Evaluating and optimizing the use of diagnostics during epidemics: Application to the 2017 plague outbreak in Madagascar

During outbreaks, the lack of diagnostic “gold standard” can mask the true burden of infection in the population and hamper the allocation of resources required for control. Here, we present an analytical framework to evaluate and optimize the use of diagnostics when multiple yet imperfect diagnostic tests are available. We apply it to laboratory results of 2,136 samples, analyzed with three diagnostic tests (based on up to seven diagnostic outcomes), collected during the 2017 pneumonic (PP) and bubonic plague (BP) outbreak in Madagascar, which was unprecedented both in the number of notified cases, clinical presentation, and spatial distribution. The extent of this outbreaks has however remained unclear due to non-optimal assays. Using latent class methods, we estimate that 7%-15% of notified cases were Yersinia pestis-infected. Overreporting was highest during the peak of the outbreak and lowest in the rural settings endemic to Yersinia pestis. Molecular biology methods offered the best compromise between sensitivity and specificity. The specificity of the rapid diagnostic test was relatively low (PP: 82%, BP: 85%), particularly for use in contexts with large quantities of misclassified cases. Comparison with data from a subsequent seasonal Yersinia pestis outbreak in 2018 reveal better test performance (BP: specificity 99%, sensitivity: 91%), indicating that factors related to the response to a large, explosive outbreak may well have affected test performance. We used our framework to optimize the case classification and derive consolidated epidemic trends. Our approach may help reduce uncertainties in other outbreaks where diagnostics are imperfect.

Cancer patients’ trade-offs for efficacy, toxicity, and cost.

6021 Background: When making treatment decisions, cancer patients (pts) must make trade-offs between efficacy, toxicity (tox) and cost. However, little is known about how individual characteristics influence these decisions, particularly as many face high out of pocket costs. Methods: We presented cancer pts with hypothetical scenarios that asked them to choose between 2 treatments of varying levels of efficacy, tox and cost. Each scenario included 9 choice pairs. Pts were given 2 of 3 scenarios described in the Table. Tox was also varied. Demographics, cost concerns and numeracy were assessed. Within each scenario, we used latent class methods to distinguish pt groups with discrete preferences. We then used regressions with group membership probabilities as covariates to identify associations. Results: We enrolled 400 pts. Median age was 61 years (range 27-90). 63% were female. 41% were college educated. 51% had an annual income ≥$60K. 25% were enrolled at a community hospital. 98% were insured. Within each of the 3 scenarios, we identified 3 pt classes with preferences for survival or aversion to high cost or toxicity. Across each of the scenarios, <6% of pts in the group averse to high cost chose the costlier treatment. >92% of pts in the group that favored survival chose the highest efficacy treatment. >65% of pts in the group with aversion to tox chose the lower tox treatment. Within each of the scenarios, pts in the group with preference for survival were more likely to have an income of >$60K (p<.05) and greater numeracy skills (p<.05). In scenarios 2 and 3, pts with concerns about treatment costs were more likely to be in the class that was averse to high cost (p<.05 for both). Conclusions: Even in hypothetical scenarios presented to insured pts, socioeconomic status was predictive of treatment choice. Higher income pts may be more likely to focus on survival when making decisions while those with greater cost concerns may be more likely to avoid costly treatment, regardless of survival or tox. This raises the possibility that health plans with greater cost-sharing may have the unintended consequence of increasing disparities in care. [Table: see text]