A growing body of evidence suggests that SARS-CoV-2 infection and its resulting disease may lead to sequelae persisting beyond the typical post-viral recovery period (1, 2). This phenomenon is referred to by a variety of terms, such as chronic COVID-19 syndrome, late sequelae of COVID-19, long COVID, long-haul COVID, long-term COVID-19, post-COVID syndrome, post-acute COVID-19, and post-acute sequelae of SARS-CoV-2 infection (PASC). To harmonize the discrepancies in nomenclature, the World Health Organization (WHO) proposed the term Post-COVID-19 Condition (PCC). PCC is defined as the continuation or the development of new symptoms 3 months after a probable or confirmed SARS-CoV-2 infection, where these symptoms last for at least 2 months and cannot be explained by an alternative diagnosis (3).

Post-COVID-19 conditions encompass a wide range of clinical outcomes, spanning from the emergence of new symptoms to both acute and chronic clinical diagnoses. In a 2021 study, Al-Aly et al. (1) identified an extensive array of sequelae within 6 months among individuals surviving at least 30 days from symptom onset. These included both pulmonary and extrapulmonary manifestations, such as neurological and neurocognitive disorders, mental health conditions, metabolic, cardiovascular, and gastrointestinal disorders, as well as general malaise, fatigue, musculoskeletal pain, and anemia. Estiri et al. (2) extended the period of observation, investigating symptoms and conditions up to 9 months following infection. Many studies have focused on hospitalized COVID-19 patients, limiting the generalizability of findings to broader populations. Several authors have argued about the need to investigate clinical sequelae in low-risk adult populations, or in individuals who experienced mild or asymptomatic infections (4). Currently, an increasing number of studies assesses these conditions at the population level, or at least across large regional areas or specific population subgroups (5–10).

Identifying health outcomes on a population level in a timely, systematic, and cost-efficient manner is crucial for implementing effective public health strategies. Health administrative data (HAD), routinely generated through the provision of health services, provide a valuable resource for this purpose. Although labeled “administrative,” these data are primarily produced within national, regional or local health information systems, and reflect both clinical and service use information. HAD-based detection algorithms to be applied to the general population can be developed by linking multiple data sources, such as billing claims, hospital discharge records, outpatient specialist services, pharmaceutical prescriptions, emergency department visits, general practitioner records and co-payments exemption data, at the individual level. The specific context of application, the quality and availability of administrative data and the extent to which different datasets can be linked strongly affect the possibility of examining isolated symptoms, acute conditions, or chronic diseases.

Recent critiques of the existing literature have highlighted methodological limitations, especially the lack of standardized study designs and the use of limited comparative methods (5). Many studies, for example, do not include SARS-CoV-2-negative individuals as a control group, limiting the ability to disentangle the effects of infection from the effect of other specific disease progressions or conditions (10). Further research is needed to investigate the potential protective role of vaccination against long-term consequences of SARS-CoV-2 infection (5).

The primary objective of the present study is to identify, via a literature review, clinical conditions potentially attributable to SARS-CoV-2 infection, and the types of health administrative data and “diagnostic criteria” used for population-level investigations of these outcomes. We also aim to analyze the study designs employed for examining medium- and long-term sequelae, comparing infected and non-infected individuals.

A narrative literature review was conducted to identify comparative cohort studies based on HAD that examine medium- and long-term outcomes of SARS-CoV-2 infection in the general population, to assess the effects of infection. A structured PubMed search, updated in March 2025, was conducted using a Boolean combination of terms related to health administrative data, cohort study design, population-level analyses, and PCC-related outcomes. Filters were applied to restrict results to English-language studies published between 2021 and 2023. The full search string is detailed in Figure 1 and in Supplementary Table S1. Additional relevant studies were identified through reference screening of included articles.

We focused on primary, comparative, non-descriptive cohort studies based on administrative data for the investigation of specific medium- or long-term clinical outcomes. Therefore, we applied the following exclusion criteria: patient-centered studies based on surveys or laboratory data; case–control studies; studies that developed prognostic models; studies focusing solely on healthcare resource utilization as outcomes; studies examining the impact of COVID-19 on healthcare service delivery; studies performed on selected population subgroups (e.g., veterans) instead of the general population; studies evaluating the effect of specific risk factors, therapeutic interventions and/or vaccination.

From each selected study, the research protocol and methods used to identify potential outcomes through HAD were retrieved. Algorithms used for outcome identification may or may not involve linkages across different data sources. We then extracted information about the health information system source(s), coding system(s), specific diagnostic or medication code(s), and time criteria/thresholds used to identify outcomes.

A total of 151 articles were initially identified via PubMed. Of these, four (5–8) met our inclusion criteria. Two more studies (9, 10) were identified through reference screening and included in our review (Figure 1; Table 1). The diagnosis and/or medication codes used for the identification of each outcome by the six studies are listed in Supplementary Tables S2–S6.

The studies reviewed provide multiple lines of evidence supporting the presence of medium- and long-term conditions potentially associated with SARS-CoV-2 infection or COVID-19. The conditions investigated in the literature cover a wide spectrum, ranging from symptoms to acute and chronic diseases, and extend beyond the respiratory system to various other organs and systems.

Conducting research at the population level requires the use of routinely collected data and the capacity to link different data sources in order to reconstruct individual subjects’ clinical histories. The data sources used are diverse and include general practitioner or specialist visits, outpatient diagnoses, hospital admissions, pharmaceutical prescriptions, and, to a lesser extent, laboratory data. The availability, completeness and quality of data directly influence the scope and extent of studies leveraging them. In the absence of coded diagnoses from general practitioner or specialist visits, it is virtually impossible to investigate non-specific symptoms such as fatigue or cough. Similarly, acute conditions are more likely to be detected through significant and specific healthcare encounters, such as hospitalizations. For less severe conditions, detection may be less sensitive. Chronic conditions, on the other hand, are more likely to be captured even with less detailed data, particularly when multiple sources can be integrated.

Several studies define the exposure window for participant inclusion based on the period during which different viral variants were dominant. Some studies do not address the influence of variant dominance in comparing test-positive and test-negative individuals, while others account for it in the analyses. Few studies limit the enrolment period to phases with minimal overlap between circulating variants, to reduce confounding.

The follow-up periods used to identify post-infection sequelae vary widely across studies. Typically, a lag of 3 weeks to 30 days post-infection is used to separate acute complications. Observation periods range from four to 12 months, although some studies extend beyond 1 year.

Possibly due to limited availability of tests during certain phases of the pandemic, many studies do not directly compare test-positive versus test-negative individuals. In some cases, the exposed group includes individuals with a positive test as well as those hospitalized for conditions consistent with COVID-19 without test confirmation. Conversely, the unexposed group is sometimes defined generically as individuals without a positive test result. These definitions may reduce the robustness of comparisons and limit the interpretation of associations.

The most frequently reported measures of association are hazard ratios and risk differences, typically estimated using Cox proportional hazards models or Kaplan–Meier methods, adjusted for confounders. Inverse probability weighting is often used to achieve covariate balance. Common confounders include comorbidities—often specific to the outcome under investigation—along with alcohol and tobacco use and sociodemographic characteristics.

The evidence emerging from the studies analyzed confirms that SARS-CoV-2 infection can lead to medium- and long-term clinical consequences, ranging from non-specific symptoms to acute and chronic diseases. Nevertheless, our understanding of these sequelae remains limited and fragmented, primarily due to heterogeneity in data sources, coding practices, and methodological designs.

The integrated use of data from health information systems offers remarkable potential for investigating the long-term consequences of the infection, as it enables the analysis of large populations—including individuals with severe disease as well as those with mild or asymptomatic infections. However, the variability in data availability and quality—along with country-specific differences in coding systems, healthcare organization, and care pathways—poses challenges to harmonizing results. In particular, the lack of precise coding in primary care can impede the early and accurate identification of post-COVID manifestations such as fatigue or cough. Likewise, for rare or milder conditions, the detection rate may decrease if there is no systematic referral to specialist consultations or hospital care.

There is an evident need to include test-negative control groups or adopt appropriate comparison strategies in order to distinguish the effects attributable to infection from those related to the natural progression of other conditions. A further step forward would be to incorporate analyses that consider the influence of protective factors, such as vaccination, as well as the role of different viral variants.

Addressing these challenges requires large-scale, multicenter studies that employ rigorous methodologies, leveraging the potential of administrative data while standardizing coding tools and outcome definitions. An integrated approach—combining clinical data, disease registries, pharmaceutical prescriptions, exemptions, and electronic medical records—can provide a more complete picture of the post-infection trajectory, ultimately guiding public health policies and service planning toward the management and prevention of long-term complications.

In conclusion, although current findings support the existence of a broad range of post-COVID sequelae, further research with robust protocols and large cohorts is essential. Only through such efforts will it be possible to fully quantify the impact of so-called “long COVID” and provide clear guidance on prevention, early diagnosis, and long-term care, with particular emphasis on the emergence of new chronic diseases. Even after the pandemic emergency phase, these conditions will continue to exert a significant influence on healthcare systems worldwide.