Use of the CUSUM and Shewhart control chart methods to identify changes of public health significance using childhood blood lead surveillance data

Detecting New Sources of Childhood Environmental Lead Exposure Using a Statistical Surveillance System, 2015-2019

Objectives. To design and implement a statistical surveillance system to prospectively identify potential clusters of elevated blood lead levels (EBLLs) in children younger than 6 years in the Denver, Colorado, metro area. Methods. We evaluated the ability of 2 independent statistical surveillance methods to detect synthetic clusters of EBLLs in Denver between 2015 and 2019. Results. Together, the statistical surveillance methods took an average of 9 months to detect the synthetic clusters. This is faster than similar real-world clusters that have been reported in the past. The system was relatively unaffected by changes in the testing rate and to the blood lead reference value. Conclusions. The adequate design of a statistical surveillance system can help increase the rate at which clusters of EBLLs are detected in Denver, but doing so requires an accurate model of the spatial distribution of EBLLs. Earlier detection of clusters can help guide more effective public health interventions at the local level. (Am J Public Health. 2022;112(S7):S715-S722. https://doi.org/10.2105/AJPH.2022.307009).

Control charts for chronic disease surveillance: testing algorithm sensitivity to changes in data coding

Background

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.

Methods

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.

Results

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.

Conclusions

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-12328-w.