Control charts for health surveillance based on residuals of negative binomial regression

Abstract

This research presents control charts based on one‐sided exponentially weighted moving average (EWMA) control charts of Pearson, studentized Pearson, Deviance, and Studentized Deviance residuals for a negative binomial regression in order to detect increases in count quality variables used in health surveillance. Data of weekly dengue cases for the year 2018 from the coast of Ecuador were used for the design of control charts in phase I because in that year the weather conditions were normal, while dengue cases in 2019 were used for phase II of control. The control charts detected an increase in the weekly cases of dengue during 2019, a year in which the phenomenon of the Niño was present. In this study by simulations, it is shown that the smallest lambda values have a faster speed in detecting changes. If the objective is greater sensitivity, using studentized residuals is better, but if we seek simplicity and interpretation, Pearson residuals can be used.

Patient‐based real‐time quality control in medical laboratories: On the design and robustness of the moving average control chart with truncation limits

“…Embracing the control charting methods, valuable insights and methodologies from the statistical process control (SPC) literature have been adapted to the specific challenges of laboratory QC activities. On the other hand, although not specifically in the domain of medical laboratories, some studies in the SPC literature also provided guidance on the use of control charts in healthcare (see, e.g., Woodall, 1 Woodall et al., 2 Woodall and Montgomery, 3 Dobi and Zempléni, 4 García‐Bustos and Zambrano, 5 and Aslam et al 6 …”