Facilitating patient self-management through telephony and web technologies in seasonal influenza

Purpose The aim of this projectwas to develop and test information technology implementations that could assist patients with influenza self-management in primary care settings. Although testing was conducted in the context of seasonal influenza, the project aimed to develop a blueprint that primary care practices could use in an influenza pandemic. Methods Four primary care practice-based research networks (PBRNs) systematically designed, implemented, tailored and tested a tiered patient selfmanagement technology model in 12 primary care practices during the peak of the 2007 to 2008 influenza season. Participating clinicians received a customised practice website that included a bilingual influenza self-triage module, a downloadable influenza toolkit and electronic messaging capability. As an alternative option, a bilingual, interactive seasonal influenza telephone hotline that patients could call for assistance was provided. Results Influenza self-management web pages presented via nine customised practice websites received 1060 hits between February and April of 2008. The Self-management Influenza Toolkit was downloaded 76 times and 185 Influenza Self-Triage Module sessions were completed via practice websites during the course of testing. Logs of the telephony hotline indicated 88 calls between February and April 2008. Seventy-two percent of callers had influenza- like symptoms and 18% were eligible for antiviral therapy. The Spanish language option was selected by 21% of callers. Qualitative feedback from 37 patients (29 English and 8 Spanish) and six clinicians from four PBRNs indicated ease of use, problem-free access and navigation, useful and adequate information that was utilised in various ways by patients and a high level of overall satisfaction with these technologies. Both patients and clinicians provided rich and meaningful feedback about future improvements. Conclusions Primary care patients and their clinicians can adopt and successfully utilise influenza self-management technologies. Our pilot study suggests that web resources combined with telephony technology are feasible to set up and easy to use in primary care settings

Rapid Assessment of Agents of Biological Terrorism: Defining the Differential Diagnosis of Inhalational Anthrax Using Electronic Communication in a Practice-Based Research Network

PURPOSE Early detection of bioterrorism requires assessment of diagnoses assigned to cases of rare diseases with which clinicians have little experience. In this study, we evaluated the process of defining the differential diagnosis for inhalational anthrax using electronic communication within a practice-based research network (PBRN) and compared the results with those obtained from a nationwide random sample of family physicians with a mailed instrument. METHODS We distributed survey instruments by e-mail to 55 physician members of the Wisconsin Research Network (WReN), a regional PBRN. The instruments consisted of 3 case vignettes randomly drawn from a set describing 11 patients with inhalational anthrax, 2 with influenza A, and 1 with Legionella pneumonia. Physicians provided their most likely nonanthrax diagnosis, along with their responses to 4 yes-or-no management questions for each case. Physicians who had not responded at 1 week received a second e-mail with the survey instrument. The comparison group consisted of the nationwide sample of physicians who completed mailed survey instruments. Primary outcome measures were response rate, median response time, and frequencies of diagnostic categories assigned to cases of inhalational anthrax. RESULTS The PBRN response rate compared favorably with that of the national sample (47.3% vs 37.0%; P = not significant). The median response time for the PBRN was significantly shorter than that for the national sample (2 vs 28 days; P <.001). No significant differences were found between the PBRN and the Midwest subset of the national sample in the frequencies of major diagnostic categories or in case management. CONCLUSIONS Electronic means of creating differential diagnoses for rare infectious diseases of national significance is feasible within PBRNs. Information is much more rapidly acquired and is consistent with that obtained by conventional methods

The Primary Care Differential Diagnosis of Inhalational Anthrax

PURPOSE Inhalational anthrax is an extremely rare infectious disease with nonspecific initial symptoms, thus making diagnosis on clinical grounds difficult. After a covert release of anthrax spores, primary care physicians will be among the first to evaluate cases. This study defines the primary care differential diagnosis of inhalational anthrax. METHODS In May 2002, we mailed survey instruments consisting of 3 randomly chosen case vignettes describing patients with inhalational anthrax to a nationwide random sample of 665 family physicians. Nonrespondents received additional mailings. Physicians were asked to provide their most likely nonanthrax diagnosis for each case. RESULTS The response rate was 36.9%. Diagnoses for inhalational anthrax were grouped into 35 diagnostic categories, with pneumonia (42%), influenza (10%), viral syndrome (9%), septicemia (8%), bronchitis (7%), central nervous system infection (6%), and gastroenteritis (4%) accounting for 86% of all diagnoses. Diagnoses differed significantly between cases that proved to be fatal and those that proved to be nonfatal. CONCLUSIONS Inhalational anthrax resembles common diagnoses in primary care. Surveillance systems for early detection of bioterrorism events that rely only on diagnostic codes will be hampered by false-positive alerts. Consequently, educating front-line physicians to recognize and respond to bioterrorism is of the highest priority

Surveillance of Respiratory Viruses in Long Term Care Facilities

ObjectiveTo assess the feasibility of conducting respiratory virus surveillance for residents of long term care facilities (LTCF) using simple nasal swab specimens and to describe the virology of acute respiratory infections (ARI) in LCTFs.IntroductionAlthough residents of LTCFs have high morbidity and mortality associated with ARIs, there is very limited information on the virology of ARI in LTCFs.[1,2] Moreover, most virological testing of LCTF residents is reactive and is triggered by a resident meeting selected surveillance criteria. We report on incidental findings from a prospective trial of introducing rapid influenza diagnostic testing (RIDT) in ten Wisconsin LTCFs over a two-year period with an approach of testing any resident with ARI.MethodsAny resident with new onset of respiratory symptoms consistent with ARI had a nasal swab specimen collected for RIDT by nursing staff. Following processing for RIDT (Quidel Sofia Influenza A+B FIA), the residual swab was placed into viral transport medium and forwarded to the Wisconsin State Laboratory of Hygiene and tested for influenza using RT-PCR (IVD CDC Human Influenza Virus Real-Time RT-PCR Diagnostic Panel), and for 17 viruses (Luminex NxTAG Respiratory Pathogen Panel [RPP]). The numbers of viruses in each of 7 categories [influenza A (FluA ), influenza B (FluB), coronaviruses (COR), human metapneumovirus (hMPV), parainfluenza (PARA), respiratory syncytial virus (RSV) and rhinovirus/enterovirus (R/E)], across the two years were compared using chi-square.ResultsTotals of 164 and 190 specimens were submitted during 2016-2017 and 2017-2018, respectively. RPP identified viruses in 56.2% of specimens, with no difference in capture rate between years (55.5% vs. 56.8%). Influenza A (21.5%), influenza B (16.5%), RSV (19.0%) and hMPV (16.5%) accounted for 73.5% of all detections, while coronaviruses (15.5%), rhino/enteroviruses (8.5%) and parainfluenza (2.5%) were less common. Specific distribution of viruses varied significantly across the two years (Table: X2=48.1, df=6; p&lt;0.001).ConclusionsSurveillance in LTCFs using nasal swabs collected for RIDT is highly feasible and yields virus identification rates similar to those obtained in clinical surveillance of ARI with collection of nasopharyngeal specimens by clinicians and those obtained in a school-based surveillance project of ARI with collection of combined nasal and oropharyngeal specimens collected by trained research assistants. Significant differences in virus composition occurred across the two study years. RSV varied little between years while hMPV demonstrated wide variation. Simple approaches to surveillance may provide a more comprehensive assessment of respiratory viruses in LTCF settings.References(1) Uršič T, Gorišek Miksić N, Lusa L, Strle F, Petrovec M. Viral respiratory infections in a nursing home: a six-month prospective study. BMC Infect Dis. 2016; 16: 637. Published online 2016 Nov 4. doi: 10.1186/s12879-016-1962-8(2) Masse S, Capai L, Falchi A. Epidemiology of Respiratory Pathogens among Elderly Nursing Home Residents with Acute Respiratory Infections in Corsica, France, 2013–2017. Biomed Res Int. 2017; 2017: 1423718. Published online 2017 Dec 17. doi: 10.1155/2017/142371

Facilitating patient self-management through telephony and web technologies in seasonal influenza

Purpose The aim of this projectwas to develop and test information technology implementations that could assist patients with influenza self-management in primary care settings. Although testing was conducted in the context of seasonal influenza, the project aimed to develop a blueprint that primary care practices could use in an influenza pandemic. Methods Four primary care practice-based research networks (PBRNs) systematically designed, implemented, tailored and tested a tiered patient selfmanagement technology model in 12 primary care practices during the peak of the 2007 to 2008 influenza season. Participating clinicians received a customised practice website that included a bilingual influenza self-triage module, a downloadable influenza toolkit and electronic messaging capability. As an alternative option, a bilingual, interactive seasonal influenza telephone hotline that patients could call for assistance was provided. Results Influenza self-management web pages presented via nine customised practice websites received 1060 hits between February and April of 2008. The Self-management Influenza Toolkit was downloaded 76 times and 185 Influenza Self-Triage Module sessions were completed via practice websites during the course of testing. Logs of the telephony hotline indicated 88 calls between February and April 2008. Seventy-two percent of callers had influenza- like symptoms and 18% were eligible for antiviral therapy. The Spanish language option was selected by 21% of callers. Qualitative feedback from 37 patients (29 English and 8 Spanish) and six clinicians from four PBRNs indicated ease of use, problem-free access and navigation, useful and adequate information that was utilised in various ways by patients and a high level of overall satisfaction with these technologies. Both patients and clinicians provided rich and meaningful feedback about future improvements. Conclusions Primary care patients and their clinicians can adopt and successfully utilise influenza self-management technologies. Our pilot study suggests that web resources combined with telephony technology are feasible to set up and easy to use in primary care settings

Utility of Nontraditional Data Sources for Early Detection of Influenza

ObjectiveThis session will provide an overview of the current systemsfor influenza surveillance; review the role of schools in influenzatransmission; discuss relationships between school closures, schoolabsenteeism, and influenza transmission; and explore the usefulnessof school absenteeism and unplanned school closure monitoring forearly detection of influenza in schools and broader communities.IntroductionInfluenza surveillance is conducted through a complex networkof laboratory and epidemiologic systems essential for estimatingpopulation burden of disease, selecting influenza vaccine viruses,and detecting novel influenza viruses with pandemic potential (1).Influenza surveillance faces numerous challenges, such as constantlychanging influenza viruses, substantial variability in the number ofaffected people and the severity of disease, nonspecific symptoms,and need for laboratory testing to confirm diagnosis. Exploringadditional components that provide morbidity information mayenhance current influenza surveillance.School-aged children have the highest influenza incidence ratesamong all age groups. Due to the close interaction of children inschools and subsequent introduction of influenza into households,it is recognized that schools can serve as amplification points ofinfluenza transmission in communities. For this reason, pandemicpreparedness recommendations include possible pre-emptive schoolclosures, before transmission is widespread within a school system orbroader community, to slow influenza transmission until appropriatevaccines become available. During seasonal influenza epidemics,school closures are usually reactive, implemented in response tohigh absenteeism of students and staff after the disease is alreadywidespread in the community. Reactive closures are often too late toreduce influenza transmission and are ineffective.To enhance timely influenza detection, a variety of nontraditionaldata sources have been explored. School absenteeism was suggestedby several research groups to improve school-based influenzasurveillance. A study conducted in Japan demonstrated that influenza-associated absenteeism can predict influenza outbreaks with highsensitivity and specificity (2). Another study found the use of all-causes absenteeism to be too nonspecific for utility in influenzasurveillance (3). Creation of school-based early warning systemsfor pandemic influenza remains an interest, and further studies areneeded. The panel will discuss how school-based surveillance cancomplement existing influenza surveillance systems

Cause-Specific School Absenteeism Monitoring Identifies Community Influenza Outbreaks

ObjectiveThe Oregon Child Absenteeism due to Respiratory Disease Study (ORCHARDS) was implemented to assess the relationships between cause-specific absenteeism within a school district and medically attended influenza visits within the same community.IntroductionTransmission and amplification of influenza within schools has been purported as a driving mechanism for subsequent outbreaks in surrounding communities. However, the number of studies assessing the utility of monitoring school absenteeism as an indicator of influenza in the community is limited. ORCHARDS was initiated to evaluate the relationships between all-cause (a-Tot), illness-related (a-I), and influenza-like illness (ILI)-related absenteeism (a-ILI) within a school district and medically attended influenza A or B visits within the same community.MethodsORCHARDS was based at the Oregon School District (OSD), which enrolls 3,640 students at six schools in south-central Wisconsin. Parents reported influenza-like symptoms on an existing phone-based absenteeism reporting system. Attendance staff identified ILI using a simple case definition. Absenteeism was logged into the OSD’s existing electronic information system (Infinite Campus), and an automated process extracted counts of a-Tot, a-I, and a-ILI each school day from 9/02/14 through 6/08/17.Parents of students with acute respiratory infections (ARI) were invited to contact study staff who assessed the students’ eligibility for the study based on presence of ILI symptoms. From 1/05/15 through 6/08/17, data and nasal swabs were collected from eligible OSD students whose parents volunteered to have a study home visit within 7 days of ILI onset. Specimens were tested for influenza A and B at the Wisconsin State Laboratory of Hygiene using the CDC Human Influenza Virus Real-time RT-PCR Diagnostic Panel.For community influenza, we used data from the Wisconsin Influenza Incidence Surveillance Project (WIISP) that monitors medically attended influenza using RT-PCR at five primary care clinics surrounding the OSD.Data analysis: Over-dispersed Poisson generalized additive log-linear regression models were fit to the daily number of medically attended influenza cases and daily absenteeism counts from three sources (a-Tot, a-I, and a-ILI) with year and season (calendar day within year) as smooth functions (thin plate regression splines). Two subgroups of a-ILI representing kindergarten through 4th grade (K-4) and 5th-12th grade (5-12) were also evaluated.ResultsDuring the study period, 168,859 total absentee days (8.57% of student days), 36,104 illness days (1.83%), and 4,232 ILI days (0.21%) were recorded. Home visits were completed on 700 children [mean age = 10.0 ± 3.5 (sd) years]. Influenza RT-PCR results were available for 695 (99.3%) children: influenza A was identified in 54 (13.3%) and influenza B in 51 (12.6%) specimens. There were one large and early outbreak of influenza A (H3N2) followed by B in 2014/15, an extremely late combined outbreak of influenza A (H1N1) and B in 2015/16, and a combined outbreak of influenza A/(H3N2) and B in 2016/17. PCR detection of influenza A or B, as compared to no influenza, was strongly associated with a child with a-ILI-positive status (OR=4.74; 95% CI: 2.78-8.18; P&lt;0.001).Nearly 2,400 medically attended ARI visits were reported during the study period. Of these, 514 patients were positive for influenza (21.5%): 371 (15.5%) influenza A and 143 (6.0%) influenza B. The temporal patterns of medically attended influenza were very similar to influenza cases in OSD students.Comparisons of the regression models demonstrated the highest correlation between absenteeism and medically attended influenza for 5th-12th grade students absent with ILI with a -1 day time lag and for all students with a-ILI with a -1 day lag (Table); a-I also had moderate correlation with a -15 day lag period.ConclusionsCause-specific absenteeism measures (a-I and a-ILI) are moderately correlated with medically attended influenza in the community and are better predictors than all-cause absenteeism. In addition, a-I preceded community influenza cases by 15 days. The monitoring system was easily implemented: a-I surveillance was fully automated and a-ILI required only minor review by attendance staff. The resulting correlations were likely lowered by the presence of other viruses that resulted in a-ILI (e.g., adenovirus) and by breaks in the school year during which absenteeism data did not accrue.Automated systems that report cause-specific absenteeism data may provide a reliable method for the early identification of influenza outbreaks in communities. From a preparedness perspective, 15-day advance warning is significant. The addition of a laboratory component could increase usefulness of the cause-specific student absenteeism monitoring as an early-warning system during influenza pandemics.