The demographic and epidemiological transitions of the past 200 years are well documented at an aggregate level. Understanding differences in individual and group risks for mortality during these transitions requires linkage between demographic data and detailed individual cause of death information. This paper describes the digitization of almost 185,000 causes of death for Ohio to supplement demographic information in the Longitudinal, Intergenerational Family Electronic Micro-database (LIFE-M). To extract causes of death, our methodology combines handwriting recognition, extensive data cleaning algorithms, and the semi-automated classification of causes of death into International Classification of Diseases (ICD) codes. Our procedures are adaptable to other collections of handwritten data, which require both handwriting recognition and semi-automated coding of the information extracted.

过去 200 年的人口和流行病学转变在总体水平上得到了很好的记录。了解这些过渡期间个人和群体死亡风险的差异需要将人口统计数据与详细的个人死因信息联系起来。本文描述了俄亥俄州近 185,000 种死因的数字化，以补充纵向、代际家庭电子微数据库 (LIFE-M) 中的人口统计信息。为了提取死因，我们的方法结合了手写识别、广泛的数据清理算法以及将死因半自动分类为国际疾病分类 (ICD) 代码。我们的程序适用于其他手写数据集合，