evaluation of the predictive power of five types of MLPs (Backpropagation Standard, Momentum, Resilient Propagation, Weight Decay and Quick Propagation) for SSI prediction. The MLPs were tested with 3, 5, 7 and 10 neurons in the hidden layer and with a division of the database for the resampling process (65% or 75% for testing, 35% or 25% for validation). They were compared by measuring the AUC (Area Under the Curve - ranging from 0 to 1) presented for each of the configurations. Results From 45,990 records, 12,811 were able for analysis. The statistical analysis results were: the average age is 49 years old (predominantly between 30 and 50); the surgeries had an average time of 134.13 minutes; the average hospital stay is 4 days (from 0 to 200 days), the death rate reached 1% and the SSI 1.49%. A maximum prediction power of 0.742 was found. Conclusion There was a loss of 60% of the database samples due to the presence of noise. However, it was possible to have a relevant sample to assess the profile of these six hospitals. The predictive process, presented some configurations with results that reached 0.742, what promises the use of the structure for the monitoring of automated SSI for patients submitted to surgeries considered clean. To optimize data collection, enable other hospitals to use the prediction tool and minimize noise from the database, two mobile application were developed: one for monitoring the patient in the hospital and other for monitoring after hospital discharge. The SSI prediction analysis tool is available at www.nois.org.br. Disclosures All Authors: No reported disclosures

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Automated Risk Analysis of Surgical Site Infection in Hip Arthroplasty Surgeries

Infection Control and Hospital Epidemiology ◽

10.1017/ice.2020.649 ◽

2020 ◽

Vol 41 (S1) ◽

pp. s135-s136

Author(s):

Flávio Souza ◽

Braulio Couto ◽

Felipe Leandro Andrade da Conceição ◽

Gabriel Henrique Silvestre da Silva ◽

Igor Gonçalves Dias ◽

...

Keyword(s):

Data Collection ◽

Infection Control ◽

Surgical Site Infection ◽

Hip Arthroplasty ◽

Hospital Infection ◽

Site Infection ◽

Weight Decay ◽

Hospital Infection Control ◽

Belo Horizonte ◽

Arthroplasty Surgery

Background: In 7 hospitals in Belo Horizonte, a city with >3,000,000 inhabitants, a survey was conducted between July 2016 and June 2018, focused on surgical site infection (SSI) in patients undergoing arthroplasty surgery procedures. The main objective is to statistically evaluate such incidences and enable a study of the prediction power of SSI through pattern recognition algorithms, the MLPs (multilayer perceptron). Methods: Data were collected on SSI by the hospital infection control committees (CCIHs) of the hospitals involved in the research. All data used in the analysis during their routine SSI surveillance procedures were collected. The information was forwarded to the NOIS (Nosocomial Infection Study) Project, which used SACIH automated hospital infection control system software to collect data from a sample of hospitals participating voluntarily in the project. After data collection, 3 procedures were performed: (1) a treatment of the database collected for the use of intact samples; (2) a statistical analysis on the profile of the hospitals collected; and (3) an assessment of the predictive power of 5 types of MLP (backpropagation standard, momentum, resilient propagation, weight decay, and quick propagation) for SSI prediction. MLPs were tested with 3, 5, 7, and 10 hidden layer neurons and a database split for the resampling process (65% or 75% for testing and 35% or 25% for validation). The results were compared by measuring AUC (area under the curve; range, 0–1) presented for each of the configurations. Results: Of 1,246 records, 535 were intact for analysis. We obtained the following statistics: the average surgery time was 190 minutes (range, 145–217 minutes); the average age of the patients was 67 years (range, 9–103); the prosthetic implant index was 98.13%; the SSI rate was 1.49%, and the death rate was 1.21%. Regarding the prediction power, the maximum prediction power was 0.744. Conclusions: Despite the considerable loss rate of almost 60% of the database samples due to the presence of noise, it was possible to perform relevant sampling for the profile evaluation of hospitals in Belo Horizonte. For the predictive process, some configurations have results that reached 0.744, which indicates the usefulness of the structure for automated SSI monitoring for patients undergoing hip arthroplasty surgery. To optimize data collection and to enable other hospitals to use the SSI prediction tool (available in www.sacihweb.com ), a mobile application was developed.Funding: NoneDisclosures: None

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XarxaSalut, network of municipalities for health, Valencia Region (Spain): policy process evaluation

European Journal of Public Health ◽

10.1093/eurpub/ckaa165.235 ◽

2020 ◽

Vol 30 (Supplement_5) ◽

Author(s):

R Peiró Pérez ◽

E Pérez Sanz ◽

E Legaz Sanchez ◽

J Quiles Izquierdo ◽

Grupo XarxaSalut

Keyword(s):

Process Evaluation ◽

Citizen Participation ◽

Policy Process ◽

Local Level ◽

Common Language ◽

Health Authorities ◽

Open Questions ◽

Economic Support ◽

Public Health Authorities ◽

The Impact

Abstract “XarxaSalut” started in 2017, with the municipalities that have taken the commitment to boost the Promotion of Health (HP) at the local level through community participation, intersectorality and equity perspective. The objective is to present a policy process evaluation (2'5 years) of the implementation of XarxaSalut. Different approaches have been used; a questionnaire addressed to the municipalities at the time of adhesion including data on intersectorality, participation, HP actions and open questions; description of instruments that Regional Public Health Authorities (RPHA) has mobilized and an analysis of barriers and strengths made by the coordination office. In 2017, 17 municipalities were joined, being 197 in February 2020 (70% of the population). 65% are in a process of an organizational change through the intersectoral, decision making and participative working group. 35% are doing analysis of determinants and /or health situation, assets maps and a prioritization of HP actions. The main barriers identified by municipalities are lack of economic and personal resources, and difficulties in achieve citizen participation. The main benefits were the optimization of resources, the exchange of experiences, training, or economic support from the RPHA. Some support instruments develop for RPHA are a collection of guides for community development, funds that the municipalities can apply to support actions related with training, HP action on vulnerable population, on asset maps, participation processes, vulnerable neighborhoods, etc.; Community actions have been included in the “Health Observatory” to give visibility and social support to XarxaSalut. Interdisciplinary training processes with health and municipal professionals have been made in order to develop a common language and strength the competences for HP. Lesson learned: The need to improve coordination and a common language between different types of participants and professionals Key messages The decision makers and professionals in the municipalities understand the impact in health of the policies developed at local level but needs guide and support to deal with it. The coordination between different administrations and primary health at local level and the misunderstandings about health and their determinants are the main aspect to reinforce.

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