Cross-National Measures of the Intensity of COVID-19 Public Health Policies

Abstract

In this paper, we present new indices for government responses to COVID-19 within six policy areas crucial for understanding the drivers and effects of the pandemic: social distancing, schools, businesses, health monitoring, health resources and mask wearing. We create these measures from combining two of the most comprehensive COVID-19 datasets, the CoronaNet COVID-19 Government Response Event Dataset and the Oxford COVID-19 Government Response Tracker, using a Bayesian time-varying measurement model. Our daily indices track government responses for each of these policy areas from January 1st, 2020 to January 15th, 2021, for over 180 countries. By using a statistical model to generate these indices, we are able to estimate uncertainty within the index and provide external validation for these two COVID-19 policy datasets, showing that though they represent distinct data sources, they show strong convergent validity. We further explore the correlation between these indices and a range of social, public health, political and economic covariates. Our results show that while business restrictions and social distancing restrictions are strongly associated with reduced general anxiety, school restrictions are not. School restrictions are, however, associated with higher rates of personal contact with people outside the home, higher levels of income inequality and bureaucratic corruption. Additionally, we find that female heads of state are more likely to implement a broad array of pandemic-related restrictions than male leaders.

Differential impact of the COVID-19 pandemic on primary care utilization related to common mental disorders in four European countries: A retrospective observational study

“…Indices of containment measures were provided by Goldszmidt et al ( 25 ) 1 and constituted six indices that measured intensity of government response to tackle the pandemic. These were social distancing , school restrictions , businesses restrictions , health monitoring , health resources , and mask wearing .…”

“…Indices consisted of country specific, daily estimates from 1 January 2020 until October 2021 for the investigated four (and additional more than 180) countries. The indices were created by combining two of the most comprehensive COVID-19 datasets: the Corona Net COVID-19 Government Response Event Dataset, and the Oxford COVID-19 Government Response Tracker, and are described in greater detail elsewhere ( 25 ). Due to the high degree of collinearity among the six available indices and in order to reduce the problem of multiple comparisons, we selected only the two most plausible to be relevant for population mental health, namely social distancing and schools’ restrictions.…”

A General Guide for Harmonizing Data: Drawing Lessons from Harmonizing COVID-19 PHSM Data

“…What are alternatives to data harmonization? While in this paper we concentrate on presenting our rationale and methodology for qualitatively harmonzing PHSM data, in a Kubinec (2021) we introduce a Bayesian item response model to create policy intensity scores of 6 different policy areas (general social distancing, business restrictions, school restrictions, mask usage, health monitoring and health resources) which combines data from both CoronaNet and OxCGRT [103]. As this latter paper shows, researchers should be cognizant that while statistical harmonization can be an effective form of data harmonization, the resulting indices or measures may sometimes need to be interpreted or used differently than the underlying raw data.…”

Changes in anxiety and depression during the COVID-19 pandemic in the European population: A meta-analysis of changes and associations with restriction policies

“…The "female ratio" and "mean age" at baseline were collected from the original publications and datasets, and age ranges of 0-18, 19-64, and 65+ were used. The containment severity index was provided by Kubinec et al [20] and measures the intensity of government responses to COVID-19 across six distinct policy areas (for more details, see Supplementary File S3). We only used the "social distancing" and "school closures" measures as independent variables.…”