Hamm Naomi C, Marrie Ruth Ann, Jiang Depeng, Irani Pourang, Lix Lisa M
Department of Community Health Sciences, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.
Department of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.
Int J Popul Data Sci. 2024 Apr 30;9(1):2358. doi: 10.23889/ijpds.v9i1.2358. eCollection 2024.
The validity of chronic disease case definitions for administrative health data may change over time due to changes in data quality. Trend control charts to identify out-of-control (OOC; i.e., unexpected) observations in a time series may indicate where disease estimates are influenced by changes in data quality.
Apply and compare trend control charts methods for multiple sclerosis (MS) incidence and prevalence estimates using previously-validated case definitions for Manitoba, Canada.
Eight case definitions were identified from published literature and applied to Manitoba administrative health data from January 1, 1972 to December 31, 2018. Incidence and prevalence trends were modeled using negative binomial and generalized estimating equation models, respectively. Trend control charts were used to plot predicted case counts against observed case counts. Control limits to identify OOC observations were calculated using two methods: predicted case count ±0.8standard deviation (0.8SD) and predicted case count ±2standard deviation (2SD). Differences in proportion of OOC observations across case definitions was assessed using McNemar's test.
The proportion of OOC observations ranged from 0.71 to 0.90 for incidence and 0.72 to 0.98 for prevalence when using the 0.8SD control limits. A lower proportion of OOC observations (0.46 to 0.74 for incidence; 0.30 to 0.74 for prevalence) was observed for the 2SD control limits. Neither method resulted in significant differences in OOC observations across case definitions.
The proportion of OOC observations in trend control charts varied with the control limit method adopted, but statistical significance did not. Trend control charts are a potentially useful tool for developing surveillance methods, but may benefit from disease-specific calibrated control limits.
由于数据质量的变化，用于行政卫生数据的慢性病病例定义的有效性可能会随时间而改变。用于识别时间序列中失控（OOC；即意外）观测值的趋势控制图可能会指出疾病估计值受数据质量变化影响的位置。
应用并比较趋势控制图方法，以使用加拿大曼尼托巴省先前验证的病例定义来估计多发性硬化症（MS）的发病率和患病率。
从已发表的文献中确定了八个病例定义，并将其应用于1972年1月1日至2018年12月31日的曼尼托巴省行政卫生数据。发病率和患病率趋势分别使用负二项式模型和广义估计方程模型进行建模。趋势控制图用于绘制预测病例数与观察到的病例数。使用两种方法计算用于识别OOC观测值的控制限：预测病例数±0.8 *标准差（0.8 * SD）和预测病例数±2 *标准差（2 * SD）。使用McNemar检验评估不同病例定义的OOC观测值比例差异。
使用0.8 * SD控制限时，OOC观测值的比例在发病率方面为0.71至0.90，在患病率方面为0.72至0.98。对于2 * SD控制限，观察到的OOC观测值比例较低（发病率为0.46至0.74；患病率为0.30至0.74）。两种方法在不同病例定义的OOC观测值方面均未产生显著差异。
趋势控制图中OOC观测值的比例随所采用的控制限方法而变化，但统计显著性未变。趋势控制图是开发监测方法的潜在有用工具，但可能受益于针对特定疾病校准的控制限。
