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. 2025 Mar 26;25:413. doi: 10.1186/s12879-025-10613-2

Measuring the disease burden of seasonal influenza in Germany 2015 - 2020 using the incidence-based disability-adjusted life years (DALYs)

Marin Stapic 1,✉, Ricarda Sophia Schulz 1, Elena Tamayo-Cuartero 1, Tobias Kurth 1,#, Ralph Brinks 2,#
PMCID: PMC11948870  PMID: 40141014

Abstract

Background

Seasonal influenza can lead to severe complications and death, resulting in high disease burden each year. The European Centre for Disease Prevention and Control introduced the Burden of Communicable diseases in Europe (BCoDE) project, quantifying the disease burden of infectious diseases in disability-adjusted life years (DALY). DALYs for influenza exceed those of Tuberculosis, HIV, and Invasive pneumococcal disease. As data on disease burden are limited, this study aims to calculate the seasonal influenza burden for Germany between 2015 and 2020.

Methods

The BCoDE-toolkit developed by the European Centre for Disease Prevention and Control was used, calculating country-specific DALYs. Information on incidence, population data, and underestimation were taken from the Robert Koch-Institute and the Federal Statistical Office of Germany. Outcome trees were created based on information from a rapid review and previous publications. Baseline, lower-bound and upper-bound scenarios were developed to assess the disease burden under varying conditions.

Results

Estimates range from 127,100 DALYs (153 DALYs per 100,000 population) and 1,171,115 DALYs (1,414 DALYs per 100,000 population) depending on the scenario and year examined. The main contributors to the disease burden are sequelae, primarily pneumonia, encephalitis, and myocarditis. The highest burden estimates are observable for infants, children under the age of five and the elderly.

Conclusions

Using a composite health measure like DALY can offer valuable insight into a disease’s impact on population health. Our results indicate a high disease burden due to seasonal influenza in Germany, indicating further research into complication rates, underestimation, and intervention programs for vulnerable populations, e.g., vaccination in infants, children under age of five and elderly population.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-025-10613-2.

Keywords: Influenza, Disability-adjusted life years, Infectious disease surveillance, Communicable diseases, Burden of disease, Population health

Background

The European Centre for Disease Prevention and Control (ECDC) launched the Burden of Communicable Disease in Europe (BCoDE) project intending to develop and use a methodology framework to measure the disease burden of communicable diseases in the European Union (EU) and European Economic Area (EEA). Using the incidence-based DALY approach to estimate the burden of 31 communicable diseases between 2009 and 2013, the authors of the BCoDE project report a total burden of 275 DALYs per 100,000 population, of which seasonal influenza accounted for 30%, corresponding to 81.8 DALYs per 100,000 population [1]. Other infectious diseases such as Tuberculosis and HIV/AIDS can cause between 8 DALYs per 100,000 population and 787 DALYs per 100,000 population, depending on the region and country (Table 1).

Table 1.

Burden of disease of various infectious diseases - Total DALYs (and DALYs per 100,000 population)

Region/Country Tuberculosis HIV/AIDS Acute Hep. B Measles LRI URI
Western Europe 67,266(15) 185,940(43) 5,573(1.28) 180(0.04) 1,802,174(413) 363,202(83)
France 14,932(23) 26,242(40) 928(1.40) 20(0.03) 234,856(355) 55,215(83)
Sweden 1,237(12) 1,273(12) 85(0.83) 0.8(0.01) 30,928(303) 9,033(88)
United Kingdom 9,000(13) 20,297(30) 662(0.98) 67(0.1) 474,724(706) 59,146(88)
Germany 10,045(12) 23,320(27) 708(0.83) 49(0.06) 317,689(374) 67,879(80)
Central Europe 69,881(61) 24,404(21) 1,882(1.65) 625(0.55) 599,840(525) 73,395(64)
Albania 317(12) 83(3) 32(1.18) 121(4.44) 12,404(456) 2,134(78)
Hungary 1,585(16) 1,620(17) 149(1.54) 1(0.01) 22,091(228) 6130(63)
Poland 17,011(44) 7,631(20) 416(1.08) 7(0.02) 206,880(538) 25,432(66)
Eastern Europe 539,277(257) 1,452,867(692) 6,682(3.18) 333(0.16) 1,423,084(678) 159,882(76)
Estonia 877(67) 2,041(156) 12(0.89) 0.2 (0.01) 4,934(376) 875(67)
Lithuania 5,489(196) 2,859(102) 32(1.15) 4(0.1) 12,351(442) 1,863(67)
Ukraine 168,093(382) 346,668(787) 2,751(6.25) 276(0.63) 298,090(677) 32,807(74)
North America 28,251(8) 433,184(119) 2,901(0.80) 69(0.02) 1,339,934(368) 379,345(104)
USA 25,031(8) 415,325(127) 2,273(0.69) 50(0.02) 1,228,694(375) 343,443(105)
Canada 3,145(9) 17,763(49) 626(1.72) 5(0.01) 110,971(304) 35,839(98)
Greenland 75(133) 88(157) 0.94(1.67) 14(24) 248(441) 58(102)
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DALYs Disability-Adjusted Life Years, LRI Lower respiratory infection, URI Upper respiratory infection

Source: GBD 2019 [2]

Similar approaches, wherein the disease burden of seasonal and pandemic influenza was studied, report 2.6 DALYs per 100,000 population to 300 DALYs per 100,000 population [3–6]. While attempts to measure the disease burden due to seasonal influenza have been undertaken internationally by the WHO and ECDC, in Europe, efforts to estimate the national disease burden for a single country have been mainly limited to the Netherlands [4–6]. In Germany, seasonal influenza has the highest incidence rates among all infectious diseases, ranging from 115 to 330 cases per 100,000 population in recent years [7]. To date, the only national disease burden estimates for seasonal influenza in Germany, expressed in DALY, have been calculated by Plass et al., using the BCoDE methodology. In addition to death and recovery, permanent disabilities due to sepsis, acute respiratory distress syndrome (ARDS), and otitis media were included as sequelae. In their modeling approach, Plass et al. report 40.2 DALYs per 100,000 population. However, the disease burden might be underestimated, as sequelae following a symptomatic influenza infection besides death were limited to three long-term disabilities only [8]. Further sequelae that can occur after a symptomatic infection, are pneumonia, bronchitis and its chronic manifestations, myocarditis, encephalitis, and sinusitis [9]. Therefore, the aim of the present study is to estimate the annual DALYs caused by seasonal influenza from 2015 to 2020 in Germany based on the incidence-based DALY approach using the BCoDE-Toolkit developed by the ECDC, taking more accurate estimates of sequelae into account.

Methods

The methodologies used were a rapid review to identify sequelae and their transition probabilities, followed by the design of an outcome tree for three scenarios, examining disease burden calculations under varying conditions. Finally, the BCoDE-toolkit was employed to analyze disease burden for all years and scenarios.

Rapid review

Following the Cochrane Rapid Reviews Methods Group guidelines, a rapid review of influenza-related complications and transition probabilities was conducted. Screening of abstract, title and full text was conducted by three researchers (MS, RSS, ETC) independently. Studies published since 2000 were included using the PubMed database. Keywords for influenza-related complications were identified and used in combination with MeSH terms and Boolean operators (Fig. 1). All searches were conducted in October 2021.

Fig. 1.

Fig. 1

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PRISMA flow diagram

Included studies were in German or English and examined complications following a symptomatic influenza infection, providing estimates on complication rates or probabilities. Eligible study designs were cross-sectional, case-control, cohort studies, and systematic reviews and meta-analyses. Editorials, conference abstracts, presentations, book excerpts, commentaries, case studies, case series and animal studies were excluded. Studies not published in peer-reviewed journals or lacking full text were also excluded. Complication probabilities were synthesized using population size weighting, commonly used in meta-analysis, wherein a summary estimate is calculated to address sample size differences [10] (Supplementary Table 1, Additional file 1).

Incidence-based DALY approach and BCoDE-Toolkit

The methodology for calculating disease-specific DALYs was introduced by Murray et al. in the GBD project. One DALY represents one year of healthy life lost, and is the sum of the years lost due to premature death (YLL) and the years lived with disability (YLD) [11]. Therefore, the DALY is a composite health measure, considering a disease’s impact beyond a single metric as frequency or mortality. The YLL is determined by multiplying fatal cases by the remaining life expectancy at the age of death. YLD is calculated by multiplying the duration of illness by a severity weight that represents the disability’s severity, aggregated across all cases and health outcomes [4]. In addition to death and recovery, sequelae like pneumonia, bronchitis, otitis media, encephalitis, myocarditis, ARDS, sepsis, sinusitis, and their complications, including long-term disability and death, are considered [9, 12] (Fig. 2).

Fig. 2.

Fig. 2

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Outcome tree

The BCoDE-Toolkit, developed by the ECDC, is a publicly available software tool for calculating disease burden. Input data includes incidence, population, underestimation, life expectancy by age and sex, and parameters such as probability of health outcomes, illness duration, disability weight, case coverage, and age distribution. Output data includes total and sequelae-specific YLLs, YLDs, and DALYs, both in absolute numbers and per 100,000 population [13, 14]. Results of previous efforts that investigated the disease burden of seasonal or pandemic influenza were compared to the present modeling approach (Table 2).

Table 2.

Influenza burden - Total DALYs, YLL, YLD, DALYs per 100,000, and DALYs per case in previous modeling approaches

Author Region/Country Total DALY Total YLL Total YLD DALYs per 100,000 DALYs per Case
Van Lier et al. (2007) [3] Seasonal Infl. EU 2,161 1,293-4,654 2,808-16,342 3.7 N/A
Wielders et al. (2012) [4] Pandemic Infl. Netherlands 4,827-7,146 1,468-2,216 2,246-4,930 29–43 N/A
Plass et al. (2014) [8] Seasonal Infl. Germany 33,116 17,077 16,040 40.2 0.03
Van Lier et al. (2016) [5] Seasonal Infl. Netherlands 8,670 4,580 4,090 N/A 0.026
Cassini et al. (2018) [1] Seasonal Infl. EU/EEA 411,334 383,999 27,334 81.8 0.01
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DALYs Disability-Adjusted Life Years, YLL Years of Life Lost due to Premature Mortality, YLD Years lived with Disability, EU European Union, EEA European Economic Area

Underestimation and uncertainty

Multiplication factors were applied to surveillance data reported by the Robert Koch-Institute (RKI) to correct for underestimation. Age- and sex-specific multiplication factors were derived from the seasonal influenza reports published by the “Arbeitsgemeinschaft Influenza” (AGI) for Germany [15–19]. These factors were based on a modeling approach by An der Heiden et al., calculating medically attended acute respiratory infections attributable to influenza in Germany [20] (Supplementary Table 2, Additional file 1). Uncertainty in parameter accuracy was accounted for using uniform and Program Evaluation and Review Technique (PERT) distributions. Furthermore, 95% uncertainty intervals (UIs) were calculated for all output parameters. For each model, 10,000 iterations of the Monte Carlo simulation were run. In line with the WHO methodology for global burden of disease estimates published in 2020, discounting was not considered [21].

Scenario analysis

In addition to a baseline scenario, lower-bound and upper-bound scenarios were developed to examine the range of DALYs calculated under varying conditions. Transition probabilities, redistributions, and disability weights were assumed to remain constant from 2015 to 2020 for each scenario. Incidence data on influenza were obtained from SurvStat@RKI2.0 [7], an online query tool provided by the Robert Koch-Institute. Population size and life expectancy data were extracted from the GENESIS-ONLINE query offered by the Federal Statistical Office of Germany [22] (Supplementary Table 3, Additional file 1). Lower transition probabilities were assumed for pneumonia, otitis media, and encephalitis in the lower-bound scenario, along with lower death probabilities for myocarditis and ARDS. In the upper-bound scenario, higher transition probabilities for ARDS were derived from the AGI seasonal influenza report 2018/2019, and higher death probabilities were assumed based on the rapid review estimate [23–26] (Supplementary Table 4, Additional file 1).

Results

Rapid review

The rapid review conducted in the present study yielded information on further sequelae that can occur after a symptomatic infection. Sequelae that were considered for the outcome tree in addition to ARDS, sepsis and otitis media are pneumonia [9, 12, 27–59], bronchitis and its chronic manifestations [9, 27, 29, 30, 36, 37, 42, 44, 45, 48, 54, 57], myocarditis [9, 27, 33, 46, 48, 50, 58], encephalitis [9, 31, 36, 45–47, 49, 57, 60–62], and sinusitis [9, 27, 29, 32, 33, 36, 38, 45, 48, 54, 57].

Seasonal influenza burden

The total burden attributed to seasonal influenza varies across scenarios and seasons, with a minimum of 127,100 DALYs and a maximum of 1,171,115 DALYs. Highest and lowest DALY estimates across every year and season are 696,543 DALYs and 276,282 DALYs for the baseline scenario, 319,756 DALYs and 127,100 DALYs for the lower-bound scenario, as well as 1,171,115 DALYs and 439,651 DALYs for the upper-bound scenario, respectively. Analogous to the total burden, estimates for the influenza burden per 100,000 population and DALYs per case also vary depending on season and scenario, ranging from 153 DALYs per 100,000 population (0.04 DALYs per case) to 1,414 DALYs per 100,000 population (0.13 DALYs per case). Highest and lowest burden estimates per 100,000 population are 841 DALYs per 100,000 population (0.08 DALYs per case) and 332 DALYs per 100,000 population (0.08 DALYs per case) for the baseline scenario, 386 DALYs per 100,000 population (0.04 DALYs per case) and 153 DALYs per 100,000 population (0.04 DALYs per case) for the lower-bound scenario, along with 1,414 DALYs per 100,000 population (0.13 DALYs per case) and 529 DALYs per 100,000 population (0.12 DALYs per case) for the upper-bound scenario (Table 3). Detailed results for all years and scenarios are listed in Additional file 2.

Table 3.

Seasonal influenza burden - Total DALYs, DALYs per 100,000 population, DALYs per case and incidence 2015–2020

Year Scenario Total DALYs DALYs/100,000 DALYs/Case Incidence
2015 Lower-bound 208,802 (208,807-213,330) 257 (251–262) 0.03 (0.03-0.03) 7,524
Baseline 455,066 (450,591-459,505) 560 (554–565) 0.07 (0.07-0.07)
Upper-bound 784,257 (779,798-788,687) 965 (960–971) 0.13 (0.13-0.13)
2016 Lower-bound 185,383 (181,545-189,201) 179 (174–183) 0.04 (0.04-0.04) 4,927
Baseline 319,314 (315,543-323,061) 388 (383–393) 0.08 (0.08-0.08)
Upper-bound 510,053 (506,299–513,802) 620 (616–625) 0.13 (0.13-0.13)
2017 Lower-bound 204,056 (199,424-208,657) 247 (241–252) 0.04 (0.04-0.04) 7,143
Baseline 444.336 (439,747-448,876) 538 (523–543) 0.08 (0.08-0.08)
Upper-bound 757,136 (752,598–761,629) 917 (912–922) 0.13 (0.13-0.13)
2018 Lower-bound 319,756 (312,630-326,879) 386 (377–394) 0.04 (0.04-0.04) 11,158
Baseline 696,543 (689,578–703,512) 841 (832–849) 0.08 (0.08-0.08)
Upper-bound 1,171,115 (1,164,082-1,178,036) 1,414 (1,406-1,422) 0.13 (0.13-0.13)
2019 Lower-bound 127,100 (123,906-130,275) 153 (149–156) 0.04 (0.03–0.04) 4,304
Baseline 276,282 (273,139–279,387) 332 (329–336) 0.08 (0.08-0.08)
Upper-bound 439,651 (436,515-442,762) 529 (525–533) 0.12 (0.12-0.12)
2020 Lower-bound 136,381 (132,716-140,039) 163 (159–168) 0.04 (0.04-0.04) 4,441
Baseline 295,678 (292,069–299,235) 355 (351–359) 0.08 (0.08-0.08)
Upper-bound 453,003 (449,495-456,553) 544 (540–548) 0.12 (0.12-0.12)
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DALYs Disability-Adjusted Life Years, Incidence Incidence of acute symptomatic influenza cases per 100,000 stratum specific population

Disease burden due to YLL and YLD

For the baseline scenarios, premature mortality due to a symptomatic infection (YLL) is the main contributor to the total DALYs observed with an average of 90% for all years. The remaining 10% are caused by years lived with disability (YLD). A similar trend is observed for both lower-bound and upper-bound estimates: while the average share of YLL and YLD for all lower-bound scenarios are 79% and 21%, respectively, YLL and YLD averages for all upper-bound scenarios are 86% and 14%.

Disease burden due to acute infection and sequelae

For all baseline, lower-bound and upper-bound scenarios, the main contributor to the total burden are sequelae after a symptomatic infection with an average between 9% and 99%. The remaining burden can be attributed to the disability caused by an acute infection (Fig. 3).

Fig. 3.

Fig. 3

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Disease burden baseline scenario, 2015

Disease burden by specific health outcomes and sequelae

In baseline scenarios, pneumonia accounts for 56% of the total disease burden, followed by encephalitis (19%), myocarditis (12%), and chronic bronchitis (8%). Other sequelae contribute 6% of the total burden. In lower-bound scenarios, pneumonia (49%) and chronic bronchitis (17%) are the leading causes, followed by encephalitis (13%), myocarditis (10%), and others (11%). In upper-bound scenarios, pneumonia (34%) is the predominant cause, followed by ARDS (32%), encephalitis (12%), myocarditis (7%) and others (15%).

Disease burden by age and sex

The highest overall burden is shared by the three oldest age groups (75 - 79; 80 - 84; 85+), while the lowest burden is found in age groups 15 - 19, 20 - 24, and 25 - 29. Burden generally increases with age from the age group 30 - 34 and older. Infants under one year and children aged 1 - 4 years have comparable burden estimates to age groups 40 - 44 and 45 - 49. The YLL is the main contributor to the total burden across all ages and scenarios for both males and females. YLD shares remain relatively constant until the age group 60 - 64, after which YLL becomes the predominant cause of burden for older age groups. Age and burden trends are similar for both sexes, with higher total burden observed in older age groups for females compared to males (Fig. 3).

Discussion

The disease burden estimates presented in this study provide first results for the national seasonal influenza burden for the period between 2015 and 2020 in Germany. Using a composite health measure that considers both mortality and morbidity, the study provides valuable insights into the impact of the disease on population health. Three different scenarios were developed to calculate a reasonable range of possible DALY estimates. Compared to previous efforts, this study reports substantially higher DALY estimates for all scenarios, ranging from 127,100 DALYs (153 DALYs per 100,000 population) to 1,171,115 DALYs (1,414 DALYs per 100,000 population) (Tables 2 and 3). Various factors are contributing to this effect. Previous exercises either did not correct for underestimation or relied on GP consultation rates to estimate multiplication factors. This plays an important role in explaining the higher estimates in the current exercise, as multiplication factors used in the present study range from 7 to 121, depending on age and sex. Previous exercises either used MFs ranging from 4.12 to 5.12 [5], used a GP consultation rate of 30% [4], used a symptomatic attack rate of 1 to 2% [8] in the population, or did not perform any adjustments [1, 3], yielding lower YLL, YLD, and DALY estimates. Thus, influenza-related consultation numbers presented by the AGI were considered appropriate to estimate the true number of yearly symptomatic influenza cases and multiplication factors. However, due to limited information on age-specific consultation, surveillance data reporting incidences for each year of age up to 80 were used in combination with multiplication factors. Influenza-related consultations derived from the AGI ranged between 3.6 million and 9 million, in line with yearly attack rates between 5% and 20% reported by the RKI, resulting in a 3 - 6-fold increase in assumed symptomatic influenza cases and 4 - 35-fold increase in the disease burden compared to the modeling approach presented by Plass et al. [8, 63] (Table 2). Additional health outcomes and sequelae were considered in the model presented, with myocarditis, encephalitis, sinusitis, bronchitis, and chronic bronchitis considered as possible consequences for the first time in measuring the disease burden of seasonal influenza. Following the addition of possible sequelae, higher disease burden estimates were identified. In line with calculations reported by Cassini et al., the main contributor to the total disease burden is premature death (YLL). Considering available evidence, previous publications assumed a fatality rate of 0.1%, including both complicated and uncomplicated influenza cases. This estimate was derived from findings in the “Community Strategy for Pandemic Influenza Mitigation in the United States” from 2007, preceding the influenza-H1N1 pandemic [64]. Death after symptomatic infection was therefore the only instance of premature death in previous modeling approaches, with all other sequelae contributing to YLD only, potentially leading to overestimation of YLDs. Further analyses applying the previous modeling approach resulted in considerably lower DALY estimates for all scenarios (see Additional file 3). Using a single category of death encompassing both complicated and uncomplicated, presuming a fatality rate of 0.1%, could lead to an underestimation of the overall disease burden. Introducing a higher fatality rate might provide a more accurate estimation of the overall burden, however, information on relative contribution of possible sequelae is lost. In their weekly influenza surveillance report, the Centers for Disease Control and Prevention provide mortality estimates due to influenza for seasons 2019-20 through 2022-23, ranging from 0.01% to 1,5% [65]. The estimates presented in this study give a good approximation of each sequela’s contribution to the overall disease burden. In all scenarios presented, pneumonia, acute infection, encephalitis, myocarditis, and chronic bronchitis account for over 90% of the total disease burden. While other sequelae combined only amount to 10% of the total disease burden, these sequelae constitute most previous outcome trees used for calculating disease burden estimates. This underlines the importance of including sequelae beyond respiratory illnesses. Stratifying the disease burden by age and sex, a higher disease burden is observed in infants, younger age groups, and in the oldest age groups. Accordingly, the highest incidences of seasonal influenza reported by the RKI in Germany lie within the same age groups, underlining the importance of vaccination coverage. In 2021, the Netherlands, Ireland and Denmark reported vaccination rates of over 70% in people aged 65 years and over, while Germany reported a rate of 47.3%, declining by 8.8% since 2011. These differences can significantly impact the observed complication rates of symptomatic influenza cases.

Limitations

Important limitations should be noted when interpreting the results. The clinical case-definition for influenza changed in 2019, introducing new definitions (D and E) in addition to the existing ones (B and C). The new definition is more sensitive and likely led to an increase in notified cases, including asymptomatic ones [66]. This change affects the comparability of notified cases before and after 2019, and should be considered when comparing DALY trends to consultation numbers and incidences. Transition probabilities for death, ARDS, myocarditis, encephalitis, and pneumonia may be overestimated due to the inclusion of publications from different regions and limited country-specific data on influenza-related complications in Germany. Studies primarily focused on inpatient settings, potentially leading to further overestimation. Age-specific transition probabilities had limited information, resulting in age redistribution only applied to pneumonia, death due to pneumonia, and ARDS. Assuming a higher death rate for most complications at very young and higher age, the disease burden in those age groups could have been underestimated. Disability weights were derived from the most recent GBD publication or previous models, with proxy disability weights used when data was lacking (e.g., COPD disability weights for ARDS). In the absence of reported 2020 influenza-related consultations, the multiplication factors for 2019 were used as a proxy. Due to the number of reported cases and incidences being almost identical for 2019 and 2020, similar number of consultations was assumed for both years. Age group 80 and older data was used as an approximation for the age group 85 and older, potentially underestimating disease burden in the latter group due to increasing mortality with age. Death counts by cause vary due to improper reporting and documentation, particularly for influenza. According to the RKI, it is common not to attribute the cause of death to influenza after clinical evaluations are performed. The cause of death is usually attributed to other conditions, even in the presence of laboratory confirmed influenza. This is due to the fact that it is difficult to determine the exact cause of death and whether a death occurred with influenza or rather due to it, considering all comorbidities. In the 2018/2019 Influenza Epidemiology Report in Germany, conservative excess deaths from influenza were estimated and were 15 to 78 times higher than confirmed influenza deaths. The present study reports death counts 0.4 to 4 times higher than excess deaths reported by the Robert Koch-Institute, aligning with previous conservative estimates [19]. Hospital discharge data in Germany shows an average of 19,300 sepsis deaths per year, while this study estimates an average of 115 influenza-related sepsis deaths annually. Similar patterns are observed for myocarditis, with 17,400 hospital deaths and 433 to 1,084 influenza-related deaths in this study. However, higher estimates are found for ARDS and encephalitis in hospital discharge data (950 deaths and 170 deaths per year, respectively) compared to the study’s estimates (498 to 10,882 ARDS deaths and 433 to 1,084 encephalitis deaths due to influenza), indicating possible overestimation of transition probabilities [67]. Depending on the year under investigation, estimates on DALYs per 100.000 population for the baseline and lower-bound scenario are comparable to the overall disease burden caused by lower respiratory infections in Germany (374 per 100.000 population) and other western European countries (Netherlands: 407 per 100.000 population; France: 354 per 100.000 population; Sweden: 302 per 100.000 population) according to the Global Burden of Disease Study 2019. However, results from upper-bound scenarios equate to the burden of chronic diseases such as Diabetes Type 2, lower back pain and ischemic heart disease in Germany, indicating an overestimation. Compared to other infectious diseases, the total burden is comparable to that caused by lower respiratory infections in Western European countries. In contrast, Tuberculosis, HIV/AIDS, Acute Hepatitis B and Measles are cause for considerably lower total DALYs in Western, Central, Eastern European and North American countries. Estimates per 100,000 population are similar to those for Tuberculosis in Lithuania and Ukraine, HIV/AIDS in Estonia and Ukraine, and lower respiratory tract infections in most of these regions [66] (Table 1). Transition probabilities and disability weights were assumed to be constant over time, without considering changes resulting from intervention programs or variations in natural immunity. This assumption is crucial for the disease burden of specific influenza subtypes, as different subtypes may have varying complication rates. Due to the limited evidence on subtype-specific complications, a single influenza model independent of subtypes was developed. Multimorbidity, the presence of multiple medical conditions in one person, can overestimate YLDs in the DALY approach. Adjusting for multimorbidity is complex due to varying onset and duration of different conditions. McDonald et al. suggested an individual-based modeling approach, but empirical evidence and practical solutions are lacking [68]. Further research is necessary to address multimorbidity in incidence-based modeling approaches. Lastly, search for literature was restricted to one database. As rapid reviews aim to yield similar conclusions as systematic reviews in a more time-efficient manner, it is likely that the inclusion of additional databases would have contributed to a more comprehensive information synthesis.

Conclusions

Disease burden models provide insights into the history, progression, and consequences of a disease using composite health measures. The quality of these models depends on available information on complications, probabilities, underestimation, and severity. Building upon findings of the present study, future research and surveillance are necessary to understand the complications following a symptomatic influenza infection. Reliable data on disease burden estimates are also of need to identify vulnerable groups. As resources are limited, reliable data on prioritisation and allocation of resources are important for an informed and evidence-based decision-making process. Evaluations of interventions can provide information on DALYs averted, cost-effectiveness, and total expenditure. Studies on the underestimation of influenza cases are crucial to accurately assess the disease’s impact on population health. Research across regions and cultures can help understand the effects of interventions or their lack thereof, e.g., vaccination rates in the population. Our results show high burden estimates in vulnerable populations such as infants, children under age of five and elderly population, indicating the need of further research and vaccination programs in members of risk groups and their close contacts. Finally, considering influenza complications beyond respiratory conditions is important for a comprehensive understanding of the disease burden.

Supplementary Information

12879_2025_10613_MOESM1_ESM.pdf (283.9KB, pdf)

Supplementary Material 1. Supplementary Table 1: Studies included in Rapid Review; Supplementary Table 2: Calculation of Multiplication Factors; Supplementary Table 3: Population Data; Supplementary Table 4: Scenario Input Parameters. Supplementary Table 1 gives information on all studies considered for the rapid review with information on author, study period, setting, country, populatioon, age range, and available data. Supplementary Table 2 gives information on the calculation of Multiplication Factors used, presenting the Influenza-related consultations reported by the AGI and Cases reported by the RKI. Supplementary Table 3 gives information on the population data used for the modeling approach, such as average number of cases per year, underestimation, age distribution, and life expectancy. Supplementary Table 4 presents all input data for all scenarios and sequelae. Information on probabilities, disability weights, duration in years and the source are given.

12879_2025_10613_MOESM2_ESM.xls (2MB, xls)

Supplementary Material 2. Detailed results for all years and scenarios.

12879_2025_10613_MOESM3_ESM.xls (1.8MB, xls)

Supplementary Material 3. Detailed results for all years and scenarios, using the previous modeling approach.

Acknowledgments

AI Statement

The Large Language Model (LLM) ChatGPT was used to edit to enhance clarity, coherence, and the overall readability of the manuscript.

Abbreviations

AGI

Arbeitsgemeinschaft influenza

ARDS

Acute respiratory distress syndrome

BCoDE

Burden of communicable disease in Europe

DALY

Disability-adjusted life years

ECDC

European centre for disease prevention and control

EEA

European economic area

EU

European union

GBD

Global burden of disease

MF

Mutliplication factor

RKI

Robert Koch-institute

WHO

World health organization

YLD

Years lived with disability

YLL

Years of life lost due to premature death

Authors’ contributions

MS conceived the project and performed the analyses. MS, RSS and ETC conducted the rapid review. MS, TK and RB wrote the manuscript, TK and RB further supervised the project. All authors reviewed the manuscript and have agreed to the content.

Funding

Open Access funding enabled and organized by Projekt DEAL. This research received no funding from any funding agency in public, commercial, or not-for-profit sectors.

Data availability

The datasets supporting the conclusions of this article are available in the Zenodo repository, reference number 10.5281/zenodo.7368150, https://doi.org/10.5281/zenodo.10053813.

Code availability

Not applicable.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

Outside the submitted work, TK reports to have received research grants from the Gemeinsamer Bundesausschuss (G-BA - Federal Joint Committee, Germany), the Bundesministerium für Gesundheit (BMG - Federal Ministry of Health, Germany). He further has received personal compensation from Eli Lilly & Company, the BMJ, and Frontiers.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Tobias Kurth and Ralph Brinks contributed equally to this work.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

12879_2025_10613_MOESM1_ESM.pdf (283.9KB, pdf)

Supplementary Material 1. Supplementary Table 1: Studies included in Rapid Review; Supplementary Table 2: Calculation of Multiplication Factors; Supplementary Table 3: Population Data; Supplementary Table 4: Scenario Input Parameters. Supplementary Table 1 gives information on all studies considered for the rapid review with information on author, study period, setting, country, populatioon, age range, and available data. Supplementary Table 2 gives information on the calculation of Multiplication Factors used, presenting the Influenza-related consultations reported by the AGI and Cases reported by the RKI. Supplementary Table 3 gives information on the population data used for the modeling approach, such as average number of cases per year, underestimation, age distribution, and life expectancy. Supplementary Table 4 presents all input data for all scenarios and sequelae. Information on probabilities, disability weights, duration in years and the source are given.

12879_2025_10613_MOESM2_ESM.xls (2MB, xls)

Supplementary Material 2. Detailed results for all years and scenarios.

12879_2025_10613_MOESM3_ESM.xls (1.8MB, xls)

Supplementary Material 3. Detailed results for all years and scenarios, using the previous modeling approach.

Data Availability Statement

The datasets supporting the conclusions of this article are available in the Zenodo repository, reference number 10.5281/zenodo.7368150, https://doi.org/10.5281/zenodo.10053813.

Not applicable.

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