Analyze

1. What is this data resource called and how should it be cited?

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The Covid-19 Pandemic Vulnerability Index (PVI) Dashboard, which relies on the Toxicological Prioritization Index (ToxiPi) to integrate diverse data into a geospatial context.

National Institute of Environmental Health Sciences (NIEHS). COVID-19 Pandemic Vulnerability Index (PVI) Dashboard. 2021. Available online: https://covid19pvi.niehs.nih.gov/ (accessed on 24 July 2021).

7. How has this data resource been used in research and advocacy?

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The PVI dashboard is included in the CDCD’s Covid-19 Data Tracker as part of the “Unique Populations” tab.  

NIEHS also developed Covid-19 PVI lesson plans for high school students (grades 9 – 12) to learn to examine risk factors associated with Covid-19 using the index. The goals of the curriculum are to provide students with a tool for examining the spread and health outcomes of a pandemic, to promote their awareness of how various factors (biological, social, behavioral, etc.) impact disease spread and outcomes, and to support the development of prevention and intervention strategies that reduce exposures to risk factors and their adverse health impacts. The lesson plans highlight the significance of social and environmental determinants in public health.

Learning objectives of the curriculum include:

  • Knowing what a mathematical model is, the purpose of using a mathematical model
  • How to examine the social factors contributing to the spread of infectious disease
  • How to analyze the environmental factors that contribute to the spread of infectious disease
  • Knowing about intervention strategies that could mitigate the impact of infectious disease on public health

The PVI dashboard was also used by anthropologist Jayajit Chakraborty to examine the relationship between Covid-19 vulnerability and disability status in the US. Chakraborty applied the dashboard and data from the 2019 American Community Survey to investigate whether vulnerability to the pandemic has been significantly greater in counties containing higher percentages of people with disabilities in four timeframes from May 2020 to February 2021. Chakraborty found that the percentage of people with disabilities (as well as those reporting other cognitive, vision, ambulatory, self-care and independent living difficulties) was significantly greater in counties with the highest 20% of the PVI. Chakraborty calls for further research to better understand the adverse impacts of Covid-19 on PwDs (people with disabilities).

 

 

Chakraborty, J. Vulnerability to the COVID-19 Pandemic for People with Disabilities in the U.S. Disabilities 2021, 1, 278-285. https://doi.org/10.3390/disabilities1030020

6. What visualizations can be produced with this data resource and what can they be used to demonstrate?

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The index produces an overall score derived from 12 indicators distributed across four domains (current infection rates, baseline population concentration, current interventions, and health and environmental vulnerabilities. Each vulnerability factor is represented as a slide of a radar chart (see below).

The dashboard can also be used to visualize changes over time in cases, deaths, PVI, and PVI rank (with a line chart and a bar chart), as well as predicted changes in cases and deaths (with a line chart), see below.

Additional visual layers can be added to the PVI map (e.g. number of cases and deaths).

5. What can be demonstrated or interpreted with this data set?

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The PVI offers a visual synthesis of information to monitor disease trajectories, identify local vulnerabilities, forecast outcomes, and guide an informed response (e.g. allocating resources). This includes short-term, local predictions of cases and deaths. The PVI dashboard creates profiles (called PVI scorecards) for every county in the United States.

The PVI dashboard can be customized to specific needs by adding or removing layers of information, filtering by region, or clustering by profile similarity. The Predictions panel connects historical tracking to local forecasts of cases and deaths. The dashboard applies an integrated concept of vulnerability composed of both dynamic (infection rate and interventions) and static (community population and health care access) factors.

The statistical modeling supporting the PVI dashboard (generalized linear models of cumulative outcome data) has indicated that following population size, the most significant predictors of cases and deaths were the proportion of Black residents, mean fine particulate matter [particulate matter ≤2.5μm in diameter (PM2.5)], percentage of population with insurance coverage, and proportion of Hispanic residents.

The ToxPi*GIS framework, from which the PVI was built, is a free tool that integrates data streams from different sources into interactive profiles that overlay geographic information systems (GIS) data. This enables people using the tool to compare, cluster, and evaluate the sensitivity of a statistical framework to component data streams. In other words, this enables the integration of data that are not normally compared (data are combined into a matrix comprised of various domains or categories, varying weights and represented by color schemes).

3. What data is drawn into the data resource and where does it come from?

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Data is drawn from the Social Vulnerability Index (SVI) of the Centers for Disease Control and Prevention (CDC), testing rates from the COVID tracking project (produced by the Atlantic Monthly Group), social distancing metrics from mobile device data, and USA Facts’ measures of disease spread and case numbers.

 

2. Who makes this data available and what is their mission?

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The creation of the Covid-19 Pandemic Vulnerability Index (PVI) Dashboard was a collaborative effort between scientists from the National Institute of Environmental Health Sciences, North Carolina State University, and Texas @&M University. Their mission was to provide a resource to support dynamic, community-level decision-making in response to the Covid-19 Pandemic.

 

Each PVI county profile is calculated using Toxicological Prioritization Index (ToxiPi) software, which integrates data within a geospatial context. ToxPi*GIS is meant to promote the development of targeted, effective community policies. ToxPi*GIS was created by the Reif Lab at North Carolina State University. The overarching goal of the lab is to understand the interactions between human health and the environment through the application of analytical/visual methods and experimental design. Data sources include epidemiological studies of human health, high-throughput screening (HTS) of environmental chemicals and model organism data. The lab is run by Dr. David Reif (Professor in the Department of Biological Sciences) and members include students from several degree programs as well as post-doctoral and senior scientists.