‘Outbreak Gold Standard’Selection to Provide Optimized Threshold for Infectious Diseases Early-alert Based on China Infectious Disease Automated-alert and Response System

The China Infectious Disease Automated-alert and Response System（CIDARS） was successfully implemented and became operational nationwide in 2008. The CIDARS plays an important role in and has been integrated into the routine outbreak monitoring efforts of the Center for Disease Control（CDC） at all levels in China. In the CIDARS, thresholds are determined using the ？Mean＋2SD？ in the early stage which have limitations. This study compared the performance of optimized thresholds defined using the ？Mean ＋2SD？ method to the performance of 5 novel algorithms to select optimal ？Outbreak Gold Standard（OGS）？ and corresponding thresholds for outbreak detection. Data for infectious disease were organized by calendar week and year. The ？Mean＋2 SD？, C1, C2, moving average（MA）, seasonal model（SM）, and cumulative sum（CUSUM） algorithms were applied. Outbreak signals for the predicted value（Px） were calculated using a percentile-based moving window. When the outbreak signals generated by an algorithm were in line with a Px generated outbreak signal for each week, this Px was then defined as the optimized threshold for that algorithm. In this study, six infectious diseases were selected and classified into TYPE A（chickenpox and mumps）, TYPE B（influenza and rubella） and TYPE C [hand foot and mouth disease（HFMD） and scarlet fever]. Optimized thresholds for chickenpox（P_（55））, mumps（P_（50））, influenza（P_（40）, P_（55）, and P_（75））, rubella（P_（45） and P_（75））, HFMD（P_（65） and P_（70））, and scarlet fever（P_（75） and P_（80）） were identified. The C1, C2, CUSUM, SM, and MA algorithms were appropriate for TYPE A. All 6 algorithms were appropriate for TYPE B. C1 and CUSUM algorithms were appropriate for TYPE C. It is critical to incorporate more flexible algorithms as OGS into the CIDRAS and to identify the proper OGS and corresponding recommended optimized threshold by different infectious disease types.

Thoughts on the Construction of Evidence-based Nursing Standard Plan for Dominant Diseases in Traditional Chinese Medicine

The evidence-based Traditional Chinese Medicine(TCM) nursing in standard plan is to be constructed by a combination of evidence-based thinking and expert consensus.(1) The symptoms of diseases with better curative effect in TCM and the nursing techniques of TCM based on scientific evidence can be preliminarily presented through bibliometric analysis.(2) Expert consultation letters can be developed based on bibliometric results, and symptoms of dominant diseases in TCM and TCM nursing techniques can be selected through those letters.(3) Based on the selected symptoms and TCM nursing techniques, the PICO can be determined, and then evidence search, evidence extraction, evidence analysis can be conducted and synthesized, and recommendations would be chosen.(4) The recommendation grade of each recommendation can be determined by applying the Delphi method and referring to GRADE.(5) The plan will be determined through discussions of experts. The construction of TCM nursing plan would make sure that TCM nursing is scientific, and internationalize TCM nursing.

Towards efficiency in rare disease research： what is distinctive and important？

Characterized by their low prevalence, rare diseases are often chronically debilitating or life threatening. Despite their low prevalence, the aggregate number of individuals suffering from a rare disease is estimated to be nearly 400 million worldwide.Over the past decades, efforts from researchers, clinicians, and pharmaceutical industries have been focused on both the diagnosis and therapy of rare diseases. However, because of the lack of data and medical records for individual rare diseases and the high cost of orphan drug development, only limited progress has been achieved. In recent years, the rapid development of next-generation sequencing(NGS)-based technologies, as well as the popularity of precision medicine has facilitated a better understanding of rare diseases and their molecular etiology. As a result, molecular subclassification can be identified within each disease more clearly, significantly improving diagnostic accuracy. However, providing appropriate care for patients with rare diseases is still an enormous challenge. In this review, we provide a brief introduction to the challenges of rare disease research and make suggestions on where and how our efforts should be focused.