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Trends in outpatient emergency department visits during the COVID-19 pandemic at a large, urban, academic hospital system

Theodoros v. giannouchos.

a Pharmacotherapy Outcomes Research Center, College of Pharmacy, University of Utah, Salt Lake City, UT, United States of America

Joseph Biskupiak

Michael j. moss.

b Division of Emergency Medicine, Department of Surgery, University of Utah, Salt Lake City, UT, United States of America

c Utah Poison Control Center, College of Pharmacy, University of Utah, Salt Lake City, UT, United States of America

Diana Brixner

Elena andreyeva.

d Department of Health Policy & Management, School of Public Health, Texas A&M University, College Station, TX, United States of America

Benjamin Ukert

Associated data.

The coronavirus disease 2019 (COVID-19) pandemic has critically affected healthcare delivery in the United States. Little is known on its impact on the utilization of emergency department (ED) services, particularly for conditions that might be medically urgent. The objective of this study was to explore trends in the number of outpatient (treat and release) ED visits during the COVID-19 pandemic.

We conducted a cross-sectional, retrospective study of outpatient emergency department visits from January 1, 2019 to August 31, 2020 using data from a large, urban, academic hospital system in Utah. Using weekly counts and trend analyses, we explored changes in overall ED visits, by patients' area of residence, by medical urgency, and by specific medical conditions.

While outpatient ED visits were higher (+6.0%) in the first trimester of 2020 relative to the same period in 2019, the overall volume between January and August of 2020 was lower (−8.1%) than in 2019. The largest decrease occurred in April 2020 (−30.4%), followed by the May to August period (−12.8%). The largest declines were observed for visits by out-of-state residents, visits classified as non-emergent, primary care treatable or preventable, and for patients diagnosed with hypertension, diabetes, headaches and migraines, mood and personality disorders, fluid and electrolyte disorders, and abdominal pain. Outpatient ED visits for emergent conditions, such as palpitations and tachycardia, open wounds, syncope and collapse remained relatively unchanged, while lower respiratory disease-related visits were 67.5% higher in 2020 relative to 2019, particularly from March to April 2020. However, almost all types of outpatient ED visits bounced back after May 2020.

Conclusions

Overall outpatient ED visits declined from mid-March to August 2020, particularly for non-medically urgent conditions which can be treated in other more appropriate care settings. Our findings also have implications for insurers, policymakers, and other stakeholders seeking to assist patients in choosing more appropriate setting for their care during and after the pandemic.

1. Introduction

The spread of the coronavirus disease 2019 (COVID-19) in spring of 2020 led to a sudden inflow of patients with acute respiratory symptoms to hospitals in the United States. Many state officials issued stay at home orders, and restricted elective medical and surgical procedures to redirect constrained hospital resources to COVID-19 patients. Uncertainty on the spread of COVID-19 and changing recommendations in the following months generated confusion and fear among many residents and disconnected many patients from potentially necessary health care [ [1] , [2] , [3] ]. One particularly hard-hit area were emergency departments (EDs), which serve as a safety net for many patients and generally treat individuals with acute conditions. However, more than one-third of all ED visits are estimated to be non-urgent and can be treated in other care settings [ [4] , [5] , [6] ]. Common reasons for ED utilization for non-urgent conditions include convenience and timely access to care, lack of alternatives, discrepancies in patient-provider perceptions, lapse of care management, and the need for a second-opinion [ 4 , 6 , 7 ]. The legal mandate to treat all patients in the ED, independent of their ability to pay, may further contribute to care seeking in such settings [ 4 , 6 , 7 ].

As shelter in place orders and fear of COVID-19 spread across the country, one could expect ED use to decline if people choose to forgo or postpone their non-urgent care needs or use other care settings. Although postponed or foregone care can result in impaired or even detrimental short- and long-term health outcomes, particularly for high-risk patients, reductions in care for non-urgent visits may provide the opportunity to shift resources to urgent care seekers. A current report indicates that patients are more likely to call their primary care provider or the hospital help line before deciding to seek care in the ED during the ongoing pandemic [ 3 ]. Early evidence indeed suggests that outpatient, ED visits, and hospital admissions declined by up to 60% from February to April 2020 in some parts of the country, followed by a bounce back after June [ 2 , [8] , [9] , [10] , [11] , [12] , [13] , [14] ]. However, little is known about the composition of reductions in urgent relative to non-urgent visits.

In this study, we aimed to analyze the pattern of outpatient (treat and release) ED visits between January and August 2020 compared to January through August 2019 based on the level of urgency and the spatial composition of the patients' home residence. Focusing only on outpatient ED visits allows us to identify common care events that can be classified as either urgent or non-urgent encounters using the New York University (NYU) ED algorithm, and review whether COVID-19 led to change in the proportion of urgent to non-urgent encounters. Our findings can provide critical information to stakeholders and health policymakers to develop evidence-based interventions towards a more patient focused and structurally competent healthcare system during the ongoing COVID-19 pandemic and beyond.

2.1. Study design and data source

This was a retrospective, cross-sectional study using data from the Enterprise Data Warehouse (EDW). The EDW, managed and maintained by the University of Utah Health Science Data Resource Center, is the long-term data mart for patient medical, financial, and administrative data. The EDW integrates the historical and comprehensive medical and clinical patient records across the University of Utah Healthcare Systems for more than 2.4 million patients for all health system interactions. The data also include demographic and clinical information. EDW data from the University of Utah Health Emergency Department, which is a fully approved Level 1 Trauma facility and tertiary referral center staffed by physicians 24 h a day, 7 days a week, was used in this study. The department is one of four EDs in the city in a metropolitan area of about 1.2 million residents and has a census of about 50,000 patient visits annually and provides care for acute emergencies in all subspecialties of medicine and surgery. Patients from all over Utah, Nevada, Wyoming, Idaho and western Colorado are referred to the emergency department for definitive medical care. During the COVID-19 pandemic, all area hospitals offered COVID-19 testing and medical care with no single facility designated specifically to care for or evaluate potential COVID-19 patients.

2.2. Study outcomes

Outpatient ED visits were identified as visits to the ED of the hospital system by all patients (including children 0 to 17 years of age) who were discharged within one day from the ED. To examine changes in trends in weekly outpatient ED visit volumes, we obtained data from January 1, 2019 to August 31, 2020. We examined overall weekly outpatient ED visit trends, by patients' area of residence to identify changes in care related to the stay-at-home recommendation and other social distancing directives that took place in April 2020. We used the patients' zip code of residence to identify changes in the number of patients coming from Salt Lake City (inner-city), the rest of the state, and out of state.

The International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) was used to identify the principal diagnoses and to characterize outpatient ED visits by medical urgency and by specific clinical diagnoses. To classify visits by medical urgency, we used the updated New York University (NYU) ED algorithm (Appendix 1) [ 15 , 16 ]. This previously validated algorithm assigns probabilities and classifies each ED visit as urgent (emergent-not preventable/avoidable: immediate care in an ED setting needed and the condition could not have been prevented/avoided with ambulatory care, such as chest pain, end stage renal disease, tachycardia, palpitations), emergent but preventable or avoidable (immediate care in an ED setting needed but the condition could have been prevented or avoided with timely and effective ambulatory care, such as dehydration, asthma with acute exacerbation, diabetes with hyper – or hypoglycemia), emergent but primary care treatable (care is needed within 12 h but could be provided in a primary care setting, such as quadrant pain, epigastric pain, abdominal pain), and non-emergent (immediate care not required within 12 h, such as headache, cough, low back pain, fatigue and weakness). Similar to previous work, we allocated each visit to one of the four categories related to emergency status when the assigned probability of the algorithm was higher than 50% for said category [ 17 ]. We also analyzed visits classified separately as injury-related by the algorithm (Appendix 1).

The Agency for Healthcare Research and Quality (AHRQ) single level Clinical Classification Software (CCS) was used to categorize outpatient ED visits into 16 expanded and clinically meaningful categories based on similarities among the individual ICD-10 codes, namely strains, sprains, and fractures; open wounds; superficial injuries; suicide and self-inflicted injuries; syncope and collapse; palpitations and tachycardia; abdominal pain; chest pain; fluid and electrolyte disorders; headaches and migraines; spondylosis, intervertebral disc disorders, and other back problems; nausea and vomiting; hypertension; diabetes; mood and personality disorders; and other lower respiratory diseases [ 18 ]. Finally, we also included information on overall inpatient ED visits (admission to the hospital through the ED).

2.3. Data analysis

We stratified data a priori into three different time periods based on the stay at home directive (January to March 2020; pre-directive: weeks 1 to 13, April 2020; directive: weeks 14 to 17, and May to August 2020; post-directive: weeks 18 to 35) and compared trends in outpatient ED visits volumes to the same periods in 2019. We then used weekly trend analyses for 2019 and 2020 to evaluate the number of outpatient ED visits that fall into one of the five categories of the NYU algorithm and the 16 CCS categories. All data analyses were performed using Stata version 16.1 (StataCorp) and SAS version 9.4 (SAS Institute) and trend graphs were generated in Microsoft Excel. The data used in this study were deidentified and the study was reviewed and approved by the institutional review board (IRB) at the University of Utah (IRB 00136921).

From January 1, 2019 to August 31, 2020 the ED had approximately 80,000 outpatient visits. Of those, 32,937 and 30,276 occurred between January and August in 2019 and 2020, respectively, which corresponded to an 8.1% decrease in ED visits volume in 2020 ( Table 1 and Fig. 1 ). Over the study period, the ED had higher shares of outpatient ED visits by adults 18 to 44 years of age (53.2%), females (53.7%), Non-Hispanic Whites (68.4%), and those with private health insurance (48.2%) or Medicaid (22.0%) coverage (Appendix 2). The most common reasons in both years for outpatient ED visits were abdominal (5.5%) and chest pain (5.4%), strains, sprains and fractures (4.0%), and fluid and electrolyte disorders (3.9%). About 31% of all outpatient ED visits were classified as emergent or were injury related, while almost 44% were primary care treatable, preventable or not emergent.

Emergency department visits from January 1 to August 31 in 2019 and 2020

Trends in overall weekly outpatient ED visits and by area of residence from January 1, 2019 to August 31, 2020.

In the first trimester of 2020, weekly volumes of outpatient ED visits were higher in 2020 than in 2019 (+6.0%), though weekly visit volume decreased sharply beginning the last week of March (week 13 of 2020) and flattened in April 2020 (−30.4% overall and −32.8% in the third week compared to the same period in 2019) ( Table 1 , Fig. 1 ). Outpatient ED visits bounced back beginning in May, but weekly visits in the May to August period were still lower compared to 2019 (−12.8%). In terms of patient's place of residence, visits by out-of-state patients dropped most sharply in March through May (−66.5% in April 2020 compared to 2019) and were still around 30% below the weekly trends observed in 2019 from June to August 2020. Inner city and in-state residents consistently visited the ED, as weekly visits were only trending slightly lower compared to 2019.

In terms of outpatient ED visits classified in the five NYU algorithm types, those visits classified as preventable or avoidable exhibited the largest decline (−48.2%) in April 2020 compared to the trend in 2019 ( Table 1 , Fig. 2 ). Primary care treatable and not preventable/avoidable outpatient ED visits were roughly 35% to 40% lower in April 2020 compared to the April 2019. Non-emergent visits drastically increased between February and March 2020 (+34.8% from week 7 to 11 of 2020 compared to the same weeks in 2019) and then dropped sharply in April. During May through August 2020 all visit types increased, except preventable/avoidable visits. By the end of August 2020, overall weekly visit numbers were generally only 11.3% lower compared to August 2019.

Trends in weekly outpatient ED visits by medical urgency classified by the NYU ED algorithm from January 1, 2019 to August 31, 2020.

Fig. 3 displays trends for eight clinical categories that can be characterized as emergent. Small changes in visits occurred before, during, and after the first COVID-19 case on March 6 in Utah for most of the clinical categories. Outpatient visits related to sprains, strains, and fractures (−23.3%) and superficial injuries (−33.3%) declined from mid-March to the end of April compared to the same period in 2019 but displayed an upward trend after May. Only outpatient ED visits for lower respiratory diseases increased substantially in February and March (+135.5%), with a 400.0% increase in the last week of March compared to 2019 and remained consistently higher (+67.5%) in all of 2020 compared to 2019. We also observed 305 outpatient ED encounters for patients diagnosed with COVID-19.

Trends in weekly outpatient ED visits by medically urgent conditions from January 1, 2019 to August 31, 2020.

Among the less-urgent clinical categories displayed in Fig. 4 , outpatient ED visits for fluid and electrolyte disorders, hypertension, diabetes and mood and personality disorders decreased the most after March 2020 and continued to be below 2019 volumes through August 2020. A reduction in outpatient visits for spondylosis, intervertebral disc and back problems (−27.0%), nausea and vomiting (−42.2%), and headaches and migraines (−32.5%) persisted between April and May 2020 relative to the same period in 2019. One outlier were abdominal pain conditions, which were much higher in January through March 2020 (+19.0%), strongly declined between March and April 2020 (−44.0%) and increased in June 2020 (+56.4%) to levels far above the 2019 visit counts.

Trends in weekly outpatient ED visits by less medically urgent conditions from January 1, 2019 to August 31, 2020.

Finally, in terms of inpatient visits coming from the ED, 8562 and 8089 visits occurred from January to August in 2019 and 2020 respectively, which corresponds to a modest 5.5% decline. The decline was driven by a 29.6% decrease in April 2020 compared to April 2019, while the pre- and post-directive study period volumes remained similar between 2019 and 2020 ( Table 1 ).

4. Discussion

The results indicate that outpatient ED visits in the emergency department of a large, academic, urban hospital system decreased in 2020 compared to 2019 and that the decline was driven by both emergent and non-emergent ED visits. Upon further separating outpatient ED visits into 8 urgent and 8 non-urgent categories, we found that urgent lower respiratory conditions increased 6-fold in April 2020, while other non-urgent conditions decreased in volume. Although the overall volume continued to be lower in August 2020 compared to August 2019 (−11%), outpatient ED visits exhibited an increasing trend beginning in May that peaked in July 2020.

Our results are similar in magnitude to those in a recent report on outpatient visits, which found a decline of almost 60% in March and April and a subsequent increase in visits through June [ 14 ]. The sharp decline in mid-March through April can be partially explained by patients' responding to intensive communication and outreach efforts and avoiding the risks of contracting COVID-19, shutdowns, and the stay at home directive in the state of Utah from March 27 through the first week of May. Reduced mobility of residents might further explain the large declines in ED visits from out-of-state residents and reductions in visits for many medical conditions that require urgent care, but commonly occur during outdoor activities (such as sprains, strains, fractures, and superficial injuries). Uncertainty and concerns about the risk of contracting the virus may have also led patients to postpone or forego care, particularly for conditions that are less medically urgent [ 3 ]. In contrast, visits for lower respiratory diseases were consistently higher beginning in early March of 2020.

Despite the fact that outpatient visits for many urgent conditions remained relatively stable during the pandemic, similar to previous work, we found large declines in overall inpatient ED visits in April 2020 compared to April 2019 and declines in outpatient ED visits classified as not preventable/avoidable [ 8 , 12 ]. These findings raise concerns about the short- and long-term patient health outcomes, particularly for individuals with acute conditions such as stroke and myocardial infarction, which require immediate hospital treatment. However, the underlying reasons for the decrease in not preventable/avoidable ED visits might be completely different relative to less urgent outpatient visits, so we note that inpatient ED visits require further investigation in the future.

Our conservative estimates suggest that more than 40% of all ED visits in 2020 were classified as non-urgent, consistent with national estimates and the existing ED literature [ [4] , [5] , [6] ]. In particular, the eight clinical conditions that fall within the non-emergent, preventable or primary care treatable categories accounted for almost half of the overall decrease in ED visits volume in 2020. This implies that patients are often able to evaluate the medical urgency of their condition [ 19 , 20 ]. However, for many patients ED may be the only option for care [ 20 ].

Of particular note is the relative steady trend in volume after May 2020, though lower compared to the 2019 period, of mood and personality disorders visits. This finding could be related to the rapid uptake, expansion and reimbursement of telemedicine services during the COVID-19 pandemic, which commonly serve patients with mental health conditions [ [21] , [22] , [23] ]. While we cannot establish a definitive assessment on whether patients avoided emergency care for less urgent conditions, including mental health, or substituted ED care with other care settings, such as primary care or telemedicine, the results highlight the potential to divert large levels of avoidable ED visits to other care settings. This is especially important as the second wave of COVID-19 in Winter may be exacerbated by the flu season.

The ongoing pandemic offers critical insight into individuals' care seeking behavior and the opportunity to rapidly redesign emergency care delivery [ 24 ]. Policymakers and providers should emphasize interventions and efforts to enhance primary care capacity, expand health insurance coverage, and extend telemedicine services to provide timely access to healthcare while simultaneously intensifying patient triage in the ED, and improve post-discharge care coordination [ 2 , 3 , 25 ]. Health authorities should also reach out to communities with educational material, resources and guidance to aid the determination of the appropriate location for care, since forgoing or delaying necessary care during the pandemic raises concerns about long-term adverse health outcomes particularly for high-risk and vulnerable individuals, while simultaneously causing staggering losses and financial hardships on hospitals.

4.1. Limitations

Our study is not without limitations. First, we used data from one emergency department and thus our findings are of limited external validity. In addition, despite the changes in ED visits, the use of emergency department data does not enable us to determine whether patients visited other healthcare facilities or went completely untreated. We also note that the use of the NYU ED algorithm with dichotomous indicators underestimates the prevalence of clinical conditions based on medical urgency, although we were able to classify around 80% of all ED visits.

5. Conclusion

In 2020, outpatient ED visits declined in a large, academic, urban hospital system in Utah from mid-March to April, particularly for non-urgent medical conditions. Visit volume increased after May 2020, highlighting the need for rapid and tailored interventions to raise patients' awareness on other outlets for non-urgent care as hospital systems remain focused on caring for large volumes of COVID-19 patients.

Author contributions

Study concept: TVG. Study design: TVG, JB, BU. Data acquisition: JB. Data analyses: TVG, JB, BU. Interpretation of the data: TVG, JB, DB, MJM, HA, BU. Drafting and review of the manuscript: TVG, JB, DB, MJM, HA, BU.

Declaration of Competing Interest

All authors report not conflict of interest.

Acknowledgements

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.ajem.2020.12.009 .

Appendix A. Supplementary data

Suppelementary material 1

Suppelementary material 2

New data identifies trends in accidental opioid overdoses in children

Overall rates in us emergency departments declined between 2008 and 2019 before an increase during the pandemic; numbers in many sub-populations remain high.

The US saw a 22% decline in rates of prescription-opioid overdose related emergency department (ED) visits in children 17 and younger between 2008 and 2019, but an uptick in the early part of the COVID-19 pandemic, according to a new study published this week in the open-access journal PLOS ONE by Henry Xiang of Nationwide Children's Hospital, US, and colleagues. The authors also note that rates of pediatric opioid overdoses remain high in many populations.

Opioid overdose has been declared a public health emergency in the United States but much of the focus has been on adults. In the new study, researchers analyzed overdoses in children by using data spanning 2008 to 2020 from the Nationwide Emergency Department Sample, which provides anonymized information on emergency department (ED) visits across the country.

Overall, prescription-opioid overdose ED visits for patients from 0-17 years old decreased by 22% from 2008 to 2019, and then increased by 12% in 2020. That overall increase could be mostly attributed to an increase in overdoses among males, children aged 12 to 17, and those in the West and Midwest. Across all time spans, the highest rates of overdoses were seen in ages 0 to 1 and ages 12 to 17, among females, and in urban teaching hospital EDs.

The authors conclude that efforts to reduce opioid overdoses should include increased focus on young children and adolescents and note that further studies could investigate the impact of the later years of the COVID-19 pandemic on the opioid epidemic.

The authors add: "Overall, prescription opioid overdose ED visits of US children had a decreasing trend during the past decade, suggesting the effectiveness of a variety of interventions and campaigns. However, 0-1 years and 12-17-year-olds still face a significant risk of prescription opioid overdose."

Children's Health
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Child Psychology
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Story Source:

Materials provided by PLOS . Note: Content may be edited for style and length.

Journal Reference :

Audrey Lu, Megan Armstrong, Robin Alexander, Eurella Vest, Jonathan Chang, Motao Zhu, Henry Xiang. Trends in pediatric prescription-opioid overdoses in U.S. emergency departments from 2008–2020: An epidemiologic study of pediatric opioid overdose ED visits . PLOS ONE , 2024; 19 (4): e0299163 DOI: 10.1371/journal.pone.0299163

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New data identifies trends in accidental opioid overdoses in children

by Public Library of Science

Overall, prescription- opioid overdose ED visits for patients from 0–17 years old decreased by 22% from 2008 to 2019, and then increased by 12% in 2020. That overall increase could be mostly attributed to an increase in overdoses among males, children aged 12 to 17, and those in the West and Midwest. Across all time spans, the highest rates of overdoses were seen in ages 0 to 1 and ages 12 to 17, among females, and in urban teaching hospital EDs.

The authors add, "Overall, prescription opioid overdose ED visits of US children had a decreasing trend during the past decade, suggesting the effectiveness of a variety of interventions and campaigns. However, 0 to 1 years and 12 to 17-year-olds still face a significant risk of prescription opioid overdose."

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Suicide Prevention in an Emergency Department Population: ED-SAFE

Contact Innovator

Suicide is the 12 th leading cause of death in the United States, and the 3 rd leading cause of death for people ages 15-24. 1 More than 4% of all emergency department visits are attributed to psychiatric conditions 2 and 3–8% of all patients have suicidal ideation when screened in the ED. 3 In addition, there are approximately 420,000 ED visits every year for intentional self-harm. 4 The emergency department (ED) is an ideal place to implement interventions designed to reduce suicidal behavior. However, there have been few trials conducted in clinical settings to reduce suicidal behavior.

Brown University and Butler Hospital created the Emergency Department Safety Assessment and Follow-Up Evaluation (ED-SAFE) innovation to reduce suicidal behavior among patients who present to the ED with suicidal ideation. They published the results from the initial clinical trial, ED-SAFE 1, a multicenter study of eight EDs that assessed the ED-SAFE innovation. The ED-SAFE 1 innovation provided participants with a standard universal suicide risk screening (any standard universal screening tool can be applied) plus a secondary suicide risk screening by an ED physician. It also included discharge resources (including a self-administered safety plan) and post-ED telephone calls based on the Coping with Long Term Active Suicide Program (CLASP) 5 focused on reducing suicide risk. 6 In ED-SAFE 1, there was a 5% absolute reduction in suicide attempts between the treatment as usual and intervention phases. 6 During the intervention phase, participants had 30% fewer total suicide attempts than participants in the treatment as usual phase. 6 The study found that universal screening alone did not reduce suicide attempts, and therefore, the reduction is most likely tied to the innovation itself. 6

The ED-SAFE 2 trial implemented two key elements that built on ED-SAFE 1: a Lean continuous quality improvement (CQI) approach and collaborative safety planning between patients and caregivers. Data were collected from 2014 to 2018 and analyzed from April 2022 to December 2022. 3 The trial included three phases: baseline (retrospective), implementation, and maintenance. 3 During implementation, each of the eight EDs formed a Lean team consisting of staff, management, information technology (IT), patient safety, and quality assurance members. The teams attended a one-day training and monthly followup meetings on Lean principles with an industrial engineering Lean expert with doctoral training. 3 The teams evaluated their workflows, identified gaps in care, designed solutions to close these gaps, and oversaw the implementation of ED-SAFE 2. 3 Additionally, the innovators implemented collaborative safety planning. Collaborative safety planning involved six-step safety plans created by clinicians and patients to help patients manage their individual suicidal crises. 3 In addition to these changes, teams were expected to increase the number of suicide risk screenings for patients. 3 The primary outcome measured was a suicide composite measure. The measure included 1) an ED visit or hospitalization due to suicidal ideation/behavior or 2) death by suicide in the six months after the index visit. 3 The composite measure improved over the three phases (baseline by 21%; implementation by 22%; and maintenance by 15.3%; p=.001). 3

Innovation Patient Safety Focus

Although the National Action Alliance for Suicide Prevention and The Joint Commission both identify EDs as an essential setting for suicide prevention, suicide prevention interventions in EDs remain underdeveloped and understudied. 3 The Joint Commission identifies suicide within 72 hours of discharge from a healthcare setting that provides around-the-clock care, including the ED, as a sentinel event (a patient safety event that results in death, permanent harm, or severe temporary harm). 3

Resources Used and Skills Needed

When implementing this innovations, organizations should consider the following:

Buy-in from hospital leadership and all staff involved in the continuum of care.
Staff to conduct post-visit phone calls.
Physicians willing to conduct secondary suicide prevention screenings.
Physicians to serve as treatment advisers for the post-visit phone calls.
Time and resources to train staff on the intervention.
Staff to conduct data analysis.
Leaders to train multidisciplinary teams on the Lean CQI strategy.
Staff to participate in the Lean teams and create collaborative safety plans with patients.
Clinicians with the bandwidth to create collaborative safety plans with patients. These clinicians must attend at least one training related to collaborative safety planning and they must demonstrate competency in collaborative safety planning through observation or other work samples.
Staff to champion the maintenance stage of the innovation.
Staff to measure and report results: at least one person with data related skills who can use the EHR for reporting per site; 40-80 hours are needed as an initial investment for setting up reports followed by one to two hours per month after the initial investment per site.

Use By Other Organizations

The innovator has received regular inquiries from other EDs about their ED-SAFE innovation. Much of the ED-SAFE innovation aligns with the Zero Suicide model, an emerging model for suicide prevention in healthcare. 7

Date First Implemented

Problem addressed.

The innovators created the Emergency Department Safety Assessment and Follow-Up Evaluation (ED-SAFE) 2 to build on the successes from ED-SAFE 1 (the reduction of suicidal behavior). The innovators wanted to sustain the suicide risk management that was achieved in ED-SAFE 1. 3 They deduced that a continuous quality improvement (CQI) strategy was the best way to ensure consistency, effectiveness, and the standard use of the new procedures and interventions from ED-SAFE 1. 3 This led to the development of the Lean CQI strategy, one of the key elements of ED-SAFE 2, and a focus on collaborative safety planning as a standard part of treating patients at risk for suicide who are discharged from the ED.

Description of the Innovative Activity

The ED-SAFE study was designed to assess the effect of universal screening for suicide risk and an intervention for people at risk for suicide in the ED setting. 6 ED-SAFE 1 included elements like standard universal screening, secondary risk screening, collaborative safety plans, and post-ED phone calls. Universal screening and secondary screening were carried over into the ED-SAFE 2 study. 3 The primary and secondary screener are both referred to as the Patient Safety Screener (PSS-3). 8 The first part of the PSS-3 is used to identify suicide risk for all patients who come to the acute care setting. 8 The second part of the PSS-3 takes those who have been identified with risk of suicide and guides risk stratification. 8 The second part of PSS-3 guides physicians through care pathways and mitigation procedures based on the patient’s risk level. The screeners also include other risk factors, like psychiatric hospitalization. This makes PSS-3 different than many other predominate screeners that focus entirely on suicidal ideation and behavior. When all elements of ED-SAFE 1 are implemented, the intervention costs around $1,000 per patient, per month. 9

For ED-SAFE 2, two key improvements were made: the implementation of the Lean CQI strategy and the introduction of collaborative safety planning with clinicians and patients. 3 Each of the eight EDs created a Lean CQI multidisciplinary team. 3 The team included members from all areas of patient care and support services, including frontline providers, patient safety professionals, information technology (IT) staff, and quality assurance staff. 3 Each team participated in a day-long training on Lean principles. 3 Following the training, the team attended monthly coaching calls to ensure that they had consistent knowledge of the Lean principles. 3 The Lean teams evaluated their workflows to find gaps that could be targeted with interventions. 3 The interventions to close these gaps focused on addressing the root causes associated with negative patient care outcomes. 3 In addition, the Lean teams oversaw the implementation of the interventions they established. 3 The eight ED Lean teams reported their metrics to a coordinating center each month to ensure continuity across the whole system. 3

Collaborative safety planning involved a six-step safety plan created by a clinician and a patient; the goal of the safety plan was to help manage individual suicidal crises for patients who screened positive for suicidal ideation but who were discharged from the ED. 3 When patients were in crisis states, they used the collaborative safety plans. The plans helped patients to cope with the crisis by providing collaborative interventions to curb suicidal thoughts. The plans included interventions that patients could do on their own and ideas for interventions they could do with others. The plans also directed patients on how and when to reach out for additional professional help. Although created collaboratively, the plans were written in the patient’s voice. The clinicians attended a training with a safety planning intervention trainer. The trainers followed up with the clinicians each month to provide additional training as needed, to review safety plans, and to provide feedback to the clinicians. 3

Context of the Innovation

Emergency departments treat many patients who are at risk for suicidal behavior. High-risk individuals are susceptible to suicide attempts after their ED visit. 10 In addition, a significant number of those who die by suicide received care in an ED in the period prior to death. 11 In a study using Medicaid data from 2008 to 2018, researchers looked at data from national cohorts of patients with mental health ED visits due to suicide attempts. Researchers wanted to determine the rate of suicide for these patients up to one year after discharge. 12 Among these patients, the suicide rate was 325.4 per 100,000 person years. 12 This finding contrasts with the rate of suicide in the general population as reported by the Centers for Disease Control and Prevention (CDC), which was 14.1 per 100,000 person years in 2021. 13 Because of these key patient safety factors, the innovators chose to conduct this intervention to improve patient safety for those at risk of suicide or suicidal behavior who are presenting to the ED.

The positive results from ED-SAFE 1 prompted the development of ED-SAFE 2. In ED-SAFE 1, compared with the treatment as usual phase, patients in the intervention phase showed a 5% absolute reduction in the risk of a suicide attempt (23% vs 18%; p=0.05). 6 Participants in the intervention phase in ED-SAFE 1 had 30% fewer total suicide attempts than participants in the treatment as usual phase. 3 For ED-SAFE 2, across all three phases (baseline, implementation, and maintenance), 2,761 patient encounters were included in the study analysis. 3 The intervention was based on a suicide composite measure. The measure included an ED visit or hospitalization due to suicidal ideation/behavior or death by suicide in the six months after the index visit. 3 The percentage of patient encounters with a suicide composite outcome decreased from 21% as baseline (216 of 1030) and 22% at implementation (213 of 967) to 15.3% during the maintenance phase (117 of 764; p=0.001). 3 The adjusted odds ratio of risk demonstrated a 43% reduction in the maintenance phase compared to the implementation phase and a 39% reduction in the maintenance phase compared to the baseline phase. 3

Planning and Development Process

When planning and developing this innovation, it is important to ensure that the innovating site is committed to change, there is support from senior leadership, a multidisciplinary team is convened, a standard definition of suicide risk is established, and a gap analysis is completed.

The innovators recommend convening a multidisciplinary team when planning and developing this innovation. The team must consist of members of the healthcare organization who are involved in the reduction of suicide and care pathways within the innovating organization, if possible. This may include senior leaders, frontline staff, patient safety officers, and nurse managers. The team will be instrumental in developing the innovation. Because there is no one-size-fits-all approach for EDs, the team should adapt ED-SAFE to fit their organization. Senior leadership should give the multidisciplinary team the authority to make changes in the organization that are necessary for the innovation’s success. For example, there must be a suicide risk screener administered with the innovation. However, an innovating organization should have the flexibility to pick which screener they would like to use, when they would like to screen patients, who they would like to administer the screener, and whether the screener is universal for all patients who present to the ED.

Before this innovation is implemented, it is critical that the innovation organization agree on a standard definition of suicide risk. This definition will be instrumental in determining who is eligible for the innovation.

In addition, the innovating organization should conduct a gap analysis. The gap analysis will evaluate the current state of managing risk of suicide among patients presenting to the ED versus what the innovating organization would like to achieve. The organization can determine the current state by convening meetings with management and frontline staff to reconcile the differences between the policies in place to reduce suicide risk and what is actually occurring at the organization. The organization will remedy the gaps identified in the analysis with targeted interventions. In addition, the organization will use data discovered in the gap analysis to monitor the success of the innovation.

If this innovation was used in a smaller emergency department, the smaller emergency department would likely need to adjust the scope and pace of the innovation to make it successful. However, if the ED has a strong champion, the innovation could be successful. For example, the smaller organization may need to establish who their quality improvement team will be, as they may not have a dedicated quality improvement department. A nurse manager could serve as the quality improvement lead. In addition, the smaller ED could conduct a modest amount of chart reviews rather than using EHR data reports to evaluate performance.

Funding Sources

The National Institute of Mental Health funded this innovation.

Getting Started with This Innovation

The innovators found that a deployment plan was necessary when getting started with this innovation. A deployment plan lays out the step-by-step process for implementing the innovation. It includes details like what to do first and includes the ways that every person involved in the innovation will be trained. The deployment plan also describes how the organization will gather and measure metrics and tracks implementation successes and challenges when implementing the innovation. For example, the deployment plan may include metrics that track whether the innovation is being adopted within the organization. If the innovator discovers that the organization is not adopting certain elements, they can make changes to rectify the situation. For example, the innovator discovered via their deployment plan that the screener was not being used as intended. Because of this, the innovator created targeted trainings to remedy the situation and improve the success of the innovation.

Sustaining This Innovation

The innovators found a continuous quality improvement (CQI) approach to be the primary investment in sustaining the innovation. The CQI approach identifies areas for improvement throughout the lifetime of the innovation. The CQI approach monitors the training of personnel, reviews performance, identifies gaps, creates ways to remediate gaps, implements, and evaluates remediation measures, and remeasures performance after remediation. The CQI approach should be iterative and provide flexibility for staff who take part in the innovation.

One component of the innovator’s CQI process were spot checks. The innovator conducted spot checks using the same measures that were created during the gap analysis. Spot checks help the innovators gauge whether the innovation is reaching the final target that was established during the gap analysis. If the spot checks find there are reoccurring gaps, an innovator may want to consider going back to a previous implementation phase to increase adoption of the innovation. The innovators found that conducting spot checks during suicide awareness month garnered strong support from leadership and frontline staff, as there was already a renewed focus on suicide prevention at that time.

The innovators found the minimum investment to maintain results included quarterly meetings with the innovation team (usually four to ten people for one hour), a core steering committee (usually three to five people for two hours a quarter, plus leading the quarterly meetings), and trainers focused on training new employees and current staff as needed (three to five hours a quarter). An annual review of current protocols to determine if the protocols should be updated due to new care expectations or evidence-based best practices is also beneficial when sustaining this innovation.

References/Related Articles

ClinicalTrials.gov. Emergency Department Safety Assessment and Follow-Up Evaluation (ED-SAFE). https://clinicaltrials.gov/study/NCT01150994

Miller I, Gaudiano B, Weinstock L. The Coping Long Term with Active Suicide Program (CLASP): description and pilot data. Suicide Life Threat Behav . 2016;46(6):752-761. doi:10.1111/sltb.12247

Save.org. U.S.A. SUICIDE: 2020 OFFICIAL FINAL DATA. https://save.org/wp-content/uploads/2022/01/2020datapgsv1a-3.pdf
Owens P, Mutter R, Stocks C. Mental Health and Substance Abuse-Related Emergency Department Visits Among Adults, 2007 . Statistical Brief #92. July 2010. Agency for Healthcare Research and Quality, Rockville, MD. https://www.hcup-us.ahrq.gov/reports/statbriefs/sb92.pdf
Boudreaux ED, Larkin C, Vallejo Sefair A, et al. Effect of an emergency department process improvement package on suicide prevention: the ED-SAFE 2 cluster randomized clinical trial. JAMA Psychiatry 2023;80(7):665-674. doi:10.1001/jamapsychiatry.2023.1304
Ting SA, Sullivan AF, Boudreaux ED, Miller I, Camargo CA Jr. Trends in US emergency department visits for attempted suicide and self-inflicted injury, 1993-2008. Gen Hosp Psychiatry . 2012;34(5):557-565. doi:10.1016/j.genhosppsych.2012.03.020
Miller I, Gaudiano B, Weinstock L. The Coping Long Term with Active Suicide Program (CLASP: A Clinicians Guide to a Multi-Modal Intervention for Suicide Prevention. Oxford University Press; 2022.
Miller IW, Camargo Jr CA, Arias SA, et al. Suicide prevention in an emergency department population: the ED-SAFE Study. JAMA Psychiatry . 2017;74(6):563-570. doi:10.1001/jamapsychiatry.2017.0678
Education Development Center. Zero Suicide. Accessed February 12, 2024. https://zerosuicide.edc.org
Boudreaux ED. The Patient Safety Screener: A Brief Tool to Detect Suicide Risk. Accessed March 18, 2024. https://sprc.org/micro-learning/the-patient-safety-screener-a-brief-tool-to-detect-suicide-risk/
Dunlap L, Orme S, Zarkin G, Miller I. Screening and Intervention for Suicide Prevention: A Cost-Effectiveness Analysis of the ED-SAFE Interventions. Accessed March 19 2024. https://pubmed.ncbi.nlm.nih.gov/31451063/
Olfson M, Marcus SC, Bridge JA. Focusing suicide prevention on periods of high risk. JAMA . 2014;311(11):1107-1108. doi:10.1001/jama.2014.501
Ahmedani BK, Simon GE, Stewart C, et al. Health care contacts in the year before suicide death. J Gen Intern Med . 2014;29(6):870-877. doi:10.1007/s11606-014-2767-3
Olfson M, Gao YN, Xie M, Wiesel Cullen S, Marcus SC. Suicide risk among adults with mental health emergency department visits with and without suicidal symptoms. J Clin Psychiatry . 2021;82(6):20m13833. doi:10.4088/JCP.20m13833
Garnett MF, Curtin SC. Suicide mortality in the United States, 2001-2021 . NCHS Data Brief, no. 464. April 2023. National Center for Health Statistics, Hyattsville, MD. https://www.cdc.gov/nchs/data/databriefs/db464.pdf

The inclusion of an innovation in PSNet does not constitute or imply an endorsement by the U.S. Department of Health and Human Services, the Agency for Healthcare Research and Quality, or of the submitter or developer of the innovation.

Contact the Innovator

ED-SAFE 1: Dr. Ivan Miller, [email protected]

ED-SAFE 2: Dr. Edwin Boudreaux, [email protected]

Perspective

Perspectives on Safety

Annual Perspective

WebM&M Cases

Pathologists, patients and diagnostic errors—part 1 and part 2. August 10, 2016

A simple surgery with harrowing complications. August 8, 2012

Amid lack of accountability for bias in maternity care, a California family seeks justice. August 16, 2023

Improving Patient Safety: The Intersection of Safety Culture, Clinician and Staff Support, and Patient Safety Organizations. October 28, 2015

Why doctors should own up to their medical mistakes. February 13, 2013

A Tale of Two Stories: Contrasting Views of Patient Safety. March 27, 2005

Patient Safety Primers

Discharge Planning and Transitions of Care

Medicines-related harm in the elderly post-hospital discharge. March 27, 2019

Pediatric safety incidents from an intensive care reporting system. May 27, 2009

A Crisis in Health Care: A Call to Action on Physician Burnout. January 30, 2019

Near-miss event analysis enhances the barcode medication administration process. January 17, 2018

Evaluating independent double checks in the pediatric intensive care unit: a human factors engineering approach. February 21, 2024

Diagnostic Error in Medicine. October 7, 2009

The 2013 John M. Eisenberg Patient Safety and Quality Awards. April 30, 2014

Simulation in Maternal Fetal Medicine. June 26, 2013

Iatrogenic Disease. March 5, 2008

Making Healthcare Safer III. March 18, 2020

Patient Safety and Quality. March 3, 2010

2011 John M. Eisenberg Patient Safety and Quality Awards. June 27, 2012

The Second Society for Simulation in Healthcare Research Summit: Beyond Our Boundaries. August 1, 2018

Risk, Safety and Reliability Special Issue. May 20, 2009

Quality and Safety Competencies in Nursing Education. January 13, 2010

Contributions from Ergonomics and Human Factors. November 17, 2010

Innovation in Perioperative Patient Safety. February 27, 2013

Making Health Care Safer: A Critical Review of Modern Evidence Supporting Strategies to Improve Patient Safety. March 6, 2013

The Science of Simulation in Healthcare: Defining and Developing Clinical Expertise. November 19, 2008

Patient Safety Papers 3. April 23, 2008

Special Issue: Progress at the Intersection of Patient Safety and Medical Liability. December 14, 2016

Quality and Safety in Medicine. December 9, 2009

Patient Safety. November 21, 2018

Special Issue on Health Information Technology. April 16, 2008

Patient Safety Papers 4. September 2, 2009

Patient Safety Innovations

Risk Mitigation Using the Anesthesia Risk Alert Program: Applying a Proactive Approach With Data Review & Collaborating With a Second Practitioner

Should dignity preservation be a precondition for safety and a design priority for healing in inpatient psychiatry spaces? April 10, 2024

Patient and Family Centered I-PASS (Family-Centered Communication Program to Reduce Medical Errors and Improve Family Experience and Communication Processes)

The e-autopsy/e-biopsy: a systematic chart review to increase safety and diagnostic accuracy innovation.

Clinicians' insights on emergency department boarding: an explanatory mixed methods study evaluating patient care and clinician well-being. August 23, 2023

Ambulatory Safety Nets to Reduce Missed and Delayed Diagnoses of Cancer

Remote response team and customized alert settings help improve management of sepsis, preventing falls through patient and family engagement to create customized prevention plans.

Effect of an emergency department process improvement package on suicide prevention: the ED-SAFE 2 cluster randomized clinical trial. May 31, 2023

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Supported by

Heat-Related E.R. Visits Rose in 2023, C.D.C. Study Finds

By Noah Weiland

Reporting from Washington

The rate of emergency room visits caused by heat illness increased significantly last year in large swaths of the country compared with the previous five years, according to a study published on Thursday by the Centers for Disease Control and Prevention.

The research, which analyzed visits during the warmer months of the year, offers new insight into the medical consequences of the record-breaking heat recorded across the country in 2023 as sweltering temperatures stretched late into the year.

What the Numbers Say: People in the South were especially affected by serious heat illness.

The researchers used data on emergency room visits from an electronic surveillance program used by states and the federal government to detect the spread of diseases. They compiled the number of heat-related emergency room visits in different regions of the country and compared them to data from the previous five years.

Nearly 120,000 heat-related emergency room visits were recorded in the surveillance program last year, with more than 90 percent of them occurring between May and September, the researchers found.

The highest rate of visits occurred in a region encompassing Arkansas, Louisiana, New Mexico, Oklahoma and Texas. Overall, the study also found that men and people between the ages of 18 and 64 had higher rates of visits.

How It Happens: Heat can be a silent killer, experts and health providers say.

Last year was the warmest on Earth in a century and a half, with the hottest summer on record . Climate scientists have attributed the trend in part to greenhouse gas emissions and their effects on global warming, and they have warned that the timing of a shift in tropical weather patterns last year could foreshadow an even hotter 2024.

Heat illness often occurs gradually over the course of hours, and it can cause major damage to the body’s organs . Early symptoms of heat illness can include fatigue, dehydration, nausea, headache, increased heart rate and muscle spasms.

People do not typically think of themselves as at high risk of succumbing to heat or at greater risk than they once were, causing them to underestimate how a heat wave could lead them to the emergency room, said Kristie L. Ebi, a professor at the University of Washington who is an expert on the health risks of extreme heat.

“The heat you were asked to manage 10 years ago is not the heat you’re being asked to manage today,” she said. One of the first symptoms of heat illness can be confusion, she added, making it harder for someone to respond without help from others.

What Happens Next: States and hospitals are gearing up for another summer of extreme heat.

Dr. Srikanth Paladugu, an epidemiologist at the New Mexico Department of Health, said the state had nearly 450 heat-related emergency room visits in July last year alone and over 900 between April and September, more than double the number recorded during that stretch in 2019.

In preparation for this year’s warmer months, state officials are working to coordinate cooling shelters and areas where people can be splashed by water, Dr. Paladugu said.

Dr. Aneesh Narang, an emergency medicine physician at Banner-University Medical Center in Phoenix, said he often saw roughly half a dozen heat stroke cases a day last summer, including patients with body temperatures of 106 or 107 degrees. Heat illness patients require enormous resources, he added, including ice packs, fans, misters and cooling blankets.

“There’s so much that has to happen in the first few minutes to give that patient a chance for survival,” he said.

Dr. Narang said hospital employees had already begun evaluating protocols and working to ensure that there are enough supplies to contend with the expected number of heat illness patients this year.

“Every year now we’re doing this earlier and earlier,” he said. “We know that the chances are it’s going to be the same or worse.”

Noah Weiland writes about health care for The Times. More about Noah Weiland

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Open access
Published: 24 April 2024

Social determinants of health and emergency department visits among older adults with multimorbidity: insight from 2010 to 2018 National Health Interview Survey

Arum Lim 1 ,
Chitchanok Benjasirisan 1 ,
Xiaoyue Liu 2 ,
Oluwabunmi Ogungbe 1 ,
Cheryl Dennison Himmelfarb 1 ,
Patricia Davidson 3 &
Binu Koirala 1

BMC Public Health volume 24 , Article number: 1153 ( 2024 ) Cite this article

103 Accesses

7 Altmetric

Metrics details

Multimorbidity is prevalent among older adults and is associated with adverse health outcomes, including high emergency department (ED) utilization. Social determinants of health (SDoH) are associated with many health outcomes, but the association between SDoH and ED visits among older adults with multimorbidity has received limited attention. This study aimed to examine the association between SDoH and ED visits among older adults with multimorbidity.

A cross-sectional analysis was conducted among 28,917 adults aged 50 years and older from the 2010 to 2018 National Health Interview Survey. Multimorbidity was defined as the presence of two or more self-reported diseases among 10 common chronic conditions, including diabetes, hypertension, asthma, stroke, cancer, arthritis, chronic obstructive pulmonary disease, and heart, kidney, and liver diseases. The SDoH assessed included race/ethnicity, education level, poverty income ratio, marital status, employment status, insurance status, region of residence, and having a usual place for medical care. Logistic regression models were used to examine the association between SDoH and one or more ED visits.

Participants’ mean (± SD) age was 68.04 (± 10.66) years, and 56.82% were female. After adjusting for age, sex, and the number of chronic conditions in the logistic regression model, high school or less education (adjusted odds ratio [AOR]: 1.10, 95% confidence interval [CI]: 1.02–1.19), poverty income ratio below the federal poverty level (AOR: 1.44, 95% CI: 1.31–1.59), unmarried (AOR: 1.19, 95% CI: 1.11–1.28), unemployed status (AOR: 1.33, 95% CI: 1.23–1.44), and having a usual place for medical care (AOR: 1.46, 95% CI 1.18–1.80) was significantly associated with having one or more ED visits. Non-Hispanic Black individuals had higher odds (AOR: 1.28, 95% CI: 1.19–1.38), while non-Hispanic Asian individuals had lower odds (AOR: 0.71, 95% CI: 0.59–0.86) of one or more ED visits than non-Hispanic White individuals.

SDoH factors are associated with ED visits among older adults with multimorbidity. Systematic multidisciplinary team approaches are needed to address social disparities affecting not only multimorbidity prevalence but also health-seeking behaviors and emergent healthcare access.

Peer Review reports

Multimorbidity is defined as the co-existence of two or more chronic conditions [ 1 ]. The number of people living with multimorbidity is dramatically increasing worldwide with the growing aging population and improved diagnostic capabilities [ 2 , 3 ]. According to the pooled data from a meta-analysis of studies published between 2000 and 2021, the prevalence of multimorbidity was 37.2% globally and 43.1% in North America [ 3 ]. Since chronic diseases are usually accompanied by aging, 51% of adults aged 60 years and older had multimorbidity in the global population [ 3 ]. Multimorbidity is also a problem for middle-aged adults, as multimorbidity stiffly increases after age 50 [ 4 ], and 47% of adults 50 years and older have multimorbidity [ 3 ]. In support of this, recent studies have extended their focus to individuals aged 50 years and older to investigate multimorbidity and chronic disease burden [ 5 , 6 ]. Multimorbidity has become a significant health issue because of the increasing complexity of healthcare needs [ 7 ]. For example, people with multimorbidity need a multidisciplinary approach to decision-making for the treatment and management of each condition and may have to deal with polypharmacy and communication with multiple health providers [ 1 ]. Thus, healthcare providers and researchers have been paying attention to prioritizing the complex needs of care for individuals with multimorbidity [ 1 , 7 ].

Multimorbidity is associated with adverse health outcomes, such as increased hospital utilization, major health decline, and mortality [ 8 , 9 , 10 ]. People with multimorbidity frequently contact general practitioners and visit emergency departments (ED) due to their complex care needs [ 11 , 12 ]. A study analyzing large electronic health record data in the Netherlands reported that 11% of individuals with multimorbidity had ≥ 12 general practitioner contacts, and 12% had ED visits in a year [ 11 ]. However, the group of patients who frequently contact general practice would be distinct from those who visit ED. In the previous study, only 29% of people with frequent general practice contacts had ED visits [ 11 ]. This implies that people who do not frequently visit general practice may be more likely to visit ED. In addition, generally, the intensity of ED resource utilization increased with age [ 13 ], and people who visited EDs had more chronic conditions and more prescribed medications than the entire multimorbid group [ 11 ].

Despite the consistently increased prevalence of multimorbidity for all racial/ethnic groups, the risk for multimorbidity may disproportionally occur due to social disparities. Multimorbidity was related to low educational attainment [ 14 ] and was more prevalent among the Black population and less prevalent among Asian and Hispanic populations compared to the White population in the United States (US) [ 15 ]. Moreover, emerging evidence has suggested that socioeconomic disadvantages worsen the burden of multimorbidity in older adults [ 16 ]. Particularly, low income is consistently associated with not only a higher prevalence of multimorbidity but also worse patient-reported health outcomes in older multimorbid patients [ 15 , 17 ]. In this context, education, race/ethnicity, and income can be tied up as social determinants of health (SDoH). In general, SDoH consists of five domains: economic stability, education access and quality, healthcare access and quality, neighborhood and built environment, and social and community context [ 18 ]. Although gender, education, and health system were the most frequently investigated as SDoH in older multimorbid populations, limited attention is paid to race/ethnicity, socioeconomic status, and political context in the current literature [ 16 ]. These indicate that investigating individuals living with multimorbidity and their SDoH factors associated with healthcare access, particularly access to emergency services, is necessary to understand which social context is related to managing multiple chronic conditions leading to ED visits.

Generally, ED visits can be considered as health care needs caused by sudden symptoms, deterioration, or injuries. The frequent complaints leading older people to visit EDs are shortness of breath, chest pain, and lower extremity pain/injury, and in approximately 75% of ED visits, older adults were triaged as urgent/emergent [ 13 ]. However, disparities in ED care access and triage processes exist based on race/ethnicity and health insurance status [ 19 ]. There is a need to identify and address disparities in emergency healthcare access in older people, which may produce disproportionated health outcomes [ 19 ]. Therefore, this study aims to describe the association between SDoH and ED visits among older adults with multimorbidity.

Study design and data source

The study employed a cross-sectional approach to examine the data from 2010 to 2018 National Health Interview Survey (NHIS) conducted by the National Center for Health Statistics (NCHS) [ 20 ]. The NHIS is a cross-sectional population-based survey for non-institutionalized civilians aged 18 years or older US adults. The data was gathered by face-to-face interviewing with one randomly selected adult per household for the Sample Adult Module. The interview questions covered healthcare services, behaviors, and health status. Detailed information regarding the design and methodology of NHIS is published elsewhere [ 20 , 21 ]. This study restricted the study period from 2010 to 2018 to avoid the potential confounding effects of the COVID-19 pandemic that occurred in December 2019 on SDoH and ED visits [ 22 ]. This study was exempt from institutional review board review because it used publicly available de-identified data published by the NCHS.

Inclusion/exclusion criteria

Individuals aged 50 years and older, those who had more than two chronic conditions defined as multimorbidity, and those with available ED visit data were included in this study. The number of chronic conditions was obtained from self-reported disease diagnoses. A total of 10 chronic conditions were selected to define multimorbidity, which were collected throughout the 2010–2018 study period. The selected chronic conditions are parts of conditions defined based on the National Quality Forum Multiple Chronic Conditions framework and aligned with conditions in a study that used this framework to define multimorbidity [ 23 , 24 ]. Having chronic conditions was identified by the questions asking if the respondents had ever been told by a healthcare professional that they had diabetes, hypertension, asthma, stroke, cancer, arthritis, chronic obstructive pulmonary disease (emphysema or chronic bronchitis), or heart disease (coronary artery disease, myocardial infarction, angina, or other heart conditions), or had been told in the past 12 months that they had weak/failing kidneys or any liver condition. Therefore, participants could have between two and ten chronic conditions.

Measurements

Emergency department visits.

The study outcome was one or more ED visits in the previous 12 months. Respondents were asked, “During the past 12 months, how many times have you gone to a hospital emergency room about your own health?” This includes emergency room visits that resulted in hospital admission. The responses were dichotomized as having either one or more instances or none.

Social determinants of health

The SDoH variables included in this study were race/ethnicity, marital status, employment and educational status, poverty income ratio, health insurance status, region of residence, and having a usual place to go for medical care when sick. Some variables were defined as dichotomous: marital status (currently married/not married, including never married, divorced, widowed, or separated); employment status (employed/unemployed), insurance status (insured/uninsured), and have a usual place to go for medical care when sick–a proxy for healthcare access (yes/no). Race/ethnicity was categorized as (non-Hispanic White, non-Hispanic Black, non-Hispanic Asian, and Hispanic). Educational status was categorized by ≤ high school, some college, and ≥ bachelor’s degree. The poverty income ratio (PIR) was used as a proxy of financial status. The midpoint of an individual’s family income was divided by the poverty threshold for the year. The variable was then categorized as < 1, between 1 and 1.99, and ≥ 2. A PIR less than one means that the individual income is below the federal poverty level, a PIR between 1 and 1.99 indicates the income is between 100% and 199% of the poverty level, and a PIR greater than two means that the income is more than 200% of the poverty level. The region of residence had four categories: northeast, midwest, south, and west. Perceived health status was categorized on a five-scale from “excellent” to “poor.”

We included three covariates: age in years (measured as a continuous variable, further categorized as 50–64 years or 65 and older years), sex (categorized as male or female), and the number of chronic conditions. Based on the rationale that having two or more chronic conditions defines multimorbidity, and having three or more is considered complex multimorbidity [ 4 ], the number of chronic conditions was categorized into 2, 3–4, or ≥ 5 conditions. The category will help stratify the participants by the severity of multimorbid conditions [ 25 , 26 ].

Statistical analysis

This study merged NHIS data from 2010 to 2018 and applied sampling weights according to NCHS guidelines [ 27 ]. Sociodemographic characteristics were presented using descriptive statistics, mean and standard deviation, and percentage. Differences and associations in characteristics between respondents with more than 1 ED visit and those without ED visits were examined by survey-weighted t-tests for continuous variables and chi-square tests for categorical variables.

This study used survey-weighted multivariable logistic regression to test the association between SDoH and ED visits within the previous 12 months in people with multimorbidity. Model 1 included multiple SDoH factors, including race/ethnicity, education, income, employment, insurance, marital status, and region of residence, to show the association of each SDoH factor with ED visits, adjusting for all other SDoH effects. Age and sex variables were added in Model 2, and the number of chronic conditions in Model 3. Cases with at least one missing data in any variable were deleted from the analysis, which may cause bias if the missing is not random [ 28 ]. Statistical significance was set as a two-sided α < 0.05. All statistical analyses were conducted using the Stata© SE statistical software.

Sample characteristics

A total of 28,917 respondents living with two or more of the 10 chronic conditions were included in the analysis. Among them, 68% ( n = 19,661) had no ED visit, while the remaining 32% ( n = 9,256) had at least one ED visit in the previous 12 months. The participants’ mean age (± SD) was 68 (± 10.7). The ED visits group had more female participants (58.5%) and adults who were not married (64.7%) than the no ED visit group (56.0% and 56.0%, respectively). The ED visits group had more non-Hispanic Black people (15.8% vs. 11.4%) and Hispanic participants (8.2% vs. 7.2%) than the no ED visit group and were more likely to have a high school or lower education (53.6% vs. 46.8%) and be unemployed (77.1% vs. 67.9%). Moreover, they were more likely to have low PIR (22.3% vs. 13.5%) and more likely to have a usual place for medical care (97.8% vs. 97.0%) than their no ED visit counterparts. Perceived health status was poorer (19.2% vs. 7.5%) in the ED visit group. Health insurance status did not differ between the two groups. In terms of chronic conditions, the most frequently reported condition was hypertension (83.0%), but there was no significant difference between the two groups. In both groups, heart disease (52.0% vs. 39.1%, p <.001) and diabetes (38.9% vs. 35.8%, p <.001) followed, and there were significant differences in the prevalence of the chronic conditions between the ED visit group and no ED visit group. COPD was the fourth most prevalent chronic condition in the ED visit group, with a higher prevalence than in the no ED visit group (37.4% vs. 24.9%, p <.001). However, cancer was the fourth most prevalent chronic condition in the no ED visit group, which was the only disease with a significantly higher prevalence in the no ED visit group than the ED visit group (32.1% vs. 30.4%, p <.001). The average numbers of chronic conditions (± SD) were 3.3 ± 1.31 in the ED visit group and 2.7 ± 0.99 in the no ED visit group, and the proportions of having more than five conditions were 9.7% and 6.3%, respectively. The characteristics of the study population can be found in Table 1 .

Social determinants of health on ED visits

The variables representing SDoH (marital status, race/ethnicity, education, financial status, region of residence, and usual healthcare access) were included in Model 1 without adjusting for other covariates. After adjusting for age and sex in Model 2, the associations of SDoH with ED visits were still preserved. Model 3 adjusted for the number of chronic conditions, which was a proxy of the severity of diseases, and all associations were still significant as Model 1 and 2. People who were not married (Adjusted Odd Ratio [AOR]: 1.19, 95% Confidence Interval [CI]: 1.11–1.28), non-Hispanic Black people (AOR: 1.28, 95% CI: 1.19–1.38), had high school education or less (AOR: 1.10, 95% CI: 1.02–1.19), had lower PIR (AOR: 1.44, 95% CI: 1.31–1.59), were unemployed (AOR: 1.33, 95% CI: 1.23–1.44), and had a usual place for medical care (AOR: 1.46, 95% CI: 1.18–1.80) were more likely to visit ED at least once in the prior 12 months, compared to their reference groups. The adjusted findings are presented in Table 2 .

This study presented multiple SDoH factors associated with ED visits among older people with multimorbidity. Particularly, people who were non-Hispanic Black people, not married, had poor financial conditions, and lower education levels showed higher odds of ED visits.

This study demonstrated racial/ethnic disparity in ED visits among older adults with multimorbidity. In this study, non-Hispanic Black people were more likely to have at least one ED visit than other racial/ethnic populations, while non-Hispanic Asian people were less likely to do so. Since multimorbidity was more prevalent among Black people and less prevalent among Asian people [ 15 ], this study result implied that race/ethnicity potentially deepened existing multimorbidity disparities through emergent healthcare access disparities. Similarly, the correlation between race/ethnicity and ED visits may indicate existing disparities in multimorbidity status. A study pointed out that Black individuals had a similar prevalence of multimorbidity as other groups who were 5–10 years older, and there was no significant change in multimorbidity prevalence between the Black and White populations from 1999 to 2018 [ 15 ]. Factors contributing to this may include the accumulated effect of the health experiences with chronic conditions in early life, producing a gap in older age and leading to higher odds of ED visits. However, since our study results were produced after other SDoH were adjusted, such as education levels and financial status, it needs to be investigated in further research to explore the other possible reasons for racial/ethnic disparity in ED visits. After exploring the mechanisms of deepening health disparities in the treatment continuum, it is necessary to eliminate the disparities led by early-onset chronic conditions and care processes through healthcare intervention and policy.

The findings reported that people who were not married showed higher odds of ED visits than those who were married. Since this study merged responses indicating not married, such as divorced, separated, and bereaved, as unmarried participants, people categorized as not married may include those living alone. Thus, they might lack caregivers, which increases their self-care burden, as well as available resources, such as health insurance, given that married people are more likely to have private insurance than unmarried people [ 29 ]. Moreover, married people are more likely to have social support than those who are not given that marital status is often used as a proxy for informal social support [ 30 , 31 ]. This finding aligns with previous studies that older adults living alone had higher odds of ED admission [ 32 , 33 ], and people with multimorbidity who live alone had significantly longer inpatient days after ED admissions than those without multimorbidity [ 32 ]. These findings may be supported by the fact that multimorbid people have more care needs due to the complexity of care, as well as greater disease and symptom burdens [ 34 , 35 ]. This study also showed that the lowest education level was associated with higher odds of ED visits than the highest. This can be related to the gap between high healthcare needs and capacity for self-management, given that education is associated with the activation of self-management in patients with multimorbidity [ 36 , 37 ]. Moreover, lower education level was also related to the greater impact of multimorbidity on activities of daily living and mental health, which may affect self-care [ 35 ]. Therefore, since people with multimorbidity have higher needs for self-care, SDoH factors related to self-care, such as marital status and education levels, may explain the higher odds of ED visits in unmarried and lowest education-level participants. Based on this finding, improving self-care and health literacy and implementing social support models in older multimorbid populations may prevent worsening health conditions, reducing ED visits.

The study demonstrated that people who are unemployed and have lower PIR levels have higher odds of ED visits. However, these findings need to be cautiously interpreted due to the cross-sectional study design. It could be explained that older people living with multimorbidity who visit EDs at least once in the previous 12 months are more likely to lose or quit their jobs or have not gotten a chance to be hired due to their poor health conditions. It also influences poverty levels, making lower PIR associated with ED visits. Moreover, this study used the variable ‘having a usual place for medical care’ as a proxy of health care access, one of the SDoH factors. Since it is assumed that people more likely to visit EDs would have worse health conditions, they need to receive regular follow-ups to assess and manage their health conditions. It is reported that multimorbid people are likely to spend more on healthcare costs, consequently making them more vulnerable to cost-related non-adherence to recommended treatment, resulting from financial strain [ 38 , 39 ]. A study found that more than one-third of participants living with multimorbidity had not sought medical care or purchased medication due to cost [ 40 ]. Non-adherence to recommended general healthcare visits and medication may lead to worsened symptoms in multimorbidity populations, which may result in higher odds of ED visits. In this context, their financial burden should be assessed and managed to prevent non-adherence to treatments and management of their multiple chronic conditions, leading to unplanned ED visits due to sudden deterioration. What is apparent is, however, that EDs are commonly the safety net of society [ 41 ]. A study reported that the most represented reasons for referral to social work in ED were financial concerns and resource counseling [ 41 ]. This indicates that EDs may play a role as the safety net to prevent deepening the disparities in SDoH among people with multimorbidity.

Lastly, the COVID-19 pandemic has tremendously influenced not only people’s SDoH, including employment status and socioeconomic level [ 42 ], but also ED visits, such as the number of ED visits and hospital admission rate from ED [ 43 ]. Although this study does not explain the impact of the COVID-19 pandemic on the association between SDoH and ED visits, we propose future studies that examine changes in the context of SDoH and emergency healthcare access pre- and post-pandemic.

Limitations

This study has significance, given that it used large-scale, nationally representative data to strengthen generalizability. Moreover, to our knowledge, this is the first study to examine the association between SDoH and ED visits in older multimorbid populations. However, this study acknowledges the following limitations. First, multimorbidity criteria did not include mental health problems, including substance use. If this study included mental health problems as multimorbidity criteria, the prevalence of multimorbidity would increase, which may influence the results. Moreover, since people with mental health problems are more likely to have multimorbidity [ 44 , 45 ], mental health may be associated with both SDoH and ED visits, producing a confounding effect. Thus, it may be beneficial to include mental health problems in regression models or multimorbid criteria for future studies to test whether they influence the association between SDoH and ED visits. In addition to mental health problems, some other possible chronic diseases that are common in middle and older age groups should also be comprehensively considered in further studies. Second, the study outcome (ED visits) and inclusion criteria (multimorbidity) were self-reported, which may yield recall bias and information bias. A more systematic way to collect clinical data, such as data extraction of health care utilization and disease diagnosis codes from electronic health records, may reduce the risk of bias in further studies. Third, the cross-sectional approach in this study could not test a causal relationship between SDoH and ED visits. Thus, longitudinal studies are needed to examine whether SDoH affects ED visits to rule out reverse causality. In addition, this study did not cover all domains of the SDoH definition (e.g., neighborhood/built environment and social/community) and adjusted for other SDoH to examine each SDoH effect on ED visits. Addressing all SDoH domains inclusively and considering the intersectionality of SDoH would be beneficial in examining the additive effects of SDoH on ED visits.

Lastly, the number of chronic conditions was adjusted in the final regression model to account for the potential confounding effect of the severity of overall chronic conditions on the association between SDoH and ED visits. Usually, the Charlson Comorbidity Index (CCI) or the number of diseases is used as a proxy to adjust for the severity of overall chronic conditions [ 46 , 47 ]. However, both CCI and the number of comorbidities may not be perfectly fitted with this study as a covariate since CCI was developed as a predictor of 1-year mortality and burden of disease [ 48 ], and the number of diseases cannot account for how comorbidities interact [ 46 ], although it is assumed that increasing the number of diseases may lead to increased overall severity. Unfortunately, the NHIS dataset in this study did not cover all the diagnoses to calculate CCI, so this study included the number of diseases in Model 3 as a proxy of the severity of the overall condition. However, it should be interpreted cautiously regarding the confounding effect of the severity of conditions in case it does not reflect the severity of the health condition very well. A few studies have tried to develop proper tools to measure the severity of multimorbid conditions, such as the multimorbidity interaction severity index [ 46 ]. However, this preliminary tool still needs to be verified for its reliability and validity in multiple populations [ 46 ]. Thus, a proper measure for the severity of multimorbid conditions is necessary to be developed to examine the association between SDoH and ED visits in older multimorbid populations more precisely.

Implications

Multimorbidity is increasing, and individuals with multimorbidity are high utilizers of health care. Prevention and management of multimorbidity is now a key priority globally. There is increasing attention toward studies focusing on etiology, epidemiology, and risk factors [ 1 ], yet there is still limited evidence to support effective healthcare interventions [ 4 ]. As there is a need for increased awareness of multimorbidity, innovation, and optimization of the use of existing resources, understanding existing disparities of emergent care needs and vulnerable groups can help determine which factors or combinations of factors are most important to target. The findings of this study underscore the importance of not only addressing early-life disparities contributing to developing multimorbidity but also the SDoH that influences health status and emergent care needs. Particularly, increased health screening and assessment in primary care settings is needed for racial/ethnic minority populations who have the disadvantage of emergent care access. Moreover, unemployed status and worsened financial burden, which may hinder treatment adherence, should be addressed in the context of the treatment continuum among multimorbid people to prevent unplanned worsening symptoms and hospitalization. Lastly, the self-care burden and need for social support in older multimorbid groups need to be paid more attention to mitigate the SDoH effect on emergent healthcare access.

Conclusions

In conclusion, this study demonstrated that SDoH are associated with increased ED visits among older adults living with multimorbidity. Systematic multidisciplinary team approaches are needed to address social disparities affecting multimorbidity prevalence, health-seeking behaviors, and emergent healthcare access. Therefore, researchers, healthcare practitioners, and policymakers should pay attention to addressing the social disparities by improving the management of chronic health conditions and promoting health equity.

Data availability

The datasets generated and/or analyzed during the current study are available in the NHIS repository: https://www.cdc.gov/nchs/nhis/data-questionnaires-documentation.htm .

Abbreviations

Adjusted Odds Ratio

Charlson Comorbidity Index

Confidence Interval

Emergency Department

National Center for Health Statistics

National Health Interview Survey

Poverty Income Ratio

Standard Deviation

Social Determinants of Health

United States

Navickas R, Petric VK, Feigl AB, Seychell M. Multimorbidity: what do we know? What should we do? J Comorb. 2016;6(1):4–11.

Article PubMed PubMed Central Google Scholar

Mortality G. Causes of death collaborators global, regional, and national life expectancy, all-cause and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the global burden of Disease Study 2015. Lancet. 2016;388:1459–544.

Article Google Scholar

Chowdhury SR, Chandra Das D, Sunna TC, Beyene J, Hossain A. Global and regional prevalence of multimorbidity in the adult population in community settings: a systematic review and meta-analysis. eClinicalMedicine 2023, 57.

Skou ST, Mair FS, Fortin M, Guthrie B, Nunes BP, Miranda JJ, Boyd CM, Pati S, Mtenga S, Smith SM. Multimorbidity. Nat Reviews Disease Primers. 2022;8(1):48.

Article PubMed Google Scholar

Lee JE, Haynes E, DeSanto-Madeya S, Kim YM. Social Determinants of Health and Multimorbidity among adults 50 years and older in the United States. Nurs Res. 2024;73(2):126–37.

Ansah JP, Chiu CT. Projecting the chronic disease burden among the adult population in the United States using a multi-state population model. Front Public Health. 2022;10:1082183.

Koirala B, Peeler A, Dennison Himmelfarb C, Davidson PM. Living with multiple chronic conditions: how we achieve holistic care and optimize health outcomes. J Adv Nurs. 2023;79(2):e7–9.

Thanakiattiwibun C, Siriussawakul A, Virotjarumart T, Maneeon S, Tantai N, Srinonprasert V, Chaiwat O, Sriswasdi P. Multimorbidity, healthcare utilization, and quality of life for older patients undergoing surgery: a prospective study. Med (Baltim). 2023;102(13):e33389.

Article CAS Google Scholar

Koroukian SM, Warner DF, Owusu C, Given CW. Multimorbidity redefined: prospective health outcomes and the cumulative effect of co-occurring conditions. Prev Chronic Dis 2015, 12.

Palladino R, Tayu Lee J, Ashworth M, Triassi M, Millett C. Associations between multimorbidity, healthcare utilisation and health status: evidence from 16 European countries. Age Ageing. 2016;45(3):431–5.

Heins M, Korevaar J, Schellevis F, Rijken M. Identifying multimorbid patients with high care needs - a study based on electronic medical record data. Eur J Gen Pract. 2020;26(1):189–95.

de Souto Barreto P, Lapeyre-Mestre M, Vellas B, Rolland Y. Multimorbidity type, hospitalizations and emergency department visits among nursing home residents: a preliminary study. J Nutr Health Aging. 2014;18(7):705–9.

Latham LP, Ackroyd-Stolarz S. Emergency department utilization by older adults: a descriptive study. Can Geriatr J. 2014;17(4):118–25.

Schiøtz ML, Stockmarr A, Høst D, Glümer C, Frølich A. Social disparities in the prevalence of multimorbidity– a register-based population study. BMC Public Health. 2017;17(1):422.

Caraballo C, Herrin J, Mahajan S, Massey D, Lu Y, Ndumele CD, Drye EE, Krumholz HM. Temporal trends in racial and ethnic disparities in multimorbidity prevalence in the United States, 1999–2018. Am J Med. 2022;135(9):1083–92. e1014.

Northwood M, Ploeg J, Markle-Reid M, Sherifali D. Integrative review of the social determinants of health in older adults with multimorbidity. J Adv Nurs. 2018;74(1):45–60.

von dem Knesebeck O, Bickel H, Fuchs A, Gensichen J, Höfels S, Riedel-Heller SG, König H-H, Mergenthal K, Schön G, Wegscheider K. Social inequalities in patient-reported outcomes among older multimorbid patients–results of the MultiCare cohort study. Int J Equity Health. 2015;14:1–11.

Google Scholar

Healthy People. 2030 [ https://health.gov/healthypeople/objectives-and-data/social-determinants-health ].

Sangal RB, Su H, Khidir H, Parwani V, Liebhardt B, Pinker EJ, Meng L, Venkatesh AK, Ulrich A. Sociodemographic Disparities in Queue Jumping for Emergency Department Care. JAMA Netw Open. 2023;6(7):e2326338–2326338.

National Center for Health Statistics. NHIS - Data, Questionnaires and Related Documentation. 2020.

Parsons V, Moriarity C, Jonas K, Moore T, Davis K, Tompkins L. Design and Estimation for the National Health Interview Survey, 2006–2015. Vital Health Stat Ser 2 Data Evaluation Methods Res 2014(165):1–53.

Hartnett KP. Impact of the COVID-19 pandemic on emergency department visits—United States, January 1, 2019–May 30, 2020. MMWR Morbidity and mortality weekly report 2020, 69.

Caraballo C, Herrin J, Mahajan S, Massey D, Lu Y, Ndumele CD, Drye EE, Krumholz HM. Temporal trends in racial and ethnic disparities in Multimorbidity Prevalence in the United States, 1999–2018. Am J Med. 2022;135(9):1083–e10921014.

Drye EE, Altaf FK, Lipska KJ, Spatz ES, Montague JA, Bao H, Parzynski CS, Ross JS, Bernheim SM, Krumholz HM. Defining multiple chronic conditions for quality measurement. Med Care. 2018;56(2):193–201.

Huang Y-q, Gou R, Diao Y-s, Yin Q-h, Fan W-x, Liang Y-p, Chen Y, Wu M, Zang L, Li L. Charlson comorbidity index helps predict the risk of mortality for patients with type 2 diabetic nephropathy. J Zhejiang Univ Sci B. 2014;15:58–66.

Article CAS PubMed PubMed Central Google Scholar

Lim A, Choi M, Jang Y, Lee H. Preoperative frailty based on laboratory data and postoperative health outcomes in patients undergoing coronary artery bypass graft surgery. Heart Lung. 2022;56:1–7.

Ingram DD, Malec DJ, Makuc DM, Kruszon-Moran D, Gindi RM, Albert M, Beresovsky V, Hamilton BE, Holmes J, Schiller JS. National center for health statistics guidelines for analysis of trends. 2018.

Kang H. The prevention and handling of the missing data. Korean J Anesthesiol. 2013;64(5):402–6.

Pandey KR, Yang F, Cagney KA, Smieliauskas F, Meltzer DO, Ruhnke GW. The impact of marital status on health care utilization among Medicare beneficiaries. Med (Baltim). 2019;98(12):e14871.

Kruse JA, Low LK, Seng JS. Validation of alternative indicators of social support in perinatal outcomes research using quality of the partner relationship. J Adv Nurs. 2013;69(7):1562–73.

Vlachantoni A. Unmet need for social care among older people. Ageing Soc. 2019;39(4):657–84.

Barrenetxea J, Tan KB, Tong R, Chua K, Feng Q, Koh W-P, Chen C. Emergency hospital admissions among older adults living alone in the community. BMC Health Serv Res. 2021;21(1):1192.

Lloyd T, Crellin E, Brine RJ, Shen JY, Wolters AT. Association between household context and emergency hospital use in older people: a retrospective cohort study on indicators for people living alone or living with somebody with frailty, developed from routine healthcare data in England. BMJ Open. 2022;12(5):e059371.

Tora Grauers W, Volkert S, Dagny Ros N, Dorte J, Ann Dorrit G, Susanne R, Rasmus K-R. Symptom burden in multimorbidity: a population-based combined questionnaire and registry study from Denmark. BMJ Open. 2021;11(4):e041877.

Chen YH, Karimi M, Rutten-van Mölken M. The disease burden of multimorbidity and its interaction with educational level. PLoS ONE. 2020;15(12):e0243275.

Bos-Touwen I, Schuurmans M, Monninkhof EM, Korpershoek Y, Spruit-Bentvelzen L, Ertugrul-van der Graaf I, de Wit N, Trappenburg J. Patient and Disease Characteristics Associated with activation for self-management in patients with diabetes, Chronic Obstructive Pulmonary Disease, Chronic Heart failure and chronic renal disease: a cross-sectional survey study. PLoS ONE. 2015;10(5):e0126400.

Schmaderer MS, Zimmerman L, Hertzog M, Pozehl B, Paulman A. Correlates of patient activation and Acute Care utilization among Multimorbid patients. West J Nurs Res. 2016;38(10):1335–53.

Laba T-L, Cheng L, Kolhatkar A, Law MR. Cost-related nonadherence to medicines in people with multiple chronic conditions. Res Social Administrative Pharm. 2020;16(3):415–21.

Larkin J, Foley L, Smith SM, Harrington P, Clyne B. The experience of financial burden for people with multimorbidity: a systematic review of qualitative research. Health Expect. 2021;24(2):282–95.

James L, Louise F, Shane T, Tony H, Barbara C, Patricia H, Susan MS. How do people with Multimorbidity Prioritise Healthcare when faced with Financial constraints? A choice experiment. medRxiv 2023:2023.2004.2010.23288383.

Selby S, Wang D, Murray E, Lang E. Emergency departments as the Health Safety Nets of Society: a descriptive and Multicenter Analysis of Social Worker support in the emergency room. Cureus. 2018;10(9):e3247.

PubMed PubMed Central Google Scholar

Paremoer L, Nandi S, Serag H, Baum F. Covid-19 pandemic and the social determinants of health. BMJ. 2021;372:n129.

Molina M, Evans J, Montoy JC, Cawley C, Graham-Squire D, Perez K, Raven M, Kanzaria HK. Analysis of Emergency Department Encounters among High Users of Health Care and Social Service systems before and during the COVID-19 pandemic. JAMA Netw Open. 2022;5(10):e2239076–2239076.

Barnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet. 2012;380(9836):37–43.

Melis R, Marengoni A, Angleman S, Fratiglioni L. Incidence and predictors of multimorbidity in the elderly: a population-based longitudinal study. PLoS ONE. 2014;9(7):e103120.

Gassmann D, Cheetham M, Siebenhuener K, Holzer BM, Meindl-Fridez C, Hildenbrand FF, Virgini V, Martin M, Battegay E. The multimorbidity interaction severity index (MISI): a proof of concept study. Medicine 2017, 96(8).

Charlson ME, Carrozzino D, Guidi J, Patierno C. Charlson Comorbidity Index: a critical review of Clinimetric Properties. Psychother Psychosom. 2022;91(1):8–35.

Roffman C, Buchanan J, Allison G. Charlson comorbidities index. J Physiotherapy 2016, 62(3).

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Dr. Koirala is supported by the NINR P30 NR18093, the Building Interdisciplinary Research Careers in Women’s Health (BIRCWH, K12HD085845; PI: Daniel Ernest Ford) program, and the Johns Hopkins School of Nursing Discovery and Innovation Award. The content is solely the responsibility of the authors and does not necessarily represent the official views of the supporting agencies.

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AL analyzed and interpreted the data, prepared a first draft of the manuscript, and revised the manuscript. CB, XL, and OO analyzed and interpreted the data and revised the manuscript. CH and PD supervised the process and revised the manuscript. BK conceptualized and designed the study, interpreted the data, and revised the manuscript. All authors read and approved the final manuscript.

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Lim, A., Benjasirisan, C., Liu, X. et al. Social determinants of health and emergency department visits among older adults with multimorbidity: insight from 2010 to 2018 National Health Interview Survey. BMC Public Health 24 , 1153 (2024). https://doi.org/10.1186/s12889-024-18613-8

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DOI : https://doi.org/10.1186/s12889-024-18613-8

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Proportion of ED visits by insurance type is reported for 2005 and 2016. Error bars represent 95% CIs.

a Statistically significant change in the trend of ED visits for all years between 2005 and 2016 ( P < .05).

eTable. Detailed statistical appendix regarding visit count, rates, standard error, and weighting

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Greenwood-Ericksen MB , Kocher K. Trends in Emergency Department Use by Rural and Urban Populations in the United States. JAMA Netw Open. 2019;2(4):e191919. doi:10.1001/jamanetworkopen.2019.1919

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Trends in Emergency Department Use by Rural and Urban Populations in the United States

1 Department of Emergency Medicine, University of New Mexico, Albuquerque
2 Department of Emergency Medicine, University of Michigan, Ann Arbor
3 Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor
Insights Transforming the Rural Health Care Paradigm Margaret B. Greenwood-Ericksen, MD, MSc; Shawn D’Andrea, MD, MPH; Scott Findley, MD JAMA Health Forum

Question How do payer status and patient demographic characteristics differ between urban and rural emergency department (ED) visits?

Findings In this cross-sectional study of National Hospital Ambulatory Medical Care Survey data, rural ED visit rates increased by more than 50%, from 36.5 to 64.5 per 100 persons, outpacing urban ED visit rates, which increased from 40.2 to 42.8 visits per 100 persons. Rural ED use increased for those aged 18 to 64 years, non-Hispanic white patients, Medicaid beneficiaries, and patients without insurance, with a larger proportion of rural EDs categorized as safety-net EDs.

Meaning Rural EDs experienced greater growth in ED use simultaneous with increased pressure as safety-net hospitals.

Importance Patterns in emergency department (ED) use by rural populations may be an important indicator of the health care needs of individuals in the rural United States and may critically affect rural hospital finances.

Objective To describe urban and rural differences in ED use over a 12-year period by demographic characteristics, payers, and characteristics of care, including trends in ambulatory care–sensitive conditions and ED safety-net status.

Design, Setting, and Participants This cross-sectional study of ED visit data from the nationally representative National Hospital Ambulatory Medical Care Survey examined ED visit rates from January 2005 to December 2016. Visits were divided by urban and rural classification and stratified by age, sex, race/ethnicity, and payer. Emergency departments were categorized as urban or rural in accordance with the US Office of Management and Budget classification. Codes from the International Classification of Diseases, Ninth Revision ( ICD-9 ), were used to extract visits related to ambulatory care–sensitive conditions. Safety-net status was determined by the Centers for Disease Control and Prevention definition. Visit rates were calculated using annual US Census Bureau estimates. National Hospital Ambulatory Medical Care Survey estimates were generated using provided survey weights and served as the numerator, yielding an annual, population-adjusted rate. Data were analyzed from June 2017 to November 2018.

Main Outcomes and Measures Emergency department visit rates for 2005 and 2016 with 95% confidence intervals, accompanying rate differences (RDs) comparing the 2 years, and annual rate change (RC) with accompanying trend tests using weighted linear regression models.

Results During the period examined, rural ED visit estimates increased from 16.7 million to 28.4 million, and urban visits increased from 98.6 million to 117.2 million. Rural ED visits increased for non-Hispanic white patients (13.5 million to 22.5 million), Medicaid beneficiaries (4.4 million to 9.7 million), those aged 18 to 64 years (9.6 million to 16.7 million), and patients without insurance (2.7 million to 3.4 million). Rural ED visit rates increased by more than 50%, from 36.5 to 64.5 visits per 100 persons (RD, 28.9; RC, 2.2; 95% CI, 1.2 to 3.3), outpacing urban ED visit rates, which increased from 40.2 to 42.8 visits per 100 persons (RD, 2.6; RC, 0.2; 95% CI, −0.1 to 0.6). By 2016, nearly one-fifth of all ED visits occurred in the rural setting. From 2005 to 2016, rural ED utilization rates increased for non-Hispanic white patients (RD, 26.1; RC, 1.6; 95% CI, 0.4 to 2.8), Medicaid beneficiaries (RD, 56.4; RC, 4.1; 95% CI, 2.1 to 6.1), those aged 18 to 44 years (46.9 to 81.6 visits per 100 persons; RD, 34.7; RC, 2.3; 95% CI, 1.1 to 3.5) as well as those aged 45 to 64 years (27.5 to 53.9 visits per 100 persons; RD, 26.5; RC, 1.6; 95% CI, 0.7 to 2.5), and patients without insurance (44.0 to 66.6 visits per 100 persons per year; RD, 22.6; RC, 2.7; 95% CI, 0.2 to 5.2), with a larger proportion of rural EDs categorized as safety-net status.

Conclusions and Relevance Rural EDs are experiencing important changes in utilization rates, increasingly serving a larger proportion of traditionally disadvantaged groups and with greater pressure as safety-net hospitals.

Recent reports suggest troubling declines in the health of individuals who live in the rural United States, with increases in mortality, 1 greater rates of chronic disease and high-risk health behaviors, 2 and widening differences between rural and urban life expectancy. 3 , 4 Rural areas are further constrained by physician shortages 5 and financially stressed hospitals with operating margins often too narrow to invest in upgrades to optimize care delivery. 6 As a result of these challenges, rural populations may engage with the health care system differently than their urban counterparts. Understanding the health care use of individuals in rural areas may yield insights into addressing these growing health disparities.

Emergency department (ED) use patterns provide a lens into the status of health care delivery in the communities they serve. Emergency departments play a unique and evolving role in the health care system as a site for the unplanned acute care needs of their communities 7 and as the chief location for admission to the hospital. 8 Emergency department visits may reflect progression or exacerbations of poorly controlled chronic diseases or potentially signal barriers in access to usual sources of care, such as primary or specialty outpatient settings. However, traditional office-based care settings require significant resource investment and a robust physician pool, which may be lacking in rural communities. 9 These factors raise the possibility that rural EDs are increasingly serving as a source of care for rural patients in ways that are distinct from their urban counterparts.

To evaluate this hypothesis, we examined changing trends in rural ED visits and assessed for associated drivers. These included patient demographic characteristics and payer status, visit types, and proportion of visits for ambulatory care–sensitive conditions, which can serve as a marker for outpatient care availability. Additionally, we examined the proportion of EDs that met the safety-net classification, as this designation can contribute to the eroding financial solvency of rural hospitals. Our analysis aims to describe use in rural EDs, which, to our knowledge, has never been done previously and has important implications for rural health care delivery.

To determine the yearly number of ED visits and associated confidence intervals, we analyzed data with provided survey weights from the National Hospital Ambulatory Medical Care Survey (NHAMCS), an annual, national probability sample survey on use and provision of services in hospital-based EDs, from January 2005 to December 2016. We included all visits to hospital-based EDs. We excluded data from 2012, as the urban/rural classification variable was not publicly available. Emergency departments were categorized as urban or rural in accordance with the US Office of Management and Budget classification from the 2010 census for all years. 10 Because the definition of urban and rural settings can change over time, we also conducted a sensitivity analysis and applied the Office of Management and Budget classification criteria from 2000 and found similar rates.

To determine the reference population on which to generate rates, we used the US Census Bureau estimates of the civilian, noninstitutionalized population, excluding patients in long-term care and incarcerated individuals. These estimates were further divided into urban and rural populations in accordance with the Office of Management and Budget 2010 classifications, then stratified by age, sex, and race/ethnicity. As a result, the Office of Management and Budget definitions for urban and rural were used for NHAMCS data and US Census Bureau data. Therefore, the NHAMCS estimate, generated using provided survey weights, served as the numerator and the US Census estimate, generated by the US Census Bureau, served as the denominator, yielding an annual, population-adjusted rate. This approach is used by NHAMCS in their yearly reports, as detailed in the microdata files, 11 and was confirmed by personal communication with NHAMCS/National Ambulatory Medical Care Survey statisticians (Don Cherry, oral communication, June 2017).

Finally, we calculated rate differences (RDs) by subtracting the 2005 rate from the 2016 rate as an absolute measure of change. We then calculated the annual rate change (RC) by regressing each year’s rate over time, weighted by the inverse of the variance.

Our analysis was conducted between June 2017 and November 2018. This study was exempted from review by the University of Michigan’s institutional review board, as it uses a publicly available data set that contains no patient identifiers, and informed consent was waived. This study is reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline. 12

Visit rates are reported by age, sex, race/ethnicity, insurance status, triage category, and disposition category (ie, hospital admission or transfer). Ambulatory care–sensitive conditions, a set of diagnoses reflecting the quality and availability of outpatient services, mirror the established definitions of the Agency for Healthcare Research and Quality definitions of prevention quality indicators by validated codes from the International Classification of Diseases, Ninth Revision ( ICD-9 ). 13 The conditions included bacterial pneumonia, hypertension, perforated appendix, congestive heart failure, diabetes (uncontrolled or complications), angina, chronic obstructive pulmonary disease, urinary tract infection, and dehydration. Safety-net status was determined by Centers for Disease Control and Prevention criteria, which are based on the proportion of patients without insurance and Medicaid populations served. 14

Annual ED visit rates were calculated using the US Census Bureau estimates of the civilian, noninstitutionalized population, which were divided into urban and rural populations in accordance with the Office of Management and Budget 2010 classifications and then further stratified by age, sex, and race/ethnicity. For the purpose of exploring racial/ethnic disparities, we categorized patients as non-Hispanic white, non-Hispanic black, or Hispanic using guidance from the NHAMCS. 15 The relative standard errors for each categorization for rural and urban are 30% or less, indicating reliable estimates. Annual estimates of persons by insurance type are based on the American Community Survey, starting in 2008 16 ; in prior years, insurance status was collected by the Current Population Survey Annual Social and Economic Supplement 17 without county-level identifiers, which prevents identification of urban and rural populations.

Visit rates are reported with 95% CIs based on standard errors provided by the NHAMCS. We report visit rates for 2005 and 2016 by age, sex, race/ethnicity, payer type, and ambulatory care–sensitive conditions. Additionally, to understand trends over time, we calculated the RD across this 12-year period, an approach previously established in the literature, 18 along with the annual RC. Rate change was generated by performing weighted linear regression tests of trend to account for the sampling scheme used by the NHAMCS. Weights were the inverse of the variance estimates calculated from the standard errors as described in previous literature. 19

We additionally reported change in acuity level and disposition category, also with accompanying RD and RC. For 2005 to 2008, the NHAMCS triage category was rated on a 5-point scale, based on the immediacy with which the patient should be seen: (1) immediate, (2) 1 to 14 minutes, (3) 15 to 60 minutes, (4) 1 to 2 hours, and (5) 2 to 24 hours. In 2009, NHAMCS renamed the 5 categories (1) immediate, (2) emergent, (3) urgent, (4) semiurgent, and (5) nonurgent, which we coded as synonymous with the earlier categories.

The number of safety-net EDs was determined by dividing the weighted estimate of urban and rural EDs that met the criteria for safety-net hospitals by the number of urban and rural EDs designated as a service line in the Annual Survey of Hospitals between 2005 and 2016. 20 The Centers for Disease Control and Prevention defines a safety-net ED as meeting 1 or more of the following criteria: (1) having more than 30% of ED visits with Medicaid as the expected source of payment, (2) having more than 30% of visits with self-pay or no charge as the expected source of payment (considered without insurance), or (3) having a combined Medicaid and uninsured pool greater than 40% of visits. 21 All analyses were performed in Stata version 14.0 (StataCorp) accounting for the complex survey design. Level of significance was set at P = .05, and tests were 2-tailed.

From 2005 to 2016, estimated rural ED visits increased from 16.7 million to 28.4 million and estimated urban visits from 98.6 million to 117.2 million ( Table 1 and Table 2 ), with rural increases in non-Hispanic white patients (13.5 million to 22.5 million), Medicaid beneficiaries (4.4 million to 9.7 million), those aged 18 to 64 years (9.6 million to 16.7 million), and patients without insurance (2.7 million to 3.4 million). Rural ED visit rates increased by more than 50%, from 36.5 to 64.5 per 100 persons (RD, 28.9; RC, 2.2; 95% CI, 1.2 to 3.3) between 2005 and 2016 ( Figure 1 and Table 1 ). This increase outpaced urban ED visit rates, which were generally flat, increasing from 40.2 to 42.8 visits per 100 persons (RD, 2.6; RC 0.2; 95% CI, −0.1 to 0.6) ( Figure 1 and Table 2 ). Detailed information on visit counts, rates, and weighting can be found in the eTable in the Supplement .

Across urban and rural EDs during the study, each age group demonstrated increase in use, with a more rapid change in rural visits. For rural EDs, 2 groups experienced statistically significant increases: those aged 18 to 44 years (46.9 to 81.6 visits per 100 persons; RD, 34.7; RC, 2.3; 95% CI, 1.1-3.5) and aged 45 to 64 years (27.5 to 53.9 visits per 100 persons; RD, 26.5; RC, 1.6; 95% CI, 0.7-2.5) ( Table 1 ). In contrast, urban EDs experienced increases in the same age groups but at a slower rate. For those aged 18 to 44 years, visits increased from 41.3 to 45.7 visits per 100 persons (RD, 4.4; RC, 0.5; 95% CI, 0.1-0.9); for those aged 45 to 65 years, visits increased from 29.4 to 39.5 visits per 100 persons (RD, 10.1; RC, 0.7; 95% CI, 0.4-1.0) ( Table 2 ).

Among race/ethnicity groups, rural Non-Hispanic white patients demonstrated the largest increases in ED visits (RD, 26.1; RC, 1.6; 95% CI, 0.4 to 2.8). The most notable differences in payer type between rural and urban ED use are for the Medicaid population and patients without insurance. Rural Medicaid visits experienced the largest change and the steepest rate increase from 56.2 to 112.6 per 100 persons (RD, 56.4; RC, 4.1; 95% CI, 2.1 to 6.1), which is in contrast to the urban population’s slower increase, from 56.6 to 88.3 visits per 100 persons (RD, 31.7; RC, 2.9; 95% CI, 1.6 to 4.4). In addition, rural visits by patients without insurance increased significantly during the period studied from 44.0 to 66.6 per 100 persons (RD, 22.6; RC, 2.7; 95% CI, 0.2 to 5.2) in comparison with a small, nonsignificant decrease in urban EDs from 45.7 to 38.8 visits per 100 persons (RD, −6.9; RC, −0.3; 95% CI, −1.6 to 1.0).

Urban and rural EDs experienced small, nonsignificant changes in ambulatory care–sensitive conditions; rural visits increased from 3.6 to 4.5 visits per 100 persons (RD, 0.9; RC, 0.1; 95% CI, 0 to 0.2) in comparison with minimal change in urban proportion of visits. The proportion of rural ED visits that led to hospital admission decreased from 9.3% (95% CI, 4.1% to 14.5%) to 6.3% (95% CI, 1.5% to 11.1%) (RD, −3.0; RC, −0.3; 95% CI, −0.5 to −0.1); the increase in transfer rates, from 3.3% (95% CI, 1.6% to 5.1%) to 4.2% (95% CI, 1.3% to 7.0%) (RD, 0.9; RC, 0.1; 95% CI, 0 to 0.2), was not significant. The overall acuity of ED visits as measured by NHAMCS triage categories lessened in rural EDs over time from a mean value of 3.1 (95% CI, 2.8 to 3.4) in 2005 to 3.4 (95% CI, 3.3 to 3.6; RD, 0.3) (RC, 0.5; 95% CI, 0.2 to 0.7) in 2016, whereas in urban EDs this was largely unchanged over time.

In 2005, the estimated count of rural safety-net EDs was 769 of 2009 US rural hospitals (38.3%). By 2016, the number of rural safety-net EDs had increased to 1187 of 1855 rural hospitals (65.0%). A rise in Medicaid visits, which increased proportionally from 25.9% in 2005 to 32.2% in 2016, was associated with this change. During the same period, rural EDs experienced a modest decrease in the proportion of patients without insurance, from 16.3% to 11.1% ( Figure 2 ). In comparison, urban EDs experienced a larger increase in their Medicaid share, from 24.2% in 2005 to 39.9% in 2016, which was offset by a larger decrease in patients without insurance, from 18.5% to 10.1%.

Patterns of ED use provide insight into a community’s health and local care delivery system, thereby serving as potential markers for access and health status. Our study demonstrates that rural EDs have experienced a substantial increase in patient visits from 2005 to 2016—growth of more than 50%—despite a 5% decline in the overall US rural population. 16 By 2016, nearly one-fifth of all ED visits occurred in the rural setting. Further, while the ratio of rural to urban ED visits was 1:1.1 visits per 100 persons in 2005, a reversal occurred by 2016, when there were 1.5 rural ED visits for every 1 urban ED visit. These changes seem associated in particular with increases in ED use by those aged 18 to 64 years, non-Hispanic white patients, Medicaid beneficiaries, and patients without insurance. Accompanying these changes in demographic characteristics, we also found an increase in lower-acuity rural visits. Finally, the proportion of rural EDs classified as safety-net EDs increased by 26.7% between 2005 and 2016, representing an increased reliance on Medicaid reimbursement.

The disproportionate rise in rural ED visits, particularly for traditionally disadvantaged populations, suggests several considerations for the health of rural residents and rural health care delivery. Increased visits by young to middle-aged white rural patients—particularly Medicaid beneficiaries and those without insurance—may indicate an increased burden of illness or challenges in access to alternative care sites. This has implications for health outcomes, as a greater and increasing reliance on EDs for care by rural patients may complicate efforts to bolster chronic disease management and lead to fragmentation of care. The traditional ED mission focuses on care for acute conditions and therefore may not have resources devoted to modifying health behaviors or addressing long-term conditions. Efforts may be further challenged by additional obstacles unique to rural settings, including fewer personal economic resources, 22 increased social and geographic isolation, older age, and greater burden of health risk factors, such as obesity, 23 smoking, 24 and opioid overdose. 25 , 26 In contrast, we found stable ED use rates for the youngest (aged ≤18 years) and oldest (aged ≥65 years) age groups regardless of urban/rural designation. This may reflect better access to primary care and long-term disease management efforts tied to more stable insurance coverage owing to options such as universally available Medicaid for children and Medicare for older adults.

Increases in lower-acuity visits to rural EDs and a similar trend for ambulatory care–sensitive conditions indicate that rural patients may face barriers to timely outpatient ambulatory and primary care services. 27 , 28 Rural EDs may be serving as the most immediately accessible source of health care for rural communities. This finding is consistent with the documented intractable rural primary care shortage, 5 misdistribution of primary care favoring urban centers, 29 and rapid rural primary care physician turnover, 30 all of which may contribute to increased ED use. Previous studies suggest that poor primary care access is associated with increased ED use, 31 , 32 with rural patients less likely to have a primary care follow-up visit and more likely to have an ED visit following an inpatient admission. 33 Historically, the use of EDs for routine and primary care conditions is perceived as low value, with efforts to reduce ED use in urban communities and health systems focused on investments in care coordination 34 - 36 and medical homes. 37 , 38 Recent attention to the decline in rural health has prompted calls for rural hospitals and clinicians to more forcefully embrace these population health management principles. 39 However, these approaches require significant practice transformation, adequate resource investment, economies of scale, and a robust physician pool—all which may be lacking in the rural setting.

Therefore, rural areas may require tailored and innovative strategies to achieve improvements in the access to and availability of health care in their communities. While existing federal support for these programs will continue, the traditional approaches to bolstering primary care, including the National Health Service Corps program, foreign medical graduates, and primary care residency training in rural communities, have yielded mixed returns while facing variable financial support. 40 - 42 Telehealth is another promising strategy, but it has struggled with large-scale implementation in rural areas because of significant development costs and reimbursement challenges, despite significant interest by primary care physicians. 43 Innovation in acute care delivery occurring in the urban setting, including urgent care clinics, home monitoring, and e-visits, have had poor penetration into rural health care delivery. This is in part owing to a lack of patient volume to support such innovations as well as limitations in telecommunication infrastructure. It has been increasingly recognized that rural-specific innovations are needed, yielding the concept of emergency medical centers (also known as rural freestanding EDs ), in which existing hospitals transition to a facility divested of inpatient beds. 44 These facilities then focus care on targeted outpatient services in coordination with an on-site comprehensive ED. 45 Alternatively, for smaller communities, a primary care clinic with extended hours could be linked with an ambulance service operating 24 hours a day, every day. Through these strategies advanced by the Centers for Medicare & Medicaid Services, 46 rural hospitals can focus on outpatient management of long-term conditions and high-risk health behaviors while simultaneously ensuring high-quality treatment for acute conditions and rapid transfer of patients requiring hospitalization to larger centers.

We additionally found an increase in the proportion of rural EDs classified as safety-net EDs, indicating an erosion in rural hospitals’ payer mix. Underlying this trend is an increasing reliance on Medicaid for reimbursement without a commensurate decline in visits by patients without insurance, which contrasts with the urban ED experience. These trends may be associated with Medicaid expansion in states with large rural populations, as Medicaid expansion in such states increased coverage for low-income rural adults. 47 Even with Medicaid expansion, rural hospitals operate on thin margins, often requiring special payment programs to remain financially viable. 48 Despite these federal efforts, more than 90 rural hospitals have closed in the last 10 years, 49 threatening rural communities’ access to necessary local health care. These developments are partially the result of reductions in inpatient admissions nationwide as well as market trends promoting increasing hospital and health system consolidation. 50 Further, states with large rural populations have generally been reluctant to expand Medicaid, which may be related to most rural hospital closures occurring in those states. 51 This may be reflected in the proportion of visits to rural EDs by patients without insurance, which experienced only a 5% decrease compared with a 9% decrease that occurred in urban EDs. In response to these cumulative financial pressures on rural hospitals, some states are experimenting with an alternative payment model of global rural health care budgets. Maryland’s rural hospitals are paid a fixed amount in advance for inpatient and outpatient hospital-based services. Pennsylvania is now attempting the same 52 with hopes that the greater certainty of prospective funding should allow rural hospitals to better invest in necessary quality and preventive care. 53 Our findings suggest that increased Medicaid reimbursement would help stabilize rural hospitals in a traditional fee-for-service model and alternative payment models, like global budgets, may be a more successful strategy given the deteriorating payer mix noted at rural EDs.

There are several limitations of this study related to the NHAMCS survey design and assumptions tied to some of the study’s outcome measures. 54 First, NHAMCS does not provide unique patient identifiers; therefore, the extent to which these visits represent repeated visits for the same patients or new patients is unknown. Second, methods for determining ambulatory care–sensitive conditions were designed for hospital inpatient discharge data, but applying them to ED discharge diagnoses has been described successfully. 55 Third, there are more than 15 ways to define rurality, which complicates urban vs rural analyses. The NHAMCS categorizes hospitals into urban or rural in alignment with the Office of Management and Budget, which relies on Metropolitan Statistical Areas. To match the NHAMCS convention, we defined our urban and rural reference populations by the US Census Bureau according to these same criteria. Fourth, while it is important to recognize that rural areas are heterogeneous and the findings reported may vary from one type of rural location to another, we were unable to explore more granular geographic estimates with this data set. For example, as there are no state identifiers in NHAMCS, we were unable to determine how the ED payer mix changed in Medicaid expansion vs nonexpansion states. In addition, NHAMCS allows parsing at the regional level, but our analysis in these cases was subject to a sample size less than 30, which produces unstable estimates. 54 Fifth, this is national survey data, and our findings are hypothesis generating; these findings will need to be explored in other data sets for confirmation of these trends. However, despite these limitations, the national trends reported in this analysis remain an important insight into the overall experience in rural health care delivery.

These findings demonstrate several important and concerning implications for rural population health care delivery. Increased ED use may reflect a deteriorating primary care infrastructure, greater fragmentation of care, and worsening disparities for several traditionally disadvantaged groups, including those with Medicaid and those without insurance. Additionally, rural EDs are increasingly serving as safety-net hospitals, potentially further destabilizing their budgets because they generally operate in the traditional fee-for-service model. To improve the health of individuals in the rural United States, improved Medicaid reimbursement and innovative payment and delivery models that integrate EDs into local health care delivery systems may prove successful.

Accepted for Publication: February 19, 2019.

Published: April 12, 2019. doi:10.1001/jamanetworkopen.2019.1919

Corresponding Author: Margaret B. Greenwood-Ericksen, MD, MSc, Department of Emergency Medicine, University of New Mexico, 700 Camino de Salud, Albuquerque, NM 87109 ( [email protected] ).

Author Contributions: Dr Greenwood-Ericksen had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Both authors.

Acquisition, analysis, or interpretation of data: Both authors.

Drafting of the manuscript: Greenwood-Ericksen.

Critical revision of the manuscript for important intellectual content: Both authors.

Statistical analysis: Greenwood-Ericksen.

Administrative, technical, or material support: Greenwood-Ericksen.

Supervision: Kocher.

Conflict of Interest Disclosures: Dr Kocher reported grants from the Agency for Healthcare Research and Quality (K08 HS024160) and from Blue Cross Blue Shield of Michigan and Blue Care Network outside the submitted work. No other disclosures were reported.

Funding/Support: Dr Greenwood-Ericksen was supported by the University of Michigan National Clinician Scholars Program at the Institute for Healthcare Policy and Innovation from June 2016 to March 2018 and through the Department of Veterans Affairs National Clinician Scholars Program.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The contents do not represent the views of the US Department of Veterans Affairs or the US government.

Meeting Presentation: Preliminary findings were previously presented at the Society for Emergency Medicine 2018 Annual Meeting; May 17, 2018; Indianapolis, Indiana; and 2018 AcademyHealth Annual Research Meeting; June 25, 2018; New Orleans, Louisiana.

Additional Contributions: HwaJung Choi, PhD, University of Michigan, Ann Arbor, provided statistical consultation related to data processing. She was not compensated for her work.

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2024 National Strategy for Suicide Prevention

Suicide is an urgent and growing public health crisis. More than 49,000 people in the United States died by suicide in 2022. That’s one death every 11 minutes.

National Strategy for Suicide Prevention

The 2024 National Strategy for Suicide Prevention is a bold new 10-year, comprehensive, whole-of-society approach to suicide prevention that provides concrete recommendations for addressing gaps in the suicide prevention field. This coordinated and comprehensive approach to suicide prevention at the national, state, tribal, local, and territorial levels relies upon critical partnerships across the public and private sectors. People with lived experience are critical to the success of this work.

The National Strategy seeks to prevent suicide risk in the first place; identify and support people with increased risk through treatment and crisis intervention; prevent reattempts; promote long-term recovery; and support survivors of suicide loss.

Four strategic directions guide the National Strategy:

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Strategic Direction 1: Community-Based Suicide Prevention

Goal 1: Establish effective, broad-based, collaborative, and sustainable suicide prevention partnerships.

Goal 2: Support upstream comprehensive community-based suicide prevention.

Goal 3: Reduce access to lethal means among people at risk of suicide.

Goal 4: Conduct postvention and support people with suicide-centered lived experience.

Goal 5: Integrate suicide prevention into the culture of the workplace and into other community settings.

Goal 6: Build and sustain suicide prevention infrastructure at the state, tribal, local, and territorial levels.

Goal 7: Implement research-informed suicide prevention communication activities in diverse populations using best practices from communication science.

Strategic Direction 2: Treatment and Crisis Services

Goal 8: Implement effective suicide prevention services as a core component of health care.

Goal 9: Improve the quality and accessibility of crisis care services across all communities.

Strategic Direction 3: Surveillance, Quality Improvement, and Research

Goal 10: Improve the quality, timeliness, scope, usefulness, and accessibility of data needed for suicide-related surveillance, research, evaluation, and quality improvement.

Goal 11: Promote and support research on suicide prevention.

Strategic Direction 4: Health Equity in Suicide Prevention

Goal 12: Embed health equity into all comprehensive suicide prevention activities.

Goal 13: Implement comprehensive suicide prevention strategies for populations disproportionately affected by suicide, with a focus on historically marginalized communities, persons with suicide-centered lived experience, and youth.

Goal 14: Create an equitable and diverse suicide prevention workforce that is equipped and supported to address the needs of the communities they serve.

Goal 15: Improve and expand effective suicide prevention programs for populations disproportionately impacted by suicide across the life span through improved data, research, and evaluation.

Federal Action Plan

The Federal Action Plan identifies more than 200 actions across the federal government to be taken over the next three years in support of those goals. These actions include:

Evaluating promising community-based suicide prevention strategies
Identifying ways to address substance use/overdose and suicide risk together in the clinical setting
Funding a mobile crisis locator for use by 988 crisis centers
Increasing support for survivors of suicide loss and others whose lives have been impacted by suicide

These actions will be monitored and evaluated regularly to determine progress and success, and to further identify barriers to suicide prevention.

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Emergency Department Visit Rates by Adults With Diabetes: United States, 2020–2021

NCHS Data Brief No. 487, December 2023

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Loredana Santo, M.D., M.P.H., Susan M. Schappert, M.A., and Jill J. Ashman, Ph.D.

Key findings

What was the ED visit rate by adults with diabetes in 2020–2021 and did it differ by sex and age?

Did the ed visit rate by adults with diabetes differ by race and ethnicity and age, did the ed visit rate by adults with diabetes differ by number of other chronic conditions and age, did ed visit rates by adults with diabetes change over the past decade, definitions, data source and methods, about the authors, suggested citation.

Data from the National Hospital Ambulatory Medical Care Survey

In 2020–2021, the emergency department visit rate by adults with diabetes was 72.2 visits per 1,000 adults, and the rate increased with age.

Emergency department visit rates by adults with diabetes were highest among Black non-Hispanic people (136.6 visits per 1,000 adults per year) and higher among White non-Hispanic people (69.9) compared with Hispanic people (52.3).

In 2020–2021, the emergency department visit rate by adults with diabetes and two to four other chronic conditions was 541.4 per 1,000 adults per year and increased with age.

Emergency department visit rates by adults with diabetes increased from 48.6 visits per 1,000 adults in 2012 to 74.9 visits per 1,000 adults in 2021.

In 2021, diabetes was the eighth leading cause of death in the United States ( 1 ). Over 37 million Americans have diabetes ( 2 ). While it most often develops in people older than age 45 ( 3 ), its frequency is increasing in young adults ( 4 ). Among people with diabetes, increasing age is a risk factor for hospitalization ( 5 ). Emergency department (ED) visits by people with diabetes have been used to monitor access to care and healthcare use ( 6 ). This report describes ED visits made by adults with diabetes, and presents selected characteristics by age.

Keywords : emergency care, National Hospital Ambulatory Medical Care Survey

In 2020–2021, the annual ED visit rate by adults with diabetes was 72.2 visits per 1,000 adults ( Figure 1 ).
ED visit rates by adults with diabetes did not differ significantly by sex, with 75.1 visits per 1,000 women and 69.1 visits per 1,000 men.
Among adults ages 18–44 with diabetes, ED visit rates were higher among women (32.2) than men (19.5). Among adults ages 45–64 with diabetes, ED rates were similar for women (86.2) and men (84.4). Among adults age 65 and older with diabetes, the observed difference in ED rates between men (159.9) and women (142.8) was not significant.
ED visit rates by adults with diabetes increased with age for both women and men.

Figure 1. Emergency department visit rate among adults with diabetes, by sex and age group: United States, 2020–2021

In 2020–2021, ED visit rates by adults with diabetes were highest among Black non-Hispanic people (subsequently, Black) (136.6 visits per 1,000 adults), followed by White non-Hispanic (subsequently, White) (69.9) and Hispanic (52.3) people ( Figure 2 ).
For each age group, ED rates by adults with diabetes were highest among Black people. Differences between Hispanic and White people were not significant.
ED visit rates by adults with diabetes increased with age among Black, White, and Hispanic people.

Figure 2. Emergency department visit rate among adults with diabetes, by race and ethnicity and age group: United States, 2020–2021

Most ED visits by adults with diabetes were made by patients with two to four other reported chronic conditions (541.4 visits per 1,000 visits) ( Figure 3 ). Rates by patients with no other chronic conditions were lowest (90.2).
Among adults with diabetes ages 18–44, ED visit rates were highest among those with two to four other chronic conditions (402.0) and lowest among those with five or more other conditions (93.8).
Among adults with diabetes ages 45–64, ED visit rates were highest among those with two to four other chronic conditions (526.4) and lowest among those with no other conditions (87.7).
Among adults with diabetes age 65 and older, ED visit rates were highest among those with two to four other conditions (605.2), followed by those with five or more conditions (217.7), one other chronic condition (140.6), and no other conditions (36.5).
The ED visit rate by adults with diabetes and two to four or five or more other chronic conditions increased by age, but the rates for those with no other chronic conditions or one other condition decreased with age.

Figure 3. Emergency department visit rate among adults with diabetes, by number of additional chronic conditions and age group: United States, 2020–2021

ED visit rates by adults with diabetes increased from 48.6 visits per 1,000 adults in 2012 to 74.9 in 2021 ( Figure 4 ).
ED visit rates by adults with diabetes age 65 and older were higher than all other age groups during 2012–2021, and increased from 113.4 in 2012 to 156.8 in 2021.
ED visit rates by adults with diabetes ages 45–64 increased from 53.1 in 2012 to 89.2 in 2021.
ED visit rates by adults with diabetes ages 18–44 increased from 20.9 in 2012 to 26.4 in 2016, but their rates remained stable for 2016–2021.

Figure 4. Emergency department visit rate among adults with diabetes, by age group: United States, 2012–2021

In 2020–2021, the annual ED visit rate by adults with diabetes was 72.2 visits per 1,000 adults. ED visit rates by adults with diabetes increased with age for both women and men. ED visit rates were highest among Black people, and higher among White people compared with Hispanic people. Among each race and ethnicity group, ED visit rates increased with age. Rates by adults with two or more other chronic conditions were higher than rates for adults with one or no other chronic conditions. From 2012 through 2021, ED visit rates among all adults with diabetes increased, as well as among adults age 45 and older. Among adults ages 18–44, ED visit rates increased during 2012–2016 and then remained stable during 2016–2021. This report includes the most recent estimates of ED visits by adults with diabetes from the National Hospital Ambulatory Medical Care Survey (NHAMCS) and shows an increasing trend in rates by adults with diabetes in the ED setting.

ED visits by adults with diabetes: In both 2020 and 2021, a checkbox item was included in NHAMCS that asked, “Does the patient have (mark all that apply).” The list included three checkboxes for diabetes: diabetes mellitus, type 1; diabetes mellitus, type 2; and diabetes mellitus, type unspecified. A separate item, provider’s diagnosis, collected information on up to five diagnoses related to the current visit, using verbatim text entries from the patient’s medical record, which were later coded by National Center for Health Statistics medical coders using the International Classification of Diseases , Ninth Revision , Clinical Modification (for survey years 2012–2015) and the International Classification of Diseases , 10th Revision, Clinical Modification (for 2016–2021) ( 7 , 8 ). To be sure that the chronic condition checkbox data were complete, a consistency check was performed during data editing to review responses to the provider’s diagnosis item. If any of the chronic conditions from the checkbox item were also listed in the diagnosis item, the relevant checkbox item was also checked. This report includes ED visits made by adults (age 18 and older) that had either a reported diagnosis of diabetes ( International Classification of Diseases , Ninth Revision , Clinical Modification codes 249–250 or International Classification of Diseases , 10th Revision, Clinical Modification codes E08–E13) or a checkbox category of diabetes. Adults that either had diabetes diagnosed at the ED visit or had pre-existing diabetes that was documented in the medical record were included in the analysis.

ED visits by adults with diabetes and additional chronic conditions reported : The number of chronic conditions, excluding diabetes, was grouped into none (referring to adults with only diabetes), one, two to four, and five or more of the conditions collected in NHAMCS. Beside the checkboxes for diabetes described above, checkboxes for alcohol misuse, abuse, or dependence; Alzheimer disease or dementia; asthma; cancer; cerebrovascular disease or history of stroke or transient ischemic attack; chronic kidney disease; chronic obstructive pulmonary disease; congestive heart failure; coronary artery disease, ischemic heart disease or history of myocardial infarction; depression; end-stage renal disease; history of pulmonary embolism or deep vein thrombosis; HIV; hyperlipidemia; hypertension; obesity; obstructive sleep apnea; osteoporosis; and substance use disorders were included in NHAMCS in 2020 and 2021. The consistency check described above was performed during data editing for each checkbox.

ED visit rate : Calculated by dividing the number of ED visits by adults with diabetes, by the July 1, 2012–2021, sets of estimates of the U.S. civilian noninstitutionalized population (obtained from the U.S. Census Bureau’s Population Division) for each demographic group. Visit rates for adults with chronic conditions are calculated by dividing the number of ED visits made by adults with diabetes and a specified number of other chronic conditions by the total number of ED visits made by adults with diabetes in 2020 and 2021. Visit rates are presented as annual average rates per year.

Race and ethnicity : Race and Hispanic ethnicity were collected separately and converted into a single combined variable that includes Hispanic, Black non-Hispanic, White non-Hispanic, and other races non-Hispanic. Other races includes American Indian and Alaska Native, Asian, and Native Hawaiian or Other Pacific Islander people, and people of two or more races, representing 4.5% of all ED visits by adults with diabetes. Data for other races are included in the denominator but not shown. For 2020–2021, 13.6% of weighted race data and 10.7% of weighted ethnicity data were missing for ED visits by adults with diabetes; race and ethnicity were imputed for these missing records ( 7 , 8 ).

This report analyzed data from NHAMCS, a nationally representative annual survey of nonfederal, general, and short-stay hospitals. NHAMCS uses a multistage probability design with samples of geographic primary sampling units, hospitals within primary sampling units, and patient visits within EDs. Analyses for this report were conducted using data from restricted-use data files. Public-use versions of these files are available from: https://www.cdc.gov/nchs/ahcd/datasets_documentation_related.htm . Count estimates and measures of variance could differ between the restricted-use and public-use files. Information for accessing the restricted-use data file is available from: https://www.cdc.gov/rdc/index.htm . Additional information on the methodology of NHAMCS is available online ( 7 , 8 ). This report presents results combining data for 2020 and 2021 for more detailed subgroup analyses. In addition to the 2020–2021 data, Figure 4 includes individual data years from the 2012–2021 NHAMCS.

Data analyses were performed using the statistical packages SAS version 9.4 (SAS Institute, Cary, N.C.) and SAS-callable SUDAAN version 11.0 (RTI International, Research Triangle Park, N.C.). To test for linear and quadratic trends over time, the null hypothesis of nonlinear or quadratic trend was examined using the POLY option in SUDAAN. If a quadratic trend was significant, Joinpoint software ( 9 ) was used to determine the change point in the trend line. Piecewise linear regression was used to test the significance of slopes according to National Center for Health Statistics trend analysis guidelines ( 10 ). Differences among groups were evaluated using two-sided significance tests at the p < 0.05 level.

Loredana Santo, Susan M. Schappert, and Jill J. Ashman are with the National Center for Health Statistics, Division of Health Care Statistics.

Xu JQ, Murphy SL, Kochanek KD, Arias E. Mortality in the United States, 2021. NCHS Data Brief, no 456. Hyattsville, MD: National Center for Health Statistics. 2022. DOI: https://dx.doi.org/10.15620/cdc:122516 .
Centers for Disease Control and Prevention. Type 2 diabetes .
Hall MJ, Rui P, Schwartzman A. Emergency department visits by patients aged 45 and over with diabetes: United States, 2015. NCHS Data Brief, no 301. Hyattsville, MD: National Center for Health Statistics. 2018.
Andes LJ, Cheng YJ, Rolka DB, Gregg EW, Imperatore G. Prevalence of prediabetes among adolescents and young adults in the United States, 2005–2016. JAMA Pediatr 174(2):e194498. 2020.
American Diabetes Association. 12. Older adults: Standards of medical care in diabetes—2020. Diabetes Care 43(Suppl 1):S152–62. 2020.
Uppal TS, Chehal PK, Fernandes G, Haw JS, Shah M, Turbow S, et al. Trends and variations in emergency department use associated with diabetes in the U.S. by sociodemographic factors, 2008–2017. JAMA Netw Open 5(5):e2213867. 2022.
National Center for Health Statistics. 2020 NHAMCS micro-data file documentation . 2022.
National Center for Health Statistics. 2021 NHAMCS micro-data file documentation . 2023.
National Cancer Institute. Joinpoint Regression Program (Version 4.6.0) [computer software]. 2019.
Ingram DD, Malec DJ, Makuc DM, Kruszon-Moran D, Gindi RM, Albert M, et al. National Center for Health Statistics guidelines for analysis of trends. Vital Health Stat 2(179). 2018.

Santo L, Schappert SM, Ashman JJ. Emergency department visit rates by adults with diabetes: United States, 2020–2021. NCHS Data Brief, no 487. Hyattsville, MD: National Center for Health Statistics. 2023. DOI: https://dx.doi.org/10.15620/cdc:134505 .

Copyright information

All material appearing in this report is in the public domain and may be reproduced or copied without permission; citation as to source, however, is appreciated.

National Center for Health Statistics

Brian C. Moyer, Ph.D., Director Amy M. Branum, Ph.D., Associate Director for Science

Division of Health Care Statistics

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1 Significant increasing trend with increasing age (p < 0.05). 2 Significant difference between women and men (p < 0.05). NOTES: Data are based on a sample of 4,051 emergency department visits made by adults during 2020-2021, representing about 18,238,000 average annual visits made by adults with diabetes.

Trends in outpatient emergency department visits during the COVID-19 pandemic at a large, urban, academic hospital system

Joseph Biskupiak

Diana Brixner

Benjamin Ukert

Conclusions

1. Introduction

2.1. Study design and data source

2.2. Study outcomes

2.3. Data analysis

4. Discussion

4.1. Limitations

5. Conclusion

Author contributions

Declaration of Competing Interest

Acknowledgements

Appendix A. Supplementary data

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