The Challenge Of Estimating The Direct And Indirect Effects Of Covid 19 Interventions Toward An Integrated Economic And
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ifo Working Paper No. 385, dated November 2022 and published by the ifo Institute – Leibniz Institute for Economic Research at the University of Munich, titled The Challenge of Estimating the Direct and Indirect Effects of Covid-19 Interventions – Toward an Integrated Economic and Epidemiological Approach. The paper, by economists and epidemiologists from institutions including the ifo Institute and the Helmholtz Centre for Infection Research, is a narrative review based on targeted literature searches. It sets out direct disease burden from COVID-19 and indirect burdens from overloaded health systems, economic damage and social distancing. Its discussion of death counts cites crude infection fatality risk estimates of between 0.5 and 2.4%. The paper notes it was accepted for publication in Economics & Human Biology and closes with a reference list.
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Dorn, Florian et al.
Working Paper
The challenge of estimating the direct and indirect effects
of Covid-19 interventions: Toward an integrated economic
and epidemiological approach
ifo Working Paper, No. 385
Provided in Cooperation with:
Ifo Institute – Leibniz Institute for Economic Research at the University of Munich
Suggested Citation: Dorn, Florian et al. (2022) : The challenge of estimating the direct and indirect
effects of Covid-19 interventions: Toward an integrated economic and epidemiological approach, ifo
Working Paper, No. 385, ifo Institute - Leibniz Institute for Economic Research at the University of
Munich, Munich
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ifo 385
2022
WORKING November 2022
PAPERS
The Challenge of Estimating the
Direct and Indirect Effects of
Covid-19 Interventions – Toward an
Integrated Economic and
Epidemiological Approach
Florian Dorn, Berit Lange, Martin Braml, David Gstrein, John L. Z. Nyirenda,
Patrizio Vanella, Joachim Winter, Clemens Fuest, Gérard Krause
Imprint:
ifo Working Papers
Publisher and distributor: ifo Institute – Leibniz Institute for Economic Research at the
University of Munich
Poschingerstr. 5, 81679 Munich, Germany
Telephone +49(0)89 9224 0, Telefax +49(0)89 985369, email ifo@ifo.de
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An electronic version of the paper may be downloaded from the ifo website:
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ifo Working Paper No. 385
The Challenge of Estimating the Direct and Indirect Effects
of Covid-19 Interventions – Toward an Integrated Economic
and Epidemiological Approach*
Abstract
Decisions on public health measures to contain a pandemic are often based on parameters such as
expected disease burden and additional mortality due to the pandemic. Both pandemics and non-
pharmaceutical interventions to fight pandemics, however, produce economic, social, and medical
costs. The costs are, for example, caused by changes in access to healthcare, social distancing, and
restrictions on economic activity. These factors indirectly influence health outcomes in the short- and
long-term perspective. In a narrative review based on targeted literature searches, we develop a com-
prehensive perspective on the concepts available as well as the challenges of estimating the overall
disease burden and the direct and indirect effects of Covid-19 interventions from both epidemiological
and economic perspectives, particularly during the early part of a pandemic. We review the literature
and discuss relevant components that need to be included when estimating the direct and indirect
effects of the Covid-19 pandemic. The review presents data sources and different forms of death
counts, and discusses empirical findings on direct and indirect effects of the pandemic and interven-
tions on disease burden as well as the distribution of health risks.
JEL Code: I15, I18, I19
Keywords: Covid-19, pandemics, health outcomes, disease burden, non-pharmaceutical interventions,
economic decline, social distancing, healthcare access, integrated approach
Florian Dorna,d,e,1,**, Berit Langeb,g,h,**, Martin Bramla,c, David Gstreina,d, John L.Z. Nyirendab,f, Patrizio
Vanellab,g,i, Joachim Winter d,e, Clemens Fuesta,d,e, Gérard Krauseb,g,h
a
ifo Institute – Leibniz Institute for Economic Research, Munich, Germany, b Epidemiology Department, Helmholtz
Centre for Infection Research (HZI), Braunschweig, Germany, c World Trade Organization, Economic Research and
Statistics Division, Geneva, Switzerland2, d Department of Economics, University of Munich (LMU), Germany,
e
CESifo Munich, Germany, f University Hospital Freiburg, University of Freiburg, Germany, g Hannover Medical
School (MHH), Germany, h German Center for Infection Research (DZIF), Braunschweig, Germany, i Department of
Health Reporting & Biometrics, aQua-Institut, Göttingen, Germany.
Declarations of interest: None
This paper has been accepted for publication in: Economics & Human Biology, Special Issue:
Economics, Health and Pandemics
* Acknowledgements: We are grateful for funding from the Helmholtz Association within the project “The Helm-
holtz Epidemiologic Response against the Covid-19 Pandemic”.
Note: The collaboration of the interdisciplinary team for this project started in April 2020.
1
Corresponding author: Florian Dorn, ifo Institute, Poschingerstr. 5, 81679 Munich, Germany; Email: Dorn@ifo.de.
2
This paper is not meant to represent the positions or opinions of the Banque de France, of the WTO or its mem-
bers, nor the official position of any WTO staff, and is without prejudice to members' rights and obligations under
the WTO.
1 Introduction
Disease burden and excess mortality are relevant parameters for decision-making on public health
measures to contain a pandemic. During the COVID-19 pandemic, most studies assess the direct
COVID-19 disease burden via data on reported cases and/or estimates of unreported cases (Liu, Magal
et al., 2020; Zhao et al., 2020), transmission parameters (Anastassopoulou et al., 2020; Huang et al.,
2020; Kucharski et al., 2020; Liu, Gayle et al., 2020), case fatality, the number of deaths, excess
mortality (Rajgor et al., 2020; Russell et al., 2020; Spychalski et al., 2020; Wilson et al., 2020), and
potential healthcare burden in terms of bed capacity constraints in intensive care unit (ICU) and hospital
beds (an der Heiden & Buchholz, 2020; Hellewell et al., 2020; Ferguson et al., 2020). Various studies
perform updated and context-specific evidence synthesis based on both statistics and mathematical
models (Burns et al., 2020; Nussbaumer-Streit et al., 2020; Viswanathan et al., 2020). However, those
approaches only cover the actual disease burden and excess mortality during the COVID-19 pandemic
to a limited degree.
Containment measures may decrease the direct effect of the pandemic on mortality and long-term health
consequences (Figure 1). However, both the pandemic and potential non-pharmaceutical measures
against the pandemic generate considerable economic, social, and medical costs. Containment policies
such as the lockdown of entire economic sectors and schools, the disruption of international supply
chains due to border closures, or quarantine orders, also affect the short- and long-term economic well-
being and the health of citizens (e.g., Walsh et al., 2021). For policy advice and modeling of intervention
strategies, it is important to disentangle which costs are caused by the pandemic and which are a result
of strategies of social distancing to fight the pandemic. Debates emphasizing a trade-off between the
fight against the pandemic and its economic and social costs are, however, misleading (an der Heiden
& Buchholz, 2020; Dorn et al., 2022; Hellewell et al., 2020; Ferguson et al., 2020). It is in the interest
of both health and economic well-being to follow a balanced strategy to contain the pandemic (see Dorn
et al., 2022).
Health and economic outcomes are interdependent. On the one hand, economic activity and economic
well-being decrease because people get sick or are afraid of getting sick and therefore limit their
consumption or their labor input. Full economic recovery is therefore impossible without containing the
spread of the virus (e.g., Dorn et al., 2022). On the other hand, unemployment, structural economic
decline, income and learning losses, or social distress due to the pandemic or social distancing measures
have effects on health outcomes and mortality (e.g., Goolsbee & Syverson, 2021). Moreover, the
capacities and resilience of healthcare systems depend on the economic performance of the economy as
well as the financial leeway and the health expenditures of national governments (OECD, 2019).
Therefore, today’s decisions on public health measures to contain the pandemic have both short- and
long-term effects on (economic) well-being, medical care, and the burden of disease.
In this paper, we present an overview of literature and data sources on relevant components and
parameters that need to be considered when estimating the direct and indirect disease burden of the
pandemic and measures to fight the pandemic from both epidemiological and economic perspectives, in
particular during the early part of a pandemic. This is based on the topic knowledge of experts from
infectious disease epidemiology and (health) economics. Moreover, we performed targeted literature
searches using compound search strategies for the relevant topics in epidemiological and economic
databases or paper series (e.g. MEDLINE, EMBASE, Web of Science, PreVIEW, Scholar, CESifo,
2
NBER, CEPR).3 However, we do not provide a systematic review for any sub-topic or meta-analyses to
identify pooled estimates of the direct and indirect effects of various relevant parameters.
Figure 1: Direct and indirect disease burden due to the pandemic and containment policies
In our view, the components that need to be considered when discussing disease burden and excess
mortality due to the pandemic in light of potential public health measures against the spread of the
SARS-CoV-2 virus are the following:
Direct effects:
(A) Direct burden of disease caused by COVID-19, such as people dying from COVID-19 or suffering
long-term consequences
Indirect effects, such as:
(B) Indirect disease burden due to overloading the health system, such as non-COVID-19 patients not
being treated in ICU because of overloaded capacities
3
We employed some criteria for searches in economics and in epidemiology. For example, in (health) economics,
we searched in the following steps: First, we systematically reviewed Covid-19 related articles in leading
working paper series (preprints) in economics (CESifo, CEPR and NBER Working Paper Series). In these
paper series, we searched for the topics that we addressed in our review. These include the search for
combinations of the words “health, deaths, unemployment, education, childcare, economic decline,
containment policies, Covid-19, Corona, Sars-Cov-2, pandemic, epidemic etc.”. We also reviewed the papers
cited in these articles. Second, we extended our search on Scholar. We used the combination of the same
keywords as mentioned above. In epidemiology, we used n two main databases to perform targeted literature
searches on main concepts of direct burden of disease: Medline and PreView. We used simple search strategies
in the combination of relevant key words.
3
(C) Disease burden due to economic damage (caused by the pandemic and by public health measures
and interventions against the pandemic), such as raised cardiovascular mortality in those additionally
unemployed, or long-term health consequences for younger cohorts because of learning and lifetime
income losses
(D) Disease burden due to social/physical distancing, such as patients with oral anticoagulants (blood
thinners, that require constant medical monitoring) not being regularly seen by their general physician,
or fewer traffic accidents due to work-from-home orders
The main aim of policies implemented during the COVID-19 pandemic globally has been to minimize
the disease burden possibly caused by a potential unmitigated scenario of a pandemic (an der Heiden
&Buchholz, 2020; Ferguson et al., 2020), by implementing non-pharmaceutical interventions (NPIs),
such as:
1. an increase in the capacity for case isolation and contact quarantine by official agencies (an
der Heiden & Buchholz, 2020; Hellewell et al., 2020; Ferguson et al., 2020),
2. protection of population groups at higher risk of COVID-19 severe disease (Hellewell et al.,
2020; Ferguson et al., 2020), and
3. physical distancing measures in all spheres of life (private, education facilities, home, and work)
( Ferguson et al., 2020; Prem et al., 2020).
Pharmaceutical measures, such as vaccination and their associated strategies are crucial at a later stage
during the pandemic, however, the effects of their use are only relevant in later estimations. In this paper
we focus more on the effects of NPIs.
Estimating only the direct disease burden of COVID-19 for these different intervention scenarios is not
sufficient. This applies to policy advice as well as to public and scientific debate. With epidemiological
evidence being collected globally since the beginning of the pandemic, we can realistically include the
indirect disease burden in the efficacy estimations of NPIs. In this paper, we discuss the main parameters
that need to be considered to understand the direct disease burden caused by COVID-19. In addition,
we review the literature on indirect disease burden due to changes in access to healthcare, due to social
distancing during the pandemic, and due to economic decline. We show evidence of risks on disease
burden for different groups in the population and emphasize the role of welfare state institutions and
development levels of countries in mitigating adverse effects.
2 Direct disease burden caused by COVID-19
The main parameters that we need to consider understanding the disease burden caused by COVID-19
are the case and infection fatality estimates, excess mortality, and the proportion of severely sick patients
needing ICU beds (e.g., Dudel et al., 2020; Klüsener et al., 2021; Vanella et al. 2021). For a
comprehensive view of the disease burden due to COVID-19, it is additionally important to understand
who is at a higher risk of severe disease and its long-term consequences. All of these parameters vary
across demographics, regions, and different time points during infection dynamics.
4
2.1 Measuring direct disease burden using death counts
Different forms of death counts have been used during the epidemic, depending on the detail of
information available. All of these share a numerator (deaths in the same population in the same region
due to COVID-19) affected by potentially misclassifying deaths that are rather a consequence of
impairment by other (infectious) diseases. This is, in particular, the case for those regions with high
assumed seroprevalences (% of persons with positive antibodies against SARS-CoV-2), as was the case
for early European outbreaks (Pollan et al., 2020) – meaning that initial death estimates due to the
pandemic may well have underestimated the actual deaths associated with COVID-19 (e.g., as being
associated with other types of pneumonia or other causes of venous embolism).
Crude infection fatality risks (IFRs) (those reported as dying from COVID-19 over everyone infected)
have been estimated at between 0.5 and 2.4% in evidence synthesis of seroprevalence studies and
estimations based on notified case data (Meyerowitz-Katz & Merone, 2020; Gornyk et al., 2021; Staerk
et al., 2021). The IFR is age-specific, with infection fatality >1% in those above the age of 65 years
(Levin et al., 2020). Estimates are therefore within originally assumed infection fatality estimates in
modeling studies (an der Heiden & Buchholz, 2020; Ferguson et al., 2020), depending on the underlying
age structure of the population (Verity et al., 2020). However, these estimates are still prone to
uncertainties both in their numerator and in their denominator. For example, in a particular population-
based serosurvey, the denominator "all infections" was initially based on tests that concluded that
seroprevalence in the regions of the study was below 10% of the population. However, because of the
low specificity of these initial tests and the low overall incidence of SARS-CoV-2 infection in the
population, the positive predictive value was less than 60% (Lisboa Bastos et al., 2020).
Crude case fatality risk (CFR) estimates (those reported as dying from COVID-19 over those reported
as infected) differ widely across countries and are skewed by the age structure and comorbidities of
those infected, by underassessment of cases, by a time lag between case reporting and death of the
respective individual, and by testing activity (Vanella et al., 2022).
COVID-19-specific mortality rates (those reported as dying from COVID-19 over the corresponding
population) in those countries with more than 100,000 reported cases of COVID-19 vary between 3-
98/100,000 inhabitants (Coronavirus Resource Center, 2020) and are similarly skewed by those
parameters also skewing the CFR estimates. There is limited evidence indicating an association between
lower mortality from COVID-19 and higher air pollution measures (Chen, Wang, Huang et al., 2020;
Wu, Nethery et al., 2020).
Years of life lost (YLL) are measured as the number of deaths multiplied by the average remaining life
expectancy at the age of death. Worldwide for 2020, estimates of YLL due to COVID-19 are at more
than 20 million (Pifarré i Arolas et al., 2021); the 2020 estimate for Germany, for example, has been at
more than 300,000 YLL (Rommel et al., 2021). A meta-analysis estimates a global YLL average of 16
for every person who dies of COVID-19 (Pifarré i Arolas et al., 2021). The main data sources to evaluate
CFRs and mortality across countries remain national and international public health authorities, such as
the European Centre for Disease Prevention and Control (ECDC) and the World Health Organization
(WHO). However, data on the population structure corresponding tothe infected cases are often not part
of these evaluations, thereby severely limiting the accuracy and interpretation of these estimates, which
makes international comparisons difficult (Vanella et al., 2022). YLL estimates are relevant for a
5
comprehensive inclusion of demographics in the disease burden estimates, with first estimates showing
a substantial burden of disease from COVID-19 also in terms of YLL (Hanlon et al., 2020).
Excess mortality (the absolute number of deaths above the ex-ante expected number of deaths per
inhabitant) is important for monitoring direct and indirect effects. Currently, excess mortality is
estimated in Europe at the state level and is for many countries readily available in the form of excess
mortality estimates, such as from the EuroMOMO network (Vestergaard et al., 2020) or via web-based
applications (Németh et al., 2021). For example, Sinnathamby et al. show that excess mortality in
England during the first months of the COVID-19 pandemic was higher than in the five previous years
in all age groups (Sinnathamby et al., 2020). For real-time and current estimation and monitoring of the
pandemic and pandemic measures, however, the estimation of cause-specific excess mortality as well
as morbidity would be important (Vanella et al., 2021).
2.2 Measuring direct disease burden by assessing the severity of disease and
populations at risk
The proportion of patients in need of hospitalization and critical care, including invasive and non-
invasive ventilation, also depends on the demographic structure of the infected population (Fernández
Villalobos et al., 2021). However, age- and sex-specific data for those with severe disease is only
available in selected European countries (Vanella et al., 2022). The overall direct disease burden of
COVID-19 largely depends on the incidents in the high-risk groups.4 It is therefore important to quantify
the risk of sub-populations with comorbidities to understand the specific severity of the disease burden
of COVID-19. Knowledge of long-term complications in those persons surviving COVID-19 is
increasing, with speculation about ongoing limitations of lung capacity as well as long-term neurological
complications (Wu, Xu et al., 2020; Grobe et al. 2022).
Table 1 provides an evidence synthesis of relative risks associated with severe outcomes in COVID-19
patients by risk groups. Several comorbidities like hypertension, diabetes, cardiovascular or respiratory
diseases as well as demographic factors like male sex and behavioral factors like smoking and obesity
appear to increase the risk of severe disease progression with ICU admission and death (Fernández
Villalobos et al., 2021). Population groups at a higher risk of a severe course and mortality have been
identified as being mainly the elderly as well as individuals with chronic conditions or treatments
affecting the cardiovascular or respiratory systems or immune status (Williamson et al., 2020; Vanella
et al. 2021). There is an ongoing debate about whether the latter are mainly found due to confounding
by age or whether age is modifying the association between predisposing conditions and severe COVID-
19 (Fernández Villalobos et al., 2021).
For example, during the first wave of the COVID-19 pandemic in Spain, over 80% of deaths were related
to patients over 70 years of age, but only 40% of all ICU patients were older than 70 years (Li et al.,
2020). Moreover, evidence synthesis of the main comorbidities and other risk factors for case fatality
and hospitalization and ICU admission shows that the relative risk for these population groups is higher
for death than for hospitalization and ICU admission. This is important as it has implications for
healthcare capacity calculations and disease burden estimation (Fernández Villalobos et al., 2021).
4
Risks may also depend on intergenerational residence patterns (e.g., Fenoll & Grossbard, 2020) and the place
of residence in the country (e.g., Armillei et al., 2021).
6
Table 1: Evidence synthesis of relative risks associated with severe outcomes in COVID-19 patients
Number of Calculated Relative Risk Pooled Analysis
Risk group Outcome
studies Ranges (95 %CI) RR (95% CI)
Death 1 0.9 (0.3-2.4) 0.9 (0.3-2.4)
Asthma
ICU admission 1 1.1 (0.7-1.6) 1.1 (0.7-1.6)
Death 1 0.7 (0.1-4.9) - 4.8 (1.1-21.5) 2.0 (1.4-2.8)
Cancer
ICU admission 5 1.4 (0.7-3.0) - 4.9 (2.7-9.0) 2.3 (1.3-4.0)
Death 15 1.3 (0.2-8.9) - 6.7 (5.4-8.4) 3.3 (2.3-4.5)
Cardiovascular disease
ICU admission 8 0.9 (0.4-2.1) - 4.1 (3.4-4.9) 2.1 (1.3-3.2)
Death 7 1.2 (0.3-4.3) - 7.1 (3.3-15.4) 2.6 (1.7-4.1)
Cerebrovascular disease
ICU admission 4 0.8 (0.5-1.3) - 3.7 (2.4-5.8) 1.9 (0.9-4.0)
Chronic obstructive Death 6 1.9 (0.4-10.1) - 6.0 (1.3-26.8) 2.4 (2.0-3.0)
pulmonary disease
ICU admission 4 1.0 (0.4-2.8) - 10.6 (6.2-16.1) 2.4 (0.6-9.8)
(COPD)
Death 7 0.8 (0.3–2.2)–3.4 (2.4–4.9) 2.2 (1.5–3.0)
Other respiratory diseases
ICU admission 3 0.4 (0.2–1.1)–2.6 (2.1–3.2) 1.3 (0.6–2.9)
Death 18 1.1 (0.6-1.9) - 4.5 (1.9-10.6) 2.2 (1.7-2.9)
Diabetes Mellitus
ICU admission 12 0.3 (0.1-2.3) - 4.6 (2.8-7.5) 1.9 (1.4-2.6)
Death 17 1.1 (0.2-6.4) - 8.1 (2.9-22.3) 2.7 (2.1-3.4)
Hypertension
ICU admission 9 0.9 (0.6-1.3) - 3.1 (1.8-5.4) 1.4 (1.1-1.7)
Death 19 0.7 (0.1–7.5)–4.7 (1.4–15.1) 1.4 (1.3–1.6)
Male sex
ICU admission 11 0.7 (0.3–1.6)–2.0 (0.5–77) 1.3 (1.1–1.4)
Death 2 2.1 (1.1–4.2)–4.0 (1.0–15.6) 2.4 (1.3–4.4)
Obesity
ICU admission 2 1.4 (1.0–1.8)–1.5 (0.9–2.3) 1.4 (1.1–1.8)
Death 4 1.2 (0.8–1.7)–8.7 (3.7–20.1) 2.6 (1.0–6.8)
Smoking
ICU admission 5 0.9 (0.5–1.8)–2.9 (1.8–4.8) 1.8 (1.1–2.9)
Note: (selection adapted from Fernández Villalobos et al., 2021)
Additionally, to be able to evaluate the direct disease burden beyond case fatality, the use of disability-
adjusted life years (DALYs) is important to include short- and long-term complications that do not end
in death. This will be particularly important to better grasp the long-term complications that may be
associated with COVID-19. There is now considerable evidence that different forms of long- and post-
acute COVID-19 syndromes exist and this may imply persisting disease burden in adults and substantial
pandemic-associated costs in the future (Davis et al., 2021; Michelen et al., 2021; Nalbandian et al.,
2021; Taquet et al. 2021; Pavli et al., 2021). For children, this is less clear and evidence is still being
gathered. And while, in particular, at the start of pandemics these long-term burdens might not be
adequately quantifiable, epidemiological studies and models assessing direct disease burden ought to
include these entities even early on. This is one of the advantages of the use of DALYs and similar
measures as they allow an integration not only of the most severe patient outcomes like death but also
of less severe patient outcomes, like milder disabilities. And while the actual – larger than expected
burden of – long-term complications of COVID-19 only became clear during the second year of the
pandemic, complications following ICU admittance and hospitalization were always expected and could
have been included in estimations of disease severity from the beginning. This will then ensure that
long-term complications are similarly included in later estimations as death counts or acute morbidities
associated with the infectious disease.
7
3 Indirect disease burden due to changes in access to healthcare
There is broad consensus that there have been disease burden changes due to the COVID-19 pandemic
that are not related to those infected but rather to the inability of the healthcare systems to adequately
cover other diseases (Remuzzi & Remuzzi, 2020; Verelst et al., 2020). Similarly, NPIs aiming at
physical and social distancing may also have effects on healthcare access when a part of the population
cannot access or is afraid of accessing adequate care.
Most obvious has been healthcare provider undercapacities in those regions with a high burden of
COVID-19 epidemics like Lombardy, Madrid, or Alsace during the first wave in the spring of 2020
(Buonanno et al., 2020; Grasselli et al., 2020; Magnani et al. 2020). This has been mirrored in relevant
excess mortality in these regions, with excess mortality exceeding the isolated numbers reported for
COVID-19 (Vestergaard et al., 2020).
Besides these immediate consequences during the peak times of the epidemic, however, there are less
obvious consequences resulting in indirect disease burden due to changes in healthcare use. There is
now considerable evidence of reduced or delayed healthcare access for chronic conditions where a)
healthcare access was reduced due to the treatment of COVID-19 patients (Aziz et al., 2020; Krenzlin
et al., 2020; Verelst et al., 2020), b) elective procedures were suspended for a considerable amount of
time (Krenzlin et al., 2020; Latz et al., 2020; Rimmer et al., 2020), and c) healthcare access was reduced
due to NPIs or precautions against COVID-19 (Lazzerini et al., 2020).
4 Indirect disease burden due to social distancing during the
pandemic
Evidence synthesis assessing mental health and psychiatric issues during the pandemic and anti-
pandemic measures indicates a high load of mental health burden in high-income countries for COVID-
19 survivors (Rogers et al., 2020) among those having to undergo quarantine and isolation (Hossain et
al., 2020) as well as for healthcare workers treating COVID-19 patients (Shaukat et al., 2020). Several
studies report a higher mental health burden for females (Shaukat et al., 2020; O'Connor et al., 2021).
Parents, adolescents, and children are also heavily affected by the mental health impact of the pandemic
and the containment measures, a finding with evidence from several countries (Gassman-Pines et al.,
2020; Orben et al., 2020; Orgilés et al., 2020; Ravens et al., 2021). For example, a nationwide
representative study from Germany shows that two-thirds of children and adolescents reported being
negatively affected by the pandemic. They experience a significantly lower health-related quality of life
(40.2% vs. 15.3%), more mental health problems (17.8% vs. 9.9%), and higher anxiety levels (24.1%
vs. 14.9%) than before the pandemic. Children with low socioeconomic status, a migration background,
and limited living space are affected significantly more (Ravens et al., 2021). Mental health of children
was affected both by social distancing but also by the pandemic itself, with a relevant number of children
experiencing the loss of a parent or caregiver (Hills et al. 2021). Moreover, Google searches for
boredom, loneliness, worry, and sadness increased significantly during the pandemic, while searches for
suicide and divorce decreased (Brodeur, Clark et al., 2021).
Both stress caused by the pandemic and by anti-pandemic measures, economic consequences, and social
distancing are factors that will likely lead to an increase in intimate partner violence (IPV) (Buttell &
Ferreira, 2020; Usher et al., 2020). In the US, police calls for domestic violence increased by 7.5%
8
between March and May 2020, starting even before stay-at-home orders went into effect (Leslie &
Wilson, 2020). Using data from the US state of Michigan, Chalfin et al. (2020) show that the relationship
between alcohol consumption and IPV has strengthened since the start of the pandemic, most likely
because of an increase in alcohol consumption at home. Baron et al. (2020) show that teachers are crucial
for reporting child maltreatment and that school closures have led to increases in underreporting, making
it difficult to assess the full extent of the problem. Anderberg et al. (2022) develop an internet search-
based measure of domestic violence to circumvent the bias of underreporting. They find that domestic
violence in the Greater London area peaked at a 40% increase compared to pre-pandemic levels, much
larger than the rise recorded by the police. While interventions against IPV are well established and can
be continued digitally (Emezue, 2020), their effectiveness was constrained by anti-pandemic measures
(Johnson et al., 2020).
By contrast, there are examples of the pandemic and the NPIs used resulting in a reduction of disease
burden. Most notably, a reduction in air pollution has been identified as a source of declining mortality
during this pandemic because of lockdowns and the economic downturn. Studies from China and the
US, for example, have estimated that the number of premature deaths from air pollution could drop by
a few thousand deaths per year (Achebak et al., 2020; Chen, Wang, Huang et al., 2020; Chen, Wang,
Kinney et al. 2020; Wu, Nethery et al., 2020; Brodeur, Cook et al., 2021).
Similarly, mortality rates from accidents, both work-related and transport-related, have decreased due
to social distancing or increased unemployment and work-from-home orders. First studies suggest a
decline in emergency department visits for orthopedic hand and trauma reasons of between 20% and
60% during the months of social distancing measures and stay-at-home regulations (Lubbe et al., 2020;
Morris et al., 2020; Regas et al., 2020; Stoker et al., 2020). Similarly, traffic accidents have fallen
significantly by 74% during the strict lockdown in Spain (Saladié et al., 2020), or, depending on the
exact time frame and state, by 20% to 47% in the US (Barnes et al., 2020; Brodeur, Cook et al., 2021).
And finally, considerable reductions in disease burden were observed in 2020 due to reductions in other
infectious diseases, like influenza and other infectious diseases (Fricke et al., 2021). However, some
studies predict larger outbreaks of respiratory viruses that affect children, such as RSV and influenza,
following the relaxation of NPIs (Baker et al., 2020), and there is some evidence that they are already
occurring (Weinberger Opek et al., 2021).
9
Table 2: Parameters to estimate direct and indirect disease burden used by epidemiologists
Definition Other names Potential bias in this estimate Data source and examples of current
estimates for COVID-19 if available
Disease counts
Number of visits Visits/period Underestimate of actual disease incidence if lower Single sources from hospitals, healthcare
to healthcare proportion of those with the disease attend healthcare providers
providers providers
Incidence Incidence: Events/person-time Rate Underestimate if notification is decreased Reported from official sources like ECDC,
Overestimate if notification is increased WHO
Prevalence Events/population at a specific time point Cumulative Underestimate if notification is decreased Reported from official sources like ECDC,
incidence Overestimate if notification is increased WHO
Proportion of Persons admitted to ICU/different denominators are used: hospitalizations, all ICU cases Underreporting of cases Reported from official sources like ECDC,
cases in ICU reported cases, all infected Misclassification of disease cause as COVID-19 WHO
(both negative/positive)
Disability Sum of the years of life lost (YLL) due to premature mortality in the population Underestimate if death counts or disease prevalence IHME (http://www.healthdata.org/);
adjusted life and the years lost due to disability (YLD) (WHO definition: misclassified global burden of disease estimations,
years (DALY) https://www.who.int/healthinfo/%20global_burden_disease/metrics_daly/en/) Overestimate if alternative explanations of attributed World Health Organization’s global
disease burden reports
Death counts
Case fatality Deaths caused by COVID-19/reported cases in one population Often misnamed Misclassification of death causes, both as being from Reported from official sources like ECDC,
estimate (CFR) case fatality rate COVID if in reality a comorbidity was to blame, as WHO (Vanella et al., 2021)
(though not a rate), well as being not from COVID if this was not tested
case fatality risk Underreporting of cases
Infection fatality Deaths caused by COVID-19/all infections in one population Infection fatality Misclassification of death causes, both as being from Serosurveys in several countries have
estimate (IFR) rate COVID if in reality a comorbidity was to blame, as reported this (Levin et al., 2020;
well as being not from COVID if this was not tested Meyerowitz-Katz & Merone, 2020)
Low positive predictive values of current serology IFR: 0.4-0.9%
assays
Mortality Deaths caused by COVID-19/number of people in one population Mortality rate Misclassification of death causes, both as being from Reported from official sources like ECDC,
estimate COVID if in reality a comorbidity was to blame, as WHO
well as being not from COVID if this was not tested
Years of life lost Number of cause-specific deaths multiplied by a loss function specifying the YLL Misclassification of death causes, both as being from Single reports from several sources, 14
(YLL) years lost for deaths as a function of the age at which death occurs COVID if in reality a comorbidity was to blame, as years for men and 12 years for women in
well as being not from COVID if this was not tested Scotland (Hanlon et al., 2020)
Excess mortality Deaths over time in a particular population in relation to deaths over time in Excess deaths, Underreporting of deaths in principle possible but European sources (EuroMOMO),
the same population from previous time periods excess mortality not likely in most European countries influenza monitoring (Vestergaard et al.,
Seasonal effects have to be taken into account 2020); Vanella et al. (2021)
Cause-specific Cause-specific deaths over time in a particular population in relation to same Excess deaths, Misclassification of death causes No sources yet
excess mortality cause-specific deaths over time from previous periods in the same population cause-specific Underreporting of deaths in principle possible but
excess deaths not likely in most European countries
Seasonal effects have to be taken into account
10
5 Disease burden due to economic decline
The pandemic and shutdown measures have had a severe impact on worldwide economic activity and
well-being. Countries all over the world were affected by a massive slump in economic output, which
gave rise to unemployment, bankruptcies, income losses for many households, and increasing public
and private debt (OECD, 2021a). In 2020, the global gross domestic product (GDP) fell by 3.1%, with
large cross-country differences.5
Increases in unemployment and short-time work were substantial in many countries, but effects on
household incomes varied across countries depending on welfare systems and unemployment insurance
schemes and policies (OECD, 2021a). Expanded government spending and declining GDP led to an
increase in debt-to-GDP ratios by 16 percentage points across OECD countries (OECD, 2021c).
Notably, a persistent decline in economic well-being and the need for fiscal consolidation may have
effects on disease burden and life expectancy in the long-term perspective (Schnabel & Eilers, 2009).
We disentangle different but related channels that transmit economic effects into individual health
outcomes. First, we review the general relationship between health outcomes and unemployment during
times of recession as well as the general effect of household income and unemployment shocks on
general and cause-specific mortality. Then, we discuss some first studies on the effect of the COVID-
19 related economic decline on health outcomes.
5.1 Health and mortality during times of recessions and high unemployment
There is a large body of evidence showing that aggregate-level mortality is procyclical – rising when
overall economic conditions improve and falling when they deteriorate. In an influential study, Ruhm
(2000) shows that total mortality and eight out of ten main sources of fatalities exhibit a procyclical
fluctuation, i.e., mortality rates tend to be lower in recessions. One exception is the suicide rate, which
was shown to be higher in recessions. These results were based on a state-level fixed effects analysis for
the US. Further, Ruhm (2017) argues that more severe economic crises affect mortality and other
dimensions of health in the same way as less severe downturns, also leading to improved physical health.
Several macro-level studies following the approach of the seminal work of Ruhm (2000) support his
finding of a procyclical relationship. Ruhm (2000) and subsequent studies argue that harmful behaviors
such as smoking and drinking as well as traffic accidents become less prevalent in a recession. Studies
using state-level data from Canada, Germany, and the US suggest a negative relationship between
unemployment and total mortality rates once controlled for time-invariant state-fixed effects and other
confounding factors (Ruhm, 2000; Neumayer, 2004; Miller et al., 2009; Ariizumi & Schirle, 2012).
According to these results, the overall mortality rate, ceteris paribus (c.p.), decreases by about 0.5-1.1%
when the unemployment rate increases by one percentage point (p.p.). For young adults (20-44 years of
age), who make up the largest share of the working-age population, the effect is the strongest: a one p.p.
rise in state unemployment rates lowers the predicted death rate of 20-44 year-olds by about 1.0-2.0%
(c.p.).
5
While it increased by 2.3% in China, it fell by 7.3% in India, by 6.3% in the Euro area, by 9.8% in the UK, and
by 3.4% in the US (IMF, 2021). However, all countries saw substantial losses compared to their initial GDP
growth projections for 2020 as expected before the crisis in 2019 (IMF, 2019): World GDP in 2020 is 6.5
percentage points (p.p.) lower than expected. While it is 3.5 p.p. lower in China, losses are higher in India (14.3
p.p.), the Euro area (7.7 p.p.), the UK (11.2 p.p.), and the US (5.1 p.p.).
11
Conversely, a macro-level study conducted in a panel of EU member states shows a positive relationship
between adverse economic conditions and total mortality rates (Economou et al., 2008). According to
the authors, a 1% increase in national unemployment rates c.p. gives rise to approx. 1.54 deaths per
100,000 inhabitants. The positive relationship is largest for the 45-54 age group, while the study
confirms that unemployment is associated with decreases in mortality for the youngest working-age
cohort. By contrast, another cross-country study among OECD countries suggests that a one p.p.
decrease in the national unemployment rate is on average, c.p., associated with a rise in total mortality
of 0.4%. The results suggest variation across countries, with larger adverse health effects of rising
unemployment in countries with relatively weak welfare states (Gerdtham & Ruhm, 2006). Other studies
support the notion that lower public health expenditure and a lack of health institutions like universal
health coverage are correlated with larger cause-specific mortality rates, such as treated cancer deaths
during economic recessions (Maruthappu et al., 2016).
Micro-level studies that use individual-level data (from natural experiments) to estimate the causal
effects of unemployment on health outcomes consistently find that these are negative, on average. These
studies require administrative datasets which cover large populations. Such datasets are accessible only
in some countries, including the US and Scandinavian countries. In the following, we illustrate the
research designs and main findings of selected studies. It remains an open question whether these results
are representative for other countries, but the fact that negative effects of individual-level unemployment
on health outcomes have been found in countries with very different labor market intuitions and
healthcare systems suggests that they do.
As pointed out by Sullivan and von Wachter (2009a), “the situation of an individual displaced worker
differs qualitatively from that of the average worker during a recession” (p. 1269). Using administrative
employment data from the US state of Pennsylvania, Sullivan and von Wachter (2009a) estimate that
mortality rates in the year after displacement are 50 to 100% higher for older male workers. While the
effect of becoming unemployed on mortality hazards declines over time, it can still be detected twenty
years after job displacement. The authors estimate a loss in life expectancy of 1.0 to 1.5 years for a
worker displaced at age 40. Similar results have been established for the Scandinavian countries. Using
administrative data covering the entire Danish workforce, Browning and Heinesen (2012) exploit plant
closures as a natural experiment. They show that job loss increases the risk of overall mortality and
cause-specific mortality for circulatory disease, suicide and suicide attempts, and death and
hospitalization due to traffic accidents, alcohol-related disease, and mental illness. The latter study
estimates the average risk of overall mortality associated with unemployment at 79% higher in the year
of displacement and 11% higher over the twenty-year period after the base year.6 Based on Swedish
data, Eliason and Storrie (2009) confirm the magnitude of such short-term effects but identify larger
long-run effects. The findings for Denmark and Sweden suggest smaller effects than those found by
Sullivan and von Wachter for the US. Institutional differences such as whether access to the healthcare
system is linked to the job, and differences in income insurance and welfare programs, could explain
differences in health-related outcomes across countries. More generous welfare institutions with
unemployment benefits and tighter labor market institutions seem to attenuate the negative health effects
6
Browning and Heinesen (2012) mention that it is very difficult to assess the social costs of the estimated increase
in mortality and incidence of serious diseases caused by plant closures. These costs are borne by a small share
of the displaced workers. Presumably, a larger share experience negative health effects which are less serious
and do not lead to hospitalization or death, so they are more difficult to detect. We are not aware of reliable
estimates of these effects.
12
of job losses. For the US, Cylus et al. (2015) find that the impact of job losses on health is lower in
states with more generous unemployment benefits. Riumallo-Herl et al. (2014) find that the effect of job
loss on depression is significantly larger in the US than in Europe. In addition, the effect is moderated
by pre-existing wealth in the US but not in Europe. Browning and Heinesen (2012) also find that the
effects are larger in local labor markets with higher unemployment.
5.2 Income shocks and mortality
There is evidence of an association between income (and wealth) levels and mortality rates, as in Chetty
et al. (2016) for the US and in von Gaudecker and Scholz (2007) for Germany.7 Evidence for the effects
of income shocks on health is mixed and findings seem to depend on the specific measure of income, or
economic resources more generally. For example, Ahammer et al. (2017) find a zero effect of short-run
variations in labor income on 10‐year death rates for workers aged 40-60 years using administrative
social security data from Austria.
The effects of shocks on lifetime income are likely larger. Sullivan and von Wachter (2009b) argue that
estimates based on single years of earnings data are likely to understate the strength of the association
between income and mortality. Using the same data as Sullivan and von Wachter (2009a, see above),
they find that relative to a single year of earnings, the average of earnings over six years predicts a 70%
larger impact of income on mortality. One of their main results is that for the group of workers younger
than age 60, a 10% increase in income over five years c.p. lowers the probability of death in the
following year by approximately 5%. These findings suggest that recessions and shocks to GDP are
more harmful to health and life expectancy with increasing duration of the economic shock.
5.3 Variation in cause-specific mortality
The evidence on overall mortality and cause-specific mortality rates such as cardiovascular, respiratory,
or liver diseases as well as homicides and infant mortality is mixed. However, there is strong evidence
that economic decline and a rise in unemployment are associated with increases in several cause-specific
mortality rates, such as suicide rates, while empirical evidence is also strong for declining death rates
from other causes such as (motor) vehicle accidents during economic downturns. In the following, we
highlight findings from this literature, focusing on the effects of economic decline and individual-level
unemployment. We acknowledge that during a pandemic such as COVID-19, other mechanisms will
also affect suicide rates. Most importantly, in contrast to recessions, major crises often induce policy
interventions that support individuals that are at risk. For instance, a recent study by Alvarez-Galvez et
al. (2021) highlights the mediating role of social cohesion and community values during a major crisis
– in their analysis, the 2011 financial crisis in Spain. The following studies typically analyze economic
7
Macro-level data is often employed to examine the long-run relationship of cross-country income levels and life
expectancy (e.g., Swift, 2011; Biggs et al., 2010; Bilas et al., 2014; Felice et al., 2016). Some scholars use
correlations to predict the influence of real GDP growth on life expectancy. The direction of causality is,
however, ambiguous. Both wealth and health positively influence each other at the personal as well as at the
country level. Using correlations as a predictor would therefore overestimate the effect of GDP on health.
Moreover, positive correlations between the two variables could also result from third variables, such as
technological progress, that positively impact health and income. These interdependencies need to be
interpreted cautiously as they only report (simple or conditional) correlations. We suggest that scholars should
avoid using estimates of correlations to predict effects of GDP on life expectancy. By contrast, Acemoglu and
Johnson (2007) use an instrumental variable approach and find no evidence that a rise of life expectancy (due
to health improvements) increases GDP per capita.
13
downturns and individual unemployment as determinants of suicide in periods with the implicit
assumption that the social environment remains stable.
Suicide mortality: Economic theory suggests that social living conditions such as income and
unemployment are determinants of suicidal behavior (Hammermesh & Soss, 1974). Several empirical
studies find evidence, both across countries and time, supporting this hypothesis. These studies show
that recessions are correlated with short-term increases in suicide mortality rates (Chang et al., 2009;
Tapia Granados & Diez Roux, 2009; Stuckler et al., 2009; Stuckler, 2011a; Stuckler et al., 2011b;
Reeves et al., 2012; Sullivan et al., 2013; Baumbach & Gulis, 2014). Empirical studies show that both
a rise in unemployment and a decline in economic growth influence suicide mortality. Using data from
63 countries, Nordt et al. (2015) find a positive association between unemployment and suicide mortality
that is stronger in countries with lower levels of baseline unemployment. Findings based on US state-
level data show an increase in suicide rates by 1.3-1.6% for each one-p.p. rise in state-level
unemployment rates (Ruhm 2000; Phillips & Nugent, 2014). However, studies show that the conditional
correlation is lower in European countries than in the US, or even lacks statistical significance
(Neumayer, 2004; Andrés, 2005; Breuer, 2015; Tapia Granados & Ionides, 2017). Using panel data of
European countries or regions, Breuer (2015) and Tapia Granados and Ionides (2017) show that suicide
rates are predicted to rise within a range of 0.4-1.3% for each one-p.p. increase in the unemployment
rate. Similarly, a decline in the real growth rate of gross value added in European regions of 1% would
increase regional suicide rates by about 0.3-0.5% (Breuer, 2015). Some studies which examine the
relationship between unemployment and suicides in European countries by using more aggregated data
suggest a negative correlation or do not find significant correlations between unemployment and suicide
rates (Neumayer, 2004; Andrés, 2005; Gerdtham & Ruhm, 2006).
However, studies relying on micro-level data support a causal inference that unemployment and job
displacement increase suicide risks in European countries (Gerdtham & Johannesson, 2003; Browning
& Heinesen, 2012). Other global regions report similar findings. Christian et al. (2019) find that the
rollout of cash transfers and agricultural productivity shocks influence suicide rates in Indonesia. Other
scholars also find evidence that a rise in unemployment implies higher suicides at the Japanese
subnational and local levels (Kuroki, 2010).
Reflecting the differences between elasticities of changes in the economic situation on health outcomes
in the US and Europe, scholars suggest variation in the rise of suicide rates depending on social
integration and the size of the welfare state (Brainerd, 2001; Baumbach & Gulis, 2014).8 Besides
regional differences, empirical evidence reveals relevant heterogeneity across demographics. First, the
elasticity of suicide behavior of men tends to be somewhat larger than for women during economic
downturns (Brainerd, 2001; Breuer, 2015). Second, economic recessions only influence suicide rates of
the working-age population, as they suffer a negative shock in expected income. Findings based on a
panel of European regions indicate a one-p.p. increase in unemployment to be associated with an approx.
1% increase in suicides among individuals aged younger than 65 years, while old-age suicides do not
respond to fluctuations in unemployment (Breuer, 2015).
8
For a sample of 22 economies during transition and lack of generous welfare system in the 1988-1998 period,
the effect was even larger: a one p.p. increase in the national unemployment rate is associated with a rise in
suicides rates by about 3%; and a decline of GNP per capita of USD 100 raised the predicted male suicide rate
by 0.1-0.2% (Brainerd, 2001).
14
Preliminary findings for the COVID-19 pandemic have shown that suicides generally decreased in the
US and several other countries during the first infection wave (Ahmad & Anderson, 2021; John et al.,
2020). However, some evidence from Japan suggests increases in subsequent waves or when
governmental (income) support has come to an end (Tanaka & Okamoto, 2021).
Vehicle accidents: Economic downturns go along with a reduction of traffic volume for two reasons:
first, the need for commuting declines when unemployment rises, or when plants are suspended (e.g.,
by short-time work). Second, the shrinking of disposable household incomes typically leads to a
reduction of expenditure on non-work-related transportation. During the COVID-19 pandemic, traffic
was additionally reduced due to work-from-home orders (see Section 2.3). The negative effect of
unemployment on commuting and thus on vehicle accident fatality has also been the subject of empirical
research. Ruhm (2000) quantifies that an increase in the unemployment rate by one p.p. reduces traffic
accident fatalities by c.p. 2.4% in the US. Examining the same relationship for the US during the Great
Depression, He (2016) estimates an elasticity of 2.9%. Wegman et al. (2018) emphasize that this effect
is mainly driven by a disproportionate reduction of traffic exposure by high-risk drivers (especially
young men).
5.4 COVID-19 economic decline and health outcomes
5.4.1 Economic decline because of the pandemic and containment policies
To evaluate and model the effects of the pandemic and health policy interventions on disease burden
during a pandemic, it is necessary to disentangle economic losses that are caused by the pandemic from
those caused by policy reactions to the pandemic, in particular NPIs. Evidence from the US and Europe
in the first COVID-19 wave suggests that the decline in economic activity was mainly caused by
behavioral adjustments, and only a minor part of the economic decline can be explained by NPI measures
(Rojas et al., 2020; Goolsbee & Syverson, 2021; Juranek et al., 2021; Sheridan et al., 2020; Chetty et
al., 2020).9
For example, legal restrictions explain less than 10% of the overall decline in consumer traffic in the US
during the first infection wave; the rest is likely due to voluntary behavioral changes because of fear of
infection with the virus (Goolsbee & Syverson, 2021). Similarly, earlier reopening policies in US states
did not have significant effects on consumer spending and employment compared to states with stricter
measures while the virus continued to rage (Chetty et al., 2020). In-person consumption in China
decreased more in cities that experienced more cases and deaths, even when mitigation policies were
the same across both groups of cities (Chen, Qian, & Wen, 2021). Similarly, Swedish VAT (value-added
tax) revenues fell more in municipalities with more infections, while NPIs were at the same level
(Angelov & Waldenström, 2021b). Sheridan et al. (2020) find that Danish customers reduced their
spending by 29%, while Swedish spending fell by 25% during the pandemic, although Sweden did not
implement a strict lockdown like Denmark. They conclude that the 4-p.p. difference between both
countries was likely caused by the Danish shutdown, while the 25% decrease happened because of the
pandemic and voluntary behavioral changes. Similar effects are found in the labor market when
comparing Denmark, Finland, Norway, and Sweden (Juranek et al., 2021). Sweden experienced a 25%
to 50% lower rise in unemployment than the other countries, suggesting that between 50% and 75% of
9
Behavioral responses could have been different in other infection waves, for example because of better
knowledge about the risks and mitigation measures, or because of different infection and fatality risks.
15
the rise in unemployment was caused by the pandemic, not by the NPI policies. Moreover, it is important
to disentangle the effect of different NPI strategies on the economy.10
Other studies have focused on separating policy-induced and voluntary mobility reductions and their
impact on death counts. Both containment policies and voluntary behavioral adjustments led to
substantial reductions in death growth rates in the US (Chernozhukov et al., 2021). Depending on the
specification, between 33% and 66% of the decline in deaths can be attributed to political orders, while
the rest is likely due to voluntary changes in behavior. Gupta et al. (2020) show that mobility began to
fall even before policies were enacted and did not respond strongly to the lifting of those policies. Their
estimates suggest that state emergency declarations account for 65% of the decline in mobility, while
the remaining 35% is explained by personal behavioral decisions.
While the studies discussed so far in this section focus on the role of “voluntary” behavioral adjustments
versus government intervention to contain the pandemic at a given point in time, it should be noted that
there is an important intertemporal dimension. In particular, early NPIs may have had immediate costs
due to limited economic activity but may enable more economic activity later because infection rates
had been reduced earlier (Dorn et al., 2022; König & Winkler, 2021).
5.4.2 Disease burden caused by COVID-19-associated economic decline
Research on the indirect effects of a COVID-19-associated economic shock on health outcomes is still
scarce. Data availability is limited and causal inference is challenging. Some first studies use parameters
from earlier recessions to predict adverse effects on disease burden. For the US, Bianchi et al. (2021)
apply vector autoregression to historical time series data on unemployment, life expectancy, and death
rates. Their simulations show that macroeconomic unemployment shocks give rise to a reduction in life
expectancy. The model predictions suggest a sizeable decline in life expectancy over the following 20
years due to a large unemployment shock. They estimate that job losses will be greatest for women and
the African-American population, but the adverse effects on life expectancy are assumed to be the largest
for the latter. For the UK, the British Office for National Statistics (2020) estimates the long-run health
impact of the economic decline caused by the pandemic. Their baseline scenario suggests a loss of 1.3
million QALYs (quality-adjusted life years), with a range from 0.23 million to 2.7 million in the upside
and downside scenarios. Such estimates are highly volatile, which makes external validity critical as the
economy, for example, may recover faster or slower than expected.
Welfare and unemployment insurance programs may moderate the unemployment shock as well as its
health consequences. Donnelly and Farina (2021) show that the relationship between income losses and
deteriorations in mental health was strong in US states with less supportive policies such as Medicaid
or unemployment and income insurance schemes. Several studies from Europe and the US show that
relative wage declines (before tax and transfers) were largest at the bottom of the income distribution
during the pandemic crisis (Palomino et al., 2020; Chetty et al., 2020; Crossley et al., 2021; Angelov &
Waldenström, 2021a; Almeida et al., 2021). Inequality after redistribution programs has, however, only
increased in some countries as government programs cushioned adverse economic effects in many
advanced economies during the crisis (see Angelov & Waldenström, 2021; Clark et al., 2021). This
10
For example, restaurant and bar limitations and non-essential business closures had an effect on unemployment
claims, responsible for around 6% (Kong et al., 2020). However, findings for other mitigation policies, such as
stay-at-home orders, large-gatherings bans, school closures, and emergency declarations do not seem to affect
unemployment to a significant size.
16
underlines the importance of unemployment and income insurance programs to avoid adverse economic
outcomes and their health consequences during a crisis. Longer-lasting adverse effects might become
more visible once such programs expire (Stantcheva, 2022).
Another specific effect of the COVID-19 pandemic and its social distancing measures is the disruption
of education and childcare. Gassmann-Pines et al. (2022), for example, find that families with children
reported disruptions to their schooling and care arrangements on 24% of days. Such disruptions have
led to worse mental health for children and parents as well as substantial learning losses, especially
among students from disadvantaged backgrounds (Agostinelli et al., 2021; Gassmann-Pines et al. 2022;
Grewenig et al., 2020). These disruptions might create substantial losses in the lifetime income of the
affected students (Wößmann, 2020; Hanushek & Wößmann, 2020). Hanushek and Wößmann (2020)
estimate that a learning loss of one school year is, on average, c.p., associated with (cumulated and
discounted) future GDP losses to its present value of more than 4%. As such, the pandemic may have a
long-term effect on economic growth, individual income opportunities, and ultimately the health
outcomes of today’s students.
At a more general level, severe diseases and shocks such as economic crises that are experienced by
children and young adults are known to have adverse long-run effects in such diverse outcomes as
mental health, human capital formation, family formation, lifetime incomes, and more. A large literature
studies the effects of wars, recessions, and early childhood diseases on long-run outcomes (e.g. Almond
et al., 2018; Bleakly 2007; Currie 2020). By their long-run nature, these effects are difficult to quantify
and to generalize but empirical evidence suggests that even relatively small shocks to parent’s or
children’s (mental) health can have large long-term consequences on children’s health, educational
attainment, and income (see Almond et al., 2018; Goodman et al., 2011; Currie & Stabile, 2006; Currie
et al., 2010; Currie 2020).11
6 Economic development, public debt, and long-term implications of
a pandemic´
Finally, we consider fiscal conditions and the level of economic development across countries as
relevant pre-conditions for the effect of economic decline on disease burden.
6.1 Public debt and fiscal consolidation
The COVID-19 pandemic revealed substantial differences in hospital bed and ICU capacities across
advanced economies. Economic determinants for differences in capacities in the health system and
health spending levels constitute another relevant pre-condition for effects on indirect disease burden.
Capacities and resilience of healthcare systems depend on the performance of the economy, the financial
leeway, and the spending of national governments.12 Public debt and fiscal consolidation may lead to
decreasing government expenditures in the healthcare sector with potentially negative effects on
intertemporal healthcare provision. While health expenditures may increase in high-income countries
during economic slowdowns (Keegan et al., 2013), a rise in public debt can force governments to
11
For instance, childhood psychological problems by age 16 are associated with 28% lower family income at age
50 (Goodman et al., 2011).
12
Blum et al. (2021) show that political institutions also influence government health spending.
17
consolidate and cut spending in the future. Debates on fiscal consolidation and its adverse effects on
health outcomes have intensified since many EU member states implemented budget consolidation
measures in the aftermath of the global financial crisis (Reeves et al., 2013; Stuckler et al., 2017). Several
scholars argue that fiscal consolidation policies in Europe have exacerbated short-term negative public
health effects of the financial crisis (Karanikolos et al., 2013). For example, European countries with
large fiscal adjustments exhibit a rise in suicide rates (McKee et al., 2012; Karakinokolos et al., 2013;
Antonakakis & Collins, 2014). Studies for Greece also suggest that the reduction in government health
expenditure is correlated with a rise in infant mortalities, stillbirths, and the number of low-birth-weight
infants (Ifanti et al., 2013; Kentikelenis et al., 2014). By contrast, countries that failed to consolidate
their public finances in the aftermath of the financial crisis had higher debt-to-GDP ratios and lower
fiscal leeway to support the health system, the economy, and the most vulnerable members of the
population during the pandemic crisis. There might be an intertemporal trade-off between fiscal
consolidation including health spending cuts in the present, and higher future spending capabilities
which might help to mitigate negative health outcomes during a future crisis.
6.2 Pandemic disease burden in low- and middle-income countries
Thus far, we have focused on sources related to high-income countries (HIC) – mainly from a European
or US perspective. Both the direct and indirect disease burden of the pandemic in low- and middle-
income countries (LMIC) were originally estimated to exceed those in HICs (Hogan et al., 2020). Several
studies initially discussed the import of cases, vulnerability, and the preparedness of low-income
countries especially in Africa (Gilbert et al., 2020). Shortages of medical supplies were predicted to
affect LMICs more severely in terms of the direct COVID-19 burden since the facilitation of sensitive
imports from China and Europe is not granted due to lockdown measures. With already overburdened
health systems in several African countries and less financial leeway in the WHO budget, a shift of
resources away from other diseases such as malaria, tuberculosis (TB), noncommunicable diseases
(NCDs), and HIV to tackle COVID-19 was projected to intensify indirect disease burdens (Hogan et al.,
2020). Studies from epidemics in African countries, however, indicate a considerable but less than
expected direct disease burden from COVID-19 in these countries. This is mainly explained by a
younger age structure in those regions (Diop et al., 2020; Kirenga et al., 2020; Mbow et al., 2020).
However, the indirect disease burden due to physical distancing and lockdown policies implemented
seems to be just as high as projected. First surveys and studies from tuberculosis and HIV treatment and
diagnostic programs, for example, reveal substantial interruptions of these programs. (Lagat et al., 2020;
Louie et al., 2020; Watts et al., 2020). These interruptions are not always as large as initially predicted
by models (Kessel et al, 2022), but they are substantial and have set programmatic efforts to eliminate
poverty-related but essentially well-treatable diseases like tuberculosis importantly (Jeremiah et al.
2022).
Scholars, moreover, have shown that citizen compliance with interventions against the pandemic
depends on household incomes and economic incentives or financial support from the government (e.g.,
Trudeau et al. 2020). Unemployment in LMICs also leads to a deterioration of personal health due to
increased exposure to unhealthy behavior (e.g., Kwon et al., 2010). This relationship is similar to
findings in HIC countries. However, the effect is even more pronounced in countries with a lack of
universal health or income insurance systems. Health insurance coverage in the Philippines, for example,
decreased significantly during the global financial crisis (Weber & Piechulek, 2010). Moreover, LMICs
suffer from the tendency of HICs to reduce donor funding for the treatment of typical third-world
18
diseases, e.g., when HIV prevention programs are not prolonged. At the beginning of the COVID-19
pandemic, for example, the US suspended their contributions to the WHO starting in March 2020. As
the US contributed about 15% to the budget of the WHO in recent years, the large budget cut likely
exacerbates the problematic healthcare situation in LMICs.13
Other research has highlighted negative income shocks for LMICs during economic crises with the
consequence of a decline in remittance payments received (Weber & Piechulek, 2010). Another, yet
underexposed income channel, relates to terms-of-trade deterioration effects: recessions in emerging
markets often go hand in hand with sharp currency devaluations (Obstfeld et al., 2019). As these
economies’ supply of drugs relies heavily on imports, currency devaluations increase prices for
medicines disproportionately and make them even less affordable.
7 Conclusion
The COVID-19 pandemic gives rise to a notable short- and long-term disease burden and excess
mortality. We present an overview of different death counts and an evidence review of the severity of
direct disease burden from COVID-19 and populations at risk. The actual disease burden and expected
additional mortality during a pandemic are, however, dependent on more than these direct estimates.
While our review is limited by not being an actual systematic review, we conduct a comprehensive
evidence review of parameters that need to be considered when estimating the overall disease burden
from both epidemiological and economic perspectives (see Table 3 for a summary of the range of
effects). Furthermore, the pandemic influences health outcomes indirectly through changes in access to
healthcare, social distancing, and economic decline. Future research on the effect of mitigation policies
in pandemics therefore should examine the effect of policies on both direct and indirect disease burdens.
While our results are mainly based on the COVID-19 pandemic and not all parameters and estimates
presented will be relevant in future pandemics of different pathogens, we still think the following is
crucial for any pandemic: When evaluating policy measures implemented to contain the pandemic, it is
important to disentangle effects that are influenced by NPIs and those that are caused by the pandemic
itself. Empirical findings in our review moreover suggest that established welfare systems and labor
market institutions providing income insurance and other support during a crisis may help to moderate
the negative (indirect) effects of a pandemic.
We conclude that a thorough assessment of a pandemic’s disease burden requests the following four
aspects:
(1) Interdisciplinary groups from epidemiological, economics and further health and social science
backgrounds should focus on collaboratively synthesizing the now emerging evidence on indirect
disease burden during this pandemic using rapid, systematic, and living review methodologies.
(2) Mathematical modeling and simulations going beyond the direct burden of disease from COVID-
19; both positive and negative indirect effects on a regional and local scale should be included to
gain reusable models of disease burden in pandemic situations.
13
Though, the US withdrawal from the WHO was compensated by increased contributions from other members
such as Germany.
19
(3) Real-time monitoring tools that cover direct as well as indirect disease burden, including cause-
specific excess mortality estimates (Parks et al. 2018) as well as morbidity estimates using measures
like DALYs need to be installed wherever possible.
(4) The role of the design of (public) health systems, welfare programs, institutions, mitigation and
containment policies as well as economic policies needs to be examined with respect to their impact
on both direct and indirect disease burden during a pandemic.
20
Table 3: Range of effects expected in direct and indirect effects
Effect Direction of Direction of Range of expected effect
disease disease
burden due to burden due
the pandemic to NPIs
Direct
Direct COVID- Additional deaths due ▲ ▼ Suppression: 10-15,000 additional deaths
19 burden to COVID-19 (excess No suppression: 150,000 – 500,000 additional deaths/year until 2021
mortality)
Indirect effects
Limited health Indirect effect due to ▲ ▲ Unclear
access limited healthcare
access
Indirect burden Unemployment rate ▲/▼ ▲/▼ Direction of the effect is unclear and depends on the country, time span, and age group examined. A one p.p. increase in the
due to economic unemployment rate is associated with a short-run reduction in total mortality of 0.4 to 1.1%. Mortality is decreasing for young
effect adults (20-44 y.) by about 1.0 to 2.0%. Other studies suggest a rise in total mortality of about 1.5/100,000 people, while the
positive relationship is largest for the 45-54 age group.
Job displacement ▲ ▲ For male workers, the risk of mortality in the year after displacement is 50-100% higher and at least 11% higher over the
twenty-year period after the displacement. A displaced worker at age forty has a loss in life expectancy of 1.0 to 1.5 years.
Income shock ▲ ▲ No short-term effects, but recessions and shocks to GDP are more harmful to life expectancy the longer they last. For the
group of workers younger than age 60, a 10% increase in income over a five-year period lowers the probability of death in
the following year by approx. 5%.
Intimate partner ▲ ▲ Increase, with high variation across countries (Muldoon et al, 2021; Lausi et al., 2021).
violence
Suicide ▲ ▲ 0.4-1.3% (1.3-1.6%) increase for each one p.p. increase in the unemployment rate in Europe (USA); and 1.0% increase in
suicides among individuals aged younger than 65 years (while old-age suicides do not respond to economic downturns). Data
on suicide mortality in the European Union is, for example, released by Eurostat. In the post-financial-crisis period 2011 to
2016, suicide mortality in the EU27 ranges between 12.3 suicides per 100,000 inhabitants in 2011 and 10.8 in 2016. Suicide
rates are lower for people aged below 65 and range between 9.2 in 2016 and 10.8 in 2011. There is, however, much variation
across the member states of the European Union, with large numbers above 30 suicides per 100,000 inhabitants in Lithuania
and about 4.2 suicides per 100,000 inhabitants in Greece. In Germany, as the largest member state, suicides range between
11 and 12 per 100,000 inhabitants in the period 2011-2016.
Air pollution -▼ ▼ Unclear
Vehicle accidents Traffic accidents have fallen significantly during the months of social distancing and stay-at-home regulations and because
of unemployment. Depending on the country, time span, strength of lockdown, and economic impact, traffic accidents
decreased by up to 74%. Emergency department visits for orthopedic hand and trauma reasons declined by up to 60%. An
increase in the unemployment rate by one p.p. reduces traffic accident fatalities by between 2.4 and 2.9%. Data on mortality
▼ ▼
due to vehicle accidents in the European Union is, for example, released by Eurostat. In the period 2011 to 2016, the number
of persons killed in road accidents in the EU27 decreased from 6.5 to 5.3/100,000 inhabitants. The rates are highest in
Romania and lowest in Sweden. In Germany, as the largest member state, the rate was 5.0/100,000 in 2011 and 3.9/100,000
in 2016.
21
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