Inaccurate ascertainment of morbidity and mortality due to influenza in administrative databases: a population-based record linkage study

Estimated Respiratory Syncytial Virus-Related Hospitalizations and Deaths Among Children and Adults in Spain, 2016-2019

INTRODUCTION

Respiratory syncytial virus (RSV) causes a substantial disease burden among infants. In older children and adults, incidence is underestimated due to nonspecific symptoms and limited standard-of-care testing. We aimed to estimate RSV-attributable hospitalizations and deaths in Spain during 2016-2019.

METHODS

Nationally representative hospitalization and mortality databases were obtained from the Ministry of Health and the National Statistical Office. A quasi-Poisson regression model was fitted to estimate the number of hospitalizations and deaths attributable to RSV as a function of periodic and aperiodic time trends and viral activity, while allowing for potential overdispersion.

RESULTS

In children, the RSV-attributable respiratory hospitalization incidence was highest among infants aged 0-5 months (3998-5453 cases/100,000 person-years, representing 72% of all respiratory hospitalizations) and decreased with age. In 2019, estimated rates in children 0-5, 6-11, 12-23 months and 6-17 years were approximately 1.3, 1.4, 1.5, and 6.5 times higher than those based on standard-of-care RSV-specific codes. In adults, the RSV-attributable cardiorespiratory hospitalization rate increased with age and was highest among persons ≥ 80 years (1325-1506 cases/100,000, 6.5% of all cardiorespiratory hospitalizations). In 2019, for persons aged 18-49, 50-59, 60-79, and ≥ 80 years, estimated rates were approximately 8, 6, 8, and 16 times higher than those based on standard-of-care RSV-specific codes. The RSV-attributable cardiorespiratory mortality rate was highest among ≥ 80 age group (126-150 deaths/100,000, 3.5-4.1% of all cardiorespiratory deaths), when reported mortality rate ranged between 0 and 0.5/100,000.

CONCLUSIONS

When accounting for under-ascertainment, estimated RSV-attributable hospitalizations were higher than those reported based on standard-of-care RSV-specific codes in all age groups but particularly among older children and older adults. Like other respiratory viruses, RSV contributes to both respiratory and cardiovascular complications. Efficacious RSV vaccines could have a high public health impact in these age and risk groups.

The impact of rapid diagnostic testing on hospital administrative coding accuracy for influenza

BACKGROUND

Hospital administrative coding may underestimate the true incidence of influenza-associated hospitalisation. Earlier availability of test results could lead to improved accuracy of administrative coding.

METHODS

In this study we evaluated International Classification of Diseases 10 (ICD-10) coding for influenza (with [J09-J10] or without [J11] virus identified) in adult inpatients who underwent testing in the year prior, compared to those in the 2.5 years after, the introduction of rapid PCR testing in 2017. Other factors associated with influenza coding were evaluated using logistic regression. Discharge summaries were audited to assess the impact of documentation and result availability on coding accuracy.

RESULTS

Influenza was confirmed by laboratory testing in 862 of 5755 (15%) patients tested after rapid PCR introduction compared with 170 of 926 (18%) prior. Following the introduction of rapid testing there was a significant increase in patients allocated J09 or J10 ICD-10 codes (768 of 860 [89%] vs 107 of 140 [79%], P = 0.001). On multivariable analysis, factors independently associated with correct coding were rapid PCR testing (aOR 4.36 95% CI [2.75-6.90]) and increasing length of stay (aOR 1.01, 95% CI [1.00-1.01]). Correctly coded patients were more likely to have documentation of influenza in their discharge summaries (95 of 101 [89%] vs 11 of 101 [10%], P < 0.001) and less likely to have pending results at discharge (8 of 101 [8%] vs 65 of 101 [61%], P < 0.001).

CONCLUSION

The introduction of rapid PCR testing for influenza was associated with more accurate hospital coding. One possible explanation is faster test turnaround leading to improvement in clinical documentation.

Excess mortality among people with communicable diseases over a 30-year period, Victoria, Australia: a whole of population cohort study

Background

Understanding mortality burden associated with communicable diseases is key to informing resource allocation, disease prevention and control efforts, and evaluating public health interventions. We quantified excess mortality among people notified with communicable diseases in Victoria, Australia.

Methods

Cases of communicable disease notified in Victoria between 1 January 1991 and 31 December 2021 were linked to the death registry. Informational gain obtained through linkage and 30-day case fatality rates were calculated for each disease. Standardised mortality ratios (SMR) and 95% confidence intervals were calculated up to a year following illness onset.

Findings

There were 1,032,619 cases and 5985 (0.58%) died ≤30 days of illness onset. Following linkage, the 30-day case fatality rate increased more than 2-fold. Diseases with high 7-day SMR signifying excess mortality included invasive pneumococcal disease (167.7, 95% CI 153.4-182.7); listeriosis (166.2, 95% CI 121.2-218.3); invasive meningococcal disease (145.9, 95% CI 116.7-178.3); legionellosis (43.3, 95% CI 28.0-62.0); and COVID-19 (21.9, 95% CI 19.7-24.3). Most diseases exhibited a strong negative gradient, with high SMRs in the first 7-days of illness onset that reduced over time.

Interpretation

We demonstrated that the rate of death in Victoria's notifiable disease surveillance dataset is underestimated. Further, compared to a general population, there is evidence of elevated all-cause mortality among people notified with communicable diseases often up to one year following illness onset. Not all elevated risk is likely directly attributable to the communicable diseases of interest, rather, it may reflect underlying comorbidities or behaviours in these individuals. Regardless of attribution, infection with communicable diseases may represent a marker of mortality. Key to preventing deaths may be through timely and appropriate transition to primary and preventive healthcare following diagnosis.

Funding

No funding was provided for this study.

Factors associated with death due to severe acute respiratory syndrome caused by influenza: Brazilian population study

Introduction

Influenza infection is characterized by acute viral infection of high transmissibility. Worsening of the case can lead to the need for hospitalization, severe acute respiratory syndrome (SARS) and even death.

Method

This is a cross-sectional population-based study that used secondary database from the Brazilian Influenza Epidemiological Surveillance Information System. Only cases of adults with diagnosis of influenza by RT-PCR and case evolution recorded were included.

Results

We identified 2,273 adults with SARS by influenza, 343 of which had death as an outcome. The main risk factors for death were lack of hospitalization, not having cough and age, both with p<0.001. In addition, without asthma, having black skin color, not receiving flu vaccine, having brown skin color and not having a sore throat (p≤ 0.005) were risk factors too.

Conclusion

Factors associated with death due to SARS caused by influenza in Brazil, risk factors and protective factors to death were identified. It was evident that those who did not receive the flu vaccine presented twice the risk of unfavorable outcome, reinforcing the need to stimulate adherence to vaccination adhering and propose changes in public policies to make influenza vaccines available to the entire population, in order to prevent severe cases and unfavorable outcomes.

Modelled estimates of hospitalisations attributable to respiratory syncytial virus and influenza in Australia, 2009-2017

Background

Respiratory syncytial virus (RSV) and influenza are important causes of disease in children and adults. In Australia, information on the burden of RSV in adults is particularly limited.

Methods

We used time series analysis to estimate respiratory, acute respiratory infection, pneumonia and influenza, and bronchiolitis hospitalisations attributable to RSV and influenza in Australia during 2009 through 2017. RSV and influenza‐coded hospitalisations in <5‐year‐olds were used as proxies for relative weekly viral activity.

Results

From 2009 to 2017, the estimated all‐age average annual rates of respiratory hospitalisations attributable to RSV and seasonal influenza (excluding 2009) were 54.8 (95% confidence interval [CI]: 20.1, 88.8) and 87.8 (95% CI: 74.5, 97.7) per 100,000, respectively. The highest estimated average annual RSV‐attributable respiratory hospitalisation rate per 100,000 was 464.2 (95% CI: 285.9, 641.2) in <5‐year‐olds. For seasonal influenza, it was 521.6 (95% CI: 420.9, 600.0) in persons aged ≥75 years. In ≥75‐year‐olds, modelled estimates were approximately eight and two times the coded estimates for RSV and seasonal influenza, respectively.

Conclusions

RSV and influenza are major causes of hospitalisation in young children and older adults in Australia, with morbidity underestimated by hospital diagnosis codes.

A multicenter evaluation of computable phenotyping approaches for SARS-CoV-2 infection and COVID-19 hospitalizations

Diagnosis codes are used to study SARS-CoV2 infections and COVID-19 hospitalizations in administrative and electronic health record (EHR) data. Using EHR data (April 2020–March 2021) at the Yale-New Haven Health System and the three hospital systems of the Mayo Clinic, computable phenotype definitions based on ICD-10 diagnosis of COVID-19 (U07.1) were evaluated against positive SARS-CoV-2 PCR or antigen tests. We included 69,423 patients at Yale and 75,748 at Mayo Clinic with either a diagnosis code or a positive SARS-CoV-2 test. The precision and recall of a COVID-19 diagnosis for a positive test were 68.8% and 83.3%, respectively, at Yale, with higher precision (95%) and lower recall (63.5%) at Mayo Clinic, varying between 59.2% in Rochester to 97.3% in Arizona. For hospitalizations with a principal COVID-19 diagnosis, 94.8% at Yale and 80.5% at Mayo Clinic had an associated positive laboratory test, with secondary diagnosis of COVID-19 identifying additional patients. These patients had a twofold higher inhospital mortality than based on principal diagnosis. Standardization of coding practices is needed before the use of diagnosis codes in clinical research and epidemiological surveillance of COVID-19.

Influenza-associated mortality in Australia, 2010 through 2019: High modelled estimates in 2017

INTRODUCTION

In Australia, the 2017 and 2019 influenza seasons were severe. High-dose or adjuvanted vaccines were introduced for ≥65 year-olds in 2018.

AIM

To compare influenza-associated mortality in 2017 and 2019 with the average for 2010-2019.

METHODS

We used time series modelling to obtain estimates of influenza-associated death rates for influenza A(H1N1)pdm09, A(H3N2) and B in Australia, in persons of all ages and <65, 65-74 and ≥75 years. Estimates were made for pneumonia and influenza (P&I, 2010-2018), respiratory (2010-2018), and all-cause outcomes (2010-2019).

RESULTS

During 2010 through 2018 (and 2019 for all-cause), influenza was estimated to be associated with an annual average of 2.1 (95% confidence interval (CI) 1.9, 2.4), 4.0 (95% CI 3.4, 4.6), and 11.6 (95% CI 8.4, 15.0) P&I, respiratory and all-cause deaths per 100,000 population, respectively. Influenza A(H1N1)pdm09 was estimated to be associated with less than one quarter of influenza-associated P&I and respiratory deaths, while A(H3N2) and B were each estimated to contribute approximately equally to the remaining influenza-associated deaths. In 2017, the respective rates were 7.8 (95% CI 7.1, 8.4), 12.3 (95% CI 10.9, 13.6) and 26.0 (95% CI 20.8, 32.0) per 100,000. In 2019, the all-cause estimate was 20.8 (95% CI 14.9, 26.7) per 100,000.

CONCLUSIONS

Seasonal influenza continues to be associated with substantial mortality in Australia, with at least double the average occurring in 2017. Age-specific monitoring of vaccine effectiveness is needed in Australia to understand higher mortality seasons.

Accuracy of Computable Phenotyping Approaches for SARS-CoV-2 Infection and COVID-19 Hospitalizations from the Electronic Health Record

Objective:

Real-world data have been critical for rapid-knowledge generation throughout the COVID-19 pandemic. To ensure high-quality results are delivered to guide clinical decision making and the public health response, as well as characterize the response to interventions, it is essential to establish the accuracy of COVID-19 case definitions derived from administrative data to identify infections and hospitalizations.

Methods:

Electronic Health Record (EHR) data were obtained from the clinical data warehouse of the Yale New Haven Health System (Yale, primary site) and 3 hospital systems of the Mayo Clinic (validation site). Detailed characteristics on demographics, diagnoses, and laboratory results were obtained for all patients with either a positive SARS-CoV-2 PCR or antigen test or ICD-10 diagnosis of COVID-19 (U07.1) between April 1, 2020 and March 1, 2021. Various computable phenotype definitions were evaluated for their accuracy to identify SARS-CoV-2 infection and COVID-19 hospitalizations.

Results:

Of the 69,423 individuals with either a diagnosis code or a laboratory diagnosis of a SARS-CoV-2 infection at Yale, 61,023 had a principal or a secondary diagnosis code for COVID-19 and 50,355 had a positive SARS-CoV-2 test. Among those with a positive laboratory test, 38,506 (76.5%) and 3449 (6.8%) had a principal and secondary diagnosis code of COVID-19, respectively, while 8400 (16.7%) had no COVID-19 diagnosis. Moreover, of the 61,023 patients with a COVID-19 diagnosis code, 19,068 (31.2%) did not have a positive laboratory test for SARS-CoV-2 in the EHR. Of the 20 cases randomly sampled from this latter group for manual review, all had a COVID-19 diagnosis code related to asymptomatic testing with negative subsequent test results. The positive predictive value (precision) and sensitivity (recall) of a COVID-19 diagnosis in the medical record for a documented positive SARS-CoV-2 test were 68.8% and 83.3%, respectively. Among 5,109 patients who were hospitalized with a principal diagnosis of COVID-19, 4843 (94.8%) had a positive SARS-CoV-2 test within the 2 weeks preceding hospital admission or during hospitalization. In addition, 789 hospitalizations had a secondary diagnosis of COVID-19, of which 446 (56.5%) had a principal diagnosis consistent with severe clinical manifestation of COVID-19 (e.g., sepsis or respiratory failure). Compared with the cohort that had a principal diagnosis of COVID-19, those with a secondary diagnosis had a more than 2-fold higher in-hospital mortality rate (13.2% vs 28.0%, P<0.001). In the validation sample at Mayo Clinic, diagnosis codes more consistently identified SARS-CoV-2 infection (precision of 95%) but had lower recall (63.5%) with substantial variation across the 3 Mayo Clinic sites. Similar to Yale, diagnosis codes consistently identified COVID-19 hospitalizations at Mayo, with hospitalizations defined by secondary diagnosis code with 2-fold higher in-hospital mortality compared to those with a primary diagnosis of COVID-19.

Conclusions:

COVID-19 diagnosis codes misclassified the SARS-CoV-2 infection status of many people, with implications for clinical research and epidemiological surveillance. Moreover, the codes had different performance across two academic health systems and identified groups with different risks of mortality. Real-world data from the EHR can be used to in conjunction with diagnosis codes to improve the identification of people infected with SARS-CoV-2.

Influenza vaccine effectiveness against all-cause mortality following laboratory-confirmed influenza in older adults, 2010-2011 to 2015-2016 seasons in Ontario, Canada

Background

Older adults are at increased risk of mortality from influenza infections. We estimated influenza vaccine effectiveness (VE) against mortality following laboratory-confirmed influenza.

Methods

Using a test-negative design study and linked laboratory and health administrative databases in Ontario, Canada, we estimated VE against all-cause mortality following laboratory-confirmed influenza for community-dwelling adults aged >65 years during the 2010–2011 to 2015–2016 influenza seasons.

Results

Among 54 116 older adults tested for influenza across the 6 seasons, 6837 died within 30 days of specimen collection. Thirteen percent (925 individuals) tested positive for influenza, and 50.6% were considered vaccinated for that season. Only 23.2% of influenza test-positive cases had influenza recorded as their underlying cause of death. Before and after multivariable adjustment, we estimated VE against all-cause mortality following laboratory-confirmed influenza to be 20% (95% confidence interval [CI], 8%–30%) and 20% (95% CI, 7%–30%), respectively. This estimate increased to 34% after correcting for influenza vaccination exposure misclassification. We observed significant VE against deaths following influenza confirmation during 2014–2015 (VE = 26% [95% CI, 5%–42%]). We also observed significant VE against deaths following confirmation of influenza A/H1N1 and A/H3N2, and against deaths with COPD as the underlying cause.

Conclusions

These results support the importance of influenza vaccination in older adults, who account for most influenza-associated deaths annually.

Comparative epidemiology, phylogenetics, and transmission patterns of severe influenza A/H3N2 in Australia from 2003 to 2017

Background

Over the last two decades, Australia has experienced four severe influenza seasons caused by a predominance of influenza A (A/H3N2): 2003, 2007, 2012, and 2017.

Methods

We compared the epidemiology, genetics, and transmission dynamics of severe A/H3N2 seasons in Australia from 2003 to 2017.

Results

Since 2003, the proportion of notifications in 0‐4 years old has decreased, while it has increased in the age group >80 years old (P < .001). The genetic diversity of circulating influenza A/H3N2 viruses has also increased over time with the number of single nucleotide polymorphisms significantly (P < .05) increasing. We also identified five residue positions within or near the receptor binding site of HA (144, 145, 159, 189, and 225) undergoing frequent mutations that are likely involved in significant antigenic drift and possibly severity. The Australian state of Victoria was identified as a frequent location for transmission either to or from other states and territories over the study years. The states of New South Wales and Queensland were also frequently implicated as locations of transmission to other states and territories but less so over the years. This indicates a stable but also changing dynamic of A/H3N2 circulation in Australia.

Conclusion

These results have important implications for future influenza surveillance and control policy in the country. Reasons for the change in age‐specific infection and increased genetic diversity of A/H3N2 viruses in recent years should be explored.

Estimated hospitalisations attributable to seasonal and pandemic influenza in Australia: 2001- 2013

Background

Influenza continues to cause seasonal epidemics and pandemics in humans. The burden of influenza is underestimated by traditional laboratory-based surveillance, and modelled estimates are required for influenza-attributable morbidity and mortality. We aimed to estimate the influenza-attributable hospitalisation in Australia, by influenza type.

Methods

A generalised-additive regression model was used to estimate type- and age-specific influenza-attributable hospitalisation rates per 100,000 population by principal diagnosis in Australia, from 2001 through 2013. Weekly counts of laboratory-confirmed influenza notifications and by type, influenza A and B were used as covariates in the model. Main principal diagnosis categories of interest were influenza and pneumonia and respiratory admissions. A smoothing spline was used to control for unmeasured time varying factors. Results for 2009, in which the pandemic influenza A(H1N1)pdm09 virus circulated, were not included in annual averages and are reported separately.

Results

During the study period, the estimated annual average, all-age, annual respiratory hospitalisation rates attributable to seasonal influenza type A, B and total influenza were 45.4 (95% CI: 34.9, 55.9), 32.6 (95% CI: 22.8, 42.4), and 76.9 (95% CI: 73.6, 80.2) per 100,000 population, respectively. During 2009, the estimated total pandemic influenza-attributable, all-age, respiratory hospitalisation rate was 56.1 (95% CI: 47.4, 64.9) per 100,000. Older adults (≥85 years of age) experienced the highest influenza-attributable hospitalisation rates for both seasonal and 2009 pandemic influenza. Collinearity between influenza A and B time series in some years limited the ability of the model to resolve differences in influenza attribution between the two virus types.

Conclusion

Both seasonal and pandemic influenza caused considerable morbidity in Australia during the years studied, particularly among older adults. The pandemic hospitalisation rate in 2009 was lower than the average overall annual rate for seasonal influenza, but young to middle aged adults experience a hospitalisation rate similar to that of severe seasonal influenza.

Association between type-specific influenza circulation and incidence of severe laboratory-confirmed cases; which subtype is the most virulent?

OBJECTIVES

Excess population mortality during winter is most often associated with influenza A(H3N2), though susceptibility differs by age. We examined differences between influenza types/subtypes in their association with severe laboratory-confirmed cases, overall and by age group, to determine which type is the most virulent.

METHODS

We used nine seasons of comprehensive nationwide surveillance data from Greece (2010-2011 to 2018-2019) to examine the association, separately for influenza A(H1N1)pdm09, A(H3N2) and B, between the number of laboratory-confirmed severe cases (intensive care hospitalizations or deaths) per type/subtype and the overall type-specific circulation during the season (expressed as a cumulative incidence proxy). Quasi-Poisson models with identity link were used, and multiple imputation to handle missing influenza A subtype.

RESULTS

For the same level of viral circulation and across all ages, influenza A(H1N1)pdm09 was associated with twice as many intensive care hospitalizations as A(H3N2) (rate ratio (RR) 1.89, 95% CI 1.38-2.74) and three times more than influenza B (RR 3.27, 95%CI 2.54-4.20). Similar associations were observed for laboratory-confirmed deaths. A(H1N1)pdm09 affected adults over 40 years at similar rates, whereas A(H3N2) affected elderly people at a much higher rate than younger persons (≥65 vs. 40-64 years, RR for intensive care 5.42, 95% CI 3.45-8.65, and RR for death 6.19, 95%CI 4.05-9.38). Within the 40-64 years age group, A(H1N1)pdm09 was associated with an approximately five times higher rate of severe disease than both A(H3N2) and B.

DISCUSSION

Influenza A(H1N1)pdm09 is associated with many more severe laboratory-confirmed cases, likely due to a more typical clinical presentation and younger patient age, leading to more testing. A(H3N2) affects older people more, with cases less often recognized and confirmed.

Flucast: A Real-Time Tool to Predict Severity of an Influenza Season

Background

Influenza causes serious illness requiring annual health system surge capacity, yet annual seasonal variation makes it difficult to forecast and plan for the severity of an upcoming season. Research shows that hospital and health system stakeholders indicate a preference for forecasting tools that are easy to use and understand to assist with surge capacity planning for influenza.

Objective

This study aimed to develop a simple risk prediction tool, Flucast, to predict the severity of an emerging influenza season.

Methods

Study data were obtained from the National Notifiable Diseases Surveillance System and Australian Influenza Surveillance Reports from the Department of Health, Australia. We tested Flucast using retrospective seasonal data for 11 Australian influenza seasons. We compared five different models using parameters known early in the season that may be associated with the severity of the season. To calibrate the tool, the resulting estimates of seasonal severity were validated against independent reports of influenza-attributable morbidity and mortality. The model with the highest predictive accuracy against retrospective seasonal activity was chosen as a best-fit model to develop the Flucast tool. The tool was prospectively tested against the 2018 and the emerging 2019 influenza season.

Results

The Flucast tool predicted the severity of all retrospectively studied years correctly for influenza seasonal activity in Australia. With the use of real-time data, the tool provided a reasonable early prediction of a low to moderate season for the 2018 and severe seasonal activity for the upcoming 2019 season. The tool meets stakeholder preferences for simplicity and ease of use to assist with surge capacity planning.

Conclusions

The Flucast tool may be useful to inform future health system influenza preparedness planning, surge capacity, and intervention programs in real time, and can be adapted for different settings and geographic locations.

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.

Geographical disparities in emergency department presentations for acute respiratory infections and risk factors for presenting: a population-based cohort study of Western Australian children

INTRODUCTION

Studies examining acute respiratory infections (ARIs) in emergency department (EDs), particularly in rural and remote areas, are rare. This study aimed to examine the burden of ARIs among Aboriginal and non-Aboriginal children presenting to Western Australian (WA) EDs from 2002 to 2012.

METHOD

Using a retrospective population-based cohort study linking ED records to birth and perinatal records, we examined presentation rates for metropolitan, rural and remote Aboriginal and non-Aboriginal children from 469 589 births. We used ED diagnosis information to categorise presentations into ARI groups and calculated age-specific rates. Negative binomial regression was used to investigate association between risk factors and frequency of ARI presentation.

RESULTS

Overall, 26% of presentations were for ARIs. For Aboriginal children, the highest rates were for those aged <12 months in the Great Southern (1233 per 1000 child-years) and Pilbara regions (1088 per 1000 child-years). Rates for non-Aboriginal children were highest in children <12 months in the Southwest and Kimberley (400 and 375 per 1000 child-years, respectively). Presentation rates for ARI in children from rural and remote WA significantly increased over time in all age groups <5 years. Risk factors for children presenting to ED with ARI were: male, prematurity, caesarean delivery and residence in the Kimberley region and lower socio-economic areas.

CONCLUSION

One in four ED presentations in WA children are for ARIs, representing a significant out-of-hospital burden with some evidence of geographical disparity. Planned linkages with hospital discharge and laboratory detection data will aid in assessing the sensitivity and specificity of ARI diagnoses in ED.

Estimates of global seasonal influenza-associated respiratory mortality: a modelling study

BACKGROUND

Estimates of influenza-associated mortality are important for national and international decision making on public health priorities. Previous estimates of 250 000-500 000 annual influenza deaths are outdated. We updated the estimated number of global annual influenza-associated respiratory deaths using country-specific influenza-associated excess respiratory mortality estimates from 1999-2015.

METHODS

We estimated country-specific influenza-associated respiratory excess mortality rates (EMR) for 33 countries using time series log-linear regression models with vital death records and influenza surveillance data. To extrapolate estimates to countries without data, we divided countries into three analytic divisions for three age groups (<65 years, 65-74 years, and ≥75 years) using WHO Global Health Estimate (GHE) respiratory infection mortality rates. We calculated mortality rate ratios (MRR) to account for differences in risk of influenza death across countries by comparing GHE respiratory infection mortality rates from countries without EMR estimates with those with estimates. To calculate death estimates for individual countries within each age-specific analytic division, we multiplied randomly selected mean annual EMRs by the country's MRR and population. Global 95% credible interval (CrI) estimates were obtained from the posterior distribution of the sum of country-specific estimates to represent the range of possible influenza-associated deaths in a season or year. We calculated influenza-associated deaths for children younger than 5 years for 92 countries with high rates of mortality due to respiratory infection using the same methods.

FINDINGS

EMR-contributing countries represented 57% of the global population. The estimated mean annual influenza-associated respiratory EMR ranged from 0·1 to 6·4 per 100 000 individuals for people younger than 65 years, 2·9 to 44·0 per 100 000 individuals for people aged between 65 and 74 years, and 17·9 to 223·5 per 100 000 for people older than 75 years. We estimated that 291 243-645 832 seasonal influenza-associated respiratory deaths (4·0-8·8 per 100 000 individuals) occur annually. The highest mortality rates were estimated in sub-Saharan Africa (2·8-16·5 per 100 000 individuals), southeast Asia (3·5-9·2 per 100 000 individuals), and among people aged 75 years or older (51·3-99·4 per 100 000 individuals). For 92 countries, we estimated that among children younger than 5 years, 9243-105 690 influenza-associated respiratory deaths occur annually.

INTERPRETATION

These global influenza-associated respiratory mortality estimates are higher than previously reported, suggesting that previous estimates might have underestimated disease burden. The contribution of non-respiratory causes of death to global influenza-associated mortality should be investigated.

FUNDING

None.

Epidemic forecasts as a tool for public health: interpretation and (re)calibration

OBJECTIVE

Recent studies have used Bayesian methods to predict timing of influenza epidemics many weeks in advance, but there is no documented evaluation of how such forecasts might support the day-to-day operations of public health staff.

METHODS

During the 2015 influenza season in Melbourne, Australia, weekly forecasts were presented at Health Department surveillance unit meetings, where they were evaluated and updated in light of expert opinion to improve their accuracy and usefulness.

RESULTS

Predictive capacity of the model was substantially limited by delays in reporting and processing arising from an unprecedented number of notifications, disproportionate to seasonal intensity. Adjustment of the predictive algorithm to account for these delays and increased reporting propensity improved both current situational awareness and forecasting accuracy.

CONCLUSIONS

Collaborative engagement with public health practitioners in model development improved understanding of the context and limitations of emerging surveillance data. Incorporation of these insights in a quantitative model resulted in more robust estimates of disease activity for public health use. Implications for public health: In addition to predicting future disease trends, forecasting methods can quantify the impact of delays in data availability and variable reporting practice on the accuracy of current epidemic assessment. Such evidence supports investment in systems capacity.

Influenza-associated mortality in South Africa, 2009-2013: The importance of choices related to influenza infection proxies

Background

Regression modeling methods are commonly used to estimate influenza‐associated mortality using covariates such as laboratory‐confirmed influenza activity in the population as a proxy of influenza incidence.

Objective

We examined the choices of influenza proxies that can be used from influenza laboratory surveillance data and their impact on influenza‐associated mortality estimates.

Method

Semiparametric generalized additive models with a smoothing spline were applied on national mortality data from South Africa and influenza surveillance data as covariates to obtain influenza‐associated mortality estimates from respiratory causes from 2009 to 2013. Proxies examined included alternative ways of expressing influenza laboratory surveillance data such as weekly or yearly proportion or rate of positive samples, using influenza subtypes, or total influenza data and expressing the data as influenza season‐specific or across all seasons.

Result

Based on model fit, weekly proportion and influenza subtype‐specific proxy formulation provided the best fit. The choice of proxies used gave large differences to mortality estimates, but the 95% confidence interval of these estimates overlaps.

Conclusion

Regardless of proxy chosen, mortality estimates produced may be broadly consistent and not statistically significant for public health practice.

Emergency Department demand associated with seasonal influenza, 2010 through 2014, New South Wales, Australia

Introduction

Influenza’s impact on health and health care is underestimated by influenza diagnoses recorded in health-care databases. We aimed to estimate total and non-admitted influenza-attributable hospital Emergency Department (ED) demand in New South Wales (NSW), Australia.

Methods

We used generalized additive time series models to estimate the association between weekly counts of laboratory-confirmed influenza infections and weekly rates of total and non-admitted respiratory, infection, cardiovascular and all-cause ED visits in NSW, Australia for the period 2010 through 2014. Visit categories were based on the coded ED diagnosis or the free-text presenting problem if no diagnosis was recorded.

Results

The estimated all-age, annual influenza-attributable respiratory, infection, cardiovascular and all-cause visit rates/100 000 population/year were, respectively, 120.6 (99.9% confidence interval [CI] 102.3 to 138.8), 79.7 (99.9% CI: 70.6 to 88.9), 14.0 (99.9% CI: 6.8 to 21.3) and 309.0 (99.9% CI: 208.0 to 410.1). Among respiratory visits, influenza-attributable rates were highest among < 5-year-olds and ≥ 85-year-olds. For infection and all-cause visits, rates were highest among children; cardiovascular rates did not vary significantly by age. Annual rates varied substantially by year and age group, and statistically significant associations were absent in several years or age groups. Of the respiratory visits, 73.4% did not require admission. The non-admitted proportion was higher for the other clinical categories. Around 1 in 100 total visits and more than 1 in 10 respiratory or infection visits were associated with influenza.

Discussion

Influenza is associated with a substantial and annually varying burden of hospital-attended illness in NSW.

Using record linkage to validate notification and laboratory data for a more accurate assessment of notifiable infectious diseases

BACKGROUND

Infectious disease burden is commonly assessed using notification data. Using retrospective record linkage in Western Australia, we described how well notification data captures laboratory detections of influenza, pertussis and invasive pneumococcal disease (IPD).

METHODS

We linked data from the Western Australian Notifiable Infectious Diseases Database (WANIDD) and the PathWest Laboratory Database (PathWest) pertaining to the Triple I birth cohort, born in Western Australia in 1996-2012. These were combined to calculate the number of unique cases captured in each dataset alone or in both datasets. To assess the impact of under-ascertainment, we compared incidence rates calculated using WANIDD data alone and using combined data.

RESULTS

Overall, there were 5550 influenza, 513 IPD (2001-2012) and 4434 pertussis cases (2000-2012). Approximately 2% of pertussis and IPD cases and 7% of influenza cases were solely recorded in PathWest. Notification of influenza and pertussis cases to WANIDD improved over time. Overall incidence rates of influenza in children aged <5 years using both datasets was 10% higher than using WANIDD data alone (IRR = 1.1, 95% CI = 1.1-1.2).

CONCLUSIONS

This is the first time WANIDD data have been validated against routinely collected laboratory data. We anticipated all cases would be captured in WANIDD but found additional laboratory-confirmed cases that were not notified. Studies investigating pathogen-specific infectious disease would benefit from using multiple data sources.

Epidemiology of influenza B in Australia: 2001-2014 influenza seasons

Background

Influenza B is characterised by two antigenic lineages: B/Victoria and B/Yamagata. These lineages circulate together with influenza A during influenza seasons, with varying incidence from year to year and by geographic region.

Objective

To determine the epidemiology of influenza B relative to influenza A in Australia.

Methods

Laboratory‐confirmed influenza notifications between 2001 and 2014 in Australia were obtained from the Australian National Notifiable Diseases Surveillance System.

Results

A total of 278 485 laboratory‐confirmed influenza cases were notified during the study period, comprising influenza A (82.2%), B (17.1%) and ‘other and untyped’ (0.7%). The proportion of notifications that were influenza B was highest in five‐ to nine‐year‐olds (27.5%) and lowest in persons aged 85 years and over (11.5%). Of all B notifications with lineage determined, 77.1% were B/Victoria and 22.9% were B/Yamagata infections. Mismatches between the dominant B lineage in a season and the trivalent vaccine B lineage occurred in over one‐third of seasons during the study years. In general, influenza B notifications peaked later than influenza A notifications.

Conclusion

The proportion of circulating influenza B in Australia during 2001‐2014 was slightly lower than the global average and was dominated by B/Victoria. Compared with influenza A, influenza B infection was more common among older children and young adults and less common in the very elderly. Influenza B lineage mismatch with the trivalent vaccine occurred about one‐third of the time.

Human temperatures for syndromic surveillance in the emergency department: data from the autumn wave of the 2009 swine flu (H1N1) pandemic and a seasonal influenza outbreak

Background

The emergency department (ED) increasingly acts as a gateway to the evaluation and treatment of acute illnesses. Consequently, it has also become a key testing ground for systems that monitor and identify outbreaks of disease. Here, we describe a new technology that automatically collects body temperatures during triage. The technology was tested in an ED as an approach to monitoring diseases that cause fever, such as seasonal flu and some pandemics.

Methods

Temporal artery thermometers that log temperature measurements were placed in a Boston ED and used for initial triage vital signs. Time-stamped measurements were collected from the thermometers to investigate the performance a real-time system would offer. The data were summarized in terms of rates of fever (temperatures ≥100.4 °F [≥38.0 °C]) and were qualitatively compared with regional disease surveillance programs in Massachusetts.

Results

From September 2009 through August 2011, 71,865 body temperatures were collected and included in our analysis, 2073 (2.6 %) of which were fevers. The period of study included the autumn–winter wave of the 2009–2010 H1N1 (swine flu) pandemic, during which the weekly incidence of fever reached a maximum of 5.6 %, as well as the 2010–2011 seasonal flu outbreak, during which the maximum weekly incidence of fever was 6.6 %. The periods of peak fever rates corresponded with the periods of regionally elevated flu activity.

Conclusions

Temperature measurements were monitored at triage in the ED over a period of 2 years. The resulting data showed promise as a potential surveillance tool for febrile disease that could complement current disease surveillance systems. Because temperature can easily be measured by non-experts, it might also be suitable for monitoring febrile disease activity in schools, workplaces, and transportation hubs, where many traditional syndromic indicators are impractical. However, the system’s validity and generalizability should be evaluated in additional years and settings.

Electronic supplementary material

The online version of this article (doi:10.1186/s12873-016-0080-7) contains supplementary material, which is available to authorized users.