Analyze

BIOETHNOGRAPHY: “Thus, instead of combining objects of inquiry (biology and culture), I conceived of bioethnography as combining two different methods for knowing the world (Mol 2002, 153)—ethnographic observation and biochemical sampling—in order to ask and answer research questions that could not be addressed through either method alone. This methodological focus involves exploring how our data collection and analysis might be shaped if we suspended the nature/culture binary” (Roberts, 2021, p. 2)

“bioethnography asks, what if we created numbers otherwise, upending the cooked data that reinforces inequality? In fact, bioethnography can enable us to identify structural forces, such as NAFTA and the global health apparatus itself, that are part of the bodily processes that make ill health. In other words, while we know that all data is cooked, it matters how it’s cooked.” (Roberts, 2021, p. 5)

Roberts describes their ongoing bioethnographic collaboration with a team of exposure scientists who are working in environmental engineering and health. Though ethnography is not easily enumerated, Roberts emphasizes that integrating it with quantitative data is worthwhile and makes for “better numbers”. As an example, Roberts describes 3 bioethnographic projects on neighborhoods, water distribution, and employment and chemical exposures. These projects were part of a longitudinal birth-cohort study in Mexico City called Early Life Exposures in Mexico to ENvironmental Toxicants (ELEMENT), created to understand the effects of early-life nutrition and exposure to toxicants (such as lead and phenols). Overtime, this project was expanded to include the study of new toxins (e.g. BPAS, mercury, and fluoride) and new health concerns (e.g. obesity, meopause, sleep).

Roberts’ focus on neighborhoods was produced from the ethnographic observation that neighborhood characteristics might influence exposure levels. Following this observation, Roberts’ and ELEMENT researchers sorted participants by neighborhood and identified significant differences in blood-lead levels. Additionally, Roberts applied previous ethnographic observation and scholarship to argue that high levels of toxicants like lead correlate with the capacity of neighborhoods to withstand other dangers, such as police violence. These findings prompted the development of two new bioethnographic project centered on water and the effect of neighborhood dynamics on health.

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).

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

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).

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).

County

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.