Utility of the Comprehensive Health and Stringency Indexes in Evaluating Government Responses for Containing the Spread of COVID-19 in India: Ecological Time-Series Study.

BACKGROUND: Many nations swiftly designed and executed government policies to contain the rapid rise in COVID-19 cases. Government actions can be broadly segmented as movement and mass gathering restrictions (such as travel restrictions and lockdown), public awareness (such as face covering and hand washing), emergency health care investment, and social welfare provisions (such as poor welfare schemes to distribute food and shelter). The Blavatnik School of Government, University of Oxford, tracked various policy initiatives by governments across the globe and released them as composite indices. We assessed the overall government response using the Oxford Comprehensive Health Index (CHI) and Stringency Index (SI) to combat the COVID-19 pandemic. OBJECTIVE: This study aims to demonstrate the utility of CHI and SI to gauge and evaluate the government responses for containing the spread of COVID-19. We expect a significant inverse relationship between policy indices (CHI and SI) and COVID-19 severity indices (morbidity and mortality). METHODS: In this ecological study, we analyzed data from 2 publicly available data sources released between March 2020 and October 2021: the Oxford Covid-19 Government Response Tracker and the World Health Organization. We used autoregressive integrated moving average (ARIMA) and seasonal ARIMA to model the data. The performance of different models was assessed using a combination of evaluation criteria: adjusted R2, root mean square error, and Bayesian information criteria. RESULTS: implementation of policies by the government to contain the COVID-19 crises resulted in higher CHI and SI in the beginning. Although the value of CHI and SI gradually fell, they were consistently higher at values of >80% points. During the initial investigation, we found that cases per million (CPM) and deaths per million (DPM) followed the same trend. However, the final CPM and DPM models were seasonal ARIMA (3,2,1)(1,0,1) and ARIMA (1,1,1), respectively. This study does not support the hypothesis that COVID-19 severity (CPM and DPM) is associated with stringent policy measures (CHI and SI). CONCLUSIONS: Our study concludes that the policy measures (CHI and SI) do not explain the change in epidemiological indicators (CPM and DPM). The study reiterates our understanding that strict policies do not necessarily lead to better compliance but may overwhelm the overstretched physical health systems. Twenty-first-century problems thus demand 21st-century solutions. The digital ecosystem was instrumental in the timely collection, curation, cloud storage, and data communication. Thus, digital epidemiology can and should be successfully integrated into existing surveillance systems for better disease monitoring, management, and evaluation.

Exploring the Utility of Google Mobility Data During the COVID-19 Pandemic in India: Digital Epidemiological Analysis.

BACKGROUND: Association between human mobility and disease transmission has been established for COVID-19, but quantifying the levels of mobility over large geographical areas is difficult. Google has released Community Mobility Reports (CMRs) containing data about the movement of people, collated from mobile devices. OBJECTIVE: The aim of this study is to explore the use of CMRs to assess the role of mobility in spreading COVID-19 infection in India. METHODS: In this ecological study, we analyzed CMRs to determine human mobility between March and October 2020. The data were compared for the phases before the lockdown (between March 14 and 25, 2020), during lockdown (March 25-June 7, 2020), and after the lockdown (June 8-October 15, 2020) with the reference periods (ie, January 3-February 6, 2020). Another data set depicting the burden of COVID-19 as per various disease severity indicators was derived from a crowdsourced API. The relationship between the two data sets was investigated using the Kendall tau correlation to depict the correlation between mobility and disease severity. RESULTS: At the national level, mobility decreased from -38% to -77% for all areas but residential (which showed an increase of 24.6%) during the lockdown compared to the reference period. At the beginning of the unlock phase, the state of Sikkim (minimum cases: 7) with a -60% reduction in mobility depicted more mobility compared to -82% in Maharashtra (maximum cases: 1.59 million). Residential mobility was negatively correlated (-0.05 to -0.91) with all other measures of mobility. The magnitude of the correlations for intramobility indicators was comparatively low for the lockdown phase (correlation ≥0.5 for 12 indicators) compared to the other phases (correlation ≥0.5 for 45 and 18 indicators in the prelockdown and unlock phases, respectively). A high correlation coefficient between epidemiological and mobility indicators was observed for the lockdown and unlock phases compared to the prelockdown phase. CONCLUSIONS: Mobile-based open-source mobility data can be used to assess the effectiveness of social distancing in mitigating disease spread. CMR data depicted an association between mobility and disease severity, and we suggest using this technique to supplement future COVID-19 surveillance.

Practical Guidelines to Develop and Evaluate a Questionnaire.

Life expectancy is gradually increasing due to continuously improving medical and nonmedical interventions. The increasing life expectancy is desirable but brings in issues such as impairment of quality of life, disease perception, cognitive health, and mental health. Thus, questionnaire building and data collection through the questionnaires have become an active area of research. However, questionnaire development can be challenging and suboptimal in the absence of careful planning and user-friendly literature guide. Keeping in mind the intricacies of constructing a questionnaire, researchers need to carefully plan, document, and follow systematic steps to build a reliable and valid questionnaire. Additionally, questionnaire development is technical, jargon-filled, and is not a part of most of the graduate and postgraduate training. Therefore, this article is an attempt to initiate an understanding of the complexities of the questionnaire fundamentals, technical challenges, and sequential flow of steps to build a reliable and valid questionnaire.

Understanding the determinants of happiness through Gallup World Poll.

BACKGROUND: The idea of happiness is as old as civilization, but breakthrough is achieved only in 20th century. Happiness can be broadly segmented into biological and behavioural component. The sufferings from illnesses hamper happiness. Happiness correlates negatively with morbidity, mortality, stress and anxiety in contrast to a positive correlation with motivation, healthy behaviours and longevity. In this article, an attempt has been made to understand the relationship between happiness and its important contributory factors. MATERIAL AND METHODS: The current study used data from the Gallup World Poll available under license CC0. Data analysis was performed using R studio version 1.0.136. Initially, descriptive analysis in the form of mean (standard deviation), violin plot, correlation matrix, and scatter plots were reported. Subsequently, robust regression estimates along with bootstrap standard errors and confidence intervals were used to report inferential statistics. RESULTS: Norway, with a happiness score of 7.537 ranked first followed by Denmark with a score of 7.522. Burundi with a score of 2.905 is at the bottom of ranking for happiness. Freedom (CI; 0.95-2.22) and Family (CI; 0.92 - 1.57) are the strongest predictors of happiness. The trust variable does not have a significant (CI; -0.27 - 1.94) relationship with happiness. CONCLUSIONS: The values and norms in society are changing at a fast pace. Therefore, the measures of happiness require consistent and innovative approaches to measure it.