IntroductionWorldwide, the 33 recognised megacities comprise approximately 7% of the global population, yet account for 20% COVID-19 deaths. The specific inequities and other factors within megacities that affect vulnerability to COVID-19 mortality remain poorly defined. We assessed individual, community-level and healthcare factors associated with COVID-19-related mortality in a megacity of Jakarta, Indonesia, during two epidemic waves spanning 2 March 2020 to 31 August 2021.MethodsThis retrospective cohort included residents of Jakarta, Indonesia, with PCR-confirmed COVID-19. We extracted demographic, clinical, outcome (recovered or died), vaccine coverage data and disease prevalence from Jakarta Health Office surveillance records, and collected subdistrict level sociodemographics data from various official sources. We used multilevel logistic regression to examine individual, community and subdistrict-level healthcare factors and their associations with COVID-19 mortality.ResultsOf 705 503 cases with a definitive outcome by 31 August 2021, 694 706 (98.5%) recovered and 10 797 (1.5%) died. The median age was 36 years (IQR 24–50), 13.2% (93 459) were <18 years and 51.6% were female. The subdistrict level accounted for 1.5% of variance in mortality (p<0.0001). Mortality ranged from 0.9 to 1.8% by subdistrict. Individual-level factors associated with death were older age, male sex, comorbidities and age <5 years during the first wave (adjusted OR (aOR)) 1.56, 95% CI 1.04 to 2.35; reference: age 20–29 years). Community-level factors associated with death were poverty (aOR for the poorer quarter 1.35, 95% CI 1.17 to 1.55; reference: wealthiest quarter) and high population density (aOR for the highest density 1.34, 95% CI 1.14 to 2.58; reference: the lowest). Healthcare factor associated with death was low vaccine coverage (aOR for the lowest coverage 1.25, 95% CI 1.13 to 1.38; reference: the highest).ConclusionIn addition to individual risk factors, living in areas with high poverty and density, and low healthcare performance further increase the vulnerability of communities to COVID-19-associated death in urban low-resource settings.

BackgroundThe 33 recognized megacities comprise approximately 7% of the global population, yet account for 20% COVID-19 deaths. The specific inequities and other factors within megacities that affect vulnerability to COVID-19 mortality remain poorly defined. We assessed individual, community-level and health care factors associated with COVID-19-related mortality in a megacity of Jakarta, Indonesia, during two epidemic waves spanning March 2, 2020, to August 31, 2021.MethodsThis retrospective cohort included all residents of Jakarta, Indonesia, with PCR-confirmed COVID-19. We extracted demographic, clinical, outcome (recovered or died), vaccine coverage data, and disease prevalence from Jakarta Health Office surveillance records, and collected sub-district level socio-demographics data from various official sources. We used multi-level logistic regression to examine individual, community and sub-district-level health care factors and their associations with COVID-19-mortality.FindingsOf 705,503 cases with a definitive outcome by August 31, 2021, 694,706 (98·5%) recovered and 10,797 (1·5%) died. The median age was 36 years (IQR 24–50), 13·2% (93,459) were <18 years, and 51·6% were female. The sub-district level accounted for 1·5% of variance in mortality (p<0.0001). Individual-level factors associated with death were older age, male sex, comorbidities, and, during the first wave, age <5 years (adjusted odds ratio (aOR) 1·56, 95%CI 1·04-2·35; reference: age 20-29 years). Community-level factors associated with death were poverty (aOR for the poorer quarter 1·35, 95%CI 1·17-1·55; reference: wealthiest quarter), high population density (aOR for the highest density 1·34, 95%CI 1·14-2·58; reference: the lowest), low vaccine coverage (aOR for the lowest coverage 1·25, 95%CI 1·13-1·38; reference: the highest).InterpretationIn addition to individual risk factors, living in areas with high poverty and density, and low health care performance further increase the vulnerability of communities to COVID-19-associated death in urban low-resource settings.FundingWellcome (UK) Africa Asia Programme Vietnam (106680/Z/14/Z).Research in contextEvidence before this studyWe searched PubMed on November 22, 2021, for articles that assessed individual, community, and healthcare vulnerability factors associated with coronavirus disease 2019 (COVID-19) mortality, using the search terms (“novel coronavirus” OR “SARS-CoV-2” OR “COVID-19”) AND (“death” OR “mortality” OR “deceased”) AND (“community” OR “social”) AND (“healthcare” OR “health system”). The 33 recognized megacities comprise approximately 7% of the global population, yet account for 20% COVID-19 deaths. The specific inequities and other factors within megacities that affect vulnerability to COVID-19 mortality remain poorly defined. At individual-level, studies have shown COVID-19-related mortality to be associated with older age and common underlying chronic co-morbidities including hypertension, diabetes, obesity, cardiac disease, chronic kidney disease and liver disease. Only few studies from North America, and South America have reported the association between lower community-level socio-economic status and healthcare performance with increased risk of COVID-19-related death. We found no studies have been done to assess individual, community, and healthcare vulnerability factors associated with COVID-19 mortality risk, especially in lower-and middle-income countries (LMIC) where accessing quality health care services is often challenging for substantial proportions of population, due to under-resourced and fragile health systems. In Southeast Asia, by November 22, 2021, COVID-19 case fatality rate had been reported at 2·2% (23,951/1,104,835) in Vietnam, 1·7% (47,288/2,826,853) in Philippines, 1·0% (20,434/2,071,009) in Thailand, 1·2% (30,063/2,591,486) in Malaysia, 2·4% (2,905/119,904) in Cambodia, and 0·3% in Singapore (667/253,649). Indonesia has the highest number of COVID-19 cases and deaths in the region, reporting 3·4% case fatality rate (143,744 /4,253,598), with the highest number of cases in the capital city of Jakarta. A preliminary analysis of the first five months of surveillance in Jakarta found that 497 of 4265 (12%) hospitalised patients had died, associated with older age, male sex; pre-existing hypertension, diabetes, or chronic kidney disease; clinical diagnosis of pneumonia; multiple (>3) symptoms; immediate intensive care unit admission, or intubation.Added value of this studyThis retrospective population-based study of the complete epidemiological surveillance data of Jakarta during the first eighteen months of the epidemic is the largest studies in LMIC to date, that comprehensively analysed the individual, community, and healthcare vulnerability associated with COVID-19-related mortality among individuals diagnosed with PCR-confirmed COVID-19. The overall case fatality rate among general population in Jakarta was 1·5% (10,797/705,503). Individual factors associated with risk of death were older age, male sex, comorbidities, and, during the first wave, age <5 years (adjusted odds ratio (aOR) 1·56, 95%CI 1·04-2·35; reference: age 20-29 years). The risk of death was further increased for people living in sub-districts with high rates of poverty (aOR for the poorer quarter 1·35, 95%CI 1·17-1·55; reference: wealthiest quarter), high population density (aOR for the highest density 1·34, 95%CI 1·14-2·58), and low COVID-19 vaccination coverage (aOR for the lowest coverage 1·25, 95%CI 1·13-1·38; reference: the highest).Implications of all available evidenceDifferences in socio-demographics and access to quality health services, among other factors, greatly influence COVID-19 mortality in low-resource settings. This study affirmed that in addition to well-known individual risk factors, community-level socio-demographics and healthcare factors further increase the vulnerability of communities to die from COVID-19 in urban low-resource settings. These results highlight the need for accelerated vaccine rollout and additional preventive interventions to protect the urban poor who are most vulnerable to dying from COVID-19.

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Since the beginning of the Covid-19 Pandemic, the world has heavily relied on the internet to acquire information. The Pandemic is growing as a complex information discourse with so many texts from many sources. Various texts about Covid-19 certainly have various meanings for their readers. It is interesting to see an information resource that presents information in many languages in parallel. This study aims to reveal the global meaning of the parallel Indonesian and English texts of Covid-19 released by the World Health Organization. More specifically, this study examines whether parallelism in the two versions of the same text will present differences in the global meaning of each text; and whether there are social and political aspects that potentially affect the differences. The data for this study consists of two pairs of question-and-answer texts about Covid-19 published by WHO in English and Indonesian. This study used van Dijk's Critical Discourse Analysis framework at the macrostructure level. This study collected all texts and macro rules (deletion, generalization, and construction) have been applied to reduce information at the texts’ micro level to macropropositions at the macro level or the global topics/themes of discourse. There are some differences in the Indonesian text when compared to the original English text. Some of these differences may appear in the Indonesian translated version as a response to Indonesia's social and political conditions during the Covid-19 pandemic.

Background and Hypothesis It is argued that availability of diagnostic models will facilitate a more rapid identification of individuals who are at a higher risk of first episode psychosis (FEP). Therefore, we developed, evaluated, and validated a diagnostic risk estimation model to classify individual with FEP and controls across six countries. Study Design We used data from a large multi-centre study encompassing 2627 phenotypically well-defined participants (aged 18-64 years) recruited from six countries spanning 17 research sites, as part of the European Network of National Schizophrenia Networks Studying Gene-Environment Interactions study. To build the diagnostic model and identify which of important factors for estimating an individual risk of FEP, we applied a binary logistic model with regularisation by the least absolute shrinkage and selection operator. The model was validated employing the internal-external cross-validation approach. The model performance was assessed with the area under the receiver operating characteristic curve (AUROC), calibration, sensitivity, and specificity. Study Results Having included preselected 22 predictor variables, the model was able to discriminate adults with FEP and controls with high accuracy across all six countries (rangesAUROC=0.84-0.86). Specificity (range=73.9%-78.0%) and sensitivity (range=75.6%-79.3%) were equally good, cumulatively indicating an excellent model accuracy; though, calibration slope for the diagnostic model showed a presence of some overfitting when applied specifically to participants from France, the UK, and The Netherlands. Conclusions The new FEP model achieved a good discrimination and good calibration across six countries with different ethnic contributions supporting its robustness and good generalizability.