Epidemiology Unit, ASL TO3 Regione Piemonte, Grugliasco, Italy,
Epidemiology Unit, ASL TO3 Regione Piemonte, Grugliasco, Italy.
Neuroepidemiology. 2021;55(2):119-125. doi: 10.1159/000513763. Epub 2021 Mar 10.
Italy is considered a high-risk country for multiple sclerosis (MS). Exploiting electronic health archives (EHAs) is highly useful to continuously monitoring the prevalence of the disease, as well as the care delivered to patients and its outcomes. The aim of this study was to validate an EHA-based algorithm to identify MS patients, suitable for epidemiological purposes, and to estimate MS prevalence in Piedmont (North Italy).
MS cases were identified, in the period between January 1, 2012 and December 31, 2017, linking data from 4 different sources: hospital discharges, drug prescriptions, exemptions from co-payment to health care, and long-term care facilities. Sensitivity of the algorithm was tested through record linkage with a cohort of 656 neurologist-confirmed MS cases; specificity was tested with a cohort of 2,966,293 residents presumably not affected by MS. Undercount was estimated by a capture-recapture method. We calculated crude, and age- and gender-specific prevalence. We also calculated age-adjusted prevalence by level of urbanization of the municipality of residence.
On December 31, 2017, the algorithm identified 8,850 MS cases. Sensitivity was 95.9%, specificity was 99.97%, and the estimated completeness of ascertainment was 91.9%. The overall prevalence, adjusted for undercount, was 152 per 100,000 among men and 286 among women; it increased with increasing age and reached its peak value in the 45- to 54-year class, followed by a progressive reduction. The age-adjusted prevalence of residents in cities was 15% higher than in those living in the countryside.
DISCUSSION/CONCLUSION: We validated an algorithm based on EHAs to identify cases of MS for epidemiological use. The prevalence of MS, adjusted for undercount, was among the highest in Italy. We also found that the prevalence was higher in highly urbanized areas.
意大利被认为是多发性硬化症（MS）的高风险国家。利用电子健康档案（EHAs）对于连续监测疾病的流行情况以及向患者提供的护理及其结果非常有用。本研究的目的是验证一种基于 EHA 的算法，以识别适合流行病学目的的 MS 患者，并估计意大利皮埃蒙特（北部意大利）的 MS 患病率。
在 2012 年 1 月 1 日至 2017 年 12 月 31 日期间，通过链接来自 4 个不同来源的数据，确定 MS 病例：医院出院记录、药物处方、医疗保健共付额豁免和长期护理机构。通过与 656 名经神经科医生确诊的 MS 病例的记录链接测试算法的敏感性；通过与 2966293 名假定未患 MS 的居民的队列测试特异性。通过捕获-再捕获法估计漏报率。我们计算了粗患病率以及按年龄和性别划分的患病率。我们还根据居住地的城市化程度计算了年龄调整后的患病率。
截至 2017 年 12 月 31 日，该算法确定了 8850 例 MS 病例。敏感性为 95.9%，特异性为 99.97%，估计的确定率为 91.9%。调整漏报率后，男性总体患病率为每 100000 人 152 例，女性为 286 例；随着年龄的增加而增加，并在 45 至 54 岁年龄段达到峰值，随后逐渐下降。城市居民的年龄调整患病率比农村居民高 15%。
讨论/结论：我们验证了一种基于 EHAs 的算法，用于识别用于流行病学的 MS 病例。调整漏报率后，MS 的患病率在意大利属于较高水平。我们还发现，在高度城市化地区，患病率更高。
