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. 2022 Jul 12;17(7):e0271263. doi: 10.1371/journal.pone.0271263

Incidence, recurring admissions and mortality of severe bacterial infections and sepsis over a 22-year period in the population-based HUNT study

Kristin Vardheim Liyanarachi 1,2,*, Erik Solligård 1,3, Randi Marie Mohus 1,3, Bjørn O Åsvold 4,5,6, Tormod Rogne 1,3,7,#, Jan Kristian Damås 1,2,8,#
Editor: Kazumichi Fujioka9
PMCID: PMC9275692  PMID: 35819970

Abstract

Purpose

Severe bacterial infections are important causes of hospitalization and loss of health worldwide. In this study we aim to characterize the total burden, recurrence and severity of bacterial infections in the general population during a 22-year period.

Methods

We investigated hospitalizations due to bacterial infection from eight different foci in the prospective population-based Trøndelag Health Study (the HUNT Study), where all inhabitants aged ≥ 20 in a Norwegian county were invited to participate. Enrollment was between 1995 and 1997, and between 2006 and 2008, and follow-up ended in February 2017. All hospitalizations, positive blood cultures, emigrations and deaths in the follow-up period were captured through registry linkage.

Results

A total of 79,393 (69.5% and 54.1% of the invited population) people were included, of which 42,237 (53%) were women and mean age was 48.5 years. There were 37,298 hospitalizations due to infection, affecting 15,496 (22% of all included) individuals. The median time of follow-up was 20 years (25th percentile 9.5–75th percentile 20.8). Pneumonia and urinary tract infections were the two dominating foci with incidence rates of 639 and 550 per 100,000 per year, respectively, and with increasing incidence with age. The proportion of recurring admissions ranged from 10.0% (central nervous system) to 30.0% (pneumonia), whilst the proportion with a positive blood culture ranged from 4.7% (skin- and soft tissue infection) to 40.9% (central nervous system). The 30-day mortality varied between 3.2% (skin- and soft tissue infection) and 20.8% (endocarditis).

Conclusions

In this population-based cohort, we observed a great variation in the incidence, positive blood culture rate, recurrence and mortality between common infectious diseases. These results may help guide policy to reduce the infectious disease burden in the population.

Introduction

Severe bacterial infections are common causes of hospital admission and are associated with adverse outcomes such as sepsis and death [1–4]. Bacterial infections and sepsis are substantial and increasing problems worldwide [5], and The World Health Organization (WHO) has called for initiatives aimed at increasing knowledge that can contribute to a reduced burden of sepsis.

In any disease process, understanding the epidemiology is mandatory as background information when deciding which measures to implement and which resources to prioritize. Many previous studies have information on the incidence rates and mortality of infections [2, 6–8], however, the population burden of infectious diseases rely on additional factors. In particular, risk of recurrence and risk of systemic infection are often not assessed.

In this paper we describe the burden of hospitalization for groups of bacterial infections in a large Norwegian population-based cohort of 79,393 patients followed over a 22-year period. In addition to describing the incidence rates, we have estimated the 30-day all-cause mortality, the proportion of recurring admissions, and the positive blood culture rate within the different infection foci.

Materials and methods

Description of the study cohort

The Trøndelag Health Study (HUNT Study) is a series of cross-sectional surveys conducted in Nord-Trøndelag from 1984 where all inhabitants aged 20 years or older were invited to participate [9]. The Nord-Trøndelag region in central Norway has a population of approximately 130,000. It consists of rural areas and small towns and is considered generally representative of Norway with regard to sources of income, age distribution, morbidity and mortality, but the average income and prevalence of higher education and current smoking are a little lower than the Norwegian average [10].

We used data from the second and third surveys, HUNT2 (1995–1997) and HUNT3 (2006–2008), respectively, in which a total of 79,393 subjects agreed to participate (69.5% and 54.1% of the invited population for HUNT2 and HUNT3). The majority of the participants (72% of the women and 69% of the men) in HUNT2 also participated in HUNT3. The participants completed questionnaires covering a wide range of health-related topics, underwent clinical examination and blood collection and were then followed from the day of first inclusion and up until February 2017. For all participants, we retrieved information on all hospital admissions to the county hospitals or the regional tertiary care hospital.

Classification of infectious diseases

The International Classification of Disease (ICD) by WHO is the foundation for the identification of health trends and statistics globally [11] and is used in many countries, including Norway, for administrative/economic purposes upon hospital discharge. The 10th revision (ICD-10) is currently being used, prior to 1999 the codes were from the 9th revision (ICD-9). We identified all ICD-9/10- codes describing a potentially serious bacterial infection and categorized them into 8 main groups: pneumonia, UTI (urinary tract infection), SSTI (skin- and soft tissue infection), IAI (intraabdominal infection), CNS (central nervous system) infection, endocarditis, bone- and joint infection and sepsis/bacteraemia. Each admission during the study period with one of the infection-codes was then identified and grouped (S1 Table).

A widely used method to identify patients with sepsis, is to use a combination of primary sepsis codes (explicit sepsis) and codes for infection with a known organ focus combined with codes for organ dysfunction (implicit sepsis) [12–16]. In 2020, Rudd et al [2] published global, regional and national sepsis incidence and mortality data using this type of approach. In our study, patients identified as having had sepsis as defined by their criteria were used in the further discussion on sepsis.

Study design and statistical analyses

We retrieved the ICD-9 and ICD-10 codes for all hospitalizations of the study subjects in the county hospitals and to the regional tertiary care hospital. All Norwegian citizens are assigned a unique identification number at birth, and this number is registered in health care contacts. In addition to accessing the ICD codes upon discharge, this identification number was used to link data from the HUNT Study with the Norwegian population registry to obtain information on date of emigration and date of death, as well as to the hospitals´ information on positive blood cultures through February 2017.

Incidence rate was defined as incidence per 100,000 person-years of a first-time infection. A recurring admission was defined as a new admission with the same infection occurring more than 30 days after the first admission, and the proportion having a recurring admission was calculated among patients who survived the first 30 days after the first admission. Mortality was defined as death within 30 days of admission of a last-time infection. A first-time infection with a positive blood culture was defined as a blood culture being positive within 30 days of the admission. The blood cultures were taken on clinical indication only. Isolates commonly associated with skin contamination were not considered (e.g.coagulase-negative Staphylococci). We performed the estimations on all sites of infections, however, chose to focus on pneumonias, UTIs and SSTIs. All analyses were carried out using StataMP version 16.

Ethics approval

The project has been approved by the Regional Committee for Medical and Health Research Ethics of Central Norway, REK Midt (2006/393-4), (2009/1717-2), (2014/144), (2016/55). All participants signed an informed consent before entering the HUNT study.

Results

From the date of HUNT entry and up until February 2017, 15,496 (22%) of the 79,393 participants were hospitalized due to a bacterial infection at least once. Background characteristics of our study populations are described in Table 1.

Table 1. Background characteristics of the HUNT2 and HUNT3 population.

All (N = 79,393) HUNT2 (N = 65,665) HUNT3 and not HUNT2 (N = 13,728)
Age on participation (years) 46.8 (34.4–61.9) 48.9 (36.4–64.3) 37.1 (27.3–48.9)
Time followed (years) 20.0 (9.5–20.8) 20.2 (15.3–20.9) 9.2 (8.8–9.7)
Male sex 37,156 (46.8) 30,710 (46.8) 6442 (46.9)
Died during follow-up 19,539 (24.6) 19,002 (28.9) 533 (3.9)
Emigrated during follow-up 353 (0.44) 246 (0.37) 107 (0.78)
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Data are presented as n (%) for dichotomous characteristics and median (25th percentile-75% percentile) for continuous characteristics.

The median follow-up-time was 20.0 years (25th percentile 9.5–75th percentile 20.8). The total number of hospital admissions with an infection (first-time and recurring events) was 37,298 (Table 2). 4628 patients had two infection-codes during the same admission, and 981 had three or more. The most common hospitalizations were due to pneumonia, UTIs and sepsis/bacteraemia. There was also a substantial number of intrabdominal infections and SSTIs. There was a small number of CNS infections, endocarditis and bone-/joint infections (Fig 1).

Table 2. Summary of results divided into eight different foci of infection.

Focus of infection Total admissions (n) First-time admissions (n) Incidence rate pr 100 000/year (95% CI) Proportion with recurrent infection (%, 95% CI) Proportion with positive blood culture (%, 95% CI) 30-day mortality (%, 95% CI)
Pneumonia 13,210 7,948 639 (625–653) 30.0 (28.9–31.0) 4.9 (4.5–5.5) 15.6 (14.8–16.4)
UTI 11,421 6,839 550 (537–563) 28.6 (27.5–29.7) 7.5 (6.9–8.1) 8.5 (7.8–9.1)
Sepsis/bacteraemia 6,956 4,156 334 (324–345) 20.9 (19.6–22.2) 37.5 (36.1–39.0) 13.3(12.3–14.4)
IAI 2,904 2,008 161 (154–169) 11.6 (10.3–13.1) 9.8 (8.6–11.1) 3.6 (2.9–4.5)
SSTI 2,204 1,429 115 (109–121) 16.1 (14.2–18.1) 4.7 (3.8–6.0) 3.2 (2.4–4.3)
Bone/joint 247 173 14.0 (12.0–16.2) 12.7 (8.5–18.6) 19.1 (13.9–25.7) 7.0 (4.0–11.9)
Endocarditis 238 96 7.7 (6.3–9.4) 22.2 (14.4–32.7) 39.6 (30.2–49.8) 20.8 (13.8–30.2)
CNS 118 65 5.2 (4.1–6.7) 10.0 (4.5–20.8) 40.9 (29.6–53.3) 7.7 (3.2–17.4)
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UTI, urinary tract infection; IAI, intraabdominal infection; SSTI, skin- and soft tissue infection

Fig 1. Number of admissions by foci of infection.

Fig 1

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Distribution of 37,298 admissions with different foci.

Pneumonias

A total of 7,948 people had a first-time pneumonia, and the incidence rate of 639 per 100,000 per year increased with age from 106 at the age of 30 to 3,200 after the age of 80 (Fig 2). Of them 4.9% had a positive blood culture. Recurring admissions with pneumonia were frequent (Table 2), as 30% of the survivors of a first-time pneumonia had a subsequent readmission with the same diagnosis. Of them 54.8% occurred within the first year, and 96.6% within the first ten years. Of the patients admitted with a first-time pneumonia, 15.6% died during or after this admission or of a subsequent pneumonia. Pneumonia was the focus of infection which seemed to have the largest seasonal difference, with a markedly higher incidence rate in September- February compared to the warmer months. (S2 Table).

Fig 2. Incidence rates, rate of recurrent infections, 30-day-mortality rate and rate of positive blood cultures by age group.

Fig 2

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The distribution of incidence rates (black line), recurrence (green line), 30-day mortality (red line) and proportion of positive blood cultures (purple line) in the 3 chosen foci of infection and in sepsis as defined by Rudd et al. On the x-axis is the age upon entering the HUNT study. The left y-axis shows incidence rate per 100,000 per year, the right y-axis shows proportions (in %) of recurrence, 30-day mortality and positive blood cultures. UTI, urinary tract infection; SSTI, skin- and soft tissue infection.

Urinary tract infections

A total of 6,839 participants had an admission with a first-time UTI, giving an incidence rate of 550 per 100,000. This again increased steeply with age from 90 at the age of 30 to 2473 after the age of 80, and there was a marked difference between men and women, with women having an incidence rate of 637 per 100,000 (S3 and S4 Tables). Of all the first-time UTIs 7.5% had a positive blood culture. The readmission rate for UTI was 28.6%. Of them 55.1% occurred within the first year, and 96.6% within the first ten years. 8.5% of the patients admitted with UTI, died during or after this admission or of a subsequent UTI (Fig 2).

Skin-/soft tissue infections

A total of 1,429 participants had a first-time SSTI, giving an incidence rate of 115 per 100,000 and this again increased significantly with age. 16.1% of patients surviving a SSTI had a readmission. In this group, the number of readmissions had a profound variation, up to 43 readmissions were registered in some patients. Of them 58.2% occurred within the first year, and 98.0% within ten years. Of the first-time SSTIs 4.7% had a positive blood culture and 3.2% of the patients admitted with a SSTI died during or after this admission or of a subsequent SSTI (Fig 2).

Sepsis

Amongst our 37,298 admissions with a bacterial infection, 3,687 fulfilled the criteria for explicit sepsis and 1,864 for implicit sepsis, making the total number of admissions being counted as caused by sepsis 5,224 (14.1%) (as defined by Rudd et al [2]). This constituted 12.2% of the pneumonias, 7,6% of the UTIs and 7.4% of the SSTIs.

3,671 admissions fulfilled the criteria for a first-time sepsis, giving an incidence rate of 295 per 100,000, increasing with age, and 1,499 (40.8%) had a positive blood culture.

The readmission rate was 12.9%. Of them, 48.3% occurred within the first year and 55.6% within 10 years. Of the patients admitted with sepsis 12.9% died during or after this admission or of a subsequent sepsis (Fig 2).

Discussion

In this prospective study of ~80,000 subjects representative of the adult Norwegian population, we observed different patterns of incidence, recurrence, blood culture positivity and mortality within the different foci of bacterial infection.

Incidence

Pneumonia was the dominating focus of infection, with the highest incidence rate, followed by the UTIs. The distribution of the different foci of infection is comparable and correlates with earlier work based on discharge codes in a hospital´s catchment area [8, 17, 18]. The incidence rate of hospitalization due to pneumonia correlates well with a calculation of incidence rate in an emergency setting in New York [7], however one should be careful when comparing our results with numbers in an emergency room or intensive care setting. A Danish study from 2006 [6] reports incidence rates that are lower than we have found, however their main conclusion was that pneumonia incidence is on the rise. They report an increase in hospitalized pneumonia from 288 to 442 per 100 000 person-years from 1994 to 2003, and we found the rate to be 639 up to 14 years later.

The incidence rates of all the different groups of infections, including the participants having an admission qualifying as sepsis, increased steeply with increasing age. In 84% of all admissions the patient was 60 years or older, and in 39% the patient was above the age of 80. This is also the case for blood stream infections (a positive blood culture), as concluded by Mehl et al in 2017 in analyses within our study region [19]. Our long follow-up time which naturally led to our participants being followed into their old years combined with the fact that most of the infections appeared in the elderly population, probably is the explanation why as many as 22% of the study participants had a hospital stay during the follow-up-time.

There are several factors possibly explaining why the elderly are predisposed to infections [20, 21]. Frailty and functional limitations, comorbidities leading to immunosuppression and polypharmacy all play important roles both in first-time and recurring cases. Milbrandt et al reviewed the epidemiology of critical illness in the elderly in 2009, describing the ageing and less responsive immune system, the higher risk of nosocomial infections and the higher risk of sepsis [22]. With an ageing population, the burden of severe bacterial infections will likely further increase, both with respect to the number of admissions and disease severity. In addition to ageing, the role of other underlying conditions and modifiable risk factors is certainly interesting factors in future research.

30-day-mortality-rate

Our mortality rates were based on the last infection of each focus. This has necessarily given a higher case-fatality rate compared to having the first-time infections as the denominator, however, this describes deaths from both the first-time infections and the recurrences, and we believe it has given a more correct description of the total burden. In the case of co-infection it was not possible to determine which of the foci of infection contributed the most to death. This could, in theory, have over-estimated the mortality rate of especially UTI, which is a commen “co-infecter”.

Our estimated mortality rate due to pneumonia is in line with a previous study [23]. Rudd et al found that globally, both sexes and all age groups combined, the most common underlying cause of sepsis-related death was pneumonia every year from 1990 to 2017 [2].

In our cohort, the incidence of pneumonia and the 30-day mortality rate from pneumonia increased with increasing age. Death from pneumonia have previously been linked to frailty. Kundi et al showed in 2019 that patients with higher frailty scores had higher observed rates of 30-day postadmission mortality and 30-day post discharge mortality in addition to a higher 30-day readmission rate [24]. For the SSTIs the mortality rate is low. Hardly any studies have reported on the overall mortality rates for SSTIs, however Kaye and colleagues found that mortality was relatively low in the United States and decreased from 0.56% in 2005 to 0.46% in 2011 [25].

Our 30-day all-cause mortality for sepsis was 12.9% and the 30-day mortality rate increased with age. The all-cause mortality was lower than others studies have found [8]. In the global estimations performed by Rudd et al in 2020, the sepsis mortality varied substantially across regions, being greatest in sub-Saharan Africa, Oceania, south Asia, east Asia, and southeast Asia. They did not calculate the total 30-day mortality rate from sepsis, however the age-standardized percentage of all global deaths related to sepsis was 20.1% [2].

Recurring infections

Our large pre-defined patient cohort gave us the opportunity to look further into the recurrent admissions. However, our rates of recurring infections and the finding that a large proportion of the readmissions are within the first year, are not easily comparable to others. Most other have counted readmission rates within 30 days [26–28], this being more a proxy for treatment failure or premature discharge. In addition, recurring infection of all causes is most often counted. This is especially common in USA, where 30-day-all-cause-readmission rate of pneumonia is being closely monitored as part of a “Hospital Readmissions Reduction Program» [24, 26].

In the group with SSTI, the rate of admission due to recurring infection was stable throughout the age group, however there was a huge variation in the number of readmissions, from 1 to 43. Recurring infections have been shown to be a major contributor to the overall SSTI burden [29], and in our population there seems to be a subgroup of patients with frequent recurring admissions. This group should be studied further, for example by assessing if genetic predisposition could play a role [30].

Positive blood cultures/bacteremia

Overall we found a low rate of positive blood cultures. This is not surprising, as we know that bacterial infections are often managed without identifying the causative microorganism [31, 32]. This is particularly true with pneumonia, however, patients with pneumonia and bacteremia are shown to have a high in-patient mortality rate [33].

The rate of 7.5% of positive blood cultures in UTI were substantially lower than described by Artero et al in 2016 [34], however in this study the patients were identified by having clinical features of UTI and not by discharge codes. As opposed to pneumonia, they then conclude that the presence or absence of bacteremia in elderly people with UTI requiring hospitalization does not influence in-hospital mortality. The low positive blood culture rate in SSTIs (< 5%) was expected and matches previous findings [25]. In this group of infection, the clinicians must, more than in other conditions, make their treatment decision without knowing the causative pathogen. Due to this low rate of blood culture positivity, blood cultures are in fact not routinely recommended in the American practice guidelines [35].

Of the first-time admissions qualifying as explicit or implicit sepsis 40.8% had a positive blood culture. This fits in with the existing knowledge that identifying a patient with bloodstream infection will identify many but not the majority of sepsis patients. In the work performed by Nygård et al, 37% of sepsis patients defined by clinical criteria had a positive blood culture [31]. It is found than in an intensive care setting, 40% of patient defined as having “severe sepsis” do not have findings in their blood cultures [32].

ICD coding

The method of retrospectively identifying infections using ICD codes is not without challenges. Henriksen et al found that using ICD codes in this way will underestimate the true burden, however it has a high degree of validity when stratifying on the different sites of infection [36–38]. Skull et al found that using ICD-10 codes to identify pneumonias was a valid method, even likely to be superior to the use of symptoms and signs or interpretation of radiology reports [39]. The first important challenge is the possibility of change in coding practice, regulations, guidelines and tradition over time, especially when the observation period is as long as 22 years. Another is the variation within each of the organ-groups when it comes to precision and practical usage of the different codes. Some codes describe a well-defined disease entity whilst others describe a whole spectrum of disease. This variation was the reason why we chose not to look closer at the IAIs, whilst on the other hand focused on the SSTIs. Pneumonias and UTIs were chosen because they were the two dominating groups, and they are, in our experience, fairly precisely coded.

The correct identification of patients with sepsis is a particular challenge. Iwashyna et al concluded that using ICD codes is “reasonable” compared to going through medical records [12], whilst others have highlighted marked discrepancies [38, 40, 41]. Gaieski et al have pointed out that even within the studies retrospectively using ICD codes, the different methods of databank abstraction will give a substantial variation [42]. Counting only the primary sepsis codes will make the sepsis definition too narrow and lead to a gross underestimation, as doctors mostly are guided to only use these codes when the focus of infections is unknown [43]. When it comes to using the criteria defined by Rudd et al, critics have claimed that their definition of sepsis is too broad and that sepsis is likely to be over diagnosed. Especially the process of choosing which infectious and non-infections conditions should count toward the entity “implicit sepsis” is complicated [44]. There is ongoing work with finding better and more refined definitions of sepsis based on ICD coding.

Strengths

The major strength of our study includes its large size, its long-term follow-up, as well as the linkage to microbiological records from all the laboratories at the local and regional hospitals. A few population cohorts have earlier looked at bacterial infections, however a median follow-up time of a 20 years, the opportunity to look at recurring events, the linkage to the positive blood cultures and lastly the fact that we look at all the different foci of bacterial infection, gives us a unique insight into the total burden of disease in this population comparable to the rest of the western world.

Sepsis is difficult to identify not only clinically but also retrospectively. Identifying bacterial infections based on the focus of the infection/infected organ and not sepsis directly, seems be a more correct way of getting av overview of the true burden of disease as this fit more in with how the coding practice actually works.

Limitations

Our long-time follow up is a strength, but also brings the challenge of having to consider changes within the healthcare system, including changes in health-seeking behaviour in a more informed cohort over time.

As always in this kind of prospective study, there is a possibility of selection bias. 54–69% of the invited population participated, and although this is a high participation rate for a health survey, different known high-risk groups for infection, such as intravenous drug users, will probably be underrepresented.

In addition, the question of how representative this population is of the general population is increasingly important, as more and more people live in cities where the risk factors of living in urban areas will have to be taken into account. We have compared our results with earlier results from different countries, however an overweight of comparable studies were from USA. It is uncertain how comparable our cohort is to an American population.

Outpatient antibiotic use prior to hospitalization could clearly have influenced the low level of positive blood cultures on most foci of infection. Unfortunately, our data does not give us the possibility to assess this further.

Conclusions

In this paper we describe the burden of severe bacterial infections in a large Norwegian population cohort during a 22-year follow up by presenting the incidence rates, 30-day mortality and proportions of positive blood cultures and recurring infections with particular focus on pneumonias, UTIs, SSTIs and sepsis. It points out the substantial number of recurring infections within all infection foci, especially in the SSTIs and it points out that most severe bacterial infections will not be identified through blood cultures, not even the admissions qualifying as “sepsis” by the internationally used sepsis criteria. Our data clearly describe the substantial number of hospitalizations due to severe bacterial infections and how both the number of admission and severity of disease most likely will increase in the future, seeing the clear increase of both incidence and mortality with increasing age.

Supporting information

S1 Table. Overview of the different ICD-9 and ICD-10 codes selected to identify the bacterial infections.

(PDF)

Click here for additional data file. (52KB, pdf)
S2 Table. Seasonal differences in incidence rates.

(PDF)

Click here for additional data file. (66.6KB, pdf)
S3 Table. Summary of results divided into eight different foci of infection, males.

(PDF)

Click here for additional data file. (70.8KB, pdf)
S4 Table. Summary of results divided into eight different foci of infection, females.

(PDF)

Click here for additional data file. (70.6KB, pdf)

Acknowledgments

The Trøndelag Health Study is a collaboration between the Trøndelag Health Study Research Centre (Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology), the Trøndelag County Council, the Central Norway Regional Health Authority, and the Norwegian Institute of Public Health.

Data Availability

Our data cannot be shared publicly due to patient confidentiality. Data from the HUNT Study used in research projects is available upon request to the HUNT Data Access Committee (hunt@medisin.ntnu.no) to research groups who meet the data availability requirements (described here: http://www.ntnu.edu/hunt/data).

Funding Statement

This study was supported by Samarbeidsorganet Helse Midt-Norge, NTNU Norwegian University of Science and Technology (Trondheim, Norway) (KL). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The funder provided support in the form of salaries for authors [KL, ES, RMM, BOÅ, TR, JKD], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.

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Decision Letter 0

Kazumichi Fujioka

2 Mar 2022

PONE-D-21-20080

Incidence, recurring admissions and mortality of severe bacterial infections and sepsis over a 22-year period in the population-based HUNT Study.

PLOS ONE

Dear Dr. Liyanarachi,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Reviewer #1: The manuscript is well written, with relevant informative figures/table. Its mainly a descriptive and observational study - so statistical analysis is limited to baseline comparisons.

Language is very good.

Reviewer #2: This study gives a population-based overview of common severe infections and sepsis in a Norwegian area with a background population of 130,000. The study is mainly descriptive.

I have some major concerns that confuse me:

It is based on patients that have agreed to participate in a study, based on questionnaires, clinical examination, and retrieval of blood collection. However, this is mentioned once and then all the data used in the study are registry-based. Does the legislation in Norway not allow retrieval of registry-based data without the consent of each individual patient? If so, this should be stated explicitly.

What is meant by “blood collection”? Does that include both biochemistry and microbiological specimens? If it also includes the latter, did that have any impact on the rate of positive blood cultures (i.e., not taken on indication only)?

The authors claim that this study is prospective. In what way is it prospective, especially given that data from the questionnaires apparently were not reported in this study? It seems to be a historic registry-based study.

The time frames are confusing. The study mentions a 22-y time period, but later we find out that this includes two separate cohorts, one from 1995-1997, the other from 2006-8. Moreover, the expression “22-y follow up” is used frequently. The follow-up period differs between the two cohorts of which the oldest has around 11 years more to get recurrent infections etc. I cannot figure out where a 22-y follow-up period comes from. A more appropriate used of follow-up would e.g. be for the 30-d mortality rate, i.e. that is a 30-day follow-up period. A study period is not the same as a follow-up period.

An example: “From the date of HUNT entry and up until February 2017, the 79,393 participants had 37,298 hospital admissions with a bacterial infection (first-time and recurring event) (Fig 1).”: These participants were found in two cohorts (how many in each?), of which the first had 11 years more follow-up time. And if the age distribution was the same in the two cohort, those from the first cohort were 11 years older at the same calendar time, which has a huge impact on incidence etc. So these data are blurred and muddled.

Nothing is mentioned about differences and similarities between the two cohorts. Were there any changes in incidence, mortality etc.??

It is difficult for non-Scandinavians to know how data from different registries were merged, as most countries do not have a unique personal identification number for their citizens.

Although the authors briefly describe the difficulties of defining sepsis, including the coding of these, they have omitted some important studies that show that the incidence of sepsis, based on ICD-codes, may vary more than three-fold [1,2]. These reviews were, amongst others, based on three studies [3-5], one of which was from Norway, but even this study is not mentioned in this manuscript [4].

I am not an expert on all the infections described in the article, but concerning pneumomia there are studies that give a thorough overview, are population-based, and have a very high number of patients [6].

It is confusing that some analyses are based on the first-time and some on the last time occurrence. The baseline should be the same (first-time occurrence) and competing-risk analyses [7] should be incorporated.

“To the best of our knowledge, this is the first time such a large population cohort have been studied with such a long follow-up, a population comparable to the population of the rest of Norway/western world”: I tend to disagree, as mentioned above for pneumonia, and much larger background populations than 130,000 have also been the basis for studies of e.g. bacteremia [8].

Minor comments:

In the abstract is written: “Thorough background information on the total burden and severity of the different foci of infection will contribute to reduce this.”: How will some information per se reduce some burden and severity of different foci of infection??

Too many results from the tables and figures are reiterated in the text in the “Results” section.

References

1. Wilhelms SB, Huss FR, Granath G, et al. Assessment of incidence of severe sepsis in Sweden using different ways of abstracting International Classification of Diseases codes: difficulties with methods and interpretation of results. Critical care medicine. 2010 Jun;38(6):1442-9.

2. Gaieski DF, Edwards JM, Kallan MJ, et al. Benchmarking the incidence and mortality of severe sepsis in the United States. Critical care medicine. 2013 May;41(5):1167-74.

3. Angus DC, Linde-Zwirble WT, Lidicker J, et al. Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Critical care medicine. 2001 Jul;29(7):1303-10.

4. Flaatten H. Epidemiology of sepsis in Norway in 1999. Crit Care. 2004 Aug;8(4):R180-4.

5. Martin GS, Mannino DM, Eaton S, et al. The epidemiology of sepsis in the United States from 1979 through 2000. The New England journal of medicine. 2003 Apr 17;348(16):1546-54.

6. Thomsen RW, Riis A, Nørgaard M, et al. Rising incidence and persistently high mortality of hospitalized pneumonia: a 10-year population-based study in Denmark. J Intern Med. 2006 Apr;259(4):410-7.

7. Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association. 1999;94:496-509.

8. Nielsen SL, Lassen AT, Gradel KO, et al. Bacteremia is associated with excess long-term mortality: A 12-year population-based cohort study. J Infect. 2015 Sep 9;70(2).

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Reviewer #1: Yes: Harriet Mayanja-Kizza

Reviewer #2: Yes: Kim Oren Gradel

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Attachment

Submitted filename: PLOS_Sever Bact Infection Norway 22 yr Cohort_Feb 2022.docx

Click here for additional data file. (14.5KB, docx)
PLoS One. 2022 Jul 12;17(7):e0271263. doi: 10.1371/journal.pone.0271263.r002

Author response to Decision Letter 0

7 Apr 2022

Dear Editor,

We are excited about the opportunity to revise and resubmit our manuscript “Incidence, recurring admissions and mortality of severe bacterial infections and sepsis over a 22-year period in the population-based HUNT Study”.

We found the comments from the reviewers highly relevant and addressing the issues raised has improved the manuscript.

The reviewers’ comments are included in this resubmission, with our point-by-point response.

The revised manuscript, one marked and one unmarked version, is enclosed.

2b:The text regarding funding can be changed according to your advice: “The funder provided support in the form of salaries for authors [KL, ES, RMM, BOÅ, TR, JKD], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.

3: Please change our Data Availability statement to reflect this correct information: Our data cannot be shared publicly due to patient confidentiality. Data from the HUNT Study used in research projects is available upon request to the HUNT Data Access Committee (hunt@medisin.ntnu.no) to research groups who meet the data availability requirements (described here: http://www.ntnu.edu/hunt/data)

On behalf of the authors,

Sincerely,

Kristin Vardheim Liyanarachi.

Comments from the reviewers:

Reviewer 1:

This is an interesting well designed cohort study looking at population severe bacterial infection, with outcomes of determining specific infection site incidence rates, 30 days’ all-cause mortality, recurring admissions, positive blood culture. The cohorts were enrolled in 1995-1997 (70% acceptance rate) and 2006-2008– (54% acceptance rate). It would be of interest to determine if they were outcome differences between the two cohorts regarding severe infection over the years

Comment 1

Was there any overlap in the HUNT 2 and 3 cohorts? What was retention like over the years.

Response: Yes, there was a substantial overlap, and this has been clarified in the revised manuscript. 72% of the women and 69% of the men that joined HUNT 2 joined HUNT 3 ten years later. The date of entry to our study was set as the day these people joined HUNT 2. They were not counted twice. The retention was high. Only 353 people (0.4% of the study population) emigrated out of the study area during the follow-up-time. 19.539 people (24.6%) died. A table with background characteristics of the two HUNT cohorts has now been included as Table 1 (line 134) and the following changes has been made in the revised manuscript on page 6, lines 84-91:

“We used data from the second and third surveys, HUNT2 (1995-1997) and HUNT3 (2006-2008), respectively, in which a total of 79,393 subjects agreed to participate (69.5% and 54.1% of the invited population for HUNT2 and HUNT3). The majority of the participants (72% of the women and 69% of the men) in HUNT2 also participated in HUNT3. The participants completed questionnaires covering a wide range of health-related topics, underwent clinical examination and blood collection and were then followed from the day of first inclusion and up until February 2017. For all participants, we retrieved information on all hospital admissions to the county hospitals or the regional tertiary care hospital.”

Comment 2

The study was conducted in what can referred to as “a rural area”. However, urban areas may have various factors impacting on risks of infections and their outcomes, such as environmental factors – outdoor vs indoor lifestyles etc – should these be considered in the limitations.

Response: This is a very good point made by the reviewer. An increasing number of people live in cities of increasing sizes also in this region of Norway. Fortunately, the next cohort in this ongoing study (HUNT4), has increased its catchment area and now includes Trondheim, Norway´s third largest city. We have now included this important point in the limitations of our study on page 17, lines 333-337:

“In addition, the question of how representative this population is of the general population is increasingly important, as more and more people live in cities where the risk factors of living in urban areas will have to be taken into account.”

Comment 3

Regarding the “implicit sepsis” related to organ dysfunction – could they have been an over diagnosis of sepsis in this group.

Response: As correctly observed by the reviewer this could most certainly be the case, and this is an important aspect of discussion when using the entity implicit sepsis as a way of retrospectively identifying sepsis. We have broadened the discussion regarding this in the revised manuscript on page 16, lines 309-311:

“When it comes to using the criteria defined by Rudd et al, critics have claimed that their definition of sepsis is too broad and that sepsis is likely to be over diagnosed. Especially the process of choosing which infectious and non-infections conditions should count toward the entity “implicit sepsis” is complicated [46].”

Comment 4

The low blood culture noted is an important finding – as clinicians often assume this is due to prior antibiotics. Was this possibility of prior outpatient antibiotic use explored?

Response: This is again a very important aspect of our study raised by the reviewer, and could clearly have influenced on our data. However, our data does not give us the possibility to assess this further as we do not have access to information on outpatient antibiotic use prior to hospitalization. This important aspect has now been included as a limitation of our study on page 17, lines 338-340:

“Outpatient antibiotic use prior to hospitalization could clearly have influenced the low level of positive blood cultures on most foci of infection. Unfortunately, our data does not give us the possibility to assess this further.”

Comment 5

About half the population got at least one infection needing admission – is this a true reflection of this Norwegian population– or due to higher cohort awareness? It is quite high especially as the age inclusion included young adults. In the questionnaire were participants advised to report to hospital for any suspected infection, or this is an expected figure in a none cohort population of a mixed age population from 20 yrs up upwards. What was effect of age on admissions?

Response: Of the total of 37,298 admissions with infection, 15,496 was a first-time admission. We see that this could appear unclear, as we also use the terms “first-time pneumonia”, first-time UTI” etc. This means that approximately 22% of the patients had one admission or more due to infections in our population. Although increased cohort awareness could have contributed to increased identification of infections, the decision to admit the patients to hospital for infections is in most cases made by the general practitioner. Accordingly, increased cohort awareness may increase numbers of diagnosed infections all over, but should not have a major impact on the high numbers of hospitalized patients as reported in our study. As suggested by the reviewer, it could also be partly explained by the long- term follow-up studying participants into their old years. In fact, most infections appeared in the elderly patients. These important aspects of our study have now been included in our revised manuscript. (Page 8 lines 131-133 and page 12 lines 216-219):

“From the date of HUNT entry and up until February 2017, 15,496 (22%) of the 79,393 participants were hospitalized due to a bacterial infection at least once. Background characteristics of our study populations are described in Table 1.”

“Our long follow-up time which naturally led to our participants being followed into their old years combined with the fact that most of the infections appeared in the elderly population, probably is the explanation why as many as 22% of the study participants had a hospital stay with infection during the follow-up time.”

Comment 6

The sex distribution would be of interest especially with UTI at different ages. This was common in young adults – were they mostly women?

Response: We agree that the sex distribution of the different foci of infection is of interest and have now included our table 1 divided into 2 tables (male and female) as supplementary tables 3 and 4. As suspected by the reviewer, the incidence rate of hospitalization due to UTI was higher in women. Interestingly, the 30-day mortality rate of UTI was higher in men, which was also the case for pneumonia and sepsis/bacteraemia. This has been addressed in the revised manuscript on page 10, lines 173-175:

“This again increased steeply with age from 90 at the age of 30 to 2473 after the age of 80, and there was a marked difference between men and women, with women having an incidence rate of 637 per 100,000 (S3 and S4 Tables).”

Comment 7

Pre morbidities – did they play a factor in the incidence of infections. Fragility and low immunity in older population was discussed – but what of underlying illnesses?

Response: The role of pre morbidities is certainly an interesting factor here, both underlying medical diagnoses and modifiable lifestyle factors. We judged this to be out of the scope of this article, however we will in future articles try to address this. These thoughts are now included in the revised manuscript on page 13, lines 226-228:

“In addition to ageing, the role of other underlying conditions and modifiable risk factors is certainly interesting factors in future research.”

Comment 8

Any idea what organisms were cultured in the different infections and age ranges – this would be useful information to guide clinical care.

Response: Yes, we do have the identity of all the different organisms in the positive blood cultures. We clearly agree that information on types of microbes and their resistance patterns stratified by different infection focuses, sex and age groups, is of importance for guiding clinical practice. In fact, we have plans to include such information in a forthcoming paper focusing in å broader approach on individual risk for infections. Accordingly, we have chosen not to include these data here, but if the reviewer has a strong opinion on this, we are willing to do additional analyses on this in the current manuscript.

Comment 9

The study was done in Norway – but most comparisons in discussion were with USA studies. Any related research in Europe – if so ow comparable are the findings?

Response: We have tried to include European studies as well, to make sure the results are as comparable as possible. Studies from France, Sweden, Denmark and Norway have been included and discussed but we do agree that there is an overweight of North-American studies. This has now been commented on the revised manuscript on page 17 lines 335-337:

“We have compared our results with earlier results from different countries, however an overweight of comparable studies were from USA. It is uncertain how comparable our cohort is to an American population.”

Comment 10

Was there some temporal variations with the infections esp. related to weather changes.

Response: The reviewer raises an important aspect of epidemiology of infectious diseases. Norway is country where the weather and temperature varies substantially throughout the year. A table showing incidence rates of the four main foci of infection divided into autumn/winter and spring/summer is now included in the supplements section, and the main findings here are discussed in the revised manuscript on page10, lines 159-161:

“Pneumonia was the focus of infection which seemed to have the largest seasonal difference, with a markedly higher incidence rate in September- February compared to the warmer months. (S2 Table).”

he

Comment 11

Minor typos – Sepsis results section line167 UTI not UVI, and line 168 a not av.

Response: Thank you, this has now been corrected.

Comment 12

Overall, the strength of this study is a large population cohort. Some biases to be considered could be changes within the health care systems over the years, as well as changes in health seeking behavior in a more informed cohort over time

Response: Thank you for highlighting these two important aspects. These 2 mentioned biases have now been taken into the revised text on page 17, lines 326-328.

“Our long-time follow up is a strength, but also brings the challenge of having to consider changes within the healthcare system and changes in health-seeking behaviour in a more informed cohort over time.”

Reviewer 2:

This study gives a population-based overview of common severe infections and sepsis in a Norwegian area with a background population of 130,000. The study is mainly descriptive. I have some major concerns that confuse me:

Comment 1

It is based on patients that have agreed to participate in a study, based on questionnaires, clinical examination, and retrieval of blood collection. However, this is mentioned once and then all the data used in the study are registry-based. Does the legislation in Norway not allow retrieval of registry-based data without the consent of each individual patient? If so, this should be stated explicitly.

Response: We thank the reviewer for raising this issue and we understand that the previous wording may have been confusing. Our work uses data from two types of sources: 1) The HUNT Study (a series of cross-sectional surveys including biological sampling and physical examination), and 2) registry data (e.g. hospital diagnosis codes and date of death). The former requires consent from each individual, while the latter does not. To make this more clear, we have made some changes in the abstract (page 3, lines 40-43), and in the text made the following changes on page 6, lines 84-91 and page 7, lines 110-116:

“We used data from the second and third surveys, HUNT2 (1995-1997) and HUNT3 (2006-2008), respectively, in which a total of 79,393 subjects agreed to participate (69.5% and 54.1% of the invited population for HUNT2 and HUNT3). The majority of the participants (72% of the women and 69% of the men) in HUNT2 also participated in HUNT3. The participants completed questionnaires covering a wide range of health-related topics, underwent clinical examination and blood collection and were then followed from the day of first inclusion and up until February 2017. For all participants, we retrieved information on all hospital admissions to the county hospitals or the regional tertiary care hospital.”

“We retrieved the ICD-9 and ICD-10 codes for all hospitalizations of the study subjects in the county hospitals and to the regional tertiary care hospital. All Norwegian citizens are assigned a unique identification number at birth, and this number is registered in health care contacts. In addition to accessing the ICD codes upon discharge, this identification number was used to link data from the HUNT Study with the Norwegian population registry to obtain information on date of emigration and date of death, as well as to the hospitals´ information on positive blood cultures through February 2017.”

Comment 2

What is meant by “blood collection”? Does that include both biochemistry and microbiological specimens? If it also includes the latter, did that have any impact on the rate of positive blood cultures (i.e., not taken on indication only)?

Response: Blood collection upon agreeing to participate in the HUNT study consisted of different biochemical test with focus of being risk factors of later diseases (such as serum creatinine and serum ALAT). Blood cultures (and other microbiological tests) were not included in the tests obtained upon inclusion in HUNT, but were taken on clinical indication for hospitalized patients. We fully agree that this could have been explained better and this has been pointed out in the revised manuscript on page 7, lines 121-122:

“The blood cultures were taken on clinical indication only.”

Comment 3

The authors claim that this study is prospective. In what way is it prospective, especially given that data from the questionnaires apparently were not reported in this study? It seems to be a historic registry-based study.

Response The study is prospective as the patients are included on the date when they agreed to participate in the HUNT cohort, and are then followed over time, until they emigrated or died. Information was gathered from all hospital admissions during the follow-up time. This complete information on time-to-event from entry into the study allowed us to calculate incidence rates and risks.

Comment 4

The time frames are confusing. The study mentions a 22-y time period, but later we find out that this includes two separate cohorts, one from 1995-1997, the other from 2006-8. Moreover, the expression “22-y follow up” is used frequently. The follow-up period differs between the two cohorts of which the oldest has around 11 years more to get recurrent infections etc. I cannot figure out where a 22-y follow-up period comes from. A more appropriate used of follow-up would e.g. be for the 30-d mortality rate, i.e. that is a 30-day follow-up period. A study period is not the same as a follow-up period.

An example: “From the date of HUNT entry and up until February 2017, the 79,393 participants had 37,298 hospital admissions with a bacterial infection (first-time and recurring event) (Fig 1).”: These participants were found in two cohorts (how many in each?), of which the first had 11 years more follow-up time. And if the age distribution was the same in the two cohort, those from the first cohort were 11 years older at the same calendar time, which has a huge impact on incidence etc. So these data are blurred and muddled.

Response: We do agree that the time frames could have been explained clearer. In the first survey – HUNT2 – 65,665 participants were recruited between 1995 and 1997. As we had complete data on emigration, date of death and hospital admissions (and blood cultures) through February 2017, the first study subjects to enter HUNT2 were followed for up to 22 years. In 2006-2008, a new survey was conducted – HUNT3 – where 50,807 participants were evaluated, of which 13,728 were new subjects (i.e. had not participated in HUNT 2). As the reviewer points out. these last 13,728 participants were followed for up to 11 years. The median follow-up-time was 20.0 years (25th percentile 9.5 and 75th percentile 20.8). The varying time-at-risk has of course been accounted for in the incidence rate-calculations and the participants have not been counted twice if they joined both cohorts.

A more thorough explanation is now included in the revised manuscript on page 6, lines 84-87 and page 8 lines 138-139:

We used data from the second and third surveys, HUNT2 (1995-1997) and HUNT3 (2006-2008), respectively, in which a total of 79,393 subjects agreed to participate (69.5% and 54.1% of the invited population for HUNT2 and HUNT3). The majority of the participants (72% of the women and 69% of the men) in HUNT2 also participated in HUNT3.

“The median follow-up-time was 20.0 years (25th percentile 9.5 - 75th percentile 20.8). ”

Comment 5

Nothing is mentioned about differences and similarities between the two cohorts. Were there any changes in incidence, mortality etc.??

Response: This is a very valid point made by the reviewer. Our reference number 6 explains and compares the different HUNT cohorts, but in the previous version of our manuscript we did not go into detail about these differences. As mentioned previously 72% percent of the women and 69% of the men that participated in HUNT2 also participated in HUNT 3. Overall, both HUNT2 and HUNT3 were representative of the adult Norwegian population. A table with background characteristics of the HUNT2 and HUNT3 population is now included. The original table 1 is renamed “Summary of results divided into eight different foci of infection” to avoid confusing it with the new table 2.

Comment 6

It is difficult for non-Scandinavians to know how data from different registries were merged, as most countries do not have a unique personal identification number for their citizens.

Response: We agree that this is important to clarify. Accordingly, we have described how the unique personal identification number of Norwegian citizens was used to link the study population to all prospectively recorded blood cultures in the catchment area. We have pointed it out on page 7, lines 111-116:

“All Norwegian citizens are assigned a unique identification number at birth, and this number is registered in health care contacts. In addition to accessing the ICD codes upon discharge, this identification number was used to link data from the HUNT Study with the Norwegian population registry to obtain information on date of emigration and date of death, as well as to the hospitals´ information on positive blood cultures through February 2017.”

Comment 7

Although the authors briefly describe the difficulties of defining sepsis, including the coding of these, they have omitted some important studies that show that the incidence of sepsis, based on ICD-codes, may vary more than three-fold [1,2]. These reviews were, amongst others, based on three studies [3-5], one of which was from Norway, but even this study is not mentioned in this manuscript [4].

Response: As stated by the reviewer, these are five important studies explaining the difficulties of retrospectively defining sepsis by using ICD-codes. To clarify, the reviewer´s reference number 1 was not omitted, but is reference number 13 in our original manuscript. With regard to reference number 2, this was omitted in the process of shortening-down the manuscript, but we agree that it highlights an important aspect in this discussion, and it is now included in the revised manuscript as reference number 43. The reviewer´s reference number 3 was listed as reference 10 in our original manuscript. We agree that Dr Flaatten has made important contributions to describe the sepsis epidemiology in Norway, and accordingly we chose to include three later studies from his research group as references (Knoop et al, Nygård et al and Nygård et al, our references no 15, 30 and 35).

Comment 8

I am not an expert on all the infections described in the article, but concerning pneumomia there are studies that give a thorough overview, are population-based, and have a very high number of patients [6].

Response: We agree with the reviewer that this paper by Thomsen et al is important. We have now included this as reference no 18 in the revised manuscript. Our estimated higher incidence rate fits well into their conclusion that pneumonia incidence is rising, as our estimations are performed up to 14 years later. Our mortality rates are similar. This study differs from ours due to the fact that we have a longer follow-up time, and we also look at recurrent infections. Our comments regarding this are now included in the revised manuscript on page 12, lines 207-211:

“A Danish study from 2006 (6) report incidence rates that are lower than we have found, however their main conclusion was that pneumonia incidence is on the rise. They report an increase in hospitalized pneumonia from 288 to 442 per 100 000 person-years from 1994 to 2003, and we found the rate to be 639 up to 14 years later.”

Comment 9

It is confusing that some analyses are based on the first-time and some on the last time occurrence. The baseline should be the same (first-time occurrence) and competing-risk analyses [7] should be incorporated.

Response: We are sorry that this important topic seem confusing. When it comes to incidence rates for diagnosis codes we assessed first time occurrences. For recurrent infections, we evaluated recurrences after the first-time occurrence. When it comes to positive blood cultures our thought was that using both first time and later occurrences as baseline would be more clinically meaningful, however we see that this is a possible source of confusion. We have therefore now changed this, and the proportion of positive blood cultures are now throughout the paper calculated from only the first-time infections, including figure 2. This, has, overall, made this proportion larger.

As is discussed in the manuscript, mortality rates are based on the last infection: This will necessarily give a higher case-fatality rate compared to not including recurring infections in the denominator. However, we feel that counting deaths from a last time occurrence will give the most correct result, seeing that this study not only describes the first-time occurrences but also the recurrences. From all the first-time occurrences, this way of counting the mortality rate will tell us who died from this first-time infection or a subsequent infection with the same focus. We have tried to clarify this in the revised manuscript in the Results section. (page 13 lines 230-233):

“Our mortality rates were based on the last infection of each focus. This has necessarily given a higher case-fatality rate compared to having the first-time infections as the denominator, however, this describes deaths from both the first-time infections and the recurrences and we believe it has given a more correct description of the total burden.”

Regarding competing risk analysis, this is a very interesting aspect, but we have chosen not to study different risk factors for infectious diseases in depth in this paper. We feel that competing risk analysis would have been more relevant if we had aimed at causal analysis between exposure and outcome.

Comment 10

“To the best of our knowledge, this is the first time such a large population cohort have been studied with such a long follow-up, a population comparable to the population of the rest of Norway/western world”: I tend to disagree, as mentioned above for pneumonia, and much larger background populations than 130,000 have also been the basis for studies of e.g. bacteremia [8].

Response: Many earlier studies have retrospectively counted the different diagnosis groups within a catchment area. We will still say that the fact that this is a population cohort followed over a longer time-period (median follow-up-time 20 years) combined with the fact that it looks at eight different foci of infection and looks at recurrence and death, makes this different and more complete. We, however, agree with the reviewer that this statement may be too categorical. The median follow-up time has been pointed out in the revised manuscript and we have now modified the wording on page 16-17, lines 316-320:

“A few population cohorts have earlier looked at bacterial infections, however a median follow-up time of a 20 years, the opportunity to look at recurring events, the linkage to the positive blood cultures and lastly the fact that we look at all the different foci of bacterial infection, gives us a unique insight into the total burden of disease in this population comparable to the rest of the western world.

”

Minor comments:

Comment 11

In the abstract is written: “Thorough background information on the total burden and severity of the different foci of infection will contribute to reduce this.”: How will some information per se reduce some burden and severity of different foci of infection??

Response: This is an interesting point made by the reviewer. The nature of infectious diseases is that they are, in large, preventable, i.e. the burden and severity can be reduced by preventative measures. Background information regarding this, in addition to information on the high risk of recurrence, will help in the decision-making regarding this on several aspects. Some examples are future research, future funding, focus on prevention of infections such as by vaccination (e.g. pneumococcal vaccine) and focus on identifying and treating different important modifiable risk factors. The abstract is now rewritten slightly in order to make this clearer.

Comment 12

Too many results from the tables and figures are reiterated in the text in the “Results” section.

Response: We appreciate the comment. Some of the results are now deleted from the text and are only in the table.

References

1. Wilhelms SB, Huss FR, Granath G, et al. Assessment of incidence of severe sepsis in Sweden using different ways of abstracting International Classification of Diseases codes: difficulties with methods and interpretation of results. Critical care medicine. 2010 Jun;38(6):1442-9.

2. Gaieski DF, Edwards JM, Kallan MJ, et al. Benchmarking the incidence and mortality of severe sepsis in the United States. Critical care medicine. 2013 May;41(5):1167-74.

3. Angus DC, Linde-Zwirble WT, Lidicker J, et al. Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Critical care medicine. 2001 Jul;29(7):1303-10.

4. Flaatten H. Epidemiology of sepsis in Norway in 1999. Crit Care. 2004 Aug;8(4):R180-4.

5. Martin GS, Mannino DM, Eaton S, et al. The epidemiology of sepsis in the United States from 1979 through 2000. The New England journal of medicine. 2003 Apr 17;348(16):1546-54.

6. Thomsen RW, Riis A, Nørgaard M, et al. Rising incidence and persistently high mortality of hospitalized pneumonia: a 10-year population-based study in Denmark. J Intern Med. 2006 Apr;259(4):410-7.

7. Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association. 1999;94:496-509.

8. Nielsen SL, Lassen AT, Gradel KO, et al. Bacteremia is associated with excess long-term mortality: A 12-year population-based cohort study. J Infect. 2015 Sep 9;70(2).

Attachment

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Decision Letter 1

Kazumichi Fujioka

9 May 2022

PONE-D-21-20080R1Incidence, recurring admissions and mortality of severe bacterial infections and sepsis over a 22-year period in the population-based HUNT StudyPLOS ONE

Dear Dr. Liyanarachi,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Reviewer #1: Authors have adequately addressed all the concerns from the first review appropriately. Some issues have been revised, some included in limitations while others re considered for potential follow-up work – which is acceptable.

One minor issue to consider is - whether patients with UTI died OF or died WITH UTI. This is more so since UTI was the second commonest cause of death following pneumonia. Were there any overlap infections at admission?

Much as one can clearly state some infections are a direct cause of death - I am not sure if one can say the same of UTI in most cases - as its common with very ill admitted patients esp. in the older population, - who may have another infection.

Comment 8 – regarding type of organisms need not be included in this manuscript.

Reviewer #2: I think the authors by and large have commented my questions and comments satisfactorily. It is, however, still a bit muddled with two cohorts and two follow-up periods although I have no doubt that incidence rates etc. have been computed correctly. I guess with the focus on age, the big differences between the two cohorts as regards median age (>10 y difference), death (28.9% vs. 3.9%), and follow-up time are okay.

There was one point that I think the authors have misunderstood (and admittedly, I did not explain it very clearly either!). They write:

“Regarding competing risk analysis, this is a very interesting aspect, but we have chosen not to study different risk factors for infectious diseases in depth in this paper. We feel that competing risk analysis would have been more relevant if we had aimed at causal analysis between exposure and outcome.”

When I referred to competing risk I did not think of different factors, but of death as competing risk to reinfection. That is, you can only be reinfected if you are alive. If e.g. a large proportion of a population dies shortly after the first infection, the denominator will change. It will actually change every time a person dies and it is therefore a good idea to incorporate a competing risk analysis, either using the Fine & Gray methods or treating death as censoring before time. Competing risk from death makes it hard to interpret your reinfection proportions. But again, as your study is mainly descriptive and not based on time-to-event regression analyses, I can live with that.

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Reviewer #1: Yes: Harriet Mayanja-Kizza

Reviewer #2: Yes: Kim Oren Gradel

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PLoS One. 2022 Jul 12;17(7):e0271263. doi: 10.1371/journal.pone.0271263.r004

Author response to Decision Letter 1

3 Jun 2022

Comments from the reviewers:

Reviewer 1:

Authors have adequately addressed all the concerns from the first review appropriately. Some issues have been revised, some included in limitations while others re considered for potential follow-up work – which is acceptable.

One minor issue to consider is - whether patients with UTI died OF or died WITH UTI. This is more so since UTI was the second commonest cause of death following pneumonia. Were there any overlap infections at admission?

Much as one can clearly state some infections are a direct cause of death - I am not sure if one can say the same of UTI in most cases - as its common with very ill admitted patients esp. in the older population, - who may have another infection.

Comment 8 – regarding type of organisms need not be included in this manuscript.

Response: Thank you for highlighting this important aspect, which will always be important to consider when reporting the all-cause 30-day mortality following any event. We agree that this is especially important when it comes to the urinary tract infections, and it should have been highlighted.

Of the 37298 participants having a hospital admission due to infection, 4628 had two infections codes and 918 had three or more. When reporting the 30-day all-cause mortality it is not possible to assess which of them contributed the most to death, and this is especially important to consider when it comes to the urinary tract infections. The manuscript has now been changed slightly on several places to make this important point clearer. (Lines 142-143, 160-162, 181, 188, 198-199 and 236-239).

Reviewer 2:

I think the authors by and large have commented my questions and comments satisfactorily. It is, however, still a bit muddled with two cohorts and two follow-up periods although I have no doubt that incidence rates etc. have been computed correctly. I guess with the focus on age, the big differences between the two cohorts as regards median age (>10 y difference), death (28.9% vs. 3.9%), and follow-up time are okay.

There was one point that I think the authors have misunderstood (and admittedly, I did not explain it very clearly either!). They write: “Regarding competing risk analysis, this is a very interesting aspect, but we have chosen not to study different risk factors for infectious diseases in depth in this paper. We feel that competing risk analysis would have been more relevant if we had aimed at causal analysis between exposure and outcome.” When I referred to competing risk I did not think of different factors, but of death as competing risk to reinfection. That is, you can only be reinfected if you are alive. If e.g. a large proportion of a population dies shortly after the first infection, the denominator will change. It will actually change every time a person dies and it is therefore a good idea to incorporate a competing risk analysis, either using the Fine & Gray methods or treating death as censoring before time. Competing risk from death makes it hard to interpret your reinfection proportions. But again, as your study is mainly descriptive and not based on time-to-event regression analyses, I can live with that.

Response: We acknowledge the reviewer’s comment on competing risk of death by other causes than the severe infections studied. To assess the amount of competing risk of death with our commonest group of infection, pneumonia, as an example, we assessed the proportion of participants who were censored because of death 1) before their first pneumonia and 2) before any recurrent pneumonia. The corresponding numbers are 13131 and 4066.

When studying a population that ages during follow-up, death will serve as a competing risk. In our study we descriptively reported the proportion who were subsequently reinfected, which we think serves the purpose of informing about the burden of reinfections on the population level. However, in the original analyses patients that did not survive their first infection were included in the denominator when calculating the proportion of recurrence. In accordance with the reviewer’s comment, this has now been changed, leading to slightly higher proportions of recurrence in all diagnosis groups. This has been changed in Table 2 on page 9 and changed and explained on lines 50, 119-120, 159, 179, 184 and 197. Figure 2 has also been changed accordingly. For future in depth investigations of reinfections, including analyses of risk factors for reinfection, we agree that time-to-event analyses accounting for competing risk is an appropriate tool.

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Decision Letter 2

Kazumichi Fujioka

28 Jun 2022

Incidence, recurring admissions and mortality of severe bacterial infections and sepsis over a 22-year period in the population-based HUNT Study

PONE-D-21-20080R2

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Acceptance letter

Kazumichi Fujioka

1 Jul 2022

PONE-D-21-20080R2

Incidence, recurring admissions and mortality of severe bacterial infections and sepsis over a 22-year period in the population-based HUNT Study.

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Associated Data

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

    Supplementary Materials

    S1 Table. Overview of the different ICD-9 and ICD-10 codes selected to identify the bacterial infections.

    (PDF)

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    S2 Table. Seasonal differences in incidence rates.

    (PDF)

    Click here for additional data file. (66.6KB, pdf)
    S3 Table. Summary of results divided into eight different foci of infection, males.

    (PDF)

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    S4 Table. Summary of results divided into eight different foci of infection, females.

    (PDF)

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    Attachment

    Submitted filename: PLOS_Sever Bact Infection Norway 22 yr Cohort_Feb 2022.docx

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    Attachment

    Submitted filename: Response to Reviewers.docx

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    Attachment

    Submitted filename: Response to reviewers.docx

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    Data Availability Statement

    Our data cannot be shared publicly due to patient confidentiality. Data from the HUNT Study used in research projects is available upon request to the HUNT Data Access Committee (hunt@medisin.ntnu.no) to research groups who meet the data availability requirements (described here: http://www.ntnu.edu/hunt/data).

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