Heat Waves and Emergency Department Visits Among the Homeless, San Diego, 2012–2019

2022 ◽  
Vol 112 (1) ◽  
pp. 98-106
Author(s):  
Lara Schwarz ◽  
Edward M. Castillo ◽  
Theodore C. Chan ◽  
Jesse J. Brennan ◽  
Emily S. Sbiroli ◽  
...  
Keyword(s):  
Emergency Department ◽  
San Diego ◽  
Heat Waves ◽  
Positive Association ◽  
Emergency Department Visits ◽  
Psychiatric Consultation ◽  
Vulnerability Factors ◽  
Exact Matching ◽  
Data Set ◽  
Ed Visits

Objectives. To determine the effect of heat waves on emergency department (ED) visits for individuals experiencing homelessness and explore vulnerability factors. Methods. We used a unique highly detailed data set on sociodemographics of ED visits in San Diego, California, 2012 to 2019. We applied a time-stratified case–crossover design to study the association between various heat wave definitions and ED visits. We compared associations with a similar population not experiencing homelessness using coarsened exact matching. Results. Of the 24 688 individuals identified as experiencing homelessness who visited an ED, most were younger than 65 years (94%) and of non-Hispanic ethnicity (84%), and 14% indicated the need for a psychiatric consultation. Results indicated a positive association, with the strongest risk of ED visits during daytime (e.g., 99th percentile, 2 days) heat waves (odds ratio = 1.29; 95% confidence interval = 1.02, 1.64). Patients experiencing homelessness who were younger or elderly and who required a psychiatric consultation were particularly vulnerable to heat waves. Odds of ED visits were higher for individuals experiencing homelessness after matching to nonhomeless individuals based on age, gender, and race/ethnicity. Conclusions. It is important to prioritize individuals experiencing homelessness in heat action plans and consider vulnerability factors to reduce their burden. (Am J Public Health. 2022;112(1):98–106. https://doi.org/10.2105/AJPH.2021.306557 )

scholarly journals Social Determinants of Emergency Department Visits in Mild Compared To Moderate and Severe Asthma

2021 ◽  
Author(s):  
Kamel Alachraf ◽  
Caroline Currie ◽  
William Wooten ◽  
Dmitry Tumin
Keyword(s):  
Emergency Department ◽  
Severe Asthma ◽  
Social Determinants ◽  
Insurance Coverage ◽  
Asthma Severity ◽  
Emergency Department Visits ◽  
Data Set ◽  
The Past ◽  
Children With Asthma ◽  
Ed Visits

Abstract Social determinants of health (SDH) influence emergency department (ED) use among children with asthma. We aimed to examine if SDH were more strongly associated with ED use among children with moderate/severe compared to mild asthma. This study utilized the 2016-2019 data from the National Survey of Children’s Health. Children with asthma ages 0-17 years (N=9,937) were included in the analysis. Asthma severity and all-cause ED use in the past year were reported by caregivers. The association between patient factors and ED visits was evaluated using ordinal logistic regression. Based on the study sample, 29% of children with asthma had moderate/severe asthma. In the mild group, 30% visited the ED at least once in the past 12 months, compared to 49% in the moderate/severe group. SDH associated with ED visits included race/ethnicity, insurance coverage, and parental educational attainment, but the strength of these associations did not vary according to asthma severity. In a nationally-representative data set, SDH were equally predictive of ED use regardless of children’s asthma severity. Interventions to reduce ED use among children with asthma should be considered for children with any severity of asthma, especially children in socially disadvantaged groups at higher risk of ED utilization.

scholarly journals Emergency Department Visits During Outpatient Parenteral Antimicrobial Therapy: A Cohort Study

2017 ◽  
Vol 4 (suppl_1) ◽  
pp. S333-S334
Author(s):  
So Lim Kim ◽  
Angela Everett ◽  
Susan J Rehm ◽  
Steven Gordon ◽  
Nabin Shrestha
Keyword(s):  
Risk Factors ◽  
Emergency Department ◽  
Hospital Admission ◽  
Vascular Access ◽  
Antimicrobial Therapy ◽  
Cleveland Clinic ◽  
Emergency Department Visits ◽  
Outpatient Parenteral Antimicrobial Therapy ◽  
Ed Visits ◽  
Access Complications

Abstract Background Outpatient parenteral antimicrobial therapy (OPAT) carries risk of vascular access complications, antimicrobial adverse effects, and worsening of infection. Both OPAT-related and unrelated events may lead to emergency department (ED) visits. The purpose of this study was to describe adverse events that result in ED visits and risk factors associated with ED visits during OPAT. Methods OPAT courses between January 1, 2013 and December 31, 2016 at Cleveland Clinic were identified from the institution’s OPAT registry. ED visits within 30 days of OPAT initiation were reviewed. Reasons and potential risk factors for ED visits were sought in the medical record. Results Among 11,440 OPAT courses during the study period, 603 (5%) were associated with 1 or more ED visits within 30 days of OPAT initiation. Mean patient age was 58 years and 57% were males. 379 ED visits (49%) were OPAT-related; the most common visit reason was vascular access complication, which occurred in 211 (56%) of OPAT-related ED visits. The most common vascular access complications were occlusion and dislodgement, which occurred in 99 and 34 patients (47% and 16% of vascular access complications, respectively). In a multivariable logistic regression model, at least one prior ED visit in the preceding year (prior ED visit) was most strongly associated with one or more ED visits during an OPAT course (OR 2.96, 95% CI 2.38 – 3.71, p-value < 0.001). Other significant factors were younger age (p 0.01), female sex (p 0.01), home county residence (P < 0.001), and having a PICC (p 0.05). 549 ED visits (71%) resulted in discharge from the ED within 24 hours, 18 (2%) left against medical advice, 46 (6%) were observed up to 24 hours, and 150 ED visits (20%) led to hospital admission. Prior ED visit was not associated with hospital admission among patients who visited the ED during OPAT. Conclusion OPAT-related ED visits are most often due to vascular access complications, especially line occlusions. Patients with a prior ED visit in the preceding year have a 3-fold higher odds of at least one ED visit during OPAT compared with patients without a prior ED visit. A strategy of managing occlusions at home and a focus on patients with prior ED visits could potentially prevent a substantial proportion of OPAT-related ED visits. Disclosures All authors: No reported disclosures.

scholarly journals Predictors of Urgent Cancer Care Clinic and Emergency Department Visits for Individuals Diagnosed with Cancer

2021 ◽  
Vol 28 (3) ◽  
pp. 1773-1789
Author(s):  
Kathleen Decker ◽  
Pascal Lambert ◽  
Katie Galloway ◽  
Oliver Bucher ◽  
Marshall Pitz ◽  
...  
Keyword(s):  
Emergency Department ◽  
Mixed Models ◽  
Cancer Care ◽  
Median Number ◽  
Emergency Department Visits ◽  
Planning Service ◽  
Care Clinic ◽  
Future Studies ◽  
Acute Complications ◽  
Ed Visits

In 2013, CancerCare Manitoba (CCMB) launched an urgent cancer care clinic (UCC) to meet the needs of individuals diagnosed with cancer experiencing acute complications of cancer or its treatment. This retrospective cohort study compared the characteristics of individuals diagnosed with cancer that visited the UCC to those who visited an emergency department (ED) and determined predictors of use. Multivariable logistic mixed models were run to predict an individual’s likelihood of visiting the UCC or an ED. Scaled Brier scores were calculated to determine how greatly each predictor impacted UCC or ED use. We found that UCC visits increased up to 4 months after eligibility to visit and then decreased. ED visits were highest immediately after eligibility and then decreased. The median number of hours between triage and discharge was 2 h for UCC visits and 9 h for ED visits. Chemotherapy had the strongest association with UCC visits, whereas ED visits prior to diagnosis had the strongest association with ED visits. Variables related to socioeconomic status were less strongly associated with UCC or ED visits. Future studies would be beneficial to planning service delivery and improving clinical outcomes and patient satisfaction.

scholarly journals Internet search and medicaid prescription drug data as predictors of opioid emergency department visits

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Sean D. Young ◽  
Qingpeng Zhang ◽  
Jiandong Zhou ◽  
Rosalie Liccardo Pacula
Keyword(s):  
Emergency Department ◽  
Prescription Drug ◽  
Drug Utilization ◽  
Prediction Models ◽  
Emergency Department Visits ◽  
Treatment Preferences ◽  
Internet Search ◽  
Utilization Data ◽  
Ed Visits ◽  
Medicaid Prescription

AbstractThe primary contributors to the opioid crisis continue to rapidly evolve both geographically and temporally, hampering the ability to halt the growing epidemic. To address this issue, we evaluated whether integration of near real-time social/behavioral (i.e., Google Trends) and traditional health care (i.e., Medicaid prescription drug utilization) data might predict geographic and longitudinal trends in opioid-related Emergency Department (ED) visits. From January 2005 through December 2015, we collected quarterly State Drug Utilization Data; opioid-related internet search terms/phrases; and opioid-related ED visit data. Modeling was conducted using least absolute shrinkage and selection operator (LASSO) regression prediction. Models combining Google and Medicaid variables were a better fit and more accurate (R2 values from 0.913 to 0.960, across states) than models using either data source alone. The combined model predicted sharp and state-specific changes in ED visits during the post 2013 transition from heroin to fentanyl. Models integrating internet search and drug utilization data might inform policy efforts about regional medical treatment preferences and needs.

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 Predicting emergency department visits in a large teaching hospital

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Nathan Singh Erkamp ◽  
Dirk Hendrikus van Dalen ◽  
Esther de Vries
Keyword(s):  
Emergency Department ◽  
The Netherlands ◽  
Teaching Hospital ◽  
Time Trend ◽  
Prediction Models ◽  
Emergency Department Visits ◽  
Weather Data ◽  
Negative Consequences ◽  
Weather Variables ◽  
Ed Visits

Abstract Background Emergency department (ED) visits show a high volatility over time. Therefore, EDs are likely to be crowded at peak-volume moments. ED crowding is a widely reported problem with negative consequences for patients as well as staff. Previous studies on the predictive value of weather variables on ED visits show conflicting results. Also, no such studies were performed in the Netherlands. Therefore, we evaluated prediction models for the number of ED visits in our large the Netherlands teaching hospital based on calendar and weather variables as potential predictors. Methods Data on all ED visits from June 2016 until December 31, 2019, were extracted. The 2016–2018 data were used as training set, the 2019 data as test set. Weather data were extracted from three publicly available datasets from the Royal Netherlands Meteorological Institute. Weather observations in proximity of the hospital were used to predict the weather in the hospital’s catchment area by applying the inverse distance weighting interpolation method. The predictability of daily ED visits was examined by creating linear prediction models using stepwise selection; the mean absolute percentage error (MAPE) was used as measurement of fit. Results The number of daily ED visits shows a positive time trend and a large impact of calendar events (higher on Mondays and Fridays, lower on Saturdays and Sundays, higher at special times such as carnival, lower in holidays falling on Monday through Saturday, and summer vacation). The weather itself was a better predictor than weather volatility, but only showed a small effect; the calendar-only prediction model had very similar coefficients to the calendar+weather model for the days of the week, time trend, and special time periods (both MAPE’s were 8.7%). Conclusions Because of this similar performance, and the inaccuracy caused by weather forecasts, we decided the calendar-only model would be most useful in our hospital; it can probably be transferred for use in EDs of the same size and in a similar region. However, the variability in ED visits is considerable. Therefore, one should always anticipate potential unforeseen spikes and dips in ED visits that are not shown by the model.

scholarly journals Prevalence and direct costs of emergency department visits and hospitalizations for selected diseases that can be transmitted by water, United States

2017 ◽  
Vol 15 (5) ◽  
pp. 673-683 ◽  
Author(s):  
E. A. Adam ◽  
S. A. Collier ◽  
K. E. Fullerton ◽  
J. W. Gargano ◽  
M. J. Beach
Keyword(s):  
Emergency Department ◽  
Otitis Externa ◽  
Hepatitis A Virus ◽  
Direct Costs ◽  
Emergency Department Visits ◽  
Escherichia Coli Infection ◽  
Waterborne Transmission ◽  
Ed Visits ◽  
National Emergency ◽  
Attribution Process

National emergency department (ED) visit prevalence and costs for selected diseases that can be transmitted by water were estimated using large healthcare databases (acute otitis externa, campylobacteriosis, cryptosporidiosis, Escherichia coli infection, free-living ameba infection, giardiasis, hepatitis A virus (HAV) infection, Legionnaires’ disease, nontuberculous mycobacterial (NTM) infection, Pseudomonas-related pneumonia or septicemia, salmonellosis, shigellosis, and vibriosis or cholera). An estimated 477,000 annual ED visits (95% CI: 459,000–494,000) were documented, with 21% (n = 101,000, 95% CI: 97,000–105,000) resulting in immediate hospital admission. The remaining 376,000 annual treat-and-release ED visits (95% CI: 361,000–390,000) resulted in $194 million in annual direct costs. Most treat-and-release ED visits (97%) and costs ($178 million/year) were associated with acute otitis externa. HAV ($5.5 million), NTM ($2.3 million), and salmonellosis ($2.2 million) were associated with next highest total costs. Cryptosporidiosis ($2,035), campylobacteriosis ($1,783), and NTM ($1,709) had the highest mean costs per treat-and-release ED visit. Overall, the annual hospitalization and treat-and-release ED visit costs associated with the selected diseases totaled $3.8 billion. As most of these diseases are not solely transmitted by water, an attribution process is needed as a next step to determine the proportion of these visits and costs attributable to waterborne transmission.

scholarly journals US Emergency Department Visits for Adverse Drug Events From Antibiotics in Children, 2011–2015

2018 ◽  
Vol 8 (5) ◽  
pp. 384-391 ◽  
Author(s):  
Maribeth C Lovegrove ◽  
Andrew I Geller ◽  
Katherine E Fleming-Dutra ◽  
Nadine Shehab ◽  
Mathew R P Sapiano ◽  
...  
Keyword(s):  
Emergency Department ◽  
Adverse Drug Events ◽  
Emergency Department Visits ◽  
Drug Event ◽  
Injury Surveillance System ◽  
Adverse Event Data ◽  
Systemic Medication ◽  
Ed Visits ◽  
Pharmacy Dispensing Data

Abstract Background Antibiotics are among the most commonly prescribed medications for children; however, at least one-third of pediatric antibiotic prescriptions are unnecessary. National data on short-term antibiotic-related harms could inform efforts to reduce overprescribing and to supplement interventions that focus on the long-term benefits of reducing antibiotic resistance. Methods Frequencies and rates of emergency department (ED) visits for antibiotic adverse drug events (ADEs) in children were estimated using adverse event data from the National Electronic Injury Surveillance System–Cooperative Adverse Drug Event Surveillance project and retail pharmacy dispensing data from QuintilesIMS (2011–2015). Results On the basis of 6542 surveillance cases, an estimated 69464 ED visits (95% confidence interval, 53488–85441) were made annually for antibiotic ADEs among children aged ≤19 years from 2011 to 2015, which accounts for 46.2% of ED visits for ADEs that results from systemic medication. Two-fifths (40.7%) of ED visits for antibiotic ADEs involved a child aged ≤2 years, and 86.1% involved an allergic reaction. Amoxicillin was the most commonly implicated antibiotic among children aged ≤9 years. When we accounted for dispensed prescriptions, the rates of ED visits for antibiotic ADEs declined with increasing age for all antibiotics except sulfamethoxazole-trimethoprim. Amoxicillin had the highest rate of ED visits for antibiotic ADEs among children aged ≤2 years, whereas sulfamethoxazole-trimethoprim resulted in the highest rate among children aged 10 to 19 years (29.9 and 24.2 ED visits per 10000 dispensed prescriptions, respectively). Conclusions Antibiotic ADEs lead to many ED visits, particularly among young children. Communicating the risks of antibiotic ADEs could help reduce unnecessary prescribing. Prevention efforts could target pediatric patients who are at the greatest risk of harm.

scholarly journals A Sample of The Relationship Between The Non-Integrated Healthcare System And National Epidemic of Emergency Department Overcrowding In Turkey

2021 ◽  
Author(s):  
Cihad Dundar ◽  
Seydanur Dal Yaylaoglu
Keyword(s):  
Emergency Department ◽  
Female Gender ◽  
Tertiary Hospital ◽  
Emergency Department Visits ◽  
Work Hours ◽  
Diagnostic Code ◽  
Diagnostic Codes ◽  
Logistic Analysis ◽  
Ed Visits ◽  
Negative Impacts

Abstract Background: The use of EDs has significantly increased, and a majority of this increase is attributed to non-urgent visits, which has negative impacts. We aim to explore the frequency of non-urgent emergency department (ED) visits and to identify risk factors for non-urgent ED visits. Methods: This retrospective, the record-based study was conducted at a tertiary hospital in Samsun province of Turkey. The records of all adult patients who visited to the ED between January 1 and December 31, 2017, were included in this study. All emergency department visits were evaluated according to age, gender, time of visit, means of arrival, ICD diagnostic codes, and the number of repeated non-urgent ED visits. The number of ED visits was 87,528 for the year 2017. Results: The non-urgent emergency visit rate was 9.9%. According to binary logistic analysis, non-urgent visits were associated with young age (OR = 2.75), female gender (OR = 1.11) and non-ambulance transportation (OR = 9.86). The prevalence of non-emergent visits was very similar between weekends and weekdays but was significantly higher in work hours on weekdays than non-work hours (p<0.001). The most frequent diagnostic code was “Pain, unspecified” (R52) and the rate of repeated visits was 14.8% of non-urgent ED visits. Conclusions: Harmonization of various databases at the primary level in terms of design and connectivity and integration with hospital information systems will contribute to the identification of problems and the generation of solutions. The next step is establishing an integrated health care system that can benefit emergency care organizations in Turkey.

Identification of patients at high risk for preventable emergency department visits and inpatient admissions after starting chemotherapy: Machine learning applied to comprehensive electronic health record data.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 1511-1511
Author(s):  
Dylan J. Peterson ◽  
Nicolai P. Ostberg ◽  
Douglas W. Blayney ◽  
James D. Brooks ◽  
Tina Hernandez-Boussard
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Electronic Health Record ◽  
Acute Care ◽  
Cancer Care ◽  
Model Performance ◽  
Emergency Department Visits ◽  
Health Record ◽  
Patients At Risk ◽  
Ed Visits

1511 Background: Acute care use is one of the largest drivers of cancer care costs. OP-35: Admissions and Emergency Department Visits for Patients Receiving Outpatient Chemotherapy is a CMS quality measure that will affect reimbursement based on unplanned inpatient admissions (IP) and emergency department (ED) visits. Targeted measures can reduce preventable acute care use but identifying which patients might benefit remains challenging. Prior predictive models have made use of a limited subset of the data available in the Electronic Health Record (EHR). We hypothesized dense, structured EHR data could be used to train machine learning algorithms to predict risk of preventable ED and IP visits. Methods: Patients treated at Stanford Health Care and affiliated community care sites between 2013 and 2015 who met inclusion criteria for OP-35 were selected from our EHR. Preventable ED or IP visits were identified using OP-35 criteria. Demographic, diagnosis, procedure, medication, laboratory, vital sign, and healthcare utilization data generated prior to chemotherapy treatment were obtained. A random split of 80% of the cohort was used to train a logistic regression with least absolute shrinkage and selection operator regularization (LASSO) model to predict risk for acute care events within the first 180 days of chemotherapy. The remaining 20% were used to measure model performance by the Area Under the Receiver Operator Curve (AUROC). Results: 8,439 patients were included, of whom 35% had one or more preventable event within 180 days of starting chemotherapy. Our LASSO model classified patients at risk for preventable ED or IP visits with an AUROC of 0.783 (95% CI: 0.761-0.806). Model performance was better for identifying risk for IP visits than ED visits. LASSO selected 125 of 760 possible features to use when classifying patients. These included prior acute care visits, cancer stage, race, laboratory values, and a diagnosis of depression. Key features for the model are shown in the table. Conclusions: Machine learning models trained on a large number of routinely collected clinical variables can identify patients at risk for acute care events with promising accuracy. These models have the potential to improve cancer care outcomes, patient experience, and costs by allowing for targeted preventative interventions. Future work will include prospective and external validation in other healthcare systems.[Table: see text]

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