The COVID-19 pandemic has had a profound impact on healthcare systems globally. The initial outbreak was first detected in Wuhan, China. As the virus’s behavior in China was studied, other countries began preparing for the impending waves of the pandemic. However, many nations, including India, lacked sufficient time to prepare adequately. In response to the pandemic’s waves, India implemented various healthcare and treatment strategies (1–3).

During the first wave, the rapid spread of the virus, coupled with uncertainty about treatment and outcomes, put immense pressure on India’s healthcare system (4). Hospitals prioritized COVID-19 care to manage the surge in patients, while non-essential medical procedures were postponed or avoided by patients (5).

The second wave brought additional challenges, severely disrupting healthcare services across the country. Shortages of medical supplies, an inadequate number of healthcare personnel, and overwhelmed hospitals led to increased mortality and morbidity rates from COVID-19 (6). In response, hospitals began streamlining resources and implementing standard operating procedures. This allowed the country to address healthcare challenges more effectively by expanding local facilities, advancing telemedicine, and integrating e-health services (7, 8).

With the onset of the third wave, the emergence of new SARS-CoV-2 variants introduced further complexities in healthcare management. The availability and distribution of vaccines, alongside vaccine hesitancy, significantly influenced healthcare decisions. The evolution of additional variants highlighted the importance of timely and appropriate care-seeking behaviors (9, 10).

To date, no comprehensive study has compared healthcare utilization across the three waves of COVID-19 in terms of hospital stay durations, admission units, and medication regimens. Such research is crucial to understanding the full burden of the pandemic and providing insights that can improve healthcare preparedness for future crises.

This study aims to analyze the factors influencing healthcare utilization patterns during the three waves of COVID-19 in India, with a focus on hospital admissions, and the determinants of Health Care Utilization (HCU). With this, a clearer understanding of the pandemic’s impact on hospital admissions and guide improvements in future healthcare responses.

A total of 1,620 cases of COVID 19 were admitted to acute COVID care facility of the National Institute of Medical Sciences and Research, Nims Hospital Rajasthan, Jaipur, India, were categorized based on their occurrence during the first, second, and third waves. Out of 1,620 cases, 1,308 patients were found eligible for the inclusion in this study. Of these 1,308 patients, 867 patients were admitted in the first wave, 418 in the second wave, and 23 cases in the third wave. In this retrospective observational study, we found that the total number of patients admitted subsequently decreased from I wave to III wave. Males to female ratio was 2.23 (p-value <0.001) indicating males were more affected than females in all the three waves. The age group most impacted by COVID-19 among the patients were 41–60 year range, which comprises 36.9% of the total. In comparison, 26.14% of the patients were in the 61–80 year range, and only 1.14% of the patients were older than 80 years. This suggests that middle-aged individuals (41–60 years) are more affected by the virus than those who are over 80 years. Patients were divided into four groups as per the length of hospitalization. In these 30% of the patients were hospitalized for 0–5 days, 25.9% for 9–13 days, 24.08% for 6–8 days, and only 19.5% of patients hospitalized for >14 days (refer Table 1). The healthcare utilization data during the COVID-19 pandemic, categorized by area of living, gender, and age groups is shown in Figure 1. Rural residents show higher utilization across all age groups compared to urban counterparts, potentially due to limited access to healthcare in rural areas. Similarly, males and middle age group (41–60 years) utilizes more healthcare resources. Understanding these patterns can inform targeted interventions to address disparities and optimize healthcare delivery during pandemics. The detail description of patients with area of living, age and LHS across all the three waves on the basis of gender has been demonstrated in Figure 2 with the respective p values. Table 2 shows hospital burden across pandemic outbreak, the HRCT score indicates that the progression in the number of severe cases from the first to the third wave with more severe cases (61.72%) in second wave, requiring more intensive medical attention and resources. The significant p-value (<0.001) suggests a substantial increase in the strain on hospital resources, especially during the second wave, which necessitated more intensive management of severe cases. Admissions to various hospital units such as ICU, CCU, general wards, and isolation wards. 39.39% of the patients were admitted in deluxe and super deluxe ward and 33.40% in critical care unit (CCU) utilizing expensive facilities and treatments, reflecting a greater strain on these critical areas of the hospital. The prevalence of comorbidities among COVID-19 patients, including diabetes 390 (29.82%), hypertension 356 (27.22%), dyslipidemia 245 (18.73%), and respiratory disease 148 (11.31%). Based on the prevalence data provided for comorbidities among COVID-19 patients we can infer that diabetes and hypertension are the most prevalent comorbidities among COVID-19 patients.

The types of treatments administered, ranging from non-invasive methods and high-flow oxygen to specific regimen like corticosteroids, hydroxychloroquine, monoclonal antibodies, and antiviral drugs including Ramdesivir, Favipiravir, Molnupiravir, Daclizumab, Bevacizumab, all these expensive treatment regimens directly influenced the healthcare utilization. The significant values across different categories highlight a clear trend toward increased hospital burden, particularly marked by an increase in severe cases, higher admissions to resource-intensive units, and shifts in treatment strategies. The future trajectory of a pandemic similar to COVID-19 was forecasted by conducting a multivariable poisson regression analysis. The analysis used age, gender, residence (region of living), HRCT, and treatment as independent factors. The results showed a statistically significant relationship male gender (p = 0.037; OR 0.90; 95% confidence interval [CI] 0.818 to 0.998), middle age and below (41–60: OR 1.26; 95% CI 0.78–2.06; 0–20: OR; 95% CI 0.78–2.33), rural population (OR 1.01; 95% CI 0.91–1.12), Non-invasive mechanical ventilation + Hydroxychloroquine + Antiviral therapy + Monoclonal antibodies (OR 1.15; 95% CI 0.93–1.43) were associated with higher utilizations of resources. In contrast less hospital stay 9–13 days (OR 0.91; 95% CI 0.79–1.05), HRCT score (mild: OR 0.96; 95% CI 0.84–1.09; moderate: OR 0.93; 95% CI 0.83–1.04) are associated with lower utilization of overall resources.

Other variables, such as age group, vaccination status, comorbidities, and HRCT severity score, did not show statistically significant associations in the multivariate model (p > 0.05). However, some variables demonstrated trends that may have clinical relevance. For instance, participants with moderate to severe HRCT scores showed a slightly increased incidence rate ratio, although not reaching statistical significance (adjusted IRR = 1.188; 95% CI: 0.913–1.546; p = 0.199) (refer Table 3).

In this study, we examined healthcare utilization patterns among COVID-19 patients admitted to the acute COVID care center at NIMS Hospital, Jaipur, Rajasthan, India. A total of 1,308 individuals were included, and their utilization patterns were analyzed based on length of hospital stay, treatment protocols, and the hospital units where they were admitted. During the second wave, a higher number of patients required admission to the ICU and CCU compared to isolation and general wards, indicating greater severity and mortality during this wave. This surge in severity led to a shortage of essential medical supplies, including medications, oxygen, hospital beds, and healthcare personnel. Similar studies have shown that the second wave was more widespread and severe, whereas the first wave was marked by stringent social distancing measures, national lockdowns, and travel restrictions (13–15). Consequently, the strain on healthcare resources during the COVID-19 pandemic exceeded that experienced during recent pandemics involving influenza and other coronaviruses (16).

In the second wave, 26.55% of patients were hospitalized for more than 14 days, compared to 16.60% in the first wave and 4.34% in the third wave. The significant difference in hospital stay duration (p < 0.001) highlights the increased severity of illness and higher resource utilization during the second wave. A study by Tendulkar P et al. and Singh S et al. similarly reported that the average hospital stay was longer during the second wave compared to the first (17, 18). Study also demonstrated a marked decline in the number of infected patients during the third wave when compared to the first and second waves. This trend suggests a potential shift in transmission dynamics or the impact of increased immunity and public health interventions over time.

During the second wave, treatments such as Hydroxychloroquine, Monoclonal antibodies, Antiviral therapy, and high-flow oxygen therapy were used more frequently than in the first and third waves. The use of drugs like Remdesivir and Bevacizumab was found to be more effective in preventing disease progression and reducing hospital stay durations (19–21).

In both the first and second waves, the majority of COVID-19 patients were from rural areas, with a p-value of 0.028, corroborating findings from Cuadros DF et al. Rural populations often face challenges such as limited access to healthcare resources, leading to delayed diagnoses and treatment, which in turn results in more severe cases and longer hospital stays. Factors such as lack of knowledge about social distancing, limited availability or use of face masks, and delayed vaccination uptake may have contributed to this trend (8, 22). In contrast, a study by Bhocal U. et al. claimed that rural populations had fewer infections and shorter hospital stays due to a stronger immune response, presenting contrary results (23). This study’s significant strengths include its large and diverse patient cohort, along with its comprehensive evaluation of healthcare utilization following a COVID-19 diagnosis.

Several studies have shown that males were more susceptible to COVID-19 compared to females, resulting in higher hospitalization and healthcare resource utilization among men. In our study, 904 males (69.11% of the total) were infected, possibly due to biological differences in immune responses and pre-existing health conditions (24–27), both of which may contribute to higher rates of hospitalization (28). Additionally, the middle-aged population was more affected by COVID-19 than pediatric and geriatric populations in this study (27, 29, 30). In comparison with the findings of the study by Gunjan Kumar et al., which analyzed data from 31 hospitals across India, our study similarly observes a higher proportion of middle-aged individuals affected by post-COVID sequelae. The broader dataset reported by Kumar et al. reinforces our findings, indicating that the middle-aged population represents a significant demographic among COVID-19 survivors experiencing long-term health impacts (30).

This could be attributed to the fact that vulnerable groups, such as children and the older adult, were often kept under strict preventive measures, despite being more immunosensitive (25). Underlying health conditions, such as obesity, diabetes, hypertension, and respiratory diseases, which were prevalent in our study, can exacerbate the severity of COVID-19. This underscores the importance of managing comorbidities in reducing the impact of the virus (31, 32). Although the older adult may have a higher risk of mortality from COVID-19, preventive measures like shielding and vaccination campaigns may have reduced their infection rates compared to other age groups (33, 34).

To the best of our knowledge, this is the first study to forecast future pandemic trends similar to COVID-19 while comparing healthcare utilization patterns across the three waves of the COVID-19 outbreak, based on hospital stay length, admission unit, and treatment regimen in a tertiary care hospital in Jaipur, Rajasthan, India.

The healthcare utilization pattern observed during three waves reveals notable shifts. The initial wave witnessed heightened healthcare utilization, primarily driven by increased hospitalizations. The second wave displayed a further surge, indicating evolving patient needs. However, the third wave demonstrated a decline, possibly reflecting enhanced preventive measures. These trends emphasize the importance of adaptable healthcare systems to accommodate varying demands while maintaining a proactive approach to healthcare management. Therefore, any future similar pandemic situation will be more dangerous for females, middle aged population, individuals residing in rural population and will stay for a long period of time in the hospital.