Evaluation of the Completeness of ALS Case Ascertainment in the U.S. National ALS Registry: Application of the Capture-Recapture Method

Prevalence of amyotrophic lateral sclerosis in the United States, 2018

OBJECTIVE

To estimate prevalent ALS cases in the United States for calendar year 2018.

METHODS

The National ALS Registry (Registry) compiled data from national administrative databases (from the Centers for Medicare and Medicaid Services, the Veterans Health Administration, and the Veterans Benefits Administration) and enrollment data voluntarily submitted through a web portal (www.cdc.gov/als). We used log-linear capture-recapture (CRC) model-based methodology to estimate the number of cases not ascertained by the Registry.

RESULTS

The Registry identified 21,655 cases of ALS in 2018, with an age-adjusted prevalence of 6.6 per 100,000 U.S. population. When CRC methods were used, an estimated 29,824 cases were identified, for an adjusted prevalence of 9.1 per 100,000 U.S. population. The demographics of cases of ALS did not change from previous year's reports. ALS continues to impact Whites, males, and persons over 50 years of age more so than other comparison groups. The results from the present report suggest case ascertainment for the Registry has improved, with the estimate of missing prevalent cases decreasing from 44% in 2017 to 27% in in 2018.

DISCUSSION

Consistent with previous estimates that used CRC, ALS prevalence in the United States is about 29,824 cases per year.

Analysis of the spatial distribution of amyotrophic lateral sclerosis in Virginia

Objective: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder that is usually fatal. Environmental exposures have been posited in the etiology of ALS, but few studies have modeled the spatial risk of ALS over large geographic areas. In this paper, our goal was to analyze the spatial distribution of ALS in Virginia and identify any areas with significantly elevated risk using Virginia ALS Association administrative data. Methods: We used Bayesian hierarchical spatial regression models to estimate the relative risk for ALS in Virginia census tracts, adjusting for several covariates posited to be associated with the disease. We used an intrinsic conditional autoregressive prior to allow for spatial correlation in the risk estimates and stabilize estimates over space. Results: Considerable variation in ALS risk existed across Virginia, with greater relative risk found in the central and western parts of the state. We identified significantly elevated relative risk in a number of census tracts. In particular, Henrico, Albemarle, and Botetourt counties all contained at least four census tracts with significantly elevated risk. Conclusions: We identified several areas with significantly elevated ALS risk across Virginia census tracts. These results can inform future studies of potential environmental triggers for the disease, whose etiology is still being understood.

A revision to the United States national ALS registry's algorithm to improve Case-Ascertainment

Objective: To evaluate the impact of 1) updating the existing algorithm to improve case-finding sensitivity and 2) reclassifying the Registry's diagnostic status nomenclature into four new categories ("confirmed ALS," "likely ALS," "undetermined ALS," or "not ALS") versus the current three ("definite ALS," "possible ALS," or "not ALS") to be more inclusive and descriptive of cases and individuals. Methods: A retrospective analysis of Registry data from 2011-2017 was conducted to follow "possible ALS" individuals over time to determine what qualifier caused them to convert, if at all and when, to Registry-eligible cases (i.e. "confirmed ALS" or "likely ALS"). Results: In 2011, 720 individuals were classified by the Registry algorithm as having "possible ALS". By 2017, 42% of these had converted to Registry-eligible ALS cases. Approximately 14% of those who were identified solely based on an ALS prescription drug never converted to Registry-eligible cases. This analysis indicates that "possible ALS" individuals with a single prescription for an ALS drug should be converted to Registry-eligible cases which would add between 300-500 cases per year on average. Conclusions: The Registry's existing algorithm likely results in the under-ascertainment of ALS cases. However, updating the algorithm with the inclusion of patients having been prescribed ALS-specific drugs, even with a single prescription, leads to improved epidemiologic estimates of ALS in the US. This and future algorithmic updates will help the Registry more accurately depict the true disease burden of ALS in the US.