scholarly journals Use of electronic health records to support a public health response to the COVID-19 pandemic in the United States: a perspective from 15 academic medical centers

2020 ◽  
Author(s):  
Subha Madhavan ◽  
Lisa Bastarache ◽  
Jeffrey S Brown ◽  
Atul J Butte ◽  
David A Dorr ◽  
...  
Keyword(s):  
Public Health ◽  
Clinical Care ◽  
Population Level ◽  
The United States ◽  
Socioeconomic Indicators ◽  
Healthcare Organizations ◽  
Public Health Response ◽  
Public Health Decision ◽  
Health Decision ◽  
Electronic Health

Abstract Our goal is to summarize the collective experience of 15 organizations in dealing with uncoordinated efforts that result in unnecessary delays in understanding, predicting, preparing for, containing, and mitigating the COVID-19 pandemic in the US. Response efforts involve the collection and analysis of data corresponding to healthcare organizations, public health departments, socioeconomic indicators, as well as additional signals collected directly from individuals and communities. We focused on electronic health record (EHR) data, since EHRs can be leveraged and scaled to improve clinical care, research, and to inform public health decision-making. We outline the current challenges in the data ecosystem and the technology infrastructure that are relevant to COVID-19, as witnessed in our 15 institutions. The infrastructure includes registries and clinical data networks to support population-level analyses. We propose a specific set of strategic next steps to increase interoperability, overall organization, and efficiencies

scholarly journals A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States

2019 ◽  
Vol 116 (8) ◽  
pp. 3146-3154 ◽  
Author(s):  
Nicholas G. Reich ◽  
Logan C. Brooks ◽  
Spencer J. Fox ◽  
Sasikiran Kandula ◽  
Craig J. McGowan ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Real Time ◽  
Forecast Accuracy ◽  
The United States ◽  
Public Health Response ◽  
Public Health Decision ◽  
Health Decision Making ◽  
Health Response ◽  
Health Decision

Influenza infects an estimated 9–35 million individuals each year in the United States and is a contributing cause for between 12,000 and 56,000 deaths annually. Seasonal outbreaks of influenza are common in temperate regions of the world, with highest incidence typically occurring in colder and drier months of the year. Real-time forecasts of influenza transmission can inform public health response to outbreaks. We present the results of a multiinstitution collaborative effort to standardize the collection and evaluation of forecasting models for influenza in the United States for the 2010/2011 through 2016/2017 influenza seasons. For these seven seasons, we assembled weekly real-time forecasts of seven targets of public health interest from 22 different models. We compared forecast accuracy of each model relative to a historical baseline seasonal average. Across all regions of the United States, over half of the models showed consistently better performance than the historical baseline when forecasting incidence of influenza-like illness 1 wk, 2 wk, and 3 wk ahead of available data and when forecasting the timing and magnitude of the seasonal peak. In some regions, delays in data reporting were strongly and negatively associated with forecast accuracy. More timely reporting and an improved overall accessibility to novel and traditional data sources are needed to improve forecasting accuracy and its integration with real-time public health decision making.

scholarly journals Forecasting seasonal influenza in the U.S.: A collaborative multi-year, multi-model assessment of forecast performance

2018 ◽  
Author(s):  
Nicholas G Reich ◽  
Logan Brooks ◽  
Spencer Fox ◽  
Sasikiran Kandula ◽  
Craig McGowan ◽  
...  
Keyword(s):  
Public Health ◽  
Real Time ◽  
Forecast Accuracy ◽  
The United States ◽  
Model Assessment ◽  
Public Health Response ◽  
Forecast Performance ◽  
Public Health Decision ◽  
The Us ◽  
Health Decision

AbstractInfluenza infects an estimated 9 to 35 million individuals each year in the United States and is a contributing cause for between 12,000 and 56,000 deaths annually. Seasonal outbreaks of influenza are common in temperate regions of the world, with highest incidence typically occurring in colder and drier months of the year. Real-time forecasts of influenza transmission can inform public health response to outbreaks. We present the results of a multi-institution collaborative effort to standardize the collection and evaluation of forecasting models for influenza in the US for the 2010/2011 through 2016/2017 influenza seasons. For these seven seasons, we assembled weekly real-time forecasts of 7 targets of public health interest from 22 different models. We compared forecast accuracy of each model relative to a historical baseline seasonal average. Across all regions of the US, over half of the models showed consistently better performance than the historical baseline when forecasting incidence of influenza-like illness 1, 2 and 3 weeks ahead of available data and when forecasting the timing and magnitude of the seasonal peak. In some regions, delays in data reporting were strongly and negatively associated with forecast accuracy. More timely reporting and an improved overall accessibility to novel and traditional data sources are needed to improve forecasting accuracy and its integration with real-time public health decision-making.

Persons with Disabilities and Public Health Ethics

2019 ◽  
pp. 218-231
Author(s):  
Monika Mitra ◽  
Linda Long-Bellil ◽  
Robyn Powell
Keyword(s):  
Public Health ◽  
Health Policy ◽  
Public Health Policy ◽  
Ethical Issues ◽  
The United States ◽  
Policy And Practice ◽  
Persons With Disabilities ◽  
Public Health Decision ◽  
Health Decision ◽  
Health Policy And Practice

This chapter draws on medical, social, and legal perspectives to identify and highlight ethical issues pertaining to the treatment, representation, and inclusion of persons with disabilities in public health policy and practice. A brief history of disability in the United States is provided as a context for examining the key ethical issues related to public health policy and practice. Conceptual frameworks and approaches to disability are then described and applied. The chapter then discusses the imperativeness of expanding access to public health programs by persons with disabilities, the need to address implicit and structural biases, and the importance of including persons with disabilities in public health decision-making.

Evidence for population-level approaches to the prevention of NCDs

2017 ◽  
Author(s):  
Mike Rayner ◽  
Kremlin Wickramasinghe ◽  
Julianne Williams ◽  
Karen McColl ◽  
Shanthi Mendis
Keyword(s):  
Public Health ◽  
Population Level ◽  
Health Interventions ◽  
Evidence Based ◽  
Public Health Decision ◽  
Solution Generation ◽  
Traditional Research ◽  
Vested Interests ◽  
Health Decision ◽  
Based Medicine

This chapter is the first of three about solution generation. It focuses on ways to evaluate the effectiveness of population health interventions and provides key questions to ask when applying evidence-based medicine to public health interventions. It also discusses how the dialogue between evidence producers and policy-makers can take various forms. Case studies illustrate how action can lag far behind even when evidence is strong and how powerful vested interests can undermine evidence-based policies. The chapter then discusses the role that modelling methods can play in improving public health decision-making, particularly when existing evidence is incomplete and traditional research methods are unable to provide solutions.

Youth Vaping and Tobacco Use in Context in the United States: Results From the 2018 National Youth Tobacco Survey

2020 ◽  
Author(s):  
Allison M Glasser ◽  
Amanda L Johnson ◽  
Raymond S Niaura ◽  
David B Abrams ◽  
Jennifer L Pearson
Keyword(s):  
Public Health ◽  
Tobacco Use ◽  
The United States ◽  
Tobacco Product ◽  
Decision Makers ◽  
Public Health Decision ◽  
Use Patterns ◽  
Product Use ◽  
Health Decision ◽  
Low Prevalence

Abstract Introduction According to the National Youth Tobacco Survey (NYTS), youth e-cigarette use (vaping) rose between 2017 and 2018. Frequency of vaping and concurrent past 30-day (p30d) use of e-cigarettes and tobacco products have not been reported. Methods We analyzed the 2018 NYTS (N = 20 189) for vaping among all students (middle and high school; 6–12th grades; 9–19 years old) by frequency of vaping, exclusive vaping, p30d poly-product use (vaping and use of one or more tobacco product), and any past tobacco product use. Results In 2018, 81.4% of students had not used any tobacco or vapor product in the p30d, and 86.2% had not vaped in the p30d. Among all students, of the 13.8% vaped in the p30d, just over half vaped on ≤5 days (7.0%), and roughly a quarter each vaped on 6–19 days (3.2%) and on 20+ days (3.6%). Almost three quarters of p30d vapers (9.9%) reported past or concurrent tobacco use and the remainder (3.9%) were tobacco naïve. 2.8% of students were tobacco naïve and vaped on ≤5 days; 0.7% were tobacco-naïve and vaped on 6–19 days, and 0.4% were tobacco-naïve and vaped on 20+ days. Conclusions Vaping increased among US youth in 2018 over 2017. The increases are characterized by patterns of low p30d vaping frequency and high poly-product use, and a low prevalence of vaping among more frequent but tobacco naïve vapers. Implications Results underscore the importance of including the full context of use patterns. The majority of vapers (60.0%–88.9% by use frequency) were concurrent p30d or ever tobacco users. About 4% of students were tobacco naïve and vaped in the p30d, but few (0.4%) vaped regularly on 20 or more days. Reporting youth vaping data with frequency and tobacco product co-use will give public health decision-makers the best possible information to protect public health.

scholarly journals Can auxiliary indicators improve COVID-19 forecasting and hotspot prediction?

2021 ◽  
Vol 118 (51) ◽  
pp. e2111453118 ◽  
Author(s):  
Daniel J. McDonald ◽  
Jacob Bien ◽  
Alden Green ◽  
Addison J. Hu ◽  
Nat DeFries ◽  
...  
Keyword(s):  
Public Health ◽  
Predictive Accuracy ◽  
The United States ◽  
Ar Model ◽  
Search Activity ◽  
Public Health Decision ◽  
Health Decision Making ◽  
Public Health Reporting ◽  
Health Decision ◽  
Google Search

Short-term forecasts of traditional streams from public health reporting (such as cases, hospitalizations, and deaths) are a key input to public health decision-making during a pandemic. Since early 2020, our research group has worked with data partners to collect, curate, and make publicly available numerous real-time COVID-19 indicators, providing multiple views of pandemic activity in the United States. This paper studies the utility of five such indicators—derived from deidentified medical insurance claims, self-reported symptoms from online surveys, and COVID-related Google search activity—from a forecasting perspective. For each indicator, we ask whether its inclusion in an autoregressive (AR) model leads to improved predictive accuracy relative to the same model excluding it. Such an AR model, without external features, is already competitive with many top COVID-19 forecasting models in use today. Our analysis reveals that 1) inclusion of each of these five indicators improves on the overall predictive accuracy of the AR model; 2) predictive gains are in general most pronounced during times in which COVID cases are trending in “flat” or “down” directions; and 3) one indicator, based on Google searches, seems to be particularly helpful during “up” trends.

scholarly journals Applying infectious disease forecasting to public health: a path forward using influenza forecasting examples

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Chelsea S. Lutz ◽  
Mimi P. Huynh ◽  
Monica Schroeder ◽  
Sophia Anyatonwu ◽  
F. Scott Dahlgren ◽  
...  
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
State Level ◽  
The United States ◽  
Federal State ◽  
Main Body ◽  
Disease Forecasting ◽  
Public Health Decision ◽  
Health Decision ◽  
State And Local

Abstract Background Infectious disease forecasting aims to predict characteristics of both seasonal epidemics and future pandemics. Accurate and timely infectious disease forecasts could aid public health responses by informing key preparation and mitigation efforts. Main body For forecasts to be fully integrated into public health decision-making, federal, state, and local officials must understand how forecasts were made, how to interpret forecasts, and how well the forecasts have performed in the past. Since the 2013–14 influenza season, the Influenza Division at the Centers for Disease Control and Prevention (CDC) has hosted collaborative challenges to forecast the timing, intensity, and short-term trajectory of influenza-like illness in the United States. Additional efforts to advance forecasting science have included influenza initiatives focused on state-level and hospitalization forecasts, as well as other infectious diseases. Using CDC influenza forecasting challenges as an example, this paper provides an overview of infectious disease forecasting; applications of forecasting to public health; and current work to develop best practices for forecast methodology, applications, and communication. Conclusions These efforts, along with other infectious disease forecasting initiatives, can foster the continued advancement of forecasting science.

Fidelity and Its Relationship to Implementation Effectiveness, Adaptation, and Dissemination

2017 ◽  
Author(s):  
Jennifer D. Allen ◽  
Rachel C. Shelton ◽  
Karen M. Emmons ◽  
Laura A. Linnan
Keyword(s):  
Public Health ◽  
Treatment Strategies ◽  
Implementation Fidelity ◽  
Population Level ◽  
The United States ◽  
Dissemination And Implementation ◽  
Evidence Based ◽  
Health Gains ◽  
Significant Public Health ◽  
Research Continuum

There is substantial variability in the implementation of evidence-based interventions across the United States, which leads to inconsistent access to evidence-based prevention and treatment strategies at a population level. Increased dissemination and implementation of evidence-based interventions could result in significant public health gains. While the availability of evidence-based interventions is increasing, study of implementation, adaptation, and dissemination has only recently gained attention in public health. To date, insufficient attention has been given to the issue of fidelity. Consideration of fidelity is necessary to balance the need for internal and external validity across the research continuum. There is also a need for a more robust literature to increase knowledge about factors that influence fidelity, strategies for maximizing fidelity, methods for measuring and analyzing fidelity, and examining sources of variability in implementation fidelity.

Sign in / Sign up

Export Citation Format

Share Document