Population-Level Effectiveness of COVID-19 Vaccination Program in the United States: Causal Analysis Based on Structural Nested Mean Model
Though COVID-19 vaccines have shown high efficacy, real-world effectiveness at the population level remains unclear. Based on the longitudinal data on vaccination coverage and daily infection cases from fifty states in the United States from March to May 2021, causal analyses were conducted using st...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Vaccines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-393X/10/5/726 |
| _version_ | 1827666019989061632 |
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| author | Rui Wang Jiahao Wang Taojun Hu Xiao-Hua Zhou |
| author_facet | Rui Wang Jiahao Wang Taojun Hu Xiao-Hua Zhou |
| author_sort | Rui Wang |
| collection | DOAJ |
| description | Though COVID-19 vaccines have shown high efficacy, real-world effectiveness at the population level remains unclear. Based on the longitudinal data on vaccination coverage and daily infection cases from fifty states in the United States from March to May 2021, causal analyses were conducted using structural nested mean models to estimate the population-level effectiveness of the COVID-19 vaccination program against infection with the original strain. We found that in the US, every 1% increase of vaccination coverage rate reduced the weekly growth rate of COVID-19 confirmed cases by 1.02% (95% CI: 0.26%, 1.69%), and the estimated population-level effectiveness of the COVID-19 program was 63.9% (95% CI: 18.0%, 87.5%). In comparison to a no-vaccination scenario, the COVID-19 vaccination campaign averted 8.05 million infections through the study period. Scenario analyses show that a vaccination program with doubled vaccination speed or with more rapid vaccination speed at the early stages of the campaign would avert more infections and increase vaccine effectiveness. The COVID-19 vaccination program demonstrated a high population-level effectiveness and significantly reduced the disease burden in the US. Accelerating vaccine rollout, especially at an early stage of the campaign, is crucial for reducing COVID-19 infections. |
| first_indexed | 2024-03-10T01:40:07Z |
| format | Article |
| id | doaj.art-25f07db73d894ef580aa51d4fd396f79 |
| institution | Directory Open Access Journal |
| issn | 2076-393X |
| language | English |
| last_indexed | 2024-03-10T01:40:07Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Vaccines |
| spelling | doaj.art-25f07db73d894ef580aa51d4fd396f792023-11-23T13:26:12ZengMDPI AGVaccines2076-393X2022-05-0110572610.3390/vaccines10050726Population-Level Effectiveness of COVID-19 Vaccination Program in the United States: Causal Analysis Based on Structural Nested Mean ModelRui Wang0Jiahao Wang1Taojun Hu2Xiao-Hua Zhou3Department of Biostatistics, School of Public Health, Peking University, Beijing 100191, ChinaSchool of Public Health, Peking University, Beijing 100191, ChinaDepartment of Biostatistics, School of Public Health, Peking University, Beijing 100191, ChinaDepartment of Biostatistics, School of Public Health, Peking University, Beijing 100191, ChinaThough COVID-19 vaccines have shown high efficacy, real-world effectiveness at the population level remains unclear. Based on the longitudinal data on vaccination coverage and daily infection cases from fifty states in the United States from March to May 2021, causal analyses were conducted using structural nested mean models to estimate the population-level effectiveness of the COVID-19 vaccination program against infection with the original strain. We found that in the US, every 1% increase of vaccination coverage rate reduced the weekly growth rate of COVID-19 confirmed cases by 1.02% (95% CI: 0.26%, 1.69%), and the estimated population-level effectiveness of the COVID-19 program was 63.9% (95% CI: 18.0%, 87.5%). In comparison to a no-vaccination scenario, the COVID-19 vaccination campaign averted 8.05 million infections through the study period. Scenario analyses show that a vaccination program with doubled vaccination speed or with more rapid vaccination speed at the early stages of the campaign would avert more infections and increase vaccine effectiveness. The COVID-19 vaccination program demonstrated a high population-level effectiveness and significantly reduced the disease burden in the US. Accelerating vaccine rollout, especially at an early stage of the campaign, is crucial for reducing COVID-19 infections.https://www.mdpi.com/2076-393X/10/5/726COVID-19vaccinescausal inferenceeffectivenessstructural nested mean models |
| spellingShingle | Rui Wang Jiahao Wang Taojun Hu Xiao-Hua Zhou Population-Level Effectiveness of COVID-19 Vaccination Program in the United States: Causal Analysis Based on Structural Nested Mean Model Vaccines COVID-19 vaccines causal inference effectiveness structural nested mean models |
| title | Population-Level Effectiveness of COVID-19 Vaccination Program in the United States: Causal Analysis Based on Structural Nested Mean Model |
| title_full | Population-Level Effectiveness of COVID-19 Vaccination Program in the United States: Causal Analysis Based on Structural Nested Mean Model |
| title_fullStr | Population-Level Effectiveness of COVID-19 Vaccination Program in the United States: Causal Analysis Based on Structural Nested Mean Model |
| title_full_unstemmed | Population-Level Effectiveness of COVID-19 Vaccination Program in the United States: Causal Analysis Based on Structural Nested Mean Model |
| title_short | Population-Level Effectiveness of COVID-19 Vaccination Program in the United States: Causal Analysis Based on Structural Nested Mean Model |
| title_sort | population level effectiveness of covid 19 vaccination program in the united states causal analysis based on structural nested mean model |
| topic | COVID-19 vaccines causal inference effectiveness structural nested mean models |
| url | https://www.mdpi.com/2076-393X/10/5/726 |
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