In managing stroke across diverse populations, how can clinicians effectively integrate social determinants of health insights?

To integrate social determinants and metabolic-hormonal factors into stroke prevention effectively, clinicians must adopt a multilayered, patient-centred approach that goes beyond conventional risk scoring.

Risk Stratification Must Evolve Traditional tools like CHA₂DS₂-VASc or ABCD2 don't capture socioeconomic adversity, chronic stress, or food insecurity — all of which contribute to stroke risk through both behavioural and biological pathways. → Example: Low-income individuals may have higher visceral adiposity, poorly controlled diabetes, and cortisol dysregulation — often masked in standard algorithms.

Screening for Social Determinants in Clinic Incorporating structured tools like the PRAPARE questionnaire or the Social Needs Screening Tool into primary and secondary stroke prevention allows clinicians to understand barriers to care such as: Medication non-adherence due to cost Limited access to healthy food or exercise spaces Psychosocial stress affecting blood pressure and glucose control

Linking to Community and Public Health A personalised plan must include referrals to social prescribing networks, housing support, or culturally competent dieticians — integrating care navigation with metabolic optimisation. → This is especially important in marginalised populations where metabolic-hormonal conditions like PCOS, obesity, and thyroid dysfunction may present earlier or more severely.

Hormonal-Metabolic Syndromes as Amplifiers Conditions like insulin resistance, hypothyroidism, or Cushing’s syndrome can worsen vascular inflammation and pro-thrombotic states. → Example: A woman with poorly managed PCOS, high androgen levels, and obesity living in a food desert has a stroke risk profile that isn’t reflected by age or standard BMI alone.

Clinical Practice Must Shift Toward Systems Thinking Clinicians should adopt integrated care models — combining endocrinology, primary care, neurology, and social care. → Electronic health records can flag at-risk patients using composite data: HbA1c + postcode + medication refill patterns + weight trends.

Research and Data Are Crucial Future risk prediction models must include biomarkers of stress, cortisol rhythm, adipokines, and social deprivation indices to truly personalise prevention.

Summary:

“Effective stroke prevention demands that we treat not just blood pressure or LDL cholesterol, but also the postcode, paycheck, and pantry — alongside oestrogen levels, insulin resistance, and cortisol spikes. It’s time to move from population risk to person-specific resilience-building.”

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