Nowcasting pandemic influenza A/H1N1 2009 hospitalizations in the Netherlands

Donker, Tjibbe; Boven, Michiel van; Ballegooijen, W. Marijn van; Klooster, Tessa M. van’t; Wielders, Cornelia C H; Wallinga, Jacco

doi:10.1007/s10654-011-9566-5

Cited by 38 publications

(42 citation statements)

References 15 publications

(14 reference statements)

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“…The clinical findings mirror reports from other countries that fever, dry cough and general malaise were the predominant symptoms upon hospital admission [35,[39][40][41][42][43]. Furthermore, the majority of patients had underlying co-morbidities, most commonly cardiovascular disease.…”

Section: Discussionsupporting

confidence: 80%

“…There were a marked lower proportion of obese patients (13.3%) in our study than reported elsewhere (32-58%) [7,36,[39][40][41], reflected by only 7% of adults (>16 years) in Norway being defined as obese [7,30,[33][34][35]. However the most obese patient in this study was also the most severely ill, surviving total organ failure.…”

Section: Discussioncontrasting

confidence: 57%

“…The study population consisted of more women than men and thus differs from the nationally reported equal gender distributions in Norway and reports from other countries where male patients dominated suspected Influenza A(H1N1)pdm09 cases [11,[35][36][37]. The difference may be due to Norway having one of the highest percentages worldwide of working women (84%), hence influencing women to seek medical care when ill, with a female/ male ratio of 4.7:3.0.…”

Section: Discussionmentioning

confidence: 88%

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Reduced Hospital Stay in Influenza Patients after Mass Vaccination during the 2009 Influenza Pandemic in Norway

Mohn¹,

Lrum²,

Skrede³

et al. 2013

J Vaccines Vaccin

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Background: Norway had a preorder of pandemic vaccine, when the pandemic influenza A (H1N1)pdm09 in 2009 was declared. Mass vaccination occurred 1-3 weeks prior to the peak of the pandemic. Emergency plans were in place, but the predicted severe numbers of hospitalizations did not occur. Objective:To study the epidemiology and clinical presentation of adult patients hospitalized with Influenza A(H1N1)pdm09, and to evaluate the impact of vaccination on the course of the pandemic at a tertiary hospital. Methods:The low dose oil-in-water adjuvanted vaccine was used to vaccinate healthcare workers (HCWs) and at risk patients groups, and vaccination rates were recorded for the community and the hospital. Demographic and clinical information was obtained for 129 patients (>15 years), hospitalized with influenza A (H1N1)pdm09 between August 2009-January 2010. A confirmed case of influenza A (H1N1)pdm09 was defined as meeting a clinical case definition and/or laboratory confirmed disease (rt-PCR or serology). Hospital stay of more than 2 days was defined as a sign of severe illness.Results: 1/3 of at risk patients in the community and >90% of frontline HCWs at the hospital were vaccinated. The median length of hospital stay of infected patients was significantly reduced 7 days after the onset of mass vaccination (p=0.029). There was a predominance of female and moderately obese (BMI 25-30) patients. Infiltration on chest X-ray upon admission was significantly associated with a hospital stay of >2 days (p=0.001). Conclusion:Mass vaccination of frontline HCWs at the hospital and at risk patients in the community contributed to the observed significant reduction in hospital stay of patients infected with influenza. Almost no absenteeism enabled staff confidence and the ability for quick and safe patient turnover. This study highlights the importance of early influenza vaccination, to protect the high-risk patients and the integrity of the healthcare system.

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Section: Discussionsupporting

confidence: 80%

Section: Discussioncontrasting

confidence: 57%

Section: Discussionmentioning

confidence: 88%

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Reduced Hospital Stay in Influenza Patients after Mass Vaccination during the 2009 Influenza Pandemic in Norway

Mohn¹,

Lrum²,

Skrede³

et al. 2013

J Vaccines Vaccin

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“…We presented models that describe the near-contemporaneous spatial and temporal patterns in the spread of WNS. This sort of nowcasting, a meteorologic term recently adopted in spatial epidemiology (Donker et al, 2011), provides an assessment of disease risk in the face of imperfect and changing information. Our models suggested more than one third of the Indiana bat population has been affected by WNS and another one sixth is at immediate risk (#2 yr) of infection.…”

Section: Discussionmentioning

confidence: 99%

Space-Time Models for a Panzootic in Bats, With a Focus on the Endangered Indiana Bat

Thogmartin

King

Szymanski

et al. 2012

Journal of Wildlife Diseases

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ABSTRACT:Knowledge of current trends of quickly spreading infectious wildlife diseases is vital to efficient and effective management. We developed space-time mixed-effects logistic regressions to characterize a disease, white-nose syndrome (WNS), quickly spreading among endangered Indiana bats (Myotis sodalis) in eastern North America. Our goal was to calculate and map the risk probability faced by uninfected colonies of hibernating Indiana bats. Model covariates included annual distance from and direction to nearest sources of infection, geolocational information, size of the Indiana bat populations within each wintering population, and total annual size of populations known or suspected to be affected by WNS. We considered temporal, spatial, and spatiotemporal formulae through the use of random effects for year, complex (a collection of interacting hibernacula), and year3complex. Since first documented in 2006, WNS has spread across much of the range of the Indiana bat. No sizeable wintering population now occurs outside of the migrational distance of an infected source. Annual rates of newly affected wintering Indiana bat populations between winter 2007 to 2008 and 2010 to 2011 were 4, 6, 8, and 12%; this rate increased each year at a rate of 3%. If this increasing rate of newly affected populations continues, all wintering populations may be affected by 2016. Our models indicated the probability of a wintering population exhibiting infection was a linear function of proximity to affected Indiana bat populations and size of the at-risk population. Geographic location was also important, suggesting broad-scale influences. For every 50-km increase in distance from a WNS-affected population, risk of disease declined by 6% (95% CI55.2-5.7%); for every increase of 1,000 Indiana bats, there was an 8% (95% CI51-21%) increase in disease risk. The increasing rate of infection seems to be associated with the movement of this disease into the core of the Indiana bat range. Our spatially explicit estimates of disease risk may aid managers in prioritizing surveillance and management for wintering populations of Indiana bats and help understand the risk faced by other hibernating bat species.

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“…For example, in a recent study in England, it was found that for each child diagnosed with influenza, the mean work absence for caregivers was approximately 4 days (Thorrington et al 2017). Also, hospitals and intensive care units have limited excess capacity during emerging epidemics of infectious diseases (Donker et al 2011). In Sweden, the hospital bed capacity is habitually overextended with on average 103 patients occupying 100 regular hospital bed units (SKL 2016).…”

Section: Introductionmentioning

confidence: 99%

Epidemiological and statistical basis for detection and prediction of influenza epidemics

Spreco¹

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Nowcasting pandemic influenza A/H1N1 2009 hospitalizations in the Netherlands

Cited by 38 publications

References 15 publications

Reduced Hospital Stay in Influenza Patients after Mass Vaccination during the 2009 Influenza Pandemic in Norway

Reduced Hospital Stay in Influenza Patients after Mass Vaccination during the 2009 Influenza Pandemic in Norway

Space-Time Models for a Panzootic in Bats, With a Focus on the Endangered Indiana Bat

Epidemiological and statistical basis for detection and prediction of influenza epidemics

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