Maintaining health service during COVID-19: A study on regional health services

Abstract

The pandemic has inevitably led to disruptions in the provision of health services for all those patients not affected by COVID-19. At the same time, we have observed differences among health services in their ability to maintain their activities in the face of shocks: while some health services were largely able to ensure core functions, other suffered delays in prevention, acute care, and rehabilitation. In this paper, we explore the effect of regional health policies in terms of governance, workforce, and health service delivery on the ability to maintain oncological services during the COVID-19 pandemic to assess the resilience of the system. The study is based on secondary data collected on the 21 Italian regional health services during the first wave of the pandemic. We discuss the theoretical and practical implications of providing health services with specific characteristics pertaining to governance, workforce, and health service delivery to support the resilience of regional health policies during a crisis or shock.

Keywords

COVID-19 resilience regional health services oncology

Introduction

SARS-CoV-2 began to spread in the Western world in March 2020, forcing governments to establish measures to reduce the diffusion of the virus, including lockdowns and border closures. Health systems worldwide were put under pressure: On the one hand, they had to provide care for COVID-19 patients, while on the other hand, they had to guarantee assistance to all other patients. In fact, the pandemic caused not only additional disease burden and mortality, but also a delay in necessary and urgent non-COVID care.¹ Because of the spreading of the virus, healthcare systems made tremendous efforts to face COVID-19: converting hospitals to COVID hospitals, suspending non-urgent services, and re-allocating workforce. The other side of the coin was that other patients suffered from delays in screening and diagnosis, which in turn reduced life expectancy and increased mortality.²

The capacity of maintaining health services in the face of shocks is commonly referred to health system resilience. Kruk and colleagues³ define it as “the capacity of health actors, institutions, and populations to prepare for and effectively respond to crises; maintain core functions when a crisis hits; and, informed by lessons learnt during the crisis, reorganise if conditions require it”. As this definition suggests, health system resilience pertains to key characteristics of health systems^4,5 that determine its capacity to enhance and reorganize the overall healthcare system in response to events ⁶ and are reflected in the degree of preparedness to shocks.³ Scholars concur that the ability to maintain core functions during a shock relies not only on the capacity to respond but is also influenced by the presence of a strong and modern health system.^4,5 In analyzing the determinants of a health system's resilience, Ling and colleagues⁷ posited the existence of two primary categories of variables. The first category, termed “slow variables,” encompasses all elements necessary to establish a foundation for robust health system functions during both crisis and non-crisis periods, such as workers and facilities. The second category, “fast variables,” comprises elements essential for an immediate response to health crises, including HAZMAT suits and vaccines. Despite its significance and potential implications, the impact of a health system's pre-shock characteristics on its resilience level has received less attention.

Recent scholarly work has focused on analyzing the responsiveness and resilience of various healthcare systems to COVID-19. Burke and colleagues¹ examined Ireland’s health system response to COVID-19; other studies have investigated commonalities and differences in policy implementation and the related economic, health, and social impacts of the pandemic among target countries.⁸ Furthermore, Tartaglia and colleagues⁹ have undertaken a comparative analysis of the pandemic management across 97 WHO-member countries.

We intend to enrich this body of literature by offering new insights into how the pre-exisiting characteristics of health systems, i.e. slow variables, bolster resilience during a shock,^6,10. Moreover, we examine how a decisions, policy and strategies have impacted the healthcare systems’ capacity for resilience.^6,7,11,12 More specifically we investigate the influence of regional health policies on governance, workforce, and health service delivery on the ability to maintain services during the COVID-19 in Italy. This inquiry is particularly intriguing as, while some health services have managed to sustain core functions, others have experienced delays in prevention, acute care, and rehabilitation efforts.¹³

In the current study, we specifically concentrate on the continuity of oncological care—a critical service area where delays in screening, diagnosis, and treatment can have significant adverse effects.¹⁴ For this reason, oncology is particularly suitable for examining the capacity of healthcare service of maintain their function during unpredictable events, shocks or emergency situations, as widely demonstrated by other studies.¹⁵

Theoretical background and hypotheses

Several scholars have identified health system characteristics that can lead to resilience. In a recent review, Nuzzo and colleagues¹⁶ identified 16 attributes of health system resilience while Khan and colleagues¹⁷ proposed a framework consisting of a cross-cutting theme (governance and leadership) plus nine specific elements (including collaborative networks, planning, and communication). An approach frequently used in studies on health system resilience^5,18 is the WHO’s health system framework,¹⁹ which encompasses six essential building blocks to achieve a health system’s goals and reinforce the system. In a similar vein, Hanefeld²⁰ identified three core dimensions (health management information systems, funding/financing mechanisms, and health workforce) and two cross-cutting dimensions (governance and values & beliefs) that are critical for successfully responding to shocks. Other researchers²¹ have expanded this framework by adding two more dimensions: health services and medical products and technologies.

In line with the approach proposed by Biddle and colleagues,²² the present study focuses on three specific organizational determinants of resilience, i.e. slow variables: governance, workforce, and service delivery.

The decision to address these three determinants is strictly correlated with the empirical context of our study: Italy. Founded in 1978, the Italian National Health Service (NHS) is a Beveridge health system that is free at the point of care and financed by the government through tax payments. Since 2001, the Italian NHS has been organized into three levels: national, regional, and local. The national layer establishes general objectives through planning, identifying priorities, guaranteeing a core benefit package of services to all citizens in every region and ensuring funding coverage. At regional level, responsibility lies with the organization of healthcare, with each of the 21 regional governments managing its own healthcare service. As such, regional health services possess significant autonomy in terms of service organization, governance models, and personnel acquisition policies. However, the majority of funding levels and the management of information flows are determined at a centralized national level, Regions, in turn, assign funds to local health authorities (LHAs), which provide healthcare services guaranteed by the State. Finally, the third level of the NHS comprises a network of LHAs, structured on a territorial basis, and hospital trusts (public and accredited private trusts) that are responsible for providing healthcare services. Essentially, LHAs acquire hospitalization services from directly managed hospitals or from other autonomous public and private hospitals, and they also manage outpatient services, GPs, and long-term care.

When the COVID-19 pandemic broke out in February 2020, and despite the Italian NHS reacted rapidly to the spread of SARS-CoV-2, it had to stop most elective care during the first wave. On March 9^th 2020, every non-urgent service was stopped, with regions gradually and intermittently reactivating services starting from May of the same year.

In the following sections, hypotheses are developed, the research design and analysis methods are set forth, and a discussion of the results and their implications are provided.

Governance domain

The governance domain refers to the dimension characterizing a healthcare system aimed at “ensur[ing that] strategic policy frameworks exist and are combined with effective oversight, coalition-building, the provision of appropriate regulations and incentives, attention to system-design, and accountability”.¹⁸ Given this definition, governance can be understood as a set of structured governing processes and related actions designed to ensure the adequacy and appropriateness of care, the widespread availability of service provision, and the sustainability of the syste²³ It also encompasses decisions regarding managerial internal processes, hierarchical structures, the level of formalization, as well as the degree of coordination and cooperation among both formal and informal actors within the health system and its associated networks.¹⁸ All these decisions aim to ensure o ensure the system's efficiency²³ in terms of resource allocation and care setting arrangements.

In sum, the governance domain refers to the natural, bottom-up dynamics of emergence and innovation of a group of processes related to governing that make a system structured, effective, and able to face challenges,²⁴ thus promoting resilience. Accordingly, we hypothesize the following:

HP1: The characteristics of governance affect a regional healthcare system’s resilience when facing a shock.

Workforce domain

The workforce is a critical dimension of health system resilience and has likely received the most extensive analysis because it is essential for a high-quality health system that ensures efficient service delivery through managing the quality and quantity of personnel assigned to staffed beds. However, a recent review²² has indicated that workforce policies have primarily been examined in the context of a shock or in the context of everyday resilience.²⁵ Nevertheless, there have been no studies on how previous workforce policies impact resilience.

Failures in workforce planning can lead to either an oversupply or a shortage of clinical staff, which can negatively impact health systems by reducing the quantity and quality of medical care, contributing to work overload, and potentially compromising patient safety.²⁶ The awareness of healthcare systems about their workforce is of quintessential importance to adopting successful strategies and decisions,²⁷ both in normal and in emergency conditions.²⁸ A proper knowledge of human resource forecasting ensures continuity of care and the fulfillment of the population’s needs.²⁷ It contributes to a dynamic assessment of workforce demography, thus helping to replace future retirees, and it handles medical staff turnover, unexpected changes in workforce composition, and new patients’ needs, in addition to being necessary to avoid excessive workloads²⁹ Basing on the foregoing evidence, we hypothesize the following:

HP2: The workforce domain affects a regional healthcare system’s resilience when facing a shock.

Health service delivery domain

Most existing studies on health service delivery focus on the capacity of a health system to continue to deliver healthcare services using the same level of resources and capacities as usual despite a shock.²² A key attribute of modern health systems is continuity of care, which has been proven to be correlated with a reduction in avoidable utilization of health services and, consequently, a reduction in costs.³⁰ Continuity of care is based on the existence of a series of inpatient and outpatient services organized around the patients’ needs,³¹ which means gradually moving selected services from hospitals to territorial care settings and thus reducing costs.³² It is well known that the existence of outpatient services, such as community health centers, first aid stations, and structures with non-acute beds, or the presence of general practitioners (GPs) working in teams, may help the resilience of the system by avoiding unnecessary hospitalizations and thus reducing pressure on hospitals as well as reducing exposure risk and delays in screening and treatment.³³ Based on the evidence reported above, we hypothesize the following:

HP3: The characteristics of regional health system delivery affect the system’s resilience when facing a shock.

Methods

Analysis and variables

To test our research hypotheses, we conducted three regression analyses, one for each domain with the aim of examining the impact of a set of measures related to each domain on the dependent variable, System Resilience. Since, it is a count variable and mean and standard deviations differed,³⁴ we opted for three negative binomial regressions. In all three analysis, we controlled for the fixed effect of COVID-19 Prevalence among 1.000 inhabitants: a categorical variable that takes into account low prevalence (less than 23 confirmed cases per 1.000 inhabitants), medium prevalence (between 23 and 34 confirmed cases per 1.000 inhabitants), and high prevalence (more than 34 confirmed cases per 1.000 inhabitants).

Dependent variable

Our dependent variable, System Resilience, is a count variable that captures the number of oncological services lost due to COVID-19. It provides evidence of the ability of a regional system to continue to carry out its core functions despite the pandemic emergency. In particular, we estimated the difference between the sum of outpatient service, day surgery treatments, day hospitals, day hospital admissions, and surgical procedures for cancer patients performed by each regional service in 2019 and 2020. In this calculation, we excluded Emergency Room accesses.

Explanatory variables: model 1 (governance)

Cost per core benefit

This variable indicates the average cost for a single point of Core Benefits Package, weighted for regional population. Until 2019, the provision of Core Benefits was evaluated annually using 33 indicators divided into primary, secondary and tertiary care. The overall summary score of these indicators enables the determination of the service performance achieved by each regional health system. Specifically, we calculate, on average, the cost of guaranteeing a single point of Core Benefit for regional inhabitants: The higher the cost, the more likely the region is to be inefficient.

Acute/non-acute beds ratio

This ratio compares the number of acute beds per 1000 inhabitants and the number of non-acute beds per 1000 inhabitants. We calculate this measure to see whether a region has planned a right balance between acute or non-acute beds. Since existing evidence suggests that technological advances and new interventions will reduce the need for hospital beds in the future and shift care towards outpatient settings,³⁵ we expect that this ratio will have a smaller value in modern regional health service.

Explanatory variables: model 2 (workforce)

Turnover compensation index

This is an index aimed at exploring the regional hiring policy. Specifically, when the rate is higher than 100, it indicates a workforce expansion strategy, while a rate lower than 100 indicates a downsizing policy.

Workload

This indicator assesses, on average, the number of discharged patients, weighted for complexity, per physicians and nurses staffed in a public hospital. The idea of measuring workload using mean number of discharges is coherent with other studies.³⁶ Specifically, our measure reports the regional average workload: The higher the workload, the more likely it is that the physicians are understaffed due to the absence of adequate planning.

Explanatory variables: model 3 (health service delivery)

Number of community health centers per population

This continuous variable is designed to measure the number of community health centers active within a region, adjusted for the regional population. These health centers are non-acute healthcare facilities situated within the community, committed to providing a range of social and health services. Services are delivered by general practitioners (GPs) and other healthcare professionals, encompassing those who offer rehabilitation and preventive care.

Active oncology networks

This is a ratio that indicates the number of active oncological networks off the number of recommended oncological networks that are recommended to be active (Breast, Prostate, Colon Rectum, Lung, Uterus, Melanoma, and Thyroid).

Table 1 summarizes formulas, source and year of each variable.

Table 1.

Formulas, source and year of variables included in the study.

Indicator	Formulas	Data	Source	Reference year
System Resilence	$N u m b e r o f O n c o l o g i c a l O u t p a t i e n t S e r v i c e s 2019 - N u m b e r o f O n c o l o g i c a l O u t p a t i e n t S e r v i c e s 2020$	Number of oncological outpatient services 2019	Italian Agency for regional health service - AGENAS	2021
System Resilence		Number of oncological outpatient services 2020	Italian Agency for regional health service - AGENAS	2020
Cost per core benefit	$(H e a l t h c a r e R e g i o n a l E x p e n d i t u r e \times R e g i o n a l C o r e B e n e f i t S c o r e) \div R e g i o n a l i n h a b i t a n t$	Expense	Italian Ministry of health	2019
		Core benefits point	Italian Ministry of health	2019
		Regional inhabitant	ISTAT 2019	2019
Acute/non-acute beds ratio	$A c u t e B e d s \div N o n - A c u t e B e d s$	Acute beds	Italian Ministry of health	2019
Acute/non-acute beds ratio	$A c u t e B e d s \div N o n - A c u t e B e d s$	Non acute beds	Italian Ministry of health	2019
Turnover compensation index	$(P h y s i c i a n s H i r e d i n 2019 \div P h y s i c i a n s r e t i r e d i n 2019) \times 100$	Physicians hired in 2019	Italian Ministry of finance	2019
Turnover compensation index		Physicians retired in 2019	Italian Ministry of finance	2019
Workload	$(D i s c h a r g e d P a t i e n t s i n 2019 \times I C M) \div n . o f p h i s c i a n a n d n u r s e s$	Discharged patients 2019	Italian Ministry of health	2019
		ICM	Italian Ministry of health	2019
		N. Of physicians	Italian Ministry of health	2019
		N. Of nurses	Italian Ministry of health	2019
Number of community health Center	$N u m b e r o f a c t i v e R e g i o n a l C o m m u n i t y H e a l t h C e n t e r$	Regional community health Center	Italian Ministry of health	2019
Active oncology networks	$A c t i v e O n c o l o g i c a l N e t w o r k \div R e c c o m e n d e d O n c o l o g i c a l N e t w o r k$	Active oncological network	Italian Agency for regional health service - AGENAS	2020
Active oncology networks		Recommended oncological network	Italian Agency for regional health service - AGENAS	2020

Results

Table 2 reports the descriptive statistics and pairwise correlations.

Table 2.

Descriptive statistics.

	Variable	Mean	S.D.	1	2	3	4	5	6	7
1	System resilience	379	621	1
2	Cost per core benefit	10	2.23	−0.295	1
3	Acute/non-acute beds ratio	0.20	0.08	0.1242	0.1751	1
4	Turnover compensation index	98.69	28.17	0.1475	−0.418	0.1479	1
5	Workload	76.13	17.37	0.4542	−0.264	0.1943	−0.093	1
6	Number of community health centers per population	0.14	0.29	−0.126	0.0573	−0.085	−0.217	−0.055	1
7	Active oncological networks	0.36	0.35	0.2052	−0.159	0.2022	0.3593	0.0874	−0.365	1

Table 3 presents the empirical results of the negative binomial regression estimations from the three models corresponding to the three domains of analysis.

Table 3.

Results of negative binomial regression models.

Dependent variable = system resilience
	Model 1- governance	Model 2- workforce	Model 3- service delivery
Cost per core benefit	−.61***	-	-
Cost per core benefit	(.069)
Acute/non-acute beds ratio	.504***	-	-
Acute/non-acute beds ratio	(.167)
Constant	.74	-	-
Constant	(.106)
Turnover compensation index	-	−.016**	-
Turnover compensation index		(.005)
Workload	-	−.038***	-
Workload		(.014)
Constant	-	1.40	-
Constant		(.1.42)
Community health center per inhabitant	-	-	−.574*
Community health center per inhabitant			(.169)
Active oncological networks	-	-	.300
Active oncological networks			(.195)
Constant	-	-	.951
Constant			(.251)

Note: Clustered robust standard error in parentheses.

Sig. levels: °.1; *.05; ** 0.01; *** 0.001.

Regarding the first model, which focuses on the governance domain, the lower the cost of a Core Benefit when adjusted for the regional population, the smaller the reduction in oncological treatments lost was observed (β = −0.614, p ≤ .001). In contrast, a greater number of acute beds compared to non-acute beds was associated with an increase in the number of oncological treatments lost (β = 0.504, p ≤ .01).

In the second model, aimed at analyzing the workforce domain, our results showed that the higher the turnover compensation index, which indicates a workforce hiring expansion policy, the lower was the number of lost health services for oncological patients, as demonstrated by the coefficient of the variable (β = −0.01,346, p ≤ .01). Continuing the analysis of workforce decisions, our model highlighted that the higher the workload, the higher was the number of oncological treatments dropped due to COVID-19 (β = 0.038, p ≤ .01).

In the third model, dedicated to the analysis of variables related to the service delivery domain, we found that the lower was the number of community health centers per population, the higher were the oncological treatments lost (β = −0.574, p ≤ .001). Finally, we found no significant effect for the number of oncological networks activated by the region.

Discussion

With the aim of exploring the effect of regional health policies in terms of governance, workforce, and health service delivery, we provide evidence that pre-existing characteristics of regional health systems (i.e., slow variables)^4,5,7 influence their resilience, in our study measured through their ability to continue to provide oncological care.

Our results confirm that the slow variables influence the ability of regional health systems to cope with challenging pressures, thus causing them to react to the emergency with different degrees of resilience.^6,7,11,12 We demonstrate that a well-structured governance model, efficient workforce planning strategies, and well-developed delivery system have contributed to maintain oncological services during the first wave of COVID-19 pandemic.¹⁰

Within the Governance domain, which encompasses the structure and processes of governing alongside actions that ensure the adequacy and appropriateness of care, our findings highlight the significance of accountability. Specifically, regional systems that display a high level of accountability (i.e., low cost per core benefit) exhibit greater resilience. This resilience stems from their enhanced capacity to utilize resources efficiently, ensuring both accessibility and equity in health services. Another critical aspect of the governance domain is the adequacy and appropriateness of care, coupled with the widespread provision of health services. We investigated the impact of achieving the right balance between acute and non-acute beds on resilience. Regional health services that maintain a balanced ratio between these two levels of care managed the pandemic more effectively. They were able to respond swiftly not only to patients in acute care beds but also to those requiring other types of care, such as long-term and rehabilitation beds. As a result, they ensured the continuity of care for all patients throughout the emergency.

In testing our second research hypothesis, we considered the workforce domain. In particular, we focus our attention on quantity and quality of personnel. These characteristics are in fact largely recognized to be fundamental to guarantee appropriateness, efficacy and efficient services. Literature has extensively demonstrated that the lack of human resources constitutes a problem in normal conditions, but it is undoubtedly a greater problem in emergency circumstances.²⁸ During the first wave of COVID-19, healthcare workers on the front lines were placed under strain in dealing with this unexpected event.³⁷ The “new normal” imposed by the events³⁸ obliged the systems to react and adapt to the COVID-19 struggle with severe repercussions on the staffing ratio. These challenges involved all the regional governments, and their successful reactions were based on human resources management policies adopted before the emergency. Resilient regions adopted robust policies aimed at increasing their ability to avoid shortages of health personnel.³⁹ Healthcare regional systems that promoted policies of expansion increased their degree of resilience since they were able to respond to the emergency with the right amount of workforce.⁴⁰ Our results also highlight the importance of workload, which refers to the amount of work to be done by a worker in a specified period. When the number of patients discharged by each physician is high, it can negatively affect the resilience of the system because professionals are obliged to take care of many patients at the same time. This is an organizational problem, but its relevance was heightened in the pandemic era when the number of patients increased due to the virus. The stress to which all the regional health systems were put hit more deeply those systems in which the physicians had a high workload even before the pandemic.

Finally, in our third research hypothesis we focused on the service domain, which measures the capacity of a system to efficiently deliver healthcare services. Specifically, we focus on the variables able to measure the continuity of care,⁴¹ which is a key factor for increasing resilience. Even in non-emergency conditions, continuity of care allows health systems to avoid inappropriate health services, thus reducing costs and increasing the perceived quality of care.³⁰ The ability of regional governments to develop a structured continuity of care system equipped with outpatient services organized around patients’ needs,³¹ such as community health centers, increased their resilience during the COVID-19 pandemic. At the start of the pandemic, regional governments that had developed strong population surveillance programs and were able to manage patients with mild or moderate illness were not congested with COVID-19 patients, making it possible to dedicate the necessary space for them while avoiding the delay or cancellation of non-COVID services. Similarly, the availability of pre-existing options for patients who were discharged but still needed of rehabilitation and support^13,33 made acute hospitals available for both COVID-19 and non-COVID-19 patients.

Interestingly, the presence of a formal clinical regional network among healthcare organizations did not impact resilience. We believe, based on prior evidence in healthcare, that this was due to the formal adoption of the network without its actual implementation. This phenomenon, known as decoupling⁴² is prevalent within institutionalized contexts such as healthcare⁴³: organizations in highly institutionalized environments are primarily interested in the social benefits of adhering to legitimate standards rather than in the actual implementation of these standards.⁴⁴

Our findings contribute theoretical advancements to organizational literature by examining the demographic evolution of organizational populations influenced by environmental dynamics.⁴⁵ This perspective suggests that the environment refines structural solutions enabling an organization to respond to external pressures.⁴⁵ In alignment with the concept of natural selection, organizations that can identify emerging needs and adapt to environmental changes are more likely to survive,⁴⁵ while those unable to adapt face elimination.

Our research reveals that regional governments which dynamically adjusted their policies and practices during the initial wave of the pandemic exhibited greater resilience, effectively meeting the burgeoning needs of their populations in governance design, workforce policies and planning strategies, and organization of service provisions. Conversely, those regional systems displaying rigidity, characterized by inertia, fell short during the emergency, delivering care less efficiently.

These insights present a perspective that is not definitive or comprehensive, indicating the need for further research. Specifically, it is worth examining whether aspects of healthcare services may not only be resilient but also strengthened through exposure to stressors, thereby exhibiting antifragility.⁴⁶

Implications

Our findings have a number of policymaking implications for present and future decision-makers aiming to make health systems more resilient, summarized in Table 4. Policymakers should make efforts to modernize health systems by embracing policies that enhance sustainability and resilience. Four specific implications can be derived from this analysis. First, at the governance level, we believe that the careful planning and efficient use of financial and structural resources will contribute substantially to ensuring a robust system that is able to face a crisis. Second, regarding the workforce, the main implication of our findings is related to workforce planning, which needs to be able to manage at least two phenomena: workforce shortages and workload. In order to build health system resilience, regional governments need to be equipped with an adequate number of healthcare professionals to minimize the impact of shocks on ordinary activities as well as avoid excessive workload. With respect to the delivery of health services, making health systems more resilient will require policymakers to invest in primary and territorial care. In this sense, is crucial that manager not only formally adopt organizational innovation due to conform to external pressure but also implement these innovations.

Table 4.

Implication for health managers.

Implication #1	Correct planning and efficient use of financial and structural resources support health system resilience
Implication #2	Workforce planning may support resilience, avoiding excessive workload during crisis and reducing the impact on ordinary activities
Implication #3	Health systems based on an strong primary and territorial assistance are better equipped in case of shocks, since they minimize the impact on hospital care
Implication #4	Manager should not only formally adopt organizational innovation due to conform to external pressure but also implement this innovations

Finally, it is of quintessential importance that at the central level, the health government bodies, which are ultimately responsible for ensuring equitable and accessible care in all parts of the country, strive to manage the differences between regional health services by facilitating the best practices, thus generating a spillover effect as more regions adopt these practices.

Limitations

Our study presents a number of limitations that future research may want to address. First, we selected three domains that vary across regional health services and did not take into consideration domains that are defined at a central level (i.e., the level of funds). However, our aim was to provide a methodological approach to evaluate the dimensions on which health systems should focus to guarantee their ability to react promptly to unexpected crises.

Second, we used a bundle of dimensions to test each research hypothesis. Although this is an unusual approach, we demonstrated in the hypothesis formulation that each domain is composed of a number of different variables that collectively describe the domain and can hence be used to analyze the domain.

Our third limitation is related to the data sample, which focused only on the first wave of the COVID-19 pandemic. We know that the three subsequent waves have also been important and challenging for the health systems; however, the first was without a doubt the most unexpected and therefore the most relevant wave for testing the health system resilience. Future studies will be directed at performing a longitudinal analysis incorporating data from the other waves, also considering the geographical location of Regions (North, Center, South Italy).

Moreover, in order to test the resilience of regional healthcare services, we focused on oncological care, because cancer is a leading cause of death worldwide. Despite our results can be generalized to other time dependent pathology, we did not consider them in the present study, Future studies will be aimed to test our hypothesis also considering other illness, such as chronic condition.

Finally, our results may have been affected by the peculiarities of the Italian NHS. However, given that its characteristics are similar to all public systems and specifically to the UK NHS, we can affirm that our results are generalizable. Considering that the COVID-19 pandemic has unfortunately affected the entire world, this generalizability is significant. The existence of a model to test the resilience of health systems could be useful in other contexts.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Luca Giorgio

Federica Morandi

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