Medical and Social Factors Contributing to Length of Stay in the Hospital

Maggie Gavin, MSW Principal Investigator: Myra Glajchen, DSW

Learning Objectives• Understand the connection between medical and social factors • Understand the impact of medical and social factors on length of stay

• Explore the role of the social worker in preventing adverse health outcomes associated with long length of stay

Introduction and Overview• The passing of Affordable Care Act of 2010 incentivized

health care systems to increase quality of care by reducing readmissions and emphasizing care transitions

• Patients with chronic illnesses experience complex interactions between the health care system and the social environment

• Although length of stay contributes to the rate of readmissions, little research has been done to explore the contribution of social and environmental factors to length of stay

(Centers for Medicare and Medicaid, 2014; Arbaje et al., 2008; Lagoe et al., 2011; Lewis & Edwards, 2015; Fabbre et al., 2011

Andersen Model of Healthcare Utilization

(Andersen, 1995)

Factors Influencing Length of StayPREDISPOSING CHARACTERISTICS

NEED

ENABLING RESOURCES LENGTH OF STAY

Protective Factors: Continuity of care in the community/seamless care transitions Family caregivers Access and resources to long-term care facilities

Medical Risk Factors: Disease-related complications Higher number of Comorbidities Multiple transitions between hospital units

Social Risk Factors: Social deprivation - lacking higher education, lacking employment, having low income, being unmarried, living alone, lacking social support, and lacking quality housing Elderly Socially isolated Homeless

(Preyde & Brassard, 2011; Lewis & Edwards, 2015; Lagoe et al., 2011)

Length of Stay Impacts Patient Health

• Longer length of stay is associated with: •  increased cost of care •  adverse health outcomes - declines in physical and cognitive

function, increased risk of falling or acquiring an infection in the hospital

• Reducing length of stay produces benefits such as: •  reduced cost, •  improved quality of life •  reduced readmissions

(Arbaje, et al., 2008; Weiss & Elixhauser, 2014).

Role of the Hospital Social Worker

(Reisch, 2012)

Role of the Hospital Social Worker• Traditional responsibilities

•  Perform biopsychosocial assessment •  Comprehensive discharge planning •  Interdisciplinary collaboration for care coordination

• Emerging responsibilities •  Develop and implement evidence-based interventions •  Take part in preventive care •  Research cost outcomes in addition to psychosocial outcomes

(Reisch, 2012)

Aims of the Study• Explore associations among medical factors, socio-

environmental factors, and length of stay in the hospital in a cohort of elderly adults with advanced chronic illness

•  What are the medical and socio-environmental characteristics of a sample of chronically ill older adults who were referred to social work for an enhanced in-hospital assessment prior to discharge and an in-person home assessment post-discharge?

•  What are the relationships among medical conditions, socio-environmental factors, and hospital length of stay?

•  Do the socio-environmental and medical characteristics vary in patients with short (≤7 days in the hospital) compared to long (>7 days in the hospital) length of stay?

•  What medical and socio-environmental characteristics are independently associated with length of stay?

Theoretical Model

Hypotheses

• A combination of medical conditions and socio-environmental factors contribute to how many days a patient spends in the hospital

• Risk factors contributing to a longer length of stay will include: a higher number of comorbid medical conditions, older age, female gender, non-White race/ethnicity, lower education (high school education or less), non-married status, and living alone

• Receiving prior in-home services will be associated with shorter length of stay

Methodology• Research Design: Secondary data analysis of cohort

study conducted 2013-2016 • Setting: Urban, acute-care hospital in New York City • Sampling: Non-probability, convenience sample

chosen from inpatient medical records and referrals from department nurses and social workers

• Eligibility: 60+ years old, cognitively capable of consent, no current alcohol or substance abuse, English-speaking, living in Manhattan

• Sample: 122 chronically ill older adults

Table 1: Socio-Environmental and Medical Characteristics (N=122) N (%)Age (mean ± standard deviation) (range) 77.78 ± 9.86 (61-100)Gender

Male 49 (40)Female 73 (60)

Race/Ethnicity White, Non-Hispanic 81 (66)Black, Hispanic, Other 41 (34)

Educational Level Less than High School 22 (18)High School Graduate & More 53 (43)College Graduate & More 47 (39)

Marital Status*** Married/Partnered 19 (16)Single 62 (51)Widowed, Divorced, Separated 41 (34)

Living Arrangement Alone 85 (70)Not Alone 37 (30)

Prior In-Home Services Received Yes 54 (47)No 61 (53)

Admitting Diagnosis Cancer 14 (12)Cardiac Disease 35 (29)Infectious Disease 18 (15)Other Diseases 55 (45)

Number of Comorbidities (mean ± standard deviation) (range) 5.16 ± 1.92 (2-12)Median 5Q1 4Q3 7IQR 3

Length of Stay (days) (mean ± standard deviation) (range) 7.5 ± 8.17 (1-50)Median 5Q1 3Q3 8IQR 5

Table 2: Length of Stay by Socio-Environmental and Medical Characteristics* (N=122)

Length of Stay (mean ± standard deviation)

Age

60-69 10.81 ± 11.81

70-79 7.45 ± 7.86

80-89 6.08 ± 5.61

90+ 5.56 ± 5.19

Gender Male 7.63 ± 8.56Female 7.41 ± 7.95

Race/Ethnicity White, Non-Hispanic 7.19 ± 6.86Black, Hispanic, Other** 8.12 ± 10.35

Educational Level Less than High School 8.73 ± 8.61High School Graduate & More 8.15 ± 10.21College Graduate & More 6.19 ± 4.55

Marital Status Married/Partner 8.47 ± 10.99Single 6.79 ± 6.03Widowed, Divorced, Separated 8.12 ± 9.51

Living Arrangement Alone 7.14 ± 7.73Not Alone 8.32 ± 9.15

Prior In-Home Services Received Yes 8.28 ± 8.67No 7.20 ± 8.11

Admitting Diagnosis Cancer 4.86 ± 3.53Cardiac Disease 8.06 ± 10.24Infectious Disease 5.67 ± 3.38Other Diseases*** 8.42 ± 8.54

Number of Comorbidities

2 4.45 ± 1.86

3 7.31 ± 5.38

4 6.81 ± 5.83

5 8.04 ± 10.51

6 7.68 ± 6.14

7 6.92 ± 7.61

8+ 12.88 ± 15.77

Analysis Strategy• What are the relationships among medical conditions,

socio-environmental factors, and hospital length of stay? •  Independent samples t-tests assessed for group differences in

length of stay for the variables of gender, race/ethnicity, living arrangement, and prior in-home services

•  ANOVA tests assessed for differences in length of stay among groups for the variables of admitting diagnosis, marital status, educational level, and categorical age

•  Pearson correlations were conducted to test the strength of the relationship between length of stay and number of comorbid conditions, and length of stay and age

Analysis Strategy

• Do the socio-environmental and medical characteristics vary in patients with short (≤7 days in the hospital) compared to long (>7 days in the hospital) length of stay?

•  Length of stay was dichotomized around the mean •  ≤ 7 days = short length of stay •  > 7 days = long length of stay

•  Chi-square analysis conducted for the independent variables of admitting diagnosis, gender, race/ethnicity, education level, marital status, living arrangement, and prior in-home services.

•  Independent samples t-test performed to evaluate mean differences in age and the number of comorbidities between short and long length of stay groups.

Analysis Strategy• What medical and socio-environmental characteristics are

independently associated with length of stay? •  Length of stay was log transformed to remove positive skew •  Categorical variables were dummy coded for analysis •  Socio-environmental variables entered into the regression were:

female sex, Black/Hispanic/other racial/ethnic status, lives alone, received prior in-home services, not married, less than a high school education, and high school graduate or more

•  Male sex, White/non-Hispanic racial/ethnic status, does not live alone, did not receive in-home services, married, and college graduate or more were control variables

•  Admitting diagnosis was dummy coded to include cancer, infectious disease, and cardiac disease as compared to other diseases in the analysis

•  Age and comorbid conditions were entered as continuous variables

Results•  The multivariate model showed that age and medical conditions

(diagnosis, comorbidities) accounted for greater variance in LOS than would have been expected due to chance

•  There was a negative correlation between age and length of stay, ie, older age was associated with shorter LOS

•  This finding was unexpected and contrary to established literature •  Negative correlation persisted even when controlling for socio-

environmental and medical characteristics that had been controlled for in previous studies (Moore, et al., 2015)

• Results support the hypothesis that both social and medical factors are associated with LOS and should be addressed in tandem to determine safe discharge from the hospital

Discussion & Conclusions• Although not reaching statistical significance, length of

stay had a dose response relationship to educational level

•  As educational level increased, length of stay decreased •  This finding is consistent with other studies

• Admitting diagnosis approached significance between those patients with short (≤7 days) and long (>7 days) length of stay

•  On average, those diagnosed with cancer had the most short lengths of stay. Those is the category “other” diseases had the highest rate of long length of stay

•  However, small number of patients with cancer and infectious disease with long length of stay, so the reliability of these results could not be verified

Discussion & Conclusions• Homogeneous study sample poses limitations to variability.

•  Greater variability in terms of race/ethnicity, gender, and living situation could reveal high- and low-risk pools

• Characteristics such as income, occupation, health habits (physical activity, smoking or excessive drinking), and functional ability could influence length of stay in the hospital

• Presence of caregiver - not analyzed – could act as patient advocate for or against early discharge

• Diagnosis-related group (DRG) drives reimbursement for the hospital and could have an impact on length of stay

Implications for Practice• Assessing the biopsychosocial drivers of length of stay

•  Ecological perspective allows social workers to think broadly and make connections across disciplines

• Performing enhanced hospital assessments to identify medical and social risk factors

•  Improve care transition between hospital and community •  Communicate with community social workers, doctors, nurses to

break the cycle of rehospitalization

• Collecting data on patients receiving social work intervention

•  Value of social work includes cost savings for medical institutions and improved continuity of care and quality of life for patients receiving interventions

References•  Arbaje, A. I., Wolff, J. L., Yu, Q., Powe, N. R., Anderson, G. F., & Boult, C. (2008). Post-discharge environmental

and socioeconomic factors and the likelihood of early hospital readmission among community-dwelling Medicare beneficiaries. The Gerontologist, (4), 495.

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•  Fabbre, V., Buffington, A., Altfeld, S., Shier, G., & Golden, R. (2011). Social work and transitions of care: Observations from an intervention for older adults. Journal of Gerontological Social Work, 54(6), 615-626. doi:10.1080/01634372.2011.589100

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•  Lewis, R., & Edwards, N. (2015). Improving length of stay: What can hospitals do? (Research report). http://www.nuffieldtrust.org.uk/sites/files/nuffield/publication/improving-length-of-stay-for-web.pdf

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•  Preyde, M., & Brassard, K. (2011). Evidence-based risk factors for adverse health outcomes in older patients after discharge home and assessment tools: A systematic review. Journal of Evidence-Based Social Work, 8(5), 445-468.

•  Reisch, M. (2012). The challenges of health care reform for hospital social work in the United States. Social Work in Health Care, 51(10), 873-893. doi:10.1080/00981389.2012.721492

•  Weiss, A., & Elixhauser, A. (2014) Overview of hospital stays in the United States, 2012. HCUP Statistical Brief #180. Agency for Healthcare Research and Quality. Rockville, MD. http://www.hcup-us.arhq.gov/reports/statbriefs/sb180-Hospitalizations-United-States-2012.pdf.

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