- In cities, access to health care is defined by time, not distance. Urban congestion turns minutes into a decisive barrier for both emergency and routine care, revealing hidden inequalities in who can reach essential services when it matters most.
- Urban congestion is a blind spot in health and urban planning. Most planning and research overlook how daily traffic fluctuations shape real-world accessibility, limiting the effectiveness of policies.
- By integrating travel-time big data with health and urban information, cities can strategically locate services, reduce inequities, and align health, mobility, and urban agendas for more equitable access.
Imagine waking up in a bustling city where traffic is part of daily life and realizing something is wrong. Your face feels numb on one side. You try to smile, but only half of your mouth moves. Your arm feels weak, and your words come out slurred. Your partner calls the emergency line. An ambulance can be sent, but it might take a while. The navigation app shows a 30-minute trip in morning traffic. At midnight, it would take ten. That difference could change everything — your outcome, your future, your life.
This scene captures what we often miss when we talk about access: in cities, what matters is not how far you are, but how long it takes. Accessibility is measured in time, not distance. A previous analysis by the Inter-American Development Bank (IDB) makes that same point, linking accessibility to health geography, mobility, and urban policy. This blog post offers a complementary perspective that responds to and builds on that IDB analysis, zooming in on congestion as the hidden driver of those time gaps. Traffic congestion is more than an urban inconvenience — it is a geographical determinant of health.
People living in large cities understand this instinctively: congestion shapes daily life, influencing where they live, work, and seek care. Figure 1 shows how traffic in Cali, Colombia shapes access; only green areas of the map are within 15 minutes of tertiary care (click here to access the interactive image).
Figure 1: Travel-time accessibility to tertiary care services within 15 minutes in Cali, Colombia
Peak Traffic.
July 6-12, 2020
Source: Cuervo et al. (2022), figshare.
Across Latin America and the Caribbean, many rural and remote populations face travel times exceeding 30 minutes to reach essential health services, affecting an estimated 55 million people across 16 countries.
Now, imagine a non-emergency situation that is just unforgiving of time. Your kidneys no longer work as they should and three times a week, you must travel across the city for hemodialysis. Each session lasts four hours, but during peak traffic the round trip can take nearly as long. You also have to keep up with other frequent medical appointments, beyond the dialysis sessions themselves.
Figure 2 makes this constraint visible. It shows travel-time accessibility to hemodialysis services by car during rush hour in Cali. Even with car travel and no major delays, large parts of the city remain more than 30 minutes from dialysis centers (map areas in red), turning a routine treatment into a costly and exhausting journey for patients.
The figure also illustrates how accessibility would change if new dialysis centers were opened. A new center would reduce travel times for nearby neighborhoods, while adding a second facility would further expand coverage and shorten journeys across the city (less than 15 minutes for areas in green). These scenarios show how strategic placement of services, combined with congestion patterns, can improve access to essential care without changing the road network. This also demonstrates that it is possible to measure accessibility while accounting for traffic. Click here to access the high-resolution image.
Quantifying travel time is an important step toward universal health coverage, helping planners understand the scale of the challenge. However, most accessibility studies overlook this reality, and their findings often fail to reflect real-world conditions. Many are conducted by teams with limited input from those delivering care or shaping policies. Participatory approaches help generate context-appropriate evidence that informs local decisions. Without such engagement, research findings rarely address the most urgent needs at the intersection of health, mobility, and urban development.
Fortunately, new analytical approaches can now combine travel-time big data with open census and transport data. Paired with predictive modelling, these tools produce assessments that align urban and health service planning. Case studies in the Cali metropolitan area demonstrate the value of combining traffic data with interactive platforms and modelling.
These methodologies show how accessibility can be measured under realistic conditions, how travel times disproportionately affect residents in low-income areas and specific ethnic groups, and how strategically locating new services can improve access and offset congestion. They illustrate how data-driven approaches can guide policies and investments that better align health and urban agendas.
Encouragingly, organizations such as the IDB, PAHO, and WHO are expanding these approaches beyond health into areas such as education and emergency response — and doing so collaboratively. They are engaging stakeholders in discussions on metrics, standards, and data governance so accessibility and mobility data become a public good. When cities plan for time — not distance — they move closer to truly universal access, benefiting families and societies alike.