As early as 380 BC, Hippocrates observed annual seasonal variation of diseases that were later acknowledged as infectious.Reference Fisman 1 Nonetheless, possible causes of seasonal variation are difficult to elucidate due to the complicated interactions between pathogens, hosts, and environment.Reference Fisman 1
Bacteremia, defined as bacteremia/fungemia associated with infection,Reference Laupland and Church 2 is a serious condition with a 30-day mortality of 15%–30%.Reference Goto and Al-Hasan 3 Bacteremia may be acquired in the community or the hospital setting.Reference Garner, Jarvis, Emori, Horan and Hughes 4 Moreover, patients with community acquisition who receive home therapy, reside in a nursing home, or have had recent hospital contact are often characterized as having a healthcare-associated bacteremia (HCA).Reference Friedman, Kaye and Stout 5
Among the many studies of seasonal variation of bacteremia,Reference Alcorn, Gerrard, Macbeth and Steele 6 – Reference Deeny, van Kleef, Bou-Antoun, Hope and Robotham 17 only 2 have assessed whether seasonal variation is related to acquisition.Reference Alcorn, Gerrard, Macbeth and Steele 6 , Reference Deeny, van Kleef, Bou-Antoun, Hope and Robotham 17 For Escherichia coli bacteremia in the United KingdomReference Deeny, van Kleef, Bou-Antoun, Hope and Robotham 17 and Gram-negative bacteremia in Australia,Reference Alcorn, Gerrard, Macbeth and Steele 6 cases acquired outside the hospital showed a summer peak, whereas no peak was seen for hospital-acquired cases.
We previously reported that seasonal variation of non-typhoid Salmonella infections diminished with increasing severity of the infection.Reference Gradel, Dethlefsen and Schønheyder 18 In this study, we tested the hypothesis that seasonal variation of bacteremia decreases in frequency of acquisition origin from community acquired to HCA to hospital acquired. We investigated this hypothesis using 2 population-based bacteremia databases,Reference Gradel, Nielsen and Pedersen 19 , Reference Gradel, Schønheyder, Arpi, Knudsen, Østergaard and Søgaard 20 and we focused on the 3 most common bacteremia species (E. coli, Staphylococcus aureus, Streptococcus pneumoniae) as well as acquisition and patient characteristics (ie, gender, age, comorbidity, location of infection) to assess which factor was most closely associated with seasonal variation.
MATERIALS AND METHODS
Setting
The Danish healthcare system is tax financed and provides care free of charge for all residents. All acutely ill patients are admitted to the nearest public hospital in their area of residence, which prompts a population-based coverage. Our data covered 3 demographically well-defined areas (ie, the North Denmark Region, Capital Region, and Funen County) served by 4 Departments of Clinical Microbiology (DCMs) in hospitals in Aalborg, Herlev, Hvidovre, and Odense (2.3 million total inhabitantsReference Gradel, Schønheyder, Arpi, Knudsen, Østergaard and Søgaard 20 , Reference Nielsen, Pedersen, Jensen, Gradel, Kolmos and Lassen 21 ).
Data Linkage
Each Danish resident has a unique personal identification number used for all health contacts, which permits unambiguous linkage between health administrative registries.Reference Schmidt, Pedersen and Sorensen 22
Core Dataset
The study period comprised 2000–2011 (the North Denmark and Capital regions) or 2000–2008 (Funen County).
All microbiological results were recorded in the following electronic laboratory information systems: Aalborg, Herlev, and Hvidovre use ADBakt (Autonik, Sköldinge, Sweden) and Odense used the local Patient Administrative System in 2000–2005 and have used the MADS system (www.madsonline.dk) thereafter. Blood culture procedures were described previously.Reference Gradel, Jensen, Kolmos, Pedersen, Vinholt and Lassen 23 , Reference Gradel, Knudsen, Arpi, Østergaard, Schønheyder and Søgaard 24
Key data included dates of blood draw and receipt of the blood culture in the DCM as well as isolate identification data. For all positive blood cultures, we used previously published computer algorithms to derive bacteremia cases.Reference Trick, Zagorski and Tokars 25 For each case, we defined the bacteremia date as reported previouslyReference Gradel, Schønheyder, Arpi, Knudsen, Østergaard and Søgaard 20 and retrieved all mono-microbial bacteremia cases with the 3 most common bacteria (E. coli, S. aureus, or S. pneumoniae), which constituted ~50% of all bacteremia cases.Reference Gradel, Schønheyder, Arpi, Knudsen, Østergaard and Søgaard 20
Linkage to the Danish National Hospital Registry
We linked the core dataset to the Danish National Hospital Registry (DNPR)Reference Lynge, Sandegaard and Rebolj 26 inpatient data and retrieved the records for hospital admissions that included the bacteremia date. We further retrieved hospital contacts up to 30 days before the bacteremia admission. For bacteremia cases, we used previously reported computer algorithms to derive acquisition of bacteremia (community acquired, HCA, hospital acquired).Reference Gradel, Knudsen, Arpi, Østergaard, Schønheyder and Søgaard 24 A case was considered to be community acquired if the bacteremia date was on the admission date or the day after, without any hospital contact in the preceding 30 days. A case was considered HCA if the bacteremia date occurred in the same time span, with hospital contact in the preceding 30 days, and a case was considered hospital acquired if the bacteremia date was ≥2 days after the admission date.
We linked the core dataset to the DNPR to retrieve all first-time diagnoses included in the Charlson comorbidity indexReference Charlson, Pompei, Ales and MacKenzie 27 within a 6-year period prior to the bacteremia date. In this index, 19 major disease categories (eg, malignancy, cardiovascular diseases, diabetes mellitus) are assigned a score; higher scores are given to prognostically more severe diseases. For each patient, a Charlson score is computed from the summation of scores for each individual disease category.
Linkage to the North Denmark Bacteremia Research Database
Bacteremia cases from North Denmark, 2000–2011, were linked to the North Denmark Bacteraemia Research Database, which comprises prospectively recorded data on the location of infection.Reference Schønheyder and Søgaard 28
Statistical Analyses
We categorized the bacteremia cases according to the following 6 subcategories: (1) bacteria (E. coli, S. aureus, or S. pneumoniae), (2) combination of bacteria and acquisition, (3) combination of bacteria and gender, (4) combination of bacteria, acquisition, and gender, (5) combination of bacteria, age group (0–64, 65–79, +80 years), and Charlson score group (0, 1–2, ≥3), and (6) combination of bacteria and location of infection. Data related to (6) were from bacteremia cases in North Denmark and were limited to 3 major categories: E. coli (intra-abdominal, urinary tract, unknown), S. aureus (skin/connective tissue/muscles, intravascular catheters, bones/joints, unknown), and S. pneumoniae (respiratory tract, unknown). For all unique combinations (n=72), we computed the number of bacteremia cases in each calendar month, cumulatively over the study period after assessing graphically that the differences between years were minor.
For each unique subcategory, we then assessed the seasonal variation by computing the peak-to-trough (PTT) ratio with 95% confidence intervals (CIs) and the peak date, based on smoothed monthly numbers of bacteremia cases adjusted for variable month lengths.Reference Edwards 29 The model assumes that the monthly incidence rates perform a single annual cycle that can be modeled using a sinusoidal curve, confirmed by visual inspection of all relevant graphs. The PTT ratio is the ratio between the peak and trough of the sinusoidal curve.
We repeated all of the above analyses for the patients’ first cases considered to be independent of each other.
For subcategory 2, we further depicted curves with monthly ratios, computed by dividing the smoothed number of monthly observations by the mean monthly smoothed number, which was computed from the annual number of bacteremia cases divided by 12, adjusting for different month lengths.
The program Stata (release 13; StataCorp) was used for all analyses, except for computing PTT ratios, CIs, and peak dates using Episheet. 30
Ethical Considerations
The study was approved by the Danish Data Protection Agency (record nos. 2007-41-0627, 2013-41-2579, and 2008-58-0028). Approval by an ethics committee or consent from participants (including next of kin/caregiver for children or deceased) is not required for registry-based research in Denmark.
RESULTS
In total, 16,006 E. coli, 6,924 S. aureus, and 4,884 S. pneumoniae bacteremia cases were recorded in 25,487 patients: 23,598 patients (92.6%) had 1 case, 1,583 (6.2%) had 2, and the remaining 306 (1.2%) had 3–12 cases (data not shown). Table 1 shows characteristics of the bacteremia cases. We were able to access data on the location of infection for 6,832 cases (Table 2).
TABLE 1 Characteristics of Bacteremia Cases
TABLE 2 Location of Infection for 6,832 Bacteremia Cases, North Denmark
Acquisition varied considerably according to bacterial species (Table 1). S. aureus had the lowest proportion of community acquisition (33.3%), but the highest proportion of hospital acquisition (41.3%). The reverse was seen for S. pneumoniae (74.8% vs 8.4%), whereas the respective proportions of E. coli were intermediate (54.8% vs 23.2%). HCA was more equally distributed between the 3 bacterial species.
Seasonal Variation and Peak Dates for E. coli and Subgroups
Overall, the diagnosis of E. coli bacteremia showed seasonal variation (PTT ratio, 1.17; 95% CI, 1.12–1.22) with August 24 as the peak date (Table 3). The seasonal variation was highest for community-acquired cases, was diminished for HCA, and was missing for hospital-acquired cases, both overall and for males and females (Table 3, Fig. 1). Little difference in seasonal variation was observed in relation to gender or location of infection, and no general trend of higher or lower seasonal variation was observed for higher age and/or Charlson comorbidity. Most peak dates for subgroups with seasonal variation occurred in August or September.
FIGURE 1 Monthly ratios (smoothed number of monthly observations divided by the mean monthly smoothed number) for Escherichia coli bacteremia, according to acquisition. HCA, health-care associated.
TABLE 3 Seasonal Variation and Peak Date for Bacteremia Cases and Subgroups
NOTE. PTT, peak-to-trough; CI, confidence interval; HCA, healthcare associated; S/CT/M, skin/connective tissues/muscles.
1 Bold type indicates seasonal variation (lower 95% CI>1).
2 Only for North Jutland cases (cf, Table 2).
Seasonal Variation and Peak Dates for S. aureus and Subgroups
No seasonal variation was encountered for S. aureus, either overall or for any of the subgroups, except for some locations of infection (ie, intravascular catheters and unknown) (Table 3 and Fig. 2). Peak dates varied throughout the year, thus corroborating the lack of seasonal variation.
FIGURE 2 Monthly ratios (smoothed number of monthly observations divided by the mean monthly smoothed number) for Staphylococcus aureus bacteremia, according to acquisition. HCA, health-care associated.
Seasonal Variation and Peak Dates for S. pneumoniae and Subgroups
With a PTT ratio of 3.42 (95% CI, 3.10–3.83), S. pneumoniae showed high seasonal variation (Table 3). Seasonal variation did not differ according to acquisition (Table 3, Fig. 3), gender, or combinations of these, whereas seasonal variation was smaller for cases with an unknown location of infection. Seasonal variation tended to increase with higher age and decrease with higher Charlson comorbidity; however, these results should be interpreted with caution due to the wide CIs. All peak dates occurred in or adjacent to February.
FIGURE 3 Monthly ratios (smoothed number of monthly observations divided by the mean monthly smoothed number) for Streptococcus pneumoniae bacteremia, according to acquisition. HCA, health-care associated.
First-Time Bacteremia Cases
Restrictions to the 25,487 first-time cases (including 6,163 with location data) yielded no material differences in any of the above analyses (data not shown).
DISCUSSION
For the 3 most common bacteremia species (E. coli, S. aureus, S. pneumoniae), seasonal variation or lack thereof occurred irrespectively of acquisition (community, HCA, hospital) or patient characteristics (gender, age, Charlson comorbidity, location of infection), thus refuting our hypothesis of decreasing seasonal variation with increasing severity of infection. An exception was a weak seasonal variation of community-acquired E. coli bacteremia that tended to diminish over HCA to hospital-acquired bacteremia cases.
In accord with our overall results, seasonal variation of bacteremia has been reported for S. pneumoniae to peak in the winter,Reference Kim, Musher, Glezen, Rodriguez-Barradas, Nahm and Wright 7 , Reference Talbot, Poehling and Hartert 10 , Reference Baine, Yu and Summe 12 , Reference Dowell, Whitney, Wright, Rose and Schuchat 14 E. coli to peak in the summer,Reference Chazan, Colodner, Edelstein and Raz 8 , Reference Kaier, Frank, Conrad and Meyer 9 , Reference Eber, Shardell, Schweizer, Laxminarayan and Perencevich 11 , Reference Al-Hasan, Lahr, Eckel-Passow and Baddour 15 , Reference Deeny, van Kleef, Bou-Antoun, Hope and Robotham 17 , Reference Perencevich, McGregor and Shardell 31 and to lack seasonal variation for S. aureus. Reference Eber, Shardell, Schweizer, Laxminarayan and Perencevich 11 , Reference Baine, Yu and Summe 12 , Reference Perencevich, McGregor and Shardell 31 , Reference Tasher, Stein, Simoes, Shohat, Bromberg and Somekh 32
While numerous studies have assessed seasonal variation in relation to climatic factorsReference Kim, Musher, Glezen, Rodriguez-Barradas, Nahm and Wright 7 , Reference Chazan, Colodner, Edelstein and Raz 8 , Reference Eber, Shardell, Schweizer, Laxminarayan and Perencevich 11 , Reference Anderson, Richet and Chen 13 , Reference Al-Hasan, Lahr, Eckel-Passow and Baddour 15 , Reference Perencevich, McGregor and Shardell 31 , Reference Freeman, Anderson and Sexton 33 , Reference Richet 34 or viral respiratory infectionsReference Kim, Musher, Glezen, Rodriguez-Barradas, Nahm and Wright 7 , Reference Talbot, Poehling and Hartert 10 , Reference Tasher, Stein, Simoes, Shohat, Bromberg and Somekh 32 , Reference Jansen, Sanders, van der Ende, van Loon, Hoes and Hak 35 fewer studies have assessed seasonal variation of host susceptibility, as reviewed by Dowell.Reference Dowell 36 Previously, we reported that seasonal variation of non-typhoid Salmonella infections diminished with increasing severity of infection. This may, in part, be explained by the higher impact of exogenous factors (eg, climate and behavioral factors, which exert seasonal variation) on the acquisition of less severe infections, whereas endogenous host characteristics (eg, age and chronic diseases, with no seasonal variation) are more important in more severe infections.
To our knowledge, only 2 studies have related the seasonal variation of bacteremia to acquisition.Reference Alcorn, Gerrard, Macbeth and Steele 6 , Reference Deeny, van Kleef, Bou-Antoun, Hope and Robotham 17 In agreement with our results, a recent study from the United Kingdom, with an impressive number of 79,155 E. coli bacteremia cases, reported seasonal variation with a summer peak for the 75% community-acquired cases, but no seasonal variation for the 25% hospital-acquired cases.Reference Deeny, van Kleef, Bou-Antoun, Hope and Robotham 17 The same was reported for Gram-negative bacteremia in Australia, although the low number of 181 community-acquired (34 E. coli) and 259 (62 E. coli) hospital-acquired cases makes comparisons to our study difficult.Reference Alcorn, Gerrard, Macbeth and Steele 6 The Australian authors mainly explained the seasonal variation for outpatients by the influence of climate. Inpatients, on the other hand, were under climate control in the hospital, and the UK study group suggested that climatic factors had little impact on the seasonal variation of community-acquired bacteremia cases.
Non-climatic factors may also contribute to the diminishing seasonal variation of E. coli parallel to closer hospital contact. Different risk factors are probably related to acquisition outside or inside the hospital. In our study, 60.5% of community acquired, 51.4% of HCA, and 46.6% of hospital-acquired E. coli bacteremia was encountered in women, whereas there were no notable differences in age distribution between males and females within any of the 3 acquisition modes (data not shown). Likewise, no conspicuous differences in seasonal variation were found in relation to gender or age/Charlson comorbidity groups. A US study also reported a summer peak for 6,035 E. coli isolates, of which 74% were isolated from urine and only 9% were isolated from blood.Reference Perencevich, McGregor and Shardell 31 Although that study did not report separate seasonal variation analyses according to acquisition, 56% of the E. coli infections were community acquired, which is comparable to the 54.8% in our study. The pathogenesis of E. coli bacteremia probably also differs in relation to location of infection; the urinary tract or intra-abdominal locations were the most common.Reference Gransden, Eykyn, Phillips and Rowe 37 – Reference Kennedy, Roberts and Collignon 39 A higher prevalence of urinary tract locations was reported in community acquired than in hospital-acquired bacteremiaReference Gransden, Eykyn, Phillips and Rowe 37 , Reference Olesen, Kolmos, Ørskov, Ørskov and Gottschau 38 and in more females than males.Reference Gransden, Eykyn, Phillips and Rowe 37 , Reference Kennedy, Roberts and Collignon 39 A recent study reported seasonal variation with a summer peak for urinary tract infections.Reference Rossignol, Pelat, Lambert, Flahault, Chartier-Kastler and Hanslik 40 For 6,832 cases, we had data on the location of infection determined prospectively and based on all microbiological and clinical evidence during admission.Reference Schønheyder and Søgaard 28 The 2,297 E. coli cases located in the urinary tract exhibited seasonal variation with a PTT ratio of 1.17 (95% CI, 1.04–1.31) and a peak date on July 5, whereas the 622 cases located intra-abdominally showed no seasonal variation. The location of infection also varied according to acquisition, most notably in the intra-abdominal area (13.2% community-acquired and 22.4% hospital-acquired, respectively) and the urinary tract (64.3% vs 43.3%) (data not shown).
The aforementioned Australian study found no seasonal variation for Gram-positive bacteremia for the 149 cases acquired outside or for the 252 acquired inside the hospital. Unfortunately, the frequency of Gram-positive bacterial species was not reported.Reference Alcorn, Gerrard, Macbeth and Steele 6 These results agree with our findings for S. aureus, but not for S. pneumoniae. The minor impact of acquisition on seasonal variation, both overall and in subgroup analyses, for S. aureus and S. pneumoniae in our study, may indicate that the risk factors for bacteremic infection were the same regardless of whether it was acquired outside or inside the hospital. Interestingly, seasonal variation was observed for 286 S. aureus cases with intravascular catheters as the location of infection and for 617 cases with an unknown location of infection. For S. pneumoniae, there was less seasonal variation in the 183 cases with an unknown location of infection. Although definite conclusions are precluded due to relatively low numbers of cases, we have not encountered other such findings in the literature.
Most other studies on the seasonal variation of bacteremia comprising specific bacteria incorporated far fewer bacteremia cases than our study, with 79,155 E. coli Reference Deeny, van Kleef, Bou-Antoun, Hope and Robotham 17 and 7,266 and 4,147 S. pneumoniae casesReference Talbot, Poehling and Hartert 10 , Reference Jansen, Sanders, van der Ende, van Loon, Hoes and Hak 35 as notable exceptions. High numbers of cases are especially important when observations are distributed over 12 months and various subgroups and our relatively narrow 95% CIs in most analyses indicated high statistical precision. Moreover, our study was population based, and valid data on bacterial species and hospital admissions enabled us to define acquisition, including HCA as an intermediate entity between community and hospital acquisition. Finally, for part of our study cohort, we had valid data on the location of infection.Reference Schønheyder and Søgaard 28
However, our study also had limitations that warrant further discussion. First, the study was mainly based on administrative registries without clinical data, of which information on location would especially be beneficial. Second, we had no data on behavioral factors, which could strengthen or weaken our hypothesis on seasonal behavioral factors (eg, traveling) being more predominant in less severe infections. Third, exact acquisition criteria may vary, as shown in a review of 23 studies of pediatric bacteremia patients.Reference Henderson, Muller-Pebody, Johnson, Wade, Sharland and Gilbert 41 However, we have recently reported that time windows do not necessarily represent sharp transitions pertaining to acquisition,Reference Gradel, Nielsen and Pedersen 19 and we believe such discrepancies are of minor importance in this study. Finally, some subgroups had a low number of cases, which could preclude the detection of seasonal variation.
In conclusion, for mono-microbial E. coli, S. aureus, and S. pneumoniae bacteremia, seasonal variation was mainly related to the bacterial species, regardless of acquisition or patient characteristics such as gender, age, comorbidity, or location of infection.
ACKNOWLEDGMENTS
Contributing members of DACOBAN: Christian Østergaard (Department of Clinical Microbiology, Copenhagen University Hospital, Hvidovre Hospital, Copenhagen, Denmark), Magnus Arpi (Department of Clinical Microbiology, Copenhagen University Hospital, Herlev Hospital, Copenhagen, Denmark), Kim Oren Gradel (Center for National Clinical Databases - South, Odense University Hospital, Odense, Denmark), Ulrich Stab Jensen (Department of Clinical Microbiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark), Sara Thønnings (Department of Clinical Microbiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark), Jenny Dahl Knudsen (Department of Clinical Microbiology, Copenhagen University Hospital, Hvidovre Hospital, Copenhagen, Denmark), Kristoffer Koch (Department of Clinical Microbiology, Aalborg University Hospital, Aalborg, Denmark), Mette Pinholt (Department of Clinical Microbiology, Copenhagen University Hospital, Herlev Hospital, Copenhagen, Denmark), Jesper Smit (Department of Clinical Microbiology, Aalborg University Hospital, Aalborg, Denmark), Henrik Carl Schønheyder (Department of Clinical Microbiology, Aalborg University Hospital, Aalborg, Denmark), Mette Søgaard (Department of Clinical Microbiology, Aalborg University Hospital, Aalborg, Denmark).
Contributing members of DORIS: Annmarie Touborg Lassen (Department of Emergency Medicine, Odense University Hospital, Odense, Denmark), Court Pedersen (Department of Infectious Diseases, Odense University Hospital, Odense, Denmark), Thøger Gorm Jensen (Department of Clinical Microbiology, Odense University Hospital, Odense, Denmark), Hans Jørn Kolmos (Department of Clinical Microbiology, Odense University Hospital, Odense, Denmark), Stig Lønberg Nielsen (Department of Infectious Diseases, Odense University Hospital, Odense, Denmark).
Financial support: No financial support was provided relevant to this article.
Potential conflicts of interest: All authors report no conflicts of interest relevant to this article.
