Gittins Matthew, McNamee Roseanne, Holland Fiona, Carter Lesley-Anne
Centre for Biostatistics, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Jean McFarlane Building, University Place, Oxford Road, Manchester M13 9PL, UK.
Centre for Biostatistics, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Jean McFarlane Building, University Place, Oxford Road, Manchester M13 9PL, UK.
J Clin Epidemiol. 2017 Jan;81:77-85. doi: 10.1016/j.jclinepi.2016.09.006. Epub 2016 Sep 17.
Accurate estimation of the true incidence of ill-health is a goal of many surveillance systems. In surveillance schemes including zero reporting to remove ambiguity with nonresponse, reporter fatigue might increase the likelihood of a false zero case report in turn underestimating the true incidence rate and creating a biased downward trend over time.
Multilevel zero-inflated negative binomial models were fitted to incidence case reports of three surveillance schemes running between 1996 and 2012 in the United Kingdom. Estimates of the true annual incidence rates were produced by weighting the reported number of cases by the predicted excess zero rate in addition to the within-scheme standard adjustment for response rate and the participation rate.
Time since joining the scheme was associated with the odds of excess zero case reports for most schemes, resulting in weaker calendar trends. Estimated incidence rates (95% confidence interval) per 100,000 person years, were approximately doubled to 30 (21-39), 137 (116-157), 33 (27-39), when excess zero-rate adjustment was applied.
If we accept that excess zeros are in reality nonresponse by busy reporters, then usual estimates of incidence are likely to be significantly underestimated and previously thought strong downward trends overestimated.
准确估计健康不良的真实发生率是许多监测系统的目标。在包括零报告以消除与无应答相关的模糊性的监测方案中,报告者疲劳可能会增加虚假零病例报告的可能性,进而低估真实发生率,并随着时间的推移产生有偏差的下降趋势。
对1996年至2012年在英国运行的三种监测方案的发病病例报告拟合了多级零膨胀负二项式模型。除了对应答率和参与率进行方案内标准调整外,通过将报告的病例数乘以预测的额外零率来得出真实年发病率的估计值。
对于大多数方案,加入方案后的时间与额外零病例报告的几率相关,导致日历趋势减弱。应用额外零率调整后,每10万人年的估计发病率(95%置信区间)约增加一倍,分别为30(21 - 39)、137(116 - 157)、33(27 - 39)。
如果我们承认额外的零实际上是忙碌报告者的无应答,那么通常的发病率估计可能会被显著低估,而之前认为的强烈下降趋势则被高估。