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Aggregated COVID-19 suicide incidences in India: Fear of COVID-19 infection is the prominent causative factor.

ABSTRACT: Many Indian COVID-19 suicide cases are turning the press-media attention and flooding in the social media platforms although, no particular studies assessed the COVID-19 suicide causative factors to a large extent. Therefore, the present study presents 69 COVID-19 suicide cases (aged 19 to 65 years; 63 cases were males). The suicide causalities are included as follows - fear of COVID-19 infection (n=21), followed by financial crisis (n=19), loneliness, social boycott and pressure to be quarantine, COVID-19 positive, COVID-19 work-related stress, unable to come back home due to lockdown, unavailability of alcohol etc. Considering the extreme psychological impacts related to COVID-19, there emerges a need for countrywide extensive tele-mental health care services.

SUBMITTER: Dsouza DD

PROVIDER: S-EPMC7832713 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Aggregated COVID-19 suicide incidences in India: Fear of COVID-19 infection is the prominent causative factor.

Dsouza Deena Dimple DD Quadros Shalini S Hyderabadwala Zainab Juzer ZJ Mamun Mohammed A MA

Psychiatry research 20200528

Many Indian COVID-19 suicide cases are turning the press-media attention and flooding in the social media platforms although, no particular studies assessed the COVID-19 suicide causative factors to a large extent. Therefore, the present study presents 69 COVID-19 suicide cases (aged 19 to 65 years; 63 cases were males). The suicide causalities are included as follows - fear of COVID-19 infection (n=21), followed by financial crisis (n=19), loneliness, social boycott and pressure to be quarantin ...[more]

PMID: 32544650

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Project description:ObjectivesSocio-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 designThis is an ecological (or contextual) study for electoral wards (subcities) of Chennai megacity.MethodsUsing 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.ResultsThe 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.ConclusionsWe 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.

Project description:BackgroundThe emergency of new COVID-19 variants over the past three years posed a serious challenge to the public health. Cities in China implemented mass daily RT-PCR tests by pooling strategies. However, a random delay exists between an infection and its first positive RT-PCR test. It is valuable for disease control to know the delay pattern and daily infection incidences reconstructed from RT-PCR test observations.MethodsWe formulated the convolution model between daily incidences and positive RT-PCR test counts as a linear inverse problem with positivity restrictions. Consequently, the Richard-Lucy deconvolution algorithm was used to reconstruct COVID-19 incidences from daily PCR tests. A real-time deconvolution was further developed based on the same mathematical principle. The method was applied to an Omicron epidemic data set of a bar outbreak in Beijing and another in Wuxi in June 2022. We estimated the delay function by maximizing likelihood via an E-M algorithm.ResultsThe delay function of the bar-outbreak in 2022 differs from that reported in 2020. Its mode was shortened to 4 days by one day. A 95% confidence interval of the mean delay is [4.43,5.55] as evaluated by bootstrap. In addition, the deconvolved infection incidences successfully detected two associated infection events after the bar was closed. The application of the real-time deconvolution to the Wuxi data identified all explosive incidence increases. The results revealed the progression of the two COVID-19 outbreaks and provided new insights for prevention and control strategies, especially for the role of mass daily RT-PCR testing.ConclusionsThe proposed deconvolution method is generally applicable to other infectious diseases if the delay model can be assumed to be approximately valid. To ensure a fair reconstruction of daily infection incidences, the delay function should be estimated in a similar context in terms of virus variant and test protocol. Both the delay estimate from the E-M algorithm and the incidences resulted from deconvolution are valuable for epidemic prevention and control. The real-time feedback is particularly useful during the epidemic's acute phase because it can help the local disease control authorities modify the control measures more promptly and precisely.

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Aggregated COVID-19 suicide incidences in India: Fear of COVID-19 infection is the prominent causative factor.

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Aggregated COVID-19 suicide incidences in India: Fear of COVID-19 infection is the prominent causative factor.

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