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BackgroundContact tracing is one of the key interventions in response to the COVID-19 pandemic but its implementation varies widely across countries. There is little guidance on how to monitor contact tracing performance, and no systematic overview of indicators to assess contact tracing systems or conceptual framework for such indicators exists to date. MethodsWe conducted a rapid scoping review using a systematic literature search strategy in the peer-reviewed and grey literature as well as open source online documents. We developed a conceptual framework to map indicators by type (input, process, output, outcome, impact) and thematic area (human resources, financial resources, case investigation, contact identification, contact testing, contact follow up, case isolation, contact quarantine, transmission chain interruption, incidence reduction). ResultsWe identified a total of 153 contact tracing indicators from 1,555 peer-reviewed studies, 894 studies from grey literature sources, and 15 sources from internet searches. Two-thirds of indicators were process indicators (102; 67%), while 48 (31%) indicators were output indicators. Only three (2%) indicators were input indicators. Indicators covered seven out of ten conceptualized thematic areas, with more than half being related to either case investigation (37; 24%) or contact identification (44; 29%). There were no indicators for the input area "financial resources", the outcome area "transmission chain interruption", and the impact area "incidence reduction". ConclusionsAlmost all identified indicators were either process or output indicators focusing on case investigation, contact identification, case isolation or contact quarantine. We identified important gaps in input, outcome and impact indicators, which constrains evidence-based assessment of contact tracing systems. A universally agreed set of indicators is needed to allow for cross-system comparisons and to improve the performance of contact tracing systems.
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In an effort to monitor coronavirus disease 2019 (COVID-19), many countries have been calculatingthe ratio of cases confirmed to tests performed (test positivity ratio – TPR). While inferior to sentinelsurveillance, TPR has the benefit of being easily calculated using readily available data; however,interpreting TPR and its trends can be complex because both the numerator and the denominator areconstantly changing. We describe a three-step process where the ratio of relative increase in cases torelative increase in tests is accounted for in an adjusted TPR. This adjusted value more appropriatelyreflects the case number and factors out the effect of changes in the number of tests done. Unadjustedand adjusted TPRs are then assessed step-wise with reference to the epidemic curve and thecumulative numbers of cases and tests. Use of this three-step analysis and its potential use in guidingpublic health interventions are demonstrated for selected countries and subnational areas of the WorldHealth Organization South-East Asia Region, together with the Republic of Korea as a reference. Todate, application of the three-step analysis to data from countries of the region has signalled potentialinadequacies of testing strategies. Further work is needed on approaches to support countries wheretesting capacity is likely to remain constrained. One example would be enumeration of the averagenumber of tests needed to detect one COVID-19 case, which could be stratified by factors such aslocation and population. Such data would allow evidence-informed strategies that best balance thehighest detection rate with the prevailing testing capacity.
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COVID-19 , PandemiasRESUMEN
On 31 March 2013, the National Health and Family Planning Commission, China notified the World Health Organization of three cases of human infection with avian influenza A(H7N9) from Shanghai and Anhui.1 By 8 May, 131 cases, including 26 deaths, had been notified from 11 provinces/municipalities.1,2 The majority (81%) of reported cases were from Shanghai municipality and Zhejiang and Jiangsu provinces. Available data indicate that more than three quarters of cases (59/77, 76%) had recent exposure to animals. Among these, 58% (34/59) had direct contact with chickens and 64% (38/59) visited a live bird market (LBM).3 Provincial and national authorities in China have collected more than 80 000 samples from LBMs, poultry slaughter houses, poultry farms, wild bird habitats, pig slaughter houses and their environments. As of 7 May, 50 samples were positive for avian influenza A(H7N9): 39 samples from poultry from LBMs in Anhui, Jiangsu, Jiangxi, Guangdong, Shanghai and Zhejiang provinces/municipalities (26 chickens, three ducks, four pigeons, six unknown) and 11 environmental samples from LBMs in Shanghai, Henan and Shandong provinces.4 None of the samples from poultry farms or pigs were positive
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Since 31 March 2013, the government of China has been notifying the World Health Organization (WHO) of human infections with the avian influenza A(H7N9) virus,1 as mandated by the International Health Regulations (2005).2 While human infections with other subgroups of H7 influenza viruses (e.g. H7N2, H7N3, and H7N7) have previously been reported,3 the current event in China is of historical significance as it is the first time that A(H7N9) viruses have been detected among humans and the first time that a low pathogenic avian influenza virus is being associated with human fatalities.4 In this rapidly evolving situation, detailed epidemiologic and clinical data from reported cases are limited—making assessments challenging—however, some key questions have arisen from the available data. Age and sex data, as one of the first and most readily available data, may be an important proxy for gender-specific behaviours/conditions and an entry point for response.5,6 Here, we describe the age and sex distribution of the human cases of avian influenza A(H7N9) to better inform risk assessments and potential next steps.