Using an expanded algorithm to estimate prevalence of amyotrophic lateral sclerosis in U.S. and UK

Abstract

Background: There is an increasing need to better understand the burden of amyotrophic lateral sclerosis (ALS) using real-world data (RWD). However, identifying ALS cases using RWD presents several challenges due to the rarity of ALS and the differences in database coding systems.

Methods: MarketScan claims, and the UK Clinical Practice Research Datalink (CPRD) databases were searched for diagnosis codes of ALS or MND, the only drugs approved for treating ALS (riluzole and edaravone) and clinical visits with 12-month enrolment prior to 1 January 2011. The main algorithm required ≥ 1 ALS diagnosis code together with prescriptions or clinical visits. We expanded the existing algorithm to identify unspecific (possible) ALS group that had codes for motor neuron disease (MND) and the ALS drugs. The study period was from 1 January 2011 until 31 December 2020.

Results: We identified 16,246 patients with ≥ 1 ALS code in Marketscan (denominator n = 85,279,619), yet only 184 were found in the UK CPRD (denominator n = 21,318,589). Using the main algorithm 9,433 ALS patients were included in MarketScan, with a prevalence ranged between 4.5 per 100,000 in 2019 and 6.2 in 2015. In MarketScan, 3,658 (4.3 per 100,000) had ≥ 1 MND code and the ALS drug codes (possible cases). In CPRD, 47.9% of 2,785 patients with ≥ 1 MND code had a prescription for riluzole (6.3 per 100,000), regarded as possible ALS cases.

Conclusions: The expanded algorithm enabled the identification of a large population with ALS, or possible ALS, and the estimation of ALS prevalence in MarketScan and CPRD.

Keywords: Algorithm; Amyotrophic lateral sclerosis; Epidemiology; U.S. Claims; UK primary care.