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2.
British Journal of Dermatology ; 183(SUPPL 1):202, 2020.
Article in English | EMBASE | ID: covidwho-1093699

ABSTRACT

Cutaneous manifestations of COVID-19 infection have been described in the literature since the onset of the pandemic. No formal classification system has been suggested, but cases reported in the literature demonstrate various subtypes, including urticarial, maculopapular, papulovesicular, purpuric and livedoid lesions. The pathogenesis of the cutaneous response is not fully understood, but may represent inflammatory and thromboinflammatory processes. Our institution in South London has treated one of the largest numbers of inpatients with confirmed COVID-19 infection in the U.K., with 2989 cases recorded between 1 February 2020 and 29 June 2020. We describe the spectrum of cutaneous disease associated with COVID-19 infection presenting to an acute liaison dermatology service over a 4-month period from March to June 2020. From a large number of referrals of COVID-19-positive patients with skin disease, 13 cases of cutaneous presentations thought to be caused by COVID-19 infection were identified [eight males, five females;mean age 44 years (range 15-59)]. We included cases from outpatient (n = 8), inpatient (n = 2) and intensive care (n = 3) departments. Eight of 13 had positive COVID-19 antigen testing, while five of 13 had symptoms indicative of COVID-19 infection but were not offered a test. Clinical manifestations included perniosis (n = 3), livedo (n = 2), urticaria (n = 2), maculopapular exanthema (n = 2), vasculitis (n = 1), panniculitis (n = 1), eccrine squamous syringometaplasia (n = 1) and digital vein thrombosis (n = 1). Five of 13 had a skin biopsy that supported the clinical diagnosis. Skin disease in COVID-19 infection reflects viral exanthematous inflammation in many cases. Thromboinflammatory pathologies also contribute to some COVID-19 dermatoses. Vascular and vaso-occlusive pathologies occur prominently in the lungs and kidneys, as well as the skin, and appear to have pathogenetic specificity for COVID-19. Immunostaining of lung tissue with an antibody to the Rp3 NP protein of severe acute respiratory syndrome-coronavirus 2 has revealed prominent expression on alveolar epithelial cells. Immunostaining of skin sections might provide further evidence for a direct viral effect in COVID-19 dermatoses. Our findings are comparable with those of European colleagues regarding the spectrum, the latency and the duration of the cutaneous symptoms. We would like to add our description of three new cutaneous manifestations of COVID-19 infection - panniculitis, eccrine squamous syringometaplasia and digital vein thrombosis - to the body of literature on this topic.

3.
Public Health ; 185: 266-269, 2020 Aug.
Article in English | MEDLINE | ID: covidwho-667019

ABSTRACT

OBJECTIVES: Socio-economic inequalities may affect coronavirus disease 2019 (COVID-19) incidence. The goal of the research was to explore the association between deprivation of socio-economic status (SES) and spatial patterns of COVID-19 incidence in Chennai megacity for unfolding the disease epidemiology. STUDY DESIGN: This is an ecological (or contextual) study for electoral wards (subcities) of Chennai megacity. METHODS: Using data of confirmed COVID-19 cases from May 15, 2020, to May 21, 2020, for 155 electoral wards obtained from the official website of the Chennai Municipal Corporation, we examined the incidence of COVID-19 using two count regression models, namely, Poisson regression (PR) and negative binomial regression (NBR). As explanatory factors, we considered area deprivation that represented the deprivation of SES. An index of multiple deprivations (IMD) was developed to measure the area deprivation using an advanced local statistic, geographically weighted principal component analysis. Based on the availability of appropriately scaled data, five domains (i.e., poor housing condition, low asset possession, poor availability of WaSH services, lack of household amenities and services, and gender disparity) were selected as components of the IMD in this study. RESULTS: The hot spot analysis revealed that area deprivation was significantly associated with higher incidences of COVID-19 in Chennai megacity. The high variations (adjusted R2: 72.2%) with the lower Bayesian Information Criteria (BIC) (124.34) and Akaike's Information Criteria (AIC) (112.12) for NBR compared with PR suggests that the NBR model better explains the relationship between area deprivation and COVID-19 incidences in Chennai megacity. NBR with two-sided tests and P <0.05 were considered statistically significant. The outcome of the PR and NBR models suggests that when all other variables were constant, according to NBR, the relative risk (RR) of COVID-19 incidences was 2.19 for the wards with high housing deprivation or, in other words, the wards with high housing deprivation having 119% higher probability (RR = e0.786 = 2.19, 95% confidence interval [CI] = 1.98 to 2.40), compared with areas with low deprivation. Similarly, in the wards with poor availability of WaSH services, chances of having COVID-19 incidence was 90% higher than in the wards with good WaSH services (RR = e0.642 = 1.90, 95% CI = 1.79 to 2.00). Spatial risks of COVID-19 were predominantly concentrated in the wards with higher levels of area deprivation, which were mostly located in the northeastern parts of Chennai megacity. CONCLUSIONS: We formulated an area-based IMD, which was substantially related to COVID-19 incidences in Chennai megacity. This study highlights that the risks of COVID-19 tend to be higher in areas with low SES and that the northeastern part of Chennai megacity is predominantly high-risk areas. Our results can guide measures of COVID-19 control and prevention by considering spatial risks and area deprivation.


Subject(s)
Coronavirus Infections/epidemiology , Health Status Disparities , Pneumonia, Viral/epidemiology , Poverty Areas , Binomial Distribution , COVID-19 , Cities/epidemiology , Female , Humans , Incidence , India/epidemiology , Male , Models, Statistical , Pandemics , Poisson Distribution , Risk Assessment
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