Santé Publique France, French National Public Health Agency, Saint-Maurice, France.
Hospices Civils de Lyon, Service de Biostatistique-Bioinformatique, Pierre-Bénite, Université Lyon 1, France.
Int J Epidemiol. 2021 Mar 3;50(1):279-292. doi: 10.1093/ije/dyaa217.
In many countries, epidemiological surveillance of chronic diseases is monitored by local registries (LR) which do not necessarily cover the whole national territory. This gap has fostered interest in using non-registry databases (e.g., health care or mortality databases) available for the whole territory as proxies for incidence at the local level. However, direct counts from these databases do not provide reliable incidence measures. Accordingly, specific methods are needed to correct proxies and assess their epidemiological usefulness.
This study's objective was to implement a three-stage turnkey methodology using national non-registry data to predict incidence in geographical areas without an LR as follows: constructing a calibration model to make predictions including accurate prediction intervals; accuracy assessment of predictions and rationale for the criteria to assess which predictions were epidemiologically useful; mapping after spatial smoothing of the latter predictions. The methodology was applied to a real-world setting, whereby we aimed to predict cancer incidence, by gender, at the district level in France over the 2007-15 period for 24 different cancer sites, using several health care indicators and mortality. In the present paper, the spatial smoothing performed on predicted incidence of epidemiological interest is illustrated for two examples.
Predicted incidence of epidemiological interest was possible for 27/34 solid site-gender combinations and for only 2/8 haematological malignancies-gender combinations. Mapping of smoothed predicted incidence provided a clear picture of the main contrasts in incidence between districts.
The methodology implemented provides a comprehensive framework to produce valuable predictions of incidence at a district level, using proxy measures and existing LR.
在许多国家，慢性病的流行病学监测由地方登记处（LR）进行，但这些登记处并不一定覆盖整个国家领土。这种差距促使人们对使用整个国家可用的非登记数据库（例如医疗保健或死亡率数据库）作为地方一级发病率的替代指标产生了兴趣。然而，这些数据库中的直接计数并不能提供可靠的发病率衡量标准。因此，需要特定的方法来校正替代指标并评估其流行病学用途。
本研究的目的是使用国家非登记数据实施一个三阶段交钥匙方法，以预测没有 LR 的地理区域的发病率，具体方法如下：构建一个校准模型来进行预测，包括准确的预测区间；对预测进行准确性评估，并为评估哪些预测具有流行病学意义的标准提供依据；对后者的预测进行空间平滑后映射。该方法应用于一个真实世界的环境中，旨在通过几种医疗保健指标和死亡率，预测 2007-15 年期间法国按性别划分的 24 个不同癌症部位的区级癌症发病率。在本文中，对具有流行病学意义的预测发病率进行空间平滑的示例进行了说明。
对于 27/34 个实体部位-性别组合和仅对于 2/8 个血液恶性肿瘤-性别组合，可能对流行病学感兴趣的预测发病率。平滑预测发病率的映射提供了各地区发病率之间主要差异的清晰图像。
实施的方法提供了一个综合框架，使用替代指标和现有 LR ，在区级水平上产生有价值的发病率预测。
