Post-Acute Sequelae of SARS-CoV-2 Infection (PASC), commonly referred to as Long COVID, encompasses a broad spectrum of persistent symptoms and medical complications that continue well beyond the initial COVID-19 infection phase (1–6). Symptoms varies widely, ranging from mild, non-specific issues like fatigue and headaches to severe, debilitating conditions including cardiopulmonary abnormalities and significant neurological deficits (7, 8). These manifestations significantly impair quality of life and pose ongoing challenges to global healthcare systems (9).

The risk factors contributing to PASC are not well understood. A complex array of demographic, clinical, environmental, and socioeconomic elements, may influence PASC (10, 11). There is substantial evidence suggesting that the interplay between biological and environmental components, including air quality, temperature, humidity, and pollution levels, significantly impacts respiratory illnesses, including COVID-19 (12–16). These conditions exacerbate respiratory symptoms and increase the risk of severe complications (14, 15). Socioeconomic disparities also play a crucial role, influencing individuals’ vulnerability and resilience to COVID-19 and its prolonged effects (17). Factors such as income inequality, access to healthcare, housing quality, and employment status profoundly affect an individual’s risk of virus exposure and their ability to receive timely and adequate medical care (18). Education level, social support networks, and community resources are pivotal in determining an individual’s capacity to manage and recover from the infection (19).

Despite established correlations between these variables and outcomes in respiratory and viral diseases, the specific impacts of environmental and socioeconomic conditions on the severity and manifestation of PASC remain insufficiently explored (20–22). Furthermore, the interactions between individual characteristics and environmental risks introduce complex pathways through which PASC develops and progresses (23). For example, residents of socioeconomically disadvantaged areas may encounter increased exposure to environmental hazards and have restricted access to healthcare services, thereby intensifying their risk of developing severe PASC symptoms. Nevertheless, there is a significant gap in comprehensively quantifying these risk factors at the level of individual patients, especially in identifying the interactions between personal characteristics and environmental risks. This deficiency underscores the necessity for an integrated approach that accounts for both individual and environmental risks in the personalized management of PASC patients. Developing a better understanding of these interactions is essential for formulating holistic strategies to prevent and mitigate the long-term health effects of COVID-19 across diverse populations. Such strategies will necessitate enhancing the precision and efficacy of interventions tailored to meet the unique needs of individual patients.

Given the complexities inherent in PASC, our research adopts a comprehensive and methodical approach to elucidate how environmental and socioeconomic variables influence the progression and recovery patterns of PASC across diverse demographic segments. This longitudinal study encompasses over 4,000 hospitalized COVID-19 patients from 14 cities across Gansu Province (425,800 square kilometers) (24), leveraging its unique geographic and climatic diversity (Figure 1). Positioned at the intersection of China’s Qinghai-Tibet, Loess, and Inner Mongolia plateaus, Gansu spans a wide range of landscapes, from mountainous terrains to vast plateaus, with elevations ranging from approximately 600 to over 5,600 meters and mean annual temperatures varying between −8 and 14°C (24–26). These environmental conditions are coupled with significant disparities in air pollution levels, as median annual concentrations of fine particulate matter (PM2.5) range from approximately 12–41 μg/m³, while sulfur dioxide (SO₂) concentrations vary from 7 to 40 μg/m³ (27, 28). Such fluctuations in air quality, combined with varying levels of urbanization and green space coverage—quantified by the normalized difference vegetation index (NDVI), which ranges from nearly 0 to 1 (29), are likely to influence respiratory health and long-term COVID-19 outcomes. Economic and healthcare disparities further compound these environmental differences, as per capita gross domestic product (GDP) across the region varies nearly tenfold, from approximately 20,000 to over 150,000 Chinese yuan (30). Access to healthcare resources also differs significantly, with some cities having nearly twice the number of hospital beds per thousand people compared to others (30). This broad spectrum of environmental, economic, and healthcare conditions provides an ideal framework for investigating how these factors collectively shape the long-term impacts of COVID-19, particularly in relation to PASC risk and recovery trajectories. By systematically integrating these variables, our study aims to develop a more comprehensive understanding of PASC and inform targeted interventions that address both individual and population-level health disparities.

The diverse environmental and socioeconomic conditions across the study region provide a unique framework for investigating how these factors interact with age-related biological differences to shape the progression and recovery of PASC. This study employs a longitudinal design to systematically track COVID-19 patients over time, allowing for an in-depth analysis of how environmental exposures, economic disparities, and individual characteristics influence PASC risk and long-term outcomes. By integrating spatiotemporal data with patient health records, we aim to capture the dynamic nature of disease progression across different demographic groups. Furthermore, examining the interplay between environmental pollutants, healthcare accessibility, and socioeconomic determinants will offer a more comprehensive understanding of the broader public health implications of PASC. Through this multidisciplinary approach, our study seeks to inform the development of targeted interventions and personalized treatment strategies, ultimately contributing to improved patient care and more effective public health policies.

Patients and public will not involve in the design, recruitment and conduct of the study.

This study adheres to the ethical standards outlined in the Declaration of Helsinki and complies with national and local regulations concerning ethical research practices and patient data protection. The Gansu Provincial Hospital research ethics committee has approved this study protocol (ID: 2024–542), and it is registered as a clinical trial (ChiCTR2400091805). Ethical monitoring and data dissemination will be maintained throughout the study.

Informed consent will be obtained from all participants. For pediatric and geriatric populations, specific considerations include obtaining parental consent for minors and ensuring appropriate consent procedures for seniors, particularly those with cognitive impairments. The study commits to maintaining the confidentiality of all medical information, disclosing it only when required by law and with appropriate consent from involved parties. All clinical research data will be retained for a minimum of 5 years following the completion of the study. Study results will be published in peer-reviewed journals.

This study will employ a longitudinal design to investigate how environmental, socioeconomic, and clinical factors shape PASC outcomes in a demographically diverse population. Comprehensive health data collected during the initial COVID-19 hospitalization help distinguish pre-existing conditions from symptoms emerging as part of PASC. By integrating these clinical records with environmental and socioeconomic variables through mixed-effects models and spatiotemporal analyses, the research provides a comprehensive understanding of how both internal factors (e.g., individual health status, comorbidities) and external factors (e.g., air quality, socioeconomic conditions) influence long-term health outcomes following COVID-19. To ensure a representative sample, the cohort is stratified by age, sex, and initial symptom severity, allowing for a detailed and subgroup-specific analysis of PASC manifestations. This multidimensional approach is essential for uncovering the complex interactions between patient-level susceptibility and broader external influences, ultimately informing targeted strategies to improve the management and mitigation of long-term COVID-19 effects.

Existing literature has widely documented the potential influences of underlying comorbidities, demographic factors, and environmental conditions on PASC outcomes (64). Pre-existing conditions such as cardiovascular disease, diabetes, and chronic respiratory disorders have been shown to exacerbate the severity of PASC symptoms, leading to prolonged recovery and an increased risk of complications (65). Individuals with chronic illnesses are more likely to experience severe acute COVID-19, which may predispose them to long-term sequelae due to sustained systemic inflammation and immune dysregulation (66). For instance, patients with diabetes and obesity have demonstrated heightened susceptibility to persistent inflammatory states post-COVID-19, contributing to ongoing fatigue, dyspnea, and cardiovascular dysfunction (67, 68). By examining how pre-existing health conditions interact with PASC, this study aims to elucidate the mechanisms through which chronic illnesses impact recovery trajectories and to identify potential intervention strategies.

Beyond comorbidities, demographic factors such as gender, education level, and marital status may also influence PASC trajectories (69). Gender differences in immune response and health-seeking behaviors have been linked to variations in symptom persistence and severity, with women more frequently reporting prolonged PASC symptoms, including fatigue, neurological issues, and autonomic dysfunction (70). Additionally, education level and marital status serve as proxies for social support and healthcare access, both of which are critical for managing chronic conditions and facilitating recovery (71). Limited health literacy, often associated with lower education levels, may impact adherence to post-COVID care recommendations, while marital status may influence mental health resilience and access to medical resources (72–74). By incorporating these demographic variables into our analysis, we aim to identify groups with greater susceptibility or resilience to PASC and assess how these interactions affect recovery trajectories.

Environmental pressures, such as air quality, urbanization, and access to green spaces, are increasingly recognized as key determinants of post-COVID-19 health outcomes (15, 20, 75). Air pollution has been linked to increased COVID-19 severity and mortality, and higher exposure to PM2.5 and NO2 has been associated with a higher prevalence of long-term respiratory and cardiovascular symptoms in PASC patients (75). Conversely, access to green spaces has been shown to provide protective benefits, improving mental and respiratory health and facilitating recovery (76). Given the environmental diversity of our study sites, this research will assess how these factors contribute to symptom persistence and recovery, leveraging existing knowledge on environmental determinants of health outcomes to refine analytical approaches and enhance the understanding of PASC progression.

A key strength of this study is its extended follow-up period of up to 48 months post-discharge, allowing for a comprehensive assessment of long-term PASC progression and persistence across different age groups. This prolonged observation provides valuable insights into age-specific recovery patterns, refining predictive models for long-term health outcomes and facilitating the development of targeted interventions. Additionally, the use of Geographic Information Systems (GIS) enhances the study by generating disease distribution maps, which help identify environmental and geographic factors associated with PASC. These insights are not only essential for detecting high-risk areas but also for guiding individualized patient care strategies.

Beyond mapping geographic influences, the integration of environmental and socioeconomic data with clinical outcomes strengthens the understanding of how external factors, such as air pollution, economic disparities, and urbanization, affect the severity and trajectory of PASC. Identifying these factors has direct implications for patient care, enabling healthcare providers to incorporate external influences into personalized treatment plans. Time-series analyses further enhance this approach by tracking temporal trends and evaluating the effects of environmental changes on health outcomes, providing a dynamic perspective on how fluctuating conditions contribute to disease progression. Meanwhile, causal inference models help establish robust causal relationships between environmental, socioeconomic, and clinical variables, ensuring a more rigorous evaluation of COVID-19’s long-term effects on PASC. By combining these analytical techniques, the study offers a multidimensional framework for understanding how environmental and socioeconomic conditions interact with individual health factors, ultimately informing more precise treatment and intervention strategies. This research also carries significant implications for patient care, particularly in identifying specific risk factors that exacerbate PASC in vulnerable populations. By considering both internal and external determinants of health, this study supports a shift toward precision medicine, ensuring that interventions address not only biological vulnerabilities but also environmental and socioeconomic disparities that influence recovery.

Despite the strengths of our study, we acknowledge several limitations and have devised strategies to address them effectively. Firstly, our study is based in Gansu Province, China, involving data from 14 cities, each with unique environmental and socioeconomic characteristics. While this provides a rich dataset within a specific geographic region, it limits broader geographic representation. To address this, we have included multiple sites chosen for their environmental diversity, particularly focusing on urban settings across different regions. This strategic selection helps control environmental factors that could influence health outcomes related to PASC. Secondly, our current study setup does not include direct comparisons with other cities or countries, which restricts our ability to generalize findings across different global environments. We plan to expand our analysis to include such comparisons as comparable data becomes available, allowing for a more comprehensive evaluation of environmental and socioeconomic impacts on PASC rates and health outcomes. Thirdly, the non-random nature of our patient recruitment might introduce selection bias, and the reliance on telephone questionnaires could lead to recall bias, particularly as participants may not accurately remember past symptoms. To mitigate these issues, we use advanced statistical methods to control for potential confounders and perform sensitivity analyses to assess the impact of data attrition. Fourthly, an oversight committee is actively involved in monitoring and evaluating the study’s processes to address any unforeseen issues that arise, thereby ensuring the integrity and validity of our research. Plans for further validation through clinical assessments are in place to enhance the reliability of our findings, particularly to address challenges associated with self-reported data. By implementing these strategies, we aim to refine our understanding of PASC and its diverse impacts across different populations, ensuring that our findings are robust and reflective of the true effects of environmental and socioeconomic factors on health outcomes post-COVID-19.

In conclusion, this research represents a critical advancement in understanding PASC by mapping recovery trajectories and identifying both internal and external factors influencing patient outcomes. The insights gained from this study have the potential to transform precision medicine approaches, allowing for more targeted and effective interventions that address the long-term consequences of COVID-19. Furthermore, these findings will contribute to public health strategies aimed at mitigating the impact of PASC, while also supporting the development of tailored patient care approaches based on individual risk factors.

Data from telephone interviews will be meticulously recorded and stored in a password-protected Microsoft Excel file. All study data will be securely housed on a dedicated server at Specific-Disease Database of Gansu Provincial Hospital, with access strictly limited to authorized members of the research team. Rigorous data management protocols will be enforced to ensure the utmost privacy and security of patient information, adhering to the highest standard of data protection.