On a humid afternoon in São Paulo, a woman waits outside a local clinic with her son. She arrived early, hoping to be seen before the day’s appointments fill. But when the system goes down—again—she is turned away. She has no private insurance, and the nearest alternative facility is several bus rides away. She weighs the cost, the lost wages, and finally heads home without care.

Scenes like this are not rare. And according to a major new study, the racial patterns behind them are not random—they are predictable, measurable, and deeply rooted in Brazil’s social and economic systems. Racial inequalities in healthcare access, long recognized but poorly explained, are the subject of one of the most comprehensive analyses to date. Using data from more than 200,000 people, researchers identified not just whether inequities exist, but why—and how much each factor contributes.

A National Picture: Who Gets Care—and Who Doesn’t

The study analyzed the 2019 Brazilian National Health Survey and found stark patterns: White Brazilians consistently experience better healthcare access than Black or Pardo (mixed-race) Brazilians across every measure.

Highlights from the study include:

• Unmet need for healthcare:
  • Black: 4.5%
  • Pardo: 4.2%
  • White: 2.95%
• Unmet need for chronic care:
  • Black: 10.7%
  • Pardo: 11.1%
  • White: 8.6%

These disparities across four dimensions: unmet care, unmet medication needs, inability to obtain services, and gaps in chronic disease management. But the real contribution of this study is what comes next: Which forces drive these inequalities—and which could help reduce them?

Breaking Down the Gap: What Really Drives Inequity

Using the Oaxaca-Blinder decomposition method, the authors separated racial inequalities into two components:

1. Explained differences

These are disparities driven by measurable factors: income, education, health insurance, sanitation, employment, etc.

2. Unexplained differences

These capture factors the data cannot directly measure: discrimination, provider bias, cultural barriers, trust, geographic segregation, and structural racism.

Across outcomes, 35% to 87% of racial inequalities were explained by measurable factors. But the unexplained portion—especially for Black vs. White comparisons—remains significant and troubling.

Key Insight 1: Income and Insurance Drive the Largest Gaps

If you look at the decomposition bars on pages 8–9, two bright red blocks dominate nearly every figure:

• Private health insurance
• Income quintile

White Brazilians are more than twice as likely to have private insurance (37% vs ~18%). They are also far more likely to fall into the top income quintiles.

Together, income and private insurance explain between one-third and two-thirds of racial inequalities across outcomes.

This means that inequality in access is not simply about individual behavior—it reflects structural economic differences.

Key Insight 2: Brazil’s Primary Care Program Helps Narrow the Gap

A powerful countervailing factor emerges: Registration with a Family Health Team (FHT).

Across outcomes, FHT coverage consistently reduced racial inequalities—even when controlling for income.

In some cases, such as unmet need for medication, FHT coverage reduced the racial gap by nearly 8%—a meaningful impact for a single program.

This reinforces what prior research has shown: Brazil’s Family Health Strategy is one of the most effective equity-focused primary care systems in the world.

Key Insight 3: Some Inequities Cannot Be Explained Away

Even after adjusting for income, education, insurance, sanitation, and health status, a large unexplained gap remains, especially for Black vs. White Brazilians. This unexplained portion likely reflects:

• discrimination and bias in care
• geographic segregation
• differences in provider communication
• stress, racism, and cumulative disadvantage
• historical patterns of exclusion
• Inconsistent Implementation of Brazil’s National Policy for the Health of the Black Population

The authors are cautious, but honest: these patterns mirror what has been documented in dozens of studies across Brazil and around the world.

What This Means in Practice (For LHDs, NGOs, and Systems Leaders)

Local Health Departments

• Expand culturally competent primary care teams in neighborhoods with high Black and Pardo populations.
• Use equity dashboards to track unmet care and chronic care engagement by race.

Hospitals and Health Systems

• Improve patient navigation for chronic disease management, with targeted outreach to historically excluded communities.
• Invest in anti-racism and communication training for clinical teams.

Policy and Public Health Officials

• Strengthen and expand the Family Health Strategy; it’s one of the few levers proven to reduce gaps.
• Reassess the role of private insurance expansion—evidence suggests it increases inequality.

Community-Based Organizations

• Partner on trust-building programs, particularly in communities reporting high unmet need.
• Develop transportation and accompaniment services for people navigating complex care pathways.

Barriers to Progress

Even with strong policies, persistent barriers work against equity:

• Geographic segregation limits access to high-quality services.
• Variable implementation of racial equity policies across states.
• Limited data on discrimination and provider bias.
• Underinvestment in public primary care relative to the private sector.
• Cultural and linguistic differences that reduce trust or care-seeking.

What’s Next? Future Directions for Research and Policy

The study highlights urgent opportunities:

• Measure discrimination directly in national surveys.
• Strengthen monitoring of Brazil’s National Policy for the Health of the Black Population.
• Expand qualitative research to understand lived experiences of exclusion.
• Test equity-focused interventions, such as culturally tailored chronic disease outreach and anti-bias training.

Brazil has the tools—and the policy framework—to make real progress. But implementation must match intention.

To Spark Conversation

1. How might your organization incorporate racial equity metrics into routine performance monitoring?
2. Where in your community are unmet healthcare needs most concentrated—and why?
3. What partnerships could help address the “unexplained” drivers of inequity, such as trust or discrimination?