Can telephone data contribute to a national influenza surveillance system in Australia? A retrospective analysis

Utilizing Syndromic Surveillance for Hurricane Irma-Related CO Poisonings in Florida

Understanding the level of recording of acute serious events in general practice electronic health records (EHRs) is critical for making decisions about the suitability of general practice datasets to address research questions and requirements for linking general practice EHRs with other datasets. To examine data source agreement of five serious acute events (myocardial infarction, stroke, venous thromboembolism (VTE), pancreatitis and suicide) recorded in general practice EHRs compared with hospital, emergency department (ED) and mortality data. Data from 61 general practices routinely contributing data to the MedicineInsight database was linked with New South Wales administrative hospital, ED and mortality data. The study population comprised patients with at least three clinical encounters at participating general practices between 2019 and 2020 and at least one record in hospital, ED or mortality data between 2010 and 2020. Agreement was assessed between MedicineInsight diagnostic algorithms for the five events of interest and coded diagnoses in the administrative data. Dates of concordant events were compared. The study included 274,420 general practice patients with at least one record in the administrative data between 2010 and 2020. Across the five acute events, specificity and NPV were excellent (>98%) but sensitivity (13%-51%) and PPV (30%-75%) were low. Sensitivity and PPV were highest for VTE (50.9%) and acute pancreatitis (75.2%), respectively. The majority (roughly 70-80%) of true positive cases were recorded in the EHR within 30 days of administrative records. Large proportions of events identified from administrative data were not detected by diagnostic algorithms applied to general practice EHRs within the specific time period. EHR data extraction and study design only partly explain the low sensitivities/PPVs. Our findings support the use of Australian general practice EHRs linked to hospital, ED and mortality data for robust research on the selected serious acute conditions.

This project examined and produced a general practice (GP) based decision support tool (DST), namely POLAR Diversion, to predict a patient's risk of emergency department (ED) presentation. The tool was built using both GP/family practice and ED data, but is designed to operate on GP data alone. GP data from 50 practices during a defined time frame were linked with three local EDs. Linked data and data mapping were used to develop a machine learning DST to determine a range of variables that, in combination, led to predictive patient ED presentation risk scores. Thirteen percent of the GP data was kept as a control group and used to validate the tool. The algorithm performed best in predicting the risk of attending ED within the 30-day time category, and also in the no ED attendance tests, suggesting few false positives. At 0 to 30 days the positive predictive value (PPV) was 74%, with a sensitivity/recall of 68%. Non-ED attendance had a PPV of 82% and sensitivity/recall of 96%. Findings indicate that the POLAR Diversion algorithm performed better than previously developed tools, particularly in the 0 to 30 day time category. Its utility increases because of it being based on the data within the GP system alone, with the ability to create real-time "in consultation" warnings. The tool will be deployed across GPs in Australia, allowing us to assess the clinical utility, and data quality needs in further iterations.

ObjeciveIdentify surveillance priorities for emergency department (ED) and Oregon Poison Center (OPC) data ahead of the 2017 Great American Solar Eclipse gatherings in Oregon and create a suite of queries for use in the Health Intelligence Section of the Oregon Public Health Division (OPHD) Incident Management Team (IMT).IntroductionOregon’s statewide syndromic surveillance system (Oregon ESSENCE) has been operational since 2012. Non-federal emergency department data (and several of their associated urgent care centers) are the primary source for the system, although other data sources have been added, including de-identified call data from OPC in 2016 (1).OPHD epidemiologists have experience monitoring mass gatherings (2) and have a strong relationship with OPC, collaborating on a regular basis for routine and heightened public health surveillance. Nevertheless, surveillance for the Great American Solar Eclipse (August 2017) presented a challenge due to the 107 reported simultaneous statewide eclipse-watching events planned for the day of the eclipse (some with estimated attendance of greater than 30,000 people and most in rural or frontier regions of the state).Scientific literature is limited on mass gathering surveillance in the developed world (3), particularly in rural settings (4), so OPC and OPHD worked together to develop a list of health conditions of interest, including some that would warrant both an ED visit and a call to OPC (e.g., snake bites). Monitoring visits in both data sources in would allow for assessment of total burden on the healthcare system, especially in the case of snake bites where only specific bites require administration of anti-venom.MethodsAhead of the planned mass gatherings, OPHD Health Intelligence and OPC compiled a list of expected risks from the literature (4,5) and input from members of the IMT including the Public Information Officer, who monitored media for stories about health. Priority health conditions presented a clear risk to public health (e.g., limited supply of snake anti-venom warranted surveillance of snake bites) or were the subject of substantial media coverage. Query development focused on risks that had specific, well-defined health effects and that would be captured by syndromic ED and OPC data.During an enhanced surveillance period (8/18-8/24), OPHD Health Intelligence reviewed and interpreted trends in common queries with OPC and disseminated a daily statewide surveillance report.ResultsOPHD and OPC created four new queries for both ED and OPC data streams: snake bites, psychedelic mushrooms, 2nd and 3rd degree body burns and eye-related calls and visits. ED queries used chief complaint, discharge diagnosis, or triage note. OPC queries used generic code, therapy and clinical effect.From 8/18-8/22, OPHD Health Intelligence distributed daily surveillance reports to the OPHD IMT and external partners. An increased in eye-related injuries was identified on the day after the eclipse, prompting OPHD Health Intelligence to consult with OPC. ED surveillance data indicated that the increase in eye-related visits was likely a seasonal trend. OPC staff reviewed the charts of patient calls captured by the query and concluded the calls were not related to retinal issues from looking at the sun. No other trends were noted in the joint OPHD/OPC queries.ConclusionsOPHD Health Intelligence piloted four new queries for surveillance during this mass gathering event and exercised the process for disseminating trend information from OPC and ED data. The eclipse event was fairly quiet and very few trends of note were captured by surveillance. Prior to this event, OPC data had not been a part of the Health Intelligence surveillance plan. However, assessing trends in OPC data provides an opportunity to better understand trends seen in ED data (e.g., whether or not a surge in ED visits for snake bites is accompanied by a surge in OPC calls for anti-venom is meaningful). By building a process to review disparate data in tandem, OPHD and OPC strengthened regional surveillance for this event. Applicable queries will continue to be used for planned event surveillance and several additional queries are currently under development.

ObjectiveEpidemiologists will understand the differences between syndromic and discharge emergency department data sources, the strengths and limitations of each data source, and how each of these different emergency department data sources can be best applied to inform a public health response to the opioid overdose epidemic.IntroductionTimely and accurate measurement of overdose morbidity using emergency department (ED) data is necessary to inform an effective public health response given the dynamic nature of opioid overdose epidemic in the United States. However, from jurisdiction to jurisdiction, differing sources and types of ED data vary in their quality and comprehensiveness. Many jurisdictions collect timely emergency department data through syndromic surveillance (SyS) systems, while others may have access to more complete, but slower emergency department discharge datasets. State and local epidemiologists must make decisions regarding which datasets to use and how to best operationalize, interpret, and present overdose morbidity using ED data. These choices may affect the number, timeliness, and accuracy of the cases identified.MethodsCDC partnered with 45 states and the District of Columbia to combat the worsening opioid overdose epidemic through three cooperative agreements: Prevention for States (PFS), Data Driven Prevention Initiative (DDPI), and Enhanced State Opioid Overdose Surveillance (ESOOS). To support funded jurisdictions in monitoring non-fatal opioid overdoses, CDC developed two different sets of indicator guidance for measuring non-fatal opioid overdoses using ED data, with each focusing on different ED data sources (SyS and discharge). We report on the following attributes for each type of ED data source1,2: 1) timeliness; 2) data quality (e.g., percent completeness by field); 3) validity; and 4) representativeness (e.g., percent of facilities included).ResultsWhen comparing timeliness across data sources, SyS data has clear advantages, with many jurisdictions receiving data within 24 hours of an event. For discharge data, timeliness is more variable with some jurisdictions receiving data within weeks while others wait over 1.5 years before receiving a complete discharge dataset. Data quality and completeness tends to be stronger in discharge datasets as facilities are required to submit complete discharge records with valid ICD-10-CM codes in order to be reimbursed by payers. By contrast, for SyS data systems, participating facilities may not consistently submit data for all possible fields, including diagnosis. Validity is dependent on the data source as well as the case definition or syndrome definition used; with this in mind, SyS data overdose indicators are designed to have high sensitivity, with less attention to specificity. Discharge data overdose indicators are designed to have a high positive predictive value, while sensitivity and specificity are both important considerations. Discharge datasets often include records for 100% of ED visits from all nonfederal, acute care-affiliated facilities in a state included. By contrast, representativeness of facilities in SyS data systems varies widely across states with some states having less than 50% of facilities reporting.ConclusionsCDC funded partners share overdose morbidity data with CDC using either ED SyS data, ED discharge data, or both. CDC indicator guidance for ED discharge data is designed for states to track changes in health outcomes over time for descriptive, performance monitoring, and evaluation purposes and to create rates that are more comparable across injury category, time, and place. Considering these objectives, CDC placed a higher priority on data quality, validity (i.e., positive predictive value), and representativeness, all of which are stronger attributes of discharge data. CDC’s indicator guidance for ED SyS data is designed for states to rapidly identify changes in nonfatal overdoses and to identify areas within a particular state that are experiencing rapid change in the frequency or types of overdose events. When considering these needs, CDC prioritized timeliness and validity in terms of sensitivity, both of which are stronger attributes of SyS data. SyS and discharge ED data each lend themselves to different informational applications and interpretations based on the strengths and limitations of each dataset. An effective, informed public health response to the opioid overdose epidemic requires continued investment in public health surveillance infrastructure, careful consideration of the needs of the data user, and transparency regarding the unique strengths and limitations of each dataset.

ObjectiveTo describe influenza laboratory testing and results in the Military Health System and how influenza laboratory results may be used in DoD Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE)IntroductionTimely influenza data can help public health decision-makers identify influenza outbreaks and respond with preventative measures. DoD ESSENCE has the unique advantage of ingesting multiple data sources from the Military Health System (MHS), including outpatient, inpatient, and emergency department (ED) medical encounter diagnosis codes and laboratory-confirmed influenza data, to aid in influenza outbreak monitoring. The Influenza-like Illness (ILI) syndrome definition includes ICD-9 or ICD-10 codes that may increase the number of false positive alerts. Laboratory-confirmed influenza data provides an increased positive predictive value (PPV). The gold standard for influenza testing is molecular assays or viral culture. However, the tests may take 3-10 days to result. Rapid influenza diagnostic tests (RIDTs) have a lower sensitivity, but the timeliness of receiving a result improves to within <15 minutes. We evaluate the utility of RIDTs for routine ILI surveillance.MethodsAdministrative medical encounters for ILI and influenza laboratory-confirmed data were analyzed from the MHS from June 2013 – September 2017 (Figure 1). The medical encounters and laboratory data include outpatient, inpatient, and ED data. The ILI syndrome case definition is a medical encounter during the study period with an ICD-9 or ICD-10 codes in any diagnostic position (ICD-9 codes = 79.99, 382.9, 460, 461.9, 465.8, 465.9, 466.0, 486, 487.0, 487.1, 487.8, 488, 490, 780.6, or 786.2; ICD-10 codes = B97.89, H66.9, J00, J01.9, J06.9, J09, J09.X, J10, J10.0, J10.1, J10.2, J10.8, J11, J11.0, J11.1, J11.2, J11.8, J12.89, J12.9, J18, J20.9, J40, R05, R50.9). The ILI dataset was limited to care provided in the MHS as laboratory data is only available for direct care. We describe influenza laboratory testing practices in the MHS. We aggregated the ILI encounters and RIDT positive results into daily counts and generated a weekly Pearson’s correlation.ResultsInfluenza tests are ordered throughout the year; the mean weekly percentage of ILI encounters in which an influenza laboratory test is ordered is 5.62%, with a range from 0.68% in the off season to 19.2% during peak influenza activity. The mean weekly percentage of positive influenza laboratory results among all ILI encounters is 0.82%, with a range from 0.01% to 5.73% (Figure 2). The percent of ILI encounters in which a test is ordered increases as the influenza season progresses. Influenza laboratory tests conducted in the MHS include RIDTs, PCR, culture, and DFA. Among all influenza tests ordered in the MHS, 66.0% were RIDTs, 22.7% were PCR, and 11.3% were viral culture. Often, a confirmatory test is ordered following a RIDT; 20% of RIDTs have follow-up tests. The mean timeliness of influenza test result data in the MHS was 11.26 days for viral culture, 2.94 days for PCR, and 0.11 days for RIDTs. The RIDT results were moderately correlated with ILI encounters for the entire year (mean weekly Pearson correlation coefficient rho=0.60, 95% CI: 0.55, 0.66, Figure 3). During the influenza season, the mean weekly Pearson correlation coefficient increases to rho=0.75, 95% CI: 0.70, 0.79.ConclusionsThe DoD has the unique advantage of access to the electronic health record and laboratory tests and results of all MHS beneficiaries. This analysis provides evidence for increased utilization of positive RIDTs in ESSENCE. The moderate correlation between the ILI syndrome and positive RIDTs may be associated with ICD-10 codes included in the ILI syndrome definition that contribute to false positive influenza cases. Ongoing research is focused on improving this ILI syndrome definition using ICD-10 codes. Rapid influenza diagnostic tests provide more timely results than other influenza test types. In conjunction with ILI medical encounter data, positive RIDT data provides a more complete and timely picture of the true burden of influenza on the MHS population for early warning of influenza outbreaks.

ObjectiveTo determine if a syndromic influenza-like illness (ILI) definition previously validated for emergency department (ED) data accurately identified ILI visits in electronic ambulatory care data.IntroductionDuring summer 2012, Washington State Department of Health (WA DOH) surveyed ILINet providers and found that more than half either utilize their electronic medical record system (EMRS) to gather and report weekly ILINet data, or intend to implement queries to do so in the future. There are a variety of EMRS being used state-wide, and providers that currently utilize these systems to report ILINet data apply a wide range of methods to query their data. There exists great interest in the evaluation of ambulatory care data within the context of Meaningful Use and little research is published in this area. WA DOH sought to evaluate electronic data from WA outpatient clinic networks in order to determine if a syndromic ILI definition previously validated for emergency department (ED) data accurately identified ILI visits in electronic ambulatory care data.MethodsPublic Health Seattle King County (PHSKC) receives electronic health data from the University of Washington Physicians Network (UWPN), comprised of ten outpatient clinics, on an automated basis. Data are sent daily for all outpatient visits that occurred the previous day and include clinic name, visit date and time, patient age, sex, zip code, chief complaint and diagnoses, and both a visit and patient key. Outpatient data from August 2007 through August 2012 were queried for ILI visits using the syndromic category for ILI previously validated for ED syndromic surveillance data: (1) ICD codes for influenza or mention of “flu” in chief complaint or diagnosis, or (2) a chief complaint or diagnosis of fever plus cough, or (3) a chief complaint or diagnosis of fever plus sore throat.Using this definition, we assessed the correlation between the proportion of visits for ILI in the UWPN data and number and percentage of positive influenza laboratory tests reported by the University of Washington (UW) Virology Laboratory. We plan to apply this methodology to evaluate outpatient data from an additional clinic network, with statewide locations, and present these findings.ResultsThe median number of weekly visits captured in the data was 6,622. Three clinics were excluded from further analyses due to insufficient data, leaving seven clinics remaining in the dataset (median number of weekly visits: 6,167). Overall, the proportion of ILI visits in the UWPN data strongly correlated with the number and percentage of positive influenza tests reported by the UW Laboratory during August 2007 through August 2012 (correlation coefficients 0.85 and 0.77, respectively). The correlation between proportion of ILI visits and number positive influenza tests among individual clinics ranged from 0.62 — 0.83. Overall, the proportion of ILI visits among the age category 5 to 24 years most strongly correlated with number positive influenza tests (correlation coefficient: 0.86).ConclusionsDuring August 2007 through August 2012, the percentage of ILI visits detected in UWPN data using a previously validated definition for ILI in ED syndromic surveillance data strongly correlated with influenza activity in the community. Based on these findings, data from the UWPN network will be incorporated into ILINet during the 2012–2013 Influenza season. Findings from our analysis support the validity of using syndromic ambulatory data for ILI surveillance. Furthermore, we plan to use these results to formulate guidance for ILINet providers who want to utilize EMRS for weekly ILINet reporting.Proportion of ILI visits within electronic clinic network data and number positive influenza tests, August 2007 – August 2012, Washington State

BackgroundPeople who inject drugs (PWID) have been identified as frequent users of emergency department (ED) and hospital inpatient services. The specific challenges of record linkage in cohorts with numerous administrative health records occurring in close proximity are not well understood. Here, we present a method for patient-specific record linkage of ED and hospital admission data for a cohort of PWID.MethodsData from 688 PWID were linked to two state-wide administrative health databases identifying all ED visits and hospital admissions for the cohort between January 2008 and June 2013. We linked patient-specific ED and hospital admissions data, using administrative date-time timestamps and pre-specified linkage criteria, to identify hospital admissions stemming from ED presentations for a given individual. The ability of standalone databases to identify linked ED visits or hospital admissions was examined.ResultsThere were 3459 ED visits and 1877 hospital admissions identified during the study period. Thirty-four percent of ED visits were linked to hospital admissions. Most links had hospital admission timestamps in-between or identical to their ED visit timestamps (n = 1035, 87%). Allowing 24-h between ED visits and hospital admissions captured more linked records, but increased manual inspection requirements. In linked records (n = 1190), the ED ‘departure status’ variable correctly reflected subsequent hospital admission in only 68% of cases. The hospital ‘admission type’ variable was non-specific in identifying if a preceding ED visit had occurred.ConclusionsLinking ED visits with subsequent hospital admissions in PWID requires access to date and time variables for accurate temporal sorting, especially for same-day presentations. Selecting time-windows to capture linked records requires discretion. Researchers risk under-ascertainment of hospital admissions if using ED data alone.

We evaluated syndromic indicators of influenza disease activity developed using emergency department (ED) data - total ED visits attributed to influenza-like illness (ILI) ('ED ILI volume') and percentage of visits attributed to ILI ('ED ILI percent') - and Google flu trends (GFT) data (ILI cases/100 000 physician visits). Congruity and correlation among these indicators and between these indicators and weekly count of laboratory-confirmed influenza in Manitoba was assessed graphically using linear regression models. Both ED and GFT data performed well as syndromic indicators of influenza activity, and were highly correlated with each other in real time. The strongest correlations between virological data and ED ILI volume and ED ILI percent, respectively, were 0·77 and 0·71. The strongest correlation of GFT was 0·74. Seasonal influenza activity may be effectively monitored using ED and GFT data.

In Maine, rabies postexposure prophylaxis (PEP) administration is reportable to public health. We sought to determine the objectives of the Maine Center for Disease Control and Prevention's (Maine CDC's) PEP administration surveillance system and whether the method of conducting surveillance through a manual health care provider (hereinafter, provider) reporting system meets these objectives. We also compared provider-reported PEP administrations with administrations identified in emergency department (ED) data. During September 2022, we interviewed 8 Maine CDC epidemiologists to determine system objectives. We obtained and compared PEP administration data from provider reporting system and ED data and summarized each dataset by year, exposing animal, and facility. We assessed the ability of each source to address surveillance system objectives by comparing data elements with each objective. Maine CDC epidemiologists described the following objectives of the surveillance system: (1) track potential human exposures to rabid or potentially rabid animals, (2) document PEP administration trends, and (3) ensure PEP is correctly administered. They determined the third objective is not being achieved by the current system. During January 2018-June 2022, we identified 538 provider-reported PEP administrations and 1191 PEP administrations through ED data. ED data were more timely than provider reports and identified more PEP administrations, but 28% of ED records did not contain information on the exposing animal. Maine CDC can use ED data to document PEP administration trends in near-real time. ED data obtained from syndromic surveillance might be used in tandem with or in place of Maine CDC's traditional PEP surveillance system. We are building more complex queries that more fully capture PEP administrations to have a thorough understanding of PEP administered in Maine.

The UK Biobank has made general practitioner (GP) data (censoring date 2016–2017) available for approximately 45% of the cohort, whilst hospital inpatient and death registry (referred to as “HES/Death”) data are available cohort-wide through 2018–2022 depending on whether the data comes from England, Wales or Scotland. We assessed the importance of case ascertainment via different data sources in UKB for three diseases that are usually first diagnosed in primary care: Parkinson’s disease (PD), type 2 diabetes (T2D), and all-cause dementia. Including GP data at least doubled the number of incident cases in the subset of the cohort with primary care data (e.g. from 619 to 1390 for dementia). Among the 786 dementia cases that were only captured in the GP data before the GP censoring date, only 421 (54%) were subsequently recorded in HES. Therefore, estimates of the absolute incidence or risk-stratified incidence are misleadingly low when based only on the HES/Death data. For incident cases present in both HES/Death and GP data during the full follow-up period (i.e. until the HES censoring date), the median time difference between an incident diagnosis of dementia being recorded in GP and HES/Death was 2.25 years (i.e. recorded 2.25 years earlier in the GP records). Similar lag periods were also observed for PD (median 2.31 years earlier) and T2D (median 2.82 years earlier). For participants with an incident GP diagnosis, only 65.6% of dementia cases, 69.0% of PD cases, and 58.5% of T2D cases had their diagnosis recorded in HES/Death within 7 years since GP diagnosis. The effect estimates (hazard ratios, HR) of established risk factors for the three health outcomes mostly remain in the same direction and with a similar strength of association when cases are ascertained either using HES only or further adding GP data. The confidence intervals of the HR became narrower when adding GP data, due to the increased statistical power from the additional cases. In conclusion, it is desirable to extend both the coverage and follow-up period of GP data to allow researchers to maximise case ascertainment of chronic health conditions in the UK.

Optimization of Linkage between North Carolina EMS and ED Data: EMS Naloxone Cases

ObjectiveTo assess how weekly percent of influenza-like illness (ILI) reported via Early Notification of Community-based Epidemics (ESSENCE) tracked weekly counts of laboratory confirmed influenza cases in five influenza seasons in order to evaluate the early warning potential of ILI in ESSENCE and improve ongoing influenza surveillance efforts in Missouri.IntroductionSyndromic surveillance is used routinely to detect outbreaks of disease earlier than traditional methods due to its ability to automatically acquire data in near real-time. Missouri has used emergency department (ED) visits to monitor and track seasonal influenza activity since 2006.MethodsThe Missouri ESSENCE system utilizes data from 84 hospitals, which represents up to 90 percent of all ED visits occurring in Missouri statewide each day. The influenza season is defined as starting during Centers for Disease Control and Prevention (CDC) week number 40 (around the first of October) and ending on CDC week 20 of the following year, which is usually at the end of May.A confirmed influenza case is laboratory confirmed by viral culture, rapid diagnostic tests, or a four-fold rise in antibody titer between acute and convalescent serum samples. Laboratory results are reported on a weekly basis. To assess the severity of influenza activity, all flu seasons were compared with the 2008–09 season, which experienced the lowest influenza activity based on laboratory data. Analysis of variance (ANOVA) was applied for this analysis using Statistical Analysis Software (SAS) (version 9.2).The standard ESSENCE ILI subsyndrome includes ED chief complaints that contain keywords such as “flu”, “flulike”, “influenza” or “fever plus cough” or “fever plus sore throat”. The ESSENCE ILI weekly percent is the number of ILI visits divided by total ED visits.Time series of weekly percent of ILI in ESSENCE were compared to weekly counts of laboratory confirmed influenza cases. Spearman correlation coefficients were calculated using SAS. The baseline refers to the mean of three flu seasons with low influenza activity (2006–07, 2008–09 and 2010–11 seasons). The threshold was calculated as this baseline plus three standard deviations.The early warning potential of the ESSENCE weekly ILI percent was evaluated for five consecutive influenza seasons, beginning in 2006. This was accomplished by calculating the time lag between the first ESSENCE ILI warning versus the first lab confirmed influenza warning. A warning was identified if either lab confirmed case counts or weekly percent of ILI crossed over their respective baselines.ResultsFor each influenza season evaluated, weekly ILI rates reported via ESSENCE were significantly correlated with weekly counts of laboratory-confirmed influenza cases (Table 1). The baseline of ILI activity in ESSENCE was 1.8 ILI /100 ED visits/week and the threshold was set at 4.1 ILI visits per 100 ED visits/week. The ESSENCE ILI baseline provided, on average, two weeks of advanced warning for seasonal influenza activity. Figure 1 shows that two influenza seasons (2007–08 and 2009–10) were more severe than others examined based on the ESSENCE percent ILI threshold analysis, this result is consistent with the examination of severity of influenza activity based on lab confirmed influenza data (p<0.05).ConclusionsThe significant correlation between ILI surveillance in ESSENCE and laboratory confirmed influenza cases justifies the use of weekly ILI percent in ESSENCE to describe seasonal influenza activity. The ESSENCE ILI baseline and threshold provided advanced warning of influenza and allowed for the classification of influenza severity in the community.

Statement of purpose The NC Division of Public Health, in collaboration with UNC Chapel Hill, is improving injury surveillance data as part of the NC Surveillance Quality Improvement (SQI) Project. The project has focused on improving emergency department (ED) data in the statewide public health surveillance system NC DETECT. Unlike statewide mortality and hospital discharge data, NC DETECT ED data are available in near real time with over 75% of ED visits assigned at least one billing code within two weeks of the visit. One task/goal of the NC SQI project was the development of 12 poisoning and drug overdose surveillance case definitions. Methods/Approach The case definitions drew from existing definitions developed by state and national organisations; content experts in injury epidemiology, surveillance methods, and public health informatics; and end user feedback. Nine of the definitions incorporate diagnosis and/or E-codes (poisoning, unintentional poisoning, acute alcohol poisoning, drug overdose, unintentional drug overdose, opioid overdose, prescription analgesic opioid overdose, methadone overdose, and heroin overdose). Two definitions use a combination of diagnosis codes, E-codes, and keyword searches (drug overdose and heroin-related ED visits). One definition consists of only a keyword search (Narcan/naloxone). Results The new case definitions were added to the NC DETECT web portal in summer 2014. Authorised users can access both current and historical ED data. Authorised users from local health departments can access line-listing data for their county and compare aggregate data to other counties and the state. These case definitions may be revised based on user feedback. In addition, custom-reports can be developed to address specific poisoning topics (e.g. fentanyl overdoses). Conclusions NC DETECT ED data are vital to NC for public health surveillance. The development of 12 poisoning and drug overdose case definitions has streamlined poisoning surveillance activities. Significance and contribution to the field Given the variation among poisoning case definitions available nationwide, the NC SQI project has developed definitions for use in NC, tested the efficacy of these definitions using ED data, and revised these definitions based on expert and user feedback. It is hoped that these definitions may inform surveillance activities in other states.

Influenza-like illness (ILI) activity has been associated with increased risk of cardiopulmonary (CP) events during the influenza season. High-dose trivalent influenza vaccine was not superior to standard-dose quadrivalent vaccine for reducing these events in patients with high-risk cardiovascular (CV) disease in the Influenza Vaccine to Effectively Stop Cardio Thoracic Events and Decompensated Heart Failure (INVESTED) trial. To evaluate whether high-dose trivalent influenza vaccination is associated with benefit over standard-dose quadrivalent vaccination in reducing CP events during periods of high, local influenza activity. This study was a prespecified secondary analysis of INVESTED, a multicenter, double-blind, active comparator randomized clinical trial conducted over 3 consecutive influenza seasons from September 2016 to July 2019. Follow-up was completed in July 2019, and data were analyzed from September 21, 2016, to July 31, 2019. Weekly Centers for Disease Control and Prevention (CDC)-reported, state-level ILI activity was ascertained to assess the weekly odds of the primary outcome. The study population included 3094 patients with high-risk CV disease from participating centers in the US. Participants were randomized to high-dose trivalent or standard-dose quadrivalent influenza vaccine and revaccinated for up to 3 seasons. The primary outcome was the time to composite of all-cause death or CP hospitalization within each season. Additional measures included weekly CDC-reported ILI activity data by state. Among 3094 participants (mean [SD] age, 65 [12] years; 2309 male [75%]), we analyzed 129 285 person-weeks of enrollment, including 1396 composite primary outcome events (1278 CP hospitalization, 118 deaths). A 1% ILI increase in the prior week was associated with an increased risk in the primary outcome (odds ratio [OR], 1.14; 95% CI, 1.07-1.21; P < .001), CP hospitalization (OR, 1.13; 95% CI, 1.06-1.21; P < .001), and CV hospitalization (OR, 1.12; 95% CI, 1.04-1.19; P = .001), after adjusting for state, demographic characteristics, enrollment strata, and CV risk factors. Increased ILI activity was not associated with all-cause death (OR, 1.00; 95% CI, 0.88-1.13; P > .99). High-dose compared with standard-dose vaccine did not significantly reduce the primary outcome, even when the analysis was restricted to weeks of high ILI activity (OR, 0.88; 95% CI, 0.65-1.20; P = .43). Traditionally warmer months in the US were associated with lower CV risk independent of local ILI activity. In this secondary analysis of a randomized clinical trial, ILI activity was temporally associated with increased CP events in patients with high-risk CV disease, and a higher influenza vaccine dose did not significantly reduce temporal CV risk. Other seasonal factors may play a role in the coincident high rates of ILI and CV events. ClinicalTrials.gov Identifier: NCT02787044.