Related Experiment Video
Updated: Jun 30, 2025
06:55
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
14.5K
Evaluating Methods for Mapping Historical Redlining to Census Tracts for Health Equity Research
Hannah De Los Santos1, Carla P Bezold2, Karen M Jiang2
1The MITRE Corporation, McLean, VA, USA. hdelossantos@mitre.org.
Summary
Historic redlining maps significantly impact present-day health outcomes. Continuous mapping methods considering current populations offer better insights for redlining and health research.
Area of Science:
- Environmental Health
- Urban Planning
- Public Health
Background:
- Neighborhood characteristics, including housing status, profoundly influence health outcomes.
- Historic redlining, a practice of area classification leading to decreased investment, is increasingly recognized for its present-day health impacts.
- Limited guidance exists on optimal methods for measuring historic redlining in studies of contemporary health.
Purpose of the Study:
- To evaluate how different redlining map alignment methods influence associations between historic redlining and present-day health outcomes.
- To compare the statistical significance and explanatory power (R² values) of various redlining measurement approaches.
- To provide recommendations for optimal redlining measurement in health research.
Main Methods:
- Identified 11 existing redlining map alignment methods and 37 logical extensions, totaling 48 methods.
- Merged these methods with census tract life expectancy data for 202 cities, constructing 9696 linear models.
- Evaluated models using statistical significance, R² values, and Root Mean Squared Error (RMSE) to assess geographic and population changes.
Main Results:
- Root Mean Squared Error (RMSE) peaked at 0.175, indicating persistent differences between historical and contemporary geographies and populations.
- Continuous methods with low thresholds demonstrated higher neighborhood coverage.
- Weighting methods showed more significant associations, while high threshold methods yielded higher R² values.
Conclusions:
- Continuous redlining measurement methods that incorporate contemporary population distributions and mapping overlap are recommended for health studies.
- The choice of redlining measurement method significantly affects observed associations with health outcomes.
- An R application, {holcmapr}, was developed to facilitate method comparison and selection for redlining and health research.
Related Concept Videos
01:25Strategies for Assessing and Addressing Confounding
99
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
99
01:27Confounding in Epidemiological Studies
169
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
169
01:25Selected Data About Geographic Locations
27
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
27
02:20Longitudinal Research
12.0K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
12.0K
01:30Thematic Layering in GIS
36
In the past, planning projects such as schools or public facilities required extensive manual effort to gather and compile data. Information such as property boundaries, soil characteristics, road networks, zoning regulations, and flood zones had to be sourced individually from courthouses, utility providers, and registry offices. Assembling these datasets into a coherent format often took several months, delaying project timelines.The introduction of Geographic Information Systems (GIS)...
36
01:26Introduction to Epidemiology
723
Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...
723