tetano
Editor, Senior Moderator
Stat Med
. 2022 Jun 5.
doi: 10.1002/sim.9482. Online ahead of print.
Evaluating effectiveness of public health intervention strategies for mitigating COVID-19 pandemic
Shanghong Xie[SUP] 1 2 [/SUP], Wenbo Wang[SUP] 3 [/SUP], Qinxia Wang[SUP] 2 [/SUP], Yuanjia Wang[SUP] 2 [/SUP], Donglin Zeng[SUP] 3 [/SUP]
Affiliations
Abstract
Coronavirus disease 2019 (COVID-19) pandemic is an unprecedented global public health challenge. In the United States (US), state governments have implemented various non-pharmaceutical interventions (NPIs), such as physical distance closure (lockdown), stay-at-home order, mandatory facial mask in public in response to the rapid spread of COVID-19. To evaluate the effectiveness of these NPIs, we propose a nested case-control design with propensity score weighting under the quasi-experiment framework to estimate the average intervention effect on disease transmission across states. We further develop a method to test for factors that moderate intervention effect to assist precision public health intervention. Our method takes account of the underlying dynamics of disease transmission and balance state-level pre-intervention characteristics. We prove that our estimator provides causal intervention effect under assumptions. We apply this method to analyze US COVID-19 incidence cases to estimate the effects of six interventions. We show that lockdown has the largest effect on reducing transmission and reopening bars significantly increase transmission. States with a higher percentage of non-White population are at greater risk of increased Rt
associated with reopening bars.
Keywords: COVID-19; difference-in-difference; heterogeneity of treatment effect; infectious disease modeling; non-pharmaceutical interventions; quasi-experiments.
. 2022 Jun 5.
doi: 10.1002/sim.9482. Online ahead of print.
Evaluating effectiveness of public health intervention strategies for mitigating COVID-19 pandemic
Shanghong Xie[SUP] 1 2 [/SUP], Wenbo Wang[SUP] 3 [/SUP], Qinxia Wang[SUP] 2 [/SUP], Yuanjia Wang[SUP] 2 [/SUP], Donglin Zeng[SUP] 3 [/SUP]
Affiliations
- PMID: 35661207
- DOI: 10.1002/sim.9482
Abstract
Coronavirus disease 2019 (COVID-19) pandemic is an unprecedented global public health challenge. In the United States (US), state governments have implemented various non-pharmaceutical interventions (NPIs), such as physical distance closure (lockdown), stay-at-home order, mandatory facial mask in public in response to the rapid spread of COVID-19. To evaluate the effectiveness of these NPIs, we propose a nested case-control design with propensity score weighting under the quasi-experiment framework to estimate the average intervention effect on disease transmission across states. We further develop a method to test for factors that moderate intervention effect to assist precision public health intervention. Our method takes account of the underlying dynamics of disease transmission and balance state-level pre-intervention characteristics. We prove that our estimator provides causal intervention effect under assumptions. We apply this method to analyze US COVID-19 incidence cases to estimate the effects of six interventions. We show that lockdown has the largest effect on reducing transmission and reopening bars significantly increase transmission. States with a higher percentage of non-White population are at greater risk of increased Rt
associated with reopening bars.
Keywords: COVID-19; difference-in-difference; heterogeneity of treatment effect; infectious disease modeling; non-pharmaceutical interventions; quasi-experiments.