Analyze

Hoover’s book is an analysis of the material and psychosocial effects of industrial pollution along the St. Lawrence River, which runs through the Mohawk community of Akwesasne. Hoover focuses on resistance to private and state efforts at land enclosures and economic rearrangements.  Hoover shows how legacy of industrialization and pollution (GM and Alocoa, primarily) ruptured Mohawk relationships with the river, and incurred on tribal sovereignty by disturbing the ability to safely farm, garden, raise livestock, gather, and recreate in ways fostered important connections between and amongst people and the land (“ecocultural relationships”). Hoover describes how confusion about risk and exposure is culturally produced and develops the "Three Bodies" analytic framework to show how individual, social and political bodies are entangled in the process of social and biophysical suffering. 

Hoover also highlights how in response to pollution, Mohawk projects of resistance emerged - a newspaper, documentary films, and  community-based health impacts research. Hoover conducts a comparative history of two research projects tracking the effects on industrial-chemical contamination on Akwesasne people and wildlife: the Mount Sinai School of Medicine’s epidemiological study in the 1980s, which failed to engage Akwesasne people in the production of knowledge or share results meaningfully, and the SUNY-Albany School of Public Health Superfund Basic Research Program study (in the 1990s and 200s), which ultimately began incorporating key theoretical and methodological principles of CBPR.

“Akwesasne residents’ main criticism of the Mount Sinai study was that at its conclusion, the researchers packed up and left, and community members felt they had not received any useful information.” (76) 

“As scholars of tribal health risk evaluation Stuart Harris and Barbara Harper explain, among most tribal people, individual and collective well-being comes from being part of a healthy community with access to heritage resources and ancestral lands, which allow community members to satisfy the personal responsibilities of participating in traditional activities and providing for their families.” (96)

“By placing “race/ethnicity” on a list of diabetes causes without qualifying why it is there, the CDC neglects the underlying root cause—that race/ethnicity is often associated also with class, education, levels of stress, and access to health care and fresh foods.” (231)

“Chaufan argues that to counter the focus on the medicalized aspects of diabetes, which has led to the individualization and depoliticization of the issue, a political ecology framework needs to be applied to the disease, one that is concerned with the social, economic, and political institutions of the human environments where diabetes is emerging.39 Such a framework would highlight how diabetes rates among Mohawk people are influenced more by changes in the natural environment and home environments than by genetic makeup.” (231 - 232)

“Understanding community conceptions of this intertwined “social and biological history” is important because, as Juliet McMullin notes, examining the intersections of health, identity, family, and the environment helps to “denaturalize biomedical definitions of health and moves us toward including knowledge that is based on a shared history of sovereignty, capitalist encounters, resistance, and integrated innovation.”61 The inclusion of this knowledge can lead to the crafting of interventions that community members see as addressing the root causes of their health conditions and promoting better health.” (249)

Katsi Cook, Mother’s Milk Project, collecting samples of breast milk: “Katsi has described this work as “barefoot epidemiology,” with Indigenous women developing their own research projects based on community concerns and then collecting their own data.” (90) - 61? – used a private lab to analyze samples because women did not trust the New York State Health Department

“Barefoot epidemiology” is a concept borrowed from China’s “barefoot doctors”—community-level health workers who brought basic care to China’s countryside in the mid-twentieth century. Hipgrave, “Communicable Disease Control.” According to a “workers’ manual” published by the International Labour Organization, barefoot research is often qualitative, and qualitative research is not the standard approach for conducting health studies, which tend to be based on laboratory experiments and clinical findings. See Keith et al., Barefoot Research” (294)

Civic Dislocation: “In many instances Mohawks experienced what Sheila Jasanoff calls “civic dislocation,” which she defines as a mismatch between what governmental institutions were supposed to do for the public, and what they did in reality. In the dislocated state, trust in government vanished and people looked to other institutions . . . for information and advice to restore their security. It was as if the gears of democracy had spun loose, causing citizens, at least temporarily, to disengage from the state” (118) 

“Dennis Wiedman describes these negative sociocultural changes and structures of disempowerment as “chronicities of modernity,” which produce everyday behaviors that limit physical activities while promoting high caloric intake and psychosocial stress” (235)

Third space of sovereignty: “This tension that arises when community members challenge political bodies while simultaneously demanding that they address the issues of the community has been theorized by political scientist Kevin Bruyneel, who describes how for centuries Indigenous political actors have demanded rights and resources from the American settler state while also challenging the imposition of colonial rule on their lives. He calls this resistance a “third space of sovereignty” that resides neither inside nor outside the American political system, but exists on the very boundaries of that system.” (259)

Elizabeth Hoover is an anthropologist and associate professor of environmental science, policy and management at Berkley, who long claimed to be native (receiving grants and research access under this assumption) but has recently admitted otherwise. She has a PhD in anthropology from Brown University  with a focus on Environmental and critical Medical Anthropology. 

GitHub Repository

“To empower additional modeling efforts, the complete time series of all daily PVI scores and data are available at https://github.com/COVID19PVI/data. “

12 Key Indicators

“[The authors] assembled U.S. county- and state-level datasets into 12 key indicators across four major domains: current infection rates (infection prevalence, rate of increase), baseline population concentration (daytime density/traffic, residential density), current interventions (social distancing, testing rates), and health and environmental vulnerabilities (susceptible populations, air pollution, age distribution, comorbidities, health disparities, and hospital beds).”

Three types of modeling

“Our modeling efforts directly address the discussion in [6], by contextualizing factors such as racial differences with corrections for socioeconomic factors, health resource allocation, and co-morbidities, plus highlighting place- based risks and resource deficits that might explain spatial distributions. Specifically, three types of modeling efforts were performed and are regularly updated. First, epidemiological modeling on cumulative case- and death-related outcomes provides insights into the epidemiology of the pandemic. Second, dynamic time-dependent modeling provides similar outcome estimates as national-level models, but with county-level resolution. Finally, a Bayesian machine learning approach provides data-driven, short-term forecasts. “

Blackness and PM 2.5

“With respect to factors affecting COVID-19 related mortality, we find that the proportion of Black residents and the PM2.5 index of small-particulate air pollution are the most significant predictors among those included, reinforcing conclusions from previous reports[7]. An increase of one percentage point of Black residents is associated with a 3.3% increase in the COVID-19 death rate. The effect of a 1 g/m3 increase in PM2.5 is associated with an approximately 16% increase in the COVID-19 death rate, a value at the high end of a previously reported confidence interval from a report in late April 2020[7] when deaths had reached 38% of the current total.”

Machine learning and prediction

“To accurately predict future cases and mortality, it is necessary to account for the fluid nature of the data. Accordingly, we developed a Bayesian spatiotemporal random-effects model that jointly describes the log-observed and log-death counts to build local forecasts. Log-observed cases for a given day are predicted using known covariates (e.g., population density, social distancing metrics), a spatiotemporal random-effect smoothing component, and the time- weighted average number of cases for these counts. This smoothed time-weighted average is related to a Euler approximation of a differential equation; it provides modeling flexibility while approximating potential mechanistic models of disease spread. The smoothed case estimates are used in a similar spatiotemporal model predicting future log-death counts based on a geometric mean estimate of the estimated number of observed cases for the previous seven days as well as the other data streams. The resulting county-level predictions and corresponding confidence intervals are shown (Fig. 1)."

Source: https://www.researchgate.net/publication/343642027_The_COVID-19_Pandemi…

Skylar W. Marvel1, John S. House2, Matthew Wheeler2, Kuncheng Song1, Yihui Zhou1, Fred A. Wright1,3, Weihsueh A. Chiu4, Ivan Rusyn4, Alison Motsinger-Reif2*, David M. Reif1*

Affiliations:

1 Bioinformatics Research Center, Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695, USA.

2 Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, USA.

3 Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA

4 Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX 77845, USA.

“Data sources in the current model (version 11.2.1) include the Social Vulnerability Index (SVI) of the Centers for Disease Control and Prevention (CDC) for emergency response and hazard mitigation planning (Horney et al. 2017), testing rates from the COVID Tracking Project (Atlantic Monthly Group 2020), social distancing metrics from mobile device data ( https://www.unacast.com/covid19/social-distancing-scoreboard), and dynamic measures of disease spread and case numbers ( https://usafacts.org/issues/coronavirus/). Methodological details concerning the integration of data streams—plus the complete, daily time series of all source data since February 2020 and resultant PVI scores—are maintained on the public Github project page (COVID19PVI 2020). Over this period, the PVI has been strongly associated with key vulnerability-related outcome metrics (by rank-correlation), with updates of its performance assessment posted with model updates alongside data at the Github project page (COVID19PVI 2020).”

Source: https://ehp.niehs.nih.gov/doi/10.1289/EHP8690

Empowering local actoors

“We present the PVI Dashboard as a dynamic container for contextualizing these disparities. It is a modular tool that will evolve to incorporate new data sources and analytics as they emerge (e.g., concurrent flu infections, school and business reopening statistics, heterogeneous public health practices). This flexibility positions it well as a resource for integrated prioritization of eventual vaccine distribution and monitoring its local impact. The PVI Dashboard can empower local and state officials to take informed action to combat the pandemic by communicating interactive, visual profiles of vulnerability atop an underlying statistical framework that enables the comparison of counties and the evaluation of the PVI’s component data.”

New Pandemic Vulnerability Index dashboard maps global spread of COVID-19 (Texas A&M, The Battalion, April 26, 2021

Centre County Commissioner's Proud of Community's Pandemic Response (WJAC TV, March 23rd, 2021)

Covid-19: how to prioritize worse-off populations in allocating safe and effective vaccines (October 5, 2020)

The COVID-19 Pandemic Vulnerability Index (PVI) Dashboard: Monitoring County-Level Vulnerability Using Visualization, Statistical Modeling, and Machine Learning (Jan 5, 2021)

The COVID-19 Pandemic Vulnerability Index (PVI) Dashboard: monitoring county level vulnerability (Aug 13, 2020)

Built with toxicology knowledge

“The software used to generate PVI scores and profiles from these data is freely available at https://toxpi.org”

General visualization capabilities

“The interactive visualization within the PVI Dashboard is intended to communicate factors underlying vulnerability and empower community action [...] The visualization and quantification of county-level vulnerability indicators are displayed by a radar chart, where each of the 12 indicators comprises a “slice” of the overall PVI profile. On loading, the Dashboard displays the top 250 PVI profiles (by rank) for the current day. The data, PVI scores, and predictions are updated daily, and users can scroll through historical PVI and county outcome data. Individual profiles are an interactive map layer with numerous display options/filters that include sorting by overall score, filtering by combinations of slice scores, clustering by profile similarity (i.e. vulnerability “shape”), and searching for counties by name or state (Additional functionality is detailed in the Supplement). User selection of any county overlays the summary Scorecard and populates surrounding panels with county- specific information (Figure 1). The scrollable panels at left include plots of vulnerability drivers relative to the nation-wide distribution across all U.S. counties, with the location of the selected county delineated. The panels across the bottom of the Dashboard report cumulative county numbers of cases and deaths; timelines of cumulative cases, deaths, PVI score, and PVI rank; daily changes in cases and deaths for the most recent 14-day period (commonly used in reopening guidelines[6]; and predicted cases and deaths for a 7-day forecast horizon.”

Visualizing comparison and "peer counties"

“the multi-criteria filtering capabilities in the Dashboard were used to find a “peer county” for comparison. “

Source: https://ehp.niehs.nih.gov/doi/10.1289/EHP8690 and https://www.researchgate.net/publication/343642027_The_COVID-19_Pandemi…