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“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).

Public Health Alliance of Southern California. California Healthy Places Index. 2019. https://healthyplacesindex.org.

© 2018 Public Health Alliance of Southern California

Permission is hereby granted to use, reproduce, and distribute these materials for noncommercial purposes, including educational, government and community uses, with proper attribution to the Public Health Alliance of Southern California including this copyright notice. Use of this publication does not imply endorsement by the Public Health Alliance of Southern California.

© 2018 California Department of Public Health (CDPH)

Permission is hereby granted to use, reproduce, and distribute these materials for noncommercial purposes, including educational, government, and community uses, with proper attribution to the CDPH, including this copyright notice. Use of this publication does not imply endorsement by the CDPH.

The index does not include certain neighborhood characteristics critical to health because they did not meet the criteria for inclusion (described in question 3). For instance, this included physician ratios (the number of physicians per 100,000 population) because data was missing for a majority of census tracts. In fact, the steering committee was unable to locate much data on health care access or quality at the census-tract level (only data on health care insurance coverage was available).  

 The index was previously critiqued in ways that led to a shift from framing data in terms of “disadvantage” towards a framework of “opportunity”. This led to not only a renaming of the index (from “the Health Disadvantage Index to the Healthy Places Index) but also a shift in reporting of data (e.g. highlight the percentage of the population with a BA degree or higher rather than the percentage of population without a college degree). 

The HPI is also limited in terms of the effects of confounding, with some indicators with strong evidence of health effects showing contrary associations with life expectancy at birth by census tract. The steering committee has also acknowledged that the HPI might not be accurate for census tracts undergoing rapid population change (e.g. due to immigration, rapid gentrification, or other changes).

The HPI notably does not correlate strongly with CalEnviroScreen, which the steering committee for the HPI noted failed to identify one-third of census tracts with the worst conditions for population health. The HPI is ultimately more centered on considering environmental factors as a part of overall health, rather than as a central determinant. However, this disconnect between CalEnviroScreen and the HPI may also be a reflection of the challenges environmental injustice advocates have faced in linking environmental factors to health outcomes (which might not be as visible and geographically direct as the links between health and other indicators).

The HPI draws data about 25 community characteristics into a single indexed HPI score. The includes sub-scores for 8 “Policy Action Areas”: Economic, Education, Housing, Health Care Access, Neighborhood, Clean Environment, Transportation, and Social Factors. These scores are meant to be used to evaluate health geographically. Each policy action area includes the following individual indicators and weights:

ECONOMIC (0.32)

• Poverty
• Employment
• Income

EDUCATION (0.19)

• Pre-school enrollment
• High school enrollment
• Bachelors attainment

HEALTHCARE (0.05)

• Insured adults

HOUSING (0.05)

• Severe cost burden low income
• Homeownership
• Kitchen and plumbing
• Crowding

NEIGHBORHOOD (0.08)

• Retail jobs
• Supermarket access
• Parks
• Tree canopy
• Alcohol establishments

CLEAN ENVIRONMENT (0.05)

• Diesel PM
• Ozone
• PM2.5
• Drinking water

SOCIAL (0.10)

• Two parent household
• Voting

TRANSPORTATION (0.16)

• Healthy community
• Automobile access

*The steering committee for the HPI sought to include race/ethnicity as a 9th policy action area, but they were prohibited from doing so by state law which does not allow California state agencies to use race as a basis for public contracting.

The primary HPI Index is designed to align with life expectancy at birth as a predictive measure of community health status. However, the Healthy Places mapping tool can also be used to create custom scores using different indicators. The mapping tool includes detailed definitions of each indicator.

Each indicator is linked to a policy guide, which outlines concrete actions (e.g. best practices, emerging policy options) that local jurisdictions can take to improve HPI indicators. These actions are sometimes aimed at addressing direct links between policy and an action area, and other times aimed at addressed the root causes of an action area. The mapping tool also enables filtering results by “Decision support layers” like health outcomes, health risk behaviors, race/ethnicity, climate change effects, and other layers that the alliance identifies as important for advancing “resilient, equitable communities in California”. Geographies (e.g. census tracts) can also be compared by indicator using a ranking tool. The pool function can be used to create customized aggregations of data to map (e.g. adding several census-tracts together).

Data is available at several different scales: census-tracts, congressional districts, state assembly districts, state senate districts, cities, core based statistical areas, elementary school districts, metropolitan planning organization and medical service study areas.  

Creating maps by different combinations of indicators or geographic aggregations could be tinkered with to produce provocative data visualizations. Ranking scores can be used to draw distinction between different census tracts. However, clear inequities are evident even without these adjustments, with the HPI index score clearly demonstrating noticeable differences across geographies. 

The California Healthy Places Index is made available by the Public Health Alliance of Southern California. Their mission is to “make health equity and racial justice a reality” through collaboration and data (https://www.thepublichealthalliance.org/). They engage in advocacy and mobilization to generate this change. They are composed of a coalition of executives representing 10 local health jurisdictions in Southern California (including Long Beach, Los Angeles, Orange, and Riverside, among others), an area they highlight as representing 60% of California’s population (with which they blur the boundaries between “California” and “Southern California”).

The alliance emphasizes pursuing equity using publicly available data and collaboration (with government agencies, legislators, hospitals, health plans, philanthropy, and community advocates). They present the Healthy Places Index (HPI) as a tool for exploring how life expectancy is impacted by community conditions.

More specifically, the HPI was created by a steering committee made up of epidemiologists and 3 public health coalitions led by the alliance.