Do Institutional Systems Contribute to Disease? How?
In the process of conducting literary research for my upcoming book titled,
"Pathological Resolve: How Institutional Systems Contribute to Disease
& How to Improve Health Outcomes with Ethical Science"
I have recognized a strong correlation between systemic oppression and cycles of stress, trauma, and lack of resources which perpetuate adverse health outcomes, including mental illness, and in the long term, higher rates of chronic conditions like diabetes, hypertension, heart disease.
Vasic (2025) points out the distinct reality of how exactly diagnosis obscures systemic oppression by labeling individual reactions to unjust circumstances as pathology in the context of mental health and chronic illness, rather than recognizing the systemic factors contributing to health issues.
Can there be an unspoken political agenda behind pathologizing?
Do implicit bias within operational structures of healthcare institutions lead to an abuse of power in the misuse of pathology?
What is your favorite metric for measuring systemic oppression's impact on health in the context of pathology?
What metric can be used to ensure resources are allocated towards improving quality of life and health rather reinforcing hidden agendas of systemic inequalities.
I don't agree with the faster, quicker, cheaper method to treat patients. This may seem more lucrative for investors, however in the search for long term solutions, it is an illusion.
There is a better formula, a better process, a better design, a better instrument for long term success within healthcare.
→ My research identifies critical issues surrounding the pathologizing of systemic oppression in healthcare. As the Vasic (2025) paper suggests, systemic factors can be obscured by an individual's diagnosis, reframing a natural human response to injustice as a personal pathology. This dynamic, particularly in the context of neoliberal healthcare systems, can be influenced by political agendas and implicit bias, leading to an abuse of power.
→ A powerful political agenda may exist behind the act of pathologizing, with historical precedent demonstrating how diagnoses have been used to control and delegitimize marginalized populations.
→ Social and political control: Pathologizing resistance, distress, and non-conformity can serve to maintain the status quo by turning collective problems into individual failures. Historically, this has manifested in various ways: Pathologizing activism, with activists and protesters labeled as mentally ill to undermine their cause. Diagnosing Black patients with schizophrenia to delegitimize their demands for civil rights. Using psychiatry to justify eugenics in Nazi Germany.
→ Neoliberal and capitalist influences: In a neoliberal system, mental health treatment may prioritize making individuals functional workers rather than addressing the systemic issues that cause their distress. For example, a person's anxiety and depression stemming from an unfulfilling, low-paying job may be treated with medication, with the underlying social and economic exploitation left unaddressed. The diagnosis effectively reframes the problem from "capitalism is making you ill" to "you are ill".
→ Weaponized language: Pathologizing is a way of "weaponizing" language to define who gets to be "normal" or "healthy". This preserves the power of the dominant group and maintains existing hierarchies. For example, laws have used pathologizing language to attack queer identities and preserve heteronormative power structures.
→ Implicit bias and the misuse of pathology: Implicit bias within healthcare operational structures can lead to an abuse of power through the misuse of pathology. Healthcare providers are susceptible to the same unconscious biases that exist throughout society, influencing how they interact with, assess, and diagnose patients.
Clinical decision-making: Implicit biases can affect clinical judgment, treatment decisions, and communication.
Erosion of trust: Patients who perceive implicit bias from their providers may lose trust in the healthcare system and feel disinclined to engage with care. This leads to worse health outcomes, as patients may avoid follow-up appointments.
Over- and misdiagnosis: Implicit bias can contribute to the misinterpretation of normal emotional responses as pathologies. For example, a provider might misdiagnose an individual's healthy suspicion of the medical establishment—developed after repeated experiences of discrimination—as paranoia or a personality disorder. Black people, in particular, are at a higher risk of being misdiagnosed with schizophrenia due to biased interpretations of their distress.
Ignoring systemic factors: When a provider focuses solely on a diagnosis, they may be less likely to recognize and address the systemic context that is the true source of a patient's suffering. This approach upholds the abuse of power embedded in existing institutional structures.
Metrics for measuring and transforming healthcare
Standard metrics are often focused on clinical outcomes or population averages. An alternative approach centers on the human experience of health and quality of life, focusing on structural and relational factors rather than individual shortcomings.
To measure the impact of systemic oppression
Instead of metrics that focus solely on clinical outcomes or self-reported psychological symptoms, a more holistic approach would include:
Intersectionality-informed health disparity metrics: Track health outcomes stratified not only by race and ethnicity but also by other intersecting identities like gender, sexual orientation, disability status, income, and geographic location. Look beyond simple ratios and use measures like the Concentration Index, which can more accurately assess health equity across different social strata.
Trauma-informed care metrics: Utilize tools like the Trauma Symptoms of Discrimination Scale (TSDS), which measures anxiety and trauma-related symptoms specifically caused by discriminatory experiences. This moves the focus from generalized psychopathology to the specific, oppressive causes of distress.
Experience-based metrics: Systematically collect data on patient experiences with provider interactions, focusing on perceptions of fairness, respect, and trust. Survey results from the Department of Veterans Affairs and other studies show that patients can perceive subtle cues of implicit bias, providing valuable data.
To ensure equitable resource allocation
To move beyond "faster, quicker, cheaper" models, metrics for resource allocation should focus on long-term, structural improvements.
Social determinants of health investment metrics: Track investments in community-level resources that impact health, such as access to healthy food, housing, transportation, and green spaces. This contrasts with a medical-only approach by measuring the healthcare institution's engagement in creating healthier communities.
Equity-focused quality improvement (QI) metrics: Require health organizations to use QI methods that explicitly address equity. For example, track not only diabetes foot exams but also the disparities in who receives them, and design interventions to eliminate that specific gap.
Participatory budgeting and resource allocation: Create a system where community members are directly involved in deciding how healthcare resources are allocated. This metric would measure the degree of community participation and how funding decisions align with expressed community needs, rather than solely with hospital profitability.
Longitudinal wellness metrics: Measure quality of life and subjective well-being over a longer term, for both patients and the surrounding community. Instead of focusing solely on disease management, a measure like the SF-36 Health Survey can help assess broader health-related quality of life outcomes.
Vasic, S. (2025). How Diagnosis Obscures Systemic Oppression. Journal of Mental Health Counseling, 47(3), 112-130. https://doi.org/10.17744/mehc.47.2.02
