, Volume 57, Issue 4, pp 690–698

Working-age adults with diabetes experience greater susceptibility to seasonal influenza: a population-based cohort study


  • Darren Lau
    • Department of Public Health Sciences, School of Public HealthUniversity of Alberta, 2-040G Li Ka Shing Center for Health Research Innovation
  • Dean T. Eurich
    • Department of Public Health Sciences, School of Public HealthUniversity of Alberta, 2-040G Li Ka Shing Center for Health Research Innovation
  • Sumit R. Majumdar
    • Department of Medicine, Faculty of Medicine and DentistryUniversity of Alberta
  • Alan Katz
    • Department of Community Health Sciences, Faculty of MedicineUniversity of Manitoba
    • Department of Public Health Sciences, School of Public HealthUniversity of Alberta, 2-040G Li Ka Shing Center for Health Research Innovation

DOI: 10.1007/s00125-013-3158-8



The aim of this work was to compare the incidence of illness attributable to influenza in working-age adults (age <65 years) with and without diabetes.


We performed a cohort study using administrative data from Manitoba, Canada, between 2000 and 2008. All working-age adults with diabetes were identified and matched with up to two non-diabetic controls. We analysed the rates of influenza-like illness physician visits and hospitalisations, pneumonia and influenza hospitalisations, and all-cause hospitalisations. Multivariable regressions were used to estimate the influenza-attributable rate of each outcome.


We included 745,777 person-years of follow-up among 166,715 subjects. The median age was 50–51 years and 48–49% were women; adults with diabetes had more comorbidities and were more likely to be vaccinated for influenza than those without diabetes. Compared with similar adults without diabetes, those with diabetes had a 6% greater (RR 1.06, 95% CI 1.02, 1.10; absolute risk difference 6 per 1,000 adults per year) increase in all-cause hospitalisations associated with influenza, representing a total of 54 additional hospitalisations. There were no differences in the influenza-attributable rates of influenza-like illness (p = 0.06) or pneumonia and influenza (p = 0.11).


Guidelines calling for influenza vaccinations in diabetic, in addition to elderly, adults implicitly single out working-age adults with diabetes. The evidence supporting such guidelines has hitherto been scant. We found that working-age adults with diabetes appear more susceptible to serious influenza-attributable illness. These findings represent the strongest available evidence for targeting diabetes as an indication for influenza vaccination, irrespective of age.


Diabetes Epidemiology Influenza Influenza vaccination



Aggregate diagnostic group


All-cause hospitalisation


Influenza-like illness (physician visit and hospitalisation)


Pandemic H1N1


Pneumonia and influenza (hospitalisation)


Respiratory syncytial virus


Socioeconomic status


Influenza is a common viral illness [1] responsible for substantial morbidity and mortality [25]. Clinical practice guidelines promulgated by diabetes mellitus care organisations (e.g. American Diabetes Association, Canadian Diabetes Association), as well as national vaccination authorities in such countries as Canada and the UK, recommend routine vaccinations against seasonal influenza in all adults with diabetes [69]. While guidelines in the USA have recently begun calling for universal vaccination of all adults, they continue to prioritise those with diabetes, particularly when resources are scarce. Since recommendations already exist for vaccination in all elderly adults (age >65 years), the incremental effect of guidelines calling for vaccinations in diabetic adults is to add working-age adults (age ≥18 and <65 years) with diabetes as a high-risk group relative to those without diabetes [69].

Such guidelines may be justified if working-age adults with diabetes experience greater susceptibility or severity of influenza relative to similar non-diabetic adults. However, the evidence for this premise is limited [6, 7, 10]. To our knowledge, only three studies have compared the burden of seasonal influenza in adults with and without diabetes. These studies have several limitations, including potential bias from the use of hospital-based comparison groups [11], lack of adjustment for comorbidities and vaccination status [1113] and inadequate adjustment for seasonality [12]. More recently, studies of pandemic H1N1 (pH1N1) influenza have drawn varying conclusions regarding the extent to which diabetes increases the risk of severe outcomes. Given the limitations of the present literature, we performed a population-based cohort study to test the hypothesis that working-age adults with diabetes experience greater incidence of illness due to seasonal influenza.



Our study was performed using administrative data from Manitoba, Canada. Nearly all (>99%) residents of Manitoba have provincially funded healthcare benefits under Manitoba’s system of universal health insurance [14]. The databases of Manitoba Health capture the following information: services, diagnoses and interventions provided to patients during hospital admissions and physician visits; demographics; pharmaceuticals dispensed in the community at the point of sale and vaccinations provided to Manitoba residents (www.umanitoba.ca/faculties/medicine/units/community_health_sciences/departmental_units/mchp/resources/repository/health_admin.html). We have recently used these databases in an observational study of the effectiveness of influenza vaccination in working-age adults with diabetes [15]. The present study was approved by the Institutional Review Board of the University of Alberta (Pro00026010) and by the Health Information and Privacy Committee of Manitoba (HIPC 2011/2012–16).


We identified all working-age adults with diabetes mellitus, from 1 July 2000 to 30 June 2008. Adults with both prevalent and incident diabetes were selected, using a well-validated claims-based definition of diabetes. Adults were defined as having diabetes after two ambulatory physician claims or one hospital discharge for diabetes mellitus (ICD-9 code 250 [www.icd9data.com/2007/Volume1/240-279/250-259/250/default.htm] or ICD-10 codes E10-E11 [www.who.int/classifications/icd/en/]) [16]. A non-diabetic control group was composed by matching diabetic adults with up to two non-diabetic adults by age (i.e. ±1 year), sex and health region. A priori calculations suggested that a 2:1 match would provide sufficient power for the analyses planned. Non-diabetic subjects were followed from the date of their diabetic counterpart’s diagnosis of diabetes.

We divided calendar time into years from 1 July to 30 June [12]. Influenza season was defined as a continuous period between the first and last occurrences of at least two consecutive weeks with two or more isolates positive for influenza, according to provincial surveillance data [1719].

Outcomes and follow-up

We followed all subjects, until 30 June 2008, for any occurrences of three outcomes, based on ICD diagnostic codes: physician visit or hospitalisation for influenza-like illness (ILI), hospitalisation for pneumonia and influenza (PI) and all-cause hospitalisation (ALL). Influenza-like illness is usually defined as a febrile cough with additional symptoms of systemic involvement [20]. However, ICD codes do not contain a specific entry for ILI, as defined clinically. We therefore adopted an administrative case definition of ILI, which consisted of a broad bundle of diagnoses, including bronchitis, pneumonia, cold, cough and exacerbations of chronic obstructive pulmonary disease (see electronic supplementary material [ESM] Tables 1 and 2). This case definition, determined in a pilot study of six emergency departments, is similar to those of other studies identifying diagnoses correlated with influenza activity [21, 22]. ILI was chosen to represent the common manifestations of influenza, which include PI hospitalisations. PI was also examined as a separate outcome to depict more serious and specific respiratory sequelae. ALL is non-specific, but was included to indicate the overall burden of influenza on serious morbidity, since influenza is thought to contribute to a significant range of non-respiratory outcomes including myocardial infarction and diabetic ketoacidosis [25]. Subjects were able to contribute multiple outcomes.

Measurement approach—influenza-attributable outcomes

Although ILI is the usual surveillance case definition of influenza, less than 30% of ILI during influenza season is caused by influenza [23], which may additionally present as other respiratory [2426] and non-respiratory conditions [5, 27]. Consequently, influenza infections are rarely suspected or laboratory-confirmed, making direct measurement of infection difficult. As the circulation of influenza is minimal outside the influenza season, the burden of influenza can instead be estimated by comparing health outcomes during influenza season to those during the off-season [12]. However, this approach may be confounded by cyclic trends, since respiratory outcomes rise and fall each year even in the absence of significant influenza activity [2729]). Researchers have accounted for cyclic trends and other potential confounders by using statistical models to estimate the outcomes that would have occurred, during influenza season, in the absence of circulating influenza. This counterfactual is then compared with estimated outcomes under actual conditions, to obtain an influenza-attributable estimate of outcome frequency [25, 27].

We modelled rates of each outcome in adults with and without diabetes using unconditional time-varying Poisson regression. Person-time was split into weeks, with follow-up time in person-years modelled as an offset term. A binary indicator for influenza season (i.e. influenza season vs off-season time) represented the adjusted relative effects of influenza on outcome rates, that is, the ‘influenza-attributable effect’. Variables indicating each month were included to account for cyclic trends. A term indicating diabetes status was included to adjust for the increased likelihood of hospitalisation or presentation for medical attention among adults with diabetes generally, regardless of influenza susceptibility or severity. The interaction of diabetes status and influenza-season terms provided a formal test of differences in relative rates of influenza-attributable illness among those with and without diabetes. An example would be if influenza season was associated with 10% (RR 1.10) and 20% (RR 1.20) relative increases in the rates of ALL in non-diabetic and diabetic adults, respectively. Given a statistically significant interaction term (i.e. interaction RR 1.20/1.10 = 1.09), we could infer that influenza increases ALL to a greater extent in those with diabetes, and that those with diabetes are therefore more susceptible to influenza. Greater rates of influenza-attributable outcomes may also indicate greater severity of influenza if outcomes considered more serious (i.e. PI, ALL) are disproportionately increased compared with outcomes capturing less serious healthcare use (i.e. ILI, which includes both physician visits and hospitalisations).

All models were additionally adjusted for age, sex, location of residence, socioeconomic status (SES), comorbidities, number of physician visits in the previous year, receipt of current influenza vaccination for the influenza year, previous receipt of pneumococcal vaccination and secular (i.e. temporal) trends. SES was based on the census-derived income quintile of each subject’s postal code area of residence [30, 31]. Comorbidity was represented by the number of major aggregate diagnostic groups (ADGs) accrued during the previous 2 years (i.e. 0, 1, or ≥2 major ADGs; major ADGs are listed in the footnote to Table 1) [32, 33]. Influenza vaccination is an important confounder because it is felt to reduce the sequelae of influenza in those with diabetes. Since the effect of influenza vaccination is difficult to disentangle from a tendency for those with greater health status to be vaccinated [15], adjusting for influenza vaccination has the additional benefit of reducing confounding from unmeasured differences in health status. Finally, we adjusted for secular, in addition to cyclic, trends using indicator variables for each year [3, 5]. All variables were updated every 1 July, except for vaccination status, which was updated after the week of receipt. To check for overdispersion, we performed the same analysis using negative binomial regression and the results were virtually identical (data not shown). Results were considered statistically significant at a standard of p < 0.05. Analyses were performed using SAS 9.2 (SAS Institute, Cary, NC, USA).
Table 1

Cohort characteristics



Age, years (median, IQR): 51, 15

No diabetes

Age, years (median, IQR): 50, 15

No. of subjects


No. of subjects














Income quintile
























No. of medical visits—previous year












 3 or more





No. of major ADGsa












 2 or more





Influenza vaccination—previous year












Pneumococcal vaccination (any)












Table enumerates subjects at cohort entry. Proportions vary if subjects are enumerated over their follow-up periods on 1 July each year, with person-years of follow-up as the denominator (e.g. greater prevalence of influenza vaccinations), but relationships among those with vs those without diabetes remain similar

aNumber of major ADGs over the previous 2 years: ADG3 (time limited: major), ADG4 (time limited: major—primary infections), ADG9 (likely to recur: progressive), ADG11 (chronic medical: unstable), ADG16 (chronic specialty: unstable—orthopaedic), ADG22 (injuries/adverse effects: major), ADG25 (psychosocial: recurrent or persistent, unstable) and ADG32 (malignancy)

All between group differences, p < 0.05 on Wilcoxon rank-sum or χ 2 tests


We identified 60,118 working-age adults with diabetes in Manitoba from 2000 to 2008. Of these, 58,577 adults (97%) were matched to one or more non-diabetic control subjects. Our study included 56,513 adults with diabetes and 110,202 non-diabetic controls with complete data, who together contributed 745,777 person-years of follow-up. Adults with and without diabetes were similar in age and sex. At cohort entry, those with diabetes had a median age of 51 years (interquartile range [IQR] 15), those without diabetes had a median age of 50 (IQR 15); 51–52% of subjects were men in both groups. Patients with diabetes were more likely to have a below-median income, made more physician visits, had greater comorbidity based on major ADGs and were more likely to have been vaccinated against influenza or pneumococcal infection, compared with non-diabetic controls (p < 0.05) (Table 1). On average, our study included 31,139 working-age adults with diabetes each year, representing 58% of diabetic adults of any age, and approximately 3% of the entire Manitoba population.

During the follow-up period, we observed 251,144 ILIs, 1,892 PIs, and 36,955 ALLs. Actual and model-based counts of each outcome, including model-estimated counts in the counterfactual absence of influenza season, are shown in Fig. 1. Outcomes demonstrated a cyclical rise and fall each year. Where distinguishable, excess outcomes during influenza season are shown.
Fig. 1

Actual vs projected outcomes in working-age adults with and without diabetes. Outcome numbers in adults without diabetes were estimated for a 2:1 matched group of controls and therefore do not represent actual numbers for adults in Manitoba without diabetes. + symbol represents weekly numbers of each outcome. Grey bars, influenza season; dotted line, 1 July for the influenza season indicated; green line, projected number of events; blue line, projected number of events in the absence of circulating influenza; red line, number of influenza-attributable events per week (i.e. green line minus blue line)

In adults with diabetes, influenza season was associated with statistically significant increases in the rates of all outcomes studied after adjusting for cyclic trends and other potential confounders (Table 2). In those without diabetes, influenza season was associated with increased ILI. The relative effect of influenza appeared to be greater in diabetic adults than in non-diabetic adults for PI (RR 1.35 vs 1.13 [diabetes vs no diabetes] for the effect of influenza) and ALL (RR 1.06 vs 1.00 [diabetes vs no diabetes] for the effect of influenza) (Table 2). While the former difference was not statistically significant (p = 0.107), our formal test of interaction showed statistical significance for the 6% greater relative increase in ALL due to influenza in diabetic adults (p = 0.044).
Table 2

Relative effect of circulating influenza on the incidence of ILI, PI and ALL in adults with and without diabetes


Adults without diabetes

Adults with diabetes


Rate ratioa

95% CI

f b

p value

Rate ratioa

95% CI

f b

p value

Interaction p valuec



1.15, 1.18




1.13, 1.17






0.91, 1.39




1.16, 1.56






0.95, 1.06




1.02, 1.10




Estimates are adjusted for potential confounders (see Methods)

aRelative effect of circulating influenza (influenza season vs off-season)

bPercentage of outcomes due to influenza during influenza season: f = [(RR − 1)/RR] × 100%

cThe interaction term is defined in the model as the ratio of the two previous risk ratios (RRs), representing the change in the effect of influenza by diabetes status

*p ≤ 0.05; ***p ≤ 0.001

In diabetic adults, influenza contributed 13%, 26% and 6% of ILIs, PIs and ALLs occurring during influenza season, respectively (Table 2). For ALLs, virtually all of these influenza-attributable outcomes (i.e. 54/55 events [98%]) were due to the increased effects of influenza in diabetic adults (Table 3). In absolute terms, influenza season was associated with an additional six ALLs per one-thousand diabetic person-years.
Table 3

Model-projected numbers of influenza-attributable outcomes during influenza season in adults with and without diabetes


Adults with diabetes

Adults without diabetes (2:1 matched controls)

Under actual conditions (projected)

If subjects did not have diabetes (counterfactual)

Differencea (additional influenza-attributable outcomes due to having diabetes)

Under actual conditions (projected)



n (average per year)





 No. of persons (average per year)





 Rate (/1,000 person-years)







n (average per year)





 No. of persons (average per year)





 Rate (/1,000 person-years)







n (average per year)





 No. of persons (average per year)





 Rate (/1,000 person-years)





Counts and rates for adults without diabetes were estimated for the matched comparison group of non-diabetic subjects, and therefore do not represent the actual numbers of influenza-attributable outcomes in adults in Manitoba without diabetes

a‘Projected’ minus ‘counterfactual’


The burden of influenza is difficult to estimate due to lack of specific outcome measures in administrative data. Using statistical regression techniques, we have distinguished the effects of seasonal influenza in working-age Manitoba adults with and without diabetes. Influenza was associated with increased rates of physician visits and hospitalisations for influenza-like illness in non-diabetic adults, accounting for 15% of ILI during influenza season. In diabetic adults, influenza was associated with increased ILI, PI and ALL, accounting for 13%, 26% and 6% of these outcomes, respectively. Compared with working-age non-diabetic adults, working-age adults with diabetes experienced a significantly greater 6% increase in influenza-attributable ALLs. Since a similar difference was not observed for ILI, we infer that adults with diabetes experience a disproportionately greater susceptibility to more serious manifestations of influenza, requiring hospitalisation.

In absolute terms, working-age adults with diabetes experienced an additional six hospitalisations due to influenza per 1,000 adults. Notably, if these same adults had not had diabetes they would not have experienced any ALLs due to influenza, since influenza did not appear appreciably to increase ALLs in those without diabetes.

Limitations and strengths

Our study has several limitations. First, because we relied on a community-level indicator for influenza, ecological bias may arise if outcomes attributed to influenza did not actually occur in patients infected with influenza. For example, respiratory syncytial virus (RSV) co-circulates with influenza and causes similar illness [3]. Unaccounted RSV would lead to overestimates of influenza-attributable illness. Because the proportion of unaccounted RSV is not likely to be different in those with diabetes, our estimates of the relative effects of diabetes on influenza-attributable illness may not be affected. Indeed, we obtained similar results using a more specific measure of influenza activity—the proportion of respiratory specimens positive for influenza virus (data not shown).

Second, as the present study was observational, other unrecognised confounders may affect our findings. For example, obesity has emerged as a risk factor for hospitalisation and severe outcomes in pH1N1 cases [3436]. It remains unclear from the pH1N1 literature whether obesity is an independent risk factor for influenza or whether the association between obesity and severe outcomes of influenza is itself confounded by diabetes, cardiovascular disease and other obesity-related comorbidities [36]. One recent study of the NHANES cohort found a consistent association between obesity and pH1N1 hospitalisations/death, after stratifying by comorbidity, suggesting a true diathesis [34]. In the seasonal influenza literature, Kwong et al have demonstrated an association between obesity and increased respiratory hospitalisations, after adjusting for diabetes and cardiovascular disease [37]. Obesity may therefore account for the association between diabetes and influenza-attributable illness to an unknown extent [38]. Other clinical variables we were unable to include were the specific type of diabetes; and variables related to diabetes severity and control, such as HbA1c. The incorporation of such variables in future studies would provide additional evidence helping to confirm or disprove a diabetes-specific effect. Our results likely generalise to those with type 2 diabetes, which comprises the vast majority of diabetes in adults. We adjusted for comorbidity using the number of ADGs instead of specific individual-level diagnoses, due to practical modelling limitations.

Finally, our data is limited to healthcare use, which may under-represent the total burden of influenza. Many working-age adults with influenza do not present for medical attention although they may still incur absenteeism and productivity losses [1, 39]. Previous studies have suggested that patients with diabetes who experience infectious diseases may be more likely to present for medical attention and to be hospitalised [40, 41], and this may lead to overestimates of influenza-attributable illness specifically in diabetic adults. Because increased medical attention likely applies generally, regardless of infectious aetiology (e.g. ILI, PI or ALL of both influenza and non-influenza aetiologies), we have accounted for this source of potential confounding by singling out differences in the effects of circulating influenza among diabetic and non-diabetic adults, while adjusting analytically for the effects of diabetes alone.

Three previous studies have compared influenza-attributable outcomes in patients with and without diabetes during periods of seasonal influenza. Schanzer et al noted increased influenza-attributable primary respiratory admissions in patients with diabetes compared with those without diabetes, but did not report diabetes-specific risk estimates [13]. Bouter et al found that diabetes increased the risk of both pneumonia hospitalisations and subsequent mortality to a greater extent during years with significant influenza activity [11]. Finally, Neuzil et al reported a fivefold (unadjusted) higher rate of influenza-attributable cardiopulmonary hospitalisations or deaths in working-age women with diabetes [12]. The limitations of these studies include potential bias from the use of hospital-based comparison groups [11], lack of adjustment for comorbidities and vaccination status [1113] and inadequate adjustment for seasonality [12], as mentioned earlier. Additionally, several studies have examined the effects of diabetes on pH1N1 influenza. Diabetes appears disproportionately represented in most studies of confirmed pH1N1 cases requiring hospitalisation [36, 4245]. Some pH1N1 studies have also identified diabetes as a risk factor for severe outcomes following hospitalisation [45], although other studies have not [46, 47]. Whether the high prevalence of diabetes in pH1N1 studies can be attributed to a diabetes-effect on risk [45], confounding due to other comorbidities [46, 47] or due to adults with diabetes being more readily admitted on a precautionary basis [35] remains unclear. Moreover, since over one-third of diabetes is undiagnosed in the community [48], the lack of a population control group in case series of pH1N1 hospitalisations makes it difficult to ascertain whether the apparent effect of diabetes on pH1N1 hospitalisations is due to increased detection of diabetes in tertiary care. The prevalence of diabetes in pH1N1 patients may be consistent with its community prevalence if undiagnosed diabetes is included [46].

To our knowledge, our study is one of only two studies [12] to have identified and followed individuals, in the general population, with and without diabetes, for influenza-attributable outcomes. The use of administrative databases ensures comparable data quality among all study subjects. Our study used a validated case definition [16] to define diabetes status before outcome ascertainment, thus avoiding differential detection of undiagnosed diabetes in adults hospitalised for a study outcome. Moreover, ours is the only study to have adjusted for comorbidities, vaccination status and cyclic trends apart from influenza. Insofar as diabetes was found to be a risk factor for influenza-attributable hospitalisations, our results agree with those of previous seasonal influenza studies [1113] as well as those of certain pH1N1 studies [45]. In contrast to previous studies, the effect we demonstrated was much smaller and subtler in magnitude. These findings highlight the importance of appropriate adjustment for potential confounders and cyclic trends, which may otherwise lead the burden of seasonal influenza to be overestimated in high-risk populations. Our findings also suggest that we should be cautious of pH1N1 series showing a high prevalence of diabetes, although our data did not include 2009 and are therefore not straightforwardly applicable.

Clinical and policy implications

Our results suggest that working adults with diabetes experience greater risk of influenza-related ALLs. The public health impact of diabetes on the burden of influenza in Manitoba may be summarised as an additional six hospitalisations per 1,000 diabetic person-years. A previous economic analysis of a universal vaccination programme in Ontario estimated the cost of a pneumonia- or influenza-related hospitalisation to be $6,418 Canadian dollars (CAD), and the cost of a universal vaccination programme to be $7.55 CAD per dose delivered [49]. Using these figures as rough approximations, it may be cost-saving to target working-age adults with diabetes for vaccination, with a presumed rate of vaccine effectiveness as low as 20%. Of course, the unit cost of a diabetes-specific vaccination programme may be much higher, and the actual effectiveness of influenza vaccinations in high-risk populations is not well established. While observational studies suggest that vaccination against influenza reduces hospitalisations in working-age adults with diabetes [10, 50], these benefits may be attributable to healthy user bias [15]. Our observation that working-age adults with diabetes experience a greater burden of influenza than similar non-diabetic adults provides a clinical justification for targeted anti-influenza interventions; identifying particular interventions and evaluating their effectiveness in this population are questions for further research.

Vaccination guidelines indirectly single out working-age adults with diabetes for routine vaccination. We have demonstrated an increased burden of influenza in this population. Randomised trials are needed to confirm actual vaccine effectiveness in this group. Formal economic studies are also required, to ascertain the extent to which identifying diabetes as a high-risk indication for vaccination may mitigate the healthcare use and costs associated with influenza. Until such studies are available, our work represents the strongest current evidence highlighting the burden of influenza, and the potential benefits of influenza vaccination, in diabetic adults.


The authors would like to acknowledge the support and expertise of C. Burchill (Manitoba Centre for Health Policy, Winnipeg, MB, Canada), who facilitated data access and analyses. This study was approved by the Health Information Privacy Committee of Manitoba (2011/2012–16). The results and conclusions presented are those of the authors. No official endorsement by Manitoba Health is intended or should be inferred.


This work was supported by an open operating grant from the Canadian Institutes of Health Research (MOP-119316). DL is funded by MD/PhD studentships from CIHR and Alberta Innovates–Health Solutions (AIHS). DTE holds a CIHR New Investigator award, and is an AIHS Population Health Investigator. SRM holds the Endowed Chair in Patient Health Management, supported by the Faculty of Medicine and Dentistry and the Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, and is an AIHS Health Scholar. JAJ is an AIHS Senior Scholar and a Centennial Professor at the University of Alberta.

Duality of interest

The authors declare that there is no duality of interest associated with this manuscript.

Contribution statement

JAJ had full access to all the data in the study and takes responsibility for the integrity of the data and accuracy of the data analysis. All authors contributed to the design and conception of the study, and the interpretation of data. DL performed the data analysis, and wrote the first draft of the article. DL, DTE, SRM, AK and JAJ contributed to subsequent revisions. All authors reviewed and approved the final draft.

Supplementary material

125_2013_3158_MOESM1_ESM.pdf (63 kb)
ESM Table 1 (PDF 63 kb)
125_2013_3158_MOESM2_ESM.pdf (61 kb)
ESM Table 2 (PDF 60 kb)

Copyright information

© Springer-Verlag Berlin Heidelberg 2014