Department of Geography, Dartmouth College, Hanover, NH, 03755, USA.
Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH, 03755, USA.
Sci Rep. 2022 Aug 30;12(1):14721. doi: 10.1038/s41598-022-18944-9.
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.
我们开发了一个疾病登记处，以收集 2016 年至 2018 年期间在俄亥俄州诊断出的所有肌萎缩侧索硬化症（ALS）新发病例。由于不完全的病例确定和传统捕获-再捕获方法的限制，我们提出了一种新方法来估计未被登记处招募的病例数量及其空间分布。具体来说，我们使用了三种统计方法来识别具有正常病例-人群关系的参考县，以建立泊松回归模型来估计可能存在未招募病例的目标县的病例数。然后，我们进行了空间平滑以局部调整异常值。我们使用 ALS 死亡率数据验证了这些估计值。我们估计有 119 例总病例（95%CI [109, 130]）未被招募，包括 36 例女性（95%CI [31, 41]）和 83 例男性（95%CI [74, 99]），且分布不均匀整个州。对于目标县，包括估计未招募的病例，使病例数与死亡率之间的相关性从总病例的 r=0.8494 增加到 0.9585，女性病例从 0.7573 增加到 0.8270，男性病例从 0.6862 增加到 0.9292。从空间角度来看，这种方法的优势使得它成为疾病登记处估计遗漏病例的替代方法。
