Observational Study That Will Evaluate Treatment Patterns in the Management of Hypertension in the Public Primary Care System

Clinical Landscape of Epidemiology and Assessment of Results in Hypertension

This is a retrospective observational cohort study based on secondary use of routinely collected electronic health data. The design was selected to enable population-level characterisation of treatment patterns and clinical inertia (both diagnostic and therapeutic) in the management of hypertension across diverse settings in Brazil's public primary care system (SUS).

This is a retrospective observational cohort study based on secondary use of routinely collected electronic health data. The design was selected to enable population-level characterisation of treatment patterns and clinical inertia (both diagnostic and therapeutic) in the management of hypertension across diverse settings in Brazil's public primary care system (SUS).

The retrospective cohort design allows for efficient use of existing clinical data to evaluate real-world practice against national guideline recommendations. The primary outcomes relate to treatment decisions and delays rather than interventional effects; therefore, an observational design is methodologically appropriate and ethically preferable.

The study will be conducted across 05 Brazilian municipalities participating in the epHealth Primary Care Solutions platform. These municipalities span multiple regions and reflect heterogeneity in socio-demographic context, healthcare infrastructure, and epidemiological profiles.

Inclusion Criteria:

• Age ≥18 years at the time of Hypertension diagnosis
• Hypertension diagnosis as retrieved from registered ICD codes or CIAP Codes (primary care codes) in the EMR , OR At least two blood pressure measurements ≥140/90 mmHg during the study period. The blood measurements should be consecutive and obtained within a 30 days period.
• Continuous follow-up in the same municipality for at least 6 months after index measurement.
• Availability of structured EMR data including clinical encounters, diagnoses, and prescriptions

Exclusion Criteria:

- Patients with incomplete data or not traceable in the registration systems