Association between vaccination rates and severe COVID-19 health outcomes in the United States: a population-level statistical analysis
Population-level vaccine efficacy is a critical component of understanding COVID-19 risk, informing public health policy, and mitigating disease impacts. Unlike individual-level clinical trials, population-level analysis characterizes how well vaccines worked in the face of real-world challenges like emerging variants, differing mobility patterns, and policy changes.
In this study, we analyze the association between time-dependent vaccination rates and COVID-19 health outcomes for 48 U.S. states. We primarily focus on case-hospitalization risk (CHR) as the outcome of interest, using it as a population-level proxy for disease burden on healthcare systems. Performing the analysis using Generalized Additive Models (GAMs) allowed us to incorporate real-world nonlinearities and control for critical dynamic (time-changing) and static (temporally constant) factors. Dynamic factors include testing rates, activity-related engagement levels in the population, underlying population immunity, and policy. Static factors incorporate comorbidities, social vulnerability, race, and state healthcare expenditures. We used SARS-CoV-2 genomic surveillance data to model the different COVID-19 variant-driven waves separately, and evaluate if there is a changing role of the potential drivers of health outcomes across waves.
Our study revealed a strong and statistically significant negative association between vaccine uptake and COVID-19 CHR across each variant wave, with boosters providing additional protection during the Omicron wave. Higher underlying population immunity is shown to be associated with reduced COVID-19 CHR. Additionally, more stringent government policies are generally associated with decreased CHR. However, the impact of activity-related engagement levels on COVID-19 health outcomes varied across different waves. Regarding static variables, the social vulnerability index consistently exhibits positive associations with CHR, while Medicaid spending per person consistently shows a negative association. However, the impacts of other static factors vary in magnitude and significance across different waves.
This study concludes that despite the emergence of new variants, vaccines remain highly correlated with reduced COVID-19 harm. Therefore, given the ongoing threat posed by COVID-19, vaccines remain a critical line of defense for protecting the public and reducing the burden on healthcare systems. .
CHR_input.csv: all data used for model relative case-hospitalization risk.CIR_input.csv: all data used for model relative reported case incidence rate.CHR_RP_lag_range.csv: data used for sensitivity analysis on previous infection variables.
State_population.csv: US state population data, source: https://www.census.gov/data/datasets/time-series/demo/popest/2020s-state-detail.html.static_variable.csv: all static variables used in the model.weekly_activity_level.pkl: weekly activity-related engagement levels. This data generates from Safegraph's weekly patterns dataset. The raw data should request from Safegraph.age_US_state.csv: US state-level population by age group, source: https://www.census.gov/data/datasets/time-series/demo/popest/2020s-state-detail.html.Weekly_cases.pkl: US state-level weekly confirmed cases, source: https://github.com/CSSEGISandData/COVID-19.Weekly_genomic.pkl: generated from COVID-19 sequencing data, the raw data were downloaded from GISAID.Weekly_hospitalized.pkl: US state-level hospitalized cases, source: https://covid.cdc.gov/covid-data-tracker/#datatracker-home.Weekly_policy.pkl: US state-level government response index, source: https://github.com/OxCGRT/covid-policy-tracker.Weekly_testing.pkl: US state-level testing rate: source: https://github.com/govex/COVID-19/tree/master/data_tables/testing_data.Weekly_vaccination.pkl: US state-level cumulative vaccination data, source: https://covid.cdc.gov/covid-data-tracker/#datatracker-home.Weekly_previous_infection.pkl: generate from https://github.com/CSSEGISandData/COVID-19.
CHR_model.R: GAMS fit to case-hospitalization risk.CIR_model.R: GAMS fit to reported case incidence rate.Create_input_data_CHR.ipynb: Code for generatingCHR_input.csv.Create_input_data_CIR.ipynb: Code for generatingCIR_input.csv.previous_infection_sensitivity_analysis.R: Sensitivity analysis on previous infection variables.Previous_infection_sensitivity_analysis.ipynb: Plotting sensitivity analysis results of previous infection variables.interaction_ALE_CHR.ipynb: Plotting results from Omicron-Booster-RCHR.interaction_ALE_CIR.ipynb: Plotting results from Omicron-Booster-RCIR.- 'plot_dynamic_var_before_after_transformation.ipynb': Plotting dynamic variables before and after variables transformation.
visualize_3_model_ALE_CHR.ipynb: Plotting results from RCHR models.visualize_3_model_ALE_CIR.ipynb: Plotting results from RCIR models.
Hongru Du, Samee Saiyed, and Lauren M. Gardner