scholarly journals Differences and similarities between emergency department syndromic surveillance and hospital discharge data for nonfatal drug overdose

Author(s):  
Alana M. Vivolo-Kantor ◽  
Herschel Smith IV ◽  
Lawrence Scholl
2019 ◽  
Vol 32 (1) ◽  
pp. 33-38 ◽  
Author(s):  
Beata Stanley ◽  
Lisa J Collins ◽  
Amanda F Norman ◽  
Jonathon Karro ◽  
Monica Jung ◽  
...  

2014 ◽  
Vol 129 (5) ◽  
pp. 437-445 ◽  
Author(s):  
Svetla Slavova ◽  
Terry L. Bunn ◽  
Jeffery Talbert

2021 ◽  
Vol 27 (S1) ◽  
pp. i35-i41
Author(s):  
Hannah Yang ◽  
Emilia Pasalic ◽  
Peter Rock ◽  
James W Davis ◽  
Sarah Nechuta ◽  
...  

IntroductionOn 1 October 2015, the USA transitioned from the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) to the International Classification of Diseases, 10th Revision (ICD-10-CM). Considering the major changes to drug overdose coding, we examined how using different approaches to define all-drug overdose and opioid overdose morbidity indicators in ICD-9-CM impacts longitudinal analyses that span the transition, using emergency department (ED) and hospitalisation data from six states’ hospital discharge data systems.MethodsWe calculated monthly all-drug and opioid overdose ED visit rates and hospitalisation rates (per 100 000 population) by state, starting in January 2010. We applied three ICD-9-CM indicator definitions that included identical all-drug or opioid-related codes but restricted the number of fields searched to varying degrees. Under ICD-10-CM, all fields were searched for relevant codes. Adjusting for seasonality and autocorrelation, we used interrupted time series models with level and slope change parameters in October 2015 to compare trend continuity when employing different ICD-9-CM definitions.ResultsMost states observed consistent or increased capture of all-drug and opioid overdose cases in ICD-10-CM coded hospital discharge data compared with ICD-9-CM. More inclusive ICD-9-CM indicator definitions reduced the magnitude of significant level changes, but the effect of the transition was not eliminated.DiscussionThe coding change appears to have introduced systematic differences in measurement of drug overdoses before and after 1 October 2015. When using hospital discharge data for drug overdose surveillance, researchers and decision makers should be aware that trends spanning the transition may not reflect actual changes in drug overdose rates.


Author(s):  
Dylan C. Kent ◽  
Rachel Z. Garcia ◽  
Samuel Packard ◽  
Graham Briggs ◽  
Clancey Hill ◽  
...  

ObjectiveUsing a syndromic surveillance system to understand the magnitude and risk factors related to heat-related illness (HRI) in Pinal County, AZ.IntroductionExtreme heat is a major cause of weather-related morbidity and mortality in the United States (US).1 HRI is the most frequent cause of environmental exposure-related injury treated in US emergency departments.2 More than 65,000 emergency room visits occur for acute HRI each summer nationwide.3 In Arizona, HRI accounts for an estimated 2,000 emergency room patients and 118 deaths each year.4 As heat-related illness becomes increasingly recognized as a public health issue, local health departments are tasked with building capacity to conduct enhanced surveillance of HRI in order to inform public health preparedness and response efforts. In Pinal County, understanding the magnitude and risk factors of HRI is important for informing prevention efforts as well as developing strategies to respond to extreme heat.MethodsTo gain a better understanding of the magnitude of HRI in Pinal County, historical cases were reviewed from hospital discharge data (HDD) from 2010-2016. Cases were included if the discharge record included any ICD codes consistent with HRI (ICD-9 codes 992 or ICD-10 codes T67 or X30) and if the patient’s county of residence was Pinal County. Recent HRI cases during the summer of 2017 were identified using the National Syndromic Surveillance Program BioSense Platform. The ESSENCE syndromic surveillance tool within the BioSense Platform includes data reported by local hospitals. This data can be used to detect abnormal activity for public health investigation. HRI cases were identified in ESSENCE based on ICD-10 codes and chief complaint terms according to a standardized algorithm developed by the Council of State and Territorial Epidemiologists.1 Both emergency department and admitted patients with a HRI were abstracted from HDD and ESSENCE. To assess HRI risk factors for the summer of 2017, a survey instrument was developed. Survey questions included the nature and location of the HRI incident, potential risk factors, and knowledge and awareness of HRI. Cases were identified in ESSENSE on a weekly basis from May 1, 2017-September 12, 2017, and follow up phone interviews were conducted with eligible cases. For HRI cases eligible for interview, three attempts were made to contact the patient by phone. Cases were excluded if the patient was incarcerated, deceased, or did not have a HRI upon medical record review. An exploratory analysis was performed for the data from HDD, ESSENCE, and interviews.ResultsPinal County Public Health Services District identified 1,321 HRI cases from 2010-2016, an average of 189 per year. Hospital discharge data suggest HRI cases are more likely to occur in males between the ages of 20-44 years old (27%). It is also notable that a sharp increase in HRI cases is observed each year in mid-to-late June, with an estimated 14% of annual cases occurring during the third week of June. Further analysis of HDD showed 31% of cases received medical treatment in Casa Grande in central Pinal County. Between May 1st and September 12th of 2017, 161 HRI cases were detected using ESSENCE. Of which 149 cases were determined to be HRI; 22 cases did not have contact information, and 4 cases were ineligible due to incarceration or death. A total of 31 HRI cases were interviewed out of the eligible 123 ESSENSE cases (25% response rate). Interview data indicated occupational exposure to extreme heat as a major risk factor for HRI. Additional risk factors reported during interviews included exposure to extreme heat while at home or traveling, although interview results are not representative due to a small sample size (n=31).ConclusionsSyndromic surveillance combined with interviews and a review of HDD provides an informative approach for monitoring and responding to HRI. Data suggest Pinal County should expect an increase in HRI cases by mid-June each year, typically coinciding with the first National Weather Service Extreme Heat Warning of the season. Preliminary results suggest that cases occur more frequently in working males ages 20-44 years old in occupations that expose workers to extreme heat conditions. Additional information is needed to assess risk factors for HRI among vulnerable populations in Pinal County who were not represented in this study, including individuals who are homeless, undocumented, elderly, or in correctional facilities. Future areas for improvement include improving the phone interview script to include English and Spanish language versions and performing medical record abstractions on all HRI cases. Enhanced syndromic surveillance is recommended to provide information on risk factors for HRI to inform prevention efforts in Pinal County.References1. Heat-Related Illness Syndrome Query: A Guidance Document For Implementing Heat-Related Illness Syndromic Surveillance in Public Health Practice. In: Epidemiologists CoSaT, ed. Vol 1.02016:1-12.2. Pillai SK, Noe RS, Murphy MW, et al. Heat illness: predictors of hospital admissions among emergency department visits-Georgia, 2002-2008. J Community Health. 2014;39(1):90-98.3. Centers for Disease Control and Prevention . Climate Change and Extreme Heat: What You Can Do to Prepare. 2016; Available from https://www.cdc.gov/climateandhealth/pubs/extreme-heat-guidebook.pdf4. Trends in Morbidity and Mortality from Exposure to Excessive Natural Heat in Arizona, 2012 report. In: Services ADoH, ed2012.


2021 ◽  
pp. 088626052199794
Author(s):  
Nakita N. Lovelady ◽  
Nickolas D. Zaller ◽  
Mary Kate Stewart ◽  
Ann M. Cheney ◽  
Austin Porter III ◽  
...  

Using statewide hospital discharge data from 2005 to 2014, this study aimed to describe and identify predictors of firearm assault among young Black men ages 18 to 44 in Arkansas. Descriptive analyses of data were performed for patient demographics (age, marital status, residential location, etc.), injury, and health care information (hospital charges, length of stay, mortality, time, day and season of injury, etc.). Logistic regression analysis was performed to identify significant predicting factors for firearm assault among this population. Most of the sample survived firearm assault injury, were ages 18–35, were not married, resided in Central Arkansas, and were admitted to a Central Arkansas hospital during night hours on weekends. The majority had a short hospital stay, and total charges exceeded $34 million during the study observation years. Most patients had no diagnosis of a mental disorder, and a little less than half had drug use disorders. Being ages 18–25, living in the Central region of Arkansas, and being married were all significant predictors of firearm assault for this population. Death was also significantly associated with firearm assault. Our findings lay the groundwork for understanding firearm assault injury among young Black men in Arkansas. Research should be expanded to examine other important data sources for firearm assault and to further explore the context of predicting factors, in order to provide a more comprehensive understanding of firearm assault and to better inform future prevention efforts.


2020 ◽  
Vol 41 (S1) ◽  
pp. s81-s82
Author(s):  
Andrew Webster ◽  
Scott Fridkin ◽  
Susan Ray

Background: Due to reliance on hospital discharge data for case identification, the burden of noninvasive and community-acquired S. aureus disease is often underestimated. To determine the full burden of S. aureus infections, we utilized population-based surveillance in a large urban county. Methods: The Georgia Emerging Infections Program (GA EIP) conducted CDC-funded, population-based surveillance by finding cases of S. aureus infections in 8 counties around Atlanta in 2017. Cases were residents with S. aureus isolated from either a normally sterile site in a 30-day period (invasive cases) or another site in a 14-day period (noninvasive cases). Medical records (all invasive and 1:4 sample of noninvasive cases) among Fulton County residents were abstracted for clinical, treatment, and outcome data. Cases treated were mapped to standard therapeutic site codes. Noninvasive specimens were reviewed and attributed to an invasive case if both occurred within 2 weeks. Incidence rates were calculated using 2017 census population and using a weight-adjusted cohort to account for sampling. Results: In total, 1,186 noninvasive (1:4 sample) and 529 invasive cases of S. aureus in Fulton county were reviewed. Only 35 of 1,186 (2.9%) noninvasive cases were temporally linked to invasive cases, resulting in 5,133 cases after extrapolation (529 invasive, 4,604 noninvasive). All invasive cases and 3,776 of 4,604 noninvasive cases (82%) were treated (4,305 total). Treatment was highest in skin (90%) and abscess (97%), lowest in urine (62%) and sputum (60%), and consisted of antibacterial agents alone (65%) or in addition to drainage procedures (35%). Overall, 41% of all cases were hospitalized, 12% required ICU admission, and 2.7% died, almost exclusively with bloodstream and pulmonary infections. Attribution of noninvasive infection was most often outside healthcare settings (87%); only 341 (7.9%) were hospital-onset cases; however, 34% of cases had had healthcare exposure in the preceding year, most often inpatient hospitalization (75%) or recent surgery (35%). Estimated countywide incidence was 414 per 100,000 (130 for MRSA and 284 for MSSA), invasive infection was 50 per 100,000. Among treated cases, 57% were SSTI, and the proportion of cases caused by MRSA was ~33% but varied slightly by therapeutic site (Fig. 1). Conclusions: The incidence of treated S. aureus infection in our large urban county is estimated to be 414 per 100,000 persons, which exceeds previously estimated rates based on hospital discharge data. Only 12% of treated infections were invasive, and <1 in 10 were hospital onset. Also, two-thirds of treated disease cases were MSSA; most were SSTIs.Funding: Proprietary Organization: Pfizer.Disclosures: Scott Fridkin, consulting fee - vaccine industry (spouse).


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