- Email: [email protected]
Association Between Hospital Bed Occupancy and Outcomes in Emergency Care: A Cohort Study in Stockholm Region, Sweden, 2012 to 2016
GENERAL MEDICINE/ORIGINAL RESEARCH
Association Between Hospital Bed Occupancy and Outcomes in Emergency Care: A Cohort Study in Stockholm Region, Sweden, 2012 to 2016 Björn af Ugglas, MSc; Therese Djärv, PhD; Petter L. S. Ljungman, PhD; Martin J. Holzmann, PhD* *Corresponding Author. E-mail: [email protected].
Study objective: We evaluate the importance of hospital bed occupancy for 30-day mortality, inhospital mortality, readmission for inpatient care within 30 days, and revisits to the emergency department (ED) within 7 days among all adult patients visiting the ED. Methods: This was an observational cohort study including all adult patients visiting 6 EDs in Stockholm Region, Sweden. ED visits from 2012 to 2016 were categorized into groups by hospital bed occupancy in 5% intervals between 85% and 105%. A proportional hazards model was used to estimate adjusted hazard ratios with 95% confidence intervals (CIs). The model was stratified by hospital and adjusted for age, sex, comorbidities, hospital stays in the year preceding the index visit, marital status, length of education, and weekday/weekend timing of visit. Results: A total of 816,832 patients with 2,084,554 visits were included. Mean hospital bed occupancy was 93.3% (SD 3.3%). In total, 28,112 patients died within 30 days, and 17,966 patients died inhospital. Hospital bed occupancy was not associated with 30-day mortality (hazard ratio for highest category of occupancy 105% was 1.10; 95% CI 0.96 to 1.27) or inhospital mortality. Patients discharged from the ED at occupancy levels greater than 89% had a 2% to 4% higher risk of revisits to the ED within 7 days. A 10% increase in hospital bed occupancy was associated with a 16-minute increase (95% CI 16 to 17 minutes) in ED length of stay and 1.9-percentage-point decrease (95% CI 1.7 to 2.0 percentage points) in admission rate. Conclusion: We did not find an association between increasing hospital bed occupancy and mortality in our sample of 6 EDs in Stockholm Region, Sweden, despite increased length of stay in the ED and a decline in admissions for inpatient care. [Ann Emerg Med. 2019;-:1-12.] Please see page XX for the Editor’s Capsule Summary of this article. 0196-0644/$-see front matter Copyright © 2019 by the American College of Emergency Physicians. https://doi.org/10.1016/j.annemergmed.2019.11.009
INTRODUCTION Background Statistics from the Organization for Economic Cooperation and Development indicate a long-term trend toward reductions in staffed hospital beds per capita. Of the 36 organization member countries, only Korea and Turkey increased hospital beds per capita from 2000 to 2015.1 This is partly a desired development and a sign of increased efficiency in health care systems. However, if the number of staffed hospital beds is reduced without concurrent improvements that reduce the need for inpatient care, increased hospital bed occupancy can be expected. Sweden, which has been a leader in the shift toward fewer staffed hospital beds per capita, had the third lowest ratio (2.4 per 1,000 population) of all Organization for Economic Cooperation and Development countries in 2015, following Mexico and Chile.1 The reduction in hospital Volume
-,
no.
-
:
-
2019
beds in Sweden has been paralleled by an increase in mean bed occupancy, from 80% in 2001 to 94% in 2015.2,3 Importance It has been suggested that the increase in bed occupancy may compromise patient safety and even lead to increased mortality, but only a few studies have investigated the association between bed occupancy and patient outcomes. These studies have shown associations of high bed occupancy with increased mortality,4-7 increased emergency department (ED) lengths of stay,4,8,9 reduced admission rates,10 and increased readmissions for inpatient care,11 but they have found no association with increased revisits to the ED.12 However, in regard to the association between bed occupancy and mortality, previous studies have included only patients who were admitted for inpatient care, disregarding a large proportion of the Annals of Emergency Medicine 1
af Ugglas et al
Hospital Bed Occupancy and Outcomes in Emergency Care
Editor’s Capsule Summary
What is already known on this topic Hospital crowding can affect emergency department (ED) care for all patients as some await beds. What question this study addressed What associations exist between hospital bed occupancy and patient outcomes after an ED visit? What this study adds to our knowledge Examination of all 2012 to 2016 adult ED visits (2,084,554) in 6 hospitals in Stockholm, Sweden, revealed no observed link between hospital occupancy and 30-day or inhospital mortality. Initial ED care intervals lengthened and later revisits within 7 days were more frequent at the higher occupancy thresholds. How this is relevant to clinical practice Although hospital-based crowding affects ED care and has negative consequences, it does not always increase short-term mortality.
patients seeking care at the ED. This may have introduced bias because admission rates from the ED for inpatient care decline when bed occupancy increases,10 leading to a higher proportion of sicker patients being admitted during bed shortages. Goals of This Investigation We examined data on all adults visiting 6 EDs in Stockholm Region from 2012 to 2016, including both patients admitted for inpatient care and patients discharged home from the ED, to evaluate the associations between bed occupancy and 30-day mortality, inhospital mortality, readmissions for inpatient care within 30 days, and revisits to the ED within 7 days. In addition, we aimed to assess the effect of bed occupancy on admission rates to inpatient care and patient lengths of stay in the ED. MATERIALS AND METHODS Study Design and Setting This was an observational cohort study conducted at 1 university hospital with 2 separate EDs at different locations, and 4 large teaching hospitals in Stockholm Region, Sweden. The health care system in the region is publicly funded and patient fees are relatively low. All patients are assigned to a primary care practice, but availability, especially outside of normal business hours, is 2 Annals of Emergency Medicine
limited. Primary care follow-up after discharge from the ED is triggered by a referral from the emergency physician but is prioritized by the primary care provider, and waiting time can be up to 2 to 3 weeks. All EDs are open to the public continuously and do not require referrals. The hospitals operate independently of one another, but there is collaboration between the chief medical officers in each hospital to balance demand and capacity imbalances in extreme cases. The policy for ambulance diversion is very strict, and during the 5-year study period there were only 36 instances of ambulance diversion caused by capacity constraints. Transfers of admitted patients between hospitals because of lack of capacity is possible but not common. In the hospitals, each medical specialty is fully responsible for bed capacity management within its area and decisions are taken by the consultant on site or on call 24 hours a day, 7 days a week. If there is a need for prioritization across medical specialties or cancellation of elective care, it is facilitated by the chief medical officer, who is on call 24 hours a day, 7 days a week. The decision is supported by real-time information on ED census, number of patients with high priority, number of patients waiting for first consultation, and hospital bed occupancy, but there are no hard thresholds defined. The competence and staffing in all the EDs are high, with a consultant on site most of the hours during the week and available on call 24 hours a day, 7 days a week. The admit or discharge decision in the ED is performed by the treating physician, with support from the consultant on site or on call. Small observation wards were piloted in 3 of the hospitals during part of the study period but were not made permanent. The observation wards were treated as normal inpatient wards in the study and included in the hospital bed occupancy. Selection of Participants We included all patients aged 18 years or older with a Swedish personal identity number who visited any of the 6 EDs at Danderyd Hospital, Karolinska University Hospital Huddinge, Karolinska University Hospital Solna, Norrtälje Hospital, South General Hospital, or Södertälje Hospital from January 1, 2012, to December 31, 2016. Visits to the remaining ED in Stockholm Region, St. Göran Hospital, could not be included because this hospital had a different electronic health care records system and was unable to provide information on historical bed occupancy. Therefore, the maximum number of participants was included that the electronic health care records allowed. Patients who registered in the ED but left before the care process was completed were included and classified as discharged patients. We were unable to capture Volume
-,
no.
-
:
-
2019
af Ugglas et al
Hospital Bed Occupancy and Outcomes in Emergency Care
information in regard to limitations of treatment, so patients with do not resuscitate/do not intubate could not be excluded from the study. Methods of Measurement A database was created by retrieving information on all adult visits to 6 EDs in Stockholm Region from 2012 to 2016. Information was extracted from the electronic health care records system, which includes prospectively collected data on all visits, with data such as times of arrival and departure, personal identity number, and information enabling the calculation of bed occupancy. Using the unique 10-digit personal identity number assigned to each person residing in Sweden, for each patient we linked comorbidity and hospitalization data from the National Swedish Patient Register, data on death from the Cause-ofDeath Register, and socioeconomic data from Statistics Sweden. The validity of the personal identity number and these registers has been found to be high.13 Hourly bed occupancy was calculated at the hospital level by dividing the sum of registered patients in all wards accepting patients admitted from the ED by the sum of staffed beds in the same wards. Only wards that cared for adult emergency cases were included; thus, pediatric, obstetric, and outpatient wards performing ambulatory care within, for example, orthopedics, cardiology, and oncology were excluded. The number of patients registered in each ward changes automatically in the electronic health care records every time a patient is admitted, transferred, or discharged. Changes in the number of staffed beds in a ward are updated manually, normally by a nurse or other person coordinating the beds in each ward. Because we used historical data, we were not able to validate the information about bed occupancy. However, during the study period, there was a control routine in place, in which the information was checked 3 times daily to ensure accuracy in reporting to senior management and government institutions. We also performed quality assurance of the bed occupancy data to identify and remove potentially incorrect observations (Appendix E1 and Table E1, available online at http://www.annemergmed.com). Bed occupancy was calculated at the hospital level during the 24-hour period preceding and including the hour the patient arrived at the ED. This removed the effect of the expected diurnal variation in bed occupancy, allowing for some induction time, and follow-up could start at arrival at the ED. Because the follow-up period started at arrival, we were able to include patients who were discharged home and to calculate inhospital mortality among admitted patients from the first day of hospital stay. For the outcome of readmission for inpatient care within Volume
-,
no.
-
:
-
2019
30 days of hospital discharge, we used the calendar day of discharge from inpatient care for the calculation of bed occupancy and as the start of follow-up. Bed occupancy was categorized in 5% intervals from less than 85% to greater than 104% to cover the bed occupancy distribution in the material and all potential choke points that have been shown to affect hospital performance.14 This included 85%, which is often mentioned as an optimal level, and 100%, in which all staffed beds are occupied and wards are forced to operate above their defined maximum capacity. Outcome Measures The primary outcome was all-cause mortality within 30 days. Secondary outcomes were inhospital mortality, readmission for inpatient care within 30 days of hospital discharge, revisits to the ED within 7 days of being discharged directly home from the ED, admission rate, and length of stay in the ED. Primary Data Analysis The baseline data on patient characteristics are presented as absolute numbers, proportions, and means with SDs. Hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated with a stratified proportional hazards model. This model allows for independent baseline hazards across hospitals but assumes that the HR is the same for all hospitals. As the underlying time dimension in the model, we used date; consequently, we allowed the baseline hazard to vary independently during the follow-up and between hospitals. Because date was chosen as the nonparametric time component, no parametric assumptions of trends in the outcome were necessary, and each event that occurred was contrasted with patients at risk at that specific day, at that hospital. The model was adjusted for patient age, sex, comorbidities, number of hospital stays in the year preceding the index visit, marital status, length of education, and weekday (Monday 7 AM to Friday 4:59 PM) versus weekend (Friday 5 PM to Monday 6:59 AM) timing of visit. Additional subgroup analysis was performed with the same methodology for patients with the highest acuity (triage priority 1), by hospital and age group. Sensitivity analysis using a different definition of bed occupancy was also performed. The bed occupancy was in this case defined as the average hospital bed occupancy at the hour of arrival to the ED. This gives a measure with more variation that is affected by the expected diurnal variation. To account for this, the sensitivity-analysis model was adjusted for hour of arrival in addition to the main model adjustments. In the analyses of all-cause mortality within 30 days, follow-up started at the date of the ED visit and ended at Annals of Emergency Medicine 3
af Ugglas et al
Hospital Bed Occupancy and Outcomes in Emergency Care
death or at 30 days after the visit. A person could have more than 1 visit within a 30-day period, and if so, the later visits were ignored until the date after the previous 30-day period ended (eg, left-truncated), ensuring that no person contributed risk time more than once for each date. For the outcome inhospital mortality, follow-up started at the date of the ED visit and ended at the date of discharge or death. For the outcome readmission within 30 days, follow-up started at the date of discharge and ended at date of readmission, 30 days after discharge, or at death. For revisits to the ED within 7 days of being discharged directly home from the ED, follow-up started at the date of the ED visit and ended at revisit, death, or 7 days. For the outcomes ED length of stay and admission rate, a linear regression model adjusted for hospital was used to assess the association between bed occupancy and the outcome. Previsit comorbidities were defined according to International Statistical Classification of Diseases and Related Health Problems, 10th Revision codes retrieved from the Patient Register and are described in Appendix E1 (available online at http://www.annemergmed.com). Weekend/ holiday was defined as any nonordinary workday (Saturdays, Sundays, and national holidays), starting at 5 PM the previous day until 7 AM the following day. The assumptions in the proportional hazards model were checked by calculating annual (2012 to 2016) HRs of the risk factors. There were no clear trends or systematic patterns in HR estimates during the individual years. All statistical analyses and data management were conducted with SAS (version 9.4; SAS Institute, Inc., Cary, NC). The study was approved by the regional ethics committee in Stockholm. RESULTS Characteristics of Study Subjects A total of 816,832 patients, who visited the 6 EDs 2,084,554 times, were included in the analysis, representing 84% of all adult visits to the 7 EDs in Stockholm Region from 2012 to 2016. The mean age of these patients was 53 years, and 44% were men. Patient characteristics were similar across the different categories of bed occupancy (Table 1) and (Table E2, Figure E1 available online at http://www.annemergmed.com). The mean bed occupancy was 93.3% (SD 3.3%), and it increased from 91.7% (SD 3.1%) in 2012 to 94.5% (SD 3.1%) in 2016 (Table E3, and Figures E2 and E3, available online at http://www.annemergmed.com). The largest percentage of patient visits (31.6%) took place in a context with a bed occupancy of 95% to 99%, and only 0.9% of visits were exposed to the highest category of bed occupancy (>104%) (Table 1). There was a diurnal variation of bed 4 Annals of Emergency Medicine
occupancy, with the highest levels early in the morning (8 to 11 AM) and the lowest ones late in the afternoon (3 PM to 7 PM) (Figure E4, available online at http://www. annemergmed.com). The mean bed occupancy was lower on weekends and holidays, at 91.3% (SD 3.2%), compared with weekdays, at 94.6% (SD 2.6%) (Table E3, available online at http://www.annemergmed.com). Bed occupancy varied by hospital; the hospital with the lowest bed occupancy did not contribute to any visits in the highest category of exposure (>104%) (Table E4 and Figure E5, available online at http://www.annemergmed.com). In total, there were 28,112 deaths within 30 days of the visit to the ED (Table 2). In all groups of bed occupancy, the incidence rate was 19 to 20 deaths per 100 personyears, except for patients in the highest occupancy category of greater than 104%, in which the incidence rate was 22 deaths per 100 person-years. None of the bed occupancy categories demonstrated statistically significant associations with 30-day mortality in the multivariable model (Table 2). The estimated HR in the group with the highest bed occupancy greater than 104% was 1.10 (95% CI 0.96 to 1.27). In the subgroup analysis including only patients with the highest acuity (triage level 1), there was also no association between bed occupancy and 30-day mortality. The estimated HR in the group with the highest bed occupancy greater than 104% was 0.97 (95% CI 0.75 to 1.27). The subgroup analysis by hospital and by age group had similar results, with no association between bed occupancy and 30-day mortality. More information is available in (Appendix E1 and table E5-E8, available online at http://www.annemergmed.com). In total, there were 17,966 inhospital deaths (Table 2). Patients died most commonly during the first day of hospitalization (12%). Among patients who died inhospital, 47% died within 7 days after admission and 89% died within 30 days after admission. The incidence rate was 208 to 223 deaths per 100 person-years, except for patients in the highest category of bed occupancy (>104%), in which the incidence rate was 249 deaths per 100 person-years. There was no association between bed occupancy and inhospital mortality in the multivariable models (Table 2). The estimated HR in the group with the highest bed occupancy greater than 104% was 1.09 (95% CI 0.92 to 1.30). A total of 192,938 revisits to the ED occurred within 7 days of patients’ being discharged home from the ED (Table 2). The risk of revisits was 3% lower (adjusted HR 0.97; 95% CI 0.95 to 0.99) among patients exposed to less than 85% bed occupancy compared with the reference group (Table 2). ED visits occurring during bed occupancies of 90% to 94%, 95% to 99%, or 100% to 104% were 2% AM
Volume
-,
no.
-
:
-
2019
-,
af Ugglas et al
Volume no. -
Table 1. Characteristics of patients visiting 6 EDs in Stockholm Region, Sweden, from 2012 to 2016, by bed occupancy.
: -
Bed Occupancy
2019
All Visits
<85%
85%–89%
90%–94%
95%–99%
100%–104%
486,273,324
50,033,988 (10.3)
87,910,960 (18.1)
142,420,461 (29.3)
156,436,405 (32.2)
45,380,001 (9.3)
>104%
Patient/visit information Patient-hours at risk, No. (%) No. of patients Visits to EDs, No. (%)
816,832 2,084,554
162,155 221,210 (10.6)
260,415 379,039 (18.2)
390,930 614,787 (29.5)
386,651 658,695 (31.6)
147,934 191,883 (9.2)
4,091,510 (0.8) 17,401 18,940 (0.9)
Age and sex Age, median (Q1, Q3), y Male sex, No. (%)
51 (34, 71)
52 (34, 71)
52 (34, 71)
51 (34, 71)
51 (34, 70)
51 (34, 70)
55 (35, 73)
913,804 (43.8)
98,175 (44.4)
164,652 (43.4)
268,273 (43.6)
289,685 (44.0)
84,479 (44.0)
8,540 (45.1)
Marital status, No. (%) Married
783,703 (37.6)
76,051 (34.4)
135,556 (35.8)
231,142 (37.6)
256,959 (39.0)
76,306 (39.8)
7,689 (40.6)
Divorced
351,358 (16.9)
37,211 (16.8)
63,835 (16.8)
103,306 (16.8)
111,843 (17.0)
32,136 (16.7)
3,027 (16.0)
Never married
752,141 (36.1)
85,627 (38.7)
141,617 (37.4)
221,883 (36.1)
231,321 (35.1)
65,540 (34.2)
6,153 (32.5)
Widowed
186,602 (9.0)
21,200 (9.6)
36,335 (9.6)
55,351 (9.0)
54,891 (8.3)
16,832 (8.8)
1,993 (10.5)
10,750 (0.5)
1,121 (0.5)
1,696 (0.4)
3,105 (0.5)
3,681 (0.6)
1,069 (0.6)
<10
515,254 (24.7)
56,125 (25.4)
92,675 (24.4)
150,513 (24.5)
163,341 (24.8)
47,593 (24.8)
5,007 (26.4)
10–12
863,698 (41.4)
93,072 (42.1)
157,900 (41.7)
253,327 (41.2)
271,275 (41.2)
79,919 (41.6)
8,205 (43.3)
>12
641,224 (30.8)
65,975 (29.8)
118,145 (31.2)
192,117 (31.2)
201,864 (30.6)
57,959 (30.2)
5,164 (27.3)
64,378 (3.1)
6,038 (2.7)
10,319 (2.7)
18,830 (3.1)
22,215 (3.4)
6,412 (3.3)
564 (3.0)
1
105,822 (5.1)
11,819 (5.3)
20,199 (5.3)
31,480 (5.1)
32,480 (4.9)
9,062 (4.7)
782 (4.1)
2
248,484 (11.9)
29,751 (13.4)
45,012 (11.9)
70,505 (11.5)
78,499 (11.9)
22,685 (11.8)
2,032 (10.7)
3
767,717 (36.8)
75,679 (34.2)
134,833 (35.6)
222,435 (36.2)
250,848 (38.1)
75,977 (39.6)
7,945 (41.9)
4–5
870,733 (41.8)
93,454 (42.2)
157,049 (41.4)
258,251 (42.0)
274,559 (41.7)
79,628 (41.5)
7,792 (41.1)
91,798 (4.4)
10,507 (4.7)
21,946 (5.8)
32,116 (5.2)
22,309 (3.4)
4,531 (2.4)
389 (2.1)
Missing
78 (0.4)
Missing Triage priority, No. (%)
Annals of Emergency Medicine 5
Missing
Hospital Bed Occupancy and Outcomes in Emergency Care
Length of education, No. (%), y
Bed Occupancy <85%
All Visits
85%–89%
90%–94%
95%–99%
100%–104%
>104%
Comorbidities, No. (%) Previous stroke
103,697 (5.0)
11,596 (5.2)
19,894 (5.2)
30,944 (5.0)
31,143 (4.7)
Previous MI
91,292 (4.4)
10,042 (4.5)
17,299 (4.6)
26,514 (4.3)
COPD
95,911 (4.6)
10,833 (4.9)
18,146 (4.8)
28,416 (4.6)
Previous heart failure
81,295 (3.9)
8,866 (4.0)
15,455 (4.1)
Diabetes
9,097 (4.7)
1,023 (5.4)
28,182 (4.3)
8,312 (4.3)
943 (5.0)
29,158 (4.4)
8,525 (4.4)
833 (4.4)
24,202 (3.9)
24,673 (3.7)
7,303 (3.8)
796 (4.2)
166,928 (8.0)
17,458 (7.9)
29,213 (7.7)
48,930 (8.0)
53,798 (8.2)
15,888 (8.3)
1,641 (8.7)
Active cancer
64,489 (3.1)
5,624 (2.5)
9,465 (2.5)
19,053 (3.1)
22,998 (3.5)
6,807 (3.5)
542 (2.9)
Chronic kidney disease
51,847 (2.5)
4,250 (1.9)
8,291 (2.2)
15,368 (2.5)
18,089 (2.7)
5,377 (2.8)
472 (2.5)
Hospital stays within 1 y, No. (%) None
1,370,253 (65.7)
143,863 (65.0)
247,881 (65.4)
403,491 (65.6)
435,324 (66.1)
127,076 (66.2)
12,618 (66.6)
1
362,724 (17.4)
39,107 (17.7)
66,624 (17.6)
107,045 (17.4)
113,715 (17.3)
32,964 (17.2)
3,269 (17.3)
2
144,722 (6.9)
15,479 (7.0)
26,236 (6.9)
42,778 (7.0)
45,599 (6.9)
13,385 (7.0)
1,245 (6.6)
3
206,855 (9.9)
22,761 (10.3)
38,298 (10.1)
61,473 (10.0)
64,057 (9.7)
18,458 (9.6)
1,808 (9.5)
Weekday (Monday 7 AM–Friday 5 PM)
1,395,071 (66.9)
68,032 (30.8)
227,317 (60.0)
425,880 (69.3)
495,930 (75.3)
161,054 (83.9)
16,858 (89.0)
Weekend/holiday (Friday 5 PM–Monday 7 AM)
689,483 (33.1)
153,178 (69.2)
151,722 (40.0)
188,907 (30.7)
162,765 (24.7)
30,829 (16.1)
2,082 (11.0)
Hospital Bed Occupancy and Outcomes in Emergency Care
6 Annals of Emergency Medicine
Table 1. Continued.
Day of visit, No. (%)
MI, Myocardial infarction; COPD, chronic obstructive pulmonary disease.
Volume -, -
: -
2019
af Ugglas et al
no.
-,
af Ugglas et al
Volume no. -
: -
2019
Table 2. Association of 30-day and inhospital mortality, revisits to the ED within 7 days, and readmission for inpatient care within 30 days of hospital discharge among 816,832 patients with 2,084,554 visits to 6 EDs in Stockholm Region, Sweden, from 2012 to 2016, by bed occupancy. Bed Occupancy <85%
85%–89%
90%–94%
95%–99%
100%–104%
>104%
30-day mortality No. of deaths
3,109
5,160
8,376
8,789
2,413
265
Person-years at risk, n
15,573
26,402
42,211
44,532
12,829
1,231
20
20
20
20
19
22
1.01 (0.97–1.06)
1 [Reference]
1.01 (0.97–1.05)
1.00 (0.96–1.05)
0.96 (0.90–1.02)
1.10 (0.96–1.27)
2,022
3,387
5,347
5,513
1,561
172
Person-years at risk, n
921
1,598
2,568
2,548
700
69
Incidence rate, cases/100 person-years
220
212
208
216
223
249
1.01 (0.95–1.08)
1
0.97 (0.92–1.02)
0.98 (0.93–1.04)
0.99 (0.92–1.07)
1.09 (0.92–1.30)
Incidence rate, cases/100 person-years Adjusted model, HR (95% CI) Inhospital mortality No. of deaths
Adjusted model, HR (95% CI) Revisits, No. (%) Adjusted model, HR (95% CI)
17,255 (12.4)
30,245 (12.5)
55,536 (13.4)
68,167 (14.5)
19,900 (14.2)
1,835 (13.3)
0.97 (0.95–0.99)
1 [Reference]
1.02 (1.00–1.04)
1.04 (1.02–1.06)
1.02 (1.00–1.05)
1.02 (0.96–1.08)
6.3 (11.5)
6.4 (13.3)
6.8 (13.3)
7.0 (13.8)
6.7 (13.1)
6.4 (11.4)
8,745 (20.2)
18,671 (20.9)
28,767 (21.2)
29,585 (22.3)
7,701 (22.0)
680 (19.8)
1.01 (0.98–1.04)
1 [Reference]
0.97 (0.95–0.99)
0.98 (0.96–1.01)
0.98 (0.95–1.01)
0.95 (0.87–1.03)
Readmission for inpatient care within 30 days of hospital discharge Length of hospital stay, days (SD) Readmissions, No. (%) Adjusted model, HR (95% CI)
Annals of Emergency Medicine 7
Adjusted model included age, sex, comorbidities, number of hospital stays in the year preceding the index visit, marital status, length of education, and weekday/weekend timing of visit.
Hospital Bed Occupancy and Outcomes in Emergency Care
Revisits to the ED within 7 days
af Ugglas et al
Hospital Bed Occupancy and Outcomes in Emergency Care
Figure 1. Patient length of stay in the ED for 2,084,554 visits to 6 emergency departments in Stockholm Region, Sweden, from 2012 to 2016, by bed occupancy and hospital. The mean is represented by the point; the median, by the vertical line in the box. The left side of the box represents quartile 1 and the right side, quartile 3; the error bar spans between the fifth and 95th percentile.
to 4% more likely to lead to revisits within 7 days compared with that for the reference group (Table 2). In total, 92,592 patients were readmitted for inpatient care within 30 days of discharge, and there was no association between bed occupancy on the day of discharge and readmission for inpatient care within 30 days (Table 2). The median hospital length of stay for the initial admission was 3 days (quartile 1¼1, quartile 3¼8), with no association with bed occupancy. The mean length of stay in the ED was 234 minutes (SD 163 minutes), with a median of 200 minutes (interquartile range 184 minutes). There were differences among the hospitals in lengths of stay in the ED, with the mean length of stay ranging from 162 minutes (SD 93 minutes; median 147 minutes; interquartile range 117 minutes) to 260 minutes (interquartile range 178 minutes; median 225 minutes; interquartile range 208 minutes) (Table E5, available online at http://www.annemergmed. com). Length of stay increased with higher bed occupancy in all 6 hospitals (Figure 1 and Table E9, available online at http://www.annemergmed.com). This pattern was most 8 Annals of Emergency Medicine
obvious for patients who were admitted for inpatient care. For each 10% increase in bed occupancy, the length of stay in the ED increased by 16 minutes (95% CI 16 to 17 minutes) for all patients, by 28 minutes (95% CI 28 to 29 minutes) for admitted patients, and by 9 minutes (95% CI 9 to 10 minutes) for discharged patients. The mean admission rate for inpatient care was 21.9%, and this varied among the studied hospitals, ranging from 18.4% to 25.8%. In all hospitals, admission rates declined as bed occupancy increased (Figure 2) and Table E10, available online at http://www.annemergmed.com). The total mean admission rate was 24.0% in the lowest bed occupancy category and 20.6% in the highest one. A 10% increase in bed occupancy was associated with a 1.9percentage-point increase (95% CI 1.7 to 2.0 percentage points) in admission rate. Sensitivity Analyses Sensitivity analysis studying the association between the hourly bed occupancy levels and 30-day mortality showed nonsignificant results similar to those of the main analysis. The Volume
-,
no.
-
:
-
2019
af Ugglas et al
Hospital Bed Occupancy and Outcomes in Emergency Care
Figure 2. Admission rate from the ED to inpatient care for 2,084,554 visits to 6 emergency departments in Stockholm Region, Sweden, from 2012 to 2016, by bed occupancy and hospital. The admission rate (proportion) is represented by a point; the error bar represents the 95% CI for the proportion.
estimated HR in the group with the highest bed occupancy greater than 104% was 1.08 (95% CI 0.99 to 1.17). LIMITATIONS One limitation was that our exposure was based on information that we were unable to fully validate. However, during the study period, there was a process in place for controlling the accuracy of the information in the electronic health care records, which we believe reduced the risk of misclassification. We also performed retrospective quality assurance of the data, removing obvious errors. The main risk of information bias that we could not control was the possibility that the nurse or person who coordinated the ward registered planned admissions in advance but delayed registration of discharges in periods of high workload to manage the work environment in his or her own unit. This would lead to a false increase in bed occupancy and would dilute the association between the exposure and outcome for the highest categories of bed occupancy. By using a hospital-level 24-hour average bed occupancy exposure, we believe that the effect of errors in the reporting of Volume
-,
no.
-
:
-
2019
individual wards during a shorter period would have been relatively small. However, this approach is less sensitive compared with a 24-hour ward-level approach to assessing bed occupancy, which could be used when admitted patients are studied exclusively. This decline in sensitivity may have diluted the association between the exposure and outcome in the present study. Even if we had access to a very large study population, the highest category (>104%) of bed occupancy included only 265 deaths during the study period, which contributed to the wide CIs for this category. Although patients may have been safe in terms of mortality, there may have been other avoidable harms associated with high bed occupancy that were not included in this study. Additionally, we did not assess the effects of increased workload and revisits on the 6 EDs. Finally, we did not evaluate the risk of negative consequences for physicians and nurses in wards and EDs when workload increases, or how bed logistics and negotiations take the focus away from medical decisions and patient care as bed occupancy increases. Annals of Emergency Medicine 9
af Ugglas et al
Hospital Bed Occupancy and Outcomes in Emergency Care
DISCUSSION In a large observational cohort study including 816,832 patients with 2,084,554 visits to 6 of the 7 EDs in Stockholm Region during 5 years, we found no association of bed occupancy with 30-day mortality or inhospital mortality. Our findings were consistent across the studied hospitals by sex, by triage priority, by admission status, and in different age groups. With increasing bed occupancy, we found 2% to 4% higher risks of revisits to the ED within 7 days for patients discharged directly home from the ED. For admitted patients, there was no association between bed occupancy and readmission for inpatient care within 30 days of hospital discharge. The main strength of our study was the large study population and the completeness in the follow-up of outcomes because of our use of high-quality health care registers with comprehensive national coverage. The detailed information, including several nationwide health care registers and data from Statistics Sweden, allowed us to analyze patient characteristics and comorbidities. Unlike previous studies,5,7 we were able to include all patients seeking care at the ED who potentially were exposed to risks associated with high bed occupancy; in particular, patients who normally would be assessed as needing inpatient care but were discharged home because of bed shortages. In addition, by including all visits to the ED, we avoided the risk of selection bias, with a higher proportion of sicker patients being admitted at times of bed shortages. Another strength was that our statistical model controlled for time-dependent confounding outside the 30-day follow-up period, such as the potential effect of seasonal influenza, summer holidays, Christmas, and other known and unknown seasonal patterns. Furthermore, we believe that our findings are generalizable to other health care systems similar to the Swedish one with EDs open to the public continuously. To our knowledge, this study is the first to include all patients whose outcomes could have been affected by hospital bed occupancy; namely, all patients seeking care at the ED. Previous studies4-7 reporting associations between high bed occupancy and increased mortality included only patients who were admitted for inpatient care. Our results showed declining admission rates for inpatient care as bed occupancy increased, which is in line with earlier findings.10 Declining admission rates indicate that medical prioritization is taking place when bed occupancy is high, and this is likely to result in a higher proportion of older and sicker patients being admitted when bed occupancy is high. Even if
10 Annals of Emergency Medicine
adjustment for patient characteristics was made in previous studies, there may still have been residual confounding that might explain the differences between our results and the findings of previous work. Although we did not find a significant association between high bed occupancy and increased mortality, the point estimates for both 30-day and inhospital mortality were 9% to 10% higher at the highest level of bed occupancy (105%). The magnitude of this estimate was similar to that found in a Danish study that concluded that both 30-day and inhospital mortality were increased by 9% at a bed occupancy of greater than or equal to 110%.5 However, our study is not completely comparable to this previous study because of differences in study design: the previous study investigated only admitted patients and defined bed occupancy at ward level instead of at hospital level. We found an association between higher bed occupancy and risk of revisits to the ED within 7 days of being discharged from the ED. Although the magnitude of the higher risk was small (2% to 4%), this translates into several thousand additional visits during the study period, further encumbering an already strained health care system. The proportion of patients readmitted to the hospital within 30 days after discharge is a commonly used indicator of quality of care.15 It has been shown that high occupancy in the ICU at discharge leads to increased readmissions.16 Another Swedish study with a design similar to ours, but including a single hospital, reported a higher odds ratio of being readmitted within 30 days for patients exposed to higher bed occupancy, indicating an 11% to 17% increase in the odds of readmission for this group.8 These results were not confirmed in our study, which included 6 hospitals. Although we found no increased risk of mortality or readmission, the associations of high bed occupancy with longer length of stay in the ED and with lower admission rates are consistent with results reported from previous studies.4,8-10 As bed occupancy increased, the length of stay in the ED increased and reached a mean of almost 5 hours in 2 of the 6 EDs at the highest bed occupancy level, indicating an imbalance between demand and capacity when bed occupancy increased. Concurrently, admission rates decreased sharply as bed occupancy increased, indicating more difficult medical prioritization in the ED when whether to admit or discharge patients was being decided. These associations were observed beginning at 85% bed occupancy. Taken together, they result in an increased workload in the ED beginning when bed occupancy reaches 85%, thus increasing the
Volume
-,
no.
-
:
-
2019
af Ugglas et al
Hospital Bed Occupancy and Outcomes in Emergency Care
risk of ED crowding and negatively affecting patients and staff.17,18 For example, time-critical interventions are delayed,19 adverse events for elderly patients increase,20 and violence toward staff occurs more frequently.21 These findings highlight the ED’s dependency on the hospital’s inpatient care and the importance of viewing the ED as part of a larger system dependent on input, throughput, and output.22 In addition, with increasing ED crowding, ED staff and the staff from wards with high bed occupancy become strained, which increases the risk of burnout.23,24 In conclusion, we found no association of bed occupancy with mortality or readmissions. Our findings indicate that our health care systems may be able to adapt to high levels of bed occupancy without an increased risk of death. High levels of bed occupancy will, however, increase ED length of stay and reduce admission rate to inpatient care, which both increase the workload in the ED. A preparedness to reallocate resources to the ED is needed when bed occupancy increases because the workload is likely to increase even when bed occupancy is at 85%. The authors acknowledge Jennifer Barrett, PhD, from Edanz Group for editing a draft of this article. Supervising editor: Donald M. Yealy, MD. Specific detailed information about possible conflict of interest for individual editors is available at https://www.annemergmed.com/editors. Author affiliations: From the Function of Emergency Medicine, Karolinska University Hospital, and the Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden (Ugglas, Djärv, Holzmann); and the Department of Cardiology, Danderyd Hospital, and the Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden (Ljungman). Author contributions: BU and MJH were involved in the conception of the study; gathered, structured, and performed the analysis of the data; and drafted the article. MJH gained necessary ethical approvals. All authors contributed to the design of the study, interpreted the results, critically revised the article, and approved the article to be submitted. MJH takes responsibility for the paper as a whole. All authors attest to meeting the four ICMJE.org authorship criteria: (1) Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; AND (2) Drafting the work or revising it critically for important intellectual content; AND (3) Final approval of the version to be published; AND (4) Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Funding and support: By Annals policy, all authors are required to disclose any and all commercial, financial, and other relationships
Volume
-,
no.
-
:
-
2019
in any way related to the subject of this article as per ICMJE conflict of interest guidelines (see www.icmje.org). Dr. Djärv was supported by the Stockholm County Council (clinical research appointment). Dr. Ljungman was supported by Karolinska Institutet’s Strategic Research Area in Epidemiology (SFO Epidemiology), the Swedish Society for Medical Research, and the Swedish Research Council for Health, Working Life and Welfare (FORTE). Dr. Holzmann reports receiving consultancy honoraria from Actelion, Idorsia, and Pfizer. He holds research positions funded by the Swedish HeartLung Foundation (grant 20170804) and the ALF agreement between the Stockholm County Council and Karolinska Institutet (grant 20170686). The construction of the database used in this study was funded by an unrestricted grant from Idorsia Pharmaceutical. Publication dates: Received for publication June 23, 2019. Revisions received October 11, 2019, and November 5, 2019. Accepted for publication November 12, 2019.
REFERENCES 1. OECD. Health at a Glance 2017: OECD Indicators. Paris, France: OECD Publishing; 2017. 2. Knezevic M. Open comparison and evaluation of hospital overcrowding [Öppen jämförelse och utvärdering av överbeläggningar—Delrapport 2011]. Stockholm, Sweden: The Swedish National Board of Health and Welfare; 2012. 3. Council for the Promotion of Municipal Analysis. Bed occupancy rate [Rådet för främjande av kommunala analyser] [Beläggningsgrad] (N72826). Available at: https://www.kolada.se/?_p¼workspace/nt. Accessed August 18, 2018. 4. Boden DG, Agarwal A, Hussain T, et al. Lowering levels of bed occupancy is associated with decreased inhospital mortality and improved performance on the 4-hour target in a UK district general hospital. Emerg Med J. 2016;33:85-90. 5. Madsen F, Ladelund S, Linneberg A. High levels of bed occupancy associated with increased inpatient and thirty-day hospital mortality in Denmark. Health Aff (Millwood). 2014;33:1236-1244. 6. Schilling PL, Campbell DA Jr, Englesbe MJ, et al. A comparison of inhospital mortality risk conferred by high hospital occupancy, differences in nurse staffing levels, weekend admission, and seasonal influenza. Med Care. 2010;48:224-232. 7. Sprivulis PC, Da Silva JA, Jacobs IG, et al. The association between hospital overcrowding and mortality among patients admitted via Western Australian emergency departments. Med J Aust. 2006;184:208-212. 8. Handel DA, Fu R, Vu E, et al. Association of emergency department and hospital characteristics with elopements and length of stay. J Emerg Med. 2014;46:839-846. 9. Hillier DF, Parry GJ, Shannon MW, et al. The effect of hospital bed occupancy on throughput in the pediatric emergency department. Ann Emerg Med. 2009;53:767-776.e3. 10. Blom MC, Jonsson F, Landin-Olsson M, et al. The probability of patients being admitted from the emergency department is negatively correlated to in-hospital bed occupancy—a registry study. Int J Emerg Med. 2014;7:8. 11. Blom MC, Erwander K, Gustafsson L, et al. The probability of readmission within 30 days of hospital discharge is positively associated with inpatient bed occupancy at discharge—a retrospective cohort study. BMC Emerg Med. 2015;15:37. 12. Blom MC, Jonsson F, Landin-Olsson M, et al. Associations between inhospital bed occupancy and unplanned 72-h revisits to the emergency department: a register study. Int J Emerg Med. 2014;7:25.
Annals of Emergency Medicine 11
af Ugglas et al
Hospital Bed Occupancy and Outcomes in Emergency Care 13. Ludvigsson JF, Otterblad-Olausson P, Pettersson BU, et al. The Swedish personal identity number: possibilities and pitfalls in healthcare and medical research. Eur J Epidemiol. 2009;24:659-667. 14. Khanna S, Boyle J, Good N, et al. Unravelling relationships: hospital occupancy levels, discharge timing and emergency department access block. Emerg Med Australas. 2012;24:510-517. 15. Axon R, Williams MV. Hospital readmission as an accountability measure. JAMA. 2011;305:504-505. 16. Chrusch CA, Olafson KP, McMillan PM, et al. High occupancy increases the risk of early death or readmission after transfer from intensive care. Crit Care Med. 2009;37:2753-2758. 17. Morley C, Unwin M, Peterson GM, et al. Emergency department crowding: a systematic review of causes, consequences and solutions. PloS One. 2018;13:e0203316. 18. Guttmann A, Schull MJ, Vermeulen MJ, et al. Association between waiting times and short term mortality and hospital admission after departure from emergency department: population based cohort study from Ontario, Canada. BMJ. 2011;342:d2983.
12 Annals of Emergency Medicine
19. Gaieski DF, Agarwal AK, Mikkelsen ME, et al. The impact of ED crowding on early interventions and mortality in patients with severe sepsis. Am J Emerg Med. 2017;35:953-960. 20. Ackroyd-Stolarz S, Read Guernsey J, Mackinnon NJ, et al. The association between a prolonged stay in the emergency department and adverse events in older patients admitted to hospital: a retrospective cohort study. BMJ Qual Saf. 2011;20:564-569. 21. Medley DB, Morris JE, Stone CK, et al. An association between occupancy rates in the emergency department and rates of violence toward staff. J Emerg Med. 2012;43:736-744. 22. Asplin BR, Magid DJ, Rhodes KV, et al. A conceptual model of emergency department crowding. Ann Emerg Med. 2003;42:173-180. 23. Aiken LH, Clarke SP, Sloane DM, et al. Hospital nurse staffing and patient mortality, nurse burnout, and job dissatisfaction. JAMA. 2002;288:1987-1993. 24. Needleman J, Buerhaus P, Pankratz VS, et al. Nurse staffing and inpatient hospital mortality. N Engl J Med. 2011;364:1037-1045.
Volume
-,
no.
-
:
-
2019
Association Between Hospital Bed Occupancy and Outcomes in Emergency Care: A Cohort Study in Stockholm Region, Sweden, 2012 to 2016 Björn af Ugglas, MSc; Therese Djärv, PhD; Petter L. S. Ljungman, PhD; Martin J. Holzmann, PhD* *Corresponding Author. E-mail: [email protected].
Study objective: We evaluate the importance of hospital bed occupancy for 30-day mortality, inhospital mortality, readmission for inpatient care within 30 days, and revisits to the emergency department (ED) within 7 days among all adult patients visiting the ED. Methods: This was an observational cohort study including all adult patients visiting 6 EDs in Stockholm Region, Sweden. ED visits from 2012 to 2016 were categorized into groups by hospital bed occupancy in 5% intervals between 85% and 105%. A proportional hazards model was used to estimate adjusted hazard ratios with 95% confidence intervals (CIs). The model was stratified by hospital and adjusted for age, sex, comorbidities, hospital stays in the year preceding the index visit, marital status, length of education, and weekday/weekend timing of visit. Results: A total of 816,832 patients with 2,084,554 visits were included. Mean hospital bed occupancy was 93.3% (SD 3.3%). In total, 28,112 patients died within 30 days, and 17,966 patients died inhospital. Hospital bed occupancy was not associated with 30-day mortality (hazard ratio for highest category of occupancy 105% was 1.10; 95% CI 0.96 to 1.27) or inhospital mortality. Patients discharged from the ED at occupancy levels greater than 89% had a 2% to 4% higher risk of revisits to the ED within 7 days. A 10% increase in hospital bed occupancy was associated with a 16-minute increase (95% CI 16 to 17 minutes) in ED length of stay and 1.9-percentage-point decrease (95% CI 1.7 to 2.0 percentage points) in admission rate. Conclusion: We did not find an association between increasing hospital bed occupancy and mortality in our sample of 6 EDs in Stockholm Region, Sweden, despite increased length of stay in the ED and a decline in admissions for inpatient care. [Ann Emerg Med. 2019;-:1-12.] Please see page XX for the Editor’s Capsule Summary of this article. 0196-0644/$-see front matter Copyright © 2019 by the American College of Emergency Physicians. https://doi.org/10.1016/j.annemergmed.2019.11.009
INTRODUCTION Background Statistics from the Organization for Economic Cooperation and Development indicate a long-term trend toward reductions in staffed hospital beds per capita. Of the 36 organization member countries, only Korea and Turkey increased hospital beds per capita from 2000 to 2015.1 This is partly a desired development and a sign of increased efficiency in health care systems. However, if the number of staffed hospital beds is reduced without concurrent improvements that reduce the need for inpatient care, increased hospital bed occupancy can be expected. Sweden, which has been a leader in the shift toward fewer staffed hospital beds per capita, had the third lowest ratio (2.4 per 1,000 population) of all Organization for Economic Cooperation and Development countries in 2015, following Mexico and Chile.1 The reduction in hospital Volume
-,
no.
-
:
-
2019
beds in Sweden has been paralleled by an increase in mean bed occupancy, from 80% in 2001 to 94% in 2015.2,3 Importance It has been suggested that the increase in bed occupancy may compromise patient safety and even lead to increased mortality, but only a few studies have investigated the association between bed occupancy and patient outcomes. These studies have shown associations of high bed occupancy with increased mortality,4-7 increased emergency department (ED) lengths of stay,4,8,9 reduced admission rates,10 and increased readmissions for inpatient care,11 but they have found no association with increased revisits to the ED.12 However, in regard to the association between bed occupancy and mortality, previous studies have included only patients who were admitted for inpatient care, disregarding a large proportion of the Annals of Emergency Medicine 1
af Ugglas et al
Hospital Bed Occupancy and Outcomes in Emergency Care
Editor’s Capsule Summary
What is already known on this topic Hospital crowding can affect emergency department (ED) care for all patients as some await beds. What question this study addressed What associations exist between hospital bed occupancy and patient outcomes after an ED visit? What this study adds to our knowledge Examination of all 2012 to 2016 adult ED visits (2,084,554) in 6 hospitals in Stockholm, Sweden, revealed no observed link between hospital occupancy and 30-day or inhospital mortality. Initial ED care intervals lengthened and later revisits within 7 days were more frequent at the higher occupancy thresholds. How this is relevant to clinical practice Although hospital-based crowding affects ED care and has negative consequences, it does not always increase short-term mortality.
patients seeking care at the ED. This may have introduced bias because admission rates from the ED for inpatient care decline when bed occupancy increases,10 leading to a higher proportion of sicker patients being admitted during bed shortages. Goals of This Investigation We examined data on all adults visiting 6 EDs in Stockholm Region from 2012 to 2016, including both patients admitted for inpatient care and patients discharged home from the ED, to evaluate the associations between bed occupancy and 30-day mortality, inhospital mortality, readmissions for inpatient care within 30 days, and revisits to the ED within 7 days. In addition, we aimed to assess the effect of bed occupancy on admission rates to inpatient care and patient lengths of stay in the ED. MATERIALS AND METHODS Study Design and Setting This was an observational cohort study conducted at 1 university hospital with 2 separate EDs at different locations, and 4 large teaching hospitals in Stockholm Region, Sweden. The health care system in the region is publicly funded and patient fees are relatively low. All patients are assigned to a primary care practice, but availability, especially outside of normal business hours, is 2 Annals of Emergency Medicine
limited. Primary care follow-up after discharge from the ED is triggered by a referral from the emergency physician but is prioritized by the primary care provider, and waiting time can be up to 2 to 3 weeks. All EDs are open to the public continuously and do not require referrals. The hospitals operate independently of one another, but there is collaboration between the chief medical officers in each hospital to balance demand and capacity imbalances in extreme cases. The policy for ambulance diversion is very strict, and during the 5-year study period there were only 36 instances of ambulance diversion caused by capacity constraints. Transfers of admitted patients between hospitals because of lack of capacity is possible but not common. In the hospitals, each medical specialty is fully responsible for bed capacity management within its area and decisions are taken by the consultant on site or on call 24 hours a day, 7 days a week. If there is a need for prioritization across medical specialties or cancellation of elective care, it is facilitated by the chief medical officer, who is on call 24 hours a day, 7 days a week. The decision is supported by real-time information on ED census, number of patients with high priority, number of patients waiting for first consultation, and hospital bed occupancy, but there are no hard thresholds defined. The competence and staffing in all the EDs are high, with a consultant on site most of the hours during the week and available on call 24 hours a day, 7 days a week. The admit or discharge decision in the ED is performed by the treating physician, with support from the consultant on site or on call. Small observation wards were piloted in 3 of the hospitals during part of the study period but were not made permanent. The observation wards were treated as normal inpatient wards in the study and included in the hospital bed occupancy. Selection of Participants We included all patients aged 18 years or older with a Swedish personal identity number who visited any of the 6 EDs at Danderyd Hospital, Karolinska University Hospital Huddinge, Karolinska University Hospital Solna, Norrtälje Hospital, South General Hospital, or Södertälje Hospital from January 1, 2012, to December 31, 2016. Visits to the remaining ED in Stockholm Region, St. Göran Hospital, could not be included because this hospital had a different electronic health care records system and was unable to provide information on historical bed occupancy. Therefore, the maximum number of participants was included that the electronic health care records allowed. Patients who registered in the ED but left before the care process was completed were included and classified as discharged patients. We were unable to capture Volume
-,
no.
-
:
-
2019
af Ugglas et al
Hospital Bed Occupancy and Outcomes in Emergency Care
information in regard to limitations of treatment, so patients with do not resuscitate/do not intubate could not be excluded from the study. Methods of Measurement A database was created by retrieving information on all adult visits to 6 EDs in Stockholm Region from 2012 to 2016. Information was extracted from the electronic health care records system, which includes prospectively collected data on all visits, with data such as times of arrival and departure, personal identity number, and information enabling the calculation of bed occupancy. Using the unique 10-digit personal identity number assigned to each person residing in Sweden, for each patient we linked comorbidity and hospitalization data from the National Swedish Patient Register, data on death from the Cause-ofDeath Register, and socioeconomic data from Statistics Sweden. The validity of the personal identity number and these registers has been found to be high.13 Hourly bed occupancy was calculated at the hospital level by dividing the sum of registered patients in all wards accepting patients admitted from the ED by the sum of staffed beds in the same wards. Only wards that cared for adult emergency cases were included; thus, pediatric, obstetric, and outpatient wards performing ambulatory care within, for example, orthopedics, cardiology, and oncology were excluded. The number of patients registered in each ward changes automatically in the electronic health care records every time a patient is admitted, transferred, or discharged. Changes in the number of staffed beds in a ward are updated manually, normally by a nurse or other person coordinating the beds in each ward. Because we used historical data, we were not able to validate the information about bed occupancy. However, during the study period, there was a control routine in place, in which the information was checked 3 times daily to ensure accuracy in reporting to senior management and government institutions. We also performed quality assurance of the bed occupancy data to identify and remove potentially incorrect observations (Appendix E1 and Table E1, available online at http://www.annemergmed.com). Bed occupancy was calculated at the hospital level during the 24-hour period preceding and including the hour the patient arrived at the ED. This removed the effect of the expected diurnal variation in bed occupancy, allowing for some induction time, and follow-up could start at arrival at the ED. Because the follow-up period started at arrival, we were able to include patients who were discharged home and to calculate inhospital mortality among admitted patients from the first day of hospital stay. For the outcome of readmission for inpatient care within Volume
-,
no.
-
:
-
2019
30 days of hospital discharge, we used the calendar day of discharge from inpatient care for the calculation of bed occupancy and as the start of follow-up. Bed occupancy was categorized in 5% intervals from less than 85% to greater than 104% to cover the bed occupancy distribution in the material and all potential choke points that have been shown to affect hospital performance.14 This included 85%, which is often mentioned as an optimal level, and 100%, in which all staffed beds are occupied and wards are forced to operate above their defined maximum capacity. Outcome Measures The primary outcome was all-cause mortality within 30 days. Secondary outcomes were inhospital mortality, readmission for inpatient care within 30 days of hospital discharge, revisits to the ED within 7 days of being discharged directly home from the ED, admission rate, and length of stay in the ED. Primary Data Analysis The baseline data on patient characteristics are presented as absolute numbers, proportions, and means with SDs. Hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated with a stratified proportional hazards model. This model allows for independent baseline hazards across hospitals but assumes that the HR is the same for all hospitals. As the underlying time dimension in the model, we used date; consequently, we allowed the baseline hazard to vary independently during the follow-up and between hospitals. Because date was chosen as the nonparametric time component, no parametric assumptions of trends in the outcome were necessary, and each event that occurred was contrasted with patients at risk at that specific day, at that hospital. The model was adjusted for patient age, sex, comorbidities, number of hospital stays in the year preceding the index visit, marital status, length of education, and weekday (Monday 7 AM to Friday 4:59 PM) versus weekend (Friday 5 PM to Monday 6:59 AM) timing of visit. Additional subgroup analysis was performed with the same methodology for patients with the highest acuity (triage priority 1), by hospital and age group. Sensitivity analysis using a different definition of bed occupancy was also performed. The bed occupancy was in this case defined as the average hospital bed occupancy at the hour of arrival to the ED. This gives a measure with more variation that is affected by the expected diurnal variation. To account for this, the sensitivity-analysis model was adjusted for hour of arrival in addition to the main model adjustments. In the analyses of all-cause mortality within 30 days, follow-up started at the date of the ED visit and ended at Annals of Emergency Medicine 3
af Ugglas et al
Hospital Bed Occupancy and Outcomes in Emergency Care
death or at 30 days after the visit. A person could have more than 1 visit within a 30-day period, and if so, the later visits were ignored until the date after the previous 30-day period ended (eg, left-truncated), ensuring that no person contributed risk time more than once for each date. For the outcome inhospital mortality, follow-up started at the date of the ED visit and ended at the date of discharge or death. For the outcome readmission within 30 days, follow-up started at the date of discharge and ended at date of readmission, 30 days after discharge, or at death. For revisits to the ED within 7 days of being discharged directly home from the ED, follow-up started at the date of the ED visit and ended at revisit, death, or 7 days. For the outcomes ED length of stay and admission rate, a linear regression model adjusted for hospital was used to assess the association between bed occupancy and the outcome. Previsit comorbidities were defined according to International Statistical Classification of Diseases and Related Health Problems, 10th Revision codes retrieved from the Patient Register and are described in Appendix E1 (available online at http://www.annemergmed.com). Weekend/ holiday was defined as any nonordinary workday (Saturdays, Sundays, and national holidays), starting at 5 PM the previous day until 7 AM the following day. The assumptions in the proportional hazards model were checked by calculating annual (2012 to 2016) HRs of the risk factors. There were no clear trends or systematic patterns in HR estimates during the individual years. All statistical analyses and data management were conducted with SAS (version 9.4; SAS Institute, Inc., Cary, NC). The study was approved by the regional ethics committee in Stockholm. RESULTS Characteristics of Study Subjects A total of 816,832 patients, who visited the 6 EDs 2,084,554 times, were included in the analysis, representing 84% of all adult visits to the 7 EDs in Stockholm Region from 2012 to 2016. The mean age of these patients was 53 years, and 44% were men. Patient characteristics were similar across the different categories of bed occupancy (Table 1) and (Table E2, Figure E1 available online at http://www.annemergmed.com). The mean bed occupancy was 93.3% (SD 3.3%), and it increased from 91.7% (SD 3.1%) in 2012 to 94.5% (SD 3.1%) in 2016 (Table E3, and Figures E2 and E3, available online at http://www.annemergmed.com). The largest percentage of patient visits (31.6%) took place in a context with a bed occupancy of 95% to 99%, and only 0.9% of visits were exposed to the highest category of bed occupancy (>104%) (Table 1). There was a diurnal variation of bed 4 Annals of Emergency Medicine
occupancy, with the highest levels early in the morning (8 to 11 AM) and the lowest ones late in the afternoon (3 PM to 7 PM) (Figure E4, available online at http://www. annemergmed.com). The mean bed occupancy was lower on weekends and holidays, at 91.3% (SD 3.2%), compared with weekdays, at 94.6% (SD 2.6%) (Table E3, available online at http://www.annemergmed.com). Bed occupancy varied by hospital; the hospital with the lowest bed occupancy did not contribute to any visits in the highest category of exposure (>104%) (Table E4 and Figure E5, available online at http://www.annemergmed.com). In total, there were 28,112 deaths within 30 days of the visit to the ED (Table 2). In all groups of bed occupancy, the incidence rate was 19 to 20 deaths per 100 personyears, except for patients in the highest occupancy category of greater than 104%, in which the incidence rate was 22 deaths per 100 person-years. None of the bed occupancy categories demonstrated statistically significant associations with 30-day mortality in the multivariable model (Table 2). The estimated HR in the group with the highest bed occupancy greater than 104% was 1.10 (95% CI 0.96 to 1.27). In the subgroup analysis including only patients with the highest acuity (triage level 1), there was also no association between bed occupancy and 30-day mortality. The estimated HR in the group with the highest bed occupancy greater than 104% was 0.97 (95% CI 0.75 to 1.27). The subgroup analysis by hospital and by age group had similar results, with no association between bed occupancy and 30-day mortality. More information is available in (Appendix E1 and table E5-E8, available online at http://www.annemergmed.com). In total, there were 17,966 inhospital deaths (Table 2). Patients died most commonly during the first day of hospitalization (12%). Among patients who died inhospital, 47% died within 7 days after admission and 89% died within 30 days after admission. The incidence rate was 208 to 223 deaths per 100 person-years, except for patients in the highest category of bed occupancy (>104%), in which the incidence rate was 249 deaths per 100 person-years. There was no association between bed occupancy and inhospital mortality in the multivariable models (Table 2). The estimated HR in the group with the highest bed occupancy greater than 104% was 1.09 (95% CI 0.92 to 1.30). A total of 192,938 revisits to the ED occurred within 7 days of patients’ being discharged home from the ED (Table 2). The risk of revisits was 3% lower (adjusted HR 0.97; 95% CI 0.95 to 0.99) among patients exposed to less than 85% bed occupancy compared with the reference group (Table 2). ED visits occurring during bed occupancies of 90% to 94%, 95% to 99%, or 100% to 104% were 2% AM
Volume
-,
no.
-
:
-
2019
-,
af Ugglas et al
Volume no. -
Table 1. Characteristics of patients visiting 6 EDs in Stockholm Region, Sweden, from 2012 to 2016, by bed occupancy.
: -
Bed Occupancy
2019
All Visits
<85%
85%–89%
90%–94%
95%–99%
100%–104%
486,273,324
50,033,988 (10.3)
87,910,960 (18.1)
142,420,461 (29.3)
156,436,405 (32.2)
45,380,001 (9.3)
>104%
Patient/visit information Patient-hours at risk, No. (%) No. of patients Visits to EDs, No. (%)
816,832 2,084,554
162,155 221,210 (10.6)
260,415 379,039 (18.2)
390,930 614,787 (29.5)
386,651 658,695 (31.6)
147,934 191,883 (9.2)
4,091,510 (0.8) 17,401 18,940 (0.9)
Age and sex Age, median (Q1, Q3), y Male sex, No. (%)
51 (34, 71)
52 (34, 71)
52 (34, 71)
51 (34, 71)
51 (34, 70)
51 (34, 70)
55 (35, 73)
913,804 (43.8)
98,175 (44.4)
164,652 (43.4)
268,273 (43.6)
289,685 (44.0)
84,479 (44.0)
8,540 (45.1)
Marital status, No. (%) Married
783,703 (37.6)
76,051 (34.4)
135,556 (35.8)
231,142 (37.6)
256,959 (39.0)
76,306 (39.8)
7,689 (40.6)
Divorced
351,358 (16.9)
37,211 (16.8)
63,835 (16.8)
103,306 (16.8)
111,843 (17.0)
32,136 (16.7)
3,027 (16.0)
Never married
752,141 (36.1)
85,627 (38.7)
141,617 (37.4)
221,883 (36.1)
231,321 (35.1)
65,540 (34.2)
6,153 (32.5)
Widowed
186,602 (9.0)
21,200 (9.6)
36,335 (9.6)
55,351 (9.0)
54,891 (8.3)
16,832 (8.8)
1,993 (10.5)
10,750 (0.5)
1,121 (0.5)
1,696 (0.4)
3,105 (0.5)
3,681 (0.6)
1,069 (0.6)
<10
515,254 (24.7)
56,125 (25.4)
92,675 (24.4)
150,513 (24.5)
163,341 (24.8)
47,593 (24.8)
5,007 (26.4)
10–12
863,698 (41.4)
93,072 (42.1)
157,900 (41.7)
253,327 (41.2)
271,275 (41.2)
79,919 (41.6)
8,205 (43.3)
>12
641,224 (30.8)
65,975 (29.8)
118,145 (31.2)
192,117 (31.2)
201,864 (30.6)
57,959 (30.2)
5,164 (27.3)
64,378 (3.1)
6,038 (2.7)
10,319 (2.7)
18,830 (3.1)
22,215 (3.4)
6,412 (3.3)
564 (3.0)
1
105,822 (5.1)
11,819 (5.3)
20,199 (5.3)
31,480 (5.1)
32,480 (4.9)
9,062 (4.7)
782 (4.1)
2
248,484 (11.9)
29,751 (13.4)
45,012 (11.9)
70,505 (11.5)
78,499 (11.9)
22,685 (11.8)
2,032 (10.7)
3
767,717 (36.8)
75,679 (34.2)
134,833 (35.6)
222,435 (36.2)
250,848 (38.1)
75,977 (39.6)
7,945 (41.9)
4–5
870,733 (41.8)
93,454 (42.2)
157,049 (41.4)
258,251 (42.0)
274,559 (41.7)
79,628 (41.5)
7,792 (41.1)
91,798 (4.4)
10,507 (4.7)
21,946 (5.8)
32,116 (5.2)
22,309 (3.4)
4,531 (2.4)
389 (2.1)
Missing
78 (0.4)
Missing Triage priority, No. (%)
Annals of Emergency Medicine 5
Missing
Hospital Bed Occupancy and Outcomes in Emergency Care
Length of education, No. (%), y
Bed Occupancy <85%
All Visits
85%–89%
90%–94%
95%–99%
100%–104%
>104%
Comorbidities, No. (%) Previous stroke
103,697 (5.0)
11,596 (5.2)
19,894 (5.2)
30,944 (5.0)
31,143 (4.7)
Previous MI
91,292 (4.4)
10,042 (4.5)
17,299 (4.6)
26,514 (4.3)
COPD
95,911 (4.6)
10,833 (4.9)
18,146 (4.8)
28,416 (4.6)
Previous heart failure
81,295 (3.9)
8,866 (4.0)
15,455 (4.1)
Diabetes
9,097 (4.7)
1,023 (5.4)
28,182 (4.3)
8,312 (4.3)
943 (5.0)
29,158 (4.4)
8,525 (4.4)
833 (4.4)
24,202 (3.9)
24,673 (3.7)
7,303 (3.8)
796 (4.2)
166,928 (8.0)
17,458 (7.9)
29,213 (7.7)
48,930 (8.0)
53,798 (8.2)
15,888 (8.3)
1,641 (8.7)
Active cancer
64,489 (3.1)
5,624 (2.5)
9,465 (2.5)
19,053 (3.1)
22,998 (3.5)
6,807 (3.5)
542 (2.9)
Chronic kidney disease
51,847 (2.5)
4,250 (1.9)
8,291 (2.2)
15,368 (2.5)
18,089 (2.7)
5,377 (2.8)
472 (2.5)
Hospital stays within 1 y, No. (%) None
1,370,253 (65.7)
143,863 (65.0)
247,881 (65.4)
403,491 (65.6)
435,324 (66.1)
127,076 (66.2)
12,618 (66.6)
1
362,724 (17.4)
39,107 (17.7)
66,624 (17.6)
107,045 (17.4)
113,715 (17.3)
32,964 (17.2)
3,269 (17.3)
2
144,722 (6.9)
15,479 (7.0)
26,236 (6.9)
42,778 (7.0)
45,599 (6.9)
13,385 (7.0)
1,245 (6.6)
3
206,855 (9.9)
22,761 (10.3)
38,298 (10.1)
61,473 (10.0)
64,057 (9.7)
18,458 (9.6)
1,808 (9.5)
Weekday (Monday 7 AM–Friday 5 PM)
1,395,071 (66.9)
68,032 (30.8)
227,317 (60.0)
425,880 (69.3)
495,930 (75.3)
161,054 (83.9)
16,858 (89.0)
Weekend/holiday (Friday 5 PM–Monday 7 AM)
689,483 (33.1)
153,178 (69.2)
151,722 (40.0)
188,907 (30.7)
162,765 (24.7)
30,829 (16.1)
2,082 (11.0)
Hospital Bed Occupancy and Outcomes in Emergency Care
6 Annals of Emergency Medicine
Table 1. Continued.
Day of visit, No. (%)
MI, Myocardial infarction; COPD, chronic obstructive pulmonary disease.
Volume -, -
: -
2019
af Ugglas et al
no.
-,
af Ugglas et al
Volume no. -
: -
2019
Table 2. Association of 30-day and inhospital mortality, revisits to the ED within 7 days, and readmission for inpatient care within 30 days of hospital discharge among 816,832 patients with 2,084,554 visits to 6 EDs in Stockholm Region, Sweden, from 2012 to 2016, by bed occupancy. Bed Occupancy <85%
85%–89%
90%–94%
95%–99%
100%–104%
>104%
30-day mortality No. of deaths
3,109
5,160
8,376
8,789
2,413
265
Person-years at risk, n
15,573
26,402
42,211
44,532
12,829
1,231
20
20
20
20
19
22
1.01 (0.97–1.06)
1 [Reference]
1.01 (0.97–1.05)
1.00 (0.96–1.05)
0.96 (0.90–1.02)
1.10 (0.96–1.27)
2,022
3,387
5,347
5,513
1,561
172
Person-years at risk, n
921
1,598
2,568
2,548
700
69
Incidence rate, cases/100 person-years
220
212
208
216
223
249
1.01 (0.95–1.08)
1
0.97 (0.92–1.02)
0.98 (0.93–1.04)
0.99 (0.92–1.07)
1.09 (0.92–1.30)
Incidence rate, cases/100 person-years Adjusted model, HR (95% CI) Inhospital mortality No. of deaths
Adjusted model, HR (95% CI) Revisits, No. (%) Adjusted model, HR (95% CI)
17,255 (12.4)
30,245 (12.5)
55,536 (13.4)
68,167 (14.5)
19,900 (14.2)
1,835 (13.3)
0.97 (0.95–0.99)
1 [Reference]
1.02 (1.00–1.04)
1.04 (1.02–1.06)
1.02 (1.00–1.05)
1.02 (0.96–1.08)
6.3 (11.5)
6.4 (13.3)
6.8 (13.3)
7.0 (13.8)
6.7 (13.1)
6.4 (11.4)
8,745 (20.2)
18,671 (20.9)
28,767 (21.2)
29,585 (22.3)
7,701 (22.0)
680 (19.8)
1.01 (0.98–1.04)
1 [Reference]
0.97 (0.95–0.99)
0.98 (0.96–1.01)
0.98 (0.95–1.01)
0.95 (0.87–1.03)
Readmission for inpatient care within 30 days of hospital discharge Length of hospital stay, days (SD) Readmissions, No. (%) Adjusted model, HR (95% CI)
Annals of Emergency Medicine 7
Adjusted model included age, sex, comorbidities, number of hospital stays in the year preceding the index visit, marital status, length of education, and weekday/weekend timing of visit.
Hospital Bed Occupancy and Outcomes in Emergency Care
Revisits to the ED within 7 days
af Ugglas et al
Hospital Bed Occupancy and Outcomes in Emergency Care
Figure 1. Patient length of stay in the ED for 2,084,554 visits to 6 emergency departments in Stockholm Region, Sweden, from 2012 to 2016, by bed occupancy and hospital. The mean is represented by the point; the median, by the vertical line in the box. The left side of the box represents quartile 1 and the right side, quartile 3; the error bar spans between the fifth and 95th percentile.
to 4% more likely to lead to revisits within 7 days compared with that for the reference group (Table 2). In total, 92,592 patients were readmitted for inpatient care within 30 days of discharge, and there was no association between bed occupancy on the day of discharge and readmission for inpatient care within 30 days (Table 2). The median hospital length of stay for the initial admission was 3 days (quartile 1¼1, quartile 3¼8), with no association with bed occupancy. The mean length of stay in the ED was 234 minutes (SD 163 minutes), with a median of 200 minutes (interquartile range 184 minutes). There were differences among the hospitals in lengths of stay in the ED, with the mean length of stay ranging from 162 minutes (SD 93 minutes; median 147 minutes; interquartile range 117 minutes) to 260 minutes (interquartile range 178 minutes; median 225 minutes; interquartile range 208 minutes) (Table E5, available online at http://www.annemergmed. com). Length of stay increased with higher bed occupancy in all 6 hospitals (Figure 1 and Table E9, available online at http://www.annemergmed.com). This pattern was most 8 Annals of Emergency Medicine
obvious for patients who were admitted for inpatient care. For each 10% increase in bed occupancy, the length of stay in the ED increased by 16 minutes (95% CI 16 to 17 minutes) for all patients, by 28 minutes (95% CI 28 to 29 minutes) for admitted patients, and by 9 minutes (95% CI 9 to 10 minutes) for discharged patients. The mean admission rate for inpatient care was 21.9%, and this varied among the studied hospitals, ranging from 18.4% to 25.8%. In all hospitals, admission rates declined as bed occupancy increased (Figure 2) and Table E10, available online at http://www.annemergmed.com). The total mean admission rate was 24.0% in the lowest bed occupancy category and 20.6% in the highest one. A 10% increase in bed occupancy was associated with a 1.9percentage-point increase (95% CI 1.7 to 2.0 percentage points) in admission rate. Sensitivity Analyses Sensitivity analysis studying the association between the hourly bed occupancy levels and 30-day mortality showed nonsignificant results similar to those of the main analysis. The Volume
-,
no.
-
:
-
2019
af Ugglas et al
Hospital Bed Occupancy and Outcomes in Emergency Care
Figure 2. Admission rate from the ED to inpatient care for 2,084,554 visits to 6 emergency departments in Stockholm Region, Sweden, from 2012 to 2016, by bed occupancy and hospital. The admission rate (proportion) is represented by a point; the error bar represents the 95% CI for the proportion.
estimated HR in the group with the highest bed occupancy greater than 104% was 1.08 (95% CI 0.99 to 1.17). LIMITATIONS One limitation was that our exposure was based on information that we were unable to fully validate. However, during the study period, there was a process in place for controlling the accuracy of the information in the electronic health care records, which we believe reduced the risk of misclassification. We also performed retrospective quality assurance of the data, removing obvious errors. The main risk of information bias that we could not control was the possibility that the nurse or person who coordinated the ward registered planned admissions in advance but delayed registration of discharges in periods of high workload to manage the work environment in his or her own unit. This would lead to a false increase in bed occupancy and would dilute the association between the exposure and outcome for the highest categories of bed occupancy. By using a hospital-level 24-hour average bed occupancy exposure, we believe that the effect of errors in the reporting of Volume
-,
no.
-
:
-
2019
individual wards during a shorter period would have been relatively small. However, this approach is less sensitive compared with a 24-hour ward-level approach to assessing bed occupancy, which could be used when admitted patients are studied exclusively. This decline in sensitivity may have diluted the association between the exposure and outcome in the present study. Even if we had access to a very large study population, the highest category (>104%) of bed occupancy included only 265 deaths during the study period, which contributed to the wide CIs for this category. Although patients may have been safe in terms of mortality, there may have been other avoidable harms associated with high bed occupancy that were not included in this study. Additionally, we did not assess the effects of increased workload and revisits on the 6 EDs. Finally, we did not evaluate the risk of negative consequences for physicians and nurses in wards and EDs when workload increases, or how bed logistics and negotiations take the focus away from medical decisions and patient care as bed occupancy increases. Annals of Emergency Medicine 9
af Ugglas et al
Hospital Bed Occupancy and Outcomes in Emergency Care
DISCUSSION In a large observational cohort study including 816,832 patients with 2,084,554 visits to 6 of the 7 EDs in Stockholm Region during 5 years, we found no association of bed occupancy with 30-day mortality or inhospital mortality. Our findings were consistent across the studied hospitals by sex, by triage priority, by admission status, and in different age groups. With increasing bed occupancy, we found 2% to 4% higher risks of revisits to the ED within 7 days for patients discharged directly home from the ED. For admitted patients, there was no association between bed occupancy and readmission for inpatient care within 30 days of hospital discharge. The main strength of our study was the large study population and the completeness in the follow-up of outcomes because of our use of high-quality health care registers with comprehensive national coverage. The detailed information, including several nationwide health care registers and data from Statistics Sweden, allowed us to analyze patient characteristics and comorbidities. Unlike previous studies,5,7 we were able to include all patients seeking care at the ED who potentially were exposed to risks associated with high bed occupancy; in particular, patients who normally would be assessed as needing inpatient care but were discharged home because of bed shortages. In addition, by including all visits to the ED, we avoided the risk of selection bias, with a higher proportion of sicker patients being admitted at times of bed shortages. Another strength was that our statistical model controlled for time-dependent confounding outside the 30-day follow-up period, such as the potential effect of seasonal influenza, summer holidays, Christmas, and other known and unknown seasonal patterns. Furthermore, we believe that our findings are generalizable to other health care systems similar to the Swedish one with EDs open to the public continuously. To our knowledge, this study is the first to include all patients whose outcomes could have been affected by hospital bed occupancy; namely, all patients seeking care at the ED. Previous studies4-7 reporting associations between high bed occupancy and increased mortality included only patients who were admitted for inpatient care. Our results showed declining admission rates for inpatient care as bed occupancy increased, which is in line with earlier findings.10 Declining admission rates indicate that medical prioritization is taking place when bed occupancy is high, and this is likely to result in a higher proportion of older and sicker patients being admitted when bed occupancy is high. Even if
10 Annals of Emergency Medicine
adjustment for patient characteristics was made in previous studies, there may still have been residual confounding that might explain the differences between our results and the findings of previous work. Although we did not find a significant association between high bed occupancy and increased mortality, the point estimates for both 30-day and inhospital mortality were 9% to 10% higher at the highest level of bed occupancy (105%). The magnitude of this estimate was similar to that found in a Danish study that concluded that both 30-day and inhospital mortality were increased by 9% at a bed occupancy of greater than or equal to 110%.5 However, our study is not completely comparable to this previous study because of differences in study design: the previous study investigated only admitted patients and defined bed occupancy at ward level instead of at hospital level. We found an association between higher bed occupancy and risk of revisits to the ED within 7 days of being discharged from the ED. Although the magnitude of the higher risk was small (2% to 4%), this translates into several thousand additional visits during the study period, further encumbering an already strained health care system. The proportion of patients readmitted to the hospital within 30 days after discharge is a commonly used indicator of quality of care.15 It has been shown that high occupancy in the ICU at discharge leads to increased readmissions.16 Another Swedish study with a design similar to ours, but including a single hospital, reported a higher odds ratio of being readmitted within 30 days for patients exposed to higher bed occupancy, indicating an 11% to 17% increase in the odds of readmission for this group.8 These results were not confirmed in our study, which included 6 hospitals. Although we found no increased risk of mortality or readmission, the associations of high bed occupancy with longer length of stay in the ED and with lower admission rates are consistent with results reported from previous studies.4,8-10 As bed occupancy increased, the length of stay in the ED increased and reached a mean of almost 5 hours in 2 of the 6 EDs at the highest bed occupancy level, indicating an imbalance between demand and capacity when bed occupancy increased. Concurrently, admission rates decreased sharply as bed occupancy increased, indicating more difficult medical prioritization in the ED when whether to admit or discharge patients was being decided. These associations were observed beginning at 85% bed occupancy. Taken together, they result in an increased workload in the ED beginning when bed occupancy reaches 85%, thus increasing the
Volume
-,
no.
-
:
-
2019
af Ugglas et al
Hospital Bed Occupancy and Outcomes in Emergency Care
risk of ED crowding and negatively affecting patients and staff.17,18 For example, time-critical interventions are delayed,19 adverse events for elderly patients increase,20 and violence toward staff occurs more frequently.21 These findings highlight the ED’s dependency on the hospital’s inpatient care and the importance of viewing the ED as part of a larger system dependent on input, throughput, and output.22 In addition, with increasing ED crowding, ED staff and the staff from wards with high bed occupancy become strained, which increases the risk of burnout.23,24 In conclusion, we found no association of bed occupancy with mortality or readmissions. Our findings indicate that our health care systems may be able to adapt to high levels of bed occupancy without an increased risk of death. High levels of bed occupancy will, however, increase ED length of stay and reduce admission rate to inpatient care, which both increase the workload in the ED. A preparedness to reallocate resources to the ED is needed when bed occupancy increases because the workload is likely to increase even when bed occupancy is at 85%. The authors acknowledge Jennifer Barrett, PhD, from Edanz Group for editing a draft of this article. Supervising editor: Donald M. Yealy, MD. Specific detailed information about possible conflict of interest for individual editors is available at https://www.annemergmed.com/editors. Author affiliations: From the Function of Emergency Medicine, Karolinska University Hospital, and the Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden (Ugglas, Djärv, Holzmann); and the Department of Cardiology, Danderyd Hospital, and the Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden (Ljungman). Author contributions: BU and MJH were involved in the conception of the study; gathered, structured, and performed the analysis of the data; and drafted the article. MJH gained necessary ethical approvals. All authors contributed to the design of the study, interpreted the results, critically revised the article, and approved the article to be submitted. MJH takes responsibility for the paper as a whole. All authors attest to meeting the four ICMJE.org authorship criteria: (1) Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; AND (2) Drafting the work or revising it critically for important intellectual content; AND (3) Final approval of the version to be published; AND (4) Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Funding and support: By Annals policy, all authors are required to disclose any and all commercial, financial, and other relationships
Volume
-,
no.
-
:
-
2019
in any way related to the subject of this article as per ICMJE conflict of interest guidelines (see www.icmje.org). Dr. Djärv was supported by the Stockholm County Council (clinical research appointment). Dr. Ljungman was supported by Karolinska Institutet’s Strategic Research Area in Epidemiology (SFO Epidemiology), the Swedish Society for Medical Research, and the Swedish Research Council for Health, Working Life and Welfare (FORTE). Dr. Holzmann reports receiving consultancy honoraria from Actelion, Idorsia, and Pfizer. He holds research positions funded by the Swedish HeartLung Foundation (grant 20170804) and the ALF agreement between the Stockholm County Council and Karolinska Institutet (grant 20170686). The construction of the database used in this study was funded by an unrestricted grant from Idorsia Pharmaceutical. Publication dates: Received for publication June 23, 2019. Revisions received October 11, 2019, and November 5, 2019. Accepted for publication November 12, 2019.
REFERENCES 1. OECD. Health at a Glance 2017: OECD Indicators. Paris, France: OECD Publishing; 2017. 2. Knezevic M. Open comparison and evaluation of hospital overcrowding [Öppen jämförelse och utvärdering av överbeläggningar—Delrapport 2011]. Stockholm, Sweden: The Swedish National Board of Health and Welfare; 2012. 3. Council for the Promotion of Municipal Analysis. Bed occupancy rate [Rådet för främjande av kommunala analyser] [Beläggningsgrad] (N72826). Available at: https://www.kolada.se/?_p¼workspace/nt. Accessed August 18, 2018. 4. Boden DG, Agarwal A, Hussain T, et al. Lowering levels of bed occupancy is associated with decreased inhospital mortality and improved performance on the 4-hour target in a UK district general hospital. Emerg Med J. 2016;33:85-90. 5. Madsen F, Ladelund S, Linneberg A. High levels of bed occupancy associated with increased inpatient and thirty-day hospital mortality in Denmark. Health Aff (Millwood). 2014;33:1236-1244. 6. Schilling PL, Campbell DA Jr, Englesbe MJ, et al. A comparison of inhospital mortality risk conferred by high hospital occupancy, differences in nurse staffing levels, weekend admission, and seasonal influenza. Med Care. 2010;48:224-232. 7. Sprivulis PC, Da Silva JA, Jacobs IG, et al. The association between hospital overcrowding and mortality among patients admitted via Western Australian emergency departments. Med J Aust. 2006;184:208-212. 8. Handel DA, Fu R, Vu E, et al. Association of emergency department and hospital characteristics with elopements and length of stay. J Emerg Med. 2014;46:839-846. 9. Hillier DF, Parry GJ, Shannon MW, et al. The effect of hospital bed occupancy on throughput in the pediatric emergency department. Ann Emerg Med. 2009;53:767-776.e3. 10. Blom MC, Jonsson F, Landin-Olsson M, et al. The probability of patients being admitted from the emergency department is negatively correlated to in-hospital bed occupancy—a registry study. Int J Emerg Med. 2014;7:8. 11. Blom MC, Erwander K, Gustafsson L, et al. The probability of readmission within 30 days of hospital discharge is positively associated with inpatient bed occupancy at discharge—a retrospective cohort study. BMC Emerg Med. 2015;15:37. 12. Blom MC, Jonsson F, Landin-Olsson M, et al. Associations between inhospital bed occupancy and unplanned 72-h revisits to the emergency department: a register study. Int J Emerg Med. 2014;7:25.
Annals of Emergency Medicine 11
af Ugglas et al
Hospital Bed Occupancy and Outcomes in Emergency Care 13. Ludvigsson JF, Otterblad-Olausson P, Pettersson BU, et al. The Swedish personal identity number: possibilities and pitfalls in healthcare and medical research. Eur J Epidemiol. 2009;24:659-667. 14. Khanna S, Boyle J, Good N, et al. Unravelling relationships: hospital occupancy levels, discharge timing and emergency department access block. Emerg Med Australas. 2012;24:510-517. 15. Axon R, Williams MV. Hospital readmission as an accountability measure. JAMA. 2011;305:504-505. 16. Chrusch CA, Olafson KP, McMillan PM, et al. High occupancy increases the risk of early death or readmission after transfer from intensive care. Crit Care Med. 2009;37:2753-2758. 17. Morley C, Unwin M, Peterson GM, et al. Emergency department crowding: a systematic review of causes, consequences and solutions. PloS One. 2018;13:e0203316. 18. Guttmann A, Schull MJ, Vermeulen MJ, et al. Association between waiting times and short term mortality and hospital admission after departure from emergency department: population based cohort study from Ontario, Canada. BMJ. 2011;342:d2983.
12 Annals of Emergency Medicine
19. Gaieski DF, Agarwal AK, Mikkelsen ME, et al. The impact of ED crowding on early interventions and mortality in patients with severe sepsis. Am J Emerg Med. 2017;35:953-960. 20. Ackroyd-Stolarz S, Read Guernsey J, Mackinnon NJ, et al. The association between a prolonged stay in the emergency department and adverse events in older patients admitted to hospital: a retrospective cohort study. BMJ Qual Saf. 2011;20:564-569. 21. Medley DB, Morris JE, Stone CK, et al. An association between occupancy rates in the emergency department and rates of violence toward staff. J Emerg Med. 2012;43:736-744. 22. Asplin BR, Magid DJ, Rhodes KV, et al. A conceptual model of emergency department crowding. Ann Emerg Med. 2003;42:173-180. 23. Aiken LH, Clarke SP, Sloane DM, et al. Hospital nurse staffing and patient mortality, nurse burnout, and job dissatisfaction. JAMA. 2002;288:1987-1993. 24. Needleman J, Buerhaus P, Pankratz VS, et al. Nurse staffing and inpatient hospital mortality. N Engl J Med. 2011;364:1037-1045.
Volume
-,
no.
-
:
-
2019