Dataset Information

Comparative 4-year risk and type of hospital admission among homeless and housed emergency department attendees: longitudinal study of hospital records in England 2013-2018.

ABSTRACT:

Objectives

People experiencing homelessness are frequent users of secondary care. Currently, there is no study of potentially preventable admissions for homeless patients in England. We aim to estimate the number of potentially preventable hospital admissions for homeless patients and compare to housed patients with similar characteristics.

Design

Retrospective matched cohort study.

Setting

Hospitals in England.

Participants

16 161 homeless patients and 74 780 housed patients aged 16-75 years who attended an emergency department (ED) in England in 2013/2014, matched on the basis of age, sex, ED attended and primary diagnosis.

Primary and secondary outcome measures

Annual counts of admissions, emergency admissions, ambulatory care-sensitive (ACS) emergency admissions, acute ACS emergency admissions and chronic ACS emergency admissions over the following 4 years (2014/2015-2017/2018). We additionally compare the prevalence of specific ACS conditions for homeless and housed patients.

Results

Mean admissions per 1000 patients per year were 470 for homeless patients and 230 for housed patients. Adjusted for confounders, annual admissions were 1.79 times higher (incident rate ratio (IRR)=1.79; 95% CI 1.69 to 1.90), emergency admissions 2.08 times higher (IRR=2.08; 95% CI 1.95 to 2.21) and ACS admissions 1.65 times higher (IRR=1.65; 95% CI 1.51 to 1.80), compared with housed patients. The effect was greater for acute (IRR=1.78; 95% CI 1.64 to 1.93) than chronic (IRR=1.45; 95% CI 1.27 to 1.66) ACS conditions. ACS conditions that were relatively more common for homeless patients were cellulitis, convulsions/epilepsy and chronic angina.

Conclusions

Homeless patients use hospital services at higher rates than housed patients, particularly emergency admissions. ACS admissions of homeless patients are higher which suggests some admissions may be potentially preventable with improved access to primary care. However, these admissions comprise a small share of total admissions.

SUBMITTER: Moss C

PROVIDER: S-EPMC8314693 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundIndividuals under age 25 years are estimated to comprise one third of the homeless population nationally. Understanding the reasons for utilization of hospitals by homeless youth is important for optimizing disposition planning.ObjectivesObjectives of the present study were to: (1) report prevalence of emergency department (ED) and inpatient admissions among homeless and unstably housed youth; (2) describe demographic characteristics of those youth who seek hospital care; (3) describe their patterns of injury, illness, psychiatric, and substance use conditions; and (4) identify demographic and diagnostic predictors of ED visit or hospital readmission.MethodsRetrospective cohort study of 15-25-year-olds (N=402) who were admitted to the ED or inpatient floors of 2 urban teaching hospitals in King County, WA between July 1, 2009 and June 30, 2012 and whose address was "homeless" or "none" or a homeless shelter or service agency (ie, homeless or unstably housed), during any recorded encounter between July 1, 2009 and June 30, 2012.ResultsA total of 1151 ED visits and 227 inpatient admissions were documented. Fifty percent of patients had an ED visit or hospital readmission within 1 year, with 43.1% receiving care within 30 days of discharge. Cox regression showed that female individuals with an injury diagnosis (hazard ratio=1.74, 95% confidence interval=1.06, 2.85) and male individuals with an acute medical condition (hazard ratio=1.59, 95% confidence interval=1.09, 2.32) at index visit were more likely to have an ED visit or hospital readmission during the following year, as were patients who provided a private address at their index visit.ConclusionsHomeless young people who seek hospital care demonstrate a high rate of ED visits and hospital readmissions, with unique predictors of utilization associated with sex and housing status. Additional research is necessary to determine how best to transition these young people from hospital-based to community-based care.

Project description:ObjectiveTo predict hospital admission at the time of ED triage using patient history in addition to information collected at triage.MethodsThis retrospective study included all adult ED visits between March 2014 and July 2017 from one academic and two community emergency rooms that resulted in either admission or discharge. A total of 972 variables were extracted per patient visit. Samples were randomly partitioned into training (80%), validation (10%), and test (10%) sets. We trained a series of nine binary classifiers using logistic regression (LR), gradient boosting (XGBoost), and deep neural networks (DNN) on three dataset types: one using only triage information, one using only patient history, and one using the full set of variables. Next, we tested the potential benefit of additional training samples by training models on increasing fractions of our data. Lastly, variables of importance were identified using information gain as a metric to create a low-dimensional model.ResultsA total of 560,486 patient visits were included in the study, with an overall admission risk of 29.7%. Models trained on triage information yielded a test AUC of 0.87 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.88) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on patient history yielded an AUC of 0.86 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.87) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91-0.91), 0.92 for XGBoost (95% CI 0.92-0.93) and 0.92 for DNN (95% CI 0.92-0.92). All algorithms reached maximum performance at 50% of the training set or less. A low-dimensional XGBoost model built on ESI level, outpatient medication counts, demographics, and hospital usage statistics yielded an AUC of 0.91 (95% CI 0.91-0.91).ConclusionMachine learning can robustly predict hospital admission using triage information and patient history. The addition of historical information improves predictive performance significantly compared to using triage information alone, highlighting the need to incorporate these variables into prediction models.

Project description:BackgroundAs the U.S. homeless population grows, so has the challenge of providing effective care to homeless individuals. Understanding hospitals that achieve better outcomes after hospital discharge for homeless patients has important implications for making our health system more sustainable and equitable.ObjectiveTo determine whether homeless patients experience higher rates of readmissions and emergency department (ED) visits after hospital discharge than non-homeless patients, and whether the homeless patients exhibit lower rates of readmissions and ED visits after hospital discharge when they were admitted to hospitals experienced with the treatment of the homeless patients ("homeless-serving" hospitals-defined as hospitals in the top decile of the proportion of homeless patients).DesignA population-based longitudinal study, using the data including all hospital admissions and ED visits in FL, MA, MD, and NY in 2014.ParticipantsParticipants were 3,527,383 patients (median age [IQR]: 63 [49-77] years; 1,876,466 [53%] women; 134,755 [4%] homeless patients) discharged from 474 hospitals.Main measuresRisk-adjusted rates of 30-day all-cause readmissions and ED visits after hospital discharge.Key resultsAfter adjusting for potential confounders, homeless patients had higher rates of readmissions (adjusted rate, 27.3% vs. 17.5%; adjusted odds ratio [aOR], 1.93; 95% CI, 1.69-2.21; p < 0.001) and ED visits after hospital discharge (37.1% vs. 23.6%; aOR, 1.98; 95% CI, 1.74-2.25; p < 0.001) compared with non-homeless patients. Homeless patients treated at homeless-serving hospitals exhibited lower rates of readmissions (23.9% vs. 33.4%; p < 0.001) and ED visits (31.4% vs. 45.4%; p < 0.001) after hospital discharge than homeless patients treated at non-homeless-serving hospitals.ConclusionsHomeless patients were more likely to be readmitted or return to ED within 30 days after hospital discharge, especially when they were treated at hospitals that treat a small proportion of homeless patients. These findings suggest that homeless patients may receive better discharge planning and care coordination when treated at hospitals experienced with caring for homeless people.

Project description:BackgroundHead injury in England is common. Evidence suggests that socio-economic factors may cause variation in incidence, and this variation may affect planning for services to meet the needs of those who have sustained a head injury.MethodsSocio-economic data were obtained from the UK Office for National Statistics and merged with Hospital Episodes Statistics obtained from the Department of Health. All patients admitted for head injury with ICD-10 codes S00.0-S09.9 during 2001-2 and 2002-3 were included and collated at the level of the extant Health Authorities (HA) for 2002, and Primary Care Trust (PCT) for 2003. Incidence was determined, and cluster analysis and multiple regression analysis were used to look at patterns and associations.Results112,718 patients were admitted during 2001-2 giving a hospitalised incidence rate for England of 229 per 100,000. This rate varied across the English HA's ranging from 91-419 per 100,000. The rate remained unchanged for 2002-3 with a similar magnitude of variation across PCT's. Three clusters of HA's were identified from the 2001-2 data; those typical of London, those of the Shire counties, and those of Other Urban authorities. Socio-economic factors were found to account for a high proportion of the variance in incidence for 2001-2. The same pattern emerged for 2002-3 at the PCT level. The use of public transport for travel to work is associated with a decreased incidence and lifestyle indicators, such as the numbers of young unemployed, increase the incidence.ConclusionHead injury incidence in England varies by a factor of 4.6 across HA's and PCT's. Planning head injury related services at the local level thus needs to be based on local incidence figures rather than regional or national estimates. Socio-economic factors are shown to be associated with admission, including travel to work patterns and lifestyle indicators, which suggests that incidence is amenable to policy initiatives at the macro level as well as preventive programmes targeted at key groups.