Article Text

Original research
Heterogeneous impact of the COVID-19 pandemic on lung, colorectal and breast cancer incidence in Hungary: results from time series and panel data models
  1. Peter Elek1,2,
  2. Marcell Csanádi3,
  3. Petra Fadgyas-Freyler4,
  4. Nóra Gervai5,
  5. Rita Oross-Bécsi6,
  6. Balázs Szécsényi-Nagy7,8,
  7. Manna Tatár9,
  8. Balázs Váradi10,11,
  9. Antal Zemplényi12
  1. 1 Institute of Economics, Centre for Economic and Regional Studies, Budapest, Hungary
  2. 2 Institute of Economics, Corvinus University of Budapest, Budapest, Hungary
  3. 3 Syreon Research Institute, Budapest, Hungary
  4. 4 Department of Health Economics, Corvinus University of Budapest, Budapest, Hungary
  5. 5 independent scholar, Budapest, Hungary
  6. 6 Mediconcept, Budapest, Hungary
  7. 7 Health Services Management Training Centre, Semmelweis University, Budapest, Hungary
  8. 8 Community Health Centre, Gyál, Hungary
  9. 9 Centre for Health Technology Assessment, Semmelweis University, Budapest, Hungary
  10. 10 Budapest Institute for Policy Analysis, Budapest, Hungary
  11. 11 Department of Economics, Eötvös Loránd Tudományegyetem, Budapest, Hungary
  12. 12 Centre for Health Technology Assessment and Pharmacoeconomic Research, Pécsi Tudományegyetem, Pécs, Hungary
  1. Correspondence to Dr Peter Elek; elek.peter{at}krtk.hu

Abstract

Objective During the COVID-19 pandemic, health system resources were reallocated to provide care for patients with COVID-19, limiting access for others. Patients themselves also constrained their visits to healthcare providers. In this study, we analysed the heterogeneous effects of the pandemic on the new diagnoses of lung, colorectal and breast cancer in Hungary.

Design Time series and panel models of quarterly administrative data, disaggregated by gender, age group and district of residence.

Participants Data for the whole population of Hungary between the first quarter of 2017 and the second quarter of 2021.

Main outcome measures Number of patients newly diagnosed with lung, colorectal and breast cancer, defined as those who were hospitalised with the appropriate primary International Classification of Diseases Tenth Revision diagnosis code but had not had hospital encounters with such a code within the previous 5 years.

Results The incidence of lung, colorectal and breast cancer decreased by 14.4% (95% CI 10.8% to 17.8%), 19.9% (95% CI 12.2% to 26.9%) and 15.5% (95% CI 2.5% to 27.0%), respectively, during the examined period of the pandemic, with different time patterns across cancer types. The incidence decreased more among people at least 65 years old than among the younger (p<0.05 for lung cancer and p<0.1 for colorectal cancer). At the district level, both the previously negative income gap in lung cancer incidence and the previously positive income gap in breast cancer incidence significantly narrowed during the pandemic (p<0.05).

Conclusions The decline in new cancer diagnoses, caused by a combination of supply-side and demand-side factors, suggests that some cancer cases have remained hidden. It calls for action by policy makers to engage individuals with high risk of cancer more in accessing healthcare services, to diagnose the disease early and to prepare for effective management of patient pathways from diagnosis to survival or end-of-life care.

  • COVID-19
  • EPIDEMIOLOGY
  • Health policy
  • ONCOLOGY

Data availability statement

Data may be obtained from a third party and are not publicly available. The administrative inpatient care data on the quarter–gender–age group–district level were obtained from the National Health Insurance Fund Administration (NEAK). District-level income was available from the National Regional Development and Spatial Planning Information System (TeIR) through the Databank of the Centre for Economic and Regional Studies (KRTK). The authors do not have permission to share the data to third parties.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

Statistics from Altmetric.com

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

Strengths and limitations of this study

  • The effect of the COVID-19 pandemic on the incidence of the three most common types of cancer was examined in Hungary based on nationwide administrative data until June 2021.

  • The aggregate effect was estimated with time series models to control for previous trend and seasonality, while the heterogeneous effects by gender, age group and the income level of the district of residence were estimated with panel data models.

  • Causal effects of the potential supply-side and demand-side mechanisms (that are outlined in the paper) could not be established.

  • Disease stages and longer term outcomes such as mortality could not be examined because of the lack of data.

Introduction

The COVID-19 pandemic is a huge challenge for healthcare systems and requires the highest level of resilience in health policy decision-making. It is a learning process with countless pandemic-related issues to be addressed, very often involving trade-offs coupled with high level of uncertainty.1 For example, it had gotten to the point where, at least temporarily, a choice had to be made between treating patients with or without COVID-19, because the overburdened healthcare systems did not have the capacity to do both.2 Effectively managing the pandemic requires thinking in terms of a complex system, with a high number of factors that are not linearly linked.3 For example, preventive measures (wearing a mask, isolation, quarantine) and the proportion of the population vaccinated can affect the number of patients with COVID-19, which then affects the necessary administrative restrictions on healthcare, which in turn can influence the availability and quality of services for patients without COVID-19. In this indirect context, reserving inpatient capacity to treat patients with COVID-19, which was a policy tool in many countries, and self-limiting patients’ access to healthcare providers play an important role. To make the impact of such decisions clearer, ex post analyses of the consequences can provide a scientific basis to the management of the crisis in the future.1

Lessons learnt are of paramount importance in the case of serious chronic diseases such as cancer, which cannot be lumped together with other deferred care because of health priorities. Cancer is a complex disease that requires patients to undergo different types of procedures and laboratory or imaging tests to be diagnosed and staged. To achieve the maximum benefit for patients, these services must work in a coordinated manner, with a high level of patient engagement and compliance. Cancer survival can be increased by detecting tumours in the asymptomatic state, that is, by screening programmes, and by rapid and effective investigation of suspected tumours, which can be enhanced by effective management of the patient cancer pathways.4–6 Failure to do so can lead to lower quality of care and poorer outcomes for patients.7 Due to the control measures of the COVID-19 pandemic,8 new patients with putative cancer are exposed to a range of harms, including suspension of screening and prevention efforts, delays in timely diagnosis and staging of new patients and delays in initiation of therapy.9

According to a recent study, the impact of the COVID-19 pandemic on cancer care has been varying across countries.10 In New Zealand, for example, the number of cancer diagnoses fell by 40% compared with previous years during the national shutdown in March to April 2020, before returning to preshutdown levels in the following months.11 In contrast, in Catalonia, Spain, and in Belgium, where reductions of similar magnitude occurred, the historical figures were not reached after the end of the lockdown.12 13 In Poland, unlike in other countries, a recent study showed no decline in the number of oncological diagnoses at hospitals during the first wave.14

In Hungary, a European country with 9.7 million inhabitants, cancer incidence (623 new cases per 100 000 people) is 10% higher, and cancer mortality (330 deaths per 100 000 people) is 25% higher than the European Union average. The three most common types are lung, colorectal and breast cancer.15 Population-level breast cancer screening has been available for women aged 45–64 since 2002,16 while colorectal cancer screening was initiated for people aged 50–70 in 2018.17

The Hungarian healthcare system is highly centralised. The state has exclusive powers to set the strategic direction, control funding, define the benefits package and issue and implement regulations. The country has a single health insurance fund. Public outpatient and inpatient services are formally free of charge at the point of care, although—as in other Central and Eastern European countries18—informal payments had been a constant challenge before they were made illegal and sanctioned in 2021. There is a growing private outpatient care sector as well.15

After a relatively mild first wave, Hungary was hit particularly hard—in international comparison—by the second (2020q4) and the third (2021q1–2021q2) waves of the COVID-19 pandemic, resulting in the death of 30 000 people (0.3% of the population) until June 2021.19 (Throughout the paper, q1, q2, q3 and q4 stand for the first, second, third and fourth calendar quarters of the year, respectively.) The aims of the corresponding health policy measures were to contain the spread of the virus and to reallocate resources to COVID-19 care. These included the suspension of population-level cancer screening programmes (such as breast and colorectal screening) altogether for about 3 months (between 16 March and 1 June 2020 and between 9 April and 29 April 2021) and of elective and 1-day surgeries for even longer periods, although oncological diagnostic and curative services were exempt from the suspensions. Other important policy measures included the replacement of performance-based reimbursement with global budgets during the whole pandemic to maintain the financial sustainability and solvency of healthcare providers. In 2021, beyond the already mentioned ban on informal payments, significant increases in physicians’ salaries were introduced.20

Despite the large direct and indirect effects on the healthcare system, no systematic mapping has taken place yet on how the diagnosis and care of patients with cancer evolved during the pandemic in Hungary. (For a specific analysis of the effect of lower screening activity on breast cancer incidence, total and partial mastectomy rates see ref 21.) To understand the impact of the COVID-19 pandemic on cancer care, it is important to examine the trends in the number of newly diagnosed cases and the areas where health policy interventions may be needed.

The aim of our study was to analyse the heterogeneous effects of the COVID-19 pandemic on the new diagnoses of lung, colorectal and breast cancer until June 2021 in Hungary by gender, age group and district-level income.

Materials and methods

Data

We used administrative inpatient care data that were collected by the National Health Insurance Fund Administration (NHIFA (NEAK)), the single payer of the Hungarian healthcare system, covering the whole population of the country (9.7 million people). We defined the number of patients with newly diagnosed cancer as those who were hospitalised with the appropriate primary International Classification of Diseases Tenth Revision (ICD-10) diagnosis code (C34 for lung cancer, C18–C21 for colorectal cancer and C50 for breast cancer) but had not had hospital encounters with such a code within the previous 5 years. The data were obtained by quarter (between 2017q1 and 2021q2), disaggregated by gender, 5-year age group and district of residence.

We note that the financing (and not register) data at hand did not provide more detailed information such as disease stage or subtype within the major groups of lung, colorectal or breast cancer. However, similar NHIFA data were applied in the past fruitfully to estimate cancer incidence in Hungary (see eg, ref 22 for lung cancer). Also, we note that although cancer screening and diagnostic procedures are practised in the private sector as well, essentially all of the main oncological treatment modalities (surgery, chemotherapy, radiation therapy) are carried out in the public sector and coded as inpatient data. Hence, patients who were diagnosed in the private sector appear in our definition when they first undergo treatment in the public sector.

Hungary is composed of 197 districts, with an average population of about 50 000 people. (Specifically, Budapest, the capital, consists of 23 districts.) For the district-level analysis, the data were merged to the year 2017 value of annual per-capita taxable income of the district, which was obtained from the National Regional Development and Spatial Planning Information System (TeIR).

Beyond the crude incidence values in the aggregate as well as the gender-specific and age-specific analyses, we used the gender-standardised and age-standardised incidence (with the 2017 population structure of Hungary as the baseline) in the district-level estimations. The (calendar year-specific) size of the population of the corresponding gender, 5-year age group and district was available from the TeIR system.

Statistical analysis

First, we performed time series modelling of the number of newly diagnosed lung, colorectal and breast cancer cases by estimating

Embedded Image (1)

where t denotes time (quarter), Embedded Image is the number of new cases, Embedded Image is the j-th calendar quarter (the first quarter being the baseline) and Embedded Image ) are dummy variables for the quarters of the pandemic. The parameters Embedded Image show the quarter-specific deviation from the usual trend and seasonality during the pandemic. Finally, Embedded Image is the error term. The models were estimated with ordinary least squares (OLS) as the error terms turned out to be serially uncorrelated in each model. Then, OLS provides unbiased estimates of the parameters (with appropriate SEs).

Second, we estimated the following equations, where ρ measures the overall effect during the first five quarters of the pandemic, between 2020q2 and 2021q2:

Embedded Image (2)

Third, we investigated heterogeneous effects by gender and age group by estimating equations

Embedded Image (3)

where g denotes gender (male or female) or age group (45–64 or 65+ years), hence Embedded Image measures the overall change of the number of new cases by group during the five quarters of the pandemic. (We did not examine the 0–44 years age group specifically because of the small sample size (only 1.8%, 3.4% and 11.7% of new patients with lung, colorectal and breast cancer, respectively, were below 45 years between 2017 and 2019). We note that the aggregate analysis contains these patients as well.) We also estimated Embedded Image and Embedded Image in difference-in-difference specifications and evaluated their statistical significance (ie, whether the effects are the same across gender or age group).

Fourth, to investigate how the effect of the pandemic varies by district-level income, we classified the districts into three income quantiles (tertiles) and showed the time series of the age-adjusted and gender-adjusted incidences by tertile. Afterwards, to formally estimate the heterogeneous effect by district-level income, we fitted the following fixed-effects models on district-quarter panel data:23

Embedded Image (4)

where i is district, t is time (quarter) and, beyond the notations of equation (1), Embedded Image is the adjusted incidence (per 100 000 inhabitants), Embedded Image is the year 2017 logarithmic district-level per-capita income and Embedded Image are district fixed effects. (Here we used Embedded Image instead of Embedded Image because of zeros in some district-quarter observations.) The parameter of interest is Embedded Image , which shows the relative effect of the pandemic in a higher income district compared with a lower income one, that is, how a 1% larger average income of the district affected the change of the incidence during the pandemic.

Patient and public involvement

Due to the nature of the study, patients or the public were not involved in the design, or conduct, or reporting, or dissemination plans of our research.

Results

Aggregate, age-specific and gender-specific effects

According to the upper panel of figure 1, the quarterly number of new cases of the three major types of cancer was between 1800 and 2400 before the pandemic, corresponding to annual unadjusted incidence rates of 78–92 per 100 000 inhabitants. The lower panel of the figure shows the changes of the new case numbers in 2020–2021 compared with the trend and seasonality of the preceding 3 years (ie, the parameter estimates of the pandemic dummies from equation (1)), with 95% CIs. (Details of the estimated models are given in online supplemental appendix table A1.) The incidence of colorectal and breast cancer decreased by 26.9% (95% CI 18.5% to 34.4%) and 30.0% (95% CI 24.1% to 35.4%), respectively, in 2020q2 and remained only slightly below the historical trend in 2020q3. Afterwards, breast cancer incidence reached its usual level in 2020q4, but colorectal cancer incidence still remained significantly lower. Then, the incidence of both types of cancer fell short of the historical trend by 20%–25% in the first half of 2021. Meanwhile, the decline of lung cancer incidence was more flat, being below the historical trend by 10%–16% during each quarter.

Supplemental material

Figure 1

Number of new cancer cases (2017q1–2021q2) and deviation from the trend and seasonality (2020q1–2021q2). The lower panel shows the parameter estimates of the dummies for 2020q1–2021q2 from the logarithmic model (1) (displayed in online supplemental appendix table A1), transformed to the percentage scale, with 95% CIs. Controls: linear trend and seasonal dummies. Period: 2017q1–2021q2. Number of quarters: 18.

Overall, as the upper panel of table 1 shows, the incidence of lung, colorectal and breast cancer decreased by 14.4% (95% CI 10.8% to 17.8%), 19.9% (95% CI 12.2% to 26.9%) and 15.5% (95% CI 2.5% to 27.0%), respectively, in the first five quarters of the pandemic, between 2020q2 and 2021q2.

Table 1

Regression results for the change of incidence during the pandemic aggregately and by gender, age group and district-level income

Figure 2 shows the cumulative number of new cases between 2020q1 and 2021q2, compared with two earlier periods (2017q1–2018q2 and 2018q1–2019q2). During this time, around 5000 fewer people than usual (around 50 fewer per 100 000 inhabitants) were diagnosed with the three major types of cancer combined.

Figure 2

Cumulative number of new cancer cases during the pandemic and in previous periods.

According to figure 3, the number of new cases declined more substantially for the population aged 65+ years than for the population aged 45–64 years; according to the second panel of table 1, the difference was 10–16 percentage points and was statistically significant for lung cancer (p<0.05) and colorectal cancer (p<0.1). On the other hand, the third panel of table 1 shows that there was no statistically significant difference across genders in the decrease of cancer incidence.

Figure 3

Number of new cancer cases by age group (2017q1–2021q2).

District-level effects

In Hungary, average income differences across districts are substantial, with a 2.6-fold difference between the richest and the poorest district, and a 1.8-fold difference between the 95% and 5% quantiles in terms of district-level taxable income. Figure 4 shows the time series of gender-standardised and age-standardised cancer incidence in three quantiles (tertiles) defined by district-level income. Before the pandemic, lung cancer incidence was higher and breast cancer incidence was lower in the lower income districts compared with the higher income ones. However, during the pandemic, lung cancer incidence decreased to a greater extent and breast cancer incidence to a smaller extent in the lower income districts (compared with the higher income ones), hence the income gradient (which was negative for lung cancer and positive for breast cancer) narrowed for both types of cancer. According to the lower panel of table 1, a 1% higher district-level income was associated with a 0.044 smaller decrease for lung cancer (95% CI 0.005 to 0.083) and a 0.045 larger decrease for breast cancer (95% CI 0.004 to 0.086) quarterly incidence per 100 000 inhabitants during the pandemic. (For comparison, the average quarterly incidence was 19–21 per 100 000 inhabitants.) Meanwhile, no clear pattern (and no statistically significant association) emerged for colorectal cancer.

Figure 4

Gender-adjusted and age-adjusted incidence (per 100 000 inhabitants) by district-level income tertile for lung, colorectal and breast cancer (2017q1–2021q2).

Discussion

Our study provided a detailed analysis of the number of diagnoses of the three most common types of cancer in Hungary during the COVID-19 pandemic and considered the changes by age, gender and income level of the district of residence.

Overall, we found a 15%–20% decrease in the number of cases between 2020q2 and 2021q2. While in principle it is possible that the true cancer incidence also decreased somewhat due to COVID-19, we conclude, in line with the experience of several other countries,10 that the significant drop in the number of diagnoses is mostly due to undiagnosed cases. Indeed, in the first five quarters of the pandemic, only around 0.5 percentage point of the decrease of observed case numbers could be explained with COVID-19 mortality even in the 65+ years age group and a negligible share in the younger population. (Between 2020q2 and 2021q2, less than 0.1% of the population aged 0–64 years and around 1.4% of the population aged 65+ years of Hungary died from COVID-19, but two-thirds of these deaths occurred in 2021, which cannot explain the drop in diagnoses in the earlier quarters.) Although we acknowledge that, beyond age, some other variables such as lifestyle or comorbidities may simultaneously increase the risk of cancer and COVID-19 death, these background factors may explain only a minor additional part of the decrease of cancer incidence. For instance, smoking, which drastically increases the risk of lung cancer, increases the COVID-19 mortality rate only moderately (OR=1.35).24

The drop in newly diagnosed cancer cases was less than what was observed with comparable methods in Catalonia, a region that took a worse hit from COVID-19 than Hungary during the first wave (11%–15% in Hungary vs 34% in Catalonia in March to September 202012), but was larger than in Belgium (7%–14% vs 6% in 202013). What is even more troubling from a health policy point of view is the fact that, unlike in New Zealand,11 at least during the period under scrutiny, with the exception of breast cancer in 2020q4, we did not observe the health system catching up fully in diagnosing putative undiagnosed cancers in the breaks between the pandemic waves. Instead, cancer incidence remained below its historical average up until 2021q2, the end of our observation period.

This is all the more of concern because it did not happen on purpose. While some health policy measures were taken to free up capacity to deal with the pandemic, cancer diagnosis and care were not among them. In what follows, we present some possible causal mechanisms—both on the supply and the demand side of healthcare—that can explain the decrease and the lack of subsequent rebound in cancer diagnosis and therapy.

First, on the supply side, as already mentioned above, organised breast cancer screening was suspended twice during the pandemic. As a result, the number of mammography examinations decreased by 68% in 2020q2, was around the normal level in 2020q3 and then decreased by 20%–35% between 2020q4 and 2021q2, which contributed to the reduction in new breast cancer diagnoses and mastectomy surgeries.21 (Specifically, ref 21 estimated the causal effects of lower screening activity during the pandemic on breast cancer patient pathways in a regression discontinuity framework.)

Second, the reallocation of healthcare provider capacities to COVID-19-related care (ie, the involvement of medical personnel and equipment in COVID-19 intensive care units, vaccination, etc) may have had an indirect impact on the number of interventions performed. A proportion of the physicians who carried out diagnostic procedures were assigned to other COVID-19-related care. The workload for radiologists was particularly heavy during COVID-19 diagnostics, for which CT was used, so this may have resulted in limited access to imaging in other areas of care. Staff availability was further limited by COVID-19 diagnosis or quarantine among healthcare workers.

Third, the performance-based reimbursement techniques for specialist outpatient and inpatient care that are normally linked to patient visits and providers’ activities (procedure codes within the German point system in outpatient care and diagnosis-related groups in inpatient care) were suspended at the very beginning of the pandemic in March 2020 and since then have remained so. Instead, in order to maintain financial sustainability and the solvency, new prospective budgets were assigned to all providers based on the performance of previous years. Hence, the financial incentives25 for providers’ performance (higher patient and case numbers, more reported interventions result in more revenues) have literally disappeared. Understandably, such a change in financial incentives on its own may have had a negative effect on the activity of healthcare providers.

Fourth, a new law on employment conditions of healthcare personnel has been in force since March 2021.20 Several provisions of this new regulation, which was a crucial step regarding the modernisation of the healthcare sector in Hungary, have an effect on healthcare delivery, for example, rules on incompatibility between private and public sector employment and penalisation of informal out-of-pocket payments, which had previously had a major impact on the organisation of patient pathways and caused inequality in the access to high-quality care.18 The ban on informal payments was accompanied by a one-off, substantial wage increase, but no performance incentive scheme was introduced to motivate more efficient care. During the third pandemic wave, this may have negatively affected finding patients with cancer who had been undiagnosed.

Fifth, on the demand side of the healthcare system, patients’ readiness to visit a doctor could also decrease. Indeed, there is evidence that symptomatic patients have avoided healthcare providers due to fear of COVID-19 infection, leading to increased morbidity and mortality.26 A recent study showed that the most significant concern expressed by oncology patients about the COVID-19 pandemic was fear.27

We note that although the second and third waves of the pandemic resulted in significantly more COVID-19 cases and deaths in Hungary, the decline in the new diagnoses (at least in breast and colorectal cancer) was more significant during the first wave. Hence, the effect of the pandemic on cancer incidence is heterogeneous over time and thus it may be difficult to extrapolate the short-term and medium-term observations into the future.

We consider it a particular strength of our paper that we could use a large set of administrative data covering the period until June 2021—a more extended interval than used in the international papers reviewed above or made publicly available specifically for Hungary. (The aggregate number of new patients with cancer in Hungary, calculated with a slightly different methodology than ours, is available from the National Cancer Register for 2020.28 According to those data, the total number of new cancer diagnoses decreased by 13% in 2020 (compared with 2019), while in our calculation the combined number of the three most frequent cancer types decreased by 12% in that year.) Also, based on the data at hand, we could examine heterogeneities by age group, gender and the income level of the district of residence. The estimated larger decrease for the older than for the younger population is in line with other papers12 13 and shows that the combined effect of the mechanisms outlined above was stronger there.

Our district-level analysis gives a more nuanced picture on socioeconomic heterogeneity than a previous study did12 because, having had access to data on 197 districts with vastly different average incomes and a population of 50 000 people on average, we had enough statistical power to estimate the effect on different cancer types separately. We note that district-level analyses have already proved fruitful for establishing socioeconomic heterogeneities in other COVID-19-related outcomes as well in Hungary.29

Time series data on cancer incidence trends show that lung cancer incidence was already declining before the pandemic, which might be explained by the fact that smoking among men has decreased in recent decades in Hungary (while smoking among women has stagnated or slightly increased).30

Our study also has some limitations. First, the causal effects of the aforementioned mechanisms could not be separated based on the available semiaggregate data. Second, disease stages and subtypes within the main groups of lung, colorectal and breast cancer were not available because of the ICD-based data and definitions. Third, longer term outcome measures such as mortality could not be examined because of the limited time span since the outbreak of the pandemic. Finally, the uncertainty of the parameter estimates is sometimes too large to draw strong conclusions about the relative magnitude of the effects.

Turning to policy conclusions, the decline in the number of newly diagnosed patients due to delayed or unavailable care is a risk for public healthcare systems as the global cancer burden is rising.31 Our findings can inform health policy actors about the projected excess cancer cases, expected interventions hence increased morbidity and mortality in the years to come due to delayed diagnosis during the pandemic.

As during the early waves of the pandemic numerous policy decisions had to be made uninformed, ‘in the fog of war’, the impact of these decisions on patient care and outcomes deserves further investigation to develop an evidence-based policy approach for the future. On the other hand, the fact that patients themselves have restricted their visits to healthcare providers out of fear calls for action by policy makers to engage patients with potential cancer in accessing healthcare services, to diagnose the disease early and to prepare for effective management of patient pathways from diagnosis to survival or end-of-life care.

Data availability statement

Data may be obtained from a third party and are not publicly available. The administrative inpatient care data on the quarter–gender–age group–district level were obtained from the National Health Insurance Fund Administration (NEAK). District-level income was available from the National Regional Development and Spatial Planning Information System (TeIR) through the Databank of the Centre for Economic and Regional Studies (KRTK). The authors do not have permission to share the data to third parties.

Ethics statements

Patient consent for publication

Acknowledgments

The authors would like to thank the useful comments made by the members of the COVID-19 Working Group of the Hungarian Health Economics Association. Balázs Mayer provided excellent research assistance.

References

Supplementary materials

  • Supplementary Data

    This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

Footnotes

  • Contributors All authors contributed to the design of the study and the interpretation of the results. PE, PF-F and MT had access to the data and performed the statistical analysis. MC, NG, RO-B, BS-N, BV and AZ drew the policy conclusions from the results. PE, MC, BV and AZ compiled the first draft of the manuscript, with contributions from the other authors. All authors critically revised the manuscript and approved its current version. PE acted as guarantor.

  • Funding PE was supported by the János Bolyai Research Scholarship and by the Lendület Research Programme (LP2018-2/2018) of the Hungarian Academy of Sciences. PE and BV were supported by OTKA (National Research, Development and Innovation Fund) research (grant number: FK 134573).

  • Competing interests None declared.

  • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.