Enhanced health event detection and influenza surveillance using a joint Veterans Affairs and Department of Defense biosurveillance application

<p>Abstract</p> <p>Background</p> <p>The establishment of robust biosurveillance capabilities is an important component of the U.S. strategy for identifying disease outbreaks, environmental exposures and bioterrorism events. Currently, U.S. Departments of Defense (DoD) and Veterans Affairs (VA) perform biosurveillance independently. This article describes a joint VA/DoD biosurveillance project at North Chicago-VA Medical Center (NC-VAMC). The Naval Health Clinics-Great Lakes facility physically merged with NC-VAMC beginning in 2006 with the full merger completed in October 2010 at which time all DoD care and medical personnel had relocated to the expanded and remodeled NC-VAMC campus and the combined facility was renamed the Lovell Federal Health Care Center (FHCC). The goal of this study was to evaluate disease surveillance using a biosurveillance application which combined data from both populations.</p> <p>Methods</p> <p>A retrospective analysis of NC-VAMC/Lovell FHCC and other Chicago-area VAMC data was performed using the ESSENCE biosurveillance system, including one infectious disease outbreak (Salmonella/Taste of Chicago-July 2007) and one weather event (Heat Wave-July 2006). Influenza-like-illness (ILI) data from these same facilities was compared with CDC/Illinois Sentinel Provider and Cook County ESSENCE data for 2007-2008.</p> <p>Results</p> <p>Following consolidation of VA and DoD facilities in North Chicago, median number of visits more than doubled, median patient age dropped and proportion of females rose significantly in comparison with the pre-merger NC-VAMC facility. A high-level gastrointestinal alert was detected in July 2007, but only low-level alerts at other Chicago-area VAMCs. Heat-injury alerts were triggered for the merged facility in June 2006, but not at the other facilities. There was also limited evidence in these events that surveillance of the combined population provided utility above and beyond the VA-only and DoD-only components. Recorded ILI activity for NC-VAMC/Lovell FHCC was more pronounced in the DoD component, likely due to pediatric data in this population. NC-VAMC/Lovell FHCC had two weeks of ILI activity exceeding both the Illinois State and East North Central Regional baselines, whereas Hines VAMC had one and Jesse Brown VAMC had zero.</p> <p>Conclusions</p> <p>Biosurveillance in a joint VA/DoD facility showed potential utility as a tool to improve surveillance and situational awareness in an area with Veteran, active duty and beneficiary populations. Based in part on the results of this pilot demonstration, both agencies have agreed to support the creation of a combined VA/DoD ESSENCE biosurveillance system which is now under development.</p

Multiple Scenario Generation of Subsurface Models:Consistent Integration of Information from Geophysical and Geological Data throuh Combination of Probabilistic Inverse Problem Theory and Geostatistics

Neutrinos with energies above 1017 eV are detectable with the Surface Detector Array of the Pierre Auger Observatory. The identification is efficiently performed for neutrinos of all flavors interacting in the atmosphere at large zenith angles, as well as for Earth-skimming \u3c4 neutrinos with nearly tangential trajectories relative to the Earth. No neutrino candidates were found in 3c 14.7 years of data taken up to 31 August 2018. This leads to restrictive upper bounds on their flux. The 90% C.L. single-flavor limit to the diffuse flux of ultra-high-energy neutrinos with an E\u3bd-2 spectrum in the energy range 1.0 7 1017 eV -2.5 7 1019 eV is E2 dN\u3bd/dE\u3bd &lt; 4.4 7 10-9 GeV cm-2 s-1 sr-1, placing strong constraints on several models of neutrino production at EeV energies and on the properties of the sources of ultra-high-energy cosmic rays

Improving the Value Proposition of Surveillance Tools: Innovative Uses for VA ESSENCE

We describe VA's experience developing innovative and alternative uses for a surveillance system. We reviewed use cases for new data elements; enhancements to system analytics and functionality; and novel use cases for original outpatient data elements. Enhancements and innovations to influenza surveillance (immunization tracking, %ILI for primary care settings, telephone triage data, inpatient influenza data, geospatial mapping); epidemiologic reviews and lookbacks (identifying ERCP procedures for a CRE review); and infection control activities (SSI procedure dominators, pneumonia hospitalizations for Legionella testing review and needlestick injury queries) were highlighted. These activities were essential for demonstrating usefulness and maintaining system support within VA

Acute Hepatitis A Infections among Veterans in Outbreak States, 2016-2018

ObjectiveTo conduct surveillance for acute Hepatitis A virus (HAV) infections in Veterans from states reporting outbreaks among high-risk individuals beginning in fiscal year (FY) 2017.IntroductionAlthough cases of acute HAV have declined in recent years, elevated numbers of HAV infections began to be reported by California and Michigan in the fall of 2016.1,2 Since this time, associated outbreaks have been reported in 9 additional states (Arizona, Utah, Kentucky, Missouri, Tennessee, Indiana, Ohio, Arkansas, and West Virginia).3 No common source of food, beverages or drugs have been identified and transmission appears to be primarily person-to-person with high-risk individuals including people experiencing homelessness, those who use illicit drugs and their close direct contacts. In June 2018, CDC issued a Health Alert Network Advisory providing additional guidance on identification and prevention of HAV and updates on the outbreaks.4 This prompted our office to more closely review our HAV surveillance, to identify Veterans who may be part of these outbreaks, and assess risk factors and outcomes of HAV infection.MethodsWe queried VA data sources starting in FY 2017 (October 1, 2016 – June 30, 2018) for HAV IgM laboratory tests and HAV-coded outpatient encounters and hospitalizations (ICD-10-CM: B15) to identify potential case patients. We performed a detailed chart review on all HAV IgM positive Veterans residing in or treated in an outbreak state during the identified outbreak time frame as reported by each state health department. Data elements collected included: (1) demographics; (2) risk factors, exposures and Hepatitis A vaccination status; (3) treatment locations (i.e. outpatient, Emergency Department, inpatient, intensive care unit); (4) presenting signs and symptoms; (5) laboratory data (including liver function tests (LFTs) and hepatitis testing); and (6) outcomes (i.e. deaths). County-level rates for positive HAV IgM test results were calculated using total unique users of VHA care for matching fiscal year time frames in each county as denominators.ResultsA total of 247 HAV IgM positive individuals were identified among 136,970 HAV IgM tests performed during the study period. Among these, 67 individuals resided in an outbreak state and were identified for further chart review. Additional laboratory review revealed that 5 of the 67 were positive for HAV Total Ab with no HAV IgM performed (all five patients came from a single facility and were asymptomatic at the time of testing). Based on review of clinical data for the remaining 62 HAV IgM positive patients, 22 (35%) did not meet the CSTE clinical case definition criteria5 of having signs or symptoms consistent with acute viral hepatitis plus either jaundice or elevated ALT/AST levels. These patients were either asymptomatic or had relevant symptoms that could be explained by other diagnoses. None had documented jaundice and only 4 had any LFT elevation, which was mild (ALT: 60-83 IU/L, AST: 36-103 IU/L). There was often no mention of the positive HAV IgM test result in the patient visit records. In the cases where the results were documented, it was thought to be a false positive or cross reactivity, related to recent receipt of HAV vaccination, or prolonged persistence of HAV IgM from a prior infection. Patient characteristics of the 40 patients meeting the case definition are summarized in Table 1. None of confirmed cases had documentation of HAV vaccination prior to their acute infection. The top 5 counties of residence among confirmed cases were Jefferson, KY (7, 18%), San Diego, CA (6, 15%), Wayne, MO (4, 10%), Butler, MO (3, 8%) and Macomb, MI (3, 8%). Additionally, the top three counties (Jefferson, San Diego and Wayne) were each noted to have clustering of cases of acute HAV with risk factors of homelessness, substance abuse and/or needle exposure. Incidence rates for HAV IgM+ test results were calculated for all reported outbreak counties and the 25 counties with the highest rates are shown in Figure 1.ConclusionsOccurrence of acute HAV infections among Veterans during October 2016 – June 2018 followed patterns reported by states with outbreaks during the same time frame, including high hospitalization rates. Risk factors of homelessness, substance abuse and/or needle exposures were noted in the Veteran population, similar to national HAV outbreak data. County-level clustering of cases in states with outbreaks was also observed among Veterans, with incidence rates of HAV IgM+ as high as 13 per 10,000 Veterans. Additional education of VA providers is needed regarding recognition of and appropriate testing for acute HAV infections. HAV IgM should not be ordered in asymptomatic patients with normal LFTs as the pretest probability of HAV infection is low, leading to false positives and confusion in interpreting test results. Improving Hepatitis A vaccination rates among Veterans is important, particularly among individuals who are at increased risk for infection or complications from HAV and in outbreak states to limit further spread of this outbreak.References1. Hepatitis A Outbreak in California. Available at: &lt;a href="https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/Immunization/Hepatitis-A-Outbreak.aspx"&gt;https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/Immunization/Hepatitis-A-Outbreak.aspx&lt;/a&gt;. Accessed September 18, 2018.2. Michigan Hepatitis A Outbreak. Available at: &lt;a href="https://www.michigan.gov/mdhhs/0,5885,7-339-71550_2955_2976_82305_82310-447907--,00.html"&gt;https://www.michigan.gov/mdhhs/0,5885,7-339-71550_2955_2976_82305_82310-447907--,00.html&lt;/a&gt;. Accessed September 18, 2018.3. CDC. 2017 – Outbreaks of hepatitis A in multiple states among people who use drugs and/or people who are homeless. Available at: &lt;a href="https://www.cdc.gov/hepatitis/outbreaks/2017March-HepatitisA.htm"&gt;https://www.cdc.gov/hepatitis/outbreaks/2017March-HepatitisA.htm&lt;/a&gt;. Accessed September 18, 2018.4. CDC . Health Alert Network Advisory: Outbreak of Hepatitis A Virus (HAV) Infections among Persons Who Use Drugs and Persons Experiencing Homelessness. June 11, 2018. Available at: &lt;a href="https://emergency.cdc.gov/han/han00412.asp"&gt;https://emergency.cdc.gov/han/han00412.asp&lt;/a&gt;. Accessed September 18, 2018.5. CSTE Position Statement. Hepatitis A, Acute 2012 Case Definition. Available at: &lt;a href="https://wwwn.cdc.gov/nndss/conditions/hepatitis-a-acute/case-definition/2012/"&gt;https://wwwn.cdc.gov/nndss/conditions/hepatitis-a-acute/case-definition/2012/&lt;/a&gt;. Accessed September 18, 2018

Challenges in Surveillance for Chikungunya Virus (CHIKV) Infection

We describe challenges and lessons learned using biosurveillance methods for identifying Chikungunya (CHIKV) infections. Surveillance was performed using VA ESSENCE, electronic laboratory data and facility reports. As of Aug. 14, 2014, 21 confirmed/probable cases were identified at 10 hospitals. The principal challenges were lack of a specific ICD-9 code for CHIKV, use of non-specific symptom codes at initial and subsequent encounters, lack of CHIKV testing, long turn-around times for results, poor uniformity in test names, and infection control not being notified of  suspected/confirmed CHIKV cases.  Based on our experience, a combination surveillance strategy using multiple data sources is essential for CHIKV detection

Carbon Monoxide Poisoning in the Veterans Health Administration, 2010 - 2016

ObjectiveTo describe characteristics of Veterans Health Administration(VHA) patients with ICD 9/10 CM inpatient discharge and/oremergency department (ED)/urgent care outpatient encounter codesfor carbon monoxide (CO) poisoning.IntroductionIt is estimated that in the United States (US), unintentional non-firerelated CO poisoning causes an average of 439 deaths annually, and in2007 confirmed CO poisoning cases resulted in 21,304 ED visits and2,302 hospitalizations (71 per million and 8 per million population,respectively)1. Despite the significant risk of morbidity and mortalityassociated with CO poisoning, existing surveillance systems in theUnited States are limited. This study is the first to focus specificallyon CO poisoning trends within the VHA population.MethodsQueries were performed in VA PraedicoTMPublic HealthSurveillance System for inpatient discharges and emergency roomand urgent care outpatient visits with ICD 9/10 CM codes for COpoisoning from 1/1/2010 – 6/30/2016. A dataset of unique patientencounters with CO poisoning was compiled and further classified asaccidental, self-harm or unspecified. Patients with carboxyhemoglobin(COHb) blood level measurements≥10%2for the same timeframewere extracted and merged with the CO poisoning dataset.We analyzed for demographic, geographic and seasonal variables.Rates were calculated using total unique users of VHA care formatching time frame and geographic area as denominators.ResultsThere were a total of 671 unique VHA patients identified with COpoisoning. Of these, 298 (44%) were classified as accidental, 104(15%) self-harm, and 269 (40%) unspecified. A total of 6 patientsdied within 30 days of their coded diagnosis, however only 1 ofthese was directly attributable to CO poisoning. The overall rate ofCO poisoning over the study time frame was 18 per million uniqueusers of VHA care. CO poisoning diagnoses were obtained from396 (59%) outpatients, 216 (32%) inpatients, and 59 (9%) patientswith both and outpatient visit and inpatient admission. Patientswith self-harm classification were less likely to be seen in the ED(only 24 (6%) unique patients compared to 190 (48%) accidental and182 (46%) unspecified classifications). Of patients seen in the ED andsubsequently admitted, patients with the classification of accidentalpoisoning made up the largest percentage with 36 unique patients(61%). There were 71 (11%) females compared to 600 (89%) males.The highest represented age group was 45-64 with 342 unique patients(51%). Rates by US Census Region were highest in the Midwestand Northeast (27 and 23 per million unique users, respectively)compared to the West and South (15 and 13 per million uniqueusers, respectively) (Figure 1). Accidental CO poisonings showed aseasonal pattern with peaks occurring in late fall, winter, and earlyspring months (Figure 2). CO poisonings classified as unspecifiedhad a similar but less pronounced pattern, while those classified asself-harm were too few to observe any pattern over time. COHb bloodlevels≥10% were present in 111 (17%) of patients with CO poisoningcodes. Of patients with COHb measures≥10%, those with self-harmclassification were least represented with only 7 unique patients (6%).Accidental and unspecified classifications were equally representedwith 53 (48%) and 51 (46%) unique patients, respectively.ConclusionsThe impact of CO poisoning on the VHA patient population hasnot been well studied. The geographic distribution of the majorityof cases in the Midwest and Northeast, and the seasonal distributionof accidental cases in colder months seems to be appropriate withrespect to what is known of unintentional CO poisoning as oftenassociated with heat-generating sources3. Opportunities for furtherinvestigation include how potential CO poisoning cases are evaluatedin VHA given the low percentage of cases with COHb blood levelmeasurements

Carbon Monoxide Poisoning in the Veterans Health Administration, 2010 - 2016

ObjectiveTo describe characteristics of Veterans Health Administration(VHA) patients with ICD 9/10 CM inpatient discharge and/oremergency department (ED)/urgent care outpatient encounter codesfor carbon monoxide (CO) poisoning.IntroductionIt is estimated that in the United States (US), unintentional non-firerelated CO poisoning causes an average of 439 deaths annually, and in2007 confirmed CO poisoning cases resulted in 21,304 ED visits and2,302 hospitalizations (71 per million and 8 per million population,respectively)1. Despite the significant risk of morbidity and mortalityassociated with CO poisoning, existing surveillance systems in theUnited States are limited. This study is the first to focus specificallyon CO poisoning trends within the VHA population.MethodsQueries were performed in VA PraedicoTMPublic HealthSurveillance System for inpatient discharges and emergency roomand urgent care outpatient visits with ICD 9/10 CM codes for COpoisoning from 1/1/2010 – 6/30/2016. A dataset of unique patientencounters with CO poisoning was compiled and further classified asaccidental, self-harm or unspecified. Patients with carboxyhemoglobin(COHb) blood level measurements≥10%2for the same timeframewere extracted and merged with the CO poisoning dataset.We analyzed for demographic, geographic and seasonal variables.Rates were calculated using total unique users of VHA care formatching time frame and geographic area as denominators.ResultsThere were a total of 671 unique VHA patients identified with COpoisoning. Of these, 298 (44%) were classified as accidental, 104(15%) self-harm, and 269 (40%) unspecified. A total of 6 patientsdied within 30 days of their coded diagnosis, however only 1 ofthese was directly attributable to CO poisoning. The overall rate ofCO poisoning over the study time frame was 18 per million uniqueusers of VHA care. CO poisoning diagnoses were obtained from396 (59%) outpatients, 216 (32%) inpatients, and 59 (9%) patientswith both and outpatient visit and inpatient admission. Patientswith self-harm classification were less likely to be seen in the ED(only 24 (6%) unique patients compared to 190 (48%) accidental and182 (46%) unspecified classifications). Of patients seen in the ED andsubsequently admitted, patients with the classification of accidentalpoisoning made up the largest percentage with 36 unique patients(61%). There were 71 (11%) females compared to 600 (89%) males.The highest represented age group was 45-64 with 342 unique patients(51%). Rates by US Census Region were highest in the Midwestand Northeast (27 and 23 per million unique users, respectively)compared to the West and South (15 and 13 per million uniqueusers, respectively) (Figure 1). Accidental CO poisonings showed aseasonal pattern with peaks occurring in late fall, winter, and earlyspring months (Figure 2). CO poisonings classified as unspecifiedhad a similar but less pronounced pattern, while those classified asself-harm were too few to observe any pattern over time. COHb bloodlevels≥10% were present in 111 (17%) of patients with CO poisoningcodes. Of patients with COHb measures≥10%, those with self-harmclassification were least represented with only 7 unique patients (6%).Accidental and unspecified classifications were equally representedwith 53 (48%) and 51 (46%) unique patients, respectively.ConclusionsThe impact of CO poisoning on the VHA patient population hasnot been well studied. The geographic distribution of the majorityof cases in the Midwest and Northeast, and the seasonal distributionof accidental cases in colder months seems to be appropriate withrespect to what is known of unintentional CO poisoning as oftenassociated with heat-generating sources3. Opportunities for furtherinvestigation include how potential CO poisoning cases are evaluatedin VHA given the low percentage of cases with COHb blood levelmeasurements

Pertussis Surveillance in Veterans Affairs Medical Centers in Western United States – 2010-2014

We describe pertussis surveillance in VA from 2010-2014. 275 cases of pertussis were identified using the Hospital-Acquired Infection and Influenza Surveillance System which utilizes QC Pathfinder and VA ESSENCE applications. Nineteen cases were identified by both applications. Sixty-two cases were laboratory confirmed. Of the 275 cases, 10% required hospitalization, 43% of patients were tested for pertussis, and 54% had documentation of a Tdap vaccine (regardless of whether vaccine was given prior to encounter). VA could improve on testing of potential pertussis cases as well as vaccination rates

Enhanced Influenza Surveillance using Telephone Triage Data in the VA ESSENCE Biosurveillance System

OBJECTIVE: To evaluate the utility and timeliness of telephone triage (TT) for influenza surveillance in the Department of Veterans Affairs (VA). INTRODUCTION: Telephone triage is a relatively new data source available to biosurveillance systems.1–2 Because early detection and warning is a high priority, many biosurveillance systems have begun to collect and analyze data from non-traditional sources [absenteeism records, over-the-counter drug sales, electronic laboratory reporting, internet searches (e.g. Google Flu Trends) and TT]. These sources may provide disease activity alerts earlier than conventional sources. Little is known about whether VA telephone program influenza data correlates with established influenza biosurveillance. METHODS: Veterans phoning VA’s TT system, and those admitted or seen at a VA facility with influenza or influenza-like-illness (ILI) diagnosis were included in this analysis. Influenza-specific ICD-9-CM coded emergency department (ED) and urgent care (UC) visits, hospitalizations, TT calls, and ILI outpatient visits were analyzed covering 2010–2011 and 2011–2012 influenza seasons (July 11, 2010–April 14, 2012). Data came from 80 VA Medical Centers and over 500 outpatient clinics with complete reporting data for the time period of interest. We calculated Spearman rank-order coefficients, 95% confidence intervals and p-values using Fisher’s z transformation to describe correlation between TT data and other influenza healthcare measures. For comparison of time trends, we plotted data for hospitalizations, ED/UC visits and outpatient ILI syndrome visits against TT encounters. We applied ESSENCE detection algorithms to identify high-level alerts for influenza activity. ESSENCE aberration detection was restricted to the 2011–2012 season because limited historical TT and outpatient data from 2009–2010 was available to accurately predict aberrancy in the 2010–2011 season. We then calculated the peak measure of healthcare utilization during both influenza seasons (2010–2011 and 2011–2012) for each data source and compared timing of peaks and alerts between TT and other healthcare encounters to assess maximum healthcare system usage and timeliness of surveillance. RESULTS: There were 7,044 influenza-coded calls, 564 hospitalizations, 1,849 emergency/urgent visits, and 416,613 ILI-coded outpatient visits. Spearman rank correlation coefficients were calculated for influenza-coded calls with hospitalizations (0.77); ED/UC visits (0.85); and ILI-outpatient visits (0.88), respectively (P< 0.0001 for all correlations). Peak influenza activity occurred on the same week or within 1 week across all settings for both seasons. For the 2011–2012 season, TT alerted with increased influenza activity before all other settings. CONCLUSIONS: Data from VA telephone care correlates well with other VA data sources for influenza activity. TT may serve to augment these existing clinical data sources and provide earlier alerts of influenza activity. As a national health care system with a large patient population, VA could provide a robust early-warning system for influenza if ongoing biosurveillance activities are combined with TT data. Additional analyses are needed to understand and correlate TT with healthcare utilization and severity of illness

Enhanced Influenza Surveillance using Telephone Triage Data in the VA ESSENCE Biosurveillance System

Telephone triage (TT) is a potential data source for biosurveillance. An analysis of Veterans Affairs (VA) TT and visit data for influenza and influenza-like-illness (ILI) was performed for 2010-2011 and 2011-2012 seasons. There were 7,044 influenza-coded calls, 564 hospitalizations, 1,849 emergency/urgent visits, and 416,613 ILI-coded outpatient visits. Spearman correlation coefficients were calculated for influenza-coded calls with hospitalizations (0.77); emergency/urgent visits (0.85); and ILI-outpatient visits (0.88), respectively. For 2011-2012, TT alerted increased influenza activity before other settings. Peak activity was similar across settings for both seasons. TT correlates well with VA healthcare utilization and may provide earlier alerts of influenza activity