scholarly journals Estimating the Impact of County Boundaries on State-wide Patient-Sharing Network Models

2020 ◽  
Vol 41 (S1) ◽  
pp. s220-s221
Author(s):  
Daniel Sewell ◽  
Samuel Justice ◽  
Amy Hahn ◽  
Sriram Pemmaraju ◽  
Alberto Segre ◽  
...  
Keyword(s):  
Network Models ◽  
Disease Spread ◽  
Network Data ◽  
Training Set ◽  
Individual Based Model ◽  
American Hospital ◽  
American Hospital Association ◽  
Edge Betweenness ◽  
The Individual ◽  
The Impact

Background: In the field of public health, network models are useful for understanding the spread of both information and infectious diseases. Collecting network data requires determining network boundaries (ie, the entities selected for data collection). These decisions, if not made carefully, have potential outsized downstream effects on study findings. In practice, collaboration and coordination between healthcare organizations are often dictated by historical or geopolitical boundaries (eg, state or county boundaries), which may distort the underlying network under study, and thereby affect the reliability and/or accuracy of the network model. Objective: We compared natural communities in a patient-sharing network with those induced by geopolitical boundaries. Methods: Using data from the Healthcare Cost and Utilization Project (HCUP), we constructed a patient-sharing network among hospitals in California, splitting the data into a training set and a holdout set. We performed edge-betweenness clustering on the training set, and with the holdout set, we compared the resulting partition with the partition by counties using modularity. We also clustered contiguous counties that might function more cohesively together than individually. We performed spatially constrained hierarchical clustering on the network constructed from total patient flow between pairs of counties. The results were again compared via modularity on the holdout set to the county partition. Lastly, we built an individual-based model (IBM) using HCUP and American Hospital Association data to perform epidemic simulations. For each of several counties, we implemented this model to estimate the proportion of patients infected over time. We then reran the individual-based model using the entire state while dividing the results into corresponding counties. Results: In total, 680,485 patients transferred between 374 hospitals in 55 counties from 2003 to 2011. The out-of-sample modularity for the edge-betweenness clustering partition was 464% higher than that of the county partition. Aggregating the counties into half as many contiguous clusters was 319% higher, and aggregating them into 6 clusters was 489% higher (Fig. 1). The epicurves from the individual-based model ranged from small to significant deviations between state versus county boundaries (Fig. 2) . Conclusions: Collecting network data using externally imposed boundaries may lead to inaccurate network models. For example, counties serve as a poor proxy for their underlying communities, resulting in poor overall disease spread simulation results when county boundaries are allowed to drive network construction. These issues should be considered when building coordination partnerships such as the Accountable Communities for Health.Funding: NoneDisclosures: None

scholarly journals Barriers to hospital electronic public health reporting and implications for the COVID-19 pandemic

2020 ◽  
Vol 27 (8) ◽  
pp. 1306-1309
Author(s):  
A Jay Holmgren ◽  
Nate C Apathy ◽  
Julia Adler-Milstein
Keyword(s):  
Public Health ◽  
Surveillance Data ◽  
Electronic Data ◽  
American Hospital ◽  
American Hospital Association ◽  
Public Health Agencies ◽  
Hospital Referral ◽  
Health Agencies ◽  
The Impact ◽  
To Receive

Abstract We sought to identify barriers to hospital reporting of electronic surveillance data to local, state, and federal public health agencies and the impact on areas projected to be overwhelmed by the COVID-19 pandemic. Using 2018 American Hospital Association data, we identified barriers to surveillance data reporting and combined this with data on the projected impact of the COVID-19 pandemic on hospital capacity at the hospital referral region level. Our results find the most common barrier was public health agencies lacked the capacity to electronically receive data, with 41.2% of all hospitals reporting it. We also identified 31 hospital referral regions in the top quartile of projected bed capacity needed for COVID-19 patients in which over half of hospitals in the area reported that the relevant public health agency was unable to receive electronic data. Public health agencies’ inability to receive electronic data is the most prominent hospital-reported barrier to effective syndromic surveillance. This reflects the policy commitment of investing in information technology for hospitals without a concomitant investment in IT infrastructure for state and local public health agencies.

scholarly journals HUMAN BEHAVIORAL CHANGES AND ITS IMPACT IN DISEASE MODELING

2015 ◽  
Vol 77 (33) ◽  
Author(s):  
Teoh Shian Li ◽  
Jane Labadin ◽  
Phang Piau ◽  
Ling Yeong Tyng ◽  
Shapiee Abd Rahman
Keyword(s):  
Uniform Distribution ◽  
Rural Areas ◽  
Disease Modeling ◽  
Demographic Factors ◽  
Behavioral Changes ◽  
Individual Based Model ◽  
Preventive Actions ◽  
Urban And Rural Areas ◽  
The Individual ◽  
The Impact

One of the threats of the world health is the infectious diseases. This leads to the raise of concern of the policymakers and disease researchers. Vaccination program is one of the methods to prevent the vaccine-preventable diseases and hence help to eradicate the diseases. The impact of the preventive actions is related to the human behavioral changes. Fear of the diseases will increase one’s incentive in taking the preventive actions to avoid the diseases. As human behavioral changes affecting the impact of the preventive actions, the individual-based model is constructed to incorporate the behavioral changes in disease modeling. The agents in the individual-based model are allowed to move randomly and interact with each other in the environment. The interactions will cause the disease viruses as well as the fearfulness to be spread in the population. In addition, the individual-based model can have different environment setups to distinguish the urban and rural areas. The results shown in this paper are divided into two subsections, which are the justification of using uniform distribution as random number generator, and the variation of disease spread dynamics in urban and rural areas. Based on the results, the uniform distribution is found to be sufficient in generating the random numbers in this model as there is no extreme outlier reported in the experiment. We have hypothesized the individuals in urban area to have higher level of fearfulness compared to those in rural area. However, the preliminary results of the survey conducted show a disagreement with the hypothesis. Nevertheless, the data collected still show two distinct classes of behavior. Thus, the distinction does not fall into the samples taken from rural or urban areas but perhaps more on the demographic factors. Therefore, the survey has to be study again and demographic factors have to be included in the survey as we could not distinguish the level of fearfulness by areas.  

scholarly journals Homebound by COVID19: The Benefits and Consequences of Non-Pharmaceutical Intervention Strategies

2020 ◽  
Author(s):  
Buse Eylul Oruc ◽  
Arden Baxter ◽  
Pinar Keskinocak ◽  
John Asplund ◽  
Nicoleta Serban
Keyword(s):  
School Closure ◽  
Disease Spread ◽  
Contact Network ◽  
Population Mixing ◽  
Individual Level ◽  
History Of ◽  
Potential Benefits ◽  
The Individual ◽  
The Impact ◽  
Infection Spread

Abstract Background. Recent research has been conducted by various countries and regions on the impact of non-pharmaceutical interventions (NPIs) on reducing the spread of COVID19. This study evaluates the tradeoffs between potential benefits (e.g., reduction in infection spread and deaths) of NPIs for COVID19 and being homebound (i.e., refraining from interactions outside of the household).Methods. An agent-based simulation model, which captures the natural history of the disease at the individual level, and the infection spread via a contact network assuming heterogeneous population mixing in households, peer groups (workplaces, schools), and communities, is adapted to project the disease spread and estimate the number of homebound people and person-days under multiple scenarios, including combinations of shelter-in-place, voluntary quarantine, and school closure in Georgia from March 1 to September 1, 2020.Results. Compared to no intervention, under voluntary quarantine, voluntary quarantine with school closure, and shelter-in-place with school closure scenarios 4.5, 23.1, and 200+ homebound adult-days were required to prevent one infection, with the maximum number of adults homebound on a given day in the range of 119K-248K, 465K-499K, 5,388K-5,389K, respectively. Compared to no intervention, school closure only reduced the percentage of the population infected by less than 16% while more than doubling the peak number of adults homebound.Conclusions. Voluntary quarantine combined with school closure significantly reduced the number of infections and deaths with a considerably smaller number of homebound person-days compared to shelter-in-place.

scholarly journals Mitigating isolation: The use of rapid antigen testing to reduce the impact of self-isolation periods

2021 ◽  
Author(s):  
Declan Bays ◽  
Timothy Whiteley ◽  
Matt Pindar ◽  
Johnathon Taylor ◽  
Brodie F Walker ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Fixed Time ◽  
Disease Spread ◽  
Essential Services ◽  
Testing Technology ◽  
Antigen Tests ◽  
Widespread Access ◽  
The Individual ◽  
The Impact ◽  
Number Of Individuals

Isolating, either enforced or self-guided, is a well-recognised and used technique in the limitation and reduction of disease spread. This usually balances the societal harm of disease transmission against the individual harm of being isolated and is typically limited to a very small number of individuals. With the widespread transmission of SARS-CoV-2 and requirements to self-isolate when symptomatic or having tested positive, the number of people affected has grown very large causing noticeable individual cost, and disruption to the provision of essential services. With widespread access to reliable rapid antigen tests (also known as LFD or LFTs), in this paper we examine strategies to utilise this testing technology to limit the individual harm whist maintaining the protective effect of isolation. We extend this work to examine how isolation may be improved and mitigate the release of infective individuals into the population caused by fixed time-periods.

Cancer survivors’ perspectives on the U.S. generic drug shortage.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. e19503-e19503 ◽  
Author(s):  
Stephanie Nutt ◽  
Ruth Rechis ◽  
Lawrence N. Shulman ◽  
Brandon M. Hayes-Lattin
Keyword(s):  
Cancer Survivors ◽  
Cancer Treatment ◽  
Generic Drug ◽  
Online Survey ◽  
The United States ◽  
Drug Shortage ◽  
American Hospital ◽  
American Hospital Association ◽  
The Impact ◽  
The U.S

e19503 Background: Approximately 1.6 million new cancer cases are expected to be diagnosed in 2012 in the United States, making the need for generic cancer treatment drugs widespread. Methods: In late 2011, LIVESTRONG fielded the Drug Shortage Survey, which mirrored the questions from the American Hospital Association drug shortage survey for providers reworded for a patient audience. The primary goals of the brief survey were to 1) assess if survivors were aware of a generic drug shortage in the U.S. and 2) gauge the impact that the drug shortage has had on people affected by cancer. Individuals were asked to participate in this online survey through a number of means (including the LIVESTRONG Blog). Results: Between October 31, 2011 and January 30, 2012, 114 individuals completed the drug shortage survey, including 54 individuals who have been personally diagnosed with cancer. A little under half of the respondents (47%) felt they or a loved one had been impacted by the drug shortage, while an additional 30% were not sure if they had been impacted. Of the individuals impacted, the most commonly reported adverse effects of the drug shortage were not receiving medications needed for cancer treatment (40%) and receiving different medications (36%). The table highlights the reported effects. Conclusions: Results from this survey indicate that for cancer survivors who chose to participate in this survey who had been impacted by the drug shortage, the impact has been very significant. Additionally, results indicate that more information may need to be disseminated to the patient population about the drug shortage. [Table: see text]

scholarly journals Impact of Network Topology on the Spread of Infectious Diseases

2020 ◽  
Vol 21 (1) ◽  
pp. 95
Author(s):  
Eduardo R. Pinto ◽  
Erivelton G. Nepomuceno ◽  
Andriana S. L. O. Campanharo
Keyword(s):  
Infectious Disease ◽  
Complex Network ◽  
Network Models ◽  
Vaccination Strategy ◽  
Small World ◽  
Disease Spread ◽  
Complex Network Theory ◽  
General Complex ◽  
The Individual ◽  
Complex Network Models

The complex network theory constitutes a natural support for the study of a disease propagation. In this work, we present a study of an infectious disease spread with the use of this theory in combination with the Individual Based Model. More specifically, we use several complex network models widely known in the literature to verify their topological effects in the propagation of the disease. In general, complex networks with different properties result in curves of infected individuals with different behaviors, and thus, the growth of a given disease is highly sensitive to the network model used. The disease eradication is observed when the vaccination strategy of 10% of the population is used in combination with the random, small world or modular network models, which opens an important space for control actions that focus on changing the topology of a complex network as a form of reduction or even elimination of an infectious disease.

scholarly journals Homebound by COVID19: the benefits and consequences of non-pharmaceutical intervention strategies

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Buse Eylul Oruc ◽  
Arden Baxter ◽  
Pinar Keskinocak ◽  
John Asplund ◽  
Nicoleta Serban
Keyword(s):  
School Closure ◽  
Disease Spread ◽  
Contact Network ◽  
Population Mixing ◽  
Individual Level ◽  
History Of ◽  
Potential Benefits ◽  
The Individual ◽  
The Impact ◽  
Infection Spread

Abstract Background Recent research has been conducted by various countries and regions on the impact of non-pharmaceutical interventions (NPIs) on reducing the spread of COVID19. This study evaluates the tradeoffs between potential benefits (e.g., reduction in infection spread and deaths) of NPIs for COVID19 and being homebound (i.e., refraining from interactions outside of the household). Methods An agent-based simulation model, which captures the natural history of the disease at the individual level, and the infection spread via a contact network assuming heterogeneous population mixing in households, peer groups (workplaces, schools), and communities, is adapted to project the disease spread and estimate the number of homebound people and person-days under multiple scenarios, including combinations of shelter-in-place, voluntary quarantine, and school closure in Georgia from March 1 to September 1, 2020. Results Compared to no intervention, under voluntary quarantine, voluntary quarantine with school closure, and shelter-in-place with school closure scenarios 4.5, 23.1, and 200+ homebound adult-days were required to prevent one infection, with the maximum number of adults homebound on a given day in the range of 119 K–248 K, 465 K–499 K, 5388 K-5389 K, respectively. Compared to no intervention, school closure only reduced the percentage of the population infected by less than 16% while more than doubling the peak number of adults homebound. Conclusions Voluntary quarantine combined with school closure significantly reduced the number of infections and deaths with a considerably smaller number of homebound person-days compared to shelter-in-place.

scholarly journals Enhancing Surveillance on the BioSense Platform through Improved Onboarding Processes

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Travis Mayo ◽  
Michael Coletta ◽  
Sophia Crossen ◽  
Kristen Oliver
Keyword(s):  
Public Health ◽  
Syndromic Surveillance ◽  
Round Table ◽  
Surveillance Program ◽  
Data Validation ◽  
American Hospital ◽  
American Hospital Association ◽  
New Public Health ◽  
Data Source ◽  
The Impact

Objective: This session will present the impacts of enhancements made to National Syndromic Surveillance Program (NSSP) BioSense Platform Onboarding in 2017 from the perspective of CDC and public health jurisdictions.Introduction: In 2017, the National Syndromic Surveillance Program (NSSP) continued to expand as a national scope data source with over 6,500 facilities registered on the BioSense Platform, including 4,000 active, 1,800 onboarding, and 700 planned or inactive facilities. 2,086 of the active facilities are Emergency Departments across 49 sites in 41 states. The growth of data available in NSSP has been driven by continued enhancements to tools and processes used by the NSSP Onboarding Team. These enhancements help to rapidly integrate new healthcare facilities and onboard new public health sites in support of American Hospital Association (AHA) Emergency Department (ED) representativeness goals. Furthermore, with these improvements to the onboarding process, including the Master Facility Table update process and automated data validation reporting, NSSP has broadened stakeholder participation in the onboarding process.Description: This panel presentation will focus on the impact of the enhancements to NSSP Onboarding processes and tools that are the key enablers for NSSP to gather a site and nationally representative data source for event detection and novel surveillance. Panelists include Mr. Travis Mayo, NSSP Onboarding Manager, who will present the key enablers to accelerating NSSP Onboarding including enhancements to the management of the Master Facility Table (MFT), tailoring of the Engage, Connect, Validate, and Operate methodology, and the introduction of automated data validation reports. Building on the enablers presented by Mr. Mayo, Mr. Michael Coletta, will present on NSSP priorities and initiatives to optimize program efficiency in support of onboarding new sites and continuing to onboard facilities in support of national objectives for ED representatives. Mrs. Sophia Crossen will present the impact of NSSP changes in Kansas onboarding and surveillance initiatives. Mrs. Kirsten Oliver, will demonstrate how NSSP onboarding has impacted syndromic surveillance activities in West Virginia.With the need to always be looking ahead, each panelist will draw on their experiences in 2017, including their perspective on opportunities in 2018 to continue to enhance NSSP onboarding. These perspectives will serve as a basis for launching into questions and discussions from the audience to collect NSSP onboarding experiences in 2017 and ideas for continued enhancement in 2018.How the Moderator Intends to Engage the Audience in Discussions on the Topic: The round table will present the improvements implemented by NSSP Onboarding and discuss the following:- What strengths and weaknesses have the enhancements surfaced in onboarding processes- How have the enhancements impacted local onboarding initiatives and priorities- How have the enhancements changed the roles of key players in the onboarding process

Abstract 230: The Impact of Hospital Closures on Outcomes for Myocardial Infarction and Stroke

2013 ◽  
Vol 6 (suppl_1) ◽  
Author(s):  
Karen E Joynt ◽  
E. John Orav ◽  
Ashish K Jha
Keyword(s):  
Myocardial Infarction ◽  
Travel Time ◽  
Patient Outcomes ◽  
Mortality Rates ◽  
Travel Times ◽  
Stroke Patients ◽  
American Hospital ◽  
American Hospital Association ◽  
Hospital Closures ◽  
The Impact

Objective: U.S. hospitals face substantial financial challenges as policymakers try to reduce healthcare spending. As a result, it is likely that hospital closures will accelerate in the coming years. Hospital closures threaten access to care, and may negatively impact patient outcomes by lengthening travel times, particularly for illnesses for which treatment depends heavily on timely receipt of treatment. Therefore, our objective was to assess the impact of hospital closures on patient outcomes for acute myocardial infarction (AMI) and stroke over the past decade. Methods: We used data from Medicare files as well as the American Hospital Association from 2002 through 2010 to identify hospital closures. For each hospital that closed, we identified Medicare fee-for-service patients that sought care at that hospital for AMI or stroke in the year prior to closure. We then determined the nearest alternative hospital using geocoding for each patient in the sample, and determined the additional travel time associated with this alternative site of care. We compared the characteristics of the closing versus alternative hospitals. We then created multivariate hierarchical logistic regression models in which we reassigned each of the patients at a hospital that closed to the nearest alternative hospital, and compared mortality rates between the closed and alternative facilities. Results: Between 2002 and 2010, we identified 121 hospital closures. There were 1,109 patients that received AMI care and 1,104 patients that received stroke care at a hospital that closed in the subsequent year. The median age was 79 for both conditions; of the AMI patients, 45% were male, and of the stroke patients, 39% were male. Travel time increased by 2.8 minutes for AMI patients and by 1.5 minutes for stroke patients. For AMI patients, the alternative hospital to which they would have traveled was more often large (0% versus 28%, p<0.001), non-profit (63% versus 79%, p<0.001), and teaching (1.1% versus 8.2%, p<0.001) compared with hospitals that closed. Patterns were similar for patients with stroke. For AMI, there was no difference in 30-day risk-adjusted mortality rates for the closed versus alternative hospitals (19.03% versus 19.17%, difference 0.14%, p=0.90). Similarly, for stroke, there was no difference in 30-day risk-adjusted mortality rates (19.57% versus 19.96%, difference 0.39%, p=0.75). Conclusions: We found no evidence that hospital closure was associated with worse clinical outcomes for AMI or stroke for patients. Though there were increased travel times for both conditions, this was likely offset by an increase in hospital quality in the alternative hospitals. These findings should provide some reassurance to those concerned that hospital closures will lead to significantly worse patient outcomes, even for conditions for which timely receipt of treatment is critical.

Analysis of Hospital Resource Availability and COVID-19 Mortality Across the United States

2021 ◽  
pp. E1-E4
Author(s):  
Alexander T Janke ◽  
Hao Mei ◽  
Craig Rothenberg ◽  
Robert D Becher ◽  
Zhenqiu Lin ◽  
...  
Keyword(s):  
United States ◽  
New York ◽  
New York Times ◽  
General Medicine ◽  
The United States ◽  
American Hospital ◽  
American Hospital Association ◽  
Hospital Referral ◽  
Hospital Resources ◽  
The Impact

While the impact of coronavirus disease 2019 (COVID-19) has varied greatly across the United States, there has been little assessment of hospital resources and mortality. We examine hospital resources and death counts among hospital referral regions (HRRs) from March 1 to July 26, 2020. This was an analysis of American Hospital Association data with COVID-19 data from the New York Times. Hospital-based resource availabilities were characterized per COVID-19 case. Death count was defined by monthly confirmed COVID-19 deaths. Geographic areas with fewer intensive care unit (ICU) beds (incident rate ratio [IRR], 0.194; 95% CI, 0.076-0.491), nurses (IRR, 0.927; 95% CI, 0.888-0.967), and general medicine/surgical beds (IRR, 0.800; 95% CI, 0.696-0.920) per COVID-19 case were statistically significantly associated with greater deaths in April. This underscores the potential impact of innovative hospital capacity protocols and care models to create resource flexibility to limit system overload early in a pandemic.

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