Health Data API Integration Guide
Technical guide to integrating with Tracka's health data API — authentication, endpoints, rate limits, error handling, code examples, and webhook setup.
Chapter 1.Authentication
The Tracka Data API uses API key authentication via the X-API-Key header. Keys are issued through the platform dashboard and scoped to specific data access tiers. Each key is validated against issued credentials, checked for active status, verified for endpoint authorization, and logged for audit and rate limiting. Failed authentication returns 401 Unauthorized. Keys can be rotated without downtime using a configurable grace period.
Treat API keys as sensitive credentials — never expose in client-side code, public repositories, or shared documents. Store them in environment variables or secrets management systems.
Chapter 2.Endpoints Overview
RESTful endpoints organized around: prevalence data (aggregated statistics by geography and time), patient demographics (anonymized aggregate distributions), treatment metrics (coverage, hydroxyurea uptake, adherence), outcomes data (complication rates, hospitalizations, mortality), and geographic data (facility locations, coverage areas, infrastructure metrics). All data is anonymized and aggregated — no individual records are ever exposed.
Aggregation levels vary by tier: Basic plans get country-level, Pro gets region-level, Enterprise gets facility-level with data use agreements. All endpoints return JSON with consistent pagination, filtering, and error conventions.
Chapter 3.Rate Limits
Limits per API key by tier: Basic 100 requests/day (10/minute burst), Pro 1,000/day (60/minute), Enterprise negotiable. Status communicated via headers: X-RateLimit-Limit, X-RateLimit-Remaining, X-RateLimit-Reset (Unix timestamp). Exceeding limits returns 429 Too Many Requests with Retry-After header.
Best practices: cache responses locally, use larger page sizes to reduce request count, schedule batch pulls during off-peak hours, implement exponential backoff for rate limit responses.
Chapter 4.Error Handling
Standard HTTP status codes with structured JSON error bodies containing machine-readable error code, human-readable message, and specific parameter details. Client errors: 400 (malformed), 401 (auth failure), 403 (insufficient permissions), 404 (not found), 422 (semantic error). Server errors: 500 (unexpected), 503 (maintenance).
Distinguish retryable errors (429, 500, 503 — retry with exponential backoff) from non-retryable (400, 401, 403, 404, 422 — fix request first). Implement circuit breaker patterns for prolonged API issues to prevent cascading failures.
Chapter 5.Code Examples
For prevalence data, construct a GET request with geographic and temporal filter parameters. The response includes case counts, prevalence rates, and confidence intervals. For paginated data, use cursor-based pagination — include the cursor from the previous response's metadata in your next request for consistent results.
Implement a local cache layer with appropriate TTL values: 24 hours for static data like geographic hierarchies, 1 hour for dynamic data like patient counts. SDKs are available for Python and JavaScript, with community libraries for R, Go, and Ruby.
Chapter 6.Webhook Setup
Webhooks provide real-time notifications for: new data availability, threshold alerts, and report publications. Configure an HTTPS endpoint URL in your dashboard. Tracka sends POST requests with JSON payloads including event type, timestamp, and data. An X-Webhook-Signature header (HMAC-SHA256) verifies authenticity.
Return 200 within 10 seconds to acknowledge. Failed deliveries retry with exponential backoff up to 5 times over 24 hours. Implement idempotent handlers to safely handle duplicate deliveries during retries.
Chapter 7.Best Practices
Version-pin your integration to a specific API version — breaking changes are never introduced within a version. Log all API interactions including parameters, response codes, and timing for debugging. Monitor usage proactively and alert at 80% of daily limits. Handle data responsibly: store securely, use only for specified purposes, implement access controls, and review citation requirements before publishing analyses.