To the Editor:
Hospitals geographically localize clinically similar patients into wards to provide specialized care that improves patient outcomes and care, and lowers costs. When these wards exceed capacity, patients become “geographically dispersed” to alternate locations. For example, critically ill patients may “board” in emergency departments or alternate intensive care units (ICUs) when the clinically appropriate ICUs are at capacity. Such geographic dispersion has been demonstrated to be associated with increased hospital length of stay (LOS), ICU and in-hospital mortality, and incidence of hospital-acquired infections, in addition to lower adherence to quality metrics (1–5). In several single-center studies and a meta-analysis across the United States and Canada, geographic dispersion among general internal medicine ward patients was associated with increased in-hospital mortality and resident burnout (6), and among a mixed medical–surgical ward population with increased hospital LOS (7).
Geographic localization is particularly pertinent for pulmonary service patients, who may benefit from specialized care focused on their unique needs, such as frequent assessments by respiratory therapists. For example, within the University of Pennsylvania Health System (UPHS), the pulmonary services are staffed by a board-certified pulmonologist and internal medicine residents. The pulmonary wards have higher respiratory therapist–to-patient ratios; and oxygen, noninvasive ventilation, and high-flow nasal cannula capabilities that other wards do not.
However, variations in outcomes among pulmonary service patients who receive care on pulmonary wards as compared with those who are geographically dispersed have never been evaluated. Therefore, our objective was to determine if geographic dispersion is associated with adverse outcomes among hospitalized pulmonary service patients. We hypothesized that geographic dispersion would be associated with increased hospital LOS.
Preliminary results of this work were presented as an abstract at the American Thoracic Society International Conference 2019 (8).
Methods
We performed a retrospective cohort study of all pulmonary service patients admitted to wards from 2014 and 2015 within three UPHS acute care hospitals. Patients are admitted to the pulmonary service through several mechanisms: 1) direct admission from a UPHS outpatient pulmonary practice; 2) through the hospital emergency department, when the patient presents with a primary respiratory issue and is followed in an outpatient pulmonary practice of UPHS; 3) through interhospital transfer of a patient with a primary respiratory issue followed in a UPHS pulmonary practice; and 4) from the hospital’s medical ICU if the patient has high respiratory care needs (e.g., new tracheostomy, secretions requiring frequent suctioning, and need for noninvasive ventilation or high-flow nasal cannula), as determined by the ICU team. If a bed is available on the pulmonary ward at each hospital, the pulmonary service patient is preferentially admitted there. If not, the pulmonary service patient will be admitted to another floor, with similar patient-to-nurse ratios and telemetry capabilities, but possibly higher patient-to–respiratory therapist ratios and different oxygen delivery capabilities.
We excluded patients transferred between geographically localized and dispersed wards (n = 33 [2%]). The primary exposure was “geographic dispersion,” defined as admission to a nonpulmonary ward, for the entirety of hospitalization, excluding any period of ICU admission. The primary outcome was the rate of discharge (i.e., a proxy for hospital LOS). Secondary outcomes included in-hospital mortality, discharge to home, discharge to skilled nursing facilities (SNFs), and 90-day hospital readmissions, as these represent patient- and surrogate-centered outcomes (9).
We first performed unadjusted analyses of geographic dispersion with each outcome separately. We then estimated separate multivariable regression models, adjusting for covariates selected for inclusion a priori based on a conceptual model of geographic dispersion and prior research of inpatients, including age, gender, race, ethnicity, insurance type, Elixhauser comorbidity scores, the Centers for Medicare and Medicaid Services four-level severity risk adjustment (based on admitting diagnosis, demographics, and comorbidities) (10, 11), admission diagnosis category (from International Classification of Diseases-9 and -10 codes), daily mean number of pulmonary service patients by hospital during the hospitalization, code status (full vs. any limitation on life-sustaining therapies), hospital admission source, ICU as the ward admission source, and season (6, 7). Because in-hospital mortality is a competing risk for being discharged alive, we analyzed hospital LOS by comparing rate of discharge using a subdistribution hazard ratio (SHR) estimated from a Fine and Gray model that treated death as a competing risk (12). We performed generalized linear models with log links for the secondary outcomes. We included hospital as a fixed effect in unadjusted and adjusted analyses to account for clustering. We calculated E values for all of the adjusted models to determine the minimum strength of an association on the SHR or risk ratio (RR) that an unmeasured confounder would need to have with both the exposure and outcome, conditional on the measured covariates, to nullify the observed exposure–outcome association for the statistically significant findings, or to change the null findings by 20% (13, 14). The University of Pennsylvania Institutional Review Board approved this protocol as exempt.
Results
The study population included 1,603 patients in 31 wards across 3 hospitals (Table 1). Median age was 60 years (interquartile range = 48–70), 51% were female, and 53% were of white race. The 31 wards had a median 32 operational beds (interquartile range = 30–43). A total of 928 (58%) patients were geographically dispersed.
Table 1.
Patient characteristics and unadjusted outcomes*
| Variable | Total (n = 1,603) | Localized (n = 675) | Dispersed (n = 928) |
|---|---|---|---|
| Patient characteristics | |||
| Hospital | |||
| 1 | 991 (62) | 421 (62) | 570 (61) |
| 2 | 332 (21) | 130 (19) | 202 (22) |
| 3 | 280 (18) | 124 (18) | 156 (17) |
| Age, median (IQR), yr | 60 (48–70) | 60 (48–68) | 61 (50–71) |
| Female | 819 (51) | 347 (51) | 472 (51) |
| Race | |||
| White | 846 (53) | 343 (51) | 503 (54) |
| Black | 657 (41) | 289 (43) | 368 (40) |
| Other† | 100 (6) | 43 (6) | 57 (6) |
| Latino/Hispanic | 52 (3) | 29 (4) | 23 (3) |
| Insurance | |||
| Private | 705 (44) | 306 (45) | 399 (43) |
| Medicare | 726 (45) | 300 (44) | 426 (46) |
| Medicaid | 87 (5) | 36 (5) | 51 (6) |
| Other‡ | 85 (5) | 33 (5) | 52 (6) |
| Elixhauser comorbidity score, median (IQR) | 4 (3–6) | 4 (2–6) | 4 (3–6) |
| Centers for Medicare and Medicaid Services severity risk adjustment | |||
| Mild | 71 (4) | 36 (5) | 35 (4) |
| Moderate | 335 (21) | 177 (26) | 158 (17) |
| Major | 570 (36) | 255 (38) | 315 (34) |
| Severe | 627 (39) | 207 (31) | 420 (45) |
| Admission diagnosis category | |||
| Symptoms, signs, and abnormal clinical and laboratory findings, not elsewhere classified | 575 (36) | 248 (37) | 327 (35) |
| Diseases of the respiratory system | 362 (23) | 182 (27) | 180 (19) |
| Diseases of the circulatory system | 126 (8) | 42 (6) | 84 (9) |
| Infectious diseases | 107 (7) | 31 (5) | 76 (8) |
| Endocrine, nutritional, and metabolic diseases | 100 (6) | 46 (7) | 54 (6) |
| Other§ | 333 (21) | 126 (19) | 207 (22) |
| Daily mean number of pulmonary service patients by hospital during the hospitalization, median (IQR) | 21 (7–27) | 22 (8–28) | 20 (7–26) |
| Code status—full | 1,266 (79) | 581 (86) | 685 (74) |
| Hospital admission source | |||
| Emergency department | 1,231 (77) | 544 (81) | 687 (74) |
| Outside facility | 280 (18) | 100 (15) | 180 (19) |
| Direct | 92 (6) | 31 (5) | 61 (7) |
| Ward admission source—intensive care unit | 712 (44) | 277 (41) | 435 (47) |
| Season | |||
| Winter | 407 (25) | 172 (26) | 235 (25) |
| Spring | 458 (29) | 180 (27) | 278 (30) |
| Summer | 429 (27) | 169 (25) | 260 (28) |
| Fall | 309 (19) | 154 (23) | 155 (17) |
| Outcomes (all patients) | |||
| Hospital length of stay, median (IQR), days | 7 (4–14) | 6 (3–10) | 8 (4–16) |
| In-hospital mortality | 151 (9) | 30 (4) | 121 (13) |
| Outcomes (survivors) | n = 1,452 | n = 645 | n = 807 |
| Discharge destination | |||
| Home | 1,088 (75) | 521 (81) | 567 (70) |
| Skilled nursing facility | 207 (14) | 66 (10) | 141 (17) |
| Hospice | 78 (5) | 25 (4) | 53 (7) |
| Rehabilitation facility | 39 (3) | 16 (2) | 23 (3) |
| Transferred to another acute care hospital | 17 (1) | 10 (2) | 7 (0.1) |
| Other|| | 23 (2) | 7 (1) | 16 (2) |
| 90-d hospital readmissions | 460 (32) | 208 (32) | 252 (31) |
Definition of abbreviation: IQR = interquartile range.
All values presented as n (%) except where otherwise specified.
Other includes: Asian; American Indian/Alaskan Native; Pacific Islander; mixed; unknown; other.
Other includes: workman’s compensation; self-pay; charity; enrolled in a study; hospice; veteran’s association; missing.
Other includes: neoplasms; diseases of the blood and blood-forming organs and certain disorders involving the immune mechanism; mental, behavioral, and neurodevelopmental disorders; diseases of the nervous system; diseases of the digestive system; diseases of the genitourinary system; pregnancy, childbirth, and the puerperium; diseases of the skin and subcutaneous tissue; diseases of the musculoskeletal system and connective tissue; congenital malformations, deformations, and chromosomal abnormalities; injury, poisoning, and certain other consequences of external causes; factors influencing health status and contact with health services; multiple diagnoses. Each of these diagnoses represented <5% of the overall population, and was derived from International Classification of Diseases-9 and -10 codes.
Other includes: correctional facility; psychiatric facility; left against medical advice; user-defined code.
In unadjusted analyses, geographic dispersion was associated with decreased rates of discharge, increased in-hospital mortality, decreased discharge to home, and increased discharge to SNFs. After adjustment for potential confounders, the SHR estimated from a Fine and Gray competing risks model was 0.8 (95% confidence interval [CI] = 0.7–0.9, P = 0.001, E = 1.3), indicating that the rate of discharge was lower for dispersed patients (suggesting longer adjusted LOS). Geographically dispersed patients had: no difference in in-hospital mortality (RR = 1.2, 95% CI = 0.9–1.6, P = 0.3, adjusted percent: localized, 8% vs. dispersed, 10%, E = 1.6); lower rate of discharge home (RR = 0.95, 95% CI = 0.91–0.99, P = 0.03, adjusted percent: localized, 77% vs. dispersed, 73%, E = 1.3); higher rate of discharge to SNFs (RR = 1.5, 95% CI = 1.1–2.0, P = 0.01, adjusted percent: localized 11% vs. dispersed 16%, E = 2.3); and no difference in 90-day hospital readmissions (RR = 1.0, 95% CI = 0.8–1.1, P = 0.6, adjusted percent: localized 33% vs. dispersed 31%, E = 1.8) (Table 2).
Table 2.
Associations of geographic dispersion with patient outcomes
| Outcomes | Unadjusted |
Adjusted* |
|||
|---|---|---|---|---|---|
| Estimate (95% CI) | P Value | Estimate (95% CI) | P Value | % | |
| Primary outcome—subdistribution hazard ratio | |||||
| Rate of discharge† | 0.6 (0.6–0.7) | <0.001 | 0.8 (0.7–0.9) | 0.001 | — |
| Secondary outcomes—risk ratios | |||||
| In-hospital mortality | 3.5 (2.3–5.3) | <0.001 | 1.2 (0.9–1.6) | 0.3 | Localized: 8 |
| Dispersed: 10 | |||||
| Discharge to home | 0.9 (0.8–0.9) | <0.001 | 0.95 (0.91–0.99) | 0.03 | Localized: 77 |
| Dispersed: 73 | |||||
| Discharge to skilled nursing facilities | 1.7 (1.3–2.2) | <0.001 | 1.5 (1.1–2.0) | 0.01 | Localized: 11 |
| Dispersed: 16 | |||||
| 90-d hospital readmissions | 1.0 (0.8–1.1) | 0.7 | 1.0 (0.8–1.1) | 0.6 | Localized: 33 |
| Dispersed: 31 | |||||
Definition of abbreviation: CI = confidence interval.
Models were adjusted for: age, gender, race, ethnicity, insurance type, Elixhauser comorbidity scores, the Centers for Medicare and Medicaid Services four-level severity risk adjustment (based on admitting diagnosis, demographics, and comorbidities), admission diagnosis category (from International Classification of Diseases-9 and -10 codes), daily mean number of pulmonary service patients by hospital during the hospitalization, code status (full vs. any limitation on life-sustaining therapies), hospital admission source, intensive care unit as the ward admission source, season, and the patient’s hospital.
Estimated using a Fine and Gray competing risks model to account for the competing risk of death.
Discussion
This is the first study to evaluate the association of geographic dispersion with adverse outcomes among ward-based pulmonary service patients. We demonstrated that geographic dispersion is independently associated with decreased rates of discharge (suggesting longer adjusted LOS) and discharge home, and increased rate of discharge to SNFs. Several mechanisms may account for these findings: geographically dispersed patients may have decreased access to respiratory therapists and pulmonary therapies; case managers on dispersed wards may not be aware of home services required for pulmonary patients; and primary teams may be less available for direct bedside care.
This study has important limitations. First, as a single–health system study, the results may not be broadly generalizable. Furthermore, many hospitals do not have subspecialty services. Having these services could create trade-offs between expertise and flexibility, which have unknown effects on hospital bed flow. Second, although we performed analyses adjusted for patient, ward, and hospital factors, there may be residual confounding. Specifically, dispersion may have been considered appropriate for patients in transition to comfort-focused care, leading to potential confounding by indication. Finally, the study may have been underpowered to detect differences in in-hospital mortality.
In summary, geographic dispersion among hospitalized pulmonary service patients is associated with decreased rates of discharge and discharge to home, and increased rate of discharge to SNFs. Future work is needed to confirm these findings in other settings and to understand the mechanisms underlying these differences.
Supplementary Material
Footnotes
Supported by U.S. National Institutes of Health (NIH)/National Heart, Lung, and Blood Institute (NHLBI) grants T32 HL007891, F32 HL139107-01, and K23 HL146894-01, and Agency for Healthcare Research and Quality (AHRQ) K12 HS026372-01 (R.K.), NIH/NHLBI K99 HL141678 (M.O.H.), and Dr. Greysen was supported by NIH/NIA K23 AG045338 (S.R.G.).
Author disclosures are available with the text of this letter at www.atsjournals.org.
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