The cost of the pandemic for the healthcare workforce: an economic evaluation of COVID-19 among healthcare professionals in Brazil based on reported cases, 2020–2022Abstract
Objective To estimate the direct and indirect costs of hospitalizations and deaths due to COVID-19 among healthcare professionals in Brazil from 2020 to 2022.
Methods A partial economic evaluation study of the cost of illness, conducted from the perspectives of the Unified Health System (SUS) and society. Data were obtained from the Influenza Epidemiological Surveillance Information System (Sivep-Gripe), the Mortality Information System (SIM), and the Hospital Information System (SIH/SUS). Direct costs were estimated based on hospital admissions; indirect costs were estimated based on lost productivity, measured by potential years of life lost (PYLL), potential years of work lost (PYWL), and potential years of income lost (PYIL), monetized using the human capital method.
Results A total of 3,698 hospital admissions and 11,231 deaths among healthcare professionals were recorded. PYLL totaled 159,800.4, with the highest concentration in the 40–49 (28.55%) and 50–59 (26.30%) age groups. PYWL totaled 72,031, with an economic loss of R$ 3,507,820,144.72. PYIL totaled 139,728.0, corresponding to R$ 6,602,378,714.38. The direct costs of hospitalizations were R$ 23,399,642.85, and the indirect costs associated with days of hospitalization were R$ 12,551,903.26.
Conclusion the findings highlight the high epidemiological and economic burden of COVID-19 on healthcare professionals.
Keywords
Life Expectancy; Health Personnel; Health Management; COVID-19; Cost of Illness; Occupational Health; Health Evaluation
Resumo
Objetivo Estimar os custos diretos e indiretos das internações e óbitos por covid-19 entre profissionais de saúde no Brasil, de 2020 a 2022.
Métodos Estudo de avaliação econômica parcial do tipo custo da doença, conduzido sob as perspectivas do Sistema Único de Saúde (SUS) e da sociedade. Utilizaram-se dados do Sistema de Informação da Vigilância Epidemiológica da Gripe (Sivep-Gripe), Sistema de Informações sobre Mortalidade (SIM) e Sistema de Informações Hospitalares (SIH/SUS). Os custos diretos foram estimados com base nas internações hospitalares; os indiretos, pela perda de produtividade, mensurada por anos potenciais de vida perdidos (APVP), de trabalho perdidos (APTP) e de renda perdidos (APRP), monetarizados pelo método do capital humano.
Resultados Foram registradas 3.698 internações e 11.231 óbitos de profissionais da saúde. Os APVP totalizaram 159.800,4, com maior concentração nas faixas etárias de 40–49 anos (28,55%) e 50–59 anos (26,30%). Os APTP somaram 72.031, com perda econômica de R$ 3.507.820.144,72. Os APRP totalizaram 139.728,0, correspondendo a R$ 6.602.378.714,38. Os custos diretos das internações foram R$ 23.399.642,85, e os indiretos associados aos dias de internação, R$ 12.551.903,26.
Conclusão Os achados evidenciam elevada carga epidemiológica e econômica da covid-19 sobre os trabalhadores da saúde.
Palavras-chave
Expectativa de Vida; Profissionais de Saúde; Gestão em Saúde; Covid-19; Custo da Doença; Saúde do Trabalhador; Avaliação em Saúde
Introduction
Since the onset of the COVID-19 pandemic, healthcare professionals have played a strategic role in sustaining the response to the health crisis, working in direct care, surveillance, diagnosis, hospital care, and service organization, often under conditions of intense occupational exposure and high physical and psychological strain1.
In Brazil, the vulnerability of this group has been exacerbated by care overload, shortages of personal protective equipment (PPE), insufficient material and human resources, and the persistence of precarious forms of employment characterized by unstable contracts, long working hours, multiple jobs, and marked inequalities in working conditions across regions and professional categories2. In this sense, the effects of the pandemic on healthcare professionals cannot be reduced to the dynamics of virus transmission but must be understood in light of the structural risks inherent in the healthcare work process and the historical limitations of occupational health protection policies.
The infection, illness, hospitalization, and, in many cases, death of these professionals have produced repercussions that extend beyond the clinical sphere, reaching social, economic, and institutional dimensions3. In addition to the physical and mental health impacts, absences and deaths have compromised the availability of the healthcare workforce, affecting the continuity of care and the system’s capacity to respond in a context of high demand for care3. These events entailed direct costs for the public system, related to hospitalizations, the use of intensive care units, and treatments provided, as well as significant indirect costs resulting from lost productivity, reduced income, and Potential Years of Life Lost (PYLL)4,5.
The precarious nature of work and the fragility of social protection policies and occupational health and safety measures contributed to exacerbating these effects, especially in settings marked by regional inequalities and reduced service responsiveness6. Thus, analyzing the economic impact of COVID-19 among healthcare professionals requires an approach that integrates the social determinants of the health-disease process, concrete working conditions, and institutional mechanisms for protecting the healthcare workforce.
In Brazil, COVID-19 cases requiring hospitalization are subject to mandatory reporting in the Influenza Epidemiological Surveillance Information System (Sivep-Gripe), while deaths are recorded in the Mortality Information System (SIM)7,8. These systems collect essential information, such as occupation, sociodemographic characteristics, clinical diagnosis, and underlying and associated causes of death, constituting relevant sources for epidemiological surveillance and for analyzing the effects of the pandemic on specific occupational groups. Complementarily, direct costs related to hospitalizations are recorded in the Hospital Information System of the Unified Health System (SIH/SUS), the Ministry of Health’s official tool for processing the Hospital Admission Authorization (AIH), for billing and payment of hospital services related to admissions in the public and affiliated networks, enabling the monitoring of healthcare provision and hospital expenditures7,8.
Despite the availability of these databases, the lack of systematic mechanisms for estimating and integrated monitoring of the economic costs associated with illness and mortality among healthcare professionals has limited strategic planning, the formulation of compensatory measures, and the strengthening of policies to protect this workforce9.
Given this situation, the aim of this study was to estimate the direct and indirect costs of hospitalizations and deaths due to COVID-19 among healthcare professionals in Brazil, based on cases reported between 2020 and 2022. The analysis was conducted using official databases, with an emphasis on measuring the economic burden of disease from the perspectives of the SUS and society, considering that the observed impacts reflect not only the epidemiological magnitude of the pandemic but also the working conditions, occupational exposure, and social protection that shape the practice of healthcare work in the country.
Methods
Study Design and Perspective
This is a partial economic evaluation study of the cost of illness (COI) type, developed from the perspectives of the SUS and society. Two components were estimated: direct costs, represented by expenditures on COVID-19 hospitalizations within the SUS, and indirect costs, related to productivity losses resulting from work absences and premature mortality among healthcare professionals.
Intervention and comparators
Because this is a COI study based on secondary data, there was no intervention, no health comparator, and no measurement of outcomes related to effectiveness, utility, or preference.
Population
The study population included healthcare professionals who were hospitalized and/or died from COVID-19 in Brazil between 2020 and 2022. To identify healthcare professionals, the “occupation” field was used, coded according to the Brazilian Classification of Occupations (CBO 2002). Records were considered eligible if the reported occupation corresponded to healthcare categories previously defined in the study and described below: 2212 – Biomedical Scientists, 2231 – Physicians, 2232 – Dental Surgeons, 2234 – Pharmacists, 2235 – Nurses, 2236 – Physical Therapists, 2237 – Nutritionists, 2238 – Speech-language Pathologists, 2239 – Occupational Therapists, 2515 – Psychologists and psychoanalysts, 2516 – Social Workers, 3221 – Complementary Therapy Technologists, 3222 – Nursing Technicians and Assistants, 3223 – Optics and Optometry Technicians, 3224 – Dental Technicians, 3225 – Orthopedic Prosthetics Technicians, 3226 – Orthopedic Immobilization Technicians, 3241 – Medical and Dental Equipment Technicians, 3242 – Clinical Pathology Technicians and Technical Assistants, 3251 – Pharmacy Technicians in Pharmaceutical Compounding, 3252 – Food Production, Preservation, and Quality Technicians, 5151 – Health Promotion and Support Service Workers, 5152 – Health Laboratory Assistants, 3253 – Biotechnology Support Technicians, 3281 – Autopsy Technicians and Taxidermists.
Setting
The study was conducted in Brazil, a country comprising 27 states distributed across five major regions (North, Northeast, Southeast, South, and Central-West), characterized by regional inequalities in the distribution of the healthcare workforce, the organization of the healthcare network, and the availability of hospital beds and Intensive Care Unit (ICU) beds. During the COVID-19 pandemic, these inequalities influenced the services’ response capacity, especially in the face of the abrupt increase in demand for care, hospitalizations, and intensive care.
Between 2020 and 2022, the pandemic placed significant pressure on the SUS, requiring a reorganization of the network, an emergency expansion of beds, and greater mobilization of healthcare professionals. This context produced varying levels of occupational exposure, work overload, and vulnerability across regions and professional categories, which is relevant for interpreting the direct and indirect costs associated with hospitalizations and deaths from COVID-19 among healthcare professionals. The time horizon adopted corresponded to the period during which the analyzed events occurred, from 2020 to 2022, covering the initial phase of the pandemic, the period of greatest healthcare pressure in 2021, and the relative reduction in severe events in 2022.
Evaluation Model
The economic evaluation was conducted as a partial cost-of-illness study, using a descriptive and retrospective approach, based on the measurement of direct and indirect costs associated with hospitalizations and deaths due to COVID-19 among healthcare professionals in Brazil. No analytical decision model, such as a decision tree or Markov model, was used, since the objective was not to compare interventions, therapeutic alternatives, or prevention strategies, but to estimate the economic burden observed based on events recorded in official information systems.
The estimation model considered two main components. The first corresponded to direct hospital costs, estimated based on the number of hospitalization days identified among healthcare professionals and the average daily hospitalization cost calculated using SIH/SUS records. The second component corresponded to indirect costs, estimated by the loss of productivity resulting from days of hospitalization and premature mortality, measured using PYLL, Potential Years of Work Lost (PYWL), and Potential Years of Income Lost (PYIL), monetized using the human capital method.
Assumptions
Methodological assumptions were adopted to enable the measurement of costs using secondary, public, and anonymized data sources. It was assumed that SIM and Sivep-Gripe records containing occupational information compatible with health care categories represented health care professionals eligible for the study. For deaths, records with ICD-10 codes related to COVID-19 as underlying or associated causes were considered; for hospitalizations, cases classified as SARS due to COVID-19 in Sivep-Gripe were considered. Since no individual linkage between the databases was performed, estimates of hospitalizations, deaths, and hospital costs were treated in an aggregated manner.
For direct costs, it was assumed that the average daily cost of hospitalization for COVID-19 estimated in the SIH/SUS could be applied to the days of hospitalization of healthcare professionals identified in Sivep-Gripe, due to underreporting and inconsistencies in the occupation variable in the SIH/SUS. For indirect costs, it was assumed that premature mortality and days of hospitalization resulted in a loss of productivity equivalent to the potential time of work or lost income. Monetization was performed using the human capital method, employing standardized salary values by occupational category, 40-hour workweeks, and annual business days. Outpatient costs, post-discharge costs, rehabilitation, long-term sequelae, costs of caregivers, psychological distress, family losses, or other intangible costs were not included, as they were not available in a standardized form in the databases used or because they fell outside the scope of the analysis.
Data sources
Data was extracted from SUS health information systems available via open access: SIM, used to identify deaths; Sivep-Gripe, to identify hospitalization cases; and SIH/SUS, to estimate the direct costs of hospitalizations.
Death records were extracted from the SIM using public files available on the website of the Department of Information Technology of the Unified Health System (Datasus)10. Cases were included whose underlying or associated causes contained the following ICD-10 codes: B34.2 (coronavirus infection, unspecified site), U07.1 (COVID-19, identified virus), and U07.2 (COVID-19, unidentified virus).
Hospitalizations were identified in Sivep-Gripe based on records with a final classification of “5 – Severe Acute Respiratory Syndrome (SARS) due to COVID-19” and with occupational information consistent with working in the healthcare sector, a variable made available in the system as of March 31, 2020. Individuals with a clinical picture consistent with SARS were considered hospitalized, including signs and symptoms such as fever, cough, dyspnea, O2 saturation ≤ 93%, respiratory rate ≥ 30 breaths per minute, PaO2/FiO2 ratio < 300, lymphopenia, and signs of alveolar edema. The datasets were obtained through public access via OpenDataSUS11.
To estimate direct costs, the SIH/SUS AIHs were used, also obtained from public files available on Datasus10. Records with the following ICD-10 codes were selected: B34.2 (coronavirus infection, unspecified site), B97.2 (coronavirus as a cause of diseases classified in other chapters), U07.1 (COVID-19, identified virus), U07.2 (COVID-19, unidentified virus), and J80 (acute respiratory distress syndrome – ARDS). Data from the National Registry of Health Facilities (CNES) were also used to obtain the number of health professionals registered during the period, employed as the denominator in the calculation of PYLL rates per 1,000 health professionals, which are publicly available. All data was retrieved from the Datasus website (https://datasus.saude.gov.br/transferencia-de-arquivos/) on January 7, 2025.
Data Organization
To increase the transparency and reproducibility of the analysis, data cleaning and processing procedures were adopted prior to the analytical stage. Initially, the databases were inspected for completeness, consistency, and duplicate records. Duplicate records were excluded when identified, as well as those with missing or inconsistent values in variables essential to the analysis, such as diagnosis, case progression, age, or occupation, according to the specific purpose of each database. Categorical variables were also standardized, particularly those related to sex, age group, year of occurrence, and occupation. For the occupation field, records were reviewed and harmonized to fit into the analyzed occupational categories, using the 2002 Brazilian Classification of Occupations (CBO 2002) as a reference. Records without sufficient information for occupational classification were excluded from the specific analyses by occupational category. Since the databases used are public and anonymized, no probabilistic or deterministic linkage was performed between the systems.
The authors had access exclusively to anonymized and public-domain data, already made available for consultation and extraction by official systems. No independent validation was performed of the record selection algorithms adopted in the study, which were based on ICD-10 codes and occupational information, as described above.
Costs
Hospital costs were estimated based on the average cost per day of hospitalization calculated in the SIH/SUS and subsequently applied to the total number of hospitalization days for cases identified in Sivep-Gripe as healthcare professionals. Different procedures were adopted depending on the availability and quality of information in each database, aiming to align the occupational identification of cases with the official hospital cost database. In SIM and Sivep-Gripe, the identification of healthcare professionals was performed directly from the “occupation” field, based on codes or descriptions that correspond to the health categories selected in the study. In the SIH/SUS, however, the occupation field exhibits high underreporting and inconsistent data entry, which made it impossible to directly and reliably identify hospitalizations of healthcare professionals.
Hospitalization costs were calculated based on the SUS procedure fee schedule, considering the costs of stays in general wards and the ICU. The average cost per day of hospitalization was obtained by dividing the total cost of hospitalizations by the total number of days spent in the hospital. All monetary values were expressed in Brazilian reais (R$) and converted to U.S. dollars (US$) using the average exchange rate for May 2025 reported by the Central Bank of Brazil (US$ 1.00 = R$ 5.71)12.
For indirect costs, two main indicators were estimated: PYLL and Potential Years of Work and Income Lost (PYWL and PYIL), calculated based on deaths recorded in the SIM. PYLL were calculated using the formula: PYLL = ∑(aidi), where ai represents the difference between life expectancy and age at death (when death occurs between the ages of i and i + 1 years), and di represents the number of deaths in that age group. Deaths occurring at an age above life expectancy were excluded from the calculation, according to estimates produced by the Brazilian Institute of Geography and Statistics (IBGE): males – 71 years (2020), 69.3 years (2021), 72 years (2022); female – 78.4 years (2020), 76.4 years (2021), 79 years (2022)13.
To allow for proportional comparisons, the PYLL rate (PYLLR) was calculated per 1,000 health professionals, using the number of health professional records in the CNES as the denominator. The average PYLL per death was also calculated, stratified by sex and age group.
PYWL were calculated as the difference between age at death and the minimum retirement age: 65 years for men and 62 years for women, according to Constitutional Amendment No. 103/201914.
PYIL were obtained by subtracting age at death from life expectancy by sex and year. Both were monetized using the human capital method, employing estimated annual net salary values, as this approach is most compatible with the objective of estimating the potential economic impact of premature mortality and work absences among health professionals. This method assigns a monetary value to work time lost based on expected income during the productive period.
For this calculation, 40-hour workweeks were considered, based on working days, including the 13th-month salary, vacation pay, and social security contributions, in accordance with the Consolidation of Labor Laws (Decree-Law No. 5,452/1943)15. The figures were based on the minimum wage for nursing (Law No. 14,434/2022)16 for health professionals with higher education and technical training; on the minimum wage for Community Health Agents (ACS) and Endemic Disease Control Agents (ACE) (Law No. 11,350/2006, as amended by Constitutional Amendment No. 120/2022)17,18 for equivalent categories; and, for physicians19, in the stipends of the Mais Médicos and Médicos pelo Brasil programs, as they constitute public, national, and standardized benchmarks for remuneration linked to the provision of medical services in the country. This approach aims to enhance the comparability of the estimates and reduce the heterogeneity associated with differences in employment relationships, specialties, work schedules, and regional remuneration contexts within the category.
To estimate the financial loss per day of hospitalization, the annual net salary was divided by the average number of working days in the year (253 days in 2020, 252 in 2021, and 251 in 2022), and this daily amount was multiplied by the total number of days of hospitalization per case. Costs related to caregivers were not included, as they are not the central focus of this estimate of productivity loss and were not available in a standardized and individualized format in the databases used.
Statistical and Sensitivity Analyses
Descriptive statistical analyses were performed to characterize hospitalizations and deaths due to COVID-19 among healthcare professionals in Brazil from 2020 to 2022. Categorical variables were described using absolute and relative frequencies, by sex, age group, race/skin color, occupational category, year of occurrence, and state of residence. For quantitative variables related to costs and potential losses, totals, percentages, means, and rates were calculated according to the nature of each indicator.
The PYLL were estimated in absolute values, percentages, average per death, and rate per 1,000 healthcare professionals, stratified by sex, age group, and year. The total number of hospitalization days, the total cost of hospitalizations, the estimated daily wage, and the wage loss associated with COVID-19 hospitalizations among healthcare professionals were also calculated, according to occupational category and year. Productivity loss resulting from premature mortality was estimated using the PYWL and PYIL and their respective monetary values, by occupational category, age group, and sex of the deaths. No hypothesis tests or inferential models were performed, as the study’s objective was to estimate and describe the economic burden observed based on records available in official information systems.
A univariate deterministic sensitivity analysis was performed to assess the robustness of the indirect cost estimates in the face of variations in critical model parameters. Three groups of parameters were tested: life expectancy (±1 year), wages (±20%), and discount rate (0%, 3%, and 5% per year). In scenarios with variations in life expectancy, the PYLL and PYIL were recalculated. In scenarios with wage variations, potential years lost remained constant, with only the monetary values of the PYWL and PYIL varying. In discount rate scenarios, potential years lost remained constant, with only future monetary values varying. The results were compared to the base-case scenario using absolute and percentage changes and are presented in supplementary tables. In the base case, no discount rate was applied to indirect costs. The influence of this parameter was examined exclusively in the sensitivity analysis, with scenarios of 0%, 3%, and 5% per year.
Data analysis was performed using Microsoft Excel® and TabWin/Datasus software.
Results
Between 2020 and 2022, 3,698 cases of hospitalization due to COVID-19 were identified in Sivep-Gripe and 11,231 deaths were recorded in SIM among healthcare professionals in Brazil. In terms of gender distribution, a predominance of females was observed among those hospitalized (59.9%) and of males among the deaths (59.3%). Regarding age group, hospitalizations were concentrated among those aged 30–59, especially among those aged 40–49 (23.4%) and 30–39 (22.7%). Deaths, however, were concentrated among professionals aged 60 years or older (60.5%), with a particular emphasis on the 70 years or older age group (36.7%) (Table 1).
Regarding race/skin color, the white category predominated among both hospitalized patients (49.1%) and deaths (62.8%). However, it is worth noting the higher rate of missing data for this variable in Sivep-Gripe, with 11.7% of records missing, while in SIM this proportion was 1.7%.
Regarding occupation, nursing technicians/assistants represented the largest group among hospitalized patients (35.1%) and also among deaths (37.2%), followed by physicians (23.3% and 19.3%, respectively) and nurses (18.1% and 12.7%). These three groups accounted for most of the morbidity and mortality burden observed during the period (Table 1).
Figure 1 shows that São Paulo, Rio de Janeiro, and Minas Gerais accounted for the highest absolute numbers of hospitalizations and deaths during the three-year period. Amazonas, Pará, and Pernambuco also reported a significant volume of cases, while states such as Acre and Roraima recorded the lowest totals, highlighting marked territorial heterogeneity in the COVID-19 burden among healthcare professionals.
Distribution of deaths (Map A) and hospitalizations (Map B) due to COVID-19 among healthcare professionals, by state. Brazil, 2020–2022
An estimated 159,800.4 PYLL were recorded during the period, corresponding to 8,116 eligible deaths, an average of 19.69 years of life lost per death, and a cumulative rate of 13.21 PYLL per 1,000 healthcare professionals (Table 2). The year 2021 had the highest number of PYLL (96,510.1), followed by 2020 (54,586.2) and 2022 (8,704.1).
The highest volumes of PYLL were concentrated in the 40–49 age group, with 45,626.7 PYLL (28.55%), and the 50–59 age group, with 42,025.2 (26.30%). Next were the 30–39 age group, with 32,796.8 PYLL (20.52%), and the 60–69 age group, with 26,002.8 (16.27%). The 70 and older age group accounted for 4,220.2 PYLL (2.64%), and the 18–29 age group, for 9,128.7 (5.71%).
When stratified by sex, females accounted for 114,346.0 PYLL, corresponding to 71.6% of the total, with an average of 21.2 years lost per death and a rate of 14.2 per 1,000 professionals. For males, 45,454.4 PYLL were estimated, with an average of 16.6 years lost per death and a rate of 11.3 per 1,000 professionals (Table 2).
Regarding productivity losses due to premature mortality, Supplementary Table 1 estimated 72,031 PYWL and 139,728.0 PYIL, corresponding to R$ 3,507,820,144.72 in work-related losses and R$ 6,602,378,714.38 in income losses across all analyzed categories.
Among occupational groups, professionals with higher education, excluding physicians, totaled 30,985 PYWL and 58,361 PYIL, equivalent to R$ 1,545,368,299.20 and R$ 2,939,392,170.00, respectively. Nursing technicians accounted for 30,959 PYWL and 63,139.4 PYIL, with monetary losses of R$ 1,118,837,827.06 and R$ 2,109,002,914.12. Among physicians, an estimated 5,582 PYLL and 10,213.8 PYIL were reported, corresponding to R$ 761,649,696.00 and R$ 1,407,515,136.60. For health workers, laboratory assistants, and midwives, the figures were 4,505 PYWL and 8,013.8 PYIL, with losses of R$ 81,964,322.66 and R$ 146,468,493.66 (Supplementary Table 1).
Productivity losses were concentrated primarily in the 40–49 and 50–59 age groups, which totaled 26,081 and 15,759 PYWL, respectively, in addition to 45,626.7 and 42,025.2 PYIL. Women accounted for the largest volume of losses, with 42,406 PYWL and 95,380.7 PYIL, surpassing those of men, who had 29,625 PYWL and 44,347.4 PYIL.
In the sensitivity analysis, variations in life expectancy produced slight changes in PYLL and PYIL, while wage variations proportionally impacted the monetary values of the losses. The introduction of discount rates of 3% and 5% reduced the monetary values of PYWL and PYIL more significantly. Nevertheless, in all the scenarios tested, indirect costs remained high and exceeded the direct costs of hospitalizations, reinforcing the robustness of the study’s main conclusion. Detailed results are presented in Supplementary Table 2.
Regarding the direct costs of COVID-19 hospitalizations, 34,249 days of hospitalization were recorded during the period, corresponding to R$ 23,399,642.85. The highest expenditure occurred in 2021, with R$ 12,104,629.99 and 17,717 days of hospitalization, followed by 2020, with R$ 10,013,973.10 and 14,657 days, and 2022, with R$ 1,281,039.76 and 1,875 days (Table 3).
Number of days of hospitalization and total cost in Brazilian reais (R$) of COVID-19 hospitalizations among healthcare professionals, by occupational category and year. Brazil, 2020–2022
The highest cumulative hospital costs were observed among nursing technicians, with R$ 8,249,895.97 and 12,075 days of hospitalization; physicians, with R$ 6,219,362.58 and 9,103 days; and nurses, with R$ 3,588,277.74 and 5,252 days. Together, these three categories accounted for approximately 77.2% of the total cost of hospitalizations.
Significant costs were also identified among dentists (R$ 1,261,226.33), pharmacists (R$ 1,201,786.09), and physical therapists (R$ 628,563.50). In contrast, categories such as laboratory technicians, ACS, ACE, and midwives showed lower absolute values, reflecting a lower volume of hospitalizations recorded during the period.
Wage losses associated with days of hospitalization totaled R$ 12,551,903.26 over the three-year period. The amount was highest in 2021 (R$ 6,204,339.61), followed by 2020 (R$ 5,692,277.74) and 2022 (R$ 655,285.91), mirroring the temporal distribution of hospitalization days (Table 4).
Number of days of hospitalization, estimated daily wage, and associated wage loss, in Brazilian Reais (R$), for COVID-19 hospitalizations among healthcare professionals, by occupational category and year. Brazil, 2020–2022
Physicians experienced the largest aggregate wage loss during the period, at R$ 5,839,392.44, followed by nursing technicians, at R$ 2,691,638.25, and nurses, at R$ 1,840,668.44. Among the categories with the lowest losses were laboratory assistants (R$ 21,722.73), ACS (R$ 38,478.24), ACE (R$ 15,580.94), and midwives (R$ 15,983.31).
Discussion
The results of this study show that COVID-19 had a significant impact on morbidity and mortality and on the costs associated with healthcare work in Brazil. During the period analyzed, 3,698 hospitalizations and 11,231 deaths were recorded among healthcare professionals. Among the deaths eligible for the calculation of PYLL, an estimated 159,800,4 PYLL, in addition to 72,031 PYWL, 139,728.0 PYIL, R$ 23,399,642.85 in direct hospitalization costs, and R$ 12,551,903.26 in lost wages associated with days of hospitalization.
The concentration of hospitalizations among those aged 30 to 59 and of deaths among professionals aged 60 or older suggests distinct patterns of severity and outcome, possibly influenced by age, the presence of comorbidities, and the intensity of occupational exposure. Nevertheless, the highest burden of PYLL, PYWL, and PYIL was concentrated among those aged 40 to 59, indicating that premature mortality among individuals of working age resulted in disproportionate social and economic losses - a pattern also observed in studies highlighting the greater vulnerability of healthcare professionals during periods of heightened care demands1,20,21.
The predominance of females among hospital admissions and of males among deaths, combined with the higher absolute volume of PYLL among females, should be interpreted considering the composition of the healthcare workforce in the country. Given that women constitute the majority in the most numerous categories, particularly nursing and other care-related occupations, the higher absolute number of deaths in this group may reflect both the larger occupational cohort and heightened exposure during the pandemic3.
The findings by occupational category reinforce the central role of nursing technicians, physicians, and nurses in the dynamics of the pandemic among healthcare professionals. These groups accounted for most hospitalizations and deaths and, together, represented the largest share of direct hospital costs. Regarding productivity losses due to premature mortality, professionals with higher education (excluding physicians) and nursing technicians stood out, while physicians experienced the greatest wage loss related to days of hospitalization, due to the higher daily rate assigned to this category. This pattern is consistent with national and international studies that have shown greater exposure and a higher burden of morbidity and mortality in core healthcare occupations2,21.
The peak in costs and losses observed in 2021 is consistent with the period of greatest pressure on the country’s health services. That year saw the highest annual figures for PYLL, direct hospital costs, and wage loss due to hospitalization, suggesting that the combination of high viral transmission, healthcare overload, and increased case severity simultaneously amplified the pandemic’s epidemiological and economic impact. The literature also describes a significant increase in pressure on health services and on workers during periods of higher viral circulation and worsening of cases1,21.
The heterogeneity observed among Brazilian states, with a concentration of hospitalizations and deaths in states such as São Paulo, Rio de Janeiro, and Minas Gerais, indicates that the burden of COVID-19 among healthcare professionals followed, at least in part, the territorial distribution of the healthcare network, the workforce, and the intensity of viral circulation. This pattern reinforces the importance of workforce protection strategies that are sensitive to regional inequalities and the response capacity of services in different contexts3.
From an economic evaluation perspective, the results confirm that the costs associated with premature mortality and lost productivity far exceed direct hospital costs. This difference highlights that the economic burden of the pandemic was not limited to healthcare financing but had a profound impact on the replenishment and sustainability of the healthcare workforce. Thus, analyses limited to healthcare costs tend to underestimate the real impact of health emergencies on healthcare systems dependent on specialized labor, in line with studies that have demonstrated the greater relative weight of indirect costs in different national contexts7,20,22,23.
The choice of the human capital method proved consistent with the aim of quantifying the social and economic impact of the loss of healthcare professionals. Although alternative approaches, such as friction costs, yield more conservative estimates, the method adopted provides a more comprehensive picture of potential losses in contexts of high demand for care and a need for a skilled workforce. In the same vein, the sensitivity analysis demonstrated that, even in the most conservative scenarios, indirect costs remained high and exceeded the direct costs of hospitalizations, reinforcing the robustness of the study’s main conclusion. This pattern is consistent with findings in the international literature on the economic burden of COVID-19, especially when considering premature deaths and loss of productivity7,20,22.
The results also have relevant implications for public policy formulation. Prevention measures, occupational protection, adequate staffing levels, continuous availability of PPE, occupational health surveillance, and psychosocial support can reduce not only morbidity and mortality, but also significant economic losses associated with work interruptions and the premature death of essential workers. In this sense, the protection of the health workforce constitutes a strategic dimension of the health response and the resilience of health systems3,24.
Some limitations should be considered when interpreting the findings. As this is a study based on secondary data, the estimates are subject to incompleteness, underreporting, and data entry inconsistencies, especially in variables such as occupation, which may have led to an underestimation of events and costs. Furthermore, the use of public and anonymized databases made it impossible to link records from the SIM, Sivep-Gripe, and SIH/SUS systems, preventing individual case matching and limiting the reconstruction of disease, hospitalization, and death trajectories.
It should also be noted that PYLL depend on the life expectancy parameters adopted and, by definition, exclude deaths occurring above that threshold, which is why the number of deaths included in this calculation is lower than the total number of deaths observed in the SIM. Furthermore, post-discharge costs, long-term sequelae, psychological distress, family repercussions, and other intangible or long-term dimensions of the pandemic were not considered, suggesting that the economic and social e impact of COVID-19 on healthcare professionals may be even greater than estimated in this study25,26.
Conclusion
COVID-19 imposed a high epidemiological and economic burden on the healthcare workforce in Brazil, producing effects that extended beyond the scope of care and impacted the sustainability of the healthcare system. The findings demonstrate that illness, hospitalizations, and deaths among healthcare professionals constitute a concrete manifestation of inequalities and vulnerabilities related to work processes and conditions, affirming the centrality of occupational health in understanding the impacts of the pandemic. Thus, the protection of the healthcare workforce should not be treated merely as a contingency measure in the face of health crises, but as a structural pillar of occupational health policies, work management, and service organization. Strengthening surveillance, prevention, protection, and care measures directed at these workers is a strategic prerequisite for the resilience of the SUS and for preparedness in the face of future public health emergencies.
Supplementary Materials
Appendix
References
-
1 Bandyopadhyay S, Baticulon RE, Kadhum M, Alser M, Ojuka DK, Badereddin Y et al. Infection and mortality of healthcare workers worldwide from COVID-19: a systematic review. BMJ Glob Health. 2020;5(12):e003097. https://doi.org/10.1136/bmjgh-2020-003097
» https://doi.org/10.1136/bmjgh-2020-003097 -
2 Duarte MMS, Haslett MIC, Freitas LJA, Gomes NTN, Malta JMAS, Alves AJS et al. Descrição dos casos hospitalizados pela COVID-19 em profissionais de saúde nas primeiras nove semanas da pandemia, Brasil, 2020. Epidemiol Serv Saúde. 2020;29(5):e2020277. https://doi.org/10.1590/S1679-49742020000500011
» https://doi.org/10.1590/S1679-49742020000500011 -
3 Helioterio MC, Lopes FQRS, Sousa CC, Souza FO, Pinho PS, Sousa FNF et al. COVID-19: por que a proteção de trabalhadores e trabalhadoras da saúde é prioritária no combate à pandemia? Trab Educ Saúde. 2020;18(3):e00289121. https://doi.org/10.1590/1981-7746-sol00289
» https://doi.org/10.1590/1981-7746-sol00289 - 4 Nunes ACGP. O absentismo dos profissionais de saúde do SNS e custos associados em tempos de pandemia COVID-19 [dissertação]. Lisboa: Universidade Nova de Lisboa; 2021.
- 5 Passos ALS. Análise do impacto econômico do absenteísmo em hospital público durante pandemia da COVID-19 [dissertação]. Recife: Universidade Federal de Pernambuco; 2022.
-
6 Rocha-Filho CR, Martins JWL, Lucchetta RC, Ramalho GS, Trevisani GFM, Rocha AP et al. Hospitalization costs of coronaviruses diseases in upper-middle-income countries: a systematic review. PLoS One. 2022;17(3):e0265003. https://doi.org/10.1371/journal.pone.0265003
» https://doi.org/10.1371/journal.pone.0265003 - 7 Marinho MF. Semelhanças e diferenças na interpretação dos dados sobre SG, SRAG e COVID-19: SIM, SIVEP-Gripe e cartórios de registro civil. In: Santos AO, Lopes LT, organizadores. Coleção COVID-19. v. 2: planejamento e gestão. Brasília: Conselho Nacional de Secretários de Saúde; 2021. p. 112-25.
-
8 Souza VMM, Arsky MLNS, Castro APB, Araujo WN. Anos potenciais de vida perdidos e custos hospitalares da leptospirose no Brasil. Rev Saúde Pública. 2011;45(6):1001-8. https://doi.org/10.1590/S0034-89102011005000070
» https://doi.org/10.1590/S0034-89102011005000070 -
9 Ghaffari Darab M, Keshavarz K, Sadeghi E, Shahmohamadi J, Kavosi Z. The economic burden of coronavirus disease 2019 (COVID-19): evidence from Iran. BMC Health Serv Res. 2021;21(1):132. https://doi.org/10.1186/s12913-021-06126-8
» https://doi.org/10.1186/s12913-021-06126-8 -
10 Ministério da Saúde (BR). DATASUS. Brasília: Departamento de Informática do Sistema Único de Saúde; 2025 [citado 7 jan 2025]. Disponível em: https://datasus.saude.gov.br/
» https://datasus.saude.gov.br/ -
11 Ministério da Saúde (BR). OpenDataSUS. Brasília: Ministério da Saúde; [citado 7 jan 2025]. Disponível em: https://opendatasus.saude.gov.br/
» https://opendatasus.saude.gov.br/ -
12 Banco Central do Brasil. Conversor de moedas. Brasília: Banco Central do Brasil; 2025 [citado 4 maio 2025]. Disponível em: https://www.bcb.gov.br/conversao
» https://www.bcb.gov.br/conversao -
13 Instituto Brasileiro de Geografia e Estatística. Tábuas completas de mortalidade para o Brasil: 2020, 2021 e 2022. Rio de Janeiro: IBGE; 2023 [citado 4 maio 2025]. Disponível em: https://www.ibge.gov.br/estatisticas/sociais/populacao/9126-tabuas-completas-de-mortalidade.html
» https://www.ibge.gov.br/estatisticas/sociais/populacao/9126-tabuas-completas-de-mortalidade.html -
14 Brasil. Emenda constitucional Nº 103, de 12 de novembro de 2019. Altera o sistema de previdência social e estabelece regras de transição e disposições transitórias. Diário Oficial da União. 13 nov 2019 [citado 7 maio 2025]. Disponível em: https://www.planalto.gov.br/ccivil_03/constituicao/emendas/emc/emc103.htm
» https://www.planalto.gov.br/ccivil_03/constituicao/emendas/emc/emc103.htm -
15 Brasil. Decreto-Lei Nº 5.452, de 1 de maio de 1943. Aprova a Consolidação das Leis do Trabalho. Diário Oficial da União. 9 ago 1943 [citado 7 maio 2025]. Disponível em: https://www.planalto.gov.br/ccivil_03/decreto-lei/del5452.htm
» https://www.planalto.gov.br/ccivil_03/decreto-lei/del5452.htm -
16 Brasil. Lei Nº 14.434, de 4 de agosto de 2022. Institui o piso salarial nacional do enfermeiro, do técnico de enfermagem, do auxiliar de enfermagem e da parteira. Diário Oficial da União. 5 ago 2022 [citado 7 maio 2025]. Disponível em: https://www.planalto.gov.br/ccivil_03/_ato2019-2022/2022/lei/L14434.htm
» https://www.planalto.gov.br/ccivil_03/_ato2019-2022/2022/lei/L14434.htm -
17 Brasil. Emenda constitucional Nº 120, de 5 de maio de 2022. Altera o art. 198 da Constituição Federal para instituir o piso salarial nacional para os Agentes Comunitários de Saúde e de Combate às Endemias. Diário Oficial da União. 6 maio 2022 [citado 7 maio 2025]. Disponível em: https://www.planalto.gov.br/ccivil_03/constituicao/emendas/emc/emc120.htm
» https://www.planalto.gov.br/ccivil_03/constituicao/emendas/emc/emc120.htm -
18 Brasil. Lei Nº 11.350, de 5 de outubro de 2006. Regula o exercício das atividades de agente comunitário de saúde e de agente de combate às endemias. Diário Oficial da União. 6 out 2006 [citado 7 maio 2025]. Disponível em: https://www.planalto.gov.br/ccivil_03/_ato2004-2006/2006/lei/l11350.htm
» https://www.planalto.gov.br/ccivil_03/_ato2004-2006/2006/lei/l11350.htm -
19 Ministério da Saúde (BR). Programa médicos pelo Brasil: perguntas frequentes. Brasília: Ministério da Saúde; 2022 [citado 4 maio 2025]. Disponível em: https://egestorab.saude.gov.br/image/?file=20220131_I_FAQPMpB.REV225.01.2022rev_5682030788201565089.pdf
» https://egestorab.saude.gov.br/image/?file=20220131_I_FAQPMpB.REV225.01.2022rev_5682030788201565089.pdf -
20 Richards F, Kodjamanova P, Chen X, Li N, Atanasov P, Bennetts L et al. Economic burden of COVID-19: a systematic review. Clinicoecon Outcomes Res. 2022;14:293-307. https://doi.org/10.2147/CEOR.S338225
» https://doi.org/10.2147/CEOR.S338225 -
21 Gholami M, Fawad I, Shadan S, Rowaiee R, Ghanem HA, Hassan Khamis A et al. COVID-19 and healthcare workers: a systematic review and meta-analysis. Int J Infect Dis. 2021;104:335-46. https://doi.org/10.1016/j.ijid.2021.01.013
» https://doi.org/10.1016/j.ijid.2021.01.013 -
22 Norouzi S, Aghlmand S, Faramarzi A. Economic burden of COVID-19 in West Azerbaijan, Iran: a societal perspective. J Iran Med Counc. 2025;8(2):257-67. https://doi.org/10.18502/jimc.v8i2.17697
» https://doi.org/10.18502/jimc.v8i2.17697 -
23 Bartsch SM, Ferguson MC, McKinnell JA, O'Shea KJ, Wedlock PT, Siegmund SS et al. The potential health care costs and resource use associated with COVID-19 in the United States. Health Aff. 2020;39(6):927-35. https://doi.org/10.1377/hlthaff.2020.00426
» https://doi.org/10.1377/hlthaff.2020.00426 -
24 Karlsson U, Fraenkel CJ. COVID-19: risks to healthcare workers and their families. BMJ. 2020;371:m3944. https://doi.org/10.1136/bmj.m3944
» https://doi.org/10.1136/bmj.m3944 -
25 Vasconcelos DSS, Pompeo DA, Antonio-Viegas MCR, Miasso AI, Arruda GO, Teston EF et al. Fatores associados à COVID longa em profissionais de enfermagem. Rev Esc Enferm USP. 2024;58:e20240082. https://doi.org/10.1590/1980-220X-REEUSP-2024-0268pt
» https://doi.org/10.1590/1980-220X-REEUSP-2024-0268pt - 26 Murray CJL, Lopez AD. The global burden of disease: a comprehensive assessment of mortality and disability from diseases, injuries, and risk factors in 1990 and projected to 2020: summary. Boston: Harvard School of Public Health; 1996.
Source: Mortality Information System (SIM) and Influenza Epidemiological Surveillance Information System (Sivep-Gripe).