A new method for estimating under-recruitment of a patient registry: a case study with the Ohio Registry of Amyotrophic Lateral Sclerosis

Abstract

We developed a disease registry to collect all incident amyotrophic lateral sclerosis (ALS) cases diagnosed during 2016-2018 in Ohio. Due to incomplete case ascertainment and limitations of the traditional capture-recapture method, we proposed a new method to estimate the number of cases not recruited by the Registry and their spatial distribution. Specifically, we employed three statistical methods to identify reference counties with normal case-population relationships to build a Poisson regression model for estimating case counts in target counties that potentially have unrecruited cases. Then, we conducted spatial smoothing to adjust outliers locally. We validated the estimates with ALS mortality data. We estimated that 119 total cases (95% CI [109, 130]) were not recruited, including 36 females (95% CI [31, 41]) and 83 males (95% CI [74, 99]), and were distributed unevenly across the state. For target counties, including estimated unrecruited cases increased the correlation between the case count and mortality count from r = 0.8494 to 0.9585 for the total, from 0.7573 to 0.8270 for females, and from 0.6862 to 0.9292 for males. The advantage of this method in the spatial perspective makes it an alternative to capture-recapture for estimating cases missed by disease registries.

Figure 1

Estimated numbers of unrecruited ALS cases in individual Ohio counties.

Figure 2

The incidence rate of ALS in Ohio. (a) Before including the estimated unrecruited cases; (b) after including the estimated unrecruited cases.

Figure 3

Sites are invited to participate in the Ohio ALS Registry. Participating sites are institutions and neurologists that have contributed cases to the Registry; Declining sites are those that have not.

Figure 4

Plots of the ranked ALS incidence and the difference and ratio between two consecutive values. All plots are for the counties with non-zero incidence rates in Ohio.

Figure 5

Spatial smoothing for adjusting the statistically estimated case counts in hotspot and cold spot areas.