Analyze

“Since asthma surveillance data were not available at the census tract level for most of Louisiana, we estimated asthma burden using the inpatient discharge data available through LDH.”  (4)

“Case counts are not provided for CTs with a 2018 population of less than 800 to safeguard privacy.” (4)

“To minimize the need for suppression, inpatient discharge data was aggregated for the three most recent years available (2017–2019) and average annual crude rates were calculated for cases where asthma (ICD-10 code J45) was the primary diagnosis, as well as where asthma was any diagnosis.” (4)

“Spearman’s Rank Correlation was utilized to analyze the correlation between various social and environmental vulnerability factors, COVID-19 incidence, and the measures of asthma risk by CT.” (4)

“This was performed by first ranking the values in each dataset using RANK.AVG function in MS Excel 2016, followed by applying the PEARSON function to compare two datasets. Significance was set at alpha less than 0.05 (α < 0.05), with degrees of freedom (df) equal to two less than the total number of data points represented in both datasets” (4)

The research team works for the Section of Environmental Epidemiology and Toxicology, Office of Public Health, Louisiana Department of Health in Baton Rouge. Team members included Arundhati Bakshi; Shanon Soileau; Collete Stewart; Kate Friedman; Collete Maser; Alexis Williams; Kathleen Aubin; and Alicia Van Doren. 

“Currently, much of the environmental focus of the pandemic remains on PM2.5 levels; however, we noted that higher levels of ozone was consistently associated with higher incidence rates of COVID-19, and it was the only environmental factor that appeared to have an additive effect over SVI on COVID-19 incidence (Fig 1).” (11)

“Specifically, our data show a moderately strong positive correlation between SVI due to minority status/language barrier and three health data variables: asthma hospitalization; estimated asthma prevalence; and cumulative COVID-19 incidence at 3 months (Table 2). Interestingly, SVI measures were either negatively or not significantly correlated COVID-19 incidence at the 9-and 12-month time points, indicating that social vulnerability factors may have played a greater role in COVID-19 spread early in the pandemic, but may have been of diminishing importance as the pandemic wore on (Fig 1 and Table 2).” (9)

Bakshi A, Van Doren A, Maser C, Aubin K, Stewart C, Soileau S, et al. (2022) Identifying Louisiana communities at the crossroads of environmental and social vulnerability, COVID-19, and asthma. PLoS ONE 17(2): e0264336. https:// doi.org/10.1371/journal.pone.0264336. 

This document uses data resources from the Center for Disease Control/Agency for Toxic Substances and Disease Registry (CDC/ATSDR), the Environmental Protection Agency (EPA), and the Louisiana Department of Health (LDH).

These data resources include the Social Vulnerability Index (2018 - CDC/ATSDR), the NATA Respiratory Hazard Index (EPA 2014), PM2.5level (average annual concentration in ug/m3, EPA 2016), ozone level (summer seasonal average of daily maximum 8-hour concentration in air in parts per billion, EPA 2016), indoor mold concerns reported to IEQES program (average annual number of calls, LDH 2017-2019), cumulative COVID-19 incidence rate at 3-, 6-, 9- and 12-month increments (LDH March 2020 - March 2021), asthma hospitalization (average annual crude rate, where asthma was a primary diagnosis among hospitalization cases, LDH 2017-2019), and estimated asthma prevalence (average annual crude rate, where asthma was any diagnosis among hospitalization cases, LDH 2017-2019).

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.