GPS Mobility Tracking, Ecological Momentary Assessment, and Qualitative Interviewing to Specify How Space Produces Intersectional Health Inequities: Development and Pilot Testing of the Spatial Intersectionality Health Framework (SIHF) and IGEMA Methodology

This paper presents the development and pilot testing of the Spatial Intersectionality Health Framework (SIHF) and the IGEMA methodology, which integrate GPS tracking, ecological momentary assessment, and qualitative interviews to empirically demonstrate how layered, positional, and conditional spatial mechanisms drive intersectional health inequities among young sexual and gender minorities of color.

The Gist

Imagine your health isn't just about what you eat or how much you sleep; it's also about where you go and who you are when you get there.

This paper is like a new set of "detective tools" designed to solve a mystery: Why do some people get sick or stressed in certain neighborhoods while others in the same neighborhood feel fine?

Here is the breakdown using simple analogies:

1. The Problem: The "One-Size-Fits-All" Map is Broken

Scientists have long known that "place matters." If you live in a neighborhood with no parks and lots of pollution, your health might suffer. But old research treated everyone in that neighborhood the same.

The Analogy: Imagine a weather app that tells you, "It's raining in New York." That's true for everyone. But it doesn't tell you that for someone without an umbrella, the rain is a disaster, while for someone with a waterproof tent, it's just background noise.

The researchers realized that for young people of color who are also LGBTQ+, the "weather" (their environment) hits them differently depending on their specific identity. They face a "double trouble" of racism, transphobia, and poverty all at once. Old tools couldn't see this complexity.

2. The New Tool: The "SIHF" (The 3-Layer Lens)

The authors created a new framework called SIHF (Spatial Intersectionality Health Framework). Think of this as a 3D X-Ray that looks at a neighborhood not just as a flat map, but as a complex layer cake of experiences. They found three specific ways a place can hurt someone:

• Layered (The Storm Cloud): Imagine walking outside and getting hit by rain, wind, and hail all at the same time. This is when multiple systems of oppression (racism, sexism, classism) crash down on a person simultaneously in one spot.
• Positional (The Same Room, Different Seats): Imagine two people sitting in the same classroom. One is the teacher's favorite; the other is being bullied. The room is the same, but the experience is totally different based on who they are. This framework sees that the same street corner can be safe for one person but terrifying for another.
• Conditional (The Trap Door): Imagine a park that looks beautiful and safe (a "protective space"), but for a specific person, it's actually a trap where they might get harassed. The place looks good on a map, but it has a hidden cost for certain people.

3. The Method: "IGEMA" (The High-Tech Detective Kit)

To prove this works, they built a high-tech toolkit called IGEMA. They recruited 32 young people in New York City and gave them a "digital detective kit" with three parts:

1. The GPS Tracker (The Footprint): Like a fitness tracker, this followed their phones to see exactly where they went. It linked their movement to data about the neighborhoods (like poverty rates or pollution levels).
2. The Mood Check-in (The Pulse): Throughout the day, the app asked them quick questions: "Are you stressed right now?" "Did someone treat you unfairly?" This captured their feelings in real-time, not just from memory.
3. The Map Interview (The Story): Later, researchers sat down with the participants, showed them the map of where they walked, and asked, "Tell me the story of this spot." This helped them understand why a place felt bad.

4. What They Found

The results were clear: The old way of looking at data was missing the big picture.

• The "Compound" Effect: When they looked at how racism and sexism worked together, it predicted bad moods and stress much better than looking at just racism or just sexism alone. It's like realizing that a car crash is caused by the combination of a slippery road and bald tires, not just one or the other.
• The "Positional" Truth: Most of the harm (71%) happened because of the "Positional" mechanism—the same place hurting different people in different ways.
• The Sleep Connection: When these young people faced intersectional discrimination during the day, they had much worse sleep that night.

The Bottom Line

This paper argues that we can't just fix "bad neighborhoods." We have to understand how a specific neighborhood interacts with a specific person's identity.

The Takeaway: If you want to fix health problems for marginalized groups, you need a map that doesn't just show streets and buildings, but shows how the street feels to the person walking it. This new toolkit (SIHF and IGEMA) gives us the magnifying glass to see those hidden connections so we can build interventions that actually work.

Technical Summary

1. Problem Statement

Young sexual and gender minorities (SGM) of color face compound health risks driven by interlocking systems of oppression, specifically racism, cisgenderism, and class inequality. While existing spatial health research acknowledges that "place" influences health, current methodologies lack the granularity to specify how specific spatial configurations generate different health outcomes for individuals with different intersectional identities. This methodological gap prevents the development of targeted interventions that address the root causes of health inequities for multiply marginalized populations.

2. Methodology

The study, titled GeoSense, serves as a pilot for the NIH R01HL169503 grant. It enrolled 32 young SGM of color (ages 18–29) in New York City to develop and validate a new framework and methodology.

• Theoretical Framework: The Spatial Intersectionality Health Framework (SIHF) was developed to specify three distinct mechanisms through which space produces health inequities:

  1. Layered: Multiple oppressive systems activate simultaneously within a space.
  2. Positional: The same physical space produces divergent health pathways depending on an individual's intersectional position.
  3. Conditional: Spaces that are nominally protective for the general population carry hidden costs for specific intersectional positions.

• Operational Methodology (IGEMA): The study introduced Intersectional Geographically-Explicit Ecological Momentary Assessment (IGEMA), a multi-level data collection approach integrating:

  • Level 1 (Structural Exposure): GPS mobility tracking via participants' smartphones, linked to census tract structural exposure indices (data analyzed for $n=19$).
  • Level 2 (Real-time Experience): Ecological Momentary Assessment (EMA) capturing intersectional discrimination, mood, stress, and sleep, analyzed using multilevel modeling.
  • Level 3 (Qualitative Context): Map-guided qualitative interviews where experiences were coded against SIHF mechanisms. Intercoder reliability was assessed across 92 coded records from 18 participants.

3. Key Contributions

• Theoretical Advancement: The SIHF moves beyond simple spatial correlation by defining specific mechanisms (Layered, Positional, Conditional) that explain why and how space affects health differently based on intersectionality.
• Methodological Innovation: The development of IGEMA provides a validated, integrated protocol that combines objective GPS data, subjective real-time EMA, and qualitative interview data to operationalize intersectional spatial analysis.
• Empirical Validation: The study successfully demonstrated that these complex mechanisms are empirically detectable and measurable in real-world settings.

4. Key Results

• Predictive Superiority: A compound structural gendered racism index significantly outperformed single-axis measures in predicting daily health outcomes.
  • Mood: $b = -0.048$, $p = .001$
  • Stress: $b = 0.121$, $p < .001$
• Mechanism Prevalence: The Positional mechanism was the dominant driver of harm, accounting for 71% of all coded harm experiences.
• Reliability: The qualitative coding of SIHF mechanisms demonstrated high reliability, with an intercoder kappa of 0.824 at Stage 2 reconciliation.
• Health Outcomes: Daily intersectional discrimination was a significant predictor of increased sleep disturbance ($b = 1.308$, $p = .004$).

5. Significance

This research establishes that mechanism specification, rather than mere spatial location, is the critical condition for designing effective research and interventions. By providing a framework (SIHF) and a tool (IGEMA) to dissect how space produces intersectional inequities, the study enables public health practitioners to move beyond broad geographic targeting. Instead, they can design interventions that specifically address the "source of harm" for multiply marginalized populations, ultimately leading to more precise and equitable health strategies.