In June and July 2022, the Public Health Disparities Geocoding Project, based at the Harvard TH Chan School of Public Health (Boston, MA), hosted new, thoroughly updated and revised FREE 4-day virtual trainings on why & how to analyze population health and health inequities in relation to census tract, county, and other georeferenced societal and environmental data. Each training included 75 participants, comprising a diverse mix of students, faculty, and staff at health departments, health care agencies, and community-based organizations from across the US and several other countries as well.
Topics covered included:
• the history and context of, and rationale for, conducting this type of work
• conceptual & methodological issues involved in creating area-based social metrics (ABSMs)
• summarizing health inequities by ABSMs
• multilevel & spatial models for health disparities research
• & the content covered is summarized in these graphics (click to download)
The following materials are now available for public use:
• The training manual [download the pdf]
• Recordings of the lectures delivered during the training: Day 1 / Day 2 / Day 3
• The data for the 5 case examples (see below), so that you can work your way through the material covered (the code is available in the training manual chapters for each case study)
| Case example #1 (Premature Mortality – Massachusetts data) |
| Case example #2 (Breast Cancer Mortality – Massachusetts data) |
| Case example #3 (Cook County COVID – Chicago, IL data) |
| Case example #4 (Temporal Trends in health insurance – American Community Survey (ACS) data, US) |
| Case example #5 (Comparing Health Insurance in ACS + CDC’s PLACES data, US) |
We invite you to explore our resources & use them to advance the work for health justice!
Background to training materials
In June and July 2022, the Public Health Disparities Geocoding Project, based at the Harvard TH Chan School of Public Health (Boston, MA), hosted new, thoroughly updated and revised FREE 4-day virtual trainings on why & how to analyze population health and health inequitiesin relation to census tract, county, and other georeferenced societal and environmental data.
Topics covered included:
• the history and context of, and rationale for, conducting this type of work
• issues affecting the numerator and denominator data
• conducting analyses with data aggregated to a specified level of geography vs. multi-level analyses with 2 or more levels of geography
• data interpretation and visualization
• the impact of changes to US census data (e.g., differential privacy)
The training consisted of:
c• lectures
• small discussion groups
• hands-on work with a variety of data sets
Limited to 75 participants per session – students, faculty, and staff at health departments, health care agencies, and community-based organizations that work with local health data were especially encouraged to apply.
The live virtual components of the training (lectures & hands-on training) took place in 2022 on:
TRAINING #1:
• Tuesday, June 21 (Day 1): 1-5 pm EDT
• Wednesday, June 22 (Day 2): 1-5 pm EDT
• Friday, June 24 (Day 3): 1-5 pm EDT
• Monday, June 27 (Day 4): 1-5 pm EDT
TRAINING #2:
• Monday, July 25 (Day 1): 1-5 pm EDT
• Wednesday, July 27 (Day 2): 1-5 pm EDT
• Friday, July 29 (Day 3): 1-5 pm EDT
• Monday, August 1 (Day 4): 1-5 pm EDT
• In the days between live virtual sessions, participants worked in small groups of 5 on their assignments.
Questions? Send us an email: geoproj@hsph.harvard.edu.
PHDGP 2.0 faculty: Nancy Krieger, Jarvis Chen, Pam Waterman, Christian Testa
PHDGP 2.0 RAs: Dena Javadi, Enjoli Hall, Justin Morgan
Grant support: American Cancer Society Clinical Research Professor Award to N. Krieger.
Citation should be listed as: Testa C, Chen JT, Hall E, Javadi D, Morgan J, Rushovich T, Saha S, Waterman PD, Krieger N. The Public Health Disparities Geocoding Project 2.0. Training Manual. Available as of October 30, 2022. https://phdgp.github.io/PHDGP2.0/index.html
