Mental Health Trends in India from 2020 to 2022: Association with Financial Stress, Food Insecurity, and COVID-19-related Illness Concerns (preprint)

This study examines the impact of pandemic-related worries on mental health in the Indian general adult population from 2020 to 2022. Using data from the Global COVID-19 Trends and Impact Survey (N = 2,576,174 respondents aged >= 18 years in India; an average weekly sample size of around 25,000), it explores the associations between worry variables (namely financial stress, food insecurity, and COVID-19-related health worries) and self-reported symptoms of depression and nervousness. The statistical analysis was conducted using complete cases only (N = 747,996). Our analysis used survey-weighted models, focusing on the three pandemic-related worries as the exposures, while also adjusting for various other covariates, including demographics and calendar time. The study finds significant associations between these worries and mental health outcomes, with financial stress being the most significant factor affecting both depression (adjusted odds ratio: 2.36, 95% confidence interval: [2.27, 2.46]) and nervousness (adjusted odds ratio: 1.91, 95% confidence interval: [1.81, 2.01]) during the first phase of the study period (June 27, 2020, to May 19, 2021). The fully adjusted models also identify additional factors related to mental health, including age, gender, residential status, geographical region, occupation, and education. Moreover, the research highlights that males and urban residents had higher odds ratios for self-reported mental health problems regarding the worry variables than females and rural residents, respectively. Furthermore, the study reveals a rise in the prevalence of self-reported depression and nervousness and their association with COVID-19-related health worries during the lethal second wave of the pandemic in May 2021 compared to the onset of the pandemic. This study shows that social media platforms like Facebook can deploy surveys to a large number of participants globally and can be useful tools in capturing mental health trends and uncovering associations during a public health crisis.

Global Prevalence of Post-Acute Sequelae of COVID-19 (PASC) or Long COVID: A Meta-Analysis and Systematic Review (preprint)

Importance As SARS-CoV-2 pervades worldwide, considerable focus has been placed on the longer lasting health effects of the virus on the human host and on the anticipated healthcare needs. Objective The primary aim of this study is to examine the prevalence of post-acute sequelae of COVID-19 (PASC), commonly known as long COVID, across the world and to assess geographic heterogeneities through a systematic review and meta-analysis. A second aim is to provide prevalence estimates for individual symptoms that have been commonly reported as PASC, based on the existing literature. Data Sources PubMed, Embase, and iSearch for preprints from medRxiv, bioRxiv, SSRN, and others, were searched on July 5, 2021 with verification extending to August 12, 2021. Study Selection Studies written in English that consider PASC (indexed as ailments persisting at least 28 days after diagnosis or recovery for SARS-CoV-2 infection) and that examine corresponding prevalence, risk factors, duration, or associated symptoms were included. A total of 40 studies were included with 9 from North America, 1 from South America, 17 from Europe, 11 from Asia, and 2 from other regions. Data Extraction and Synthesis Data extraction was performed and separately cross-validated on the following data elements: title, journal, authors, date of publication, outcomes, and characteristics related to the study sample and study design. Using a random effects framework for meta-analysis with DerSimonian-Laird pooled inverse-variance weighted estimator, we provide an interval estimate of PASC prevalence, globally, and across regions. This meta-analysis considers variation in PASC prevalence by hospitalization status during the acute phase of infection, duration of symptoms, and specific symptom categories. Main Outcomes and Measures Prevalence of PASC worldwide and stratified by regions. Results Global estimated pooled PASC prevalence derived from the estimates presented in 29 studies was 0.43 (95% confidence interval [CI]: 0.35, 0.63), with a higher pooled PASC prevalence estimate of 0.57 (95% CI: 0.45, 0.68), among those hospitalized during the acute phase of infection. Females were estimated to have higher pooled PASC prevalence than males (0.49 [95% CI: 0.35, 0.63] versus 0.37 [95% CI: 0.24, 0.51], respectively). Regional pooled PASC prevalence estimates in descending order were 0.49 (95% CI: 0.21, 0.42) for Asia, 0.44 (95% CI: 0.30, 0.59) for Europe, and 0.30 (95% CI: 0.32, 0.66) for North America. Global pooled PASC prevalence for 30, 60, 90, and 120 days after index test positive date were estimated to be 0.36 (95% CI: 0.25, 0.48), 0.24 (95% CI: 0.13, 0.39), 0.29 (95% CI: 0.12, 0.57) and 0.51 (95% CI: 0.42, 0.59), respectively. Among commonly reported PASC symptoms, fatigue and dyspnea were reported most frequently, with a prevalence of 0.23 (95% CI: 0.13, 0.38) and 0.13 (95% CI: 0.09, 0.19), respectively. Conclusions and Relevance The findings of this meta-analysis suggest that, worldwide, PASC comprises a significant fraction (0.43 [95% CI: 0.35, 0.63]) of COVID-19 tested positive cases and more than half of hospitalized COVID-19 cases, based on available literature as of August 12, 2021. Geographic differences appear to exist, as lowest to highest PASC prevalence is observed for North America (0.30 [95% CI: 0.32, 0.66]) to Asia (0.49 [95% CI: 0.21, 0.42]). The case-mix across studies, in terms of COVID-19 severity during the acute phase of infection and variation in the clinical definition of PASC, may explain some of these differences. Nonetheless, the health effects of COVID-19 appear to be prolonged and can exert marked stress on the healthcare system, with 237M reported COVID-19 cases worldwide as of October 12, 2021.

SARS-CoV-2 infection fatality rates in India: systematic review, meta-analysis and model-based estimation (preprint)

IntroductionFervorous investigation and dialogue surrounding the true number of SARS-CoV-2 related deaths and implied infection fatality rates in India have been ongoing throughout the pandemic, and especially pronounced during the nations devastating second wave. We aim to synthesize the existing literature on the true SARS-CoV-2 excess deaths and infection fatality rates (IFR) in India, through a systematic search followed by viable meta-analysis. We then provide updated epidemiological model-based estimates of the wave 1, wave 2 and combined IFRs using an extension of the Susceptible-Exposed-Infected-Removed (SEIR) model, using data from April 1, 2020 to June 30, 2021. MethodsFollowing PRISMA guidelines, the databases PubMed, Embase, Global Index Medicus, as well as BioRxiv, MedRxiv, and SSRN for preprints (accessed through iSearch), were searched on July 3, 2021 (with results verified through August 15, 2021). Altogether using a two-step approach, 4,765 initial citations were screened resulting in 37 citations included in the narrative review and 19 studies with 41 datapoints included in the quantitative synthesis. Using a random effects model with DerSimonian-Laird estimation, we meta-analyze IFR1 which is defined as the ratio of the total number of observed reported deaths divided by the total number of estimated infections and IFR2 (which accounts for death underreporting in the numerator of IFR1). For the latter, we provide lower and upper bounds based on the available range of estimates of death undercounting, often arising from an excess death calculation. The primary focus is to estimate pooled nationwide estimates of IFRs with the secondary goal of estimating pooled regional and state-specific estimates for SARS-CoV-2 related IFRs in India. We also try to stratify our empirical results across the first and the second wave. In tandem, we present updated SEIR model estimates of IFRs for waves 1, 2, and combined across the waves with observed case and death count data from April 1, 2020 to June 30, 2021. ResultsFor India countrywide, underreporting factors (URF) for cases (sourced from serosurveys) range from 14.3-29.1 in the four nationwide serosurveys; URFs for deaths (sourced from excess deaths reports) range from 4.4-11.9 with cumulative excess deaths ranging from 1.79-4.9 million (as of June 2021). Nationwide pooled IFR1 and IFR2 estimates for India are 0.097% (95% confidence interval [CI]: 0.067 - 0.140) and 0.365% (95% CI: 0.264 - 0.504) to 0.485% (95% CI: 0.344 - 0.685), respectively, again noting that IFR2 changes as excess deaths estimates vary. Among the included studies in this meta-analysis, the IFR1 generally appear to decrease over time from the earliest study end date to the latest study end date (from 4 June 2020 to 6 July 2021, IFR1 changed from 0.199 to 0.055%), whereas a similar trend is not as readily evident for IFR2 due to the wide variation in excess death estimates (from 4 June 2020 to 6 July 2021, IFR2 ranged from (0.290-1.316) to (0.241-0.651) %). Nationwide SEIR model-based combined estimates for IFR1 and IFR2 are 0.101% (95% CI: 0.097 - 0.116) and 0.367% (95% CI: 0.358 - 0.383), respectively, which largely reconcile with the empirical findings and concur with the lower end of the excess death estimates. An advantage of such epidemiological models is the ability to produce daily estimates with updated data with the disadvantages being that these estimates are subject to numerous assumptions, arduousness of validation and not directly using the available excess death data. Whether one uses empirical data or model-based estimation, it is evident that IFR2 is at least 3.6 times more than IFR1. ConclusionWhen incorporating case and death underreporting, the meta-analyzed cumulative infection fatality rate in India varies from 0.36%-0.48%, with a case underreporting factor ranging from 25-30 and a death underreporting factor ranging from 4-12. This implies, by June 30, 2021, India may have seen nearly 900 million infections and 1.7-4.9 million deaths when the reported numbers stood at 30.4 million cases and 412 thousand deaths (covid19india.org) with an observed case fatality rate (CFR) of 1.35%. We reiterate the need for timely and disaggregated infection and fatality data to examine the burden of the virus by age and other demographics. Large degrees of nationwide and state-specific death undercounting reinforce the call to improve death reporting within India.

Resurgence of SARS-CoV-2 in India: Potential role of the B.1.617.2 (Delta) variant and delayed interventions (preprint)

India has seen a surge of SARS-CoV-2 infections and deaths in early part of 2021, despite having controlled the epidemic during 2020. Building on a two-strain, semi-mechanistic model that synthesizes mortality and genomic data, we find evidence that altered epidemiological properties of B.1.617.2 (Delta) variant play an important role in this resurgence in India. Under all scenarios of immune evasion, we find an increased transmissibility advantage for B.1617.2 against all previously circulating strains. Using an extended SIR model accounting for reinfections and wanning immunity, we produce evidence in support of how early public interventions in March 2021 would have helped to control transmission in the country. We argue that enhanced genomic surveillance along with constant assessment of risk associated with increased transmission is critical for pandemic responsiveness.

Estimating COVID-19 Related Mortality in India: An Epidemiological Challenge with Insufficient Data (preprint)

The harrowing second wave of COVID-19 in India has led to much discussion over the quality and timeliness of reporting of deaths attributed to the pandemic. In this brief report, we aim to present the existing evidence, as well as the broader complexities surrounding the mortality burden of COVID-19 in India. This article sheds light on the following epidemiological issues: (1) general and India-specific challenges to COVID-19 death reporting, (2) latest COVID-19 mortality estimates in India as of May 16, 2021, (3) the apparent scale of uncaptured COVID-19 deaths, and (4) the role of disaggregated historic mortality trends in quantification of excess deaths attributed to COVID-19. We conclude with a set of high-level policy recommendations for improving the vital surveillance system and tracking of causes of death in India. We encourage direct efforts to integrate health data and indirect strategies for cross-validation of registered deaths. Such system-wide advances would drastically aid epidemiological research efforts and strengthen India’s position to overcome future public health crises.