Webb G R
Department of Employment, Vocational Education, Training and Industrial Relations, Cairns, Australia.
Accid Anal Prev. 1995 Oct;27(5):687-97. doi: 10.1016/0001-4575(95)00014-q.
The paper describes a filter model that can be used to explore the possible biases that occur in reporting of alcohol-related injuries. These biases occur because of loss of data at various stages of injury reporting systems. The filter model consists of four levels or incidences, with a filter between each level. Level 1 is the total incidence of alcohol-related injuries. Level 2 is the incidence of reported alcohol-related injuries. Level 3 is the incidence of reported alcohol-related injuries where the contribution of alcohol has been correctly identified. Level 4 consists of official statistics on alcohol-related injuries. Between each of these levels is a filtering mechanism that allows some but not all data to pass through to the next level. The paper describes the nature of data at each level and the mechanisms operating at each filter to result in progressive loss of data. Estimates are provided regarding the extent of loss of data at each filter. Suggestions are provided for improving the quality of official statistics on alcohol-related injuries.
本文描述了一种过滤模型，可用于探究酒精相关伤害报告中可能出现的偏差。这些偏差的出现是由于伤害报告系统各个阶段的数据丢失。该过滤模型由四个层级或发生率组成，每个层级之间有一个过滤器。第一层是酒精相关伤害的总发生率。第二层是报告的酒精相关伤害的发生率。第三层是已正确识别酒精作用的报告的酒精相关伤害的发生率。第四层由酒精相关伤害的官方统计数据组成。在这些层级中的每一层之间都有一个过滤机制，该机制允许部分而非全部数据传递到下一层级。本文描述了每个层级的数据性质以及每个过滤器处运行的机制，以导致数据逐渐丢失。提供了关于每个过滤器处数据丢失程度的估计。还提供了提高酒精相关伤害官方统计数据质量的建议。
