Introduction: Lyme disease (LD) is common in the US but the total burden is unknown. We previously validated a claims-based algorithm (LD diagnosis code and indicated antibiotic) via medical record review (PPV 93.8%). The current work explores additional claims-based algorithms to identify LD cases without LD-specific diagnoses.
Methods: We developed algorithms using non-LD diagnosis codes, LD-indicated antibiotics, and procedure codes for LD diagnostic tests. Diagnosis codes reflected musculoskeletal, nervous system, and cardiovascular manifestations and were not specific to LD. For each manifestation, we assessed 3 algorithms; each required a diagnosis code and: (1) antibiotic and diagnostic test; (2) antibiotic, with no diagnostic test; (3) diagnostic test, regardless of antibiotic. We applied all algorithms to claims from Massachusetts residents, where LD is endemic.
Results: We identified 144,058 individuals who met ≥1 algorithm, among whom only 10% had a LD diagnosis code within +/-90 days. The algorithms identified the greatest patient count for musculoskeletal manifestations, followed by neurologic manifestations, then cardiovascular. The most common diagnoses identifying cases were: musculoskeletal: knee pain; neurologic: radiculopathy, Bell’s palsy, polyneuropathy; cardiovascular: atrioventricular block, other specified conduction disorders.
Among patients identified by algorithms requiring an antibiotic (algorithms 1 and 2), patients with a diagnostic test (algorithm 1) were more likely than those without a diagnostic test (algorithm 2) to receive doxycycline (43% vs 18%); most other patients received amoxicillin. Algorithm 3 had a low prevalence of treatment (16%). Algorithms 1 and 3, both requiring a diagnostic test, reflected LD seasonality, with higher burden in summer; algorithm 2, which did not require a diagnostic test, had an inverse seasonal pattern.
Conclusion: The claims-based algorithm requiring a diagnosis code for a LD manifestation, antibiotic treatment, and LD diagnostic test identified patients whose characteristics were consistent with those of LD cases. We will validate this algorithm via medical record review.
View the poster
Kluberg SA, Cocoros NM, Rosen E, Jin R, Aucott JN, Pugh SJ, Stark JH. Extending an Under-Recognized Burden of Lyme Disease with Administrative Claims Data. International Symposium on Tick-borne Pathogens and Disease 2023; 2023 Oct 22; Vienna, Austria.
