Exploring the relation of active surveillance schedules and prostate cancer mortality

BACKGROUND

Active surveillance (AS), where treatment is deferred until cancer progression is detected by a biopsy, is acknowledged as a way to reduce overtreatment in prostate cancer. However, a consensus on the frequency of taking biopsies while in AS is lacking. In former studies to optimize biopsy schedules, the delay in progression detection was taken as an evaluation indicator and believed to be associated with the long-term outcome, prostate cancer mortality. Nevertheless, this relation was never investigated in empirical data. Here, we use simulated data from a microsimulation model to fill this knowledge gap.

METHODS

In this study, the established MIcrosimulation SCreening Analysis model was extended with functionality to simulate the AS procedures. The biopsy sensitivity in the model was calibrated on the Canary Prostate Cancer Active Surveillance Study (PASS) data, and four (tri-yearly, bi-yearly, PASS, and yearly) AS programs were simulated. The relation between detection delay and prostate cancer mortality was investigated by Cox models.

RESULTS

The biopsy sensitivity of progression detection was found to be 50%. The Cox models show a positive relation between a longer detection delay and a higher risk of prostate cancer death. A 2-year delay resulted in a prostate cancer death risk of 2.46%-2.69% 5 years after progression detection and a 10-year risk of 5.75%-5.91%. A 4-year delay led to an approximately 8% greater 5-year risk and an approximately 25% greater 10-year risk.

CONCLUSION

The detection delay is confirmed as a surrogate for prostate cancer mortality. A cut-off for a "safe" detection delay could not be identified.

What We Can Learn from the Exported Cases in Detecting Disease Outbreaks - A Case Study of the COVID-19 Epidemic

Purpose

Early warning in the travel origins is crucial to prevent disease spreading. When travel origins have delays in reporting disease outbreaks, the exported cases could be used to estimate the epidemic.

Methods

We developed a Bayesian model to jointly estimate the epidemic prevalence and detection delay using the exported cases and their arrival and detection dates. We used simulation studies to discuss potential biases generated by the exported cases. We proposed a hypothesis testing framework to determine the epidemic severity.

Results

We applied the method to the early phase of the COVID-19 epidemic of Wuhan, United States, Italy, and Iran and found that the indicators estimated from the exported cases were consistent with the domestic data under certain scenarios. The exported cases could generate various biases if not modeled properly. We presented the required number of exported cases for determining different severity levels of the outbreak.

Conclusions

The exported case data is a good addition to the domestic data but also has its drawbacks. Utilizing the diagnosis resources from all countries, we advocate that countries work collaboratively to strengthen the global infectious disease surveillance system.