Pumarola et al. BMC Infectious Diseases (2023) 23:86 BMC Infectious Diseases https://doi.org/10.1186/s12879-023-08015-3 RESEARCH Open Access Excess hospitalizations and mortality associated with seasonal influenza in Spain, 2008–2018 T. Pumarola1,13*, J. Díez‑Domingo2, F. Martinón‑Torres3,4,5, E. Redondo Margüello6, R. Ortiz de Lejarazu Leonardo7, M. Carmo8, G. Bizouard9, G. Drago10, J. L. López‑Belmonte10, H. Bricout11, C. de Courville11 and A. Gil‑de‑Miguel12 Abstract Background Influenza may trigger complications, particularly in at-risk groups, potentially leading to hospitalization or death. However, due to lack of routine testing, influenza cases are infrequently coded with influenza-specific diag‑ nosis. Statistical models using influenza activity as an explanatory variable can be used to estimate annual hospitaliza‑ tions and deaths associated with influenza. Our study aimed to estimate the clinical and economic burden of severe influenza in Spain, considering such models. Methods The study comprised ten epidemic seasons (2008/2009–2017/2018) and used two approaches: (i) a direct method of estimating the seasonal influenza hospitalization, based on the number of National Health Service hospitalizations with influenza-specific International Classification of Diseases (ICD) codes (ICD-9: 487–488; ICD-10: J09-J11), as primary or secondary diagnosis; (ii) an indirect method of estimating excess hospitalizations and deaths using broader groups of ICD codes in time-series models, computed for six age groups and four groups of diagnoses: pneumonia or influenza (ICD-9: 480–488, 517.1; ICD-10: J09–J18), respiratory (ICD-9: 460–519; ICD-10: J00–J99), respira‑ tory or cardiovascular (C&R, ICD-9: 390–459, 460–519; ICD-10: I00–I99, J00–J99), and all-cause. Means, excluding the H1N1pdm09 pandemic (2009/2010), are reported in this study. Results The mean number of hospitalizations with a diagnosis of influenza per season was 13,063, corresponding to 28.1 cases per 100,000 people. The mean direct annual cost of these hospitalizations was €45.7 million, of which 65.7% was generated by patients with comorbidities. Mean annual influenza-associated C&R hospitalizations were estimated at 34,894 (min: 16,546; max: 52,861), corresponding to 75.0 cases per 100,000 (95% confidence interval [CI]: 63.3–86.3) for all ages and 335.3 (95% CI: 293.2–377.5) in patients aged ≥ 65 years. We estimate 3.8 influenza-associated excess C&R hospitalizations for each hospitalization coded with an influenza-specific diagnosis in patients aged ≥ 65 years. The mean direct annual cost of the estimated excess C&R hospitalizations was €142.9 million for all ages and €115.9 million for patients aged ≥ 65 years. Mean annual influenza-associated all-cause mortality per 100,000 people was estimated at 27.7 for all ages. Conclusions Results suggest a relevant under-detected burden of influenza mostly in the elderly population, but not neglectable in younger people. *Correspondence: T. Pumarola tpsvhebron@gmail.com Full list of author information is available at the end of the article © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/. The Creative Commons Public Domain Dedication waiver (http://​creat​iveco​ mmons.​org/​publi​cdoma​in/​zero/1.​0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Pumarola et al. BMC Infectious Diseases (2023) 23:86 Page 2 of 16 Keywords Influenza, Burden, Excess, Mortality, Hospitalization, Cardiovascular, Respiratory, Spain Introduction increasing use of regression techniques to estimate Influenza is a viral infection accountable for seasonal excess morbidity and mortality due to influenza [13]. epidemics, leading to a substantial disease burden In Spain, available studies are mostly focused on hos- throughout the world [1]. The infection not only trig- pitalized cases coded with an International Classification gers mild symptoms but also causes severe complica- of Diseases (ICD) influenza code and 2014/15 is the most tions, which may result in hospitalization or even death recently analyzed season [14–20]. There are no national of the patient [1–3]. estimates available for morbidity and, those available for Some patients are at a higher risk of developing mortality during this study, were not updated [21, 22]. severe complications due to their age or medical con- León-Gómez et al. (2015) published estimates on Pneu- ditions. The World Health Organization (WHO) rec- monia and Influenza (P&I) excess mortality for popula- ommends that those countries initiating or expanding tion aged ≥ 65 years, but these include only data until their programs for seasonal influenza vaccination season 2011/2012 [21]. The annual estimates published should consider the following targets for vaccination by the surveillance system include a characterization of (not in the order of priority): health workers, indi- confirmed severe influenza cases that are hospitalized viduals with comorbidities and underlying conditions, and indicates excess mortality periods as well. However, older adults, and pregnant women [4]. The WHO also influenza-associated excess deaths were not quantified at states that countries may consider additional (sub) the time of this study neither in total nor by age. populations for vaccination, such as children, and other There is an undeniable need for updated national esti- groups at a high risk of severe influenza in congregate- mates on the influenza-associated excess hospitalization living settings [4]. and mortality in Spain, considering the impact of influ- Increasing seasonal vaccination within priority enza on other respiratory and non-respiratory complica- groups is a key strategy to lessen the burden of influ- tions. The contrast of these estimates with those obtained enza [5]. Prior to the COVID-19 pandemic, Spanish through the direct analysis of influenza-coded cases can official estimates indicated vaccination coverage rates be of added value, to understand the influenza under- (VCRs) in those aged 65 or above below the 75% VCR detection factor, and its evolution over the course of time. goal [6, 7]. During 2008/2009, VCR in this at-risk group This study aims to address this knowledge gap by esti- was 65.4%. It has gradually declined over the following mating the clinical and economic burden of severe influ- nine years, reaching 55.7% in 2017/2018 [8]. enza in Spain from season 2008/09 to season 2017/18 The intensity and severity of influenza epidemics across distinct age groups, by: (i) describing and esti- are variable and often unpredictable, depending on mating the cumulative seasonal incidence of Spanish the VCRs, the circulating strains of influenza viruses, National Health Service (NHS) hospitalizations with a and vaccine effectiveness against the circulating virus, diagnosis of influenza by age group and comorbidities; among other variables [3, 9, 10]. Therefore, it is impor- (ii) estimating the number of excess influenza-associated tant to continuously monitor the morbidity and mor- hospitalizations and deaths during influenza epidemics, tality, and specific impacts of influenza by age, risk considering broader groups of diagnoses. groups, and groups of potential complications [3, 11]. Health administrative data can be used to efficiently assess the burden of diseases in broad populations Methods [12]. These algorithms may be applied to hospitalized Study design patients even when laboratory data are not available The Burden of Acute Respiratory Infections (BARI) is a and tests are not routinely performed, to help improve real-world evidence study assessing the clinical and eco- the epidemiologic studies of influenza. However, there nomic burden of acute respiratory infections (influenza may be variations in terms of the national coding prac- and respiratory syncytial virus) in Spain and Portugal tices [12]. In Canada, a study by Hamilton et al. reports [23–26]. We are reporting the results for the burden of that even the best performing influenza algorithm had severe influenza in Spain, measured through hospitali- a specificity of 99% but a sensitivity of 73% [12]. The zations and deaths. Results from a similar analysis per- challenges of estimating the disease burden of influ- formed for Portugal are reported by Froes et al. [24]. enza infection in the context of insufficient influenza This publication uses the following two approaches: laboratory testing and coding practices have led to an (i) a direct method of estimating seasonal influenza Pumarola et al. BMC Infectious Diseases (2023) 23:86 Page 3 of 16 incidence, based on the number of NHS hospitaliza- who have been hospitalized for each specific pathology tions with influenza-specific ICD codes; (ii) an indirect [29]. This methodology estimates data on hospitalization method of estimating excess hospitalizations and deaths for the total Spanish NHS universe, which were used in using broader groups of ICD codes in time-series eco- this study. The PHDB database was used to identify the logical models. hospitalizations with a diagnosis of influenza and the hospitalizations associated with other groups of diagno- Hospitalizations with a diagnosis of influenza ses used for the influenza-associated excess hospitaliza- Influenza episodes were defined as those coded with tion analysis. ICD-9-MC 487 or 488, or ICD-10-ES J09, J10 or J11, in These data were also used to describe the in-hospital any primary or secondary diagnosis field. These codes case fatality risk based on discharge status, correspond- were found to be highly accurate ICD algorithms to ing to the in-hospital death during influenza episode at identify influenza hospitalizations as per Hamilton et al. hospitalization. [12]. Total hospitalizations excluded admissions related to routine birth, programmed activity, and those admis- Death certificate data sions in which the primary diagnosis was related to The monthly reports for death per cause per age group, musculoskeletal, alcohol, or mental disease. Diagnostic as in the death certificate, were obtained from the Insti- information from the influenza episode was also used tuto Nacional de Estadística (INE, National Statistics to identify individuals who had at least one medical Institute) in October 2020 [30]. condition regarded as a risk factor for severe influenza (Additional file 1: Table S1), in any primary or second- Influenza activity data ary diagnosis field, considering the following conditions: The primary predictor of influenza excess hospitaliza- pregnancy, diabetes mellitus, respiratory or lung disease, tions and deaths was the overall weekly incidence rate cardiovascular, immunocompromised, chronic liver dis- of influenza-like-illness (ILI), which was obtained from ease, and chronic kidney disease. ScVGE—Sistema centinela de Vigilancia de la Gripe en España (National Epidemiology Surveillance Network) Influenza‑associated excess hospitalization and mortality in November 2020 [31]. This rate is estimated by ScVGE According to the primary diagnosis, influenza-associated based on data collected by each sentinel network belong- excess hospitalization was computed for four groups of ing to the ScVGE. The ScVGE uses the the clinical defi- diagnoses [27, 28]: pneumonia or influenza (P&I, ICD- nition of influenza—as proposed by the European Union 9-MC: 480–488, 517.1; or ICD-10-ES: J09-J18), respira- [32]—to compute this rate [33]. Being close to the WHO tory (R, ICD-9-MC: 460–519; or ICD-10-ES: J00-J99), definition of ILI, it will be treated as such throughout this respiratory or cardiovascular (C&R, ICD-9-MC: 390– manuscript [34]. 459, 460–519; or ICD-10-MC: I00-I99, J00-J99), and all-cause (any ICD-9/10 diagnosis). The aforementioned Demographic data groups of diagnoses and the primary cause of death in Data on age-specific annual resident population esti- the death certificate were used to compute influenza- mates were downloaded from INE’s website to calculate associated excess mortality. rates the incidence rates of cases per 100,000 people [35]. Data sources Statistical analysis Hospital discharge data An ecological approach was used to estimate the hospi- Anonymized administrative data on hospitalizations talization costs, number of influenza-associated excess (January 2008-December 2018) were retrieved from the hospitalizations, and deaths; weekly morbidity and IASIST Projected Hospitalizations Database (PHDB) for monthly mortality data were explicitly modeled against Spain, including public hospital episodes. The Minimum indicators of influenza activity (weekly moving average, Basic Datasets (MBDS) collect anonymous information broken down by season with a 1-week lag for hospitaliza- from inpatient episodes, including demographic, admin- tions and monthly indicator broken down by season for istrative, and clinical information data, including any death) using cyclic regression models. ICD diagnosis and procedures coded for every inpatient Different Poisson cyclic models (time series) were used, episode. The PHDB receives MBDS from 189 hospitals where age- and cause-specific hospitalization and mortal- of the NHS. The PHDB represents approximately 55% to ity data were explained by the ILI incidence [13, 36, 37], as 65% of the total NHS episodes per year. Inferential meth- well as time trends and seasonal terms, using a log link [38, odology is used to estimate the final universe of patients 39]. The model used for hospitalization is given below. Pumarola et al. BMC Infectious Diseases (2023) 23:86 Page 4 of 16 (c,a) Ht The estimated influenza cases were divided by the log E =α 0 + α 1 t + α 2 t 2 Spanish population in the respective age group to calcu- Pop late cases per 100,000 people. 12 itπ The indicator of cost per unit of hospital production + β i cos i=1 52, 14 (UHP) was used to compute costs of those hospitaliza- itπ tions with a diagnosis of influenza. This indicator relates +γ i sin + δIN F t the operating costs incurred by the hospital to carry out 52, 14 i its activity to the production carried out by the hospital. where H t (c,a) the weekly number of hospitalizations for The UHP value was established for each type and level of cause c in a specific age group a, and Pop the population hospital in the Spanish National Health System [40, 41]. offset. The terms t, t2, cos, and sin are terms represent- The average cost per UHP was updated annually through ing the time trend and seasonality. IN F t represents the the Hospital TOP 20 Program; the degree of complexity weekly influenza activity data). A backward selection of hospitalization episode, considering variables related to 3 M™ All Patient Refined Diagnosis Related Groups (APR method was used to identify significant time trends and the patient and specific episode, was calculated using the seasonal terms in the age- and cause-specific models. Baseline hospitalization and mortality data were cal- DRGs) system (version 32). The cost of influenza-associ- culated using the Poisson models as the model-expected ated excess hospitalization was computed by multiplying values when the variable ILI incidence was set to zero. the number of estimated excess hospitalizations by the The number of influenza-associated excess hospitali- mean cost per hospitalization, by cause and by age group. zations and mortality was the difference between the The estimates of influenza-associated excess hospitali- expected hospitalizations and deaths, as estimated by zation and mortality were compared to the number of the Poisson model, which incorporated the ILI incidence primary or secondary influenza‐specific diagnoses cap- rate as the indicator of influenza activity; the number of tured from NHS hospital discharge records and fatalities hospitalizations and deaths were estimated by the base- observed during these episodes. line model and the indicator was not considered in this The following characteristics of hospitalizations with a regard. The number of influenza-associated hospitaliza- diagnosis of influenza—approach (i) – were analyzed: the tions was defined as the sum of the weekly excesses for contribution (in percentage) of each age group or comor- each epidemic season. The number of influenza-asso- bidity status to total hospitalizations with a diagnosis of ciated deaths was defined as the sum of the monthly influenza; the mean length of stay (LoS), calculated as the excesses. Every year, influenza season was defined as the time from hospitalization until discharge; the in-hospital period from September to June. case fatality risk (corresponding to the hospitalizations The correlation between the values predicted by the with a diagnosis of influenza that resulted in death) dur- model and those observed indicated the performance ing the respective inpatient episode; and the mean hospi- of the model, using Pearson’s correlation. The mean talization cost per patient, computed by dividing the total absolute percentage error (MAPE), defined as the aver- cost of hospitalizations with a diagnosis of influenza by age of the percentage difference between predicted and the number of patients hospitalized with a diagnosis of observed values for all weeks (or months in case of mor- influenza. The analysis was stratified by age group and tality), was also computed. All analyses were performed comorbidity status (with or without comorbidities). using SAS 9.4. Means are reported only for nine seasons, exclud- For the excess hospitalization and mortality analysis, ing the H1N1pdm09 pandemic (2009/2010). Three-year the hospitalizations and deaths were categorized into means, including the most recent seasons (2015/2016 to four groups of diagnoses (P&I, respiratory, respiratory, or 2017/2018), are also included. cardiovascular, and all-cause). The population was strati- fied in the following age groups: 0–4 years, 5–18, 19–49, Results 50–64, 65–74, ≥ 75, and ≥ 65 years. Descriptive statistics for unmodeled hospitalizations Confidence intervals (CIs) of 95% were calculated and deaths for the estimated influenza-associated excess hospi- In 2018, Spain had a resident population of 46.7 million talizations and deaths by causes and by age groups. A people, of which 19.2% were ≥ 65 years old [35]. During significance level of 5% was considered for the associa- the study period, a total of 37.0 million all-cause hos- tion of excess estimates with the ILI incidence variable pitalizations were estimated in Spanish public hospitals. (p-value < 0.05). C&R, R, and P&I diagnoses accounted for 26.4%, 10.8%, and 2.9% of hospitalizations, respectively. Patients Pumarola et al. BMC Infectious Diseases (2023) 23:86 Page 5 of 16 Table 1 Hospitalizations with a diagnosis of influenza by age group and epidemic season, in absolute number of cases and incidence rates per 100,000 people, in Spanish Public Hospitals between 2008/2009 and 2017/2018 Hospitalizations with a diagnosis of influenza per age group 0–4 5–18 19–49 50–64 65–74 ≥ 75 ≥ 65 All ages 2008/2009 N 597 127 267 184 120 306 426 1601 Ratec 24.1 2.1 1.2 2.3 3.2 7.9 5.6 3.5 2009/2010 N 552 438 1476 682 288 387 675 3823 Ratec 22.1 7.0 6.7 8.4 7.6 9.7 8.6 8.2 2010/2011 N 775 263 902 715 314 398 712 3367 Ratec 31.0 4.2 4.2 8.7 8.1 9.7 8.9 7.2 2011/2012 N 683 174 437 465 298 639 937 2696 Ratec 27.5 2.7 2.0 5.5 7.6 15.1 11.5 5.8 2012/2013 N 1004 422 807 839 485 745 1230 4302 Ratec 41.4 6.6 3.8 9.8 12.2 17.4 14.9 9.2 2013/2014 N 1528 359 1983 2110 1574 2674 4248 10,229 Ratec 65.8 5.6 9.6 24.2 38.0 62.1 50.3 22.0 2014/2015 N 1232 486 1138 1753 1894 5357 7251 11,860 Ratec 54.6 7.5 5.6 19.7 44.0 125.0 84.4 25.5 2015/2016 N 3612 1622 4092 5600 4593 6625 11,218 26,144 Ratec 164.0 24.7 20.6 61.5 106.2 151.6 129.0 56.3 2016/2017 N 996 470 1270 2336 3167 11,513 14,680 19,752 Ratec 46.3 7.1 6.5 25.1 71.5 261.9 166.4 42.5 2017/2018 N 2341 981 2887 5483 6804 19,123 25,927 37,619 Ratec 111.1 14.7 14.9 57.6 150.1 431.9 289.3 80.6 9 years ­meana N 1419 545 1532 2165 2139 5264 7403 13,063 Ratec 61.1 8.4 7.4 24.8 51.7 123.8 88.2 28.1 3 years ­meanb N 2316 1024 2,750 4,473 4,854 12,420 17,275 27,838 Ratec 107.6 15.4 14.0 48.0 109.6 282.4 195.7 59.8 N: Number of hospitalizations with a diagnosis of influenza a Includes the following seasons: 2008/2009, 2010/2011, 2011/2012, 2012/2013, 2013/2014, 2014/2015, 2015/2016, 2016/2017, 2017/2018. Excludes season 2009/10, since it was affected by the 2009 influenza A (H1N1) pandemic; b Includes the most recent seasons, namely 2015/2016, 2016/2017, 2017/2018; c Rate of hospitalizations with a diagnosis of influenza per 100,000 people aged ≥ 65 years contributed to 44.2% of all-cause, 65.6% Patients aged ≥ 65 years contributed to 56.7% of hospi- of C&R, 55.5% of R, and 65.6% of P&I hospitalizations. talizations with a diagnosis of ­influenza1, with a mean of A total of 4.0 million people died in Spain over the same 88.2 cases per 100,000 over nine seasons, reaching 289.3 period, of whom 3.4 million (84.9%) were ≥ 65 years old. cases per 100,000 in 2017/18 (Table 1). Patients with comorbidities accounted for 59.0% of hospitalizations, Influenza epidemiologic burden 32.0% of whom were < 65 years old (Table 2)1. Hospitalizations with a diagnosis of influenza The mean LoS stood at 9.4 d ­ ays1. The highest mean A total of 121,393 hospitalizations with a diagnosis LoS was observed in patients aged < 65 with comorbidi- of influenza were registered between 2008/2009 and ties (12.0 days)1. Those patients between 50 and 64 years 2017/2018, of which 83,515 (68.8%) were during the last of age presented the highest mean LoS (11.7 days)1. three analyzed seasons. Excluding 2009/2010, which was The mean in-hospital case fatality risk was 5.2%, which affected by the H1N1 pandemic, the mean annual num- increased with age—from 0.3% in those aged < 5 years to ber of hospitalizations was 13,063 per season and 28.1 7.8% in those aged ≥ 75 ­years1. Patients with comorbidi- cases per 100,000 people1 (Table 1). The maximum num- ties, who were < 65 years old, presented 2.8 times higher ber of annual hospitalizations was observed in 2017/18 mean in-hospital case fatality risk than those patients of (37,619; 80.6 per 100,000). the same age and without comorbidities (4.5% vs. 1.6%, respectively)1. These indicators were detailed by age 1 Mean for nine seasons (2008/2009 to 2017/2018), ie, excluding the group and presence of comorbidities in Table 2. H1N1pdm09 pandemic (2009/10). Table 2 Summary of the Characteristics of the Hospitalizations With a Diagnosis of Influenza by Age Group and Presence of Comorbidities in Spanish Public Hospitals Between 2008/2009 and 2017/2018 Pumarola et al. BMC Infectious Diseases Variable Age group (in years) With comorbidities Without comorbidities 0–4 5–18 19–49 50–64 65–74 ≥ 75 ≥ 65 All Ages < 65 ≥ 65 < 65 ≥ 65 Share of hospitalizations (%) 9 years ­meana 10.9 4.2 11.7 16.6 16.4 40.3 56.7 100.0 18.9 40.1 24.4 16.5 (2023) 23:86 3 years ­meanb 8.3 3.7 9.9 16.1 17.4 44.6 62.1 100.0 16.0 40.3 22.0 21.8 [Min; ­Max]c [5.0; 37.3] [2.4; 11.5] [6.4; 38.6] [11.5; 21.4] [7.5; 18.1] [10.1; 58.3] [17.7; 74.3] 100.0 [8.6; 35.6] [15.5; 67.4] [11; 55.1] [2.1; 41.8] LoS (in days) 9 years ­meana 6.1 6.2 10.2 11.7 11.0 9.3 9.8 9.4 12.0 10.1 6.9 8.8 3 years ­meanb 5.7 5.5 8.8 10.6 9.7 8.8 9.1 8.8 10.8 9.4 6.5 8.4 [Min; ­Max]c [5.1; 7.0] [4.6; 7.4] [6.0; 14.8] [7.8; 14.8] [7.4; 12.8] [7.7; 10.7] [7.9; 11.1] [6.6; 11.4] [7.1; 15.1] [8; 11.3] [4.9; 9.2] [6; 11.3] In-hospital case fatality risk (%) 9 years ­meana 0.3 0.7 2.9 5.0 5.1 7.8 7.0 5.2 4.5 7.1 1.6 6.7 3 years ­meanb 0.3 0.6 2.6 4.7 4.7 7.7 6.9 5.3 4.4 7.0 1.6 6.8 c [Min; ­Max] [0.1; 0.5] [0.0; 1.4] [0.4; 4.4] [2.2; 7.1] [3.8; 8] [3.6; 9.3] [3.8; 8.5] [1.4; 5.8] [1.7; 5.8] [4.2; 8.7] [0.1; 2.6] [0; 8.2] Mean hospitalization cost per patientd (€) 9 years ­meana 3,584 3,172 4,619 5,305 4,580 3,639 3,981 4,159 5,437 4,030 3,404 3,581 3 years ­meanb 4,709 3,252 4,234 4,752 4,231 3,714 3,898 4,162 4,856 3,973 3,901 3,556 [Min; ­Max]c [2,160; 7,281] [1,944; 3,896] [3,045; 7,241] [3,850; 6,695] [3,558; 5,783] [3,000; 4,152] [3,148; 4,544] [3,103; 5,295] [3,285; 6,806] [3,391; 4,514] [2,178; 5,490] [2,443; 4,632] LoS: length of stay a Includes the following seasons: 2008/2009, 2010/2011, 2011/2012, 2012/2013, 2013/2014, 2014/2015, 2015/2016, 2016/2017, 2017/2018. Excludes season 2009/10, since it was affected by the 2009 influenza A (H1N1) pandemic; b Includes the most recent seasons, namely 2015/2016, 2016/2017, 2017/2018; c Minimum and maximum values observed throughout the analyzed seasons for each age group; d Includes > 1 influenza hospitalization/patient Page 6 of 16 Table 3 Estimated Influenza-Associated Excess Hospitalizations for All Ages and for Population Aged 65 Years Old or Over, in Absolute Number of Cases and per 100,000 People, by Group of Diagnoses and Epidemic Season in Spanish Public Hospitals Between 2008/2009 and 2017/2018 2008/2009 2009/2010 2010/2011 2011/2012 2012/2013 2013/2014 2014/2015 2015/2016 2016/2017 2017/2018 P&I All agess N 4,859 14,616 12,286 11,777 6,191 15,294 19,874 15,785 24,188 37,821 Pumarola et al. BMC Infectious Diseases Ratea 10.6 31.5 26.4 25.2 13.2 32.8 42.8 34.0 52.0 81.2 95% CI 7.6–13.8 29.1–33.6 23.6–29.3 22.5–28.1 10.4–16.1 30.4–35 40–44.7 31–36.8 49.6–54.6 78–83.6 ≥ 65 N 3,703 1,426 4,438 9,147 3,033 7,730 14,061 6,344 19,361 27,274 Ratea 48.8 18.4 56.2 113.6 37.0 92.5 165.1 73.4 221.1 306.8 95% CI 37.2–60.2 9–27.8 45.8–66.6 103–123.6 26.5–48.4 83.4–100.5 154.6–172.2 62.3–83.7 212.4–230.3 295.5–314.6 (2023) 23:86 R All ages N 9,069 16,518 23,410 32,885 13,764 26,384 39,304 20,115 36,407 64,390 Ratea 19.7 35.6 50.3 70.4 29.4 56.6 84.6 43.3 78.3 138.2 95% CI 11.3–27.8 29.5–41.8 42.6–57.3 63.3–77.6 22.4–36.3 50.4–62.1 77.9–90.5 35.4–50.6 71–85.2 129.5–145.7 ≥ 65 N 11,690 1808 11,149 29,812 11,025 15,541 30,959 7,956 33,124 52,781 a Rate 154.1 23.4 141.2 370.1 134.5 186.1 363.4 92.1 378.2 593.7 95% CI 121.9–184.7 − 0.5 to 48.8 113.7–167.7 343.6–397.7 107.2–160.4 163–208.5 337.6–384.4 63.7–121.5 351.7–403.9 561.4–619.4 C&R All ages N 16,546 12,844 30,444 41,665 16,743 29,924 49,761 30,430 45,672 52,861 Ratea 36.0 27.7 65.4 89.1 35.8 64.2 107.1 65.5 98.3 113.5 95% CI 23.1–47.9 17.7–37.2 53.7–77 77.2–100.7 24.4–47.1 53.8–73.7 96.7–116.9 52.9–78 86.7–109.6 101.3–126 ≥ 65 N 18,438 −b 15,963 40,098 12,559 18,382 40,351 16,479 42,722 48,824 a Rate 243.1 −b 202.2 497.8 153.2 220.1 473.7 190.7 487.8 549.2 95% CI 196.3–288.3 − 55.9 to 18.2 156.7–245.5 454.1–541.9 110.7–195.1 183.3–256 435.9–511.7 146.1–236.8 449.5–528.6 505.9–593.4 All-cause All ages N –b 3,029 36,546 34,244 960 35,560 35,464 53,181 32,775 48,313 a Rate –b 6.5 78.5 73.3 2.1 76.3 76.3 114.5 70.5 103.7 95% CI − 46 to 31.2 − 21.5 to 36.2 42.8–109.9 35.7–110.4 − 31.3 to 34.9 44.9–107.2 48.2–108.2 80.3–150.5 38.6–103 68.3–142.4 ≥ 65 N 6,862 –b 13,759 39,696 8,980 18,970 35,989 24,477 38,290 48,898 a Rate 90.5 –b 174.2 492.8 109.6 227.1 422.5 283.2 437.2 550.0 95% CI − 10.2 to 180.8 − 111.4 to 32.1 85.7–254.9 404.3–583.5 25.4–190.9 143.4–300.7 350.1–500.1 196.8–373.2 352.2–513.7 465.8–641.7 CI—Confidence interval; C&R—Respiratory or cardiovascular; N—Number of excess hospitalizations; R – Respiratory; P&I—Pneumonia or influenza a Rate of influenza-associated excess hospitalizations per 100,000 people; b No influenza-associated excess hospitalizations estimated for this age group/season Page 7 of 16 Pumarola et al. BMC Infectious Diseases (2023) 23:86 Page 8 of 16 Table 4 Estimated Influenza-Associated Excess Respiratory or Cardiovascular Hospitalizations, in Absolute Number of Cases and per 100,000 People, by Age Group and Epidemic Season in Spanish Public Hospitals Between 2008/2009 and 2017/2018 Influenza-Associated Excess C&R Hospitalizations per Age Group 0–4 5–18 19–49 50–64 65–74 ≥ 75 ≥ 65 All ages 2008/2009 N –d –a,d 189a 2,099 4,724 12,459 18,438 16,546 e d d Rate – – 0.9 26.9 125.5 326.1 243.1 36 95% CI –d –d − 3.2 to 5 14.8–38.8 96.7–154.2 249.8–401.6 196.3–288.3 23.1–47.9 a 2009/2010 N 455 2,412 6,621 3,542 817a –a,d –a,d 12,844 Ratee 18.3 38.9 30.2 44.3 21.5 –d –d 27.7 95% CI − 14.8 to 0 34–0 27.5–0 35.1–0 0.4–0 vd –d 17.7–0 2010/2011 N 1,202a 393a 5,250 6,804 5,457 10,432 15,963 30,444 Ratee 48.1 6.3 24.1 83.4 142 257.4 202.2 65.4 95% CI 11.7–86.2 0.3–12.6 20.7–27.4 73.1–93 116.4–166.3 187.9–328 156.7–245.5 53.7–77 2011/2012 N –d –a,d 332a 3,227 6,860 33,068 40,098 41,665 e d d Rate – – 1.5 38.7 176.5 793.4 497.8 89.1 95% CI –d –d − 2 to 5.3 27.7–49.5 151.5–202.1 725.6–860.4 454.1–541.9 77.2–100.7 d 2012/2013 N – –a,d 1,763 3,643 2,680 10,013 12,559 16,743 Ratee –d –d 8.3 42.8 68 235.5 153.2 35.8 95% CI –d –d 4.8–11.7 32.5–52.9 42.9–91.7 168.5–301.7 110.7–195.1 24.4–47.1 2013/2014 N 1432 188a 3,553 5,702 5,493 13,716 18,382 29,924 Ratee 60.4 2.9 17.1 66 135.2 319.6 220.1 64.2 95% CI 30.8–90.6 − 2.2–8.2 14.2–20 57–74.6 112.9–155.5 261.1–376.8 183.3–256 53.8–73.7 2014/2015 N 632a 724 1,743 4,449 6,556 35,472 40,351 49,761 Ratee 27.6 11.2 8.5 50.5 155.2 826.1 473.7 107.1 95% CI − 2.9 to 61.8 6.3–16.5 5.5–11.7 41–60.1 133.3–177.3 766.3–881.8 435.9–511.7 96.7–116.9 2015/2016 N 887a 2,155 4,669 6,181 4,948 12,127 16,479 30,430 Ratee 39.8 32.9 23.3 68.7 114.7 280.2 190.7 65.5 95% CI 1–77.2 27.2–38.8 19.9–26.7 57.4–79.1 89.3–139.1 208.6–354.9 146.1–236.8 52.9–78 2016/2017 N –d –a,d 946 4,120 7,513 34,664 42,722 45,672 Ratee –d vd 4.8 44.7 171.7 790.9 487.8 98.3 95% CI –d –d 1.3–8.3 34.7–54.2 147.9–194.9 727.2–855.4 449.5–528.6 86.7–109.6 2017/2018 N –d 1,172 1,683 6,704 10,540 36,490 48,824 52,861 Ratee –d 17.6 8.6 71.2 235.3 827.3 549.2 113.5 95% CI –d 11.5–24.1 5.2–12.2 60.9–82 209.8–261.3 758–897.2 505.9–593.4 101.3–126 9 years ­meanb N 461 515 2,236 4,770 6,086 22,049 28,202 34,894 Ratee 19.5 7.9 10.8 54.8 147.1 517.4 335.3 75 95% CI − 79.8 to 79 0.5–12.4 7.4–14.2 44.3–64.9 122.3–171.4 450.4–584.2 293.2–377.5 63.3–86.3 3 years ­meanc N 296 1,109 2,432 5,668 7,667 27,761 36,008 42,988 Ratee 13.3 16.8 12.2 61.5 173.9 632.8 409.2 92.4 95% CI − 88.7 to 77.2 9.6–21.6 8.8–15.7 51–71.8 149–198.4 564.6–702.5 367.2–452.9 80.3–104.5 CI: Confidence interval; C&R: Respiratory or cardiovascular; N: Number of influenza-associated excess C&R hospitalizations a P-value associated to the ILI incidence variable > 0.05; b Includes the following seasons: 2008/2009, 2010/2011, 2011/2012, 2012/2013, 2013/2014, 2014/2015, 2015/2016, 2016/2017, 2017/2018. Excludes season 2009/10, since it was affected by the 2009 influenza A (H1N1) pandemic. Seasonal negative excess estimates were truncated to zero to compute the mean; c Includes the most recent seasons, namely 2015/2016, 2016/2017, 2017/2018. Seasonal negative excess estimates were truncated to zero to compute the mean; d No influenza-associated excess hospitalizations estimated for this age group/season; e Rate of influenza-associated excess C&R hospitalizations per 100,000 people Influenza‑associated excess hospitalization 280,072 in the all-cause model (Table 3). The estimated The estimated all-age influenza-associated excess hos- excess hospitalizations per group of diagnoses for patients pitalizations over the 10-year study period was 162,691 aged ≥ 65 years was 96,517 in the P&I, 205,845 in the R, in the P&I, 282,246 in the R, 326,890 in the C&R, and 253,816 in the C&R, and 235,921 in the all-cause model. Pumarola et al. BMC Infectious Diseases (2023) 23:86 Page 9 of 16 The maximum number of influenza-associated excess Influenza economic burden hospitalizations was observed in 2017/18 for P&I, R, and Hospitalizations with a diagnosis of influenza C&R diagnoses, whereas the maximum excess hospitali- Over ten years, the 121,393 hospitalizations with a diag- zations was estimated to have occurred in 2015/2016 for nosis of influenza have costed NHS €422.8 million among all-cause diagnoses, followed by 2017/2018. The models the Spanish population. Costs varied with the epidemic using C&R hospitalizations showed the best performance season, from €5.9 million in 2008/09 to €120.5 million for the all ages and the ≥ 65 years age groups (Additional in 2017/18. The mean annual cost was €45.7 million file 1: Table S2). (excluding the 2009/10 pandemic season), of which 52.8% The mean annual influenza-associated C&R hospi- was generated by patients aged ≥ 65 ­years1 (Fig. 1). Over talizations over nine seasons were estimated at 34,8941 the study period, patients with comorbidities, regard- (Table 4). The mean annual excess C&R hospitalization less of age, accounted for 65.7% of the costs of hospitali- rate per 100,000 for the Spanish population was esti- zations with a diagnosis of i­nfluenza1. At-risk patients, mated at 75.0 (95% CI: 63.3–86.3) for all ages and 335.3 either because of their age (≥ 65 years) or underlying (95% CI: 293.2–377.5) in the population aged ≥ 65 ­years1. medical conditions, accounted for 79.9% of influenza In the population aged < 65 years, the highest mean hospitalization ­costs1. Table 2 presents the mean influ- annual excess C&R hospitalization rate per 100,000 was enza hospitalization cost per patient, according to their estimated in those aged between 50 to 64 years (54.8 age and comorbidities profile. [95% CI: 44.8–64.9])1. The absolute and relative influenza-associated excess Influenza‑associated excess hospitalization hospitalizations by age groups and by diagnoses (P&I, The direct cost of all-age influenza-associated excess respiratory cause alone and all-causes) are displayed in hospitalizations over the ten-year study period was esti- Supplementary Materials (Additional file 1: Table S4). mated at €484.9 million in the P&I, €826.1 million in the R, €1339.1 million in the C&R, and €1015.2 million in the Influenza‑associated excess mortality all-cause model. The total number of all-age influenza-associated excess The mean annual cost of influenza-associated excess deaths in the ten-year study period was 12,063 in the P&I, C&R hospitalizations over 9 seasons was estimated at 38,075 in the R, 74,694 in the C&R, and 117,389 in the all- 142.9 M€ (min: 68.6 M€; max: 216.6 M€) for the all ages cause model (Table 5). The estimated excess deaths per group, €115.9 million for the population aged ≥ 65 years, group of diagnoses in the population aged ≥ 65 years was €24.2 million for the population aged 50 to 64 years, and 11,010 in the P&I, 35,364 in the R, 68,484 in the C&R, €8.8 million for the population aged 19 to 49 years. The and 108,939 in the all-cause model. The highest estimated population aged ≥ 65 years accounted for 76.4% of the influenza-associated deaths in P&I and R groups of diag- cost, and younger adults accounted for 22.1% of the cost noses was seen during 2017/2018. The C&R and all-cause (Fig. 2). groups showed maximum influenza-associated deaths in 2014/2015. The models using all-cause deaths yielded the Discussion best model performance (Additional file 1: Table S3). This is the first study to estimate the global epidemio- The mean annual influenza-associated all-cause deaths logic and economic morbidity and mortality burden of per 100,000 people – over nine seasons –was estimated influenza and to capture complications associated with at 27.7 for the all ages group (Table 6). This ratio (9.7 per influenza in Spain by integrating two approaches: cases 100,000) was 10.8 times higher in those aged 50–64 years actually coded with influenza-specific diagnosis and compared with the population aged 19–49 years (0.9 per cases associated with influenza based on statistical excess 100,000)1. The population aged ≥ 65 years showed a high modeling techniques. The results were assessed across annual excess all-cause influenza-associated deaths in ten epidemic seasons, six age groups, and four groups of five seasons; the specifics are as follows: 2008/09 (14,193 diagnoses. The mortality data represent all deaths reg- deaths; 187.2 deaths per 100,000 people); 2011/12 istered in Spain. The hospitalization data represent all (18,744; 232.7), 2014/15 (24,282; 285.1), 2016/17 (19,074; estimated NHS hospitalizations, exclusive of the private 217.8), and 2017/18 (16,685; 187.7). All these seasons sector. showed a dominance or co-predominance of A(H3N2) Findings from this study confirm the high burden of and/or B/Yamagata strains. severe influenza in Spain. Furthermore, regardless of The absolute and relative influenza-associated excess the selected group of diagnoses, the excess modeling all-cause deaths stratified by age groups and by diagnoses analyses show that the burden of severe influenza will groups are displayed in Additional file 1: Table S5. Table 5 Estimated Influenza-Associated Excess Deaths for All Ages and for Population Aged 65 Years Old or Over, in Absolute Number of Cases and per 100,000 People, by Group of Diagnoses and Epidemic Season in Spain Between 2008/2009 and 2017/2018 2008/2009 2009/2010 2010/2011 2011/2012 2012/2013 2013/2014 2014/2015 2015/2016 2016/2017 2017/2018 P&I All ages N 1,301 227 488 1,301 63 866 1,943 395 2,340 3,139 Pumarola et al. BMC Infectious Diseases Ratea 2.8 0.5 1.0 2.8 0.1 1.9 4.2 0.9 5.0 6.7 95% CI 2.2–3.5 0–1 0.5–1.6 2.2–3.3 − 0.4 to 0.8 1.3–2.4 3.6–4.7 0.3–1.5 4.6–5.5 6.1–7.2 ≥ 65 N 1,134 26 331 1,338 140 700 1,909 277 2,260 2,895 Ratea 15.0 0.3 4.2 16.6 1.7 8.4 22.4 3.2 25.8 32.6 95% CI 11.4–18.5 1.5–6.9 13.5–19.5 5.6–11 19.4–25.3 0.3–6.4 23.7–28.2 29.4–35.3 (2023) 23:86 − 2.3 to 3.1 − 1.4 to 4.8 R All ages N 4,685 597 1,511 5,807 643 2,464 7,428 749 6,394 7,797 Ratea 10.2 1.3 3.2 12.4 1.4 5.3 16.0 1.6 13.8 16.7 95% CI 8.3–12.1 − 0.3 to 2.8 1.7–4.8 10.7–14.2 − 0.5 to 3.1 3.7–6.8 14.2–17.6 − 0.2 to 3.4 12.3–15.2 14.9–18.3 ≥ 65 N 4,492 378 1,064 5,548 402 1,981 6,983 479 6,264 7,773 Ratea 59.2 4.9 13.5 68.9 4.9 23.7 82.0 5.5 71.5 87.4 95% CI 49.3–69.8 − 4 to 13.2 5.1–22.6 59.2–78.3 − 5.4 to 14.9 14.7–31.9 72.8–90.7 − 4.2 to 14.9 63.8–79.8 78.3–95.2 C&R All ages N 9,019 185 3,001 13,220 1,974 4,933 15,443 2,228 11,884 12,807 Ratea 19.6 0.4 6.4 28.3 4.2 10.6 33.2 4.8 25.6 27.5 95% CI 15.8–23.9 − 2.7 to 3.8 3.4–9.9 24.7–32 0.4–8 7.3–13.7 29.7–36.8 1–8.6 22.9–28.7 23.8–31 ≥ 65 N 8,140 –b 2,327 12,855 1,662 3,990 14,464 1,472 11,328 12,246 Ratea 107.4 –b 29.5 159.6 20.3 47.8 169.8 17.0 129.3 137.7 95% CI 86.6–129.9 − 17.6 to 16.2 13.1–48.3 140.6–178.5 0.2–40.5 30.6–63.6 151.2–188.5 − 3.7 to 36.8 114.5–145.3 118.3–156.3 All-cause All ages N 14,078 1,525 5,169 20,974 2,070 7,353 24,268 3,883 18,763 19,306 Ratea 30.6 3.3 11.1 44.9 4.4 15.8 52.2 8.4 40.4 41.4 95% CI 24.4–37.5 − 1.7 to 0 5.8–16.8 38.8–51.2 − 1.9 to 10.5 10.4–20.5 46.5–58 1.9–14.8 35.6–45.5 35.3–47.3 ≥ 65 N 14,193 520 5,693 18,744 3,810 4,923 24,282 1,015 19,074 16,685 Ratea 187.2 6.7 72.1 232.7 46.5 58.9 285.1 11.7 217.8 187.7 95% CI 149.4–225.5 − 22.4 to 0 42.1–104.2 201.3–266 8.9–80.1 32.1–85.7 251.4–317.6 − 21.4 to 47.8 191.5–245.6 156.1–219.2 CI: Confidence interval; C&R: Respiratory or cardiovascular; N: Number of excess deaths; R: Respiratory; P&I: Pneumonia or influenza a Rate of influenza-associated excess deaths per 100,000 people; b No influenza-associated excess deaths estimated for this age group/season Page 10 of 16 Pumarola et al. BMC Infectious Diseases (2023) 23:86 Page 11 of 16 Table 6 Estimated Influenza-Associated Excess All-Cause Deaths, in Absolute Number of Cases and per 100,000 People, by Age Group and Epidemic Season in Spain Between 2008/2009 and 2017/2018 Influenza-Associated Excess All-Cause Deaths per Age Group 0–4 5–18 19–49 50–64 65–74 ≥ 75 ≥ 65 All Ages 2008/2009 N 79 –a,d 234 566 1,509 12,824 14,193 14,078 e Rate 3.2 –d 1.1 7.2 40.1 335.9 187.2 30.6 95% CI 1.3–5.3 –d 0.4–1.8 4–11 30.4–49.7 264.1–405.4 149.4–225.5 24.4–37.5 2009/2010 N – a,d 38 291 214a 155a 633a 520a 1,525a e d Rate – 0.6 1.3 2.7 4.1 16.1 6.7 3.3 95% CI –d 0.2–0 0.8–1.9 0–5.4 − 3.5 to 11.4 − 39.7 to 70.6 − 22.4 to 37.1 − 1.7 to 8.8 2010/2011 N 42a –a,d 351 898 841 4,695 5,693 5,169 e Rate 1.7 –d 1.6 11 21.9 115.8 72.1 11.1 95% CI − 0.1 to 3.3 –d 1.1–2.1 8.4–14 13.3–30.1 60–175.7 42.1–104.2 5.8–16.8 2011/2012 N 53a 7a 176 1,054 1,317 16,913 18,744 20,974 Ratee 2.1 0.1 0.8 12.7 33.9 405.8 232.7 44.9 95% CI 0.2–4 − 0.3 to 0.6 0.2–1.4 9.9–15.8 24.4–42.7 347.9–468.1 201.3–266 38.8–51.2 2012/2013 N –a,d –a,d 94a 664 14a 3,205a 3,810a 2,070a e d d Rate – – 0.4 7.8 0.4 75.4 46.5 4.4 95% CI –d –d − 0.2 to 1 4.8–10.9 − 9 to 9.1 4.7–139.7 8.9–80.1 − 1.9 to 10.5 2013/2014 N 35a –a,d 240 799 795 4,419 4,923 7,353 Ratee 1.5 −d 1.2 9.2 19.6 103 58.9 15.8 d 95% CI 0.1–2.9 – 0.6–1.7 6.7–11.7 11.6–27.2 54.1–154.3 32.1–85.7 10.4–20.5 2014/2015 N 17a 15a 130a 935 1,931 23,510 24,282 24,268 Ratee 0.8 0.2 0.6 10.6 45.7 547.5 285.1 52.2 95% CI − 0.9 to 2.5 − 0.2 to 0.6 0.1–1.2 7.9–13.5 36.9–54.2 487.2–608.2 251.4–317.6 46.5–58 2015/2016 N –a,d –a,d 224 713 755 804a 1,015a 3,883a e d d Rate – – 1.1 7.9 17.5 18.6 11.7 8.4 95% CI –d –d 0.6–1.7 5.1–11.1 8.3–25.8 − 43.8 to 85.1 −21.4 to 47.8 1.9–14.8 2016/2017 N 33a 5a 182 726 1,473 17,607 19,074 18,763 Ratee 1.5 0.1 0.9 7.9 33.7 401.7 217.8 40.4 95% CI 0.1–3.2 − 0.3 to 0.4 0.4–1.4 5.4–10.5 26.2–41.6 353.3–452.9 191.5–245.6 35.6–45.5 2017/2018 N 63 7a 161 1,247 1,778 14,736 16,685 19,306 Ratee 3 0.1 0.8 13.2 39.7 334.1 187.7 41.4 95% CI 1.2–4.6 − 0.3 to 0.5 0.2–1.4 10.6–16 30.8–48 274.3–391.9 156.1–219.2 35.3–47.3 9 years ­meanb N 36 4 199 845 1,157 10,968 12,047 12,874 Ratee 1.5 0.1 0.9 9.7 28.1 259.8 144.4 27.7 95% CI − 0.2 to 3.3 − 0.5 to 0.4 0.4–1.5 7–12.7 19.2–36.5 200.2–320.2 112.4–176.9 21.9–33.5 3 years ­meanc N 32 4 189 895 1,335 11,049 12,258 13,984 Ratee 1.5 0.1 0.9 9.7 30.3 251.5 139.1 30.1 95% CI − 0.2 to 3.3 − 0.4 to 0.4 0.4–1.5 7–12.5 21.8–38.4 194.6–310 108.7–170.9 24.3–35.9 CI: Confidence interval; N: Number of influenza-associated excess all-cause deaths a P-value associated to the ILI incidence variable > 0.05; b Includes the following seasons: 2008/2009, 2010/2011, 2011/2012, 2012/2013, 2013/2014, 2014/2015, 2015/2016, 2016/2017, 2017/2018. Excludes season 2009/10, since it was affected by the 2009 influenza A (H1N1) pandemic. Seasonal negative excess estimates were truncated to zero to compute the mean; c Includes the most recent seasons, namely 2015/2016, 2016/2017, 2017/2018. Seasonal negative excess estimates were truncated to zero to compute the mean; d No influenza-associated excess deaths estimated for this age group/season; e Rate of influenza-associated excess all-cause deaths per 100,000 people be underestimated if only administrative data with influ- Mean NHS hospitalizations with a diagnosis of influ- enza-specific codes is used (both for hospitalization and enza per 100,000 per season was 28.1 for all ages and mortality). 88.2 in the population aged ≥ 65 years. These are higher than those reported by San-Román-Montero et al. [14] Pumarola et al. BMC Infectious Diseases (2023) 23:86 Page 12 of 16 Fig. 1 Direct Cost of Hospitalizations With a Diagnosis of Influenza by Age Group and Epidemic Season in Spanish Public Hospitals Between 2008/2009 and 2017/2018 (Million €) for seasons 2009/10 to 2014/15 due to the much higher increased to 64.8% in all-age and 48.0% in population admission rates observed in seasons 2015/16 to 2017/18. aged ≥ 65. If the mean annual influenza-associated excess NHS The mean annual influenza-associated deaths per hospitalizations are considered instead, this ratio would 100,000 people were estimated at 2.8 (95% CI: 2.3–3.4) rise to 35.3 cases per 100,000 (95% CI: 32.6–38.0) for all for all ages and 14.4 (95% CI: 11.5–17.3) for population ages and 123.8 (95% CI: 113.4–133.4) for the population aged ≥ 65 years for P&I; 9.0 (95% CI: 7.2–10.6) and 46.3 aged ≥ 65 years in the P&I; 63.4 (95% CI: 56.0–70.3) and (95% CI: 37.1–55.3) for R; 17.8 (95% CI: 14.3–21.4) and 268.1 (95% CI: 240.4–294.2) in the R; 75.0 (95% CI: 63.3– 90.9 (95% CI: 72.4–109.8) for C&R; and, 27.7 (95% CI: 86.3) and 335.3 (95% CI: 293.2–377.5) in the C&R; and 21.9–33.5) and 144.4 (95% CI: 112.4–176.9) for all-cause 66.1 (95% CI: 31.3–99.8) and 309.7 (95% CI: 223.7–393.3) deaths, respectively. Deaths observed during NHS hos- in the all-cause model, respectively. Hospitalizations with pitalizations with a diagnosis of influenza represented a diagnosis of influenza during the study period repre- only 5.3% and 4.3% of the estimated influenza-associ- sented only 37.4% of the estimated influenza-associated ated excess all-cause deaths (regardless of place of death, excess C&R hospitalizations in all-age group and 26.3% within Spain) in the all ages group and in the population in the population aged ≥ 65 years. Czaja et al. reported aged ≥ 65 years, respectively. similar ratios for Colorado, USA, estimating four influ- In both approaches, most of the hospitalizations and enza-associated excess C&R hospitalizations for each deaths were observed in the elderly patients. However, influenza-coded hospitalization from hospital discharge relevant excess hospitalization was also observed in records, in the population aged ≥ 65 years (vs. our 3.8 younger age groups, particularly in those aged between ratio over 9 seasons) [27]. However, the data also suggest 50 and 64 years old. This age group also reported the an improvement in testing and/or coding for influenza in longest hospital stays and the highest mean hospitaliza- Spain, as the mean over the last three analyzed seasons tion cost per patient. Beyond age, underlying medical conditions play an important role and must be adequately Pumarola et al. BMC Infectious Diseases (2023) 23:86 Page 13 of 16 Fig. 2 Direct Cost of Influenza-Associated Excess Respiratory or Cardiovascular Hospitalizations by Age Group and Epidemic Season in Spanish Public Hospitals Between 2008/2009 and 2017/2018 (Million €). The total cost corresponds to the cost of all-age influenza-associated excess C&R hospitalization. The percentage for each age group was computed as a proportion of the sum of influenza-associated excess C&R hospitalization estimated for each individual age group addressed. However, the population with comorbidities could potentially double when cardiovascular deaths had longer hospital stays, higher hospitalization costs, were included [43, 44]. This duplication was indeed and higher in-hospital case fatality risk irrespective of the observed in our study between the R and C&R models, age. The highest mean LoS was observed in patients with both for all ages and for those aged ≥ 65 years. comorbidities, aged < 65 years (12.0 days), which had an Other authors have explored the influenza-associ- impact on hospitalization costs but also on lost produc- ated excess mortality in Spain using different groups of tivity; the latter was not quantified in this study. diagnoses. In the GLaMOR project, Paget et al. (2022) As expected, excess morbidity and mortality esti- estimated the average influenza-associated respiratory mates varied considerably with the groups of diagno- deaths in Spain per 100,000 people between 2002 and ses, age, and seasons used in the model. Although the 2011 (excluding 2009) to be 5.4 (2.9–7.3) for all ages and performance of each model was computed, the analysis 28.3 (14.4–41.4) for population aged ≥ 65 years, which is alone cannot determine the importance of all‐cause or lower than our estimates for the same group of diagnoses cause‐specific data in estimating morbidity and mortal- [43]. In another study, Schmidt et al. estimated the influ- ity, which was also an issue raised by other authors [42]. enza‐associated excess mortality for (i) all‐cause, (ii) res- Nonetheless, it is extensively studied that the influenza piratory and circulatory, (iii) circulatory, (iv) respiratory, infection can trigger various complications—beyond res- and (v) pneumonia and influenza, for the 2015/2016 and piratory complications -, leading to hospitalizations and 2016/2017 influenza seasons, in three countries, includ- deaths [3]. Some authors who have computed influenza- ing Spain [42]. In Spain, the mean excess mortality for associated respiratory deaths for Spain and other geogra- the two seasons based on all‐cause, C&R, R, and P&I was phies have stated that their estimates were conservative, 37.9, 19.6, 10.1, and 3.3 per 100,000 people, respectively as evidence suggested that excess mortality estimates (all ages) [42]. In all groups of diagnoses, Schmidt et al. Pumarola et al. BMC Infectious Diseases (2023) 23:86 Page 14 of 16 estimates are slightly higher but closer to the ones from ILI cannot strictly be attributed to influenza alone. Still, our study. the models employed in the BARI study presented global These differences reflect the variability of results and high performance in terms of correlation and MAPE, as difficulty in comparing different studies of this nature presented in the Supplementary Materials. In the excess [45]. The reasons for the variability in results may hospitalization and mortality models, we used the “all- come mainly from differences in seasons considered in ages” ILI incidence as an indicator of influenza activity, each study (different circulating strains, level of match as reported by the national authorities [31] and without between vaccine and circulating strains, vaccine cov- further control variables. The use of age-stratified inci- erage rates) and possibly from differences in the used dence rates throughout the study period, if made avail- methods (e.g., statistical models, causes and age groups able, should improve the performance of the models per definitions, data source); however, the accurate reason age group. is yet to be explored [45]. A possible explanation for Cost estimates are based on mean estimated DRG costs the lower estimates reported by Paget et al. may be the at a national level, although distinct DRG costs may be different viral characteristics across the analyzed sea- observed across autonomous communities. Also, despite sons. In our study, A(H3N2) was predominant or co- providing a broader perspective on the cost of treating predominant in 66.7% of the seasons [46] versus 44.4% severe influenza, the study still shows only a fraction of as observed in the GLaMOR study [43, 47]. In another the problem. Hospitalizations in the private setting, the article, the authors report that the dominant subtype burden of milder influenza cases with self-management, circulating in each season was a predictor of excess res- or those cases managed in primary health care set- piratory mortality [47]. The most severe seasons coin- ting were not included. Indirect costs of lost productiv- cided with influenza A(H3N2) in older adults and it was ity (from patients or their parents) and the medium and A(H1N1)pdm09 in the younger group [47]. Methods long-term costs related to the posterior treatment of and data sources also differed with those from Paget complications caused or aggravated by influenza were et al. not computed. Published studies for Spain report that Results of this study should be interpreted in light of hospitalization costs represent less than half of the total several limitations. First, the study is based on extrapo- direct costs of influenza (with its weight varying accord- lated administrative data, and subject to coding errors or ing to patients’ age and comorbidities status), reflecting missing information. No laboratory data or information the importance of considering the burden in other set- on the vaccination status of subjects were available. Data tings of care [20, 23]. on deaths per cause per age group from death certificate Overall, the present study supports the need for could be retrieved only on a monthly basis. The study increased testing for influenza in Spain to tackle the cur- period has included two ICD systems, but ICD10 brought rent underestimation of influenza burden [27]. The use of a greater granularity and it may have impacted coding a multiplier approach when computing influenza’s bur- practices. The excess modeling analysis helped over- den, as performed in other geographies could be helpful come some of these limitations. As for the excess hos- [50]. Further studies analyzing the impact of influenza on pitalization and mortality model, we used ILI incidence specific complications could enable more targeted inter- as an indicator of influenza activity, without further con- ventions. Regional assessments of the burden of influenza trol variables, based on published literature [24, 27, 38]. could also provide further insights for policy evaluation Other variables could have also been used, such as labo- and decision-making. ratory data on the circulation of other respiratory viruses [48, 49]. In France, a comparison of models using ILI and Conclusion percent influenza positive demonstrated that ILI was the The BARI study contributed to a better understanding most statistically relevant indicator of mortality and that, of the burden of influenza in Spain, showing the impact most importantly, both indicators produced similar influ- of influenza and its complications on hospitalization and enza burden estimates [38]. Nonetheless, as indicated by mortality. Also, administrative data without a systematic Froes et al., these findings may not necessarily hold in testing for influenza would lead to under-detection of other countries [24]. The choice to use ILI indicator for influenza, thus underestimating the impact of influenza influenza activity in the model may be a limitation of our virus. We conclude that there is still a substantial burden work, as ILI may capture diseases caused by other respir- of influenza on the elderly population and younger popu- atory pathogens. Hence excess mortality modelled with lation in Spain, which needs to be addressed. Pumarola et al. BMC Infectious Diseases (2023) 23:86 Page 15 of 16 Supplementary Information part in advisory boards and expert meetings and for acting as a speaker in congresses outside the scope of the submitted work. Martinón-Torres F has The online version contains supplementary material available at https://​doi.​ also acted as the principal investigator in randomized controlled trials of the org/​10.​1186/​s12879-​023-​08015-3. above-mentioned companies as well as Ablynx, Gilead, Regeneron, Roche, Abbott, Novavax, and MedImmune, with honoraria paid to his institution. Ortiz Additional file 1. Table S1. Diagnostic codes used to identify comorbidi‑ de Lejarazu R received honoraria from AstraZeneca, GSK, MSD, Roche, Sanofi, ties/risk factors for influenza: a) in people ≥ 5 years old; b) in children < 5 and Seqirus for taking part in advisory boards and expert meetings and for years old. Table S2. Performance of the excess hospitalization model per acting as a speaker in congresses outside the scope of the submitted work. age group and cause. Table S3. Performance of the excess deaths model Ortiz de Lejarazu R has been the Director of the National Influenza Centre, per age group and cause. Table S4. Estimated influenza-associated excess Valladolid, Spain, until 2019, without any salary for that position and has pneumonia or influenza, respiratory, and all-cause excess hospitaliza‑ participated in regular commitments of influenza and other respiratory virus tions, in absolute and per 100,000 people, by age group and epidemic surveillance. Pumarola T received honoraria from Roche, Sanofi, Seegene, and season in Spanish public hospitals between 2008/2009 and 2017/2018. Seqirus for taking part in advisory boards and expert meetings and for acting Table S5. Estimated influenza-associated excess pneumonia or influenza, as a speaker in congresses outside the scope of the submitted work. Redondo respiratory, and respiratory or cardiovascular deaths, in absolute and per Margüello E received honoraria from GSK group of companies, Pfizer Inc, 100,000 people, by age group and epidemic season in Spain between Sanofi, and MSD, for taking part in advisory boards and expert meetings and 2008/2009 and 2017/2018. Table S6. P-value associated to the ILI vari‑ for acting as a speaker in congresses outside the scope of the submitted work. ables of estimated influenza-associated excess hospitalization. Table S7. P-value associated to the ILI variables of estimated influenza-associated Author details excess deaths. 1 Department of Microbiology, Hospital Universitari Vall d’Hebron, Barcelona, Spain. 2 Vaccine Research Department, University of Valencia, Valencia, Spain. 3 Translational Pediatrics and Infectious Diseases, Hospital Clínico Universi‑ Acknowledgements tario and Universidad de Santiago de Compostela, Galicia, Spain. 4 Genetics, The authors would like to thank all investigators who participated in the BARI Vaccines and Pediatric Infectious Diseases Research Group (GENVIP), Instituto study, and José María Guillén for assistance during the project design and de Investigación Sanitaria de Santiago and Universidad de Santiago de Com‑ results assessment. postela (USC), Galicia, Spain. 5 Consorcio Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Madrid, Spain. 6 International Author contributions Health Center Madrid Health, City Council of Madrid, Madrid, Spain. 7 Valladolid All authors have contributed to the protocol revision, critical analysis, and National Influenza Centre, Hospital Clínico Universitario de Valladolid, Vallado‑ discussion of the study results, and collaborated for the redaction and revision lid, Spain. 8 IQVIA, Barcelona, Spain. 9 IQVIA, Paris, France. 10 Sanofi, Barcelona, of the manuscript. MC and GB have conducted the technical analysis of the Spain. 11 Sanofi, Lyon, France. 12 Public Health and Medical Specialties Depart‑ BARI study. All authors read and approved the final manuscript. ment, Health Sciences Faculty, Juan Carlos University, Madrid, Spain. 13 Univer‑ sitat Autònoma de Barcelona, Plaça Cívica, 08193 Bellaterra, Barcelona, Spain. Funding The BARI study was funded by Sanofi. Martinón-Torres F has received sup‑ Received: 3 July 2022 Accepted: 18 January 2023 port for the present work from the Instituto de Salud Carlos III (Proyecto de Investigación en Salud, Acción Estratégica en Salud): Fondo de Investigación Sanitaria (FIS; PI070069/PI1000540/PI1601569/PI1901090) del plan nacional de I + D + I and ‘fondos FEDER’ and Proyectos GAIN Rescata-Covid_IN845D 2020/23 (GAIN, Xunta de Galicia). References 1. Troeger CE, Blacker BF, Khalil IA, Zimsen SRM, Albertson SB, Abate D, Availability of data and materials Abdela J, Adhikari TB, Aghayan SA, Agrawal S, et al. 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