Race boxes can look like harmless paperwork: check one, sign here, try not to lose the clipboard. In health care, communities appear in disease statistics, which disparities receive research funding, and whether a hospital notices that part of its patient population is being underserved. For decades, many people with roots in South West Asia and North Africa were classified as White in federal data. That administrative decision did not magically provide the social advantages associated with whiteness. It often did something less impressive and more damaging: it made inequity harder to see.
The term SWANASouth West Asian and North Africanis used by many scholars, organizers, and community members as a geographically grounded alternative to “Middle Eastern.” The federal government uses MENA, meaning Middle Eastern or North African. The terms overlap but are not perfectly interchangeable, and neither describes a single culture, language, religion, migration story, or health profile. That distinction matters. Better categorization should increase visibility without replacing one giant demographic bucket with another slightly newer giant bucket.
How a “White” label created statistical invisibility
Under the federal standards adopted in 1997, people with origins in the Middle East or North Africa were generally counted within the White category. In the 2020 Census, respondents could write in detailed identities such as Lebanese, Iranian, or Egyptian, but there was no separate MENA checkbox. Census research found that many respondents used the White write-in area, while others selected “Some Other Race,” demonstrating that the official label and lived identity frequently did not line up. The Census Bureau later reported that roughly 3.5 million people identified with MENA descent in the 2020 count. hen these respondents are merged into an enormous White comparison group, their health outcomes may be averaged away. Imagine adding a cup of strong coffee to a swimming pool and then announcing that the pool is not caffeinated. The coffee is still there; the measurement strategy simply cannot detect it. In the same way, elevated risks within particular SWANA populations can disappear when researchers calculate a single average for all non-Hispanic White people.
Peer-reviewed public health researchers have repeatedly described Arab and MENA Americans as historically understudied. They have also warned that classification as White obscures discrimination, barriers to care, and differences in chronic disease, mental health, and preventive services. This is not a complaint about terminology for terminology’s sake. It is a problem of denominators, sampling frames, funding priorities, and institutional accountability. Why race and ethnicity data affect real health outcomes
Public-health surveillance cannot fix what it cannot count
Health departments rely on demographic data to compare infection rates, cancer diagnoses, maternal outcomes, vaccination coverage, disability, insurance status, and mortality. If SWANA patients are automatically reported as White, a local spike in diabetes or delayed cancer screening may look like ordinary variation within the White population. Officials may never generate the targeted question that should follow: What is happening in this community, and why?
The damage compounds over time. Poor classification produces weak baseline data. Weak baseline data make grant applications less persuasive. Limited funding produces smaller studies. Small studies are then dismissed as insufficient evidence. The loop is tidy, circular, and about as helpful as a smoke alarm that waits for a peer-reviewed fire.
Aggregated data can hide cardiovascular and metabolic risk
Research focused on Arab American communities has identified concerns involving cardiovascular disease, hypertension, obesity, smoking patterns, and type 2 diabetes, although estimates vary by subgroup, location, age, migration history, and study design. A 2024 literature review emphasized both meaningful cardiovascular risk and the serious limitations created by sparse, nonrepresentative data. In other words, the evidence says “pay attention,” while the data system has often replied, “Please select White and continue.” isaggregation is essential because SWANA communities are internally diverse. A recently arrived refugee managing trauma, interrupted medical care, and unstable housing does not have the same exposure profile as a third-generation professional with reliable insurance. Patients with different national, ethnic, linguistic, and migration backgrounds may encounter different diets, environmental exposures, forms of racialization, and access barriers. Treating them as medically interchangeable is not cultural competence; it is demographic improv.
Preventive care gaps may remain hidden
Studies have documented barriers involving cancer screening and prevention among Arab and MENA populations, including lower screening in some samples, limited culturally and linguistically appropriate information, modesty concerns, transportation problems, lack of insurance, and insufficient provider recommendation. Research involving Arab American women in Michigan, for example, has raised concerns about cervical cancer screening. Yet national systems often lack a reliable way to compare outcomes because MENA identity has not been consistently captured. his matters at the clinic level. A health system may congratulate itself for equitable screening rates among “White” patients while a SWANA subgroup within that category is receiving fewer reminders, fewer translated materials, or fewer completed screenings. The dashboard glows green. The inequity remains stubbornly red.
Racism, migration, and mental health do not fit neatly into the old box
SWANA people can experience anti-Arab racism, Islamophobia, xenophobia, surveillance, workplace discrimination, bullying, and political violence directed at their perceived identity. These experiences can affect stress, anxiety, depression, sleep, trust in institutions, and willingness to seek care. Refugees and immigrants may also carry grief, war exposure, family separation, or uncertainty about legal status. Classification as White can erase these social exposures from analysis, even when they are central to the patient’s health. Research has linked discrimination among MENA Americans with psychological distress and has called for models that recognize cumulative racial and ethnic trauma. ace is not a biological switch hidden behind the liver. It is a social and political classification that can help measure exposure to racism, unequal resources, environmental conditions, and institutional treatment. When clinicians treat race as genetics, they risk stereotyping. When institutions refuse to collect race and ethnicity carefully, they risk blindness. The goal is not to turn identity into a diagnosis; it is to understand how social conditions shape diagnosis, treatment, and survival.
The 2024 federal change is historicbut implementation matters
In March 2024, the Office of Management and Budget revised Statistical Policy Directive No. 15. The update created a separate MENA minimum reporting category, combined race and ethnicity into one question, allowed people to select multiple identities, and generally required collection of more detailed group information. The standards took effect for new federal recordkeeping requirements on March 28, 2024. Existing systems must comply as soon as possible and no later than March 28, 2029. hat is a major correction. It can improve federal surveys, public-health surveillance, civil-rights monitoring, clinical research, and eventually the demographic fields used by hospitals and insurers. Census testing also found that MENA participants responded positively to having a category that more accurately reflected their identity. The American Medical Association has similarly supported a distinct MENA category in medical records, research, and medical education. till, a policy announcement does not instantly repair every electronic health record, state registry, insurance database, university study, or intake form. During the transition, some systems will use the new standards while others continue using older fields. Researchers will need methods to bridge old and new datasets without pretending they are identical. Health organizations should not wait until the 2029 deadline and then attempt a five-year modernization project during one heroic weekend.
Why a MENA checkbox is necessary but not sufficient
SWANA and MENA are not exact synonyms
Many community members prefer SWANA because it avoids a term historically defined from a European geopolitical viewpoint. Others use MENA, Arab American, a national identity, an ethnic identity, a religious identity, or several labels at once. Institutions should explain why they are collecting information, permit multiple selections, and provide write-in options. Self-identification is more accurate and more respectful than staff guessing identity from a surname, appearance, language, or the highly scientific method known as “vibes.”
Large categories still conceal differences
A single MENA category improves visibility compared with automatic classification as White, but aggregation can still mask within-group disparities. Health outcomes may differ by national origin, race, language, religion, sex, gender, disability, income, neighborhood, refugee status, generation, and time in the United States. Data should therefore be collected at enough detail to support meaningful analysis while protecting privacy, especially in small communities where overly specific tables could identify individuals.
Disaggregation should also avoid a deficit-only lens. SWANA communities possess strong family networks, mutual-aid traditions, multilingual knowledge, professional expertise, religious and cultural institutions, and community organizations that support health. Good data should reveal both unmet needs and protective resources. Otherwise, public health turns communities into collections of risk factors, which is efficient only if the goal is to make everyone stop answering surveys.
What health systems and researchers should do now
Collect identity through self-report
Intake forms should include MENA as a co-equal option, allow multiple categories, include detailed ancestry or ethnic identity, and separate identity from preferred language, birthplace, and migration history. Those variables answer different questions and should not be used as clumsy substitutes for one another. Staff should be trained to explain that the information supports equitable care and is protected under applicable privacy rules.
Audit electronic records and quality dashboards
Hospitals should examine how demographic data move from registration desks into electronic health records, billing systems, cancer registries, patient portals, and quality reports. A beautifully designed intake form is useless if the software later recodes every MENA response as White. The CDC has highlighted collaborative approaches to improving race and ethnicity data quality, while medical organizations have emphasized disaggregation as a tool for exposing hidden inequities. Design studies with communities, not merely about them
Researchers should partner with SWANA-led organizations when defining categories, translating instruments, recruiting participants, interpreting results, and deciding how findings will be shared. Community-based methods can identify concerns that standard datasets miss, such as medical mistrust, culturally specific stigma, remittance obligations, caregiving expectations, or the health consequences of foreign-policy events and hate incidents.
National studies should oversample populations that would otherwise be too small for reliable analysis. Researchers should publish denominators, explain how identity was measured, distinguish self-report from observer assignment, and avoid treating the White category as a neutral control group. Studies of cognitive aging already suggest that disaggregating MENA older adults can reveal differences that were hidden when they were grouped with non-Latino White participants. Turn better data into better care
Collecting more accurate information is not the finish line. Health systems must use it to improve interpreter access, preventive-care reminders, mental-health referrals, chronic-disease programs, workforce training, and partnerships with trusted community institutions. They should publish progress, invite community review, and correct programs that are not working. A new checkbox without changed practice is simply a more inclusive way to file the same inequity.
Conclusion: Visibility is a health intervention
Race categorizations do not merely describe populations; they shape which populations become legible to institutions. The long-standing classification of SWANA people as White created a misleading picture of health by blending distinct communities into a group whose average often concealed their experiences. The new federal MENA category is an overdue and important step, but its value will depend on fast implementation, detailed self-reported data, careful disaggregation, privacy protections, and genuine community authority.
Health equity begins with asking better questions. Who is being counted? Who is missing? Which differences are hidden by the average? And, most importantly, what will an institution do after the numbers become visible? Data cannot prescribe medication, drive someone to a screening appointment, or sit with a family after a frightening diagnosis. But accurate data can direct attention, money, research, and accountability. That makes classification far more than paperwork. It makes it part of care.
Experiences behind the categories: what statistical erasure can feel like
The following scenarios are composites based on patterns described in U.S. health research and community accounts. They do not represent specific identifiable patients.
Consider a patient whose family is from Iraq. At registration, the form offers White, Black, Asian, American Indian or Alaska Native, Native Hawaiian or Pacific Islander, and “Other.” He hesitates. The receptionist, trying to keep the line moving, says, “You can choose White.” He does. Later, the clinic reviews hypertension outcomes by race. His blood pressure is uncontrolled, but his case is absorbed into the White total. Nobody asks whether Arabic-language medication instructions, shift work, transportation, diet, stress, or previous interruptions in care affected his treatment. To the patient, the form felt inaccurate. To the clinic, the data looked complete. Both experiences are true, and that is precisely the problem.
Now imagine an Iranian American woman scheduling a cancer screening. She speaks English fluently, has insurance, and is highly educated, so a broad demographic profile suggests few barriers. Yet she has postponed the appointment because prior visits felt rushed and culturally dismissive. The health system sees another White patient who is “noncompliant.” It does not see a pattern among patients with similar experiences because it never collected the identity needed to find one. Her delay becomes an individual failure rather than a possible signal about trust, communication, or care design.
A third patient is a recently resettled Syrian refugee seeking help for insomnia, headaches, and stomach pain. The symptoms may be connected to trauma, family separation, financial pressure, or uncertainty about relatives abroad. On paper, he is placed in the same racial category as U.S.-born White patients whose migration histories and exposures are entirely different. Without language support and trauma-informed screening, his distress may be treated only as a collection of physical complaints. He leaves with an antacid and the familiar feeling that the most important part of his story did not fit inside the appointment.
The experience can also affect clinicians and researchers from SWANA backgrounds. A medical student may be counted as White in workforce diversity reports while being subjected to anti-Arab comments or assumptions about religion and politics. A researcher may know that a community faces a screening gap but struggle to prove it with national data because the relevant population cannot be isolated. A community organization may repeatedly describe rising mental-health needs, only to hear that there is not enough evidence for targeted funding. Everyone is asked for data; the system has declined to create usable data.
Better categorization changes these encounters in small but meaningful ways. A patient can select MENA and a detailed identity without surrendering another part of who they are. A clinic can detect that one subgroup has lower screening completion and investigate the cause. A researcher can compare outcomes without using surnames as an imperfect proxy. A health department can fund outreach through trusted community centers. None of this guarantees equity, and no dropdown menu has ever defeated structural racism by itself. But accurate recognition creates the possibility of action. For SWANA communities, being counted correctly is not a request for special treatment. It is a request to stop letting an outdated category make unequal treatment look equal.
