Strategies for population-level identification of post-acute sequelae of COVID-19 through health administrative data

Abstract

Introduction: Post-acute sequelae of COVID-19 (PASC) encompass several clinical outcomes, from new-onset symptoms to both acute and chronic diagnoses, including pulmonary and extrapulmonary manifestations. Health administrative data (HAD) from health information systems allow population-level analyses of such outcomes. Our primary aim was to identify clinical conditions potentially attributable to SARS-CoV-2 infection, and the types of HAD and "diagnostic criteria" used for their detection.

Methods: We performed a literature review to identify HAD-based cohort studies assessing the association between SARS-CoV-2 infection and medium-/long-term outcomes in the general population. From each included study, we extracted data on design, algorithms used for outcome identification (sources, coding systems, codes, time criteria/thresholds), and whether significant associations with SARS-CoV-2 infection were reported.

Results: We identified six studies investigating acute and chronic conditions grouped by clinical domain (cardiovascular, respiratory, neurologic, mental health, endocrine/metabolic, pediatric, miscellaneous). Two studies also addressed the onset of specific symptoms. Cardio/cerebrovascular conditions were most studied, with significant associations reported for deep vein thrombosis, heart failure, atrial fibrillation, and coronary artery disease. Conditions in other domains were less investigated, with inconsistent findings. Only three studies were designed as test-positive vs. test-negative comparisons.

Discussion: Heterogeneity in data sources, study design, and outcome definitions hinder the comparability of studies and explain the inconsistencies in findings about associations with SARS-CoV-2 infection. Rigorously designed studies on large populations with wide availability of data from health information systems are needed for population-level analyses on PASC, and especially on its impact on chronic diseases and their future burden on healthcare systems.

Keywords: COVID-19; PASC; case-detection algorithm; health administrative data; long COVID; routinely collected data.

Figure 1

Study selection flowchart illustrating the identification, screening, eligibility assessment, and inclusion of studies in the review. Adapted from Page et al. (11).

Figure 2

Bar chart illustrating, for each cardio/cerebrovascular outcome, the number of included studies that investigated that outcome. The colored portion of each bar indicates the number of included studies where a significant association of the outcome with COVID-19 was found. Within the category arrhythmias, associations were found for atrial fibrillation. CHF, Congestive Heart failure; IHD, Ischemic heart disease; AMI, acute myocardial infarction; CAD, coronary artery disease; DVT, Deep vein thrombosis; PE, pulmonary embolism; TIA, transient ischemic attack.