Lag time between state-level policy interventions and change points in COVID-19 outcomes in the United States

Dey, Tanujit; Lee, Jaechoul; Chakraborty, Suman Bhusan; Chandra, Jay; Bhaskar, Anushka; Zhang, Kenneth; Bhaskar, Anchal; Dominici, Francesca

doi:10.1016/j.patter.2021.100306

Cited by 9 publications

(11 citation statements)

References 23 publications

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“…As such, we use a single index of containment/health policies constructed by OxCGRT (0 to 100, for level of state response), with a rolling average of two weeks to allow for delayed impacts of policies. We use two week lag times given this is identified as the time span between onset of COVID symptoms and occurrence of COVID-19 death as well as between policy implementation and effects on COVID-19 outcomes [ 15 , 16 ]. In robustness checks we utilize three and four week lags in the main model which result in similar substantive findings.…”

Section: Resultsmentioning

confidence: 99%

The polarization of politics and public opinion and their effects on racial inequality in COVID mortality

Arolas

Renshon

et al. 2022

PLoS ONE

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Evidence from the early months of the COVID-19 pandemic in the U.S. indicated that the virus had vastly different effects across races, with black Americans faring worse on dimensions including illness, hospitalization and death. New data suggests that our understanding of the pandemic’s racial inequities must be revised given the closing of the gap between black and white COVID-related mortality. Initial explanations for inequality in COVID-related outcomes concentrated on static factors—e.g., geography, urbanicity, segregation or age-structures—that are insufficient on their own to explain observed time-varying patterns in inequality. Drawing from a literature suggesting the relevance of political factors in explaining pandemic outcomes, we highlight the importance of political polarization—the partisan divide in pandemic-related policies and beliefs—that varies over time and across geographic units. Specifically, we investigate the role of polarization through two political factors, public opinion and state-level public health policies, using fine-grained data on disparities in public concern over COVID and in state containment/health policies to understand the changing pattern of inequality in mortality. We show that (1) apparent decreases in inequality are driven by increasing total deaths—mostly among white Americans—rather than decreasing mortality among black Americans (2) containment policies are associated with decreasing inequality, likely resulting from lower relative mortality among Blacks (3) as the partisan disparity in Americans who were “unconcerned” about COVID increased, racial inequality in COVID mortality decreased, generating the appearance of greater equality consistent with a “race to the bottom’’ explanation as overall deaths increased and substantively swamping the effects of containment policies.

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Section: Resultsmentioning

confidence: 99%

The polarization of politics and public opinion and their effects on racial inequality in COVID mortality

Arolas

Renshon

et al. 2022

PLoS ONE

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“…Our analyses are based on observed disease dynamics to make inferences about differences in protective measures taken by citizens of states and counties. There has been considerable research effort to assess attitudes, such as surveys on mask use (44) and vaccination hesitancy (45), and to identify effective proxies of protective behaviors, such as analyses of government policies (31) and changes in individual movement patterns using cell-phone signals (46). While acknowledging the value of these studies, our approach of analyzing the dynamics of COVID-19 focuses on the effects of protective behaviors, rather than the protective behaviors themselves.…”

Section: Discussionmentioning

confidence: 99%

“…We base our analyses on the disease spread rate, r ( t ), of COVID-19 in the USA, estimated at the county and state levels (henceforth jurisdictions) using weekly death counts (29) from 9 May 2020 to 12 February 2021 (40 weeks). We did not consider the initial outbreak (March-early May 2020) because there was pronounced among-jurisdiction variation in the time of onset (30), and because protective measures (individual behavior and NPIs) built up differently during the first outbreak (31). We ended the data on 12 February 2021 because vaccinations had started to influence the disease transmission and death rates (32).…”

Section: Methodsmentioning

confidence: 99%

“…Preprint posted on medRxiv, url: https://www.medrxiv.org/ Of the 100 counties and 49 states, 96 and 41 showed cyclic dynamics in the stationary domain (SI Appendix, Fig.S6), and all analyzed jurisdictions had mean r(t) of nearly zero (not shown). The estimated period was similar at the county and state levels (SI Appendix, Fig.S7a,b): counties had a median of 23 weeks (interquartile range[20][21][22][23][24][25][26][27][28][29], and states had a median of 26 weeks(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33). The damping factor (d) was also similar (SI Appendix, Fig.S7c,d): counties had median d = 0.91 (0.85-0.96), and states had median d = 0.91 (0.83-0.94).…”

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confidence: 99%

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Differences in COVID-19 cyclicity and predictability among U.S. counties and states reflect the effectiveness of protective measures

Bozzuto¹,

Ives

2022

Preprint

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Data available for COVID-19 in the USA make it possible to assess the dynamics of disease spread with 20:20 hindsight. Here, we analyze archived data to explain variation among counties and states in the cyclicity and predictability (that is, the extent to which predictions are possible) of disease dynamics, using a combination of statistical and simulation models. For the period after the initial outbreak but before widespread vaccination (May 2020 - February 2021), we show that for half the counties and states the spread rate of COVID-19, r(t), was predictable at most 9 weeks and 8 weeks ahead, respectively, corresponding to at most 40% and 35% of an average cycle length of 23 weeks and 26 weeks. However, there were large differences among counties and states, and high predictability was associated with high cyclicity of r(t). Furthermore, predictability was negatively associated with R0 values from the pandemic's onset. This suggests that a severe initial outbreak induced strong and sustained protective measures to lower disease transmission, and these protective measures in turn reduced both cyclicity and predictability. Thus, decreased predictability of disease spread should be viewed as a by-product of positive and sustained steps that people take to protect themselves and others.

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“…We recorded the Stringency Index on a scale of 0 to 100 for each country at 2, 5 and 11 months into the pandemic, reflecting a time lag between restrictive government policies and any potential impact on COVID-19 deaths (which were measured at 3, 6 and 12 months). This assumed a median lag of 10 days between a change in government mobility restrictions and community rates of infection [8], five days from infection to symptom onset, and a further 16 days from symptom onset to possible death [9]. We obtained 2019 data on national population size, the proportion of the population aged �65, Gross National Income per capita, % Gross Domestic Product (GDP) spent on healthcare, and universal health coverage (UHC) service index (2017) from the World Bank public database [10].…”

Section: Investigated Variables and Sourcesmentioning

confidence: 99%

Joint External Evaluation scores and communicable disease deaths: An ecological study on the difference between epidemics and pandemics

Jain

Sharp

Neilson

et al. 2022

PLOS Glob Public Health

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The Joint External Evaluation (JEE) assesses national capacities to implement the International Health Regulations (IHR). Previous studies have found that higher JEE scores are associated with fewer communicable disease deaths. But given the impact of COVID-19 in many countries, including those believed to have developed IHR capacities, the validity of the JEE for pandemic preparedness has been questioned. We constructed univariable and multivariable linear regression models to investigate the relationship between JEE scores and i) deaths from communicable diseases before the pandemic and ii) deaths from COVID-19. We adjusted for country differences in age, health system access, national wealth, health expenditure, democratic governance, government restrictions, pre-pandemic tourist arrivals and testing capacity (estimated by test positivity rates). For COVID-19 deaths, we calculated cumulative deaths per 100,000 at 3, 6 and 12 months into the pandemic. A total of 91 countries were included, with a median JEE score of 50%. On multivariable linear regression the association between JEE scores and log COVID-19 deaths was significant and positive at 3 months (β 0.05, p = 0.02), becoming statistically non-significant, at 6 (β 0.02, p = 0.27) and 12 months (β -0.03, p = 0.19), while the association with log communicable disease deaths was significant and negative (β -0.03, p = 0.003). A higher Stringency Index was significantly associated with higher log COVID-19 deaths at 3 (β 0.04, p = 0.003) and 6 (β 0.04, p = 0.001) months, but not at 12 months (β 0.02, p = 0.08). Higher test positivity rates were associated with higher log COVID-19 deaths at all time points, at least partially attenuating the positive association between Stringency Index and log COVID-19 deaths. While universal health coverage indices (β -0.04 p<0.001) and international tourist arrivals were associated with log communicable disease deaths (β 0.02, p = 0.002), they were not associated with log COVID-19 deaths. Although the same tool is used to assess capacities for both epidemics and pandemics, the JEE may be better suited to small outbreaks of known diseases, compared to pandemics of unknown pathogens.

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Lag time between state-level policy interventions and change points in COVID-19 outcomes in the United States

Cited by 9 publications

References 23 publications

The polarization of politics and public opinion and their effects on racial inequality in COVID mortality

The polarization of politics and public opinion and their effects on racial inequality in COVID mortality

Differences in COVID-19 cyclicity and predictability among U.S. counties and states reflect the effectiveness of protective measures

Joint External Evaluation scores and communicable disease deaths: An ecological study on the difference between epidemics and pandemics

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