Eurosurveillance - Volume 24, Issue 20, 16 May 2019

In a study from 2013 that prioritised communicable diseases for surveillance in Sweden, we identified Lyme borreliosis as one of the diseases with highest priority. In 2014, when the present study was designed, there were also plans to make neuroborreliosis notifiable within the European Union.

We compared possibilities of surveillance of neuroborreliosis in Sweden through two different sources: the hospital discharge register and reporting from the clinical microbiology laboratories.

We examined the validity of ICD-10 codes in the hospital discharge register by extracting personal identification numbers for all cases of neuroborreliosis, defined by a positive cerebrospinal fluid–serum anti-Borrelia antibody index, who were diagnosed at the largest clinical microbiology laboratory in Sweden during 2014. We conducted a retrospective observational study with a questionnaire sent to all clinical microbiology laboratories in Sweden requesting information on yearly number of cases, age group and sex for the period 2010 to 2014.

Among 150 neuroborreliosis cases, 67 (45%) had received the ICD-10 code A69.2 (Lyme borreliosis) in combination with G01.9 (meningitis in bacterial diseases classified elsewhere), the combination that the Swedish National Board of Health and Welfare recommends for neuroborreliosis. All 22 clinical laboratories replied to our questionnaire. Based on laboratory reporting, the annual incidence of neuroborreliosis in Sweden was 6.3 cases per 100,000 in 2014.

The hospital discharge register was unsuitable for surveillance of neuroborreliosis, whereas laboratory-based reporting was a feasible alternative. In 2018, the European Commission included Lyme neuroborreliosis on the list of diseases under epidemiological surveillance.

The detection of a cluster of travel-associated Legionnaires’ disease (TALD) cases in any European Union/European Economic Area (EU/EEA) country prompts action at the accommodation, follow-up by health authorities and reporting of measures taken. Some accommodations incur further cases despite presumed implementation of adequate control measures.

To identify factors associated with the occurrence of a further TALD case after the implementation of control measures.

We conducted a retrospective cohort study of hotel and holiday rental accommodations in the EU/EEA associated with two or more TALD cases with onset dates less than 2 years apart (a ‘cluster’) and notification between 1 June 2011−31 December 2016. We fitted Cox regression models to estimate the association between accommodation characteristics and the occurrence of a further case, defined as any case with onset date after the report on measures taken.

Of the 357 accommodations in the analysis, 90 (25%) were associated with at least one further case after the report on measures taken (12.4/100 accommodation-years). Accommodations associated with two or more cases before the cluster notification were more likely to be associated with a further case, compared with those not previously associated with any case (adjusted hazard ratio 1.85; 95% confidence interval: 1.14–3.02). Neither the detection of Legionella in the water system nor the type of disinfection were found to be associated with the risk of a further case.

Accommodation size and previous TALD cases were predictive of further Legionnaires’ disease cases after implementation of control measures.

Antimicrobial resistance is widely considered an urgent global health issue due to associated mortality and disability, societal and healthcare costs.

To estimate the past, current and projected future proportion of infections resistant to treatment for eight priority antibiotic-bacterium combinations from 2000 to 2030 for 52 countries.

We collated data from a variety of sources including ResistanceMap and World Bank. Feature selection algorithms and multiple imputation were used to produce a complete historical dataset. Forecasts were derived from an ensemble of three models: exponential smoothing, linear regression and random forest. The latter two were informed by projections of antibiotic consumption, out-of-pocket medical spending, populations aged 64 years and older and under 15 years and real gross domestic product. We incorporated three types of uncertainty, producing 150 estimates for each country-antibiotic-bacterium-year.

Average resistance proportions across antibiotic-bacterium combinations could grow moderately from 17% to 18% within the Organisation for Economic Co-operation and Development (OECD; growth in 64% of uncertainty sets), from 18% to 19% in the European Union/European Economic Area (EU/EEA; growth in 87% of uncertainty sets) and from 29% to 31% in Group of Twenty (G20) countries (growth in 62% of uncertainty sets) between 2015 and 2030. There is broad heterogeneity in levels and rates of change across countries and antibiotic-bacterium combinations from 2000 to 2030.

If current trends continue, resistance proportions are projected to marginally increase in the coming years. The estimates indicate there is significant heterogeneity in resistance proportions across countries and antibiotic-bacterium combinations.

To control respiratory syncytial virus (RSV), which causes acute respiratory infections, data and methods to assess its epidemiology are important.

We sought to describe RSV seasonality, affected age groups and RSV-type distribution over 12 consecutive seasons in the Netherlands, as well as to validate the moving epidemic method (MEM) for monitoring RSV epidemics.

We used 2005−17 laboratory surveillance data and sentinel data. For RSV seasonality evaluation, epidemic thresholds (i) at 1.2% of the cumulative number of RSV-positive patients per season and (ii) at 20 detections per week (for laboratory data) were employed. We also assessed MEM thresholds.

In laboratory data RSV was reported 25,491 times (no denominator). In sentinel data 5.6% (767/13,577) of specimens tested RSV positive. Over 12 seasons, sentinel data showed percentage increases of RSV positive samples. The average epidemic length was 18.0 weeks (95% confidence intervals (CI):  16.3–19.7) and 16.5 weeks (95% CI: 14.0–18.0) for laboratory and sentinel data, respectively. Epidemics started on average in week 46 (95% CI: 45–48) and 47 (95% CI:  46–49), respectively. The peak was on average in the first week of January in both datasets. MEM showed similar results to the other methods. RSV incidence was highest in youngest (0–1 and >1–2 years) and oldest (>65–75 and > 75 years) age groups, with age distribution remaining stable over time. RSV-type dominance alternated every one or two seasons.

Our findings provide baseline information for immunisation advisory groups. The possibility of employing MEM to monitor RSV epidemics allows prospective, nearly real-time use of surveillance data.