scholarly journals Enhanced Surveillance of Heat-Related Illness in Pinal County

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Dylan C. Kent ◽  
Rachel Z. Garcia ◽  
Samuel Packard ◽  
Graham Briggs ◽  
Clancey Hill ◽  
...  
Keyword(s):  
Public Health ◽  
Risk Factors ◽  
Emergency Department ◽  
Emergency Room ◽  
Medical Record ◽  
Hospital Discharge ◽  
Syndromic Surveillance ◽  
Extreme Heat ◽  
Discharge Data ◽  
Icd 10

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.

Transitions in “Privatized” Prison Health Systems: Emergency Department Visits and Hospitalizations Among Incarcerated People in Florida, 2011–2018

2021 ◽  
pp. e1-e4
Author(s):  
Jessica L. Adler ◽  
Weiwei Chen ◽  
Timothy F. Page
Keyword(s):  
Public Health ◽  
Health Care ◽  
Emergency Department ◽  
Hospital Discharge ◽  
Emergency Department Visits ◽  
Care Utilization ◽  
Discharge Data ◽  
Prison Health ◽  
Discharge Rates ◽  
Care Problems

Objectives. To examine rates of emergency department (ED) visits and hospitalizations among incarcerated people in Florida during a period when health care management in the state’s prisons underwent transitions. Methods. We used Florida ED visit and hospital discharge data (2011–2018) to depict the trend in ED visit and hospital discharge rates among incarcerated people. We proxied incarcerated people using individuals admitted from and discharged or transferred to a court or law enforcement agency. We fitted a regression with year indicators to examine the significance of yearly changes. Results. Among incarcerated people in Florida, ED visit rates quadrupled, and hospitalization rates doubled, between 2015 and 2018, a period when no similar trends were evident in the nonincarcerated population. Public Health Implications. Increasing the amount and flexibility of payments to contractors overseeing prison health services may foster higher rates of hospital utilization among incarcerated people and higher costs, without addressing major quality of care problems. Hospitals and government agencies should transparently report on health care utilization and outcomes among incarcerated people to ensure better oversight of services for a highly vulnerable population. (Am J Public Health. Published online ahead of print March 18, 2021: e1–e4. https://doi.org/10.2105/AJPH.2020.305988 )

scholarly journals Interrupted time series design to evaluate ICD-9-CM to ICD-10-CM coding changes on trends in Colorado emergency department visits related to traumatic brain injury

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Lauren Alexis De Crescenzo ◽  
Barbara Alison Gabella ◽  
Jewell Johnson
Keyword(s):  
Public Health ◽  
Traumatic Brain Injury ◽  
Emergency Department ◽  
Brain Injury ◽  
Interrupted Time Series ◽  
Emergency Department Visits ◽  
Control And Prevention ◽  
Icd 10 ◽  
Ed Visits ◽  
The Impact

Abstract Background The transition in 2015 to the Tenth Revision of the International Classification of Disease, Clinical Modification (ICD-10-CM) in the US led the Centers for Disease Control and Prevention (CDC) to propose a surveillance definition of traumatic brain injury (TBI) utilizing ICD-10-CM codes. The CDC’s proposed surveillance definition excludes “unspecified injury of the head,” previously included in the ICD-9-CM TBI surveillance definition. The study purpose was to evaluate the impact of the TBI surveillance definition change on monthly rates of TBI-related emergency department (ED) visits in Colorado from 2012 to 2017. Results The monthly rate of TBI-related ED visits was 55.6 visits per 100,000 persons in January 2012. This rate in the transition month to ICD-10-CM (October 2015) decreased by 41 visits per 100,000 persons (p-value < 0.0001), compared to September 2015, and remained low through December 2017, due to the exclusion of “unspecified injury of head” (ICD-10-CM code S09.90) in the proposed TBI definition. The average increase in the rate was 0.33 visits per month (p < 0.01) prior to October 2015, and 0.04 visits after. When S09.90 was included in the model, the monthly TBI rate in Colorado remained smooth from ICD-9-CM to ICD-10-CM and the transition was no longer significant (p = 0.97). Conclusion The reduction in the monthly TBI-related ED visit rate resulted from the CDC TBI surveillance definition excluding unspecified head injury, not necessarily the coding transition itself. Public health practitioners should be aware that the definition change could lead to a drastic reduction in the magnitude and trend of TBI-related ED visits, which could affect decisions regarding the allocation of TBI resources. This study highlights a challenge in creating a standardized set of TBI ICD-10-CM codes for public health surveillance that provides comparable yet clinically relevant estimates that span the ICD transition.

scholarly journals Validation of ICD-10-CM codes for injuries complicating pregnancy, childbirth and the puerperium: a medical record review

2021 ◽  
Vol 27 (Suppl 1) ◽  
pp. i9-i12
Author(s):  
Anna Hansen ◽  
Dana Quesinberry ◽  
Peter Akpunonu ◽  
Julia Martin ◽  
Svetla Slavova
Keyword(s):  
Emergency Department ◽  
Medical Record ◽  
International Classification Of Diseases ◽  
Medical Record Review ◽  
Full Sample ◽  
Emergency Department Data ◽  
Classification Of Diseases ◽  
Icd 10 ◽  
Record Review

IntroductionThe purpose of this study was to estimate the positive predictive value (PPV) of International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes for injury, poisoning, physical or sexual assault complicating pregnancy, childbirth and the puerperium (PCP) to capture injury encounters within both hospital and emergency department claims data.MethodsA medical record review was conducted on a sample (n=157) of inpatient and emergency department claims from one Kentucky healthcare system from 2015 to 2017, with any diagnosis in the ICD-10-CM range O9A.2-O9A.4. Study clinicians reviewed medical records for the sampled cases and used an abstraction form to collect information on documented presence of injury and PCP complications. The study estimated the PPVs and the 95% CIs of O9A.2-O9A.4 codes for (1) capturing injuries and (2) capturing injuries complicating PCP.ResultsThe estimated PPV for the codes O9A.2-O9A.4 to identify injury in the full sample was 79.6% (95% CI 73.3% to 85.9%) and the PPV for capturing injuries complicating PCP was 72.0% (95% CI 65.0% to 79.0%). The estimated PPV for an inpatient principal diagnosis O9A.2-O9A.4 to capture injuries was 90.7% (95% CI 82.0% to 99.4%) and the PPV for capturing injuries complicating PCP was 88.4% (95% CI 78.4% to 98.4%). The estimated PPV for any mention of O9A.2-O9A.4 in emergency department data to capture injuries was 95.2% (95% CI 90.6% to 99.9%) and the PPV for capturing injuries complicating PCP was 81.0% (95% CI 72.4% to 89.5%).DiscussionThe O9A.2-O9A.4 codes captured high percentage true injury cases among pregnant and puerperal women.

The prevalence of pre-existing mental health, drug and alcohol conditions in major trauma patients

2017 ◽  
Vol 41 (3) ◽  
pp. 283 ◽  
Author(s):  
Tu Q. Nguyen ◽  
Pamela M. Simpson ◽  
Belinda J. Gabbe
Keyword(s):  
Mental Health ◽  
Medical Record ◽  
Major Trauma ◽  
Health Conditions ◽  
Trauma Patients ◽  
Discharge Data ◽  
Mental Health Conditions ◽  
Icd 10 ◽  
Record Review ◽  
Drug And Alcohol

Objective Capturing information about mental health, drug and alcohol conditions in injury datasets is important for improving understanding of injury risk and outcome. This study describes the prevalence of pre-existing mental health, drug and alcohol conditions in major trauma patients based on routine discharge data coding. Methods Data were extracted from the population-based Victorian State Trauma Registry (July 2005 to June 2013, n = 16 096). Results Seventeen percent of major trauma patients had at least one mental health condition compared with the Australian population prevalence of 21%. The prevalence of mental health conditions was similar to the Australian population prevalence in men (19% v. 18%), but lower in women (14% v. 25%) and across all age groups. Mental health conditions were more prevalent in intentional self-harm cases (56.3%) compared with unintentional (13.8%) or other intentional (31.2%) cases. Substance use disorders were more prevalent in major trauma patients than the general population (15% v. 5%), higher in men than women (17% v. 10%) and was highest in young people aged 25–34 years (24%). Conclusions Under-reporting of mental health conditions in hospital discharge data appears likely, reducing the capacity to characterise the injury population. Further validation is needed. What is known about the topic? Medical record review, routine hospital discharge data and self-report have been used by studies previously to characterise mental health, drug and alcohol conditions in injured populations, with medical record review considered the most accurate and reliance on self-report measures being considered at risk of recall bias. The use of routinely collected data sources provides an efficient and standardised method of characterising pre-existing conditions, but may underestimate the true prevalence of conditions. What does this paper add? No study to date has explored the prevalence of Abbreviated Injury Scale and International Classification of Diseases and Health Related Problems, Tenth Revision, Australian Modification (ICD-10-a.m)-coded mental health, alcohol and drug conditions in seriously injured populations. The results of this study show the incidence of mental health conditions appeared to be under-reported in major trauma patients, suggesting limitations in the use of ICD-10-a.m. to measure mental health comorbidities. What are the implications for practitioners? In order to achieve improvements in measuring mental health, drug and alcohol comorbidities, we suggest the use of a series of different diagnostic systems to be used in conjunction with ICD-10-a.m., such as medical record review and self-reporting as well as linkage to other datasets. When applied simultaneously, diagnosis and outcomes of mental health may be compared and validated across diagnostic systems and deviations in diagnoses could be more readily accounted for.

scholarly journals Descriptive exploration of overdose codes in hospital and emergency department discharge data to inform development of drug overdose morbidity surveillance indicator definitions in ICD-10-CM

2021 ◽  
Vol 27 (S1) ◽  
pp. i27-i34
Author(s):  
Leigh M Tyndall Snow ◽  
Katelyn E Hall ◽  
Cody Custis ◽  
Allison L Rosenthal ◽  
Emilia Pasalic ◽  
...  
Keyword(s):  
Emergency Department ◽  
Adverse Effects ◽  
Drug Overdose ◽  
International Classification Of Diseases ◽  
Primary Diagnosis ◽  
Epidemiological Surveillance ◽  
Discharge Data ◽  
Aggregated Data ◽  
Data Coding ◽  
Icd 10

BackgroundIn October 2015, discharge data coding in the USA shifted to the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM), necessitating new indicator definitions for drug overdose morbidity. Amid the drug overdose crisis, characterising discharge records that have ICD-10-CM drug overdose codes can inform the development of standardised drug overdose morbidity indicator definitions for epidemiological surveillance.MethodsEight states submitted aggregated data involving hospital and emergency department (ED) discharge records with ICD-10-CM codes starting with T36–T50, for visits occurring from October 2015 to December 2016. Frequencies were calculated for (1) the position within the diagnosis billing fields where the drug overdose code occurred; (2) primary diagnosis code grouped by ICD-10-CM chapter; (3) encounter types; and (4) intents, underdosing and adverse effects.ResultsAmong all records with a drug overdose code, the primary diagnosis field captured 70.6% of hospitalisations (median=69.5%, range=66.2%–76.8%) and 79.9% of ED visits (median=80.7%; range=69.8%–88.0%) on average across participating states. The most frequent primary diagnosis chapters included injury and mental disorder chapters. Among visits with codes for drug overdose initial encounters, subsequent encounters and sequelae, on average 94.6% of hospitalisation records (median=98.3%; range=68.8%–98.8%) and 95.5% of ED records (median=99.5%; range=79.2%–99.8%), represented initial encounters. Among records with drug overdose of any intent, adverse effect and underdosing codes, adverse effects comprised an average of 74.9% of hospitalisation records (median=76.3%; range=57.6%–81.1%) and 50.8% of ED records (median=48.9%; range=42.3%–66.8%), while unintentional intent comprised an average of 11.1% of hospitalisation records (median=11.0%; range=8.3%–14.5%) and 28.2% of ED records (median=25.6%; range=20.8%–40.7%).ConclusionResults highlight considerations for adapting and standardising drug overdose indicator definitions in ICD-10-CM.

scholarly journals Use of Emergency Department Data to Monitor and Respond to an Increase in Opioid Overdoses in New Hampshire, 2011-2015

2017 ◽  
Vol 132 (1_suppl) ◽  
pp. 73S-79S ◽  
Author(s):  
Elizabeth R. Daly ◽  
Kenneth Dufault ◽  
David J. Swenson ◽  
Paul Lakevicius ◽  
Erin Metcalf ◽  
...  
Keyword(s):  
Public Health ◽  
Emergency Department ◽  
New Hampshire ◽  
Syndromic Surveillance ◽  
Situational Awareness ◽  
The United States ◽  
Chief Complaint ◽  
Public Health Response ◽  
Syndromic Surveillance System ◽  
Icd Codes

Objectives: Opioid-related overdoses and deaths in New Hampshire have increased substantially in recent years, similar to increases observed across the United States. We queried emergency department (ED) data in New Hampshire to monitor opioid-related ED encounters as part of the public health response to this health problem. Methods: We obtained data on opioid-related ED encounters for the period January 1, 2011, through December 31, 2015, from New Hampshire’s syndromic surveillance ED data system by querying for (1) chief complaint text related to the words “fentanyl,” “heroin,” “opiate,” and “opioid” and (2) opioid-related International Classification of Diseases ( ICD) codes. We then analyzed the data to calculate frequencies of opioid-related ED encounters by age, sex, residence, chief complaint text values, and ICD codes. Results: Opioid-related ED encounters increased by 70% during the study period, from 3300 in 2011 to 5603 in 2015; the largest increases occurred in adults aged 18-29 and in males. Of 20 994 total opioid-related ED visits, we identified 18 554 (88%) using ICD code alone, 690 (3%) using chief complaint text alone, and 1750 (8%) using both chief complaint text and ICD code. For those encounters identified by ICD code only, the corresponding chief complaint text included varied and nonspecific words, with the most common being “pain” (n = 3335, 18%), “overdose” (n = 1555, 8%), “suicidal” (n = 816, 4%), “drug” (n = 803, 4%), and “detox” (n = 750, 4%). Heroin-specific encounters increased by 827%, from 4% of opioid-related encounters in 2011 to 24% of encounters in 2015. Conclusions: Opioid-related ED encounters in New Hampshire increased substantially from 2011 to 2015. Data from New Hampshire’s ED syndromic surveillance system provided timely situational awareness to public health partners to support the overall response to the opioid epidemic.

scholarly journals Interrupted time Series Design to Evaluate ICD-9-CM to ICD-10-CM Coding Changes on Trends in Colorado Emergency Department visits Related to Traumatic Brain Injury

2020 ◽  
Author(s):  
Lauren Alexis De Crescenzo ◽  
Barbara Alison Gabella ◽  
Jewell Johnson
Keyword(s):  
Public Health ◽  
Traumatic Brain Injury ◽  
Emergency Department ◽  
Brain Injury ◽  
Interrupted Time Series ◽  
Emergency Department Visits ◽  
P Value ◽  
Icd 10 ◽  
The Usa ◽  
Definition Of

Abstract Background. The transition in 2015 to the Tenth Revision of the International Classification of Disease, Clinical Modification (ICD-10-CM) in the USA led public health professionals to propose a surveillance definition of traumatic brain injury (TBI) that uses ICD-10-CM codes. The proposed definition excludes “unspecified injury of the head,” previously included in the ICD-9-CM TBI definition. The purpose of this study was to evaluate this change in surveillance methods on monthly rates of TBI-related emergency department visits in Colorado from 2012 to 2017.Results. The monthly rate of TBI-related emergency department visits in the transition month to ICD-10-CM (October 2015) decreased 41 visits per 100,000 population (p-value <0.0001), compared to September 2015, and remained low through December 2017, due to the exclusion of “unspecified injury of head” (ICD-10-CM code S09.90) in the proposed TBI definition. Conclusion. This study highlights a challenge in creating a standardized set of TBI ICD-10-CM codes for public health surveillance that provides comparable yet clinically relevant estimates over time. The findings inform estimation of TBI magnitude based on ICD coded data and decisions about allocating TBI resources based on an estimated TBI magnitude.

scholarly journals Opioid prescribing in the emergency department of a tertiary hospital: A retrospective audit of hospital discharge data

2019 ◽  
Vol 32 (1) ◽  
pp. 33-38 ◽  
Author(s):  
Beata Stanley ◽  
Lisa J Collins ◽  
Amanda F Norman ◽  
Jonathon Karro ◽  
Monica Jung ◽  
...  
Keyword(s):  
Emergency Department ◽  
Hospital Discharge ◽  
Tertiary Hospital ◽  
Hospital Discharge Data ◽  
Discharge Data ◽  
Opioid Prescribing ◽  
Retrospective Audit

scholarly journals Syndromic Surveillance of Emergency Department Visits for Acute Adverse Effects of Marijuana, Tri-County Health Department, Colorado, 2016-2017

2019 ◽  
Vol 134 (2) ◽  
pp. 132-140 ◽  
Author(s):  
Grace E. Marx ◽  
Yushiuan Chen ◽  
Michele Askenazi ◽  
Bernadette A. Albanese
Keyword(s):  
Emergency Department ◽  
Adverse Effects ◽  
Medical Records ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Marijuana Use ◽  
Emergency Department Visits ◽  
Diagnostic Codes ◽  
Icd 10 ◽  
Ed Visits

Objectives: In Colorado, legalization of recreational marijuana in 2014 increased public access to marijuana and might also have led to an increase in emergency department (ED) visits. We examined the validity of using syndromic surveillance data to detect marijuana-associated ED visits by comparing the performance of surveillance queries with physician-reviewed medical records. Methods: We developed queries of combinations of marijuana-specific International Classification of Diseases, Tenth Revision (ICD-10) diagnostic codes or keywords. We applied these queries to ED visit data submitted through the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE) syndromic surveillance system at 3 hospitals during 2016-2017. One physician reviewed the medical records of ED visits identified by ≥1 query and calculated the positive predictive value (PPV) of each query. We defined cases of acute adverse effects of marijuana (AAEM) as determined by the ED provider’s clinical impression during the visit. Results: Of 44 942 total ED visits, ESSENCE queries detected 453 (1%) as potential AAEM cases; a review of 422 (93%) medical records identified 188 (45%) true AAEM cases. Queries using ICD-10 diagnostic codes or keywords in the triage note identified all true AAEM cases; PPV varied by hospital from 36% to 64%. Of the 188 true AAEM cases, 109 (58%) were among men and 178 (95%) reported intentional use of marijuana. Compared with noncases of AAEM, cases were significantly more likely to be among non-Colorado residents than among Colorado residents and were significantly more likely to report edible marijuana use rather than smoked marijuana use ( P < .001). Conclusions: ICD-10 diagnostic codes and triage note keyword queries in ESSENCE, validated by medical record review, can be used to track ED visits for AAEM.

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