Trend Control Charts for Multiple Sclerosis Case Definitions

Overview

Journal Int J Popul Data Sci

Specialty Public Health

Date 2024 Dec 2

PMID 39620120

Authors

Naomi C Hamm

Ruth Ann Marrie

DePeng Jiang

Pourang Irani

Lisa M Lix

Affiliations

Soon will be listed here.

Abstract

Introduction: 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.

Objective: Apply and compare trend control charts methods for multiple sclerosis (MS) incidence and prevalence estimates using previously-validated case definitions for Manitoba, Canada.

Methods: 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 ±0.8*standard deviation (0.8*SD) and predicted case count ±2*standard deviation (2*SD). Differences in proportion of OOC observations across case definitions was assessed using McNemar's test.

Results: 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 0.8*SD 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 2*SD control limits. Neither method resulted in significant differences in OOC observations across case definitions.

Conclusions: 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.

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