UC San Diego

Aims

The aim of this study was to assess the validity of undertaking time-series analyses on both fatal and non-fatal drug overdose outcomes for the surveillance of emerging drug threats, and to determine the validity of analyzing non-fatal indicators to support the early detection of fatal overdose outbreaks.

Design, setting and participants

Time-series analyses using county-level data containing fatal overdoses and non-fatal overdose counts were collected at monthly intervals between 2015 and 2021 in California and Florida, USA. To analyze these data, we used the Farrington algorithm (FA), a method used to detect aberrations in time-series data such that an abnormal increase in counts relative to previous observations would result in an alert. The FA's performance was compared with a bench-mark approach, using the standard deviation as an aberration detection threshold. We evaluated whether monthly alerts in non-fatal overdose can aid in identifying fatal drug overdose outbreaks, defined as a statistically significant increase in the 6-month overdose death rate. We also conducted analyses across regions, i.e. clusters of counties.

Measurements

Measurements were taken during emergency department and emergency medical service visits.

Findings

Both methods yielded a similar proportion of alerts across scenarios for non-fatal overdoses, while the bench-mark method yielded more alerts for fatal overdoses. For both methods, the correlations between surveillance evaluations were relatively poor in the detection of aberrations (typically < 35%) but were high between evaluations yielding no alerts (typically > 75%). For ongoing fatal overdose outbreaks, a strategy based on the detection of alerts at the county level from either method yielded a sensitivity of 66% for both California and Florida. At the regional level, the equivalent analyses had sensitivities of 81% for California and 77% for Florida.

Conclusion

Aberration detection methods can support the early detection of fatal drug overdose outbreaks, particularly when methodologies are applied in combination rather than individual methods separately.