Establishing an ICD-10 code based SARI-surveillance in Germany - description of the system and first results from five recent influenza seasons

Implementation and evaluation of a SARI surveillance system in a tertiary hospital in Scotland in 2021/2022

OBJECTIVE

To set up and evaluate a new surveillance system for severe acute respiratory infection (SARI) in Scotland.

STUDY DESIGN

Cross-sectional study and evaluation of surveillance system.

METHODS

The SARI case definition comprised patients aged 16 years or over with an acute respiratory illness presentation requiring testing for influenza and SARS-CoV-2 and hospital admission. Data were collected from SARI cases by research nurses in one tertiary teaching hospital using a bespoke data collection tool from November 2021 to May 2022. Descriptive analyses of SARI cases were carried out. The following attributes of the surveillance system were evaluated according to Centers for Disease Control and Prevention (CDC) guidelines: stability, data quality, timeliness, positive predictive value, representativeness, simplicity, acceptability and flexibility.

RESULTS

The final surveillance dataset comprised 1163 records, with cases peaking in ISO week 50 (week ending 19/12/2021). The system produced a stable stream of surveillance data, with the proportion of SARI records with sufficient information for effective surveillance increasing from 65.4% during the first month to 87.0% over time. Similarly, the proportion where data collection was completed promptly was low initially, but increased to 50%-65% during later periods.

CONCLUSION

SARI surveillance was successfully established in one hospital, but for a national system, additional sentinel hospital sites across Scotland, with flexibility to ensure consistently high data completeness and timeliness are needed. Data collection should be automated where possible, and demands on clinicians minimised. SARI surveillance should be embedded and resourced as part of a national respiratory surveillance strategy.

Atypical age distribution and high disease severity in children with RSV infections during two irregular epidemic seasons throughout the COVID-19 pandemic, Germany, 2021 to 2023

BackgroundNon-pharmaceutical interventions (NPIs) during the COVID-19 pandemic affected respiratory syncytial virus (RSV) circulation worldwide.AimTo describe, for children aged < 5 years, the 2021 and 2022/23 RSV seasons in Germany.MethodsThrough data and 16,754 specimens from outpatient sentinel surveillance, we investigated RSV seasonality, circulating lineages, and affected children's age distributions in 2021 and 2022/23. Available information about disease severity from hospital surveillance was analysed for patients with RSV-specific diagnosis codes (n = 13,104). Differences between RSV seasons were assessed by chi-squared test and age distributions trends by Mann-Kendall test.ResultsRSV seasonality was irregular in 2021 (weeks 35-50) and 2022/23 (weeks 41-3) compared to pre-COVID-19 2011/12-2019/20 seasons (median weeks 51-12). RSV positivity rates (RSV-PR) were higher in 2021 (40% (522/1,291); p < 0.001) and 2022/23 (30% (299/990); p = 0.005) than in prior seasons (26% (1,430/5,511)). Known globally circulating RSV-A (lineages GA2.3.5 and GA2.3.6b) and RSV-B (lineage GB5.0.5a) strains, respectively, dominated in 2021 and 2022/23. In 2021, RSV-PRs were similar in 1 - < 2, 2 - < 3, 3 - < 4, and 4 - < 5-year-olds. RSV hospitalisation incidence in 2021 (1,114/100,000, p < 0.001) and in 2022/23 (1,034/100,000, p < 0.001) was approximately double that of previous seasons' average (2014/15-2019/20: 584/100,000). In 2022/23, proportions of RSV patients admitted to intensive care units rose (8.5% (206/2,413)) relative to pre-COVID-19 seasons (6.8% (551/8,114); p = 0.004), as did those needing ventilator support (6.1% (146/2,413) vs 3.8% (310/8,114); p < 0.001).ConclusionsHigh RSV-infection risk in 2-4-year-olds in 2021 and increased disease severity in 2022/23 possibly result from lower baseline population immunity, after NPIs diminished exposure to RSV.

Evaluating the coding accuracy of type 2 diabetes mellitus among patients with non-alcoholic fatty liver disease

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) describes a spectrum of chronic fattening of liver that can lead to fibrosis and cirrhosis. Diabetes has been identified as a major comorbidity that contributes to NAFLD progression. Health systems around the world make use of administrative data to conduct population-based prevalence studies. To that end, we sought to assess the accuracy of diabetes International Classification of Diseases (ICD) coding in administrative databases among a cohort of confirmed NAFLD patients in Calgary, Alberta, Canada.

METHODS

The Calgary NAFLD Pathway Database was linked to the following databases: Physician Claims, Discharge Abstract Database, National Ambulatory Care Reporting System, Pharmaceutical Information Network database, Laboratory, and Electronic Medical Records. Hemoglobin A1c and diabetes medication details were used to classify diabetes groups into absent, prediabetes, meeting glycemic targets, and not meeting glycemic targets. The performance of ICD codes among these groups was compared to this standard. Within each group, the total numbers of true positives, false positives, false negatives, and true negatives were calculated. Descriptive statistics and bivariate analysis were conducted on identified covariates, including demographics and types of interacted physicians.

RESULTS

A total of 12,012 NAFLD patients were registered through the Calgary NAFLD Pathway Database and 100% were successfully linked to the administrative databases. Overall, diabetes coding showed a sensitivity of 0.81 and a positive predictive value of 0.87. False negative rates in the absent and not meeting glycemic control groups were 4.5% and 6.4%, respectively, whereas the meeting glycemic control group had a 42.2% coding error. Visits to primary and outpatient services were associated with most encounters.

CONCLUSION

Diabetes ICD coding in administrative databases can accurately detect true diabetic cases. However, patients with diabetes who meets glycemic control targets are less likely to be coded in administrative databases. A detailed understanding of the clinical context will require additional data linkage from primary care settings.

Trends in Outpatient Influenza Antiviral Use Among Children and Adolescents in the United States

BACKGROUND

Influenza antivirals improve outcomes in children with duration of symptoms <2 days and those at high risk for influenza complications. Real-world prescribing of influenza antivirals in the pediatric population is unknown.

METHODS

We performed a cross-sectional study of outpatient and emergency department prescription claims in individuals <18 years of age included in the IBM Marketscan Commercial Claims and Encounters Database between July 1, 2010 and June 30, 2019. Influenza antiviral use was defined as any dispensing of oseltamivir, baloxavir, or zanamivir. The primary outcome was the rate of antiviral dispensing per 1000 enrolled children. Secondary outcomes included antiviral dispensing per 1000 influenza diagnoses and inflation-adjusted costs of antiviral agents. Outcomes were calculated and stratified by age, acute versus prophylactic treatment, influenza season, and geographic region.

RESULTS

The analysis included 1 416 764 unique antiviral dispensings between 2010 and 2019. Oseltamivir was the most frequently prescribed antiviral (99.8%). Dispensing rates ranged from 4.4 to 48.6 per 1000 enrolled children. Treatment rates were highest among older children (12-17 years of age), during the 2017 to 2018 influenza season, and in the East South Central region. Guideline-concordant antiviral use among young children (<2 years of age) at a high risk of influenza complications was low (<40%). The inflation-adjusted cost for prescriptions was $208 458 979, and the median cost ranged from $111 to $151.

CONCLUSIONS

There is wide variability and underuse associated with influenza antiviral use in children. These findings reveal opportunities for improvement in the prevention and treatment of influenza in children.

Establishing severe acute respiratory infection (SARI) surveillance in a sentinel hospital, Ireland, 2021 to 2022

BackgroundIn 2020, due to the COVID-19 pandemic, the European Centre for Disease Prevention and Control (ECDC) accelerated development of European-level severe acute respiratory infection (SARI) surveillance.AimWe aimed to establish SARI surveillance in one Irish hospital as part of a European network E-SARI-NET.MethodsWe used routine emergency department records to identify cases in one adult acute hospital. The SARI case definition was adapted from the ECDC clinical criteria for a possible COVID-19 case. Clinical data were collected using an online questionnaire. Cases were tested for SARS-CoV-2, influenza and respiratory syncytial virus (RSV), including whole genome sequencing (WGS) on SARS-CoV-2 RNA-positive samples and viral characterisation/sequencing on influenza RNA-positive samples. Descriptive analysis was conducted for SARI cases hospitalised between July 2021 and April 2022.ResultsOverall, we identified 437 SARI cases, the incidence ranged from two to 28 cases per week (0.7-9.2/100,000 hospital catchment population). Of 431 cases tested for SARS-CoV-2 RNA, 226 (52%) were positive. Of 349 (80%) cases tested for influenza and RSV RNA, 15 (4.3%) were positive for influenza and eight (2.3%) for RSV. Using WGS, we identified Delta- and Omicron-dominant periods. The resource-intensive nature of manual clinical data collection, specimen management and laboratory supply shortages for influenza and RSV testing were challenging.ConclusionWe successfully established SARI surveillance as part of E-SARI-NET. Expansion to additional sentinel sites is planned following formal evaluation of the existing system. SARI surveillance requires multidisciplinary collaboration, automated data collection where possible, and dedicated personnel resources, including for specimen management.

Comprehensive surveillance of acute respiratory infections during the COVID-19 pandemic: a methodological approach using sentinel networks, Castilla y León, Spain, January 2020 to May 2022

BackgroundSince 1996, epidemiological surveillance of acute respiratory infections (ARI) in Spain has been limited to seasonal influenza, respiratory syncytial virus (RSV) and potential pandemic viruses. The COVID-19 pandemic provides opportunities to adapt existing systems for extended surveillance to capture a broader range of ARI.AimTo describe how the Influenza Sentinel Surveillance System of Castilla y León, Spain was rapidly adapted in 2020 to comprehensive sentinel surveillance for ARI, including influenza and COVID-19.MethodsUsing principles and methods of the health sentinel network, we integrated electronic medical record data from 68 basic surveillance units, covering 2.6% of the regional population between January 2020 to May 2022. We tested sentinel and non-sentinel samples sent weekly to the laboratory network for SARS-CoV-2, influenza viruses and other respiratory pathogens. The moving epidemic method (MEM) was used to calculate epidemic thresholds.ResultsARI incidence was estimated at 18,942 cases per 100,000 in 2020/21 and 45,223 in 2021/22, with similar seasonal fold increases by type of respiratory disease. Incidence of influenza-like illness was negligible in 2020/21 but a 5-week epidemic was detected by MEM in 2021/22. Epidemic thresholds for ARI and COVID-19 were estimated at 459.4 and 191.3 cases per 100,000 population, respectively. More than 5,000 samples were tested against a panel of respiratory viruses in 2021/22.ConclusionExtracting data from electronic medical records reported by trained professionals, combined with a standardised microbiological information system, is a feasible and useful method to adapt influenza sentinel reports to comprehensive ARI surveillance in the post-COVID-19 era.

Monitoring COVID-19 and Influenza: The Added Value of a Severe Acute Respiratory Infection Surveillance System in Portugal

Background

Severe acute respiratory infections (SARI) surveillance is recommended to assess the severity of respiratory infections disease. In 2021, the National Institute of Health Doutor Ricardo Jorge, in collaboration with two general hospitals, implemented a SARI sentinel surveillance system based on electronic health registries. We describe its application in the 2021/2022 season and compare the evolution of SARI cases with the COVID-19 and influenza activity in two regions of Portugal.

Methods

The main outcome of interest was the weekly incidence of patients hospitalized due to SARI, reported within the surveillance system. SARI cases were defined as patients containing ICD-10 codes for influenza-like illness, cardiovascular diagnosis, respiratory diagnosis, and respiratory infection in their primary admission diagnosis. Independent variables included weekly COVID-19 and influenza incidence in the North and Lisbon and Tagus Valley regions. Pearson and cross-correlations between SARI cases, COVID-19 incidence and influenza incidence were estimated.

Results

A high correlation between SARI cases or hospitalizations due to respiratory infection and COVID-19 incidence was obtained (ρ = 0.78 and ρ = 0.82, respectively). SARI cases detected the COVID-19 epidemic peak a week earlier. A weak correlation was observed between SARI and influenza cases (ρ = -0.20). However, if restricted to hospitalizations due to cardiovascular diagnosis, a moderate correlation was observed (ρ = 0.37). Moreover, hospitalizations due to cardiovascular diagnosis detected the increase of influenza epidemic activity a week earlier.

Conclusion

In the 2021/2022 season, the Portuguese SARI sentinel surveillance system pilot was able to early detect the COVID-19 epidemic peak and the increase of influenza activity. Although cardiovascular manifestations associated with influenza infection are known, more seasons of surveillance are needed, to confirm the potential use of cardiovascular hospitalizations as an indicator of influenza activity.

Guideline-Concordant Antiviral Treatment in Children at High Risk for Influenza Complications

BACKGROUND

National guidelines recommend antiviral treatment for children with influenza at high risk for complications regardless of symptom duration. Little is known about concordance of clinical practice with this recommendation.

METHODS

We performed a cross-sectional study of outpatient children (aged 1-18 years) at high risk for complications who were diagnosed with influenza during the 2016-2019 influenza seasons. High-risk status was determined using an existing definition that includes age, comorbidities, and residence in a long-term care facility. The primary outcome was influenza antiviral dispensing within 2 days of influenza diagnosis. We determined patient- and provider-level factors associated with guideline-concordant treatment using multivariable logistic regression.

RESULTS

Of the 274 213 children with influenza at high risk for influenza complications, 159 350 (58.1%) received antiviral treatment. Antiviral treatment was associated with the presence of asthma (aOR, 1.13; 95% confidence interval [CI], 1.11-1.16), immunosuppression (aOR, 1.10; 95% CI, 1.05-1.16), complex chronic conditions (aOR, 1.04; 95% CI, 1.01-1.07), and index encounter in the urgent care setting (aOR, 1.3; 95% CI, 1.26-1.34). Factors associated with decreased odds of antiviral treatment include age 2-5 years compared with 6-17 years (aOR, 0.95; 95% CI, .93-.97), residing in a chronic care facility (aOR, .61; 95% CI, .46-.81), and index encounter in an emergency department (aOR, 0.66; 95% CI, .63-.71).

CONCLUSIONS

Among children with influenza at high risk for complications, 42% did not receive guideline-concordant antiviral treatment. Further study is needed to elucidate barriers to appropriate use of antivirals in this vulnerable population.

COVID-19 and Severe Acute Respiratory Infections: Monitoring Trends in 421 German Hospitals During the First Four Pandemic Waves

Introduction

Reliable surveillance systems to monitor trends of COVID-19 case numbers and the associated healthcare burden play a central role in efficient pandemic management. In Germany, the federal government agency Robert-Koch-Institute uses an ICD-code-based inpatient surveillance system, ICOSARI, to assess temporal trends of severe acute respiratory infection (SARI) and COVID-19 hospitalization numbers. In a similar approach, we present a large-scale analysis covering four pandemic waves derived from the Initiative of Quality Medicine (IQM), a German-wide network of acute care hospitals.

Methods

Routine data from 421 hospitals for the years 2019-2021 with a "pre-pandemic" period (01-01-2019 to 03-03-2020) and a "pandemic" period (04-03-2020 to 31-12-2021) was analysed. SARI cases were defined by ICD-codes J09-J22 and COVID-19 by ICD-codes U07.1 and U07.2. The following outcomes were analysed: intensive care treatment, mechanical ventilation, in-hospital mortality.

Results

Over 1.1 million cases of SARI and COVID-19 were identified. Patients with COVID-19 and additional codes for SARI were at higher risk for adverse outcomes when compared to non-COVID SARI and COVID-19 without any coding for SARI. During the pandemic period, non-COVID SARI cases were associated with 28%, 23% and 27% higher odds for intensive care treatment, mechanical ventilation and in-hospital mortality, respectively, compared to pre-pandemic SARI.

Conclusion

The nationwide IQM network could serve as an excellent data source to enhance COVID-19 and SARI surveillance in view of the ongoing pandemic. Future developments of COVID-19/SARI case numbers and associated outcomes should be closely monitored to identify specific trends, especially considering novel virus variants.

Digitalizing and Upgrading Severe Acute Respiratory Infections Surveillance in Malta: System Development

BACKGROUND

In late 2020, the European Centre for Disease Prevention and Control and Epiconcept started implementing a surveillance system for severe acute respiratory infections (SARI) across Europe.

OBJECTIVE

We sought to describe the process of digitizing and upgrading SARI surveillance in Malta, an island country with a centralized health system, during the COVID-19 pandemic from February to November 2021. We described the characteristics of people included in the surveillance system and compared different SARI case definitions, including their advantages and disadvantages. This study also discusses the process, output, and future for SARI and other public health surveillance opportunities.

METHODS

Malta has one main public hospital where, on admission, patient data are entered into electronic records as free text. Symptoms and comorbidities are manually extracted from these records, whereas other data are collected from registers. Collected data are formatted to produce weekly and monthly reports to inform public health actions. From October 2020 to February 2021, we established an analogue incidence-based system for SARI surveillance. From February 2021 onward, we mapped key stakeholders and digitized most surveillance processes.

RESULTS

By November 30, 2021, 903 SARI cases were reported, with 380 (42.1%) positive for SARS-CoV-2. Of all SARI hospitalizations, 69 (7.6%) were admitted to the intensive care unit, 769 (85.2%) were discharged, 27 (3%) are still being treated, and 107 (11.8%) died. Among the 107 patients who died, 96 (89.7%) had more than one underlying condition, the most common of which were hypertension (n=57, 53.3%) and chronic heart disease (n=49, 45.8%).

CONCLUSIONS

The implementation of enhanced SARI surveillance in Malta was completed by the end of May 2021, allowing the monitoring of SARI incidence and patient characteristics. A future shift to register-based surveillance should improve SARI detection through automated processes.

Real-time surveillance of severe acute respiratory infections in Scottish hospitals: an electronic register-based approach, 2017-2022

OBJECTIVES

The COVID-19 pandemic highlighted the importance of routine syndromic surveillance of respiratory infections, specifically new cases of severe acute respiratory infection (SARI). This surveillance often relies on questionnaires carried out by research nurses or transcriptions of doctor's notes, but existing, routinely collected electronic healthcare data sets are increasingly being used for such surveillance. We investigated how patient diagnosis codes, recorded within such data sets, could be used to capture SARI trends in Scotland.

STUDY DESIGN

We conducted a retrospective observational study using electronic healthcare data sets between 2017 and 2022.

METHODS

Sensitive, specific and timely case definition (CDs) based on patient diagnosis codes contained within national registers in Scotland were proposed to identify SARI cases. Representativeness and sensitivity analyses were performed to assess how well SARI cases captured by each definition matched trends in historic influenza and SARS-CoV-2 data.

RESULTS

All CDs accurately captured the peaks seen in laboratory-confirmed positive influenza and SARS-CoV-2 data, although the completeness of patient diagnosis records was discovered to vary widely. The timely CD provided the earliest detection of changes in SARI activity, whilst the sensitive CD provided insight into the burden and severity of SARI infections.

CONCLUSIONS

A universal SARI surveillance system has been developed and demonstrated to accurately capture seasonal SARI trends. It can be used as an indicator of emerging secondary care burden of emerging SARI outbreaks. The system further strengthens Scotland's existing strategies for respiratory surveillance, and the methods described here can be applied within any country with suitable electronic patient records.

Effectiveness of early warning systems in the detection of infectious diseases outbreaks: a systematic review

Background

Global pandemics have occurred with increasing frequency over the past decade reflecting the sub-optimum operationalization of surveillance systems handling human health data. Despite the wide array of current surveillance methods, their effectiveness varies with multiple factors. Here, we perform a systematic review of the effectiveness of alternative infectious diseases Early Warning Systems (EWSs) with a focus on the surveillance data collection methods, and taking into consideration feasibility in different settings.

Methods

We searched PubMed and Scopus databases on 21 October 2022. Articles were included if they covered the implementation of an early warning system and evaluated infectious diseases outbreaks that had potential to become pandemics. Of 1669 studies screened, 68 were included in the final sample. We performed quality assessment using an adapted CASP Checklist.

Results

Of the 68 articles included, 42 articles found EWSs successfully functioned independently as surveillance systems for pandemic-wide infectious diseases outbreaks, and 16 studies reported EWSs to have contributing surveillance features through complementary roles. Chief complaints from emergency departments’ data is an effective EWS but it requires standardized formats across hospitals. Centralized Public Health records-based EWSs facilitate information sharing; however, they rely on clinicians’ reporting of cases. Facilitated reporting by remote health settings and rapid alarm transmission are key advantages of Web-based EWSs. Pharmaceutical sales and laboratory results did not prove solo effectiveness. The EWS design combining surveillance data from both health records and staff was very successful. Also, daily surveillance data notification was the most successful and accepted enhancement strategy especially during mass gathering events. Eventually, in Low Middle Income Countries, working to improve and enhance existing systems was more critical than implementing new Syndromic Surveillance approaches.

Conclusions

Our study was able to evaluate the effectiveness of Early Warning Systems in different contexts and resource settings based on the EWSs’ method of data collection. There is consistent evidence that EWSs compiling pre-diagnosis data are more proactive to detect outbreaks. However, the fact that Syndromic Surveillance Systems (SSS) are more proactive than diagnostic disease surveillance should not be taken as an effective clue for outbreaks detection.

Characteristics and outcomes of COVID-19 patients during B.1.1.529 (Omicron) dominance compared to B.1.617.2 (Delta) in 89 German hospitals

Background

The SARS-CoV-2 variant B.1.1.529 (Omicron) was first described in November 2021 and became the dominant variant worldwide. Existing data suggests a reduced disease severity with Omicron infections in comparison to B.1.617.2 (Delta). Differences in characteristics and in-hospital outcomes of COVID-19 patients in Germany during the Omicron period compared to Delta are not thoroughly studied. ICD-10-code-based severe acute respiratory infections (SARI) surveillance represents an integral part of infectious disease control in Germany.

Methods

Administrative data from 89 German Helios hospitals was retrospectively analysed. Laboratory-confirmed SARS-CoV-2 infections were identified by ICD-10-code U07.1 and SARI cases by ICD-10-codes J09-J22. COVID-19 cases were stratified by concomitant SARI. A nine-week observational period between December 6, 2021 and February 6, 2022 was defined and divided into three phases with respect to the dominating virus variant (Delta, Delta to Omicron transition, Omicron). Regression analyses adjusted for age, gender and Elixhauser comorbidities were applied to assess in-hospital patient outcomes.

Results

A total cohort of 4,494 inpatients was analysed. Patients in the Omicron dominance period were younger (mean age 47.8 vs. 61.6; p < 0.01), more likely to be female (54.7% vs. 47.5%; p < 0.01) and characterized by a lower comorbidity burden (mean Elixhauser comorbidity index 5.4 vs. 8.2; p < 0.01). Comparing Delta and Omicron periods, patients were at significantly lower risk for intensive care treatment (adjusted odds ratio 0.72 [0.57–0.91]; p = 0.005), mechanical ventilation (adjusted odds ratio 0.42 [0.31–0.57]; p < 0.001), and in-hospital mortality (adjusted odds ratio 0.42 [0.32–0.56]; p < 0.001). This also applied mostly to the separate COVID-SARI group. During the Delta to Omicron transition, case numbers of COVID-19 without SARI exceeded COVID-SARI for the first time in the pandemic’s course.

Conclusion

Patient characteristics and outcomes differ during the Omicron dominance period as compared to Delta suggesting a reduced disease severity with Omicron infections. SARI surveillance might play a crucial role in assessing disease severity of future SARS-CoV-2 variants.

Supplementary information

The online version contains supplementary material available at 10.1186/s12879-022-07781-w.

Machine learning-derived prediction of in-hospital mortality in patients with severe acute respiratory infection: analysis of claims data from the German-wide Helios hospital network

BACKGROUND

Severe acute respiratory infections (SARI) are the most common infectious causes of death. Previous work regarding mortality prediction models for SARI using machine learning (ML) algorithms that can be useful for both individual risk stratification and quality of care assessment is scarce. We aimed to develop reliable models for mortality prediction in SARI patients utilizing ML algorithms and compare its performances with a classic regression analysis approach.

METHODS

Administrative data (dataset randomly split 75%/25% for model training/testing) from years 2016-2019 of 86 German Helios hospitals was retrospectively analyzed. Inpatient SARI cases were defined by ICD-codes J09-J22. Three ML algorithms were evaluated and its performance compared to generalized linear models (GLM) by computing receiver operating characteristic area under the curve (AUC) and area under the precision-recall curve (AUPRC).

RESULTS

The dataset contained 241,988 inpatient SARI cases (75 years or older: 49%; male 56.2%). In-hospital mortality was 11.6%. AUC and AUPRC in the testing dataset were 0.83 and 0.372 for GLM, 0.831 and 0.384 for random forest (RF), 0.834 and 0.382 for single layer neural network (NNET) and 0.834 and 0.389 for extreme gradient boosting (XGBoost). Statistical comparison of ROC AUCs revealed a better performance of NNET and XGBoost as compared to GLM.

CONCLUSION

ML algorithms for predicting in-hospital mortality were trained and tested on a large real-world administrative dataset of SARI patients and showed good discriminatory performances. Broad application of our models in clinical routine practice can contribute to patients' risk assessment and quality management.

Burden of Hospital-acquired SARS-CoV-2 Infections in Germany

Background

Avoiding in-hospital transmissions has been crucial in the COVID-19 pandemic. Little is known on the extent to which hospital-acquired SARS-CoV-2 variants have caused infections in Germany.

Aim

To analyse the occurrence and the outcomes of HAI with regard to different SARS-CoV-2 variants.

Methods

Patients with SARS-CoV-2 infections hospitalized between March 1^st, 2020 and May 17^th, 2022 in 79 hospitals of the Helios Group were included. Information on patients' characteristics and outcomes were retrieved from claims data. In accordance with the Robert Koch Institute, infections were classified as hospital-acquired when tested positive >6 days after admission and if no information hinted at a different source.

Findings

In all, 62,875 SARS-CoV-2 patients were analysed, of whom 10.6% had HAI. HAIs represented 14.7% of SARS-CoV-2 inpatients during the Wildtype period, 3.5% during Alpha (odds ratio: 0.21; 95% confidence interval: 0.19–0.24), 8.8% during Delta (2.70; 2.35–3.09) and 10.1% during Omicron (1.10; 1.03–1.19). When age and comorbidities were accounted for, HAI had lower odds for death than community-acquired infections (0.802; 0.740–0.866). Compared to the Wildtype period, HAIs during Omicron were associated with lower odds for ICU (0.78; 0.69–0.88), ventilation (0.47; 0.39–0.56), and death (0.33; 0.28–0.40).

Conclusion

Hospital-acquired SARS-CoV-2 infections occurred throughout the pandemic, affecting highly vulnerable patients. Although transmissibility increased with newer variants, the proportion of HAIs decreased, indicating improved infection prevention and/or the effect of immunization. Furthermore, the Omicron period was associated with improved outcomes. However, the burden of hospital-acquired SARS-CoV-2 infections remains high.

Using routine emergency department data for syndromic surveillance of acute respiratory illness, Germany, week 10 2017 until week 10 2021

Background

The COVID-19 pandemic expanded the need for timely information on acute respiratory illness at population level.

Aim

We explored the potential of routine emergency department data for syndromic surveillance of acute respiratory illness in Germany.

Methods

We used routine attendance data from emergency departments, which continuously transferred data between week 10 2017 and 10 2021, with ICD-10 codes available for > 75% of attendances. Case definitions for acute respiratory infection (ARI), severe acute respiratory infection (SARI), influenza-like illness (ILI), respiratory syncytial virus infection (RSV) and COVID-19 were based on a combination of ICD-10 codes, and/or chief complaints, sometimes combined with information on hospitalisation and age.

Results

We included 1,372,958 attendances from eight emergency departments. The number of attendances dropped in March 2020 during the first COVID-19 pandemic wave, increased during summer, and declined again during the resurge of COVID-19 cases in autumn and winter of 2020/21. A pattern of seasonality of respiratory infections could be observed. By using different case definitions (i.e. for ARI, SARI, ILI, RSV) both the annual influenza seasons in the years 2017–2020 and the dynamics of the COVID-19 pandemic in 2020/21 were apparent. The absence of the 2020/21 influenza season was visible, parallel to the resurge of COVID-19 cases. SARI among ARI cases peaked in April–May 2020 (17%) and November 2020–January 2021 (14%).

Conclusion

Syndromic surveillance using routine emergency department data can potentially be used to monitor the trends, timing, duration, magnitude and severity of illness caused by respiratory viruses, including both influenza viruses and SARS-CoV-2.

Antibiotic use for empirical therapy in the critical care units in primary and secondary hospitals in Vietnam: a multicenter cross-sectional study

Background

The high rate of infections among patients admitted to critical care units (CCUs) is associated with high rate of antibiotic consumption, especially broad-spectrum antibiotics. This study is to describe the antibiotics use in CCUs in primary and secondary hospitals in Vietnam, a setting with high burden of antibiotic resistance.

Methods

This was a 7-day observational study in 51 CCUs in hospitals from 5 provinces in Vietnam from March to July 2019. Patients aged ≥ 18 years admitted to the participating CCUs was enrolled consecutively. We collected data on patient's demographics, initial diagnosis and antibiotic therapy within the first 24 hours. Antibiotic therapy was classified by the Anatomical Therapeutic Chemical (ATC) Index and the 2019 WHO Access, Watch, Reserve (AWaRe) groups.

Findings

Out of 1747 enrolled patients, empirical antibiotic treatments were initiated in 1112 (63.6%) patients. The most frequently prescribed antibiotics were cefotaxime (22.3%), levofloxacin (19%) and ceftazidime (10.8%). Antibiotics were given in 31.5% of patients without diagnosis of infection. Watch and/or Reserve group antibiotic were given in 87.3% of patients and associated with patient's age (aOR 1.01 per 1-year increment, 95%CI 1.00-1.02) and the presence of SIRS on admission (aOR 2.1, 95%CI 1.38-3.2).

Interpretation

We observed a high frequency use and a substantial variation in patterns of empirical antibiotic use in the CCUs in Vietnam. It highlights the importance of continuous monitoring antibiotic consumption in CCUs.

[The different periods of COVID-19 in Germany: a descriptive analysis from January 2020 to February 2021]

The first case of coronavirus SARS-CoV‑2 infection in Germany was diagnosed on 27 January 2020. To describe the pandemic course in 2020, we regarded four epidemiologically different periods and used data on COVID-19 cases from the mandatory reporting system as well as hospitalized COVID-19 cases with severe acute respiratory infection from the syndromic hospital surveillance.Period 0 covers weeks 5 to 9 of 2020, where mainly sporadic cases of younger age were observed and few regional outbreaks emerged. In total, 167 cases with mostly mild outcomes were reported. Subsequently, the first COVID-19 wave occurred in period 1 (weeks 10 to 20 of 2020) with a total of 175,013 cases throughout Germany. Increasingly, outbreaks in hospitals and nursing homes were registered. Moreover, elderly cases and severe outcomes were observed more frequently. Period 2 (weeks 21 to 39 of 2020) was an interim period with more mild cases, where many cases were younger and often travel-associated. Additionally, larger trans-regional outbreaks in business settings were reported. Among the 111,790 cases, severe outcomes were less frequent than in period 1. In period 3 (week 40 of 2020 to week 8 of 2021), the second COVID-19 wave started and peaked at the end of 2020. With 2,158,013 reported cases and considerably more severe outcomes in all age groups, the second wave was substantially stronger than the first wave.Irrespective of the different periods, more elderly persons and more men were affected by severe outcomes.

Development and assessment of a hospital admissions-based syndromic surveillance system for COVID-19 in Ontario, Canada: ACES Pandemic Tracker

BACKGROUND

The COVID-19 pandemic has continued to pose a major global public health risk. The importance of public health surveillance systems to monitor the spread and impact of COVID-19 has been well demonstrated. The purpose of this study was to describe the development and effectiveness of a real-time public health syndromic surveillance system (ACES Pandemic Tracker) as an early warning system and to provide situational awareness in response to the COVID-19 pandemic in Ontario, Canada.

METHODS

We used hospital admissions data from the Acute Care Enhanced Surveillance (ACES) system to collect data on pre-defined groupings of symptoms (syndromes of interest; SOI) that may be related to COVID-19 from 131 hospitals across Ontario. To evaluate which SOI for suspected COVID-19 admissions were best correlated with laboratory confirmed admissions, laboratory confirmed COVID-19 hospital admissions data were collected from the Ontario Ministry of Health. Correlations and time-series lag analysis between suspected and confirmed COVID-19 hospital admissions were calculated. Data used for analyses covered the period between March 1, 2020 and September 21, 2020.

RESULTS

Between March 1, 2020 and September 21, 2020, ACES Pandemic Tracker identified 22,075 suspected COVID-19 hospital admissions (150 per 100,000 population) in Ontario. After correlation analysis, we found laboratory-confirmed hospital admissions for COVID-19 were strongly and significantly correlated with suspected COVID-19 hospital admissions when SOI were included (Spearman's rho = 0.617) and suspected COVID-19 admissions when SOI were excluded (Spearman's rho = 0.867). Weak to moderate significant correlations were found among individual SOI. Laboratory confirmed COVID-19 hospital admissions lagged in reporting by 3 days compared with suspected COVID-19 admissions when SOI were excluded.

CONCLUSIONS

Our results demonstrate the utility of a hospital admissions syndromic surveillance system to monitor and identify potential surges in severe COVID-19 infection within the community in a timely manner and provide situational awareness to inform preventive and preparatory health interventions.

[Monitoring of COVID-19 by extending existing surveillance for acute respiratory infections]

Im Rahmen der nationalen Influenzapandemieplanung wurden in Deutschland neben dem Meldewesen gemäß Infektionsschutzgesetz (IfSG) weitere Überwachungssysteme etabliert. Ziel dieser Systeme sind die Beschreibung, Analyse und Bewertung der Situation bei akuten respiratorischen Erkrankungen (ARE), die Identifikation der hauptsächlich zirkulierenden Atemwegserreger und die Beschreibung des zeitlichen Verlaufs. Seit Beginn der COVID-19-Pandemie wurden die Systeme erweitert, um auch Infektionen mit SARS-CoV‑2 erfassen zu können.

In diesem Beitrag werden drei verschiedene Surveillance-Systeme für ARE vorgestellt: GrippeWeb, die Arbeitsgemeinschaft Influenza mit dem SEED^ARE-Modul (Sentinel zur elektronischen Erfassung von Diagnosecodes) und das Krankenhaus-Sentinel ICOSARI (ICD-10-code-basierte Krankenhaus-Surveillance schwerer akuter respiratorischer Infektionen). Mit diesen Systemen können ARE auf Bevölkerungsebene, im ambulanten und im stationären Bereich überwacht werden. Zusammen mit dem Monitoring der Mortalität liefern sie wichtige Hinweise zur Häufigkeit verschieden schwerer Krankheitsverläufe in der Bevölkerung. Um die Systeme für SARS-CoV‑2 zu erweitern, waren nur wenige Anpassungen notwendig.

Da die Falldefinitionen für ARE nicht geändert wurden, können in den beschriebenen Systemen historische Zeitreihen zum Vergleich herangezogen werden. Alle Systeme sind so aufgebaut, dass stabile und etablierte Bezugsgrößen für die Berechnung von wöchentlichen Anteilen und Raten zur Verfügung stehen. Dies ist eine wichtige Ergänzung zum Meldewesen gemäß IfSG, welches stark von Testkapazitäten und -strategien sowie veränderten Falldefinitionen abhängt. Die Surveillance-Systeme haben sich in der COVID-19-Pandemie auch im internationalen Vergleich als praktikabel und effizient erwiesen.

Validating International Classification of Disease 10th Revision algorithms for identifying influenza and respiratory syncytial virus hospitalizations

Objective

Routinely collected health administrative data can be used to efficiently assess disease burden in large populations, but it is important to evaluate the validity of these data. The objective of this study was to develop and validate International Classification of Disease 10^th revision (ICD -10) algorithms that identify laboratory-confirmed influenza or laboratory-confirmed respiratory syncytial virus (RSV) hospitalizations using population-based health administrative data from Ontario, Canada.

Study design and setting

Influenza and RSV laboratory data from the 2014–15, 2015–16, 2016–17 and 2017–18 respiratory virus seasons were obtained from the Ontario Laboratories Information System (OLIS) and were linked to hospital discharge abstract data to generate influenza and RSV reference cohorts. These reference cohorts were used to assess the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the ICD-10 algorithms. To minimize misclassification in future studies, we prioritized specificity and PPV in selecting top-performing algorithms.

Results

83,638 and 61,117 hospitalized patients were included in the influenza and RSV reference cohorts, respectively. The best influenza algorithm had a sensitivity of 73% (95% CI 72% to 74%), specificity of 99% (95% CI 99% to 99%), PPV of 94% (95% CI 94% to 95%), and NPV of 94% (95% CI 94% to 95%). The best RSV algorithm had a sensitivity of 69% (95% CI 68% to 70%), specificity of 99% (95% CI 99% to 99%), PPV of 91% (95% CI 90% to 91%) and NPV of 97% (95% CI 97% to 97%).

Conclusion

We identified two highly specific algorithms that best ascertain patients hospitalized with influenza or RSV. These algorithms may be applied to hospitalized patients if data on laboratory tests are not available, and will thereby improve the power of future epidemiologic studies of influenza, RSV, and potentially other severe acute respiratory infections.

Preventive effects of influenza and pneumococcal vaccination in the elderly - results from a population-based retrospective cohort study

Influenza and pneumococcal vaccinations are recommended in the elderly to reduce life-threatening complications like sepsis. Protection may be reduced with increasing age. We aimed to assess the effectiveness of both vaccines in the elderly by performing a retrospective cohort study of 138,877 individuals aged ≥60 y in Germany, who were insured in a large statutory health insurance (AOK PLUS). We used longitudinal claims data to classify individuals according to vaccination status 2008–2014, and assessed vaccine effectiveness (VE) in 2015 and 2016. Inverse probability weighting based on generalized propensity scores was used to adjust for systematic between-group differences. Influenza vaccination was associated with a reduction of hospital treatment in laboratory-confirmed influenza in 2015 (VE = 41.32 [95%CI 0.85, 65.26]), but had no significant impact on the overall influenza incidence. Complications of influenza (pneumonia and sepsis) were reduced in 2016. We found a rise in influenza-like illness and acute respiratory infections in both years and an increased 90-d mortality after hospital-treated pneumonia in vaccinees in 2015. Pneumococcal vaccination was effective in preventing hospital-treated pneumonia within the first and second year after vaccination (VE = 52.45 [13.31, 73.92] and 46.04 [5.46, 69.21], respectively), but had no impact on sepsis incidence or pneumonia mortality. Influenza and pneumococcal vaccination can prevent severe complications from influenza and hospital-treated pneumonia in the elderly, respectively. Vaccine effects differ between years and seasons and are partly difficult to interpret. Despite extensive efforts to adjust for between-group differences, residual bias cannot be ruled out, possibly explaining signals like increased ILI or pneumonia mortality.

Experience of establishing severe acute respiratory surveillance in the Netherlands: Evaluation and challenges

The 2009 influenza A (H1N1) pandemic prompted the World Health Organization (WHO) to recommend countries to establish a national severe acute respiratory infections (SARI) surveillance system for preparedness and emergency response. However, setting up or maintaining a robust SARI surveillance system has been challenging. Similar to other countries, surveillance data on hospitalisations for SARI in the Netherlands are still limited, in contrast to the robust surveillance data in primary care.

The objective of this narrative review is to provide an overview, evaluation, and challenges of already available surveillance systems or datasets in the Netherlands, which might be used for near real-time surveillance of severe respiratory infections.

Seven available surveillance systems or datasets in the Netherlands were reviewed. The evaluation criteria, including data quality, timeliness, representativeness, simplicity, flexibility, acceptability and stability were based on United States Centers for Disease Control and Prevention (CDC) and European Centre for Disease Prevention and Control (ECDC) guidelines for public health surveillance. We added sustainability as additional evaluation criterion.

The best evaluated surveillance system or dataset currently available for SARI surveillance is crude mortality monitoring, although it lacks specificity. In contrast to influenza-like illness (ILI) in primary care, there is currently no gold standard for SARI surveillance in the Netherlands.

Based on our experience with sentinel SARI surveillance, a fully or semi-automated, passive surveillance system seems most suited for a sustainable SARI surveillance system. An important future challenge remains integrating SARI surveillance into existing hospital programs in order to make surveillance data valuable for public health, as well as hospital quality of care management and individual patient care.

•

Multiple surveillance systems or datasets are available in the Netherlands with potential use for SARI surveillance.

•

There is currently no gold standard for SARI surveillance in the Netherlands.

•

A potential sustainable SARI surveillance system for the long-term is a fully or semi-automated, passive surveillance system.

•

SARI surveillance data should be valuable for both public health and individual patient care.

•

An important future challenge remains integrating SARI surveillance into existing hospital programs.

Risk factors for hospitalized respiratory syncytial virus disease and its severe outcomes

Introduction

Respiratory syncytial virus (RSV) is a major cause of hospital admission for acute lower respiratory tract infection in young children.

Objectives

We aimed to identify risk factors for hospitalized RSV disease and its severe outcomes.

Methods

We conducted a retrospective cohort study analyzing data of a ICD‐10‐code‐based hospital surveillance for severe acute respiratory infections (SARI). Using univariable and multivariable logistic regression analysis, we assessed age‐group, gender, season, and underlying medical conditions as possible risk factors for RSV and its severe outcomes including ICU admission, application of ventilator support, and death, respectively.

Results

Of the 413 552 patients hospitalized with SARI in the database, 8761 were diagnosed with RSV from week 01/2009 to 20/2018 with 97% (8521) aged <5 years. Among children aged <5 years, age‐groups 0‐5 months (OR: 20.29, 95% CI: 18.37‐22.41) and 6 months‐1 year (OR: 4.59, 95% CI: 4.16‐5.06), and underlying respiratory and cardiovascular disorders specific to the perinatal period (OR: 1.32, 95% CI: 1.11‐1.57) were risk factors for being diagnosed with RSV. Age‐group 0‐5 months (OR: 2.39, 95% CI: 1.45‐3.94), low birth weight (OR: 6.77, 95% CI: 1.28‐35.71), preterm newborn (OR: 6.71, 95% CI: 2.19‐20.61), underlying respiratory and cardiovascular disorders specific to the perinatal period (OR: 4.97, 95% CI: 3.36‐7.34), congenital malformation of the heart (OR: 3.65, 95% CI: 1.90‐7.02), congenital malformation of the great vessels (OR: 3.50, 95% CI: 1.10‐11.18), congenital defect originating in perinatal period (OR: 4.07, 95% CI: 1.71‐9.70), cardiovascular disease (OR: 5.19, 95% CI: 2.77‐9.72), neurological disorders (OR: 6.48, 95% CI: 3.76‐11.18), blood disease (OR: 3.67, 95% CI: 1.98‐6.79), and liver disease (OR: 14.99, 95% CI: 1.49‐150.82) contributed to ICU admission in RSV cases.

Conclusions

Using ICD‐10‐based surveillance data allows to identify risk factors for hospitalized RSV disease and its severe outcomes, and quantify the risk in different age‐groups.

[Infectious diseases and their ICD coding : What could be improved by the introduction of ICD-11?]

Die Revision der Internationalen statistischen Klassifikation der Krankheiten und verwandter Gesundheitsprobleme (International Classification of Diseases – ICD) geht mit grundlegenden Änderungen der Morbiditäts- und Mortalitätsstatistik einher, die auch den Bereich der Infektionskrankheiten betreffen. Die Zuordnung der einzelnen Infektionskrankheiten zu den Kapiteln in der aktuellen ICD-10 erfolgt aufgrund unterschiedlicher Konzepte, teilweise nach auslösendem Agens, nach betroffenem Organsystem oder nach Lebensperiode. Besondere Herausforderungen der Klassifizierung der Infektionskrankheiten bestehen u. a. darin, dass regelmäßig ein Anpassungsbedarf durch neu auftretende Erreger entstehen kann. Außerdem reichen die Angaben hinsichtlich Umfang und Tiefe in der ICD-10 teilweise nicht aus, um epidemiologische Auswertungen der Daten durchzuführen.

Die ICD ermöglicht den weltweiten Vergleich von Statistiken zu Infektionskrankheiten. Zunehmend wird die ICD jedoch auch für die Erhebung von Surveillance- und Forschungsdaten eingesetzt, z. B. im Rahmen des Meldewesens (Identifizierung von Meldetatbeständen), aber auch in der syndromischen Surveillance akuter Atemwegsinfektionen und für den Aufbau neuer Surveillance-Systeme sowie der Evaluation der Datenqualität durch Abgleich mit Sekundärdaten.

Die Chancen der ICD-11 liegen vor allem darin, dass Infektionskrankheiten eindeutiger codiert werden können und ihre Codierung mehr relevante Informationen für die epidemiologische Bewertung enthält. Durch die hohe Komplexität können jedoch Verzerrungen in den Daten entstehen, die die Fortschreibung der Morbiditäts- und Mortalitätsstatistiken erschweren.

Evaluation of using ICD-10 code data for respiratory syncytial virus surveillance

Background

Respiratory syncytial virus (RSV) is the most common cause of acute lower respiratory tract infection (ALRI) in young children. ICD‐10‐based syndromic surveillance can transmit data rapidly in a standardized way.

Objectives

We investigated the use of RSV‐specific ICD‐10 codes for RSV surveillance.

Methods

We performed a retrospective descriptive data analysis based on existing ICD‐10‐based surveillance systems for ALRI in primary and secondary care and a linked virological surveillance in Germany. We described RSV epidemiology and compared the epidemiological findings based on ICD‐10 and virological data. We calculated sensitivity and specificity of RSV‐specific ICD‐10 codes and in combination with ICD‐10 codes for acute respiratory infections (ARI) for the identification of laboratory‐confirmed RSV infections.

Results

Based on the ICD‐10 and virological data, epidemiology of RSV was described, and common findings were found. The RSV‐specific ICD‐10 codes had poor sensitivity 6% (95%‐CI: 3%‐12%) and high specificity 99.8% (95%‐CI: 99.6%‐99.9%). In children <5 years and in RSV seasons, the sensitivities of RSV‐specific ICD‐10 codes combined with general ALRI ICD‐10 codes J18.‐, J20.‐ and with J12.‐, J18.‐, J20.‐, J21.‐, J22 were moderate (44%, 95%‐CI: 30%‐59%). The specificities of both combinations remained high (91%, 95%‐CI: 86%‐94%; 90%, 95%‐CI: 85%‐94%).

Conclusions

The use of RSV‐specific ICD‐10 codes may be a useful indicator to describe RSV epidemiology. However, RSV‐specific ICD‐10 codes underestimate the number of actual RSV infections. This can be overcome by combining RSV‐specific and general ALRI ICD‐10 codes. Further investigations are required to validate this approach in other settings.

Getting a grippe on severity: a retrospective comparison of influenza-related hospitalizations and deaths captured in reportable disease and administrative data sources in Ontario, Canada

Background

Since 2009, in Ontario, reportable disease surveillance data has been used for timely in-season estimates of influenza severity (i.e., hospitalizations and deaths). Due to changes in reporting requirements influenza reporting no longer captures these indicators of severity, necessitating exploration of other potential sources of data. The purpose of this study was to complete a retrospective analysis to assess the comparability of influenza-related hospitalizations and deaths captured in the Ontario reportable disease information system to those captured in Ontario’s hospital-based discharge database.

Methods

Hospitalizations and deaths of laboratory-confirmed influenza cases reported during the 2010–11 to 2013–14 influenza seasons were analyzed. Information on hospitalizations and deaths for laboratory-confirmed influenza cases were obtained from two databases; the integrated Public Health Information System, which is the provincial reportable disease database, and the Discharge Abstract Database, which contains information on all in-patient hospital visits using the International Classification of Diseases, 10th Revision, Canada (ICD-10-CA) coding standards. Analyses were completed using the ICD-10 J09 and J10 diagnosis codes as an indicator for laboratory-confirmed influenza, and a secondary analysis included the physician-diagnosed influenza J11 diagnosis code.

Results

For each season, reported hospitalizations for laboratory-confirmed influenza cases in the reportable disease data were higher compared to hospitalizations with J09 and J10 diagnoses codes, but lower when J11 codes were included. The number of deaths was higher in the reportable disease data, whether or not J11 codes were included. For all four seasons, the weekly trends in the number of hospitalizations and deaths were similar for the reportable disease and hospital data (with and without J11), with seasonal peaks occurring during the same week or within 1 week of each other.

Conclusion

In our retrospective analyses we found that hospital data provided a reliable estimate of the trends of influenza-related hospitalizations and deaths compared to the reportable disease data for the 2010–11 to 2013–14 influenza seasons in Ontario, but may under-estimate the total seasonal number of deaths. Hospital data could be used for retrospective end-of-season assessments of severity, but due to delays in data availability are unlikely to be timely estimates of severity during in-season surveillance.

Influenza-associated hospitalisation, vaccine uptake and socioeconomic deprivation in an English city region: an ecological study

OBJECTIVES

Every year, influenza poses a significant burden on the National Health Service in England. Influenza vaccination is an effective measure to prevent severe disease, hence, maximising vaccine coverage in the most vulnerable is a priority. We aimed to identify the extent to which socioeconomic status is associated with influenza-associated illness (IAI) and influenza vaccine coverage.

DESIGN

Retrospective observational study using hospital episode statistics.

SETTING

Merseyside, North-West of England, including the city of Liverpool.

PARTICIPANTS

Residents of Merseyside hospitalised with IAI between April 2004 and March 2016, and Merseyside general practice registered patients eligible for influenza vaccination in 2014/2015 and 2015/2016 influenza seasons.

EXPOSURES

Socioeconomic deprivation based on lower super output area English Indices of Deprivation scores.

PRIMARY AND SECONDARY OUTCOME MEASURES

Incidence and risk of IAI hospitalisation, and vaccine uptake.

RESULTS

There were 89 058 hospitalisations related to IAI among Merseyside residents (mean yearly rate=4.9 per 1000 population). Hospitalisations for IAI were more frequent in the most socioeconomically deprived areas compared with the least deprived in adults aged 15-39 years (incidence rate ratio (IRR) 2.08;95% CI 1.76 to 2.45; p<0.001), 60-64 years (IRR 2.65; 95% CI 2.35 to 2.99; p<0.001) and 65+ years (IRR 1.90; 95% CI 1.73 to 2.10; p<0.001), whereas rates in children were more homogeneous across deprivation strata. Vaccine uptake was lower than the nationally set targets in most neighbourhoods. The odds of vaccine uptake were 30% lower (OR 0.70; 95% CI 0.66 to 0.74; p<0.001) and 10% lower (OR 0.90; 95% CI 0.88 to 0.92; p<0.001) in the most socioeconomically deprived quintile compared with the least deprived, among children aged 24-59 months and 65+ years, respectively.

CONCLUSIONS

Higher rates of IAI hospitalisations and lower vaccine uptake in the most socioeconomically deprived populations suggest that health promotion policies and interventions that target these populations should be a priority.

Estimating severity of influenza epidemics from severe acute respiratory infections (SARI) in intensive care units

BACKGROUND

While influenza-like-illness (ILI) surveillance is well-organized at primary care level in Europe, few data are available on more severe cases. With retrospective data from intensive care units (ICU) we aim to fill this current knowledge gap. Using multiple parameters proposed by the World Health Organization we estimate the burden of severe acute respiratory infections (SARI) in the ICU and how this varies between influenza epidemics.

METHODS

We analyzed weekly ICU admissions in the Netherlands (2007-2016) from the National Intensive Care Evaluation (NICE) quality registry (100% coverage of adult ICUs in 2016; population size 14 million) to calculate SARI incidence, SARI peak levels, ICU SARI mortality, SARI mean Acute Physiology and Chronic Health Evaluation (APACHE) IV score, and the ICU SARI/ILI ratio. These parameters were calculated both yearly and per separate influenza epidemic (defined epidemic weeks). A SARI syndrome was defined as admission diagnosis being any of six pneumonia or pulmonary sepsis codes in the APACHE IV prognostic model. Influenza epidemic periods were retrieved from primary care sentinel influenza surveillance data.

RESULTS

Annually, an average of 13% of medical admissions to adult ICUs were for a SARI but varied widely between weeks (minimum 5% to maximum 25% per week). Admissions for bacterial pneumonia (59%) and pulmonary sepsis (25%) contributed most to ICU SARI. Between the eight different influenza epidemics under study, the value of each of the severity parameters varied. Per parameter the minimum and maximum of those eight values were as follows: ICU SARI incidence 558-2400 cumulated admissions nationwide, rate 0.40-1.71/10,000 inhabitants; average APACHE score 71-78; ICU SARI mortality 13-20%; ICU SARI/ILI ratio 8-17 cases per 1000 expected medically attended ILI in primary care); peak-incidence 101-188 ICU SARI admissions in highest-incidence week, rate 0.07-0.13/10,000 population).

CONCLUSIONS

In the ICU there is great variation between the yearly influenza epidemic periods in terms of different influenza severity parameters. The parameters also complement each other by reflecting different aspects of severity. Prospective syndromic ICU SARI surveillance, as proposed by the World Health Organization, thereby would provide insight into the severity of ongoing influenza epidemics, which differ from season to season.