Factors Associated with Revision Surgery after Internal Fixation of Hip Fractures

Background: Femoral neck fractures are associated with high rates of revision surgery after management with internal fixation. Using data from the Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trial evaluating methods of internal fixation in patients with femoral neck fractures, we investigated associations between baseline and surgical factors and the need for revision surgery to promote healing, relieve pain, treat infection or improve function over 24 months postsurgery. Additionally, we investigated factors associated with (1) hardware removal and (2) implant exchange from cancellous screws (CS) or sliding hip screw (SHS) to total hip arthroplasty, hemiarthroplasty, or another internal fixation device. Methods: We identified 15 potential factors a priori that may be associated with revision surgery, 7 with hardware removal, and 14 with implant exchange. We used multivariable Cox proportional hazards analyses in our investigation. Results: Factors associated with increased risk of revision surgery included: female sex, [hazard ratio (HR) 1.79, 95% confidence interval (CI) 1.25-2.50; P = 0.001], higher body mass index (fo

Identifying and Communicating the Importance of the Variable Nature of SyS Data

ObjectiveThis roundtable will provide a forum for national, state, and localmanagers of syndromic surveillance systems to discuss how theyidentify, monitor, and respond to changes in the nature of their data.Additionally, this session will focus on the strengths and weaknessof the syndromic surveillance systems for supporting programevaluation and trend analysis. This session will also provide a forumwhere subject matter experts can discuss the ways in which this deepunderstanding of their data can be leveraged to forge and improvepartnerships with academic partners.IntroductionAs syndromic surveillance systems continue to grow, newopportunities have arisen to utilize the data in new or alternativeways for which the system was not initially designed. For example,in many jurisdictions syndromic surveillance has recently becomepopulation-based, with 100% coverage of targeted emergencydepartment encounters. This makes the data more valuable for real-time evaluation of public health and prevention programs. There hasalso been increasing pressure to make more data publicly available –to the media, academic partners, and the general public

Identifying and Communicating the Importance of the Variable Nature of SyS Data

ObjectiveThis roundtable will provide a forum for national, state, and localmanagers of syndromic surveillance systems to discuss how theyidentify, monitor, and respond to changes in the nature of their data.Additionally, this session will focus on the strengths and weaknessof the syndromic surveillance systems for supporting programevaluation and trend analysis. This session will also provide a forumwhere subject matter experts can discuss the ways in which this deepunderstanding of their data can be leveraged to forge and improvepartnerships with academic partners.IntroductionAs syndromic surveillance systems continue to grow, newopportunities have arisen to utilize the data in new or alternativeways for which the system was not initially designed. For example,in many jurisdictions syndromic surveillance has recently becomepopulation-based, with 100% coverage of targeted emergencydepartment encounters. This makes the data more valuable for real-time evaluation of public health and prevention programs. There hasalso been increasing pressure to make more data publicly available –to the media, academic partners, and the general public

Monitoring Respiratory Syncytial Virus Regionally In Children Aged < 5 Years Old Using Emergency Department and Urgent Care Center Chief Complaint Data in Florida’s Syndromic Surveillance System, Week 1, 2010 - Week 32, 2014

ED chief complaint and discharge diagnosis data accessed through a syndromic surveillance system can be used for effective, timely monitoring of RSV hospitalizations in children &lt; 5 years old and may be a more efficient and complete means of monitoring seasonality of RSV activity by region and statewide compared to hospital-based laboratory data reporting. Additionally, this surveillance technique can efficiently monitor RSV activity as well as estimate hospital admissions due to RSV and may be a useful approach for other states with syndromic surveillance systems

Characterizing Fentanyl-Associated Mortality using the Literal Causes of Death

ObjectiveTo characterize fentanyl-associated mortality in Florida using freetext queries of the literal causes of death listed on death certificates.IntroductionIn October 2015, the Centers for Disease Control and Prevention(CDC) released health advisory #384 to inform people about increasesin fentanyl fatalities. Florida’s statewide syndromic surveillancesystem, Electronic Surveillance System for the Early Notification ofCommunity-based Epidemics (ESSENCE-FL), captures electronicdeath record data in near real time which allows for the monitoringof mortality trends across the state. One limitation of using deathrecord data for fentanyl surveillance is the lack of a fentanyl-specificoverdose ICD-10 code; however, the literal cause of death fields(“literals”) provide a level of detail that is rich enough to capturementions of fentanyl use. The “literals” are a free text field on thedeath certificate, recorded by a physician at the time of death anddetail the factors that led to the death. ESSENCE-FL has the benefitof not only receiving death record data in near real-time, but alsoreceiving the literal cause of death fields. This work analyzes trendsin fentanyl-associated mortality in Florida over time by using theliteral cause of death fields within death records data obtained fromESSENCE-FL.MethodsThe “literals” elements of Florida Vital Statistics mortality datafrom 2010 through 2015 accessed via ESSENCE-FL were queriedfor the term ^fent^. No necessary negations or extra term inclusionswere deemed necessary after looking at the records pulled with ^fent^alone. Deaths were analyzed by various demographic and geographicvariables to characterize this population in order to assess whichgroups are most heavily burdened by fentanyl-associated mortality.Population estimates by county for 2015 were obtained from the U.S.Census Bureau to calculate mortality rates. Language processing in RStudio was used to determine which other substances were commonlyreported when fentanyl was listed on the death certificate, in order toassess polydrug use and its impact on increased mortality.ResultsCompared to the number of fentanyl-associated mortalities in 2010(82), fentanyl-associated mortality in 2015 (599) was 6.5 times higherafter controlling for the natural increase in total mortality between2010 and 2015. Almost three-fourths of the deaths in 2015 were male(73%), which is higher than the proportion of male deaths in 2010(55%). The age group with the largest burden of fentanyl-associatedmortality was the 30 – 39 age group, with almost one-third of thedeaths in 2015 coming from this age group (31%) compared to only10% in 2010, a roughly 200% increase. Fentanyl-associated mortalitywas almost exclusive to people that are Caucasian, with 94% of thefentanyl-associated mortalities in 2015 occurring among Caucasians.Multi-drug use was also identified for those with fentanyl-associatedmortality. Mentions of other drugs were present in at least 10% of thedeaths. Some of the other drugs mentioned in the “literals” includedheroin, cocaine, and alprazolam. There was county variation in thenumber of fentanyl morality deaths ranging from 21.19 deaths per100,000 to 0.29 deaths per 100,000 residents. Two counties with thehighest rates were located adjacent to one another.ConclusionsHaving death record data readily available within the statesyndromic surveillance system is beneficial for rapid analysisof mortality trends and the analytic methods used for syndromicsurveillance can be applied to mortality data. Free text querying ofthe “literals” in the vital statistics death records data allowed forsurveillance of fentanyl-associated mortality, similar to methods usedfor querying emergency department chief complaint data. Althoughunderlying ICD-10 codes can lack detail about certain causes ofdeath, the “literals” provide a clearer picture as to what caused thedeath. The “literals” also make it possible to look at potential drugcombinations that may have increased risk of mortality, which willbe explored more thoroughly. Further work will explore other datasources for fentanyl usage and mortality trends, as well as examinepotential risk factors and confounders

Characterizing Fentanyl-Associated Mortality using the Literal Causes of Death

ObjectiveTo characterize fentanyl-associated mortality in Florida using freetext queries of the literal causes of death listed on death certificates.IntroductionIn October 2015, the Centers for Disease Control and Prevention(CDC) released health advisory #384 to inform people about increasesin fentanyl fatalities. Florida’s statewide syndromic surveillancesystem, Electronic Surveillance System for the Early Notification ofCommunity-based Epidemics (ESSENCE-FL), captures electronicdeath record data in near real time which allows for the monitoringof mortality trends across the state. One limitation of using deathrecord data for fentanyl surveillance is the lack of a fentanyl-specificoverdose ICD-10 code; however, the literal cause of death fields(“literals”) provide a level of detail that is rich enough to capturementions of fentanyl use. The “literals” are a free text field on thedeath certificate, recorded by a physician at the time of death anddetail the factors that led to the death. ESSENCE-FL has the benefitof not only receiving death record data in near real-time, but alsoreceiving the literal cause of death fields. This work analyzes trendsin fentanyl-associated mortality in Florida over time by using theliteral cause of death fields within death records data obtained fromESSENCE-FL.MethodsThe “literals” elements of Florida Vital Statistics mortality datafrom 2010 through 2015 accessed via ESSENCE-FL were queriedfor the term ^fent^. No necessary negations or extra term inclusionswere deemed necessary after looking at the records pulled with ^fent^alone. Deaths were analyzed by various demographic and geographicvariables to characterize this population in order to assess whichgroups are most heavily burdened by fentanyl-associated mortality.Population estimates by county for 2015 were obtained from the U.S.Census Bureau to calculate mortality rates. Language processing in RStudio was used to determine which other substances were commonlyreported when fentanyl was listed on the death certificate, in order toassess polydrug use and its impact on increased mortality.ResultsCompared to the number of fentanyl-associated mortalities in 2010(82), fentanyl-associated mortality in 2015 (599) was 6.5 times higherafter controlling for the natural increase in total mortality between2010 and 2015. Almost three-fourths of the deaths in 2015 were male(73%), which is higher than the proportion of male deaths in 2010(55%). The age group with the largest burden of fentanyl-associatedmortality was the 30 – 39 age group, with almost one-third of thedeaths in 2015 coming from this age group (31%) compared to only10% in 2010, a roughly 200% increase. Fentanyl-associated mortalitywas almost exclusive to people that are Caucasian, with 94% of thefentanyl-associated mortalities in 2015 occurring among Caucasians.Multi-drug use was also identified for those with fentanyl-associatedmortality. Mentions of other drugs were present in at least 10% of thedeaths. Some of the other drugs mentioned in the “literals” includedheroin, cocaine, and alprazolam. There was county variation in thenumber of fentanyl morality deaths ranging from 21.19 deaths per100,000 to 0.29 deaths per 100,000 residents. Two counties with thehighest rates were located adjacent to one another.ConclusionsHaving death record data readily available within the statesyndromic surveillance system is beneficial for rapid analysisof mortality trends and the analytic methods used for syndromicsurveillance can be applied to mortality data. Free text querying ofthe “literals” in the vital statistics death records data allowed forsurveillance of fentanyl-associated mortality, similar to methods usedfor querying emergency department chief complaint data. Althoughunderlying ICD-10 codes can lack detail about certain causes ofdeath, the “literals” provide a clearer picture as to what caused thedeath. The “literals” also make it possible to look at potential drugcombinations that may have increased risk of mortality, which willbe explored more thoroughly. Further work will explore other datasources for fentanyl usage and mortality trends, as well as examinepotential risk factors and confounders

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

ObjectiveThis study describes how Florida Poison Information Center Network (FPICN) and emergency department (ED) data accessed through Florida’s syndromic surveillance system were used to conduct near real-time carbon monoxide (CO) poisoning surveillance and active case finding in response to Hurricane Irma in Florida.IntroductionOn September 10, 2017, Hurricane Irma made landfall in Florida. Over 90% of Florida counties reported power outages as of September 11. During power outages, CO poisonings often occur due to indoor use of fuel combustion sources (e.g., cooking, heating) or generators for electricity.CO poisoning is a reportable condition in Florida; health care providers and laboratories are required to report suspected cases to the Florida Department of Health (FDOH). In Florida, approximately 202 cases of CO poisoning are reported each year (three-year average from 2014 to 2016). In addition to passive surveillance, FDOH uses the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE-FL) to find cases of CO poisoning. ESSENCE-FL provides access to ED data from 98% (255 out of 260) of EDs in Florida and all statewide FPICN call data (includes three poison control centers). ESSENCE-FL provides near real-time access to these data sets, as ED data are uploaded every 2 hours or once a day (depending on the hospital system) and FPICN data are uploaded every 10 minutes. The statewide FPICN database includes information about substance, signs and symptoms, exposure scenario, and patient identification information provided by the individual caller or clinician from a health care facility.MethodsIn addition to receipt of health care provider reports through traditional disease reporting, active case finding was conducted using ESSENCE-FL during Hurricane Irma. Exposure calls to the FPICN indicating CO exposure were extracted from the statewide database. Calls coded with the following medical outcomes were excluded: no health effect, not followed – judged as nontoxic exposure, not followed – minimal clinical effects possible, unrelated effect – the exposure was probably not responsible for the effect(s), and confirmed non-exposure. To query ESSENCE-FL ED data, a free-text query was created and executed against the concatenated chief complaint and discharge diagnosis (CCDD) field: (^carbon^,andnot,(,^retention^ ,or,^narcosis^,),),or,^monox^,or,(,^generator^,and, (,^fumes^,or,^expos^,or,^nausea^, or,^headach^,or,^exhaust^,or,^garage^,or,^inhale^,),) . Results of these queries were analyzed and sent to county and regional epidemiologists daily for investigation.Reports of CO poisoning exposures were investigated by collecting medical records and conducting interviews using an expanded risk factor questionnaire.1 Cases were classified using Florida’s reportable disease case definition2 and documented in the electronic reportable disease surveillance system, Merlin (see process flow chart). Descriptive analysis of Hurricane Irma-related CO poisoning cases reported in Merlin was conducted to characterize morbidity, mortality, and exposure scenarios.ResultsIn September 2017, FDOH investigated 666 reports of CO poisoning and identified 529 people (79.4%) who met the case definition for CO poisoning. Among 529 cases, 56.3% were reported by ED data, 5.7% by FPICN data, 29.1% from both data sets, and the remaining 8.9% by other sources (e.g., self-report, media). About 60.1% of cases were only reported by FPICN and ED data, 33.1% by health care providers and laboratories, and 6.8% by other sources. Among 15 deaths, 20% were identified through active case finding using ED and FPICN data. CO poisoning cases peaked on September 12 (within two days of hurricane landfall) and decreased by September 16, as power was restored. About 95% of cases reported CO exposures within the first week of hurricane landfall.Merlin data analysis of 529 cases identified some notable findings related to Hurricane Irma. CO poisoning rates were highest among those aged 5–14 years (4.8 per 100,000 population), and the mean age was 33.2 years (median: 31 years, range: 3 months – 89 years). Most cases were in females (55.6%), non-Hispanics (58.3%), and whites (73%). CO exposures were predominantly caused by generator use (97.5%). Among 516 generator-related exposures, 15.7% of people had a CO detector, 62.8% did not have CO detector, and it was unknown for 21.5%. Among 516 residential exposures due to generator use, 31.3% of people reported generator use inside the home, attached garage, or other attached structures, and 66% reported generator use outside the home, including covered decks and carports. Among 340 people who reported generator use outside the home, 63.5% reported having a generator within 20 feet of windows, doors, air conditioners, or air intake vents.ConclusionsEven though CO poisoning is a reportable condition in Florida, use of active surveillance was key in the public health response to Hurricane Irma-related CO poisonings. FDOH would not have identified 60% of these hurricane-related CO poisoning cases without access to FPICN and ED data. During Hurricane Irma, active case finding complemented routine disease surveillance not only in early detection of CO poisonings but also in guiding rapid public health response. Similarly, in the 2005 hurricane season, FDOH monitored FPICN data and identified an increase in CO poisonings.3 Based on near-real-time CO poisoning surveillance, FDOH produced daily situation reports, sent out a press release about the dangers of CO poisoning from generator use, prepared a YouTube video, and conducted educational outreach through social media and text alert. Other jurisdictions may benefit from use of near real-time ED and poison control center data to better understand the magnitude and characteristics of CO poisonings during power outages in their areas. Public education messages need to emphasize outdoor use of generators (at least 20 feet away from doors, windows, and air conditioners) and use of CO detectors.References1. Florida Department of Health. Carbon monoxide poisoning enhanced case report form; October 2017. Available at: www.floridahealth.gov/diseases-and-conditions/disease-reporting-and-management/disease-reporting-and-surveillance/_documents/crf-co-hurricane-irma-enhanced-surveillance.pdf2. Florida Department of Health. Carbon monoxide poisoning case definition; 2018. Available at: www.floridahealth.gov/diseases-and-conditions/disease-reporting-and-management/disease-reporting-and-surveillance/_documents/cd-carbon-monoxide.pdf3. Monitoring Poison Control Center Data to Detect Health Hazards During Hurricane Season—Florida, 2003-2005. JAMA. 2006;295(21):2469–2470.

MERS PUI Surveillance and Restrospective Identification in ESSENCE-FL, 2013-2015

ObjectiveTo retrospectively identify initial emergency department (ED)and urgent care center (UCC) visits for Florida’s Middle Eastrespiratory syndrome coronavirus disease (MERS-CoV) patientsunder investigation (PUIs) in the Florida Department of Health’s(DOH) syndromic surveillance system, the Electronic SurveillanceSystem for the Early Notification of Community-based Epidemics(ESSENCE-FL), using information gathered from PUI case reportforms and corresponding medical records for the purpose ofimproving syndromic surveillance for MERS-CoV. The results ofthis study may be further utilized in an effort to evaluate the currentMERS-CoV surveillance query.IntroductionHuman MERS-CoV was first reported in September 2012. Globally,all reported cases have been linked through travel to or residence inthe Arabian Peninsula with the exception of cases associated with anoutbreak involving multiple health care facilities in the Republic ofKorea ending in July 2015. While the majority of MERS-CoV caseshave been reported in the Arabian Peninsula, several cases have beenreported outside of the region. Most cases are believed to have beenacquired in the Middle East and then exported elsewhere, with no orrare instances of secondary transmission. Two cases of MERS-CoVwere exported to the United States and identified in May 2014. Oneof these cases traveled from Saudi Arabia to Florida.DOH conducts regular surveillance for MERS-CoV through theinvestigation of persons with known risk factors. PUIs have mostoften been identified by physicians reporting directly to local healthdepartments and by DOH staff regularly querying ED and UCC chiefcomplaint data in ESSENCE-FL. ESSENCE-FL currently capturesdata from 265 EDs and UCCs statewide and has been useful inidentifying cases associated with reportable disease and emergingpathogens.MethodsFrom 2013-2015 DOH identified and investigated 62 suspectedcases of MERS-CoV, including one confirmed case in May 2014.Specimens were collected from all 62 patients under investigation(PUIs) and 61 were ruled out. Of the 61 PUIs who were ruled out,ten were part of the contact investigation initiated following theidentification of MERS-CoV in May 2014 and were not included inthis analysis. DOH utilizes a MERS-CoV PUI case report form tocollect data regarding demographics, clinical presentation, and riskfactors. Retrospectively, additional documents including medicalrecords and discharge summaries were gathered and utilized toevaluate PUIs identified in ESSENCE-FL.Name of the facility where PUIs presented, date and time of visit,age at event, and sex were identified using PUI case report forms andcorresponding medical records and discharge summaries. Visit detailsfor each of the identified facilities were queried in ESSENCE-FLand pulled for all visits with corresponding age at event and sex forthe patient’s visit date. Additional PUI information including chiefcomplaint, discharge diagnosis, ZIP code, race, and ethnicity weregathered for the purpose of matching corresponding ESSENCE-FLdata fields. ESSENCE-FL visit details were narrowed by ZIP code (orlack of ZIP code for residents of other countries) and match detailswere recorded and evaluated. The fields examined were not alwayscomplete in ESSENCE-FL. Visits were considered matches when allavailable data in the fields examined were consistent with informationobtained in the PUI case report form and available medical recordsand discharge summaries.ResultsOf the 52 PUIs included in this analysis, 39 sought treatmentat facilities participating in ESSENCE-FL at their time of visit.Comparing information obtained from PUI documents with dataprovided in ESSENCE-FL, 30 ED visits were successfully matchedto PUIs, including an initial ED visit for the patient with a confirmedcase of MERS-CoV.ConclusionsFollowing preliminary identification, all matches are to beconfirmed with the appropriate hospitals. Future work to examine thechief complaints associated with patients’ initial ED visits identifiedin ESSENCE-FL will serve as a way to validate and improve uponthe query currently being used as a surveillance tool for MERS-CoV.Detailing these methods also has value in the replication of thisstudy for other diseases and in the development and validation ofother disease-specific queries. Summarizing the reasons why PUIswere unable to be matched to ESSENCE-FL visits is also useful inimproving system robustness

MERS PUI Surveillance and Restrospective Identification in ESSENCE-FL, 2013-2015

ObjectiveTo retrospectively identify initial emergency department (ED)and urgent care center (UCC) visits for Florida’s Middle Eastrespiratory syndrome coronavirus disease (MERS-CoV) patientsunder investigation (PUIs) in the Florida Department of Health’s(DOH) syndromic surveillance system, the Electronic SurveillanceSystem for the Early Notification of Community-based Epidemics(ESSENCE-FL), using information gathered from PUI case reportforms and corresponding medical records for the purpose ofimproving syndromic surveillance for MERS-CoV. The results ofthis study may be further utilized in an effort to evaluate the currentMERS-CoV surveillance query.IntroductionHuman MERS-CoV was first reported in September 2012. Globally,all reported cases have been linked through travel to or residence inthe Arabian Peninsula with the exception of cases associated with anoutbreak involving multiple health care facilities in the Republic ofKorea ending in July 2015. While the majority of MERS-CoV caseshave been reported in the Arabian Peninsula, several cases have beenreported outside of the region. Most cases are believed to have beenacquired in the Middle East and then exported elsewhere, with no orrare instances of secondary transmission. Two cases of MERS-CoVwere exported to the United States and identified in May 2014. Oneof these cases traveled from Saudi Arabia to Florida.DOH conducts regular surveillance for MERS-CoV through theinvestigation of persons with known risk factors. PUIs have mostoften been identified by physicians reporting directly to local healthdepartments and by DOH staff regularly querying ED and UCC chiefcomplaint data in ESSENCE-FL. ESSENCE-FL currently capturesdata from 265 EDs and UCCs statewide and has been useful inidentifying cases associated with reportable disease and emergingpathogens.MethodsFrom 2013-2015 DOH identified and investigated 62 suspectedcases of MERS-CoV, including one confirmed case in May 2014.Specimens were collected from all 62 patients under investigation(PUIs) and 61 were ruled out. Of the 61 PUIs who were ruled out,ten were part of the contact investigation initiated following theidentification of MERS-CoV in May 2014 and were not included inthis analysis. DOH utilizes a MERS-CoV PUI case report form tocollect data regarding demographics, clinical presentation, and riskfactors. Retrospectively, additional documents including medicalrecords and discharge summaries were gathered and utilized toevaluate PUIs identified in ESSENCE-FL.Name of the facility where PUIs presented, date and time of visit,age at event, and sex were identified using PUI case report forms andcorresponding medical records and discharge summaries. Visit detailsfor each of the identified facilities were queried in ESSENCE-FLand pulled for all visits with corresponding age at event and sex forthe patient’s visit date. Additional PUI information including chiefcomplaint, discharge diagnosis, ZIP code, race, and ethnicity weregathered for the purpose of matching corresponding ESSENCE-FLdata fields. ESSENCE-FL visit details were narrowed by ZIP code (orlack of ZIP code for residents of other countries) and match detailswere recorded and evaluated. The fields examined were not alwayscomplete in ESSENCE-FL. Visits were considered matches when allavailable data in the fields examined were consistent with informationobtained in the PUI case report form and available medical recordsand discharge summaries.ResultsOf the 52 PUIs included in this analysis, 39 sought treatmentat facilities participating in ESSENCE-FL at their time of visit.Comparing information obtained from PUI documents with dataprovided in ESSENCE-FL, 30 ED visits were successfully matchedto PUIs, including an initial ED visit for the patient with a confirmedcase of MERS-CoV.ConclusionsFollowing preliminary identification, all matches are to beconfirmed with the appropriate hospitals. Future work to examine thechief complaints associated with patients’ initial ED visits identifiedin ESSENCE-FL will serve as a way to validate and improve uponthe query currently being used as a surveillance tool for MERS-CoV.Detailing these methods also has value in the replication of thisstudy for other diseases and in the development and validation ofother disease-specific queries. Summarizing the reasons why PUIswere unable to be matched to ESSENCE-FL visits is also useful inimproving system robustness