A population-based model for rationing COVID-19 vaccine

Abstract

BACKGROUND As COVID-19 vaccines develop, methods for identifying vulnerability within groups to prioritized vaccination remain un-established. This paper presents a novel approach based on population-based analysis of viral pneumonia vulnerability, as an example. METHODS The analysis employed an anonymous, 16-year, population dataset (n = 768,460) consisting of International Classification of Diseases (ICD-9) diagnoses, demographics, and dates identifying those with viral pneumonia and permitting linkage of these individuals to all their associated diagnoses for calculation of odds ratios and proportions of disorders before and after the index viral pneumonia diagnosis. RESULTS Females and males had results of differing magnitude. For those with viral pneumonia, the mean number of diagnoses was greater in both the subsample and whole sample, with associated diagnoses arising about 4 years on average before the viral pneumonia index diagnosis. Within the subsample, compared to those without, the temporal analysis revealed distinct over-representation for those with viral pneumonia at visit one and over the first fifty visits. Further, those with viral pneumonia had diagnoses not represented in the group without viral pneumonia. CONCLUSIONS The population-based analysis of temporal hyper-morbidity may be a viable and economical approach to identifying viral pneumonia vulnerability. The approach presented in this paper may provide an economical means of identifying vulnerability to COVID-19 in regions where comparable data are available for analysis. Rational approaches may optimize vaccination and help to limit the spread of the disease and to some extent alleviate the health service burden.