Dynamic mapping of cholera outbreak during the Yemeni Civil War, 2016-2019.

Widespread destruction from the Yemeni Civil War (2014-present) triggered the world's largest cholera outbreak. We compiled a comprehensive health dataset and created dynamic maps to demonstrate spatiotemporal changes in cholera infections and war conflicts. We aligned and merged daily, weekly, and monthly epidemiological bulletins of confirmed cholera infections and daily conflict events and fatality records to create a dataset of weekly time series for Yemen at the governorate level (subnational regions administered by governors) from 4 January 2016 through 29 December 2019. We demonstrated the use of dynamic mapping for tracing the onset and spread of infection and manmade factors that amplify the outbreak. We report curated data and visualization techniques to further uncover associations between infectious disease outbreaks and risk factors and to better coordinate humanitarian aid and relief efforts during complex emergencies.

Racial disparities in skin tone representation of dermatomyositis rashes: a systematic review.

OBJECTIVE: This systemic review assesses skin tone representation in images of DM rashes in medical education literature. METHODS: A review was performed of 59 dermatology, 11 neurology, 10 neuromuscular, 7 rheumatology and 6 internal medicine textbooks published between 2011 and 2021 and 3 online image databases (UpToDate, VisualDx and DermNet NZ) that were available through an online medical school library. After extracting images, images with poor lighting or unclear rashes were removed. Authors graded skin tone independently on the Massey and Martin Skin Colour Scale (MMSCS) from 1 (very light) to 10 (very dark). The median score was taken for a final score, grouped within MMSCS 1-2, 3-4, 5-7 or 8-10. Inter-rater reliability was assessed using Kendall's coefficient of concordance (W). RESULTS: Six hundred and twenty-one images were extracted after reviewing 93 textbooks and 3 online databases. Of the 561 images analysed, 73.1% of images represented MMSCS 1-2, followed by 3-4 (13.4%), 5-7 (11.8%) and 8-10 (1.8%). Inter-rater reliability was high (W = 0.835). Of the images in MMSCS 5-10, 59.2% were in online databases and 80.6% of textbook images were in dermatology books. CONCLUSIONS: Patients with lighter skin tones were represented in a higher number of DM-related educational materials compared with patients with darker skin tones. Our findings add to current research implicating that darker skin tones are under-represented in cutaneous educational materials, specifically for DM. This leads to the inability to properly characterize skin involvement in DM and may lead to inappropriate exclusion from clinical trials due to erroneous skin scoring.

Signatures of Cholera Outbreak during the Yemeni Civil War, 2016-2019.

The Global Task Force on Cholera Control (GTFCC) created a strategy for early outbreak detection, hotspot identification, and resource mobilization coordination in response to the Yemeni cholera epidemic. This strategy requires a systematic approach for defining and classifying outbreak signatures, or the profile of an epidemic curve and its features. We used publicly available data to quantify outbreak features of the ongoing cholera epidemic in Yemen and clustered governorates using an adaptive time series methodology. We characterized outbreak signatures and identified clusters using a weekly time series of cholera rates in 20 Yemeni governorates and nationally from 4 September 2016 through 29 December 2019 as reported by the World Health Organization (WHO). We quantified critical points and periods using Kolmogorov-Zurbenko adaptive filter methodology. We assigned governorates into six clusters sharing similar outbreak signatures, according to similarities in critical points, critical periods, and the magnitude of peak rates. We identified four national outbreak waves beginning on 12 September 2016, 6 March 2017, 28 May 2018, and 28 January 2019. Among six identified clusters, we classified a core regional hotspot in Sana'a, Sana'a City, and Al-Hudaydah-the expected origin of the national outbreak. The five additional clusters differed in Wave 2 and Wave 3 peak frequency, timing, magnitude, and geographic location. As of 29 December 2019, no governorates had returned to pre-Wave 1 levels. The detected similarity in outbreak signatures suggests potentially shared environmental and human-made drivers of infection; the heterogeneity in outbreak signatures implies the potential traveling waves outwards from the core regional hotspot that could be governed by factors that deserve further investigation.