Educational status and COVID-19 related outcomes in India: hospital-based cross-sectional study

Acute and long COVID-19 symptoms and associated factors in the omicron-dominant period: a nationwide survey via the online platform Wenjuanxing in China

BACKGROUNDS

To our knowledge, there is no available nationwide data on omicron symptom patterns in China mainland. We aim to determine the acute and long COVID-19 symptoms in the omicron-dominant period and to evaluate its association with risk factors.

METHODS

We designed a cross-sectional nationwide study and data about self-reported symptoms were collected by an online platform named Wenjuanxing. Eligible participants were aged 25-65 years and were symptomatic. In this study, the ratios of the number of people of different ages and genders were weighted by the data from the Seventh National Census (2020 years), and validated by a published nationwide representative study through comparing smoking rates. Descriptive indicators were calculated for demographic characteristics, diagnosis ways, and duration time, acute symptoms, hospitalization, severity and long COVID-19 symptoms. And, the associations between risk factors and acute and long COVID-19 symptoms were analyzed by multivariable logistic regression models.

RESULTS

A total of 32,528 individuals diagnosed as COVID-19 infection from October 1, 2022 to February 21, 2023 were included. The first three acute symptoms of COVID-19 infection were fever (69.90%), headache (62.63%), and sore throat (54.29%), respectively. The hospitalization rate within 7 days was 3.07% and symptoms disappearance rate within 21 days was 68.84%, respectively. Among 3983 COVID-19 patients with 3 months or more time difference between first infection and participation into the study, the long COVID-19 rate was 19.68% and the primary symptoms were muscle weakness (19.39%), headache (17.98%) and smell/taste disorder (15.18%). Age groups, smoking, marriage status and vaccination were risk factors for numbers of acute phase symptoms and long COVID-19 symptoms. Lastly, female and current smokers also showed more numbers of symptoms during acute infection period.

CONCLUSIONS

In Chinese mainland, our respondent indicated that current smokers and women were associated with acute COVID-19 symptoms, which should be treated with caution due to the lack of representative.

Protocol for a longitudinal study examining the trajectory of COVID-19, post-COVID, multidimensional disadvantage and health-related quality of life in India: the IndiQol Project

INTRODUCTION

The COVID-19 pandemic has raised concerns about the persistence of symptoms after infection, commonly referred to as 'post-COVID' or 'long-COVID'. While countries in high-resource countries have highlighted the increased risk of disadvantaged communities, there is limited understanding of how COVID-19 and post-COVID conditions affect marginalised populations in low-income and middle-income countries. We study the longitudinal patterns of COVID-19, post-COVID symptoms and their impact on the health-related quality of life through the IndiQol Project.

METHODS AND ANALYSIS

The IndiQol Project conducts household surveys across India to collect data on the incidence of COVID-19 and multidimensional well-being using a longitudinal design. We select a representative sample across six states surveyed over four waves. A two-stage sampling design was used to randomly select primary sampling units in rural and urban areas of each State. Using power analysis, we select an initial sample of 3000 household and survey all adult household members in each wave. The survey data will be analysed using limited dependent variable models and matching techniques to provide insights into the impact of COVID-19 pandemic and post-COVID on health and well-being of individuals in India.

ETHICS AND DISSEMINATION

Ethics approval for the IndiQol Project was obtained from the Macquarie University Human Research Ethics Committee in Sydney, Australia and Institutional Review Board of Morsel in India. The project results will be published in peer-reviewed journals. Data collected from the IndiQol project will be deposited with the EuroQol group and will be available to use by eligible researchers on approval of request.

Influence of COVID-19 pandemic in India on coronary artery disease clinical presentation, angiography, interventions and in-hospital outcomes: a single centre prospective registry-based observational study

OBJECTIVE

The study examined the influence of the COVID-19 pandemic in India on variation in clinical features, management and in-hospital outcomes in patients undergoing percutaneous coronary intervention (PCI).

DESIGN

Prospective registry-based observational study.

SETTING

A tertiary care hospital in India participant in the American College of Cardiology CathPCI Registry.

PARTICIPANTS

7089 successive patients who underwent PCI from April 2018 to March 2023 were enrolled (men 5627, women 1462). Details of risk factors, clinical presentation, coronary angiography, coronary interventions, clinical management and in-hospital outcomes were recorded. Annual data were classified into specific COVID-19 periods according to Government of India guidelines as pre-COVID-19 (April 2018 to March 2019, n=1563; April 2019 to March 2020, n=1594), COVID-19 (April 2020 to March 2020, n=1206; April 2021 to March 2022, n=1223) and post-COVID-19 (April 2022 to March 2023, n=1503).

RESULTS

Compared with the patients in pre-COVID-19 and post-COVID-19 periods, during the first COVID-19 year, patients had more hypertension, non-ST elevation myocardial infarction (NSTEMI), lower left ventricular ejection fraction (LVEF) and multivessel coronary artery disease (CAD). In the second COVID-19 year, patients had more STEMI, lower LVEF, multivessel CAD, primary PCI, multiple stents and more vasopressor and mechanical support. There were 99 (1.4%) in-hospital deaths which in the successive years were 1.2%, 1.4%, 0.8%, 2.4% and 1.3%, respectively (p=0.019). Compared with the baseline year, deaths were slightly lower in the first COVID-19-year (age-sex adjusted OR 0.68, 95% CI 0.31 to 1.47) but significantly more in the second COVID-19-year (OR 1.97, 95% CI 1.10 to 3.54). This variation attenuated following adjustment for clinical presentation, extent of CAD, in-hospital treatment and duration of hospitalisation.

CONCLUSIONS

In-hospital mortality among patients with CAD undergoing PCI was significantly higher in the second year of the COVID-19 pandemic in India and could be one of the reasons for excess deaths in the country. These patients had more severe CAD, lower LVEF, and more vasopressor and mechanical support and duration of hospitalisation.

Inequalities in healthcare use during the COVID-19 pandemic

The COVID-19 pandemic led to reductions in non-COVID related healthcare use, but little is known whether this burden is shared equally. This study investigates whether reductions in administered care disproportionately affected certain sociodemographic strata, in particular marginalised groups. Using detailed medical claims data from the Dutch universal health care system and rich full population registry data, we predict expected healthcare use based on pre-pandemic trends (2017 - Feb 2020) and compare these expectations with observed healthcare use in 2020 and 2021. Our findings reveal a 10% decline in the number of weekly treated patients in 2020 and a 3% decline in 2021 relative to prior years. These declines are unequally distributed and are more pronounced for individuals below the poverty line, females, older people, and individuals with a migrant background, particularly during the initial wave of COVID-19 hospitalisations and for middle and low urgency procedures. While reductions in non-COVID related healthcare decreased following the initial shock of the pandemic, inequalities persist throughout 2020 and 2021. Our results demonstrate that the pandemic has not only had an unequal toll in terms of the direct health burden of the pandemic, but has also had a differential impact on the use of non-COVID healthcare.

Vaccination Can Prevent Severe Pulmonary Disease in COVID-19 Positive Patients: A Case-Control Study

BACKGROUND

The COVID-19 pandemic was a global health emergency, which brought lives to a standstill. To combat this deadly virus, two vaccines were deployed widely: COVISHIELD (ChAdOx1 nCoV-19) and COVAXIN (BBV152). These were approved based on the immunological response they elicit in standardized conditions; however, the real-life scenario after deployment was completely different. Only in such situations can the true effectiveness of vaccines be assessed. The primary objective was to assess the effectiveness (VE) of COVAXIN/COVISHIELD in preventing severe pulmonary disease in RT-PCR-positive COVID-19 patients greater than 18 years of age.

MATERIALS AND METHODS

A case-control study was conducted among 260 subjects aged above 18 years, positive for COVID-19 through RT-PCR. 130 cases and 130 controls were enrolled. Radiological findings were obtained and subjects with >50% lung involvement were considered as cases. Subjects were interviewed about their vaccination status. Odds ratio was calculated, and the adjusted odds ratio was estimated for vaccine effectiveness, using the formula (1-adjusted ODDS ratio)*100.

RESULTS

The vaccine effectiveness for a single dose of vaccine was 55.2% (95% C.I. 11.0%-77.5%) and with two doses was 98.0% (95% C.I. 85.0%-99.7%). Hence two doses are highly effective than a single dose of vaccine in reducing lung involvement.

CONCLUSION

Two doses of vaccine are more effective than a single dose vaccine in reducing lung involvement. Since sporadic cases of COVID-19 still persist, it is important to emphasize the role of vaccination in preventing severe COVID-19 infections, particularly in the elderly and those with comorbidities.

Preparing Cities for Future Pandemics: Unraveling the Influence of Urban and Housing Variables on COVID-19 Incidence in Santiago de Chile

In this study, we analyzed how urban, housing, and socioeconomic variables are related to COVID-19 incidence. As such, we have analyzed these variables along with demographic, education, employment, and COVID-19 data from 32 communes in Santiago de Chile between March and August of 2020, before the release of the vaccines. The results of our Principal Component Analysis (PCA) confirmed that those communes with more economic, social, organizational, and infrastructural resources were overall less affected by COVID-19. As the dimensions affecting COVID-19 are based on structural variables, this study discusses to what extent our cities can be prepared for the next pandemic. Recommendations for local decision-makers in controlling illegal immigration and investing in housing and urban parks are drawn.

Biomarkers and outcomes in hospitalised patients with COVID-19: a prospective registry

OBJECTIVES

To determine association of biomarkers-high-sensitivity C reactive protein (hsCRP), D-dimer, interleukin-6 (IL-6), lactic dehydrogenase (LDH), ferritin and neutrophil-lymphocyte ratio (NLR)-at hospitalisation with outcomes in COVID-19.

DESIGN AND SETTING

Tertiary-care hospital based prospective registry.

PARTICIPANTS

Successive virologically confirmed patients with COVID-19 hospitalised from April 2020 to July 2021 were prospectively recruited. Details of clinical presentation, investigations, management and outcomes were obtained.

PRIMARY AND SECONDARY OUTCOME MEASURES

All biomarkers were divided into tertiles to determine associations with clinical features and outcomes. Primary outcome was all-cause deaths and secondary outcome was oxygen requirement, non-invasive and invasive ventilation, dialysis, duration of stay in ICU and hospital. Numerical data are presented in median and interquartile range (IQR 25-75). Univariate and multivariate (age, sex, risk factors, comorbidities, treatments) ORs and 95% CIs were calculated.

RESULTS

3036 virologically confirmed patients with COVID-19 were detected and 1251 hospitalised. Men were 70.0%, aged >60 years 44.8%, hypertension 44.1%, diabetes 39.6% and cardiovascular disease 18.9%. Median symptom duration was 5 days (IQR 4-7) and oxygen saturation 95% (90%-97%). Total white cell count was 6.9×10⁹/L (5.0-9.8), neutrophils 79.2% (68.1%-88.2%), lymphocytes 15.8% (8.7%-25.5%) and creatinine 0.93 mg/dL (0.78-1.22). Median (IQR) for biomarkers were hsCRP 6.9 mg/dL (2.2-18.9), D-dimer 464 ng/dL (201-982), IL-6 20.1 ng/dL (6.5-60.4), LDH 284 mg/dL (220-396) and ferritin 351 mg/dL (159-676). Oxygen support at admission was in 38.6%, subsequent non-invasive or invasive ventilatory support in 11.0% and 11.6%, and haemodialysis in 38 (3.1%). 173 (13.9%) patients died and 15 (1.2%) transferred to hospice care. For each biomarker, compared with the first, those in the second and third tertiles had more clinical and laboratory abnormalities, and oxygen, ventilatory and dialysis support. Multivariate-adjusted ORs (95% CI) for deaths in second and third versus first tertiles, respectively, were hsCRP 2.24 (1.11 to 4.50) and 12.56 (6.76 to 23.35); D-dimer 3.44 (1.59 to 7.44) and 14.42 (7.09 to 29.30); IL-6 2.56 (1.13 to 5.10) and 10.85 (5.82 to 20.22); ferritin 2.88 (1.49 to 5.58) and 8.19 (4.41 to 15.20); LDH 1.75 (0.81 to 3.75) and 9.29 (4.75 to 18.14); and NLR 3.47 (1.68 to 7.14) and 17.71 (9.12 to 34.39) (p<0.001).

CONCLUSION

High levels of biomarkers-hsCRP, D-dimer, IL-6, LDH, ferritin and NLR-in COVID-19 are associated with more severe illness and higher in-hospital mortality. NLR, a widely available investigation, provides information similar to more expensive biomarkers.

Spatial mapping and socio-demographic determinants of COVID-19 mortality in India

Background

COVID-19 is caused by SARS-CoV-2. The first case of COVID-19 was detected in Wuhan city of China in December 2019. Geographic information system (GIS) mapping is important for the surveillance of infectious diseases.

Objectives

The objectives of the study are to map spatially total cases and case fatality rate of COVID-19 and to build a linear regression model for mortality based on socio-demographic variables.

Methology

We plotted the epidemiological data of COVID-19 of Indian states as on 11^th May 2021 using the Q-GIS software. We used socio-demographic variables as the predictors of COVID-19 mortality and developed a linear regression model.

Results

Adjusted R-squared in linear regression model based on socio-demographic variables for COVID-19 deaths is 0.82.

Conclusions

There are spatial variations in COVID-19 cases and deaths.