Hamm Naomi C, Jiang Depeng, Marrie Ruth Ann, Irani Pourang, Lix Lisa M
Department of Community Health Sciences, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, S113-750 Bannatyne Avenue, Winnipeg, MB, R3E 0W3, Canada.
Department of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, R3A 1R9, Canada.
BMC Public Health. 2022 Feb 28;22(1):406. doi: 10.1186/s12889-021-12328-w.
Algorithms used to identify disease cases in administrative health data may be sensitive to changes in the data over time. Control charts can be used to assess how variations in administrative health data impact the stability of estimated trends in incidence and prevalence for administrative data algorithms. We compared the stability of incidence and prevalence trends for multiple juvenile diabetes algorithms using observed-expected control charts.
Eighteen validated algorithms for juvenile diabetes were applied to administrative health data from Manitoba, Canada between 1975 and 2018. Trends in disease incidence and prevalence for each algorithm were modelled using negative binomial regression and generalized estimating equations; model-predicted case counts were plotted against observed counts. Control limits were set as predicted case count ±0.8*standard deviation. Differences in the frequency of out-of-control observations for each algorithm were assessed using McNemar's test with Holm-Bonferroni adjustment.
The proportion of out-of-control observations for incidence and prevalence ranged from 0.57 to 0.76 and 0.45 to 0.83, respectively. McNemar's test revealed no difference in the frequency of out-of-control observations across algorithms. A sensitivity analysis with relaxed control limits (2*standard deviation) detected fewer out-of-control years (incidence 0.19 to 0.33; prevalence 0.07 to 0.52), but differences in stability across some algorithms for prevalence.
Our study using control charts to compare stability of trends in incidence and prevalence for juvenile diabetes algorithms found no differences for disease incidence. Differences were observed between select algorithms for disease prevalence when using wider control limits.
用于在行政卫生数据中识别疾病病例的算法可能会随时间对数据变化敏感。控制图可用于评估行政卫生数据中的变化如何影响行政数据算法估计的发病率和患病率趋势的稳定性。我们使用观察-预期控制图比较了多种青少年糖尿病算法的发病率和患病率趋势的稳定性。
将18种经过验证的青少年糖尿病算法应用于1975年至2018年加拿大曼尼托巴省的行政卫生数据。使用负二项回归和广义估计方程对每种算法的疾病发病率和患病率趋势进行建模；将模型预测的病例数与观察到的病例数进行绘制。控制限设定为预测病例数±0.8*标准差。使用经Holm-Bonferroni调整的McNemar检验评估每种算法失控观察频率的差异。
发病率和患病率的失控观察比例分别为0.57至0.76和0.45至0.83。McNemar检验显示各算法之间失控观察频率没有差异。使用放宽的控制限（2*标准差）进行的敏感性分析检测到较少的失控年份（发病率0.19至0.33；患病率0.07至0.52），但某些算法在患病率稳定性方面存在差异。
我们使用控制图比较青少年糖尿病算法发病率和患病率趋势稳定性的研究发现疾病发病率没有差异。在使用更宽的控制限时，特定算法在疾病患病率方面存在差异。
