Towards the elimination of dog-mediated rabies: development and application of an evidence-based management tool.

BACKGROUND: International organizations advocate for the elimination of dog-mediated rabies, but there is only limited guidance on interpreting surveillance data for managing elimination programmes. With the regional programme in Latin America approaching elimination of dog-mediated rabies, we aimed to develop a tool to evaluate the programme's performance and generate locally-tailored rabies control programme management guidance to overcome remaining obstacles. METHODS: We developed and validated a robust algorithm to classify progress towards rabies elimination within sub-national administrative units, which we applied to surveillance data from Brazil and Mexico. The method combines criteria that are easy to understand, including logistic regression analysis of case detection time series, assessment of rabies virus variants, and of incursion risk. Subjecting the algorithm to robustness testing, we further employed simulated data sub-sampled at differing levels of case detection to assess the algorithm's performance and sensitivity to surveillance quality. RESULTS: Our tool demonstrated clear epidemiological transitions in Mexico and Brazil: most states progressed rapidly towards elimination, but a few regressed due to incursions and control lapses. In 2015, dog-mediated rabies continued to circulate in the poorest states, with foci remaining in only 1 of 32 states in Mexico, and 2 of 27 in Brazil, posing incursion risks to the wider region. The classification tool was robust in determining epidemiological status irrespective of most levels of surveillance quality. In endemic settings, surveillance would need to detect less than 2.5% of all circulating cases to result in misclassification, whereas in settings where incursions become the main source of cases the threshold detection level for correct classification should not be less than 5%. CONCLUSION: Our tool provides guidance on how to progress effectively towards elimination targets and tailor strategies to local epidemiological situations, while revealing insights into rabies dynamics. Post-campaign assessments of dog vaccination coverage in endemic states, and enhanced surveillance to verify and maintain freedom in states threatened by incursions were identified as priorities to catalyze progress towards elimination. Our finding suggests genomic surveillance should become increasingly valuable during the endgame for discriminating circulating variants and pinpointing sources of incursions.

Trends in Cigarette Smoking Prevalence Among Refinery and Petrochemical Plant Workers, 1950 to 1999.

OBJECTIVE: Smoking is a potential confounder in studies of workplace exposures and smoking-related disease, but little data exist to quantitatively adjust for smoking in statistical models. METHODS: We estimated smoking prevalence trends between 1950 and 1999 for 12,299 female and 43,307 male hourly and salaried petrochemical workers using company physical examination data. RESULTS: Nearly half of hourly male and female employees smoked during the study period, compared with 38% of salaried males and 29% of females. Smoking prevalence in the 1950s reached 80% and 66% among female and male hourly workers, respectively, significantly higher than the US general population. CONCLUSIONS: As hourly workers typically comprise higher exposure groups and expected case counts are typically generated from the US general population, biased risk estimates may result from standardized mortality ratio analyses if smoking rate differences are not accounted for.

Optimizing medical resources for spaceflight using the integrated medical model.

INTRODUCTION: Efficient allocation of medical resources for spaceflight is important for crew health. The Integrated Medical Model (IMM) was developed to estimate medical event occurrences, mitigation, and resource requirements. An optimization module was created for IMM that uses a systematic process of elimination and preservation to maximize crew health outcomes subject to resource constraints. METHODS: A maximum medical kit is identified and resources are eliminated according to their relative impact on outcomes of interest. Additional steps allow opportunities for resources to be added back into the medical kit if possible. The effectiveness of the module is demonstrated under six alternative mission profiles by optimizing the medical kit to maximize the expected Crew Health Index (CHI), and comparisons are made with minimum and maximum kits. RESULTS: The optimum and maximum kits had similar expected CHI, but CHI was more variable for the optimum kit. The maximum kit resulted in the best outcomes, but required at least 13.7 times the mass of the optimum kit and 26.6 times the volume. The largest difference in mean CHI between the optimum and maximum kits occurred for four crewmembers on a 180-d mission (91.1% vs. 95.4%). CONCLUSIONS: The optimization module may be used as an objective tool to assist with the efficient allocation of medical resources for spaceflight. The module provides a flexible algorithm that may be used in conjunction with the IMM model to assist in medical kit requirements and design.