Affiliations
Section of General Internal Medicine, Boston University School of Medicine/ Boston Medical Center, Boston, Massachusetts
Given name(s)
Suzanne E.
Family name
Mitchell
Degrees
MD, MSc

Depressive Symptoms and Readmission

Article Type
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Sun, 05/21/2017 - 14:15
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Dose‐response relationship between depressive symptoms and hospital readmission

Unplanned hospital readmission within 30 days is an important marker of the quality of care provided in the hospital and in the immediate posthospital setting.[1] In the United States, 1 in 5 Medicare patients is readmitted within 30 days, and related Medicare costs are estimated to be $17 billion annually.[2] Public policy is attempting to drive down healthcare costs in the United States via the Affordable Care Act by reducing payments to hospitals that have high 30‐day readmission rates.[3]

The prevalence of depression is 6.7% of adults,[4] and depressive symptomatology has been linked to hospital readmission.[5, 6] Depressive symptoms are associated with poor health outcomes and increased utilization. Patients with cardiac disease and depressive symptoms have worse outcomes.[7] Mild symptoms are associated with increased primary care and mental health care visits.[8] Both mild and moderate‐to‐severe depressive symptomatology are associated with symptom burden, physical limitation, quality of life, and overall health.[9] Importantly, many hospitalized patients, who do not have a diagnosis of depression, display depressive symptomatology.[10] A gap in knowledge exists as to the utility of stratifying depressive symptomatology between mild and moderate to severe in determining risk for hospital readmission.

Although the causal pathway to worse outcomes in patients with a diagnosis of depression has been studied,[11] the reasons for poor outcomes of patients with depressive symptomatology have not been well described, particularly in hospitalized general medical patients. Nonadherence to physician recommendations, decreased cognitive function, and poor adherence to self‐care recommendations[12, 13, 14] may explain increased healthcare utilization. Physiological models hypothesize heightened levels of proinflammatory markers[15, 16] and vascular depression[17] may play a role. There remains a gap in knowledge as to the postdischarge utilization of hospitalized patients with depressive symptomatology.

We studied the rate of hospital readmission among hospitalized adult patients with mild and moderate‐to‐severe depressive symptoms as defined by the 9‐Item Patient Health Questionnaire (PHQ‐9) depression screening tool.[18]

METHODS

Setting, Data, and Participants

Data from 2 Project Re‐Engineered Discharge (RED) clinical trials were included in a secondary analysis.[19, 20, 21] Depressive symptom screening data were available for 1418 participants originally randomized to the control and experimental groups in each trial.

The Project RED trials were a set of 2‐armed randomized controlled trials studying hospital utilization following a standardized hospital‐based discharge process. Inclusion criteria included English‐speaking patients who were 18 years or older and admitted to the adult medical service at Boston Medical Center, a large urban safety‐net hospital with an ethnically diverse patient population. Patients were required to have a telephone and have plans to return home after discharge. Patients were excluded if admitted from a skilled nursing facility or other hospital, transferred to a different hospital service before enrollment, admitted for a planned hospitalization, on hospital precautions, on suicide watch, or were deaf or blind. A full description of the methods has been described.[19] The second RED trial differed in that a health information technology system was used to teach the discharge plan to subjects at the time of discharge rather than a nurse.[20, 21]

The institutional review board of Boston University approved all study activities.

Outcome Variables

The primary end point for this analysis was the hospital readmission rate, defined as the total number of readmissions per subject within 30 days of the index discharge. Data were collected by hospital electronic medical record (EMR) review and by contacting subjects by telephone 30 days after discharge. Research staff collected data and were blinded to study group assignment. We examined emergency department (ED) utilization and primary care physician (PCP) follow‐up visit attendance rates within 30 days of discharge. Any ED or ambulatory visit resulting in hospital admission within 30 days of the index discharge date was counted as 1 readmission. Readmission and ED utilization dates occurring at Boston Medical Center were obtained from its EMR, whereas those at other hospitals were collected through subject report.

Primary Independent Variable

The primary independent variable was the PHQ‐9 screening questionnaire score, designed to identify patients with depressive symptoms. Data were collected at the time of enrollment during the index admission. Symptoms were categorized into 3 groups: no depression (PHQ‐9 score of 04, or less than a score of 2 on the first 2 questions of the PHQ‐9), mild depressive symptoms (PHQ‐9 score of 59), and moderate‐to‐severe symptoms (PHQ‐9 score of 1027).[18]

Statistical Analysis

Potential confounders were identified a priori from available literature on factors associated with hospital readmission. These included age,[22] race, gender,[23] marital status, health literacy (using the Rapid Estimate of Health Literacy in Adult Medicine [REALM] tool),[24] insurance type, employment status, income level,[25] Charlson Comorbidity Index,[26] homelessness within the past 3 months, hospital utilization within the 6 months before the index hospitalization,[27] educational attainment, length of hospital stay,[28] and study group assignment.

Demographic and other characteristics were compared by dichotomized healthcare reutilization outcomes using bivariate analysis to identify potential confounders of the relationship between depressive symptom severity and hospital readmission. [2] tests were used for categorical variables and t tests for continuous variables. Age (years) and length of stay (days) were used as continuous variables. Gender (male/female), frequent hospital admission within 6 months (01 vs 2 or more), homelessness, and presence of substance use disorder diagnosis (defined by the International Statistical Classification of Disease, 9th Revision [ICD‐9] diagnosis codes, which included 303.0 [alcohol dependence], 305.0 [alcohol abuse], 291.0 [alcohol‐induced mental disorders], 304.0 [drug dependence], 305.2305.7, 305.9 [drug abuse] and 292.0 [drug‐induced mental disorders]) were treated as dichotomous variables. Categorical variables were created for marital status (single, married, divorced/widowed), educational attainment (less than or incomplete high school, high school graduate, some college, college graduate), insurance type (Medicare, Medicaid, private insurance, or Free Care), income level (<$19,999 per year, $20,00039,000, $40,00074,999, >$75,000, or unknown/refused to answer), employment status (working full time, working part time, not working, or no answer), and health literacy level according to REALM score (044 [grade 6 or below], 4560 [grade 78], or 6166 [grade 9 or above]).

The readmission rate reflects the number of hospital readmission events per subject within 30 days of discharge. The PCP follow‐up rate reflects the number of subjects attending a posthospital PCP visit within 30 days of discharge. The incidence rate ratio (IRR) was calculated to compare the rates of readmission between those with mild, moderate to severe, and no depressive symptoms. Readmission data at 30 days are cumulative. Poisson models were used to test for significant differences between the predicted and observed number of events at 30 days. A stepwise selection process (with =0.2 as entry and exit criteria) was conducted to identify relevant confounders and to construct the final model for the association between depressive symptom screen severity and readmission. Using sensitivity analysis, we tested a model including substance abuse as a variable. This did not substantially change our final model, and therefore we did not include this variable in the final model.

A Kaplan‐Meier hazard curve was generated for time from the index discharge to the first hospital readmission within 30 days. The hazard of readmission was compared among the 3 depressive symptom screen categories using Cox proportional hazards. Two‐sided significance tests were used. P values <0.05 were considered to indicate statistical significance. All data were analyzed with SAS 9.3 (SAS Institute, Cary, NC).

Adjusted Poisson regression results identified individuals with readmission and/or ED utilization rates much higher than the sample mean. Data points with 5 or more hospital readmissions or 7 or more combined readmissions and ED utilizations were removed from analysis.

RESULTS

Of the total study population, 15.9% (225/1418) demonstrated mild depressive symptoms, and 23.7% (336/1418) demonstrated moderate‐to‐severe symptoms. Of those in our study, 38.9% (551/1418) self‐reported being told by a healthcare professional that they had depression. Of those self‐reporting depression, 27.9% (273/540) were currently taking antidepressant medication at the time of the index hospitalization. Table 1 shows baseline characteristics by dichotomized utilization outcome. Mean age, marital status, health insurance, employment status, mean length of stay, admissions in the previous 6 months, mean Charlson score, and substance abuse were significantly associated with hospital readmission. Of these characteristics, all but mean length of stay and previous history of substance abuse were significantly associated with ED utilization.

Baseline Demographics and Utilization Outcomes
 Hospital ReadmissionED Utilization
No, n=1,240Yes, n=193No, n=1,231Yes, n=202
  • NOTE: Abbreviations: ED, emergency department; GED, General Educational Development; PCP, primary care physician; PHQ‐9, 9‐Item Patient Health Questionnaire; SD, standard deviation.

  • A significant association with the outcome.

  • Free Care is a Massachusetts state program for uninsured patients.

  • Health literacy scores correspond to total score as determined by Rapid Estimate of Health Literacy in Adult Medicine tool. The 2 categories of lowest literacy were combined because of the distribution of scores. Score of 0 to 44 corresponds to 6th‐grade level or below, 45 to 60 corresponds to 7th‐ to 8th‐grade level, and 61 to 66 corresponds to 9th‐grade level or above.

  • PCP at enrollment refers to subject self‐identifying PCP at time of Project RED study enrollment.

  • Charlson Comorbity Index score reflects the cumulative increased likelihood of 1‐year mortality. Higher scores indicate more comorbidity. The minimum score is 0. There is no maximum score.

  • Defined by the presence of a substance use disorder defined by International Classification of Diseases, 9th Revision diagnosis code for the index hospitalization. Diagnostic codes included, 303 (alcohol dependence), 305.0 (alcohol abuse), 291 (alcohol induced mental disorders), 304 (drug dependence), 305.2305.7, 305.9 (drug abuse), and 292 (drug‐induced mental disorders).

Male, n (%)602 (48.6)105 (54.4)606 (49.3)101 (50.0)
Mean age, y (SD)49.14 (14.21)a52.24 (14.69)a50.06 (14.57)a46.45 (12.19)a
Race, n (%)    
White non‐Hispanic332 (26.8)73 (37.8)357 (29.0)48 (23.8)
Black non‐Hispanic666 (53.7)89 (46.1)646 (52.5)109 (54.0)
Hispanic135 (10.9)19 (9.8)124 (10.1)30 (14.9)
Other or mixed race59 (4.8)7 (3.6)56 (4.6)10 (5.0)
Unknown48 (3.9)5 (2.6)48 (3.9)5 (2.5)
Marital status, n (%)    
Single593 (47.8)a74 (38.3)a552 (44.8)a115 (56.9)a
Married286 (23.1)a42 (21.8)a296 (24.1)a32 (15.8)a
Divorced/widowed346 (27.9)a74 (38.3)a369 (20.0)a51 (25.3)a
Unknown15 (1.2)a3 (1.6)a14 (1.1)a4 (2.0)a
Annual personal income, n (%)    
<$19,999511 (41.2)88 (45.6)509 (41.4)90 (44.6)
$20,000$39,999184 (14.8)22 (11.4)175 (14.2)31 (15.4)
$40,000$74,999107 (8.6)14 (7.3)111 (9.0)10 (5.0)
>$75,00041 (3.3)8 (4.2)44 (3.6)5 (2.5)
Unknown/refused397 (32.0)61 (31.6)392 (31.8)66 (32.7)
Health insurance, n (%)    
Private321 (25.9)a34 (17.6)a316 (25.7)a39 (19.3)a
Medicaid510 (41.1)a90 (46.6)a485 (39.4)a115 (56.9)a
Medicare138 (11.1)a43 (22.3)a170 (13.8)a11 (5.5)a
Free Care207 (16.7)a14 (7.3)a190 (15.4)a31 (15.4)a
Other/unknown64 (5.2)a12 (6.2)a70 (5.7)a6 (3.0)a
Education level, n (%)    
Incomplete high school290 (23.4)45 (23.3)288 (23.4)47 (23.3)
High school graduate/GED492 (39.7)87 (45.1)489 (39.7)90 (44.6)
Some college257 (20.7)34 (17.6)255 (20.7)36 (17.8)
College degree183 (14.8)25 (13.0)183 (14.9)25 (12.4)
Unknown18 (1.5)2 (1.0)16 (1.3)4 (2.0)
Employment status, n (%)    
Full time322 (26.4)a31 (16.6)a316 (26.2)a37 (18.7)a
Part time136 (11.2)a11 (5.9)a124 (10.3)a23 (11.6)a
Retired172 (14.1)a37 (19.8)a196 (16.2)a13 (6.6)a
Disabled278 (22.8)a72 (38.5)a287 (23.8)a63 (31.8)a
Unemployed286 (23.5)a31 (16.6)a258 (21.4)a59 (29.8)a
Student24 (2.0)a5 (2.7)a26 (2.2)a3 (1.5)a
Homeless in past 6 months, n (%)143 (11.6)24 (12.5)126 (10.3)41 (20.4)
Health literacy, n (%)b    
6th‐grade level230 (19.2)41 (22.4)228 (19.2)43 (22.4)
7th8th‐grade level342 (28.5)60 (32.8)342 (28.7)60 (31.3)
9th‐grade level627 (52.3)82 (44.8)620 (52.1)89 (46.4)
Mean length of stay, d (SD)2.69 (2.60)a3.57 (3.50)a2.81 (2.80)2.84 (2.18)
PCP at enrollment, n (%)c1,005 (81.1)166 (86.0)1,005 (81.7)166 (82.2)
2 Admissions in past 6 months, n (%)300 (24.2)a81 (42.0)a292 (23.7)a89 (44.1)a
Mean Charlson score (SD)d2.19 (2.53)a2.85 (2.78)a2.34 (2.60)a1.92 (2.18)a
Substance abuse, n (%)e138 (12.0)a36 (19.7)a151 (13.1)23 (12.6)

Table 2 shows the unadjusted 30 day hospital readmission, ED utilization, and PCP follow‐up rates. Participants with mild symptoms had higher readmission rates than those without symptoms (0.20 vs 0.13). In other words, 20 readmissions occurred per 100 subjects with mild symptoms, compared with 13 readmissions per 100 subjects without symptoms (P<0.001). The readmission rate was 0.21 for subjects with moderate‐to‐severe depression. The rate of ED utilization for subjects with mild symptoms was 0.18. This was significantly different from ED utilization rates of those with no depression and those with moderate‐to‐severe symptoms, which were 0.16 and 0.28, respectively (P<0.001). The postdischarge follow‐up rates were different for those without depression compared to those with mild and moderate‐to‐severe symptoms (58.7, 49.5, and 51.1, respectively), but this did not reach statistical significance (P=0.06).

Hospital Readmission, Emergency Department, and Primary Care Physician Utilization Rates 30 Days After Discharge by Depressive Symptom Screen Status
 Depressive Symptom Severity Based on PHQ‐9 Score, N=1,418P Value
No Depression, n=857Mild Depressive Symptoms, n=225Moderate‐to‐Severe Depressive Symptoms, n=336
  • NOTE: Abbreviations: ED, emergency department; PCP, primary care physician; PHQ‐9, 9‐Item Patient Health Questionnaire.

Hospital readmission, n (rate per 100)108 (12.6)44 (19.6)71 (21.1)<0.001
ED utilization, n (rate per 100)133 (15.5)41 (18.2)94 (28.0)<0.001
PCP follow‐up, n (rate per 100)420 (58.7)103 (49.5)157 (51.1)0.06

Poisson analyses were conducted to control for potential confounding in the relationship between symptom severity and readmission or ED utilization (Table 3). Compared to subjects with no depression, the association between mild symptoms and readmission remained significant (adjusted IRR: 1.49; 95% confidence interval [CI]: 1.11‐2.00) after controlling for relevant confounders. For those with moderate‐to‐severe symptoms, the adjusted IRR was 1.96 (95% CI: 1.51‐2.49). When compared to those without depression, the adjusted IRR for ED reutilization was not found to be significant for those with mild symptoms (1.30; 95% CI: 0.96‐1.76) and significant for those participants with moderate‐to‐severe symptoms (1.48; 95% CI: 1.16‐1.89).

Adjusted Hospital Readmission, Emergency Department Utilization Rates, and IRR 30 Days After Discharge by Depressive Symptom Screen Status
 Depressive Symptom Severity Based on PHQ‐9 Score, N=1418P Value
No Depression, n=857Mild Depressive Symptoms, n=225Moderate‐to‐Severe Depressive Symptoms, n=336
  • NOTE: Abbreviations: CI, confidence interval; ED, emergency department; IRR, incidence rate ratio; PHQ‐9, 9‐item Patient Health Questionnaire.

  • Adjusted for intervention group, Rapid Estimate of Health Literacy in Adult Medicine tool score, Charlson Comorbidity Index, gender, homelessness, employment, insurance, frequent utilizer, age (years), length of stay (days).

Hospital readmission, n (rate per 100)96 (11.9)36 (17.1)67 (21.1)<0.001
Hospital readmission IRR (95% CI)Ref1.49 (1.11‐2.00)1.96 (1.51‐2.49) 
ED utilization, n (rate per 100)124 (15.2)40 (19.0)85 (26.7)0.007
ED utilization IRR (95% CI)Ref1.30 (0.96‐1.76)1.48 (1.16‐1.89) 

Figure 1 depicts the hazard curve generated for the time to first hospital readmission, stratified by depressive symptom severity. A readmission within 30 days following an index discharge date occurred in 10% of participants without depression, 14% of those with mild symptoms, and 19% of those with moderate‐to‐severe symptoms (P=0.03).

Figure 1
Hazard of hospital readmission in the 30 days following hospital discharge among subjects with mild, moderate‐to‐severe, and no depressive symptoms.

DISCUSSION

Our study shows hospitalized medical patients at an urban academic hospital with a positive screen for depressive symptoms are significantly more likely to be readmitted within 30 days of discharge as compared to those who do not screen positive. The significant association of depressive symptoms and readmission remains even after stratifying by severity and controlling for relevant confounders. Further, there appears to be a dose‐response relationship between depressive symptom severity and readmission. This graded effect makes the distinction between mild and moderate‐to‐severe depressive symptoms a better instrument at predicting rehospitalization than a diagnostic code for depression. Few studies have analyzed the readmission of general medical patients stratified by depressive symptomatology, and even fewer have addressed the presence of mild depressive symptomatology as it relates to readmission. A diagnosis of mild depression is associated with similar though less severe outcomes as compared to major depression, including negative effects on quality of life, functional disability, health status, and mortality.[29] Patients with heart failure and mild depressive symptoms have higher rates of readmission at 3 months and 1 year as compared with those without depressive symptoms, but these findings were not found to be significant.[30] Mild depressive symptoms may contribute to readmission, accrued medical cost, and burden of disease.

We extend previous research[5, 31, 32] by showing that, compared to those without and those with mild symptoms, the readmission risk is even greater for those who screen positive for moderate‐to‐severe symptoms. The mechanism linking depressive symptoms and readmission is not well understood. Behavioral mechanisms such as physical symptom amplification or anxiety about symptoms link depressive symptoms to healthcare utilization after discharge.[33] Depressive symptoms among patients with diabetes, asthma, hypertension, or human immunodeficiency virus (HIV) impairs medication adherence and self‐care behavior.[14, 34, 35, 36] Depressed patients might have reduced social support leading to increased stress, worsened symptoms, and prolonged recovery.[37] These mechanisms may prompt patients to present to hospitals for reevaluation. The direct physiologic consequences of depressive symptoms may be similar to that of the diagnosis of depression. Patients with cardiovascular disease and depression have poor outcomes, which may be related to decreased heart rate variability, hypercoagulability, high burden of inflammatory markers, and severity of left ventricular dysfunction.[38, 39] Among patients with HIV/acquired immunodeficiency syndrome and coronary artery disease, depression is linked to increased proinflammatory marker levels and less favorable outcomes, which may signal a more severe form of the disease or an impaired response to treatment.[15, 16]

Our data have several implications. Though disease burden may play a role in the presence of depressive symptomatology in hospitalized patients, screen‐positive patients still experienced more readmission events as compared to those without depressive symptoms after controlling for relevant confounders. Further, there appears to be a dose‐dependent relationship between depressive symptom severity and rate of readmission. Use of a categorical ICD‐9 code often implies that the diagnosis of depression has been confirmed. Rather than using administrative ICD‐9 codes to account for readmission risk, hospitals may consider screening patients for depressive symptoms during hospitalizations to both identify and risk‐stratify patients at high risk for readmission. Procedures should be implemented to address barriers to safe transitions in care in the screen‐positive population. The relationship between symptom severity and readmission rate may aid in the decision to devote resources to those at highest risk of readmission. Lastly, though research on the treatment of depressive symptoms in medical inpatients has been inconclusive in determining whether this approach is better than usual care or structured pharmacotherapies,[40] further study is needed to determine whether treatment of mild and moderate‐to‐severe depressive symptoms during an acute medical hospitalization will decrease readmission.

Strengths of the current study include the large dataset, the broad range of covariates available for analyses, and the inclusive nature of the sample, which was not restricted to factors such as age or medical condition.

Several limitations should be noted. We did not conduct a psychiatric evaluation to evaluate screen‐positive patients who met diagnostic criteria for minor or major depressive disorder, nor did we reconfirm the presence of symptoms at the time of or following hospital discharge. Our data, then, may not reflect patients' depressive symptomatology prior to the index hospitalization or at the time of discharge. Although such data might further refine the use of depressive symptomatology in identifying patients at high risk for readmission, our findings demonstrate that simply screening positive for depressive symptomatology at time of admission is associated with increased risk of readmission. We do not know the direction of the reported associations. If depressive symptoms are the consequence of higher disease burden, treatment of the underlying disease may be the most important intervention. Although this is possible, our model does include variables (eg, length of stay, Charlson Comorbidity Index), which are likely to adjust for disease severity, pointing to the likelihood that depressive symptoms truly predict hospital readmission independent of disease severity. Data on utilization outside Boston Medical Center (about 9% of outcomes) were determined by patient self‐report and not confirmed by document review. Our results may not be generalizable to populations other than those served by an urban safety‐net hospital or other populations excluded from analysis (eg, nonEnglish‐speaking patients, patients from nursing homes). Finally, social factors such as social support may residually confound the relationship between depressive symptom severity and readmission.

Our finding linking both mild and moderate‐to‐severe depressive symptoms to increased readmission when compared to those without depressive symptoms is significant for future policy. If future studies demonstrate that the initiation of treatment of patients who screen positive for depressive symptoms during an acute hospitalization leads to reduced readmission, policymakers should increase support for mental health screening and programming as an integral portion of general medical patient management.

In conclusion, screening positive for mild or moderate‐to‐severe depressive symptoms is associated with an increased rate of early hospital readmission as compared to those without depressive symptoms, even after controlling for relevant confounders. The rate and hazard of hospital readmission increase with symptom severity. This finding has important implications for future research for hospital screening programming and interventions for patients who screen positive for depressive symptoms.

Disclosures: This research was funded in part by grants from the Agency for Healthcare Research and Quality (NCT00252057) and the National Heart, Lung, and Blood Institute (NCT00217867). Dr. Cancino has been paid for consulting work for PracticeUpdate, a subsidiary of Elsevier. Dr. Culpepper has been paid for participation in advisory boards by AstraZeneca, Eli Lilly and Co., Boehringer Ingelheim Pharmaceuticals Inc., Forest Labs, Janssen Pharmaceuticals, Inc., Jazz Pharmaceuticals plc, H. Lundbeck A/S, Merck & Co., Pfizer Inc., Reckitt Benckiser Pharmaceuticals Inc., Sunovion Pharmaceuticals Inc., and Takeda Pharmaceuticals Inc. He has received payment for educational presentations regarding hospital readmission without mention of any pharmaceutical or other products from Merck. Dr. Mitchell has received honoraria from Merck for lectures on health behavior counseling. Trial registration: NCT00252057, NCT00217867.

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  36. Schoenthaler A, Ogedegbe G, Allegrante JP. Self‐efficacy mediates the relationship between depressive symptoms and medication adherence among hypertensive African Americans. Health Educ Behav. 2009;36(1):127137.
  37. Tse WS, Bond AJ. The impact of depression on social skills. J Nerv Ment Dis. 2004;192(4):260268.
  38. Melle JP, Jonge P, Ormel J, et al. Relationship between left ventricular dysfunction and depression following myocardial infarction: data from the MIND‐IT. Eur Heart J. 2005;26(24):26502656.
  39. Serebruany VL, Glassman AH, Malinin AI, et al. Platelet/endothelial biomarkers in depressed patients treated with the selective serotonin reuptake inhibitor sertraline after acute coronary events: the Sertraline AntiDepressant Heart Attack Randomized Trial (SADHART) Platelet Substudy. Circulation. 2003;108(8):939944.
  40. Cuijpers P, Clignet F, Meijel B, Straten A, Li J, Andersson G. Psychological treatment of depression in inpatients: a systematic review and meta‐analysis. Clin Psychol Rev. 2011;31(3):353360.
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Unplanned hospital readmission within 30 days is an important marker of the quality of care provided in the hospital and in the immediate posthospital setting.[1] In the United States, 1 in 5 Medicare patients is readmitted within 30 days, and related Medicare costs are estimated to be $17 billion annually.[2] Public policy is attempting to drive down healthcare costs in the United States via the Affordable Care Act by reducing payments to hospitals that have high 30‐day readmission rates.[3]

The prevalence of depression is 6.7% of adults,[4] and depressive symptomatology has been linked to hospital readmission.[5, 6] Depressive symptoms are associated with poor health outcomes and increased utilization. Patients with cardiac disease and depressive symptoms have worse outcomes.[7] Mild symptoms are associated with increased primary care and mental health care visits.[8] Both mild and moderate‐to‐severe depressive symptomatology are associated with symptom burden, physical limitation, quality of life, and overall health.[9] Importantly, many hospitalized patients, who do not have a diagnosis of depression, display depressive symptomatology.[10] A gap in knowledge exists as to the utility of stratifying depressive symptomatology between mild and moderate to severe in determining risk for hospital readmission.

Although the causal pathway to worse outcomes in patients with a diagnosis of depression has been studied,[11] the reasons for poor outcomes of patients with depressive symptomatology have not been well described, particularly in hospitalized general medical patients. Nonadherence to physician recommendations, decreased cognitive function, and poor adherence to self‐care recommendations[12, 13, 14] may explain increased healthcare utilization. Physiological models hypothesize heightened levels of proinflammatory markers[15, 16] and vascular depression[17] may play a role. There remains a gap in knowledge as to the postdischarge utilization of hospitalized patients with depressive symptomatology.

We studied the rate of hospital readmission among hospitalized adult patients with mild and moderate‐to‐severe depressive symptoms as defined by the 9‐Item Patient Health Questionnaire (PHQ‐9) depression screening tool.[18]

METHODS

Setting, Data, and Participants

Data from 2 Project Re‐Engineered Discharge (RED) clinical trials were included in a secondary analysis.[19, 20, 21] Depressive symptom screening data were available for 1418 participants originally randomized to the control and experimental groups in each trial.

The Project RED trials were a set of 2‐armed randomized controlled trials studying hospital utilization following a standardized hospital‐based discharge process. Inclusion criteria included English‐speaking patients who were 18 years or older and admitted to the adult medical service at Boston Medical Center, a large urban safety‐net hospital with an ethnically diverse patient population. Patients were required to have a telephone and have plans to return home after discharge. Patients were excluded if admitted from a skilled nursing facility or other hospital, transferred to a different hospital service before enrollment, admitted for a planned hospitalization, on hospital precautions, on suicide watch, or were deaf or blind. A full description of the methods has been described.[19] The second RED trial differed in that a health information technology system was used to teach the discharge plan to subjects at the time of discharge rather than a nurse.[20, 21]

The institutional review board of Boston University approved all study activities.

Outcome Variables

The primary end point for this analysis was the hospital readmission rate, defined as the total number of readmissions per subject within 30 days of the index discharge. Data were collected by hospital electronic medical record (EMR) review and by contacting subjects by telephone 30 days after discharge. Research staff collected data and were blinded to study group assignment. We examined emergency department (ED) utilization and primary care physician (PCP) follow‐up visit attendance rates within 30 days of discharge. Any ED or ambulatory visit resulting in hospital admission within 30 days of the index discharge date was counted as 1 readmission. Readmission and ED utilization dates occurring at Boston Medical Center were obtained from its EMR, whereas those at other hospitals were collected through subject report.

Primary Independent Variable

The primary independent variable was the PHQ‐9 screening questionnaire score, designed to identify patients with depressive symptoms. Data were collected at the time of enrollment during the index admission. Symptoms were categorized into 3 groups: no depression (PHQ‐9 score of 04, or less than a score of 2 on the first 2 questions of the PHQ‐9), mild depressive symptoms (PHQ‐9 score of 59), and moderate‐to‐severe symptoms (PHQ‐9 score of 1027).[18]

Statistical Analysis

Potential confounders were identified a priori from available literature on factors associated with hospital readmission. These included age,[22] race, gender,[23] marital status, health literacy (using the Rapid Estimate of Health Literacy in Adult Medicine [REALM] tool),[24] insurance type, employment status, income level,[25] Charlson Comorbidity Index,[26] homelessness within the past 3 months, hospital utilization within the 6 months before the index hospitalization,[27] educational attainment, length of hospital stay,[28] and study group assignment.

Demographic and other characteristics were compared by dichotomized healthcare reutilization outcomes using bivariate analysis to identify potential confounders of the relationship between depressive symptom severity and hospital readmission. [2] tests were used for categorical variables and t tests for continuous variables. Age (years) and length of stay (days) were used as continuous variables. Gender (male/female), frequent hospital admission within 6 months (01 vs 2 or more), homelessness, and presence of substance use disorder diagnosis (defined by the International Statistical Classification of Disease, 9th Revision [ICD‐9] diagnosis codes, which included 303.0 [alcohol dependence], 305.0 [alcohol abuse], 291.0 [alcohol‐induced mental disorders], 304.0 [drug dependence], 305.2305.7, 305.9 [drug abuse] and 292.0 [drug‐induced mental disorders]) were treated as dichotomous variables. Categorical variables were created for marital status (single, married, divorced/widowed), educational attainment (less than or incomplete high school, high school graduate, some college, college graduate), insurance type (Medicare, Medicaid, private insurance, or Free Care), income level (<$19,999 per year, $20,00039,000, $40,00074,999, >$75,000, or unknown/refused to answer), employment status (working full time, working part time, not working, or no answer), and health literacy level according to REALM score (044 [grade 6 or below], 4560 [grade 78], or 6166 [grade 9 or above]).

The readmission rate reflects the number of hospital readmission events per subject within 30 days of discharge. The PCP follow‐up rate reflects the number of subjects attending a posthospital PCP visit within 30 days of discharge. The incidence rate ratio (IRR) was calculated to compare the rates of readmission between those with mild, moderate to severe, and no depressive symptoms. Readmission data at 30 days are cumulative. Poisson models were used to test for significant differences between the predicted and observed number of events at 30 days. A stepwise selection process (with =0.2 as entry and exit criteria) was conducted to identify relevant confounders and to construct the final model for the association between depressive symptom screen severity and readmission. Using sensitivity analysis, we tested a model including substance abuse as a variable. This did not substantially change our final model, and therefore we did not include this variable in the final model.

A Kaplan‐Meier hazard curve was generated for time from the index discharge to the first hospital readmission within 30 days. The hazard of readmission was compared among the 3 depressive symptom screen categories using Cox proportional hazards. Two‐sided significance tests were used. P values <0.05 were considered to indicate statistical significance. All data were analyzed with SAS 9.3 (SAS Institute, Cary, NC).

Adjusted Poisson regression results identified individuals with readmission and/or ED utilization rates much higher than the sample mean. Data points with 5 or more hospital readmissions or 7 or more combined readmissions and ED utilizations were removed from analysis.

RESULTS

Of the total study population, 15.9% (225/1418) demonstrated mild depressive symptoms, and 23.7% (336/1418) demonstrated moderate‐to‐severe symptoms. Of those in our study, 38.9% (551/1418) self‐reported being told by a healthcare professional that they had depression. Of those self‐reporting depression, 27.9% (273/540) were currently taking antidepressant medication at the time of the index hospitalization. Table 1 shows baseline characteristics by dichotomized utilization outcome. Mean age, marital status, health insurance, employment status, mean length of stay, admissions in the previous 6 months, mean Charlson score, and substance abuse were significantly associated with hospital readmission. Of these characteristics, all but mean length of stay and previous history of substance abuse were significantly associated with ED utilization.

Baseline Demographics and Utilization Outcomes
 Hospital ReadmissionED Utilization
No, n=1,240Yes, n=193No, n=1,231Yes, n=202
  • NOTE: Abbreviations: ED, emergency department; GED, General Educational Development; PCP, primary care physician; PHQ‐9, 9‐Item Patient Health Questionnaire; SD, standard deviation.

  • A significant association with the outcome.

  • Free Care is a Massachusetts state program for uninsured patients.

  • Health literacy scores correspond to total score as determined by Rapid Estimate of Health Literacy in Adult Medicine tool. The 2 categories of lowest literacy were combined because of the distribution of scores. Score of 0 to 44 corresponds to 6th‐grade level or below, 45 to 60 corresponds to 7th‐ to 8th‐grade level, and 61 to 66 corresponds to 9th‐grade level or above.

  • PCP at enrollment refers to subject self‐identifying PCP at time of Project RED study enrollment.

  • Charlson Comorbity Index score reflects the cumulative increased likelihood of 1‐year mortality. Higher scores indicate more comorbidity. The minimum score is 0. There is no maximum score.

  • Defined by the presence of a substance use disorder defined by International Classification of Diseases, 9th Revision diagnosis code for the index hospitalization. Diagnostic codes included, 303 (alcohol dependence), 305.0 (alcohol abuse), 291 (alcohol induced mental disorders), 304 (drug dependence), 305.2305.7, 305.9 (drug abuse), and 292 (drug‐induced mental disorders).

Male, n (%)602 (48.6)105 (54.4)606 (49.3)101 (50.0)
Mean age, y (SD)49.14 (14.21)a52.24 (14.69)a50.06 (14.57)a46.45 (12.19)a
Race, n (%)    
White non‐Hispanic332 (26.8)73 (37.8)357 (29.0)48 (23.8)
Black non‐Hispanic666 (53.7)89 (46.1)646 (52.5)109 (54.0)
Hispanic135 (10.9)19 (9.8)124 (10.1)30 (14.9)
Other or mixed race59 (4.8)7 (3.6)56 (4.6)10 (5.0)
Unknown48 (3.9)5 (2.6)48 (3.9)5 (2.5)
Marital status, n (%)    
Single593 (47.8)a74 (38.3)a552 (44.8)a115 (56.9)a
Married286 (23.1)a42 (21.8)a296 (24.1)a32 (15.8)a
Divorced/widowed346 (27.9)a74 (38.3)a369 (20.0)a51 (25.3)a
Unknown15 (1.2)a3 (1.6)a14 (1.1)a4 (2.0)a
Annual personal income, n (%)    
<$19,999511 (41.2)88 (45.6)509 (41.4)90 (44.6)
$20,000$39,999184 (14.8)22 (11.4)175 (14.2)31 (15.4)
$40,000$74,999107 (8.6)14 (7.3)111 (9.0)10 (5.0)
>$75,00041 (3.3)8 (4.2)44 (3.6)5 (2.5)
Unknown/refused397 (32.0)61 (31.6)392 (31.8)66 (32.7)
Health insurance, n (%)    
Private321 (25.9)a34 (17.6)a316 (25.7)a39 (19.3)a
Medicaid510 (41.1)a90 (46.6)a485 (39.4)a115 (56.9)a
Medicare138 (11.1)a43 (22.3)a170 (13.8)a11 (5.5)a
Free Care207 (16.7)a14 (7.3)a190 (15.4)a31 (15.4)a
Other/unknown64 (5.2)a12 (6.2)a70 (5.7)a6 (3.0)a
Education level, n (%)    
Incomplete high school290 (23.4)45 (23.3)288 (23.4)47 (23.3)
High school graduate/GED492 (39.7)87 (45.1)489 (39.7)90 (44.6)
Some college257 (20.7)34 (17.6)255 (20.7)36 (17.8)
College degree183 (14.8)25 (13.0)183 (14.9)25 (12.4)
Unknown18 (1.5)2 (1.0)16 (1.3)4 (2.0)
Employment status, n (%)    
Full time322 (26.4)a31 (16.6)a316 (26.2)a37 (18.7)a
Part time136 (11.2)a11 (5.9)a124 (10.3)a23 (11.6)a
Retired172 (14.1)a37 (19.8)a196 (16.2)a13 (6.6)a
Disabled278 (22.8)a72 (38.5)a287 (23.8)a63 (31.8)a
Unemployed286 (23.5)a31 (16.6)a258 (21.4)a59 (29.8)a
Student24 (2.0)a5 (2.7)a26 (2.2)a3 (1.5)a
Homeless in past 6 months, n (%)143 (11.6)24 (12.5)126 (10.3)41 (20.4)
Health literacy, n (%)b    
6th‐grade level230 (19.2)41 (22.4)228 (19.2)43 (22.4)
7th8th‐grade level342 (28.5)60 (32.8)342 (28.7)60 (31.3)
9th‐grade level627 (52.3)82 (44.8)620 (52.1)89 (46.4)
Mean length of stay, d (SD)2.69 (2.60)a3.57 (3.50)a2.81 (2.80)2.84 (2.18)
PCP at enrollment, n (%)c1,005 (81.1)166 (86.0)1,005 (81.7)166 (82.2)
2 Admissions in past 6 months, n (%)300 (24.2)a81 (42.0)a292 (23.7)a89 (44.1)a
Mean Charlson score (SD)d2.19 (2.53)a2.85 (2.78)a2.34 (2.60)a1.92 (2.18)a
Substance abuse, n (%)e138 (12.0)a36 (19.7)a151 (13.1)23 (12.6)

Table 2 shows the unadjusted 30 day hospital readmission, ED utilization, and PCP follow‐up rates. Participants with mild symptoms had higher readmission rates than those without symptoms (0.20 vs 0.13). In other words, 20 readmissions occurred per 100 subjects with mild symptoms, compared with 13 readmissions per 100 subjects without symptoms (P<0.001). The readmission rate was 0.21 for subjects with moderate‐to‐severe depression. The rate of ED utilization for subjects with mild symptoms was 0.18. This was significantly different from ED utilization rates of those with no depression and those with moderate‐to‐severe symptoms, which were 0.16 and 0.28, respectively (P<0.001). The postdischarge follow‐up rates were different for those without depression compared to those with mild and moderate‐to‐severe symptoms (58.7, 49.5, and 51.1, respectively), but this did not reach statistical significance (P=0.06).

Hospital Readmission, Emergency Department, and Primary Care Physician Utilization Rates 30 Days After Discharge by Depressive Symptom Screen Status
 Depressive Symptom Severity Based on PHQ‐9 Score, N=1,418P Value
No Depression, n=857Mild Depressive Symptoms, n=225Moderate‐to‐Severe Depressive Symptoms, n=336
  • NOTE: Abbreviations: ED, emergency department; PCP, primary care physician; PHQ‐9, 9‐Item Patient Health Questionnaire.

Hospital readmission, n (rate per 100)108 (12.6)44 (19.6)71 (21.1)<0.001
ED utilization, n (rate per 100)133 (15.5)41 (18.2)94 (28.0)<0.001
PCP follow‐up, n (rate per 100)420 (58.7)103 (49.5)157 (51.1)0.06

Poisson analyses were conducted to control for potential confounding in the relationship between symptom severity and readmission or ED utilization (Table 3). Compared to subjects with no depression, the association between mild symptoms and readmission remained significant (adjusted IRR: 1.49; 95% confidence interval [CI]: 1.11‐2.00) after controlling for relevant confounders. For those with moderate‐to‐severe symptoms, the adjusted IRR was 1.96 (95% CI: 1.51‐2.49). When compared to those without depression, the adjusted IRR for ED reutilization was not found to be significant for those with mild symptoms (1.30; 95% CI: 0.96‐1.76) and significant for those participants with moderate‐to‐severe symptoms (1.48; 95% CI: 1.16‐1.89).

Adjusted Hospital Readmission, Emergency Department Utilization Rates, and IRR 30 Days After Discharge by Depressive Symptom Screen Status
 Depressive Symptom Severity Based on PHQ‐9 Score, N=1418P Value
No Depression, n=857Mild Depressive Symptoms, n=225Moderate‐to‐Severe Depressive Symptoms, n=336
  • NOTE: Abbreviations: CI, confidence interval; ED, emergency department; IRR, incidence rate ratio; PHQ‐9, 9‐item Patient Health Questionnaire.

  • Adjusted for intervention group, Rapid Estimate of Health Literacy in Adult Medicine tool score, Charlson Comorbidity Index, gender, homelessness, employment, insurance, frequent utilizer, age (years), length of stay (days).

Hospital readmission, n (rate per 100)96 (11.9)36 (17.1)67 (21.1)<0.001
Hospital readmission IRR (95% CI)Ref1.49 (1.11‐2.00)1.96 (1.51‐2.49) 
ED utilization, n (rate per 100)124 (15.2)40 (19.0)85 (26.7)0.007
ED utilization IRR (95% CI)Ref1.30 (0.96‐1.76)1.48 (1.16‐1.89) 

Figure 1 depicts the hazard curve generated for the time to first hospital readmission, stratified by depressive symptom severity. A readmission within 30 days following an index discharge date occurred in 10% of participants without depression, 14% of those with mild symptoms, and 19% of those with moderate‐to‐severe symptoms (P=0.03).

Figure 1
Hazard of hospital readmission in the 30 days following hospital discharge among subjects with mild, moderate‐to‐severe, and no depressive symptoms.

DISCUSSION

Our study shows hospitalized medical patients at an urban academic hospital with a positive screen for depressive symptoms are significantly more likely to be readmitted within 30 days of discharge as compared to those who do not screen positive. The significant association of depressive symptoms and readmission remains even after stratifying by severity and controlling for relevant confounders. Further, there appears to be a dose‐response relationship between depressive symptom severity and readmission. This graded effect makes the distinction between mild and moderate‐to‐severe depressive symptoms a better instrument at predicting rehospitalization than a diagnostic code for depression. Few studies have analyzed the readmission of general medical patients stratified by depressive symptomatology, and even fewer have addressed the presence of mild depressive symptomatology as it relates to readmission. A diagnosis of mild depression is associated with similar though less severe outcomes as compared to major depression, including negative effects on quality of life, functional disability, health status, and mortality.[29] Patients with heart failure and mild depressive symptoms have higher rates of readmission at 3 months and 1 year as compared with those without depressive symptoms, but these findings were not found to be significant.[30] Mild depressive symptoms may contribute to readmission, accrued medical cost, and burden of disease.

We extend previous research[5, 31, 32] by showing that, compared to those without and those with mild symptoms, the readmission risk is even greater for those who screen positive for moderate‐to‐severe symptoms. The mechanism linking depressive symptoms and readmission is not well understood. Behavioral mechanisms such as physical symptom amplification or anxiety about symptoms link depressive symptoms to healthcare utilization after discharge.[33] Depressive symptoms among patients with diabetes, asthma, hypertension, or human immunodeficiency virus (HIV) impairs medication adherence and self‐care behavior.[14, 34, 35, 36] Depressed patients might have reduced social support leading to increased stress, worsened symptoms, and prolonged recovery.[37] These mechanisms may prompt patients to present to hospitals for reevaluation. The direct physiologic consequences of depressive symptoms may be similar to that of the diagnosis of depression. Patients with cardiovascular disease and depression have poor outcomes, which may be related to decreased heart rate variability, hypercoagulability, high burden of inflammatory markers, and severity of left ventricular dysfunction.[38, 39] Among patients with HIV/acquired immunodeficiency syndrome and coronary artery disease, depression is linked to increased proinflammatory marker levels and less favorable outcomes, which may signal a more severe form of the disease or an impaired response to treatment.[15, 16]

Our data have several implications. Though disease burden may play a role in the presence of depressive symptomatology in hospitalized patients, screen‐positive patients still experienced more readmission events as compared to those without depressive symptoms after controlling for relevant confounders. Further, there appears to be a dose‐dependent relationship between depressive symptom severity and rate of readmission. Use of a categorical ICD‐9 code often implies that the diagnosis of depression has been confirmed. Rather than using administrative ICD‐9 codes to account for readmission risk, hospitals may consider screening patients for depressive symptoms during hospitalizations to both identify and risk‐stratify patients at high risk for readmission. Procedures should be implemented to address barriers to safe transitions in care in the screen‐positive population. The relationship between symptom severity and readmission rate may aid in the decision to devote resources to those at highest risk of readmission. Lastly, though research on the treatment of depressive symptoms in medical inpatients has been inconclusive in determining whether this approach is better than usual care or structured pharmacotherapies,[40] further study is needed to determine whether treatment of mild and moderate‐to‐severe depressive symptoms during an acute medical hospitalization will decrease readmission.

Strengths of the current study include the large dataset, the broad range of covariates available for analyses, and the inclusive nature of the sample, which was not restricted to factors such as age or medical condition.

Several limitations should be noted. We did not conduct a psychiatric evaluation to evaluate screen‐positive patients who met diagnostic criteria for minor or major depressive disorder, nor did we reconfirm the presence of symptoms at the time of or following hospital discharge. Our data, then, may not reflect patients' depressive symptomatology prior to the index hospitalization or at the time of discharge. Although such data might further refine the use of depressive symptomatology in identifying patients at high risk for readmission, our findings demonstrate that simply screening positive for depressive symptomatology at time of admission is associated with increased risk of readmission. We do not know the direction of the reported associations. If depressive symptoms are the consequence of higher disease burden, treatment of the underlying disease may be the most important intervention. Although this is possible, our model does include variables (eg, length of stay, Charlson Comorbidity Index), which are likely to adjust for disease severity, pointing to the likelihood that depressive symptoms truly predict hospital readmission independent of disease severity. Data on utilization outside Boston Medical Center (about 9% of outcomes) were determined by patient self‐report and not confirmed by document review. Our results may not be generalizable to populations other than those served by an urban safety‐net hospital or other populations excluded from analysis (eg, nonEnglish‐speaking patients, patients from nursing homes). Finally, social factors such as social support may residually confound the relationship between depressive symptom severity and readmission.

Our finding linking both mild and moderate‐to‐severe depressive symptoms to increased readmission when compared to those without depressive symptoms is significant for future policy. If future studies demonstrate that the initiation of treatment of patients who screen positive for depressive symptoms during an acute hospitalization leads to reduced readmission, policymakers should increase support for mental health screening and programming as an integral portion of general medical patient management.

In conclusion, screening positive for mild or moderate‐to‐severe depressive symptoms is associated with an increased rate of early hospital readmission as compared to those without depressive symptoms, even after controlling for relevant confounders. The rate and hazard of hospital readmission increase with symptom severity. This finding has important implications for future research for hospital screening programming and interventions for patients who screen positive for depressive symptoms.

Disclosures: This research was funded in part by grants from the Agency for Healthcare Research and Quality (NCT00252057) and the National Heart, Lung, and Blood Institute (NCT00217867). Dr. Cancino has been paid for consulting work for PracticeUpdate, a subsidiary of Elsevier. Dr. Culpepper has been paid for participation in advisory boards by AstraZeneca, Eli Lilly and Co., Boehringer Ingelheim Pharmaceuticals Inc., Forest Labs, Janssen Pharmaceuticals, Inc., Jazz Pharmaceuticals plc, H. Lundbeck A/S, Merck & Co., Pfizer Inc., Reckitt Benckiser Pharmaceuticals Inc., Sunovion Pharmaceuticals Inc., and Takeda Pharmaceuticals Inc. He has received payment for educational presentations regarding hospital readmission without mention of any pharmaceutical or other products from Merck. Dr. Mitchell has received honoraria from Merck for lectures on health behavior counseling. Trial registration: NCT00252057, NCT00217867.

Unplanned hospital readmission within 30 days is an important marker of the quality of care provided in the hospital and in the immediate posthospital setting.[1] In the United States, 1 in 5 Medicare patients is readmitted within 30 days, and related Medicare costs are estimated to be $17 billion annually.[2] Public policy is attempting to drive down healthcare costs in the United States via the Affordable Care Act by reducing payments to hospitals that have high 30‐day readmission rates.[3]

The prevalence of depression is 6.7% of adults,[4] and depressive symptomatology has been linked to hospital readmission.[5, 6] Depressive symptoms are associated with poor health outcomes and increased utilization. Patients with cardiac disease and depressive symptoms have worse outcomes.[7] Mild symptoms are associated with increased primary care and mental health care visits.[8] Both mild and moderate‐to‐severe depressive symptomatology are associated with symptom burden, physical limitation, quality of life, and overall health.[9] Importantly, many hospitalized patients, who do not have a diagnosis of depression, display depressive symptomatology.[10] A gap in knowledge exists as to the utility of stratifying depressive symptomatology between mild and moderate to severe in determining risk for hospital readmission.

Although the causal pathway to worse outcomes in patients with a diagnosis of depression has been studied,[11] the reasons for poor outcomes of patients with depressive symptomatology have not been well described, particularly in hospitalized general medical patients. Nonadherence to physician recommendations, decreased cognitive function, and poor adherence to self‐care recommendations[12, 13, 14] may explain increased healthcare utilization. Physiological models hypothesize heightened levels of proinflammatory markers[15, 16] and vascular depression[17] may play a role. There remains a gap in knowledge as to the postdischarge utilization of hospitalized patients with depressive symptomatology.

We studied the rate of hospital readmission among hospitalized adult patients with mild and moderate‐to‐severe depressive symptoms as defined by the 9‐Item Patient Health Questionnaire (PHQ‐9) depression screening tool.[18]

METHODS

Setting, Data, and Participants

Data from 2 Project Re‐Engineered Discharge (RED) clinical trials were included in a secondary analysis.[19, 20, 21] Depressive symptom screening data were available for 1418 participants originally randomized to the control and experimental groups in each trial.

The Project RED trials were a set of 2‐armed randomized controlled trials studying hospital utilization following a standardized hospital‐based discharge process. Inclusion criteria included English‐speaking patients who were 18 years or older and admitted to the adult medical service at Boston Medical Center, a large urban safety‐net hospital with an ethnically diverse patient population. Patients were required to have a telephone and have plans to return home after discharge. Patients were excluded if admitted from a skilled nursing facility or other hospital, transferred to a different hospital service before enrollment, admitted for a planned hospitalization, on hospital precautions, on suicide watch, or were deaf or blind. A full description of the methods has been described.[19] The second RED trial differed in that a health information technology system was used to teach the discharge plan to subjects at the time of discharge rather than a nurse.[20, 21]

The institutional review board of Boston University approved all study activities.

Outcome Variables

The primary end point for this analysis was the hospital readmission rate, defined as the total number of readmissions per subject within 30 days of the index discharge. Data were collected by hospital electronic medical record (EMR) review and by contacting subjects by telephone 30 days after discharge. Research staff collected data and were blinded to study group assignment. We examined emergency department (ED) utilization and primary care physician (PCP) follow‐up visit attendance rates within 30 days of discharge. Any ED or ambulatory visit resulting in hospital admission within 30 days of the index discharge date was counted as 1 readmission. Readmission and ED utilization dates occurring at Boston Medical Center were obtained from its EMR, whereas those at other hospitals were collected through subject report.

Primary Independent Variable

The primary independent variable was the PHQ‐9 screening questionnaire score, designed to identify patients with depressive symptoms. Data were collected at the time of enrollment during the index admission. Symptoms were categorized into 3 groups: no depression (PHQ‐9 score of 04, or less than a score of 2 on the first 2 questions of the PHQ‐9), mild depressive symptoms (PHQ‐9 score of 59), and moderate‐to‐severe symptoms (PHQ‐9 score of 1027).[18]

Statistical Analysis

Potential confounders were identified a priori from available literature on factors associated with hospital readmission. These included age,[22] race, gender,[23] marital status, health literacy (using the Rapid Estimate of Health Literacy in Adult Medicine [REALM] tool),[24] insurance type, employment status, income level,[25] Charlson Comorbidity Index,[26] homelessness within the past 3 months, hospital utilization within the 6 months before the index hospitalization,[27] educational attainment, length of hospital stay,[28] and study group assignment.

Demographic and other characteristics were compared by dichotomized healthcare reutilization outcomes using bivariate analysis to identify potential confounders of the relationship between depressive symptom severity and hospital readmission. [2] tests were used for categorical variables and t tests for continuous variables. Age (years) and length of stay (days) were used as continuous variables. Gender (male/female), frequent hospital admission within 6 months (01 vs 2 or more), homelessness, and presence of substance use disorder diagnosis (defined by the International Statistical Classification of Disease, 9th Revision [ICD‐9] diagnosis codes, which included 303.0 [alcohol dependence], 305.0 [alcohol abuse], 291.0 [alcohol‐induced mental disorders], 304.0 [drug dependence], 305.2305.7, 305.9 [drug abuse] and 292.0 [drug‐induced mental disorders]) were treated as dichotomous variables. Categorical variables were created for marital status (single, married, divorced/widowed), educational attainment (less than or incomplete high school, high school graduate, some college, college graduate), insurance type (Medicare, Medicaid, private insurance, or Free Care), income level (<$19,999 per year, $20,00039,000, $40,00074,999, >$75,000, or unknown/refused to answer), employment status (working full time, working part time, not working, or no answer), and health literacy level according to REALM score (044 [grade 6 or below], 4560 [grade 78], or 6166 [grade 9 or above]).

The readmission rate reflects the number of hospital readmission events per subject within 30 days of discharge. The PCP follow‐up rate reflects the number of subjects attending a posthospital PCP visit within 30 days of discharge. The incidence rate ratio (IRR) was calculated to compare the rates of readmission between those with mild, moderate to severe, and no depressive symptoms. Readmission data at 30 days are cumulative. Poisson models were used to test for significant differences between the predicted and observed number of events at 30 days. A stepwise selection process (with =0.2 as entry and exit criteria) was conducted to identify relevant confounders and to construct the final model for the association between depressive symptom screen severity and readmission. Using sensitivity analysis, we tested a model including substance abuse as a variable. This did not substantially change our final model, and therefore we did not include this variable in the final model.

A Kaplan‐Meier hazard curve was generated for time from the index discharge to the first hospital readmission within 30 days. The hazard of readmission was compared among the 3 depressive symptom screen categories using Cox proportional hazards. Two‐sided significance tests were used. P values <0.05 were considered to indicate statistical significance. All data were analyzed with SAS 9.3 (SAS Institute, Cary, NC).

Adjusted Poisson regression results identified individuals with readmission and/or ED utilization rates much higher than the sample mean. Data points with 5 or more hospital readmissions or 7 or more combined readmissions and ED utilizations were removed from analysis.

RESULTS

Of the total study population, 15.9% (225/1418) demonstrated mild depressive symptoms, and 23.7% (336/1418) demonstrated moderate‐to‐severe symptoms. Of those in our study, 38.9% (551/1418) self‐reported being told by a healthcare professional that they had depression. Of those self‐reporting depression, 27.9% (273/540) were currently taking antidepressant medication at the time of the index hospitalization. Table 1 shows baseline characteristics by dichotomized utilization outcome. Mean age, marital status, health insurance, employment status, mean length of stay, admissions in the previous 6 months, mean Charlson score, and substance abuse were significantly associated with hospital readmission. Of these characteristics, all but mean length of stay and previous history of substance abuse were significantly associated with ED utilization.

Baseline Demographics and Utilization Outcomes
 Hospital ReadmissionED Utilization
No, n=1,240Yes, n=193No, n=1,231Yes, n=202
  • NOTE: Abbreviations: ED, emergency department; GED, General Educational Development; PCP, primary care physician; PHQ‐9, 9‐Item Patient Health Questionnaire; SD, standard deviation.

  • A significant association with the outcome.

  • Free Care is a Massachusetts state program for uninsured patients.

  • Health literacy scores correspond to total score as determined by Rapid Estimate of Health Literacy in Adult Medicine tool. The 2 categories of lowest literacy were combined because of the distribution of scores. Score of 0 to 44 corresponds to 6th‐grade level or below, 45 to 60 corresponds to 7th‐ to 8th‐grade level, and 61 to 66 corresponds to 9th‐grade level or above.

  • PCP at enrollment refers to subject self‐identifying PCP at time of Project RED study enrollment.

  • Charlson Comorbity Index score reflects the cumulative increased likelihood of 1‐year mortality. Higher scores indicate more comorbidity. The minimum score is 0. There is no maximum score.

  • Defined by the presence of a substance use disorder defined by International Classification of Diseases, 9th Revision diagnosis code for the index hospitalization. Diagnostic codes included, 303 (alcohol dependence), 305.0 (alcohol abuse), 291 (alcohol induced mental disorders), 304 (drug dependence), 305.2305.7, 305.9 (drug abuse), and 292 (drug‐induced mental disorders).

Male, n (%)602 (48.6)105 (54.4)606 (49.3)101 (50.0)
Mean age, y (SD)49.14 (14.21)a52.24 (14.69)a50.06 (14.57)a46.45 (12.19)a
Race, n (%)    
White non‐Hispanic332 (26.8)73 (37.8)357 (29.0)48 (23.8)
Black non‐Hispanic666 (53.7)89 (46.1)646 (52.5)109 (54.0)
Hispanic135 (10.9)19 (9.8)124 (10.1)30 (14.9)
Other or mixed race59 (4.8)7 (3.6)56 (4.6)10 (5.0)
Unknown48 (3.9)5 (2.6)48 (3.9)5 (2.5)
Marital status, n (%)    
Single593 (47.8)a74 (38.3)a552 (44.8)a115 (56.9)a
Married286 (23.1)a42 (21.8)a296 (24.1)a32 (15.8)a
Divorced/widowed346 (27.9)a74 (38.3)a369 (20.0)a51 (25.3)a
Unknown15 (1.2)a3 (1.6)a14 (1.1)a4 (2.0)a
Annual personal income, n (%)    
<$19,999511 (41.2)88 (45.6)509 (41.4)90 (44.6)
$20,000$39,999184 (14.8)22 (11.4)175 (14.2)31 (15.4)
$40,000$74,999107 (8.6)14 (7.3)111 (9.0)10 (5.0)
>$75,00041 (3.3)8 (4.2)44 (3.6)5 (2.5)
Unknown/refused397 (32.0)61 (31.6)392 (31.8)66 (32.7)
Health insurance, n (%)    
Private321 (25.9)a34 (17.6)a316 (25.7)a39 (19.3)a
Medicaid510 (41.1)a90 (46.6)a485 (39.4)a115 (56.9)a
Medicare138 (11.1)a43 (22.3)a170 (13.8)a11 (5.5)a
Free Care207 (16.7)a14 (7.3)a190 (15.4)a31 (15.4)a
Other/unknown64 (5.2)a12 (6.2)a70 (5.7)a6 (3.0)a
Education level, n (%)    
Incomplete high school290 (23.4)45 (23.3)288 (23.4)47 (23.3)
High school graduate/GED492 (39.7)87 (45.1)489 (39.7)90 (44.6)
Some college257 (20.7)34 (17.6)255 (20.7)36 (17.8)
College degree183 (14.8)25 (13.0)183 (14.9)25 (12.4)
Unknown18 (1.5)2 (1.0)16 (1.3)4 (2.0)
Employment status, n (%)    
Full time322 (26.4)a31 (16.6)a316 (26.2)a37 (18.7)a
Part time136 (11.2)a11 (5.9)a124 (10.3)a23 (11.6)a
Retired172 (14.1)a37 (19.8)a196 (16.2)a13 (6.6)a
Disabled278 (22.8)a72 (38.5)a287 (23.8)a63 (31.8)a
Unemployed286 (23.5)a31 (16.6)a258 (21.4)a59 (29.8)a
Student24 (2.0)a5 (2.7)a26 (2.2)a3 (1.5)a
Homeless in past 6 months, n (%)143 (11.6)24 (12.5)126 (10.3)41 (20.4)
Health literacy, n (%)b    
6th‐grade level230 (19.2)41 (22.4)228 (19.2)43 (22.4)
7th8th‐grade level342 (28.5)60 (32.8)342 (28.7)60 (31.3)
9th‐grade level627 (52.3)82 (44.8)620 (52.1)89 (46.4)
Mean length of stay, d (SD)2.69 (2.60)a3.57 (3.50)a2.81 (2.80)2.84 (2.18)
PCP at enrollment, n (%)c1,005 (81.1)166 (86.0)1,005 (81.7)166 (82.2)
2 Admissions in past 6 months, n (%)300 (24.2)a81 (42.0)a292 (23.7)a89 (44.1)a
Mean Charlson score (SD)d2.19 (2.53)a2.85 (2.78)a2.34 (2.60)a1.92 (2.18)a
Substance abuse, n (%)e138 (12.0)a36 (19.7)a151 (13.1)23 (12.6)

Table 2 shows the unadjusted 30 day hospital readmission, ED utilization, and PCP follow‐up rates. Participants with mild symptoms had higher readmission rates than those without symptoms (0.20 vs 0.13). In other words, 20 readmissions occurred per 100 subjects with mild symptoms, compared with 13 readmissions per 100 subjects without symptoms (P<0.001). The readmission rate was 0.21 for subjects with moderate‐to‐severe depression. The rate of ED utilization for subjects with mild symptoms was 0.18. This was significantly different from ED utilization rates of those with no depression and those with moderate‐to‐severe symptoms, which were 0.16 and 0.28, respectively (P<0.001). The postdischarge follow‐up rates were different for those without depression compared to those with mild and moderate‐to‐severe symptoms (58.7, 49.5, and 51.1, respectively), but this did not reach statistical significance (P=0.06).

Hospital Readmission, Emergency Department, and Primary Care Physician Utilization Rates 30 Days After Discharge by Depressive Symptom Screen Status
 Depressive Symptom Severity Based on PHQ‐9 Score, N=1,418P Value
No Depression, n=857Mild Depressive Symptoms, n=225Moderate‐to‐Severe Depressive Symptoms, n=336
  • NOTE: Abbreviations: ED, emergency department; PCP, primary care physician; PHQ‐9, 9‐Item Patient Health Questionnaire.

Hospital readmission, n (rate per 100)108 (12.6)44 (19.6)71 (21.1)<0.001
ED utilization, n (rate per 100)133 (15.5)41 (18.2)94 (28.0)<0.001
PCP follow‐up, n (rate per 100)420 (58.7)103 (49.5)157 (51.1)0.06

Poisson analyses were conducted to control for potential confounding in the relationship between symptom severity and readmission or ED utilization (Table 3). Compared to subjects with no depression, the association between mild symptoms and readmission remained significant (adjusted IRR: 1.49; 95% confidence interval [CI]: 1.11‐2.00) after controlling for relevant confounders. For those with moderate‐to‐severe symptoms, the adjusted IRR was 1.96 (95% CI: 1.51‐2.49). When compared to those without depression, the adjusted IRR for ED reutilization was not found to be significant for those with mild symptoms (1.30; 95% CI: 0.96‐1.76) and significant for those participants with moderate‐to‐severe symptoms (1.48; 95% CI: 1.16‐1.89).

Adjusted Hospital Readmission, Emergency Department Utilization Rates, and IRR 30 Days After Discharge by Depressive Symptom Screen Status
 Depressive Symptom Severity Based on PHQ‐9 Score, N=1418P Value
No Depression, n=857Mild Depressive Symptoms, n=225Moderate‐to‐Severe Depressive Symptoms, n=336
  • NOTE: Abbreviations: CI, confidence interval; ED, emergency department; IRR, incidence rate ratio; PHQ‐9, 9‐item Patient Health Questionnaire.

  • Adjusted for intervention group, Rapid Estimate of Health Literacy in Adult Medicine tool score, Charlson Comorbidity Index, gender, homelessness, employment, insurance, frequent utilizer, age (years), length of stay (days).

Hospital readmission, n (rate per 100)96 (11.9)36 (17.1)67 (21.1)<0.001
Hospital readmission IRR (95% CI)Ref1.49 (1.11‐2.00)1.96 (1.51‐2.49) 
ED utilization, n (rate per 100)124 (15.2)40 (19.0)85 (26.7)0.007
ED utilization IRR (95% CI)Ref1.30 (0.96‐1.76)1.48 (1.16‐1.89) 

Figure 1 depicts the hazard curve generated for the time to first hospital readmission, stratified by depressive symptom severity. A readmission within 30 days following an index discharge date occurred in 10% of participants without depression, 14% of those with mild symptoms, and 19% of those with moderate‐to‐severe symptoms (P=0.03).

Figure 1
Hazard of hospital readmission in the 30 days following hospital discharge among subjects with mild, moderate‐to‐severe, and no depressive symptoms.

DISCUSSION

Our study shows hospitalized medical patients at an urban academic hospital with a positive screen for depressive symptoms are significantly more likely to be readmitted within 30 days of discharge as compared to those who do not screen positive. The significant association of depressive symptoms and readmission remains even after stratifying by severity and controlling for relevant confounders. Further, there appears to be a dose‐response relationship between depressive symptom severity and readmission. This graded effect makes the distinction between mild and moderate‐to‐severe depressive symptoms a better instrument at predicting rehospitalization than a diagnostic code for depression. Few studies have analyzed the readmission of general medical patients stratified by depressive symptomatology, and even fewer have addressed the presence of mild depressive symptomatology as it relates to readmission. A diagnosis of mild depression is associated with similar though less severe outcomes as compared to major depression, including negative effects on quality of life, functional disability, health status, and mortality.[29] Patients with heart failure and mild depressive symptoms have higher rates of readmission at 3 months and 1 year as compared with those without depressive symptoms, but these findings were not found to be significant.[30] Mild depressive symptoms may contribute to readmission, accrued medical cost, and burden of disease.

We extend previous research[5, 31, 32] by showing that, compared to those without and those with mild symptoms, the readmission risk is even greater for those who screen positive for moderate‐to‐severe symptoms. The mechanism linking depressive symptoms and readmission is not well understood. Behavioral mechanisms such as physical symptom amplification or anxiety about symptoms link depressive symptoms to healthcare utilization after discharge.[33] Depressive symptoms among patients with diabetes, asthma, hypertension, or human immunodeficiency virus (HIV) impairs medication adherence and self‐care behavior.[14, 34, 35, 36] Depressed patients might have reduced social support leading to increased stress, worsened symptoms, and prolonged recovery.[37] These mechanisms may prompt patients to present to hospitals for reevaluation. The direct physiologic consequences of depressive symptoms may be similar to that of the diagnosis of depression. Patients with cardiovascular disease and depression have poor outcomes, which may be related to decreased heart rate variability, hypercoagulability, high burden of inflammatory markers, and severity of left ventricular dysfunction.[38, 39] Among patients with HIV/acquired immunodeficiency syndrome and coronary artery disease, depression is linked to increased proinflammatory marker levels and less favorable outcomes, which may signal a more severe form of the disease or an impaired response to treatment.[15, 16]

Our data have several implications. Though disease burden may play a role in the presence of depressive symptomatology in hospitalized patients, screen‐positive patients still experienced more readmission events as compared to those without depressive symptoms after controlling for relevant confounders. Further, there appears to be a dose‐dependent relationship between depressive symptom severity and rate of readmission. Use of a categorical ICD‐9 code often implies that the diagnosis of depression has been confirmed. Rather than using administrative ICD‐9 codes to account for readmission risk, hospitals may consider screening patients for depressive symptoms during hospitalizations to both identify and risk‐stratify patients at high risk for readmission. Procedures should be implemented to address barriers to safe transitions in care in the screen‐positive population. The relationship between symptom severity and readmission rate may aid in the decision to devote resources to those at highest risk of readmission. Lastly, though research on the treatment of depressive symptoms in medical inpatients has been inconclusive in determining whether this approach is better than usual care or structured pharmacotherapies,[40] further study is needed to determine whether treatment of mild and moderate‐to‐severe depressive symptoms during an acute medical hospitalization will decrease readmission.

Strengths of the current study include the large dataset, the broad range of covariates available for analyses, and the inclusive nature of the sample, which was not restricted to factors such as age or medical condition.

Several limitations should be noted. We did not conduct a psychiatric evaluation to evaluate screen‐positive patients who met diagnostic criteria for minor or major depressive disorder, nor did we reconfirm the presence of symptoms at the time of or following hospital discharge. Our data, then, may not reflect patients' depressive symptomatology prior to the index hospitalization or at the time of discharge. Although such data might further refine the use of depressive symptomatology in identifying patients at high risk for readmission, our findings demonstrate that simply screening positive for depressive symptomatology at time of admission is associated with increased risk of readmission. We do not know the direction of the reported associations. If depressive symptoms are the consequence of higher disease burden, treatment of the underlying disease may be the most important intervention. Although this is possible, our model does include variables (eg, length of stay, Charlson Comorbidity Index), which are likely to adjust for disease severity, pointing to the likelihood that depressive symptoms truly predict hospital readmission independent of disease severity. Data on utilization outside Boston Medical Center (about 9% of outcomes) were determined by patient self‐report and not confirmed by document review. Our results may not be generalizable to populations other than those served by an urban safety‐net hospital or other populations excluded from analysis (eg, nonEnglish‐speaking patients, patients from nursing homes). Finally, social factors such as social support may residually confound the relationship between depressive symptom severity and readmission.

Our finding linking both mild and moderate‐to‐severe depressive symptoms to increased readmission when compared to those without depressive symptoms is significant for future policy. If future studies demonstrate that the initiation of treatment of patients who screen positive for depressive symptoms during an acute hospitalization leads to reduced readmission, policymakers should increase support for mental health screening and programming as an integral portion of general medical patient management.

In conclusion, screening positive for mild or moderate‐to‐severe depressive symptoms is associated with an increased rate of early hospital readmission as compared to those without depressive symptoms, even after controlling for relevant confounders. The rate and hazard of hospital readmission increase with symptom severity. This finding has important implications for future research for hospital screening programming and interventions for patients who screen positive for depressive symptoms.

Disclosures: This research was funded in part by grants from the Agency for Healthcare Research and Quality (NCT00252057) and the National Heart, Lung, and Blood Institute (NCT00217867). Dr. Cancino has been paid for consulting work for PracticeUpdate, a subsidiary of Elsevier. Dr. Culpepper has been paid for participation in advisory boards by AstraZeneca, Eli Lilly and Co., Boehringer Ingelheim Pharmaceuticals Inc., Forest Labs, Janssen Pharmaceuticals, Inc., Jazz Pharmaceuticals plc, H. Lundbeck A/S, Merck & Co., Pfizer Inc., Reckitt Benckiser Pharmaceuticals Inc., Sunovion Pharmaceuticals Inc., and Takeda Pharmaceuticals Inc. He has received payment for educational presentations regarding hospital readmission without mention of any pharmaceutical or other products from Merck. Dr. Mitchell has received honoraria from Merck for lectures on health behavior counseling. Trial registration: NCT00252057, NCT00217867.

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References
  1. Ashton CM, Del Junco DJ, Souchek J, Wray NP, Mansyur CL. The association between the quality of inpatient care and early readmission: a meta‐analysis of the evidence. Med Care. 1997;35(10):10441059.
  2. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee‐for‐service program. N Engl J Med. 2009;360(14):14181428.
  3. Medicare program. Final rule. Fed Regist. 2012;77(170):5325753750.
  4. Kessler RC, Chiu WT, Demler O, Merikangas KR, Walters EE. Prevalence, severity, and comorbidity of 12‐month DSM‐IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry. 2005;62(6):617627.
  5. Mitchell SE, Paasche‐Orlow MK, Forsythe SR, et al. Post‐discharge hospital utilization among adult medical inpatients with depressive symptoms. J Hosp Med. 2010;5(7):378384.
  6. Prina AM, Deeg D, Brayne C, Beekman A, Huisman M. The association between depressive symptoms and non‐psychiatric hospitalisation in older adults. PLoS One. 2012;7(4):e34821.
  7. Vaccarino V, Kasl SV, Abramson J, Krumholz HM. Depressive symptoms and risk of functional decline and death in patients with heart failure. J Am Coll Cardiol. 2001;38(1):199205.
  8. Fogarty CT, Sharma S, Chetty VK, Culpepper L. Mental health conditions are associated with increased health care utilization among urban family medicine patients. J Am Board Fam Med. 2008;21(5):398407.
  9. Ruo B, Rumsfeld JS, Hlatky MA, Liu H, Browner WS, Whooley MA. Depressive symptoms and health‐related quality of life: the heart and soul study. JAMA. 2003;290(2):215221.
  10. Koenig HG, Meador KG, Shelp F, Goli V, Cohen HJ, Blazer DG. Major depressive disorder in hospitalized medically ill patients: an examination of young and elderly male veterans. J Am Geriatr Soc. 1991;39(9):881890.
  11. Scherer M, Herrmann‐Lingen C. Single item on positive affect is associated with 1‐year survival in consecutive medical inpatients. Gen Hosp Psychiatry. 2009;31(1):813.
  12. Egede LE, Ellis C, Grubaugh AL. The effect of depression on self‐care behaviors and quality of care in a national sample of adults with diabetes. Gen Hosp Psychiatry. 2009;31(5):422427.
  13. McCusker J, Cole M, Dufouil C, et al. The prevalence and correlates of major and minor depression in older medical inpatients. J Am Geriatr Soc. 2005;53(8):13441353.
  14. Cukor D, Rosenthal DS, Jindal RM, Brown CD, Kimmel PL. Depression is an important contributor to low medication adherence in hemodialyzed patients and transplant recipients. Kidney Int. 2009;75(11):12231229.
  15. Gold SM, Irwin MR. Depression and immunity: inflammation and depressive symptoms in multiple sclerosis. Neurol Clin. 2006;24(3):507519.
  16. Brydon L, Walker C, Wawrzyniak A, et al. Synergistic effects of psychological and immune stressors on inflammatory cytokine and sickness responses in humans. Brain Behav Immun. 2009;23(2):217224.
  17. Teodorczuk A, Firbank MJ, Pantoni L, et al. Relationship between baseline white‐matter changes and development of late‐life depressive symptoms: 3‐year results from the LADIS study. Psychol Med. 2010;40(4):603610.
  18. Kroenke K, Spitzer RL, Williams JB. The PHQ‐9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606613.
  19. Jack BW, Chetty VK, Anthony D, et al. A reengineered hospital discharge program to decrease rehospitalization: a randomized trial. Ann Intern Med. 2009;150(3):178187.
  20. Bickmore TW, Pfeifer LM, Jack BW. Taking the time to care: empowering low health literacy hospital patients with virtual nurse agents. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York, NY: Association for Computing Machinery; 2009:1265–1274.
  21. Bickmore TW, Pfeifer LM, Byron D, et al. Usability of conversational agents by patients with inadequate health literacy: evidence from two clinical trials. J Health Commun. 2010;15(suppl 2):197210.
  22. Marcantonio ER, McKean S, Goldfinger M, Kleefield S, Yurkofsky M, Brennan TA. Factors associated with unplanned hospital readmission among patients 65 years of age and older in a Medicare managed care plan. Am J Med. 1999;107(1):1317.
  23. Woz S, Mitchell S, Hesko C, et al. Gender as risk factor for 30 days post‐discharge hospital utilisation: a secondary data analysis. BMJ Open. 2012;2(2):e000428.
  24. Davis TC, Long SW, Jackson RH, et al. Rapid estimate of adult literacy in medicine: a shortened screening instrument. Fam Med. 1993;25(6):391395.
  25. Weissman JS, Stern RS, Epstein AM. The impact of patient socioeconomic status and other social factors on readmission: a prospective study in four Massachusetts hospitals. Inquiry. 1994;31(2):163172.
  26. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373383.
  27. Walraven C, Mamdani M, Fang J, Austin PC. Continuity of care and patient outcomes after hospital discharge. J Gen Intern Med. 2004;19(6):624631.
  28. Krumholz HM, Parent EM, Tu N, et al. Readmission after hospitalization for congestive heart failure among Medicare beneficiaries. Arch Intern Med. 1997;157(1):99104.
  29. Cuijpers P, Graaf R, Dorsselaer S. Minor depression: risk profiles, functional disability, health care use and risk of developing major depression. J Affect Disord. 2004;79(1‐3):7179.
  30. Jiang W, Alexander J, Christopher E, et al. Relationship of depression to increased risk of mortality and rehospitalization in patients with congestive heart failure. Arch Intern Med. 2001;161(15):18491856.
  31. Fournier JC, DeRubeis RJ, Hollon SD, et al. Antidepressant drug effects and depression severity: a patient‐level meta‐analysis. JAMA. 2010;303(1):4753.
  32. Kartha A, Anthony D, Manasseh CS, et al. Depression is a risk factor for rehospitalization in medical inpatients. Prim Care Companion J Clin Psychiatry. 2007;9(4):256262.
  33. Pirmohamed M, James S, Meakin S, et al. Adverse drug reactions as cause of admission to hospital: prospective analysis of 18 820 patients. BMJ. 2004;329(7456):1519.
  34. Lima VD, Geller J, Bangsberg DR, et al. The effect of adherence on the association between depressive symptoms and mortality among HIV‐infected individuals first initiating HAART. AIDS. 2007;21(9):11751183.
  35. Gonzalez JS, Safren SA, Delahanty LM, et al. Symptoms of depression prospectively predict poorer self‐care in patients with Type 2 diabetes. Diabet Med. 2008;25(9):11021107.
  36. Schoenthaler A, Ogedegbe G, Allegrante JP. Self‐efficacy mediates the relationship between depressive symptoms and medication adherence among hypertensive African Americans. Health Educ Behav. 2009;36(1):127137.
  37. Tse WS, Bond AJ. The impact of depression on social skills. J Nerv Ment Dis. 2004;192(4):260268.
  38. Melle JP, Jonge P, Ormel J, et al. Relationship between left ventricular dysfunction and depression following myocardial infarction: data from the MIND‐IT. Eur Heart J. 2005;26(24):26502656.
  39. Serebruany VL, Glassman AH, Malinin AI, et al. Platelet/endothelial biomarkers in depressed patients treated with the selective serotonin reuptake inhibitor sertraline after acute coronary events: the Sertraline AntiDepressant Heart Attack Randomized Trial (SADHART) Platelet Substudy. Circulation. 2003;108(8):939944.
  40. Cuijpers P, Clignet F, Meijel B, Straten A, Li J, Andersson G. Psychological treatment of depression in inpatients: a systematic review and meta‐analysis. Clin Psychol Rev. 2011;31(3):353360.
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Address for correspondence and reprint requests: Ramon S. Cancino, MD, Department of Family Medicine, Dowling 5, Boston Medical Center, 1 BMC Place, Boston, MA 02118; Telephone: 617‐414‐6324; Fax: 617‐414‐3345; E‐mail: [email protected]
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Post‐Discharge Inpatients With Depressive Symptoms

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Post‐discharge hospital utilization among adult medical inpatients with depressive symptoms

Fully 19% of Medicare patients are readmitted to the hospital within 30 days of discharge.1 This represents a large amount of potentially avoidable morbidity and cost. Indeed, projects to improve the discharge process and post‐hospital care have shown that as much as one‐third of hospital utilization in the month after discharge can be avoided.2 Consequently, the rate of early, unplanned hospital utilization after discharge has emerged as an important indicator of hospital quality and the Centers for Medicare and Medicaid Services (CMS) has proposed a policy to decrease payments to hospitals with high rates of early unplanned hospital utilization. Thus, there is great interest in identifying modifiable risk factors for rehospitalization that could be used to refine intervention models and lead to improvements in quality of care, patient outcomes, and cost savings.

To date, known predictors of readmission include: lower socioeconomic status,3 history of prior hospitalization4 and advanced age,5 length of stay greater than 7 days,6 a high burden of comorbid illnesses (based on Charlson score),7 poor social support,8 and specific diagnoses (eg, congestive heart failure, chronic obstructive pulmonary disease [COPD] and myocardial infarction).5, 9, 10 In addition, unplanned readmissions and emergency department (ED) visits have been linked to polypharmacy and adverse drug events related to treatment with medications such as warfarin, digoxin and narcotics.11, 12 Another characteristic that has also been linked to readmission is depression;13 however5 reports supporting this association are from studies of elderly patients or with patients who have specific diagnoses (eg, congestive heart failure [CHF], COPD, myocardial infarction).1416

Depression is common, affecting 13% to 16% of people in the US, and is recognized as an important risk factor for poor outcomes among patients with various chronic illnesses.1719 The mechanisms by which depression can be linked to health outcomes and health service utilization have been studied in age‐specific or disease‐specific cohorts such as cardiac patients or frail elders and include both physiologic factors such as hypercoagulability and hyperinflammatory conditions, as well as behavioral factors such as poor self‐care behaviors and heightened sensitivity to somatic symptoms. How these mechanisms link depression to health outcomes and hospital utilization in a general medical population is not clearly understood. Kartha et al.13 reported findings indicating that depression is a risk factor for rehospitalization in general medical inpatients, but the study sample was relatively small and the study design methodology significantly limited its generalizability.12 It would be useful to provide supporting evidence showing depression as an important risk factor for readmission in the general medical in‐patient population using more rigorous study methods and a larger cohort.

We hypothesized that depressive symptoms would be an independent risk factor for early unplanned hospital utilization after discharge for all medical patients. Therefore, we conducted a secondary analysis of the Project RED clinical trial dataset to assess the association between a positive depression screen during inpatient hospitalization and the rate of subsequent hospital utilization.

Methods

Data from the Project RED clinical trial were reviewed for inclusion in a secondary analysis. Complete data were available for 738 of the 749 subjects recruited for Project RED.

Project RED Setting and Participants

Project RED was a two‐armed randomized controlled trial of English‐speaking adult patients, 18 years or older, admitted to the teaching service of Boston Medical Center, a large urban safety‐net hospital with an ethnically diverse patient population. A total of 749 subjects were enrolled and randomized between January 3, 2006 and October 18, 2007. Patients were required to have a telephone, be able to comprehend study details and the consent process in English, and have plans to be discharged to a US community. Patients were not enrolled if they were admitted from a skilled nursing facility or other hospital, transferred to a different hospital service prior to enrollment, admitted for a planned hospitalization, on hospital precautions, on suicide watch, deaf or blind. The Institutional Review Board of Boston University approved all study activities. A full description of the methods for the Project RED trial has been described previously.2

Outcome Variable

The primary endpoint was rate of hospital utilization within 30 days of discharge from the index admission, defined as the total number of ED visits and readmissions per subject within 30 days of the index discharge. Hospital utilization rates within 60 and 90 days of the index hospitalization discharge were also analyzed as secondary outcomes. Any ED visit in which a subject was subsequently admitted to the hospital was only counted as a readmission. Outcome data were collected by reviewing the hospital's electronic medical records (EMRs) and by contacting subjects by telephone 30 days after discharge. Dates of hospital utilization occurring at Boston Medical Center were obtained from the EMR, while those at other hospitals were collected through subject report. Subjects who could not be reached within 60 days of discharge were assumed alive.

Primary Independent Variable

The primary independent variable of interest was depressive symptoms defined as a positive score for minor or major depression on the nine‐item Patient Health Questionnaire (PHQ‐9) depression screening tool.20 A dichotomized variable was created using a standardized scoring system to determine the screening cut‐off for major or minor depressive symptoms.19

Statistical Analysis

Demographic and other characteristics of the subjects were compared by depression status (Table 1). Potential confounders were identified a priori from the available literature on factors associated with rehospitalization. These included age, gender, marital status, health literacy score (rapid estimate of health literacy in adult medicine tool [REALM]),21 Charlson score,22 insurance type, employment status, income level, homelessness status within past three months, hospital utilization within the 6 months prior to the index hospitalization, educational attainment, length of hospital stay and Project RED study group assignment. Bivariate analyses were conducted to determine which covariates were significant confounders of the relationship between depression and hospital utilization within 30 days of discharge. Chi‐square tests were used for categorical variables and t‐tests for continuous variables.

Baseline Characteristics of Study Subjects by Depression Screen Status
CharacteristicDepression Screen*
Negative (n = 500)Positive (n = 238)P Value
  • NOTE: Some columns may not add up to 100% due to omission of Other categories.

  • Abbreviations: PCP, primary care provider; PHQ9, Patient Health Questionnaire‐9; REALM, Rapid Estimate of Health Literacy in Adult Medicine tool; SD, standard deviation.

  • Positive depressive symptom screen determined by PHQ9 screen tool, a nine‐item 4‐point Likert scale, standard scoring algorithm to screen for major and minor depression. A score of 5 or higher indicates a positive depression symptom screen.17

  • Free Care is a Massachusetts state program for uninsured patients.

  • Health literacy categories correspond to total score as determined by REALM.18

  • Have PCP refers to subject self‐identifying PCP at time of Project RED study enrollment.

  • Unmarried marital status includes subjects identified as divorced, widow, single, partnered.

  • Charlson Comorbity Index Score reflects the cumulative increased likelihood of 1‐year mortality. The higher the score the more severe the comorbid condition. A 33% increase in risk for death is reflected in a 1‐point increase in weights. The minimum score is 0. There is no maximum score.19

  • Project RED study intervention group refers to subjects who received the 3‐armed discharge intervention.

  • Frequent Utilizer is defined as a subject with 2 or more hospital utilizations in 6 months prior to Project RED clinical trial index admission.

Race, No. (%)   
White140 (30)66 (30) 
Black268 (58)117 (54) 
Hispanic47 (10)29 (13)0.760
Insurance, No. (%)   
Private95 (19)22 (9) 
Medicare69 (14)30 (13) 
Medicaid214 (43)143 (61) 
Free care118 (24)40 (17)<0.001
Education, No. (%)   
<8th grade33 (7)21 (9) 
Some high school82 (17)52 (22) 
High school grad192 (38)90 (38) 
Some college126 (25)51 (22) 
College grad67 (13)22 (9)0.135
Health Literacy   
Grade 3 and below64 (13)44 (19) 
Grade 4654 (11)22 (10) 
Grade 78156 (32)73 (32) 
Grade 9 and above213 (44)89 (39)0.170
Income, $, No. (%)   
No income61 (12)37 (16) 
<10K77 (15)61 (26) 
1020K96 (19)35 (15) 
2050K97 (19)34 (14) 
50100K35 (8)7 (2) 
No answer132 (27)64 (27)0.002
Employment status, No. (%)   
Full time142 (28)34 (14) 
Part time57 (11)30 (13) 
Not Working297 (59)171 (72)<0.001
Age, mean (SD), years49.9 (16.0)49.6 (13.3)0.802
Gender: No. (%) Female239 (48)133 (56)0.040
Have PCP, No. (%) Yes399 (80)197 (83)0.340
Marital status, No. (%) unmarried365 (73)201 (85)<0.001
Charlson score, mean (SD)1.058 (1.6)1.56 (2.39)0.001
RED study group,# No. (%)   
Intervention243 (49)127 (53)0.22
Length of stay, days, mean (SD)2.5 (2.8)3.1 (3.8)0.016
Homeless in last 3 months, No. (%)45 (9)30 (13)0.130
Frequent utilizer,** No. (%)159 (32)104 (44)0.002

Age, length of stay, and Charlson score were used as continuous variables. Gender, marital status, frequent prior utilization (01 vs. 2 or more), and homelessness were treated as dichotomous variables. Categorical variables were created for, educational attainment (less than eighth grade, some high school, high school graduate, some college, college graduate), insurance type (Medicare, Medicaid, private insurance or free care), income level (no income, less than $10,000 per year, $10,00020,000, $20,00050,000, $50,000100,000, no answer), level of health literacy (grade 3 and below, grade 46, grade 78, grade 9 or above) and employment status(working full‐time, working part‐time, not working, no answer).

The 30‐day hospital utilization rate reflects the number of hospital utilization events within 30 days of discharge per subject. The same method was used to calculate hospital utilization rates within 60 and 90 days of discharge respectively. The unadjusted incident rate ratio (IRR) was calculated as the ratio of the rate of hospital utilizations among patients with depressive symptoms versus patients without depressive symptoms. Data for hospital utilization at 30, 60, and 90 days are cumulative.

Poisson models were used to test for significant differences between the predicted and observed number of hospitalization events at 30 days. A backward stepwise regression was conducted to identify and control for relevant confounders and construct the final, best‐fit model for the association between depression and hospital reutilization. A statistical significance level of P = 0.10 was used for the stepwise regression. To evaluate potential interactions between depression and the Project RED intervention, interaction terms were included. Two‐sided significance tests were used. P values of less than 0.05 were considered to indicate statistical significance. All data were analyzed with S‐Plus 8.0 (Seattle, WA).

In addition, a Kaplan‐Meier hazard curve was generated for the first hospital utilization event, ED visit or readmission, for the 30‐day period following discharge and compared with a log‐rank test.

Results

A total of 28% of subjects were categorized as having a positive depression screen. More women (36%) had positive depression screens than men (28%). Among patients with a positive depression screen, 58% had a history of depression and 53% were currently taking medications at the time of enrollment, compared with 25% and 22% respectively for subjects with a negative depression screen. Table 1 presents the means or percentages for baseline characteristics by depression status in the analytic cohort. Subjects with Medicaid for insurance had a higher rate of depression (61%) than subjects with Medicare (13%), private insurance (9%), or those who qualified for the Free Care pool (17%) which is the Massachusetts state funding for healthcare to uninsured persons. Subjects who were unemployed, unmarried, or who reported earnings less than $10,000 per year were also more likely to screen positive for depression. In addition, depressed subjects had a higher severity of co‐morbid disease and longer length of stay for the index hospitalization. Patients categorized as frequent utilizers (2 or more prior admissions) for the 6 months prior to the index hospitalization were also more likely to be depressed. Of further note, is the relatively younger average age among both depressive patients (49.6 years) and non‐depressive patients (49.9) of these study subjects.

The unadjusted hospital utilization rate at 30, 60, and 90 days post‐discharge by depression status is shown in Table 2. At 30 days post‐discharge, those with depressive symptoms had a higher rate of hospital utilization than those without depressive symptoms (0.563 vs. 0.296). In other words, 56 utilization events occurred per 100 patients with depressive symptoms, compared with 30 utilization events per 100 patients without depressive symptoms. The unadjusted 30‐day post‐discharge hospital utilization rate among those with depressive symptoms was higher compared with those without symptoms (IRR, 1.90, 95% confidence interval [CI], 1.242.71). A similar trend was found among subjects at 60 and 90 days post‐discharge.

Number of Hospital Utilizations, Hospital Utilization Rate, Unadjusted IRR at 30, 60 and 90 Days by Depression Screen Status*
Hospital UtilizationDepression Screen*P ValueIRR (CI)
Negative, n = 500 (68%)Positive, n = 238 (32%)
  • Abbreviations: CI, confidence interval; ED, emergency department; IRR, incident rate ratio.

  • Depression screen determined by scoring of Patient Health Questionnaire‐9 (PHQ9). Depressive symptom score of 5 points or higher is designated as positive.17

  • Number of hospital utilizations include all ED visits and hospital readmissions following discharge from Project RED index admission. ED visits leading to hospital admission are counted as one event. Sum reflects cumulative number of events over 30, 60 and 90 days.

No. of hospital utilizations140134 1.90 (1.51,2.40)
30‐day hospital utilization rate0.2960.563<0.001 
No. of hospital utilizations231205 1.87 (1.55,2.26)
60‐day hospital utilization rate0.4630.868<0.001 
No. of hospital utilizations324275 1.79 (1.53,2.10)
90‐day hospital utilization rate0.6481.165<0.001 

Poisson regression analyses were conducted to control for potential confounding in the relationship between depressive symptoms and hospital utilization rate within 30 days after discharge (Table 3). After controlling for relevant confounders, including age, gender, employment status, frequent prior hospitalization status, marital status, Charlson score, Project RED study group assignment and the interaction variable for RED study group assignment and depression, the association between symptoms of depression, and hospital utilization rate remained significant (IRR, 1.73; 95% CI, 1.272.36).

Adjusted Incident Rate Ratio of Hospital Utilization Within 30 Days of Discharge
CharacteristicsIRRCIP Value
  • Abbreviations: CI, confidence interval; ED, emergency department; IRR, incident rate ratio, PHQ9, Patient Health Questionnaire‐9.

  • Positive depressive symptom screen determined by PHQ9 screen tool, a nine‐item 4‐point Likert scale, standard scoring algorithm to screen for major and minor depression. A score of 5 or higher indicates a positive depression symptom screen.17

  • Unmarried refers to subjects whose self‐reported marital status includes divorced, single, partnered or widowed.

  • Frequent utilizer: 2 or more ED or hospital admissions visits in prior 6 months from index admission.

  • Refers to Project RED study group assignment.

  • Charlson Comorbity Index Score reflects the cumulative increased likelihood of 1‐year mortality. The higher the score the more severe the comorbid condition. A 33% increase in risk for death is reflected in a 1‐point increase in weights. The minimum score is 0. There is no maximum score.19

  • Interaction term of Project RED study group assignment and depressive symptom category (positive or negative).

Depression symptoms*  <0.001
Positive1.731.272.36 
NegativeREF1.0 
Gender  <0.001
Male1.871.472.40 
FemaleREF1.0 
Marital status  0.005
Married0.6250.440.89 
Unmarried1.0REF 
Frequent utilizer  <0.001
2+ prior visits2.451.923.15 
<2 prior visits1.0REF 
Study group  0.054
Intervention0.760.551.06 
Control1.0REF 
Employment   
Part time1.400.852.300.095
Not working1.671.152.440.003
Other0.520.073.850.262
Full time1.0REF 
Charlson Score0.980.921.040.250
Group* depression0.840.521.360.236
Age1.000.991.010.375

Figure 1 depicts the Kaplan‐Meier hazard curve generated for time to first hospital utilization, stratified by depression status. While 21% of participants without symptoms of depression had a hospital utilization within 30 days, fully 29% of participants with symptoms of depression had a hospital utilization within 30 days (P = 0.011).

Figure 1
Hazard for hospital utilization among subjects with and without depressive symptoms in 30 days following hospital discharge.

Discussion

Our study shows hospitalized patients who screen positive for depressive symptoms are significantly more likely to have a hospital visit (emergency room or rehospitalization) within 30 days of discharge than those who do not screen positive for depressive symptoms among medical patients admitted to an urban, academic, safety‐net hospital. These findings are consistent with, and extend, prior reports regarding depression and rehospitalization in specific populations (ie, geriatrics) and specific diagnoses (ie, cardiovascular disease [CVD] and COPD).1012 We observed a 73% higher incidence rate for hospital utilization within 30 days of discharge for those with symptoms of depression. This puts symptoms of depression on par with frequent prior rehospitalization, advanced age and low social support, as known risk factors for rehospitalization.4, 5, 23

Also of significance is the relatively young age of this study population (49.9 years non‐depressive patients and 49.6 years for depressive patients) compared with the study cohorts used for research in the majority of the existing literature. The chief reason for the young age of our cohort is that potential subjects were excluded if they came from a skilled nursing facility or other hospital. This may limit the generalizability of our findings; however, it seems likely that interventions relating to depression and transitions of care will need to be quite different for patients that reside in long‐term care facilities vs. patients that live in the community. For example, patients living in the community may have significant barriers to access post‐discharge services due to insurance status and are more likely to be sensitive to variations in social support.

Early rehospitalization is associated with significant morbidity, mortality, and expense. It is also a potential marker for poor quality of care.24 Concerns for patient safety, escalating healthcare costs, and possible change in hospital reimbursement mechanisms are fueling the search for modifiable risk factors associated with early rehospitalization. Our data provide evidence that symptoms of depression may be an important focus of attention. We do not know, however whether treating hospitalized patients who screen positive for depression will decrease early rehospitalization and emergency room utilization rates.

Various physiologic and behavioral mechanisms may link symptoms of depression to hospital utilization after discharge. For example, depressed patients with features of somatization may be more likely to experience worrisome physical symptoms after discharge and present prematurely for reevaluation. Patients who are sicker in some fashion not captured by our measured confounders may have symptoms of depression related to chronic, debilitating disease warranting early return to the hospital. Depression may also yield nonadherence to aspects of the discharge treatment plan leading to rehospitalization as a result of poor post‐discharge disease management. For example, research shows that patients with depression following coronary artery bypass surgery are less likely to adhere with cardiac rehabilitation programs.25 Likewise, depression among chronically ill patients such as diabetics, asthmatics, or human immunodeficiency virus (HIV)‐positive patients impairs medication adherence and self‐care behavior which may lead to disease relapse or recurrence.2628 One study examining depression effects on hypertensive medicine adherence in African Americans identified self‐efficacy as a mediating factor between depression and nonadherence.29 This implies that interventions such as self‐management education, a program through which chronically‐ill patients learn to better manage their illnesses through enhanced self‐confidence and problem‐solving strategies (including mood disorder challenges) may reduce early rehospitalization among depressed patients.30

There is also evidence that depression may have direct physiologic consequences. In patients with CVD, depression is associated with poor outcomes possibly related to decreased heart rate variability, hypercoagulability, high burdens of inflammatory markers, and severity of left ventricular dysfunction.3134 Similarly, depression among HIV/acquired immune deficiency syndrome (AIDS), diabetics and multiple sclerosis (MS) patients is linked to heightened levels of proinflammatory markers and less favorable outcomes that may signal a more severe form of the disease or an impaired response to treatment.3538 Indeed, MS investigators now hypothesize that the proinflammatory environment associated with the neurologic manifestations of MS are also causing depression symptoms among MS patients.34 This theory contrasts the common belief that depression in the chronically ill manifests independent of the chronic illness or in response to living with chronic disease.

A major strength of the current study is the large dataset and the broad range of covariates available for analyses. However, several limitations should be noted. First, data on hospital utilization outside Boston Medical Center were determined by patient self‐report and were not confirmed by document review. Second, we do not know the direction of the associations we report. If symptoms of depression are merely the consequence of having a higher disease burden, treatment of the underlying disease may be the most important response. While this is possible, our model does include several variables (eg, Charlson score and length of stay) that are likely to adjust for disease severity, pointing to the likelihood that symptoms of depression truly predict hospital utilization in a fashion that is independent of disease severity. Third, our results may not be generalizable to populations other than those served by urban safety‐net hospitals or other populations excluded from the Project RED trial (eg, non‐English speaking patients and patients from nursing homes). Finally, social factors such as substance use and social support system variables may residually confound the relationship between depression and hospital reutilization demonstrated in this study. While this dataset does not include a measure of social support other than marital status and housing status, data is available on substance use. Analyses conducted by our colleagues using Project RED data found that in this study population depression was significantly more prevalent among substance users (29% vs. 14%) compared with non‐users and that substance use is an independent risk factor for hospital reutilization (unpublished data).

Our findings linking depression to increased hospital utilization also warrant further consideration from healthcare policymakers. Central to the Obama Administration's February 2009 healthcare reform proposal is the pursuit of cost savings through reductions in unplanned hospital readmissions.39 Thus, identifying potentially modifiable risk factors for readmission, such as depression, is of great concern to healthcare providers and policymakers across the nation. If, through testing of interventions, depression proves to be a modifiable risk for readmission, policymakers, while negotiating healthcare reform measures, must provide for the services required to address this comorbidity at the time of discharge. For example, if a patient screens positive for depressive symptoms during a hospitalization for COPD exacerbation, will the proposed payment reforms allow for mental health services during the immediate post‐discharge period in order to reduce the likelihood of hospital readmission? Will those mental health services be readily available? Payment reforms that account for all necessary transitional care services will indeed help reduce readmission costs with less risk for untoward consequences.

In conclusion, our results indicate that a positive depression screen is a significant risk factor for early post‐discharge hospital utilization among hospitalized adults on a general medical service, even after controlling for relevant confounders. Screening for depression during acute hospitalizations may be an important step in identifying patients at increased risk for readmission. Future research should focus on further characterizing and stratifying populations at highest risk for depression. Efforts should also include developing and evaluating targeted interventions for patients with symptoms of depression among hospitalized patients as part of discharge planning. Timely depression therapy during the hospitalization or following hospital discharge might reduce costly readmissions and enhance patient safety.

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  14. Almagro P,Barreiro Bienvenido ,Ochoa de Echaguen A, et al.Risk factors for hospital readmission in patients with chronic obstructive pulmonary disease.Respiration.2006;73:311317.
  15. Frasure‐Smith N,Lesperance F,Gravel G, et al.Depression and healthcare costs during the first year following myocardial infarction.J Psychosom Res.2000;48(4–5):471478.
  16. Jiang W,Alexander J,Christopher E, et al.Relationship of depression to increased risk of mortality and rehospitalization.Arch Intern Med.2001;161(15):18491856.
  17. Parashar S,Rumsfeld JS,Spertus JA, et al.Time course of depression and outcome of myocardial infarction.Arch Intern Med.2006;166:20352043.
  18. Scherer M,Herrmann‐Lingen C.Single item on positive affect is associated with 1‐year survival in consecutive medical inpatients.J Gen Hosp Psych.2009;31:813.
  19. Hasin DS,Goodwin RD,Stinson FS,Grant BF.Epidemiology of major depressive disorder: results from the National Epidemiologic Survey on Alcoholism and Related Conditions.Arch Gen Psychiatry.2005;62(10):1097106.
  20. Kroenke K,Spitzer RL,Williams JB.The PHQ‐9: Validity of a brief depression severity measure.J Gen Intern Med.2001;16:606613. [PMID:11556941]
  21. Davis TC,Long SW,Jackson RH, et al.Rapid estimate of adult literacy in medicine: a shortened screening instrument.Fam Med.1993;25:391395. [PMID:8349060]
  22. Charlson ME,Pompei P,Ales KL,MacKenzie CR.A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.J Chronic Dis.1987;40:373383. [PMID: 3558716]
  23. Rodriguez‐Artalejo F,Guallar‐Castillon P,Herrera MC, et al.Social network as a predictor of hospital readmission and mortality among older patients with heart failure.J Card Fail.2006;12(8):621627.
  24. Ashton CM,Del Junco DJ,Souchek J,Wray NP,Mansyr CL.The association between the quality of inpatient care and early readmission: a meta‐analysis of the evidence.Med Care.1997;35(10):10441059.
  25. Kronish IM,Rieckmann N,Halm FA, et al.Persistent depression affects adherence to secondary prevention behaviors after acute coronary syndromes.J Gen Intern Med.2006;21(11):11781183.
  26. Cukor D,Rosenthal DS,Jindal RM,Brown CD,Kimmel PL.Depression is an important contributor to low medication adherence in hemodialyzed patients and transplant recipients.Kidney Int.2009;75(11):12231229.
  27. Gonzalez JS,Safren SA,Delahanty LM, et al.Symptoms of depression prospectively predict poorer self‐care in patients with Type 2 diabetes.Diabet Med.2008;25(9):11021107.
  28. Lima VD,Geller J,Bangsberg DR, et al.The effect of adherence on the association between depressive symptoms and mortality among HIV‐infected individuals first initiating HAART.AIDS.2007;21(9):11751183.
  29. Schoenthaler A,Ogedegbe G,Allegrante JP.Self‐efficacy mediates the relationship between depressive symptoms and medication adherence.Health Educ Behav.2009;36(1):127137.
  30. Bodenheimer T,Lorig K,Holman H,Grumbach K.Patient self‐management of chronic disease in primary care.JAMA.2002;288(19):24692475.
  31. McFarlane AM.Effects of sertraline on the recovery rate of cardiac autonomic function in depressed patients after acute myocardial infarction.Am Heart J.2001;142:617623.
  32. van Melle JP,de Jonge P,Ormel J, et al.Relationship between left ventricular dysfunction and depression following myocardial infarction: data from the MIND‐IT.Eur Heart J.2005;26:26502656.
  33. Serebruany VL,Glassman AH,Malinin AI, et al.Platelet/endothelial biomarkers in depressed patients treated with the selective serotonin reuptake inhibitor sertraline after acugte coronary events: the Sertraline AntiDepressant Heart Attack Randomized Trial (SADHART) Platelet SubStudy.Circulation.2003;108:939944.
  34. Mulvihill NT,Foley JB.Inflammation in acute coronary syndromes.Cleve Clin J Med.2002;69(Suppl2):SII130SII142.
  35. Gold SM,Irwin MR.Depression and immunity: inflammation and depressive symptoms in multiple sclerosis.Neurol Clin.2006;24(3):507519.
  36. Brydon L,Walker C,Wawrzyniak A, et al.Synergistic effects of psychological and immune stressors on inflammatory cytokines and sickness responses in humans.Brain Behav Immun.2009;23(2):217224.
  37. Gresson JM,Hurwitz BE,Llabre MM,Schneiderman N,Penedo FJ,Klimas NG.Psychological distress, killer lymphocytes and disease severity in HIV/AIDS.Brain Behav Immun.2008;22(6):901911.
  38. Pizzi C,Manzoli L,Mancini S,Costa GM.Analysis of potential predictors of depression among coronary heart disease risk factors including heart rate variability, markers of inflammation, and endothelial function.Eur Heart J.2008;29(9):11101117.
  39. Connolly C.Obama proposes $634 billion fund for health care.Washington Post. February 26,2009:A1.
Article PDF
Issue
Journal of Hospital Medicine - 5(7)
Publications
Page Number
378-384
Legacy Keywords
depression, hospital discharge, patient safety, readmission, rehospitalization
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Article PDF
Article PDF

Fully 19% of Medicare patients are readmitted to the hospital within 30 days of discharge.1 This represents a large amount of potentially avoidable morbidity and cost. Indeed, projects to improve the discharge process and post‐hospital care have shown that as much as one‐third of hospital utilization in the month after discharge can be avoided.2 Consequently, the rate of early, unplanned hospital utilization after discharge has emerged as an important indicator of hospital quality and the Centers for Medicare and Medicaid Services (CMS) has proposed a policy to decrease payments to hospitals with high rates of early unplanned hospital utilization. Thus, there is great interest in identifying modifiable risk factors for rehospitalization that could be used to refine intervention models and lead to improvements in quality of care, patient outcomes, and cost savings.

To date, known predictors of readmission include: lower socioeconomic status,3 history of prior hospitalization4 and advanced age,5 length of stay greater than 7 days,6 a high burden of comorbid illnesses (based on Charlson score),7 poor social support,8 and specific diagnoses (eg, congestive heart failure, chronic obstructive pulmonary disease [COPD] and myocardial infarction).5, 9, 10 In addition, unplanned readmissions and emergency department (ED) visits have been linked to polypharmacy and adverse drug events related to treatment with medications such as warfarin, digoxin and narcotics.11, 12 Another characteristic that has also been linked to readmission is depression;13 however5 reports supporting this association are from studies of elderly patients or with patients who have specific diagnoses (eg, congestive heart failure [CHF], COPD, myocardial infarction).1416

Depression is common, affecting 13% to 16% of people in the US, and is recognized as an important risk factor for poor outcomes among patients with various chronic illnesses.1719 The mechanisms by which depression can be linked to health outcomes and health service utilization have been studied in age‐specific or disease‐specific cohorts such as cardiac patients or frail elders and include both physiologic factors such as hypercoagulability and hyperinflammatory conditions, as well as behavioral factors such as poor self‐care behaviors and heightened sensitivity to somatic symptoms. How these mechanisms link depression to health outcomes and hospital utilization in a general medical population is not clearly understood. Kartha et al.13 reported findings indicating that depression is a risk factor for rehospitalization in general medical inpatients, but the study sample was relatively small and the study design methodology significantly limited its generalizability.12 It would be useful to provide supporting evidence showing depression as an important risk factor for readmission in the general medical in‐patient population using more rigorous study methods and a larger cohort.

We hypothesized that depressive symptoms would be an independent risk factor for early unplanned hospital utilization after discharge for all medical patients. Therefore, we conducted a secondary analysis of the Project RED clinical trial dataset to assess the association between a positive depression screen during inpatient hospitalization and the rate of subsequent hospital utilization.

Methods

Data from the Project RED clinical trial were reviewed for inclusion in a secondary analysis. Complete data were available for 738 of the 749 subjects recruited for Project RED.

Project RED Setting and Participants

Project RED was a two‐armed randomized controlled trial of English‐speaking adult patients, 18 years or older, admitted to the teaching service of Boston Medical Center, a large urban safety‐net hospital with an ethnically diverse patient population. A total of 749 subjects were enrolled and randomized between January 3, 2006 and October 18, 2007. Patients were required to have a telephone, be able to comprehend study details and the consent process in English, and have plans to be discharged to a US community. Patients were not enrolled if they were admitted from a skilled nursing facility or other hospital, transferred to a different hospital service prior to enrollment, admitted for a planned hospitalization, on hospital precautions, on suicide watch, deaf or blind. The Institutional Review Board of Boston University approved all study activities. A full description of the methods for the Project RED trial has been described previously.2

Outcome Variable

The primary endpoint was rate of hospital utilization within 30 days of discharge from the index admission, defined as the total number of ED visits and readmissions per subject within 30 days of the index discharge. Hospital utilization rates within 60 and 90 days of the index hospitalization discharge were also analyzed as secondary outcomes. Any ED visit in which a subject was subsequently admitted to the hospital was only counted as a readmission. Outcome data were collected by reviewing the hospital's electronic medical records (EMRs) and by contacting subjects by telephone 30 days after discharge. Dates of hospital utilization occurring at Boston Medical Center were obtained from the EMR, while those at other hospitals were collected through subject report. Subjects who could not be reached within 60 days of discharge were assumed alive.

Primary Independent Variable

The primary independent variable of interest was depressive symptoms defined as a positive score for minor or major depression on the nine‐item Patient Health Questionnaire (PHQ‐9) depression screening tool.20 A dichotomized variable was created using a standardized scoring system to determine the screening cut‐off for major or minor depressive symptoms.19

Statistical Analysis

Demographic and other characteristics of the subjects were compared by depression status (Table 1). Potential confounders were identified a priori from the available literature on factors associated with rehospitalization. These included age, gender, marital status, health literacy score (rapid estimate of health literacy in adult medicine tool [REALM]),21 Charlson score,22 insurance type, employment status, income level, homelessness status within past three months, hospital utilization within the 6 months prior to the index hospitalization, educational attainment, length of hospital stay and Project RED study group assignment. Bivariate analyses were conducted to determine which covariates were significant confounders of the relationship between depression and hospital utilization within 30 days of discharge. Chi‐square tests were used for categorical variables and t‐tests for continuous variables.

Baseline Characteristics of Study Subjects by Depression Screen Status
CharacteristicDepression Screen*
Negative (n = 500)Positive (n = 238)P Value
  • NOTE: Some columns may not add up to 100% due to omission of Other categories.

  • Abbreviations: PCP, primary care provider; PHQ9, Patient Health Questionnaire‐9; REALM, Rapid Estimate of Health Literacy in Adult Medicine tool; SD, standard deviation.

  • Positive depressive symptom screen determined by PHQ9 screen tool, a nine‐item 4‐point Likert scale, standard scoring algorithm to screen for major and minor depression. A score of 5 or higher indicates a positive depression symptom screen.17

  • Free Care is a Massachusetts state program for uninsured patients.

  • Health literacy categories correspond to total score as determined by REALM.18

  • Have PCP refers to subject self‐identifying PCP at time of Project RED study enrollment.

  • Unmarried marital status includes subjects identified as divorced, widow, single, partnered.

  • Charlson Comorbity Index Score reflects the cumulative increased likelihood of 1‐year mortality. The higher the score the more severe the comorbid condition. A 33% increase in risk for death is reflected in a 1‐point increase in weights. The minimum score is 0. There is no maximum score.19

  • Project RED study intervention group refers to subjects who received the 3‐armed discharge intervention.

  • Frequent Utilizer is defined as a subject with 2 or more hospital utilizations in 6 months prior to Project RED clinical trial index admission.

Race, No. (%)   
White140 (30)66 (30) 
Black268 (58)117 (54) 
Hispanic47 (10)29 (13)0.760
Insurance, No. (%)   
Private95 (19)22 (9) 
Medicare69 (14)30 (13) 
Medicaid214 (43)143 (61) 
Free care118 (24)40 (17)<0.001
Education, No. (%)   
<8th grade33 (7)21 (9) 
Some high school82 (17)52 (22) 
High school grad192 (38)90 (38) 
Some college126 (25)51 (22) 
College grad67 (13)22 (9)0.135
Health Literacy   
Grade 3 and below64 (13)44 (19) 
Grade 4654 (11)22 (10) 
Grade 78156 (32)73 (32) 
Grade 9 and above213 (44)89 (39)0.170
Income, $, No. (%)   
No income61 (12)37 (16) 
<10K77 (15)61 (26) 
1020K96 (19)35 (15) 
2050K97 (19)34 (14) 
50100K35 (8)7 (2) 
No answer132 (27)64 (27)0.002
Employment status, No. (%)   
Full time142 (28)34 (14) 
Part time57 (11)30 (13) 
Not Working297 (59)171 (72)<0.001
Age, mean (SD), years49.9 (16.0)49.6 (13.3)0.802
Gender: No. (%) Female239 (48)133 (56)0.040
Have PCP, No. (%) Yes399 (80)197 (83)0.340
Marital status, No. (%) unmarried365 (73)201 (85)<0.001
Charlson score, mean (SD)1.058 (1.6)1.56 (2.39)0.001
RED study group,# No. (%)   
Intervention243 (49)127 (53)0.22
Length of stay, days, mean (SD)2.5 (2.8)3.1 (3.8)0.016
Homeless in last 3 months, No. (%)45 (9)30 (13)0.130
Frequent utilizer,** No. (%)159 (32)104 (44)0.002

Age, length of stay, and Charlson score were used as continuous variables. Gender, marital status, frequent prior utilization (01 vs. 2 or more), and homelessness were treated as dichotomous variables. Categorical variables were created for, educational attainment (less than eighth grade, some high school, high school graduate, some college, college graduate), insurance type (Medicare, Medicaid, private insurance or free care), income level (no income, less than $10,000 per year, $10,00020,000, $20,00050,000, $50,000100,000, no answer), level of health literacy (grade 3 and below, grade 46, grade 78, grade 9 or above) and employment status(working full‐time, working part‐time, not working, no answer).

The 30‐day hospital utilization rate reflects the number of hospital utilization events within 30 days of discharge per subject. The same method was used to calculate hospital utilization rates within 60 and 90 days of discharge respectively. The unadjusted incident rate ratio (IRR) was calculated as the ratio of the rate of hospital utilizations among patients with depressive symptoms versus patients without depressive symptoms. Data for hospital utilization at 30, 60, and 90 days are cumulative.

Poisson models were used to test for significant differences between the predicted and observed number of hospitalization events at 30 days. A backward stepwise regression was conducted to identify and control for relevant confounders and construct the final, best‐fit model for the association between depression and hospital reutilization. A statistical significance level of P = 0.10 was used for the stepwise regression. To evaluate potential interactions between depression and the Project RED intervention, interaction terms were included. Two‐sided significance tests were used. P values of less than 0.05 were considered to indicate statistical significance. All data were analyzed with S‐Plus 8.0 (Seattle, WA).

In addition, a Kaplan‐Meier hazard curve was generated for the first hospital utilization event, ED visit or readmission, for the 30‐day period following discharge and compared with a log‐rank test.

Results

A total of 28% of subjects were categorized as having a positive depression screen. More women (36%) had positive depression screens than men (28%). Among patients with a positive depression screen, 58% had a history of depression and 53% were currently taking medications at the time of enrollment, compared with 25% and 22% respectively for subjects with a negative depression screen. Table 1 presents the means or percentages for baseline characteristics by depression status in the analytic cohort. Subjects with Medicaid for insurance had a higher rate of depression (61%) than subjects with Medicare (13%), private insurance (9%), or those who qualified for the Free Care pool (17%) which is the Massachusetts state funding for healthcare to uninsured persons. Subjects who were unemployed, unmarried, or who reported earnings less than $10,000 per year were also more likely to screen positive for depression. In addition, depressed subjects had a higher severity of co‐morbid disease and longer length of stay for the index hospitalization. Patients categorized as frequent utilizers (2 or more prior admissions) for the 6 months prior to the index hospitalization were also more likely to be depressed. Of further note, is the relatively younger average age among both depressive patients (49.6 years) and non‐depressive patients (49.9) of these study subjects.

The unadjusted hospital utilization rate at 30, 60, and 90 days post‐discharge by depression status is shown in Table 2. At 30 days post‐discharge, those with depressive symptoms had a higher rate of hospital utilization than those without depressive symptoms (0.563 vs. 0.296). In other words, 56 utilization events occurred per 100 patients with depressive symptoms, compared with 30 utilization events per 100 patients without depressive symptoms. The unadjusted 30‐day post‐discharge hospital utilization rate among those with depressive symptoms was higher compared with those without symptoms (IRR, 1.90, 95% confidence interval [CI], 1.242.71). A similar trend was found among subjects at 60 and 90 days post‐discharge.

Number of Hospital Utilizations, Hospital Utilization Rate, Unadjusted IRR at 30, 60 and 90 Days by Depression Screen Status*
Hospital UtilizationDepression Screen*P ValueIRR (CI)
Negative, n = 500 (68%)Positive, n = 238 (32%)
  • Abbreviations: CI, confidence interval; ED, emergency department; IRR, incident rate ratio.

  • Depression screen determined by scoring of Patient Health Questionnaire‐9 (PHQ9). Depressive symptom score of 5 points or higher is designated as positive.17

  • Number of hospital utilizations include all ED visits and hospital readmissions following discharge from Project RED index admission. ED visits leading to hospital admission are counted as one event. Sum reflects cumulative number of events over 30, 60 and 90 days.

No. of hospital utilizations140134 1.90 (1.51,2.40)
30‐day hospital utilization rate0.2960.563<0.001 
No. of hospital utilizations231205 1.87 (1.55,2.26)
60‐day hospital utilization rate0.4630.868<0.001 
No. of hospital utilizations324275 1.79 (1.53,2.10)
90‐day hospital utilization rate0.6481.165<0.001 

Poisson regression analyses were conducted to control for potential confounding in the relationship between depressive symptoms and hospital utilization rate within 30 days after discharge (Table 3). After controlling for relevant confounders, including age, gender, employment status, frequent prior hospitalization status, marital status, Charlson score, Project RED study group assignment and the interaction variable for RED study group assignment and depression, the association between symptoms of depression, and hospital utilization rate remained significant (IRR, 1.73; 95% CI, 1.272.36).

Adjusted Incident Rate Ratio of Hospital Utilization Within 30 Days of Discharge
CharacteristicsIRRCIP Value
  • Abbreviations: CI, confidence interval; ED, emergency department; IRR, incident rate ratio, PHQ9, Patient Health Questionnaire‐9.

  • Positive depressive symptom screen determined by PHQ9 screen tool, a nine‐item 4‐point Likert scale, standard scoring algorithm to screen for major and minor depression. A score of 5 or higher indicates a positive depression symptom screen.17

  • Unmarried refers to subjects whose self‐reported marital status includes divorced, single, partnered or widowed.

  • Frequent utilizer: 2 or more ED or hospital admissions visits in prior 6 months from index admission.

  • Refers to Project RED study group assignment.

  • Charlson Comorbity Index Score reflects the cumulative increased likelihood of 1‐year mortality. The higher the score the more severe the comorbid condition. A 33% increase in risk for death is reflected in a 1‐point increase in weights. The minimum score is 0. There is no maximum score.19

  • Interaction term of Project RED study group assignment and depressive symptom category (positive or negative).

Depression symptoms*  <0.001
Positive1.731.272.36 
NegativeREF1.0 
Gender  <0.001
Male1.871.472.40 
FemaleREF1.0 
Marital status  0.005
Married0.6250.440.89 
Unmarried1.0REF 
Frequent utilizer  <0.001
2+ prior visits2.451.923.15 
<2 prior visits1.0REF 
Study group  0.054
Intervention0.760.551.06 
Control1.0REF 
Employment   
Part time1.400.852.300.095
Not working1.671.152.440.003
Other0.520.073.850.262
Full time1.0REF 
Charlson Score0.980.921.040.250
Group* depression0.840.521.360.236
Age1.000.991.010.375

Figure 1 depicts the Kaplan‐Meier hazard curve generated for time to first hospital utilization, stratified by depression status. While 21% of participants without symptoms of depression had a hospital utilization within 30 days, fully 29% of participants with symptoms of depression had a hospital utilization within 30 days (P = 0.011).

Figure 1
Hazard for hospital utilization among subjects with and without depressive symptoms in 30 days following hospital discharge.

Discussion

Our study shows hospitalized patients who screen positive for depressive symptoms are significantly more likely to have a hospital visit (emergency room or rehospitalization) within 30 days of discharge than those who do not screen positive for depressive symptoms among medical patients admitted to an urban, academic, safety‐net hospital. These findings are consistent with, and extend, prior reports regarding depression and rehospitalization in specific populations (ie, geriatrics) and specific diagnoses (ie, cardiovascular disease [CVD] and COPD).1012 We observed a 73% higher incidence rate for hospital utilization within 30 days of discharge for those with symptoms of depression. This puts symptoms of depression on par with frequent prior rehospitalization, advanced age and low social support, as known risk factors for rehospitalization.4, 5, 23

Also of significance is the relatively young age of this study population (49.9 years non‐depressive patients and 49.6 years for depressive patients) compared with the study cohorts used for research in the majority of the existing literature. The chief reason for the young age of our cohort is that potential subjects were excluded if they came from a skilled nursing facility or other hospital. This may limit the generalizability of our findings; however, it seems likely that interventions relating to depression and transitions of care will need to be quite different for patients that reside in long‐term care facilities vs. patients that live in the community. For example, patients living in the community may have significant barriers to access post‐discharge services due to insurance status and are more likely to be sensitive to variations in social support.

Early rehospitalization is associated with significant morbidity, mortality, and expense. It is also a potential marker for poor quality of care.24 Concerns for patient safety, escalating healthcare costs, and possible change in hospital reimbursement mechanisms are fueling the search for modifiable risk factors associated with early rehospitalization. Our data provide evidence that symptoms of depression may be an important focus of attention. We do not know, however whether treating hospitalized patients who screen positive for depression will decrease early rehospitalization and emergency room utilization rates.

Various physiologic and behavioral mechanisms may link symptoms of depression to hospital utilization after discharge. For example, depressed patients with features of somatization may be more likely to experience worrisome physical symptoms after discharge and present prematurely for reevaluation. Patients who are sicker in some fashion not captured by our measured confounders may have symptoms of depression related to chronic, debilitating disease warranting early return to the hospital. Depression may also yield nonadherence to aspects of the discharge treatment plan leading to rehospitalization as a result of poor post‐discharge disease management. For example, research shows that patients with depression following coronary artery bypass surgery are less likely to adhere with cardiac rehabilitation programs.25 Likewise, depression among chronically ill patients such as diabetics, asthmatics, or human immunodeficiency virus (HIV)‐positive patients impairs medication adherence and self‐care behavior which may lead to disease relapse or recurrence.2628 One study examining depression effects on hypertensive medicine adherence in African Americans identified self‐efficacy as a mediating factor between depression and nonadherence.29 This implies that interventions such as self‐management education, a program through which chronically‐ill patients learn to better manage their illnesses through enhanced self‐confidence and problem‐solving strategies (including mood disorder challenges) may reduce early rehospitalization among depressed patients.30

There is also evidence that depression may have direct physiologic consequences. In patients with CVD, depression is associated with poor outcomes possibly related to decreased heart rate variability, hypercoagulability, high burdens of inflammatory markers, and severity of left ventricular dysfunction.3134 Similarly, depression among HIV/acquired immune deficiency syndrome (AIDS), diabetics and multiple sclerosis (MS) patients is linked to heightened levels of proinflammatory markers and less favorable outcomes that may signal a more severe form of the disease or an impaired response to treatment.3538 Indeed, MS investigators now hypothesize that the proinflammatory environment associated with the neurologic manifestations of MS are also causing depression symptoms among MS patients.34 This theory contrasts the common belief that depression in the chronically ill manifests independent of the chronic illness or in response to living with chronic disease.

A major strength of the current study is the large dataset and the broad range of covariates available for analyses. However, several limitations should be noted. First, data on hospital utilization outside Boston Medical Center were determined by patient self‐report and were not confirmed by document review. Second, we do not know the direction of the associations we report. If symptoms of depression are merely the consequence of having a higher disease burden, treatment of the underlying disease may be the most important response. While this is possible, our model does include several variables (eg, Charlson score and length of stay) that are likely to adjust for disease severity, pointing to the likelihood that symptoms of depression truly predict hospital utilization in a fashion that is independent of disease severity. Third, our results may not be generalizable to populations other than those served by urban safety‐net hospitals or other populations excluded from the Project RED trial (eg, non‐English speaking patients and patients from nursing homes). Finally, social factors such as substance use and social support system variables may residually confound the relationship between depression and hospital reutilization demonstrated in this study. While this dataset does not include a measure of social support other than marital status and housing status, data is available on substance use. Analyses conducted by our colleagues using Project RED data found that in this study population depression was significantly more prevalent among substance users (29% vs. 14%) compared with non‐users and that substance use is an independent risk factor for hospital reutilization (unpublished data).

Our findings linking depression to increased hospital utilization also warrant further consideration from healthcare policymakers. Central to the Obama Administration's February 2009 healthcare reform proposal is the pursuit of cost savings through reductions in unplanned hospital readmissions.39 Thus, identifying potentially modifiable risk factors for readmission, such as depression, is of great concern to healthcare providers and policymakers across the nation. If, through testing of interventions, depression proves to be a modifiable risk for readmission, policymakers, while negotiating healthcare reform measures, must provide for the services required to address this comorbidity at the time of discharge. For example, if a patient screens positive for depressive symptoms during a hospitalization for COPD exacerbation, will the proposed payment reforms allow for mental health services during the immediate post‐discharge period in order to reduce the likelihood of hospital readmission? Will those mental health services be readily available? Payment reforms that account for all necessary transitional care services will indeed help reduce readmission costs with less risk for untoward consequences.

In conclusion, our results indicate that a positive depression screen is a significant risk factor for early post‐discharge hospital utilization among hospitalized adults on a general medical service, even after controlling for relevant confounders. Screening for depression during acute hospitalizations may be an important step in identifying patients at increased risk for readmission. Future research should focus on further characterizing and stratifying populations at highest risk for depression. Efforts should also include developing and evaluating targeted interventions for patients with symptoms of depression among hospitalized patients as part of discharge planning. Timely depression therapy during the hospitalization or following hospital discharge might reduce costly readmissions and enhance patient safety.

Fully 19% of Medicare patients are readmitted to the hospital within 30 days of discharge.1 This represents a large amount of potentially avoidable morbidity and cost. Indeed, projects to improve the discharge process and post‐hospital care have shown that as much as one‐third of hospital utilization in the month after discharge can be avoided.2 Consequently, the rate of early, unplanned hospital utilization after discharge has emerged as an important indicator of hospital quality and the Centers for Medicare and Medicaid Services (CMS) has proposed a policy to decrease payments to hospitals with high rates of early unplanned hospital utilization. Thus, there is great interest in identifying modifiable risk factors for rehospitalization that could be used to refine intervention models and lead to improvements in quality of care, patient outcomes, and cost savings.

To date, known predictors of readmission include: lower socioeconomic status,3 history of prior hospitalization4 and advanced age,5 length of stay greater than 7 days,6 a high burden of comorbid illnesses (based on Charlson score),7 poor social support,8 and specific diagnoses (eg, congestive heart failure, chronic obstructive pulmonary disease [COPD] and myocardial infarction).5, 9, 10 In addition, unplanned readmissions and emergency department (ED) visits have been linked to polypharmacy and adverse drug events related to treatment with medications such as warfarin, digoxin and narcotics.11, 12 Another characteristic that has also been linked to readmission is depression;13 however5 reports supporting this association are from studies of elderly patients or with patients who have specific diagnoses (eg, congestive heart failure [CHF], COPD, myocardial infarction).1416

Depression is common, affecting 13% to 16% of people in the US, and is recognized as an important risk factor for poor outcomes among patients with various chronic illnesses.1719 The mechanisms by which depression can be linked to health outcomes and health service utilization have been studied in age‐specific or disease‐specific cohorts such as cardiac patients or frail elders and include both physiologic factors such as hypercoagulability and hyperinflammatory conditions, as well as behavioral factors such as poor self‐care behaviors and heightened sensitivity to somatic symptoms. How these mechanisms link depression to health outcomes and hospital utilization in a general medical population is not clearly understood. Kartha et al.13 reported findings indicating that depression is a risk factor for rehospitalization in general medical inpatients, but the study sample was relatively small and the study design methodology significantly limited its generalizability.12 It would be useful to provide supporting evidence showing depression as an important risk factor for readmission in the general medical in‐patient population using more rigorous study methods and a larger cohort.

We hypothesized that depressive symptoms would be an independent risk factor for early unplanned hospital utilization after discharge for all medical patients. Therefore, we conducted a secondary analysis of the Project RED clinical trial dataset to assess the association between a positive depression screen during inpatient hospitalization and the rate of subsequent hospital utilization.

Methods

Data from the Project RED clinical trial were reviewed for inclusion in a secondary analysis. Complete data were available for 738 of the 749 subjects recruited for Project RED.

Project RED Setting and Participants

Project RED was a two‐armed randomized controlled trial of English‐speaking adult patients, 18 years or older, admitted to the teaching service of Boston Medical Center, a large urban safety‐net hospital with an ethnically diverse patient population. A total of 749 subjects were enrolled and randomized between January 3, 2006 and October 18, 2007. Patients were required to have a telephone, be able to comprehend study details and the consent process in English, and have plans to be discharged to a US community. Patients were not enrolled if they were admitted from a skilled nursing facility or other hospital, transferred to a different hospital service prior to enrollment, admitted for a planned hospitalization, on hospital precautions, on suicide watch, deaf or blind. The Institutional Review Board of Boston University approved all study activities. A full description of the methods for the Project RED trial has been described previously.2

Outcome Variable

The primary endpoint was rate of hospital utilization within 30 days of discharge from the index admission, defined as the total number of ED visits and readmissions per subject within 30 days of the index discharge. Hospital utilization rates within 60 and 90 days of the index hospitalization discharge were also analyzed as secondary outcomes. Any ED visit in which a subject was subsequently admitted to the hospital was only counted as a readmission. Outcome data were collected by reviewing the hospital's electronic medical records (EMRs) and by contacting subjects by telephone 30 days after discharge. Dates of hospital utilization occurring at Boston Medical Center were obtained from the EMR, while those at other hospitals were collected through subject report. Subjects who could not be reached within 60 days of discharge were assumed alive.

Primary Independent Variable

The primary independent variable of interest was depressive symptoms defined as a positive score for minor or major depression on the nine‐item Patient Health Questionnaire (PHQ‐9) depression screening tool.20 A dichotomized variable was created using a standardized scoring system to determine the screening cut‐off for major or minor depressive symptoms.19

Statistical Analysis

Demographic and other characteristics of the subjects were compared by depression status (Table 1). Potential confounders were identified a priori from the available literature on factors associated with rehospitalization. These included age, gender, marital status, health literacy score (rapid estimate of health literacy in adult medicine tool [REALM]),21 Charlson score,22 insurance type, employment status, income level, homelessness status within past three months, hospital utilization within the 6 months prior to the index hospitalization, educational attainment, length of hospital stay and Project RED study group assignment. Bivariate analyses were conducted to determine which covariates were significant confounders of the relationship between depression and hospital utilization within 30 days of discharge. Chi‐square tests were used for categorical variables and t‐tests for continuous variables.

Baseline Characteristics of Study Subjects by Depression Screen Status
CharacteristicDepression Screen*
Negative (n = 500)Positive (n = 238)P Value
  • NOTE: Some columns may not add up to 100% due to omission of Other categories.

  • Abbreviations: PCP, primary care provider; PHQ9, Patient Health Questionnaire‐9; REALM, Rapid Estimate of Health Literacy in Adult Medicine tool; SD, standard deviation.

  • Positive depressive symptom screen determined by PHQ9 screen tool, a nine‐item 4‐point Likert scale, standard scoring algorithm to screen for major and minor depression. A score of 5 or higher indicates a positive depression symptom screen.17

  • Free Care is a Massachusetts state program for uninsured patients.

  • Health literacy categories correspond to total score as determined by REALM.18

  • Have PCP refers to subject self‐identifying PCP at time of Project RED study enrollment.

  • Unmarried marital status includes subjects identified as divorced, widow, single, partnered.

  • Charlson Comorbity Index Score reflects the cumulative increased likelihood of 1‐year mortality. The higher the score the more severe the comorbid condition. A 33% increase in risk for death is reflected in a 1‐point increase in weights. The minimum score is 0. There is no maximum score.19

  • Project RED study intervention group refers to subjects who received the 3‐armed discharge intervention.

  • Frequent Utilizer is defined as a subject with 2 or more hospital utilizations in 6 months prior to Project RED clinical trial index admission.

Race, No. (%)   
White140 (30)66 (30) 
Black268 (58)117 (54) 
Hispanic47 (10)29 (13)0.760
Insurance, No. (%)   
Private95 (19)22 (9) 
Medicare69 (14)30 (13) 
Medicaid214 (43)143 (61) 
Free care118 (24)40 (17)<0.001
Education, No. (%)   
<8th grade33 (7)21 (9) 
Some high school82 (17)52 (22) 
High school grad192 (38)90 (38) 
Some college126 (25)51 (22) 
College grad67 (13)22 (9)0.135
Health Literacy   
Grade 3 and below64 (13)44 (19) 
Grade 4654 (11)22 (10) 
Grade 78156 (32)73 (32) 
Grade 9 and above213 (44)89 (39)0.170
Income, $, No. (%)   
No income61 (12)37 (16) 
<10K77 (15)61 (26) 
1020K96 (19)35 (15) 
2050K97 (19)34 (14) 
50100K35 (8)7 (2) 
No answer132 (27)64 (27)0.002
Employment status, No. (%)   
Full time142 (28)34 (14) 
Part time57 (11)30 (13) 
Not Working297 (59)171 (72)<0.001
Age, mean (SD), years49.9 (16.0)49.6 (13.3)0.802
Gender: No. (%) Female239 (48)133 (56)0.040
Have PCP, No. (%) Yes399 (80)197 (83)0.340
Marital status, No. (%) unmarried365 (73)201 (85)<0.001
Charlson score, mean (SD)1.058 (1.6)1.56 (2.39)0.001
RED study group,# No. (%)   
Intervention243 (49)127 (53)0.22
Length of stay, days, mean (SD)2.5 (2.8)3.1 (3.8)0.016
Homeless in last 3 months, No. (%)45 (9)30 (13)0.130
Frequent utilizer,** No. (%)159 (32)104 (44)0.002

Age, length of stay, and Charlson score were used as continuous variables. Gender, marital status, frequent prior utilization (01 vs. 2 or more), and homelessness were treated as dichotomous variables. Categorical variables were created for, educational attainment (less than eighth grade, some high school, high school graduate, some college, college graduate), insurance type (Medicare, Medicaid, private insurance or free care), income level (no income, less than $10,000 per year, $10,00020,000, $20,00050,000, $50,000100,000, no answer), level of health literacy (grade 3 and below, grade 46, grade 78, grade 9 or above) and employment status(working full‐time, working part‐time, not working, no answer).

The 30‐day hospital utilization rate reflects the number of hospital utilization events within 30 days of discharge per subject. The same method was used to calculate hospital utilization rates within 60 and 90 days of discharge respectively. The unadjusted incident rate ratio (IRR) was calculated as the ratio of the rate of hospital utilizations among patients with depressive symptoms versus patients without depressive symptoms. Data for hospital utilization at 30, 60, and 90 days are cumulative.

Poisson models were used to test for significant differences between the predicted and observed number of hospitalization events at 30 days. A backward stepwise regression was conducted to identify and control for relevant confounders and construct the final, best‐fit model for the association between depression and hospital reutilization. A statistical significance level of P = 0.10 was used for the stepwise regression. To evaluate potential interactions between depression and the Project RED intervention, interaction terms were included. Two‐sided significance tests were used. P values of less than 0.05 were considered to indicate statistical significance. All data were analyzed with S‐Plus 8.0 (Seattle, WA).

In addition, a Kaplan‐Meier hazard curve was generated for the first hospital utilization event, ED visit or readmission, for the 30‐day period following discharge and compared with a log‐rank test.

Results

A total of 28% of subjects were categorized as having a positive depression screen. More women (36%) had positive depression screens than men (28%). Among patients with a positive depression screen, 58% had a history of depression and 53% were currently taking medications at the time of enrollment, compared with 25% and 22% respectively for subjects with a negative depression screen. Table 1 presents the means or percentages for baseline characteristics by depression status in the analytic cohort. Subjects with Medicaid for insurance had a higher rate of depression (61%) than subjects with Medicare (13%), private insurance (9%), or those who qualified for the Free Care pool (17%) which is the Massachusetts state funding for healthcare to uninsured persons. Subjects who were unemployed, unmarried, or who reported earnings less than $10,000 per year were also more likely to screen positive for depression. In addition, depressed subjects had a higher severity of co‐morbid disease and longer length of stay for the index hospitalization. Patients categorized as frequent utilizers (2 or more prior admissions) for the 6 months prior to the index hospitalization were also more likely to be depressed. Of further note, is the relatively younger average age among both depressive patients (49.6 years) and non‐depressive patients (49.9) of these study subjects.

The unadjusted hospital utilization rate at 30, 60, and 90 days post‐discharge by depression status is shown in Table 2. At 30 days post‐discharge, those with depressive symptoms had a higher rate of hospital utilization than those without depressive symptoms (0.563 vs. 0.296). In other words, 56 utilization events occurred per 100 patients with depressive symptoms, compared with 30 utilization events per 100 patients without depressive symptoms. The unadjusted 30‐day post‐discharge hospital utilization rate among those with depressive symptoms was higher compared with those without symptoms (IRR, 1.90, 95% confidence interval [CI], 1.242.71). A similar trend was found among subjects at 60 and 90 days post‐discharge.

Number of Hospital Utilizations, Hospital Utilization Rate, Unadjusted IRR at 30, 60 and 90 Days by Depression Screen Status*
Hospital UtilizationDepression Screen*P ValueIRR (CI)
Negative, n = 500 (68%)Positive, n = 238 (32%)
  • Abbreviations: CI, confidence interval; ED, emergency department; IRR, incident rate ratio.

  • Depression screen determined by scoring of Patient Health Questionnaire‐9 (PHQ9). Depressive symptom score of 5 points or higher is designated as positive.17

  • Number of hospital utilizations include all ED visits and hospital readmissions following discharge from Project RED index admission. ED visits leading to hospital admission are counted as one event. Sum reflects cumulative number of events over 30, 60 and 90 days.

No. of hospital utilizations140134 1.90 (1.51,2.40)
30‐day hospital utilization rate0.2960.563<0.001 
No. of hospital utilizations231205 1.87 (1.55,2.26)
60‐day hospital utilization rate0.4630.868<0.001 
No. of hospital utilizations324275 1.79 (1.53,2.10)
90‐day hospital utilization rate0.6481.165<0.001 

Poisson regression analyses were conducted to control for potential confounding in the relationship between depressive symptoms and hospital utilization rate within 30 days after discharge (Table 3). After controlling for relevant confounders, including age, gender, employment status, frequent prior hospitalization status, marital status, Charlson score, Project RED study group assignment and the interaction variable for RED study group assignment and depression, the association between symptoms of depression, and hospital utilization rate remained significant (IRR, 1.73; 95% CI, 1.272.36).

Adjusted Incident Rate Ratio of Hospital Utilization Within 30 Days of Discharge
CharacteristicsIRRCIP Value
  • Abbreviations: CI, confidence interval; ED, emergency department; IRR, incident rate ratio, PHQ9, Patient Health Questionnaire‐9.

  • Positive depressive symptom screen determined by PHQ9 screen tool, a nine‐item 4‐point Likert scale, standard scoring algorithm to screen for major and minor depression. A score of 5 or higher indicates a positive depression symptom screen.17

  • Unmarried refers to subjects whose self‐reported marital status includes divorced, single, partnered or widowed.

  • Frequent utilizer: 2 or more ED or hospital admissions visits in prior 6 months from index admission.

  • Refers to Project RED study group assignment.

  • Charlson Comorbity Index Score reflects the cumulative increased likelihood of 1‐year mortality. The higher the score the more severe the comorbid condition. A 33% increase in risk for death is reflected in a 1‐point increase in weights. The minimum score is 0. There is no maximum score.19

  • Interaction term of Project RED study group assignment and depressive symptom category (positive or negative).

Depression symptoms*  <0.001
Positive1.731.272.36 
NegativeREF1.0 
Gender  <0.001
Male1.871.472.40 
FemaleREF1.0 
Marital status  0.005
Married0.6250.440.89 
Unmarried1.0REF 
Frequent utilizer  <0.001
2+ prior visits2.451.923.15 
<2 prior visits1.0REF 
Study group  0.054
Intervention0.760.551.06 
Control1.0REF 
Employment   
Part time1.400.852.300.095
Not working1.671.152.440.003
Other0.520.073.850.262
Full time1.0REF 
Charlson Score0.980.921.040.250
Group* depression0.840.521.360.236
Age1.000.991.010.375

Figure 1 depicts the Kaplan‐Meier hazard curve generated for time to first hospital utilization, stratified by depression status. While 21% of participants without symptoms of depression had a hospital utilization within 30 days, fully 29% of participants with symptoms of depression had a hospital utilization within 30 days (P = 0.011).

Figure 1
Hazard for hospital utilization among subjects with and without depressive symptoms in 30 days following hospital discharge.

Discussion

Our study shows hospitalized patients who screen positive for depressive symptoms are significantly more likely to have a hospital visit (emergency room or rehospitalization) within 30 days of discharge than those who do not screen positive for depressive symptoms among medical patients admitted to an urban, academic, safety‐net hospital. These findings are consistent with, and extend, prior reports regarding depression and rehospitalization in specific populations (ie, geriatrics) and specific diagnoses (ie, cardiovascular disease [CVD] and COPD).1012 We observed a 73% higher incidence rate for hospital utilization within 30 days of discharge for those with symptoms of depression. This puts symptoms of depression on par with frequent prior rehospitalization, advanced age and low social support, as known risk factors for rehospitalization.4, 5, 23

Also of significance is the relatively young age of this study population (49.9 years non‐depressive patients and 49.6 years for depressive patients) compared with the study cohorts used for research in the majority of the existing literature. The chief reason for the young age of our cohort is that potential subjects were excluded if they came from a skilled nursing facility or other hospital. This may limit the generalizability of our findings; however, it seems likely that interventions relating to depression and transitions of care will need to be quite different for patients that reside in long‐term care facilities vs. patients that live in the community. For example, patients living in the community may have significant barriers to access post‐discharge services due to insurance status and are more likely to be sensitive to variations in social support.

Early rehospitalization is associated with significant morbidity, mortality, and expense. It is also a potential marker for poor quality of care.24 Concerns for patient safety, escalating healthcare costs, and possible change in hospital reimbursement mechanisms are fueling the search for modifiable risk factors associated with early rehospitalization. Our data provide evidence that symptoms of depression may be an important focus of attention. We do not know, however whether treating hospitalized patients who screen positive for depression will decrease early rehospitalization and emergency room utilization rates.

Various physiologic and behavioral mechanisms may link symptoms of depression to hospital utilization after discharge. For example, depressed patients with features of somatization may be more likely to experience worrisome physical symptoms after discharge and present prematurely for reevaluation. Patients who are sicker in some fashion not captured by our measured confounders may have symptoms of depression related to chronic, debilitating disease warranting early return to the hospital. Depression may also yield nonadherence to aspects of the discharge treatment plan leading to rehospitalization as a result of poor post‐discharge disease management. For example, research shows that patients with depression following coronary artery bypass surgery are less likely to adhere with cardiac rehabilitation programs.25 Likewise, depression among chronically ill patients such as diabetics, asthmatics, or human immunodeficiency virus (HIV)‐positive patients impairs medication adherence and self‐care behavior which may lead to disease relapse or recurrence.2628 One study examining depression effects on hypertensive medicine adherence in African Americans identified self‐efficacy as a mediating factor between depression and nonadherence.29 This implies that interventions such as self‐management education, a program through which chronically‐ill patients learn to better manage their illnesses through enhanced self‐confidence and problem‐solving strategies (including mood disorder challenges) may reduce early rehospitalization among depressed patients.30

There is also evidence that depression may have direct physiologic consequences. In patients with CVD, depression is associated with poor outcomes possibly related to decreased heart rate variability, hypercoagulability, high burdens of inflammatory markers, and severity of left ventricular dysfunction.3134 Similarly, depression among HIV/acquired immune deficiency syndrome (AIDS), diabetics and multiple sclerosis (MS) patients is linked to heightened levels of proinflammatory markers and less favorable outcomes that may signal a more severe form of the disease or an impaired response to treatment.3538 Indeed, MS investigators now hypothesize that the proinflammatory environment associated with the neurologic manifestations of MS are also causing depression symptoms among MS patients.34 This theory contrasts the common belief that depression in the chronically ill manifests independent of the chronic illness or in response to living with chronic disease.

A major strength of the current study is the large dataset and the broad range of covariates available for analyses. However, several limitations should be noted. First, data on hospital utilization outside Boston Medical Center were determined by patient self‐report and were not confirmed by document review. Second, we do not know the direction of the associations we report. If symptoms of depression are merely the consequence of having a higher disease burden, treatment of the underlying disease may be the most important response. While this is possible, our model does include several variables (eg, Charlson score and length of stay) that are likely to adjust for disease severity, pointing to the likelihood that symptoms of depression truly predict hospital utilization in a fashion that is independent of disease severity. Third, our results may not be generalizable to populations other than those served by urban safety‐net hospitals or other populations excluded from the Project RED trial (eg, non‐English speaking patients and patients from nursing homes). Finally, social factors such as substance use and social support system variables may residually confound the relationship between depression and hospital reutilization demonstrated in this study. While this dataset does not include a measure of social support other than marital status and housing status, data is available on substance use. Analyses conducted by our colleagues using Project RED data found that in this study population depression was significantly more prevalent among substance users (29% vs. 14%) compared with non‐users and that substance use is an independent risk factor for hospital reutilization (unpublished data).

Our findings linking depression to increased hospital utilization also warrant further consideration from healthcare policymakers. Central to the Obama Administration's February 2009 healthcare reform proposal is the pursuit of cost savings through reductions in unplanned hospital readmissions.39 Thus, identifying potentially modifiable risk factors for readmission, such as depression, is of great concern to healthcare providers and policymakers across the nation. If, through testing of interventions, depression proves to be a modifiable risk for readmission, policymakers, while negotiating healthcare reform measures, must provide for the services required to address this comorbidity at the time of discharge. For example, if a patient screens positive for depressive symptoms during a hospitalization for COPD exacerbation, will the proposed payment reforms allow for mental health services during the immediate post‐discharge period in order to reduce the likelihood of hospital readmission? Will those mental health services be readily available? Payment reforms that account for all necessary transitional care services will indeed help reduce readmission costs with less risk for untoward consequences.

In conclusion, our results indicate that a positive depression screen is a significant risk factor for early post‐discharge hospital utilization among hospitalized adults on a general medical service, even after controlling for relevant confounders. Screening for depression during acute hospitalizations may be an important step in identifying patients at increased risk for readmission. Future research should focus on further characterizing and stratifying populations at highest risk for depression. Efforts should also include developing and evaluating targeted interventions for patients with symptoms of depression among hospitalized patients as part of discharge planning. Timely depression therapy during the hospitalization or following hospital discharge might reduce costly readmissions and enhance patient safety.

References
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  2. Jack BW,Chetty VK,Anthony D, et al.The reengineered hospital discharge program to decrease rehospitalization.Ann Intern Med.2009;150(3):178187.
  3. Weissman JS,Stern RS,Epstein AM.The impact of patient socioeconomic status and other social factors on readmission: a prospective study in four Massachusetts hospitals.Inquiry.1994;31(2):163172.
  4. van Walraven C,Mamdani M,Fang J,Austin PC.Continuity of care and patient outcomes after hospital discharge.J Gen Intern Med.2004;19:624631. [PMID: 15209600]
  5. Marcantonio ER,McKean S,Goldfinger M,Kleenfield S,Yurkofsky M,Brennan TA.Factors associated with unplanned hospital readmission among patients 65 years of age and older in a Medicare managed care plan.Am J Med.1999;107(1):1317.
  6. Krumholz HM,Parent EM,Tu N, et al.Readmission after hospitalization for congestive heart failure among Medicare beneficiaries.Arch Intern Med.1997;157(1):99104.
  7. Librero J,Peiro S,Ordinana R.Chronic comorbidity and outcomes of hospital care: length of stay, mortality and readmission at 30 and 365 days.J Clin Epidemiol.1999;52(3):171179.
  8. Rodríguez‐Artalejo F,Guallar‐Castillón P,Herrera MC, et al.Social network as a predictor of hospital readmission and mortality among older patients with heart failure.J Card Fail.2006;12:621627.
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  11. Budpitz DS,Shebab N,Kegler SR, et al.Medication use leading to emergency department visits for adverse drug events in older adults.Ann Intern Med.2007;147(11):755765.
  12. Campbell SE,Seymour DG,Primrose WR.A systematic literature review of factors affecting outcomes in older medical patients admitted to hospital.Age Ageing.2004;33(2):110115.
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  14. Almagro P,Barreiro Bienvenido ,Ochoa de Echaguen A, et al.Risk factors for hospital readmission in patients with chronic obstructive pulmonary disease.Respiration.2006;73:311317.
  15. Frasure‐Smith N,Lesperance F,Gravel G, et al.Depression and healthcare costs during the first year following myocardial infarction.J Psychosom Res.2000;48(4–5):471478.
  16. Jiang W,Alexander J,Christopher E, et al.Relationship of depression to increased risk of mortality and rehospitalization.Arch Intern Med.2001;161(15):18491856.
  17. Parashar S,Rumsfeld JS,Spertus JA, et al.Time course of depression and outcome of myocardial infarction.Arch Intern Med.2006;166:20352043.
  18. Scherer M,Herrmann‐Lingen C.Single item on positive affect is associated with 1‐year survival in consecutive medical inpatients.J Gen Hosp Psych.2009;31:813.
  19. Hasin DS,Goodwin RD,Stinson FS,Grant BF.Epidemiology of major depressive disorder: results from the National Epidemiologic Survey on Alcoholism and Related Conditions.Arch Gen Psychiatry.2005;62(10):1097106.
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References
  1. Jenks SF,Williams MV,Coleman EA.Rehospitalizations among patients in the Medicare fee‐for‐service program.N Engl J Med.2009;360(14):14571459.
  2. Jack BW,Chetty VK,Anthony D, et al.The reengineered hospital discharge program to decrease rehospitalization.Ann Intern Med.2009;150(3):178187.
  3. Weissman JS,Stern RS,Epstein AM.The impact of patient socioeconomic status and other social factors on readmission: a prospective study in four Massachusetts hospitals.Inquiry.1994;31(2):163172.
  4. van Walraven C,Mamdani M,Fang J,Austin PC.Continuity of care and patient outcomes after hospital discharge.J Gen Intern Med.2004;19:624631. [PMID: 15209600]
  5. Marcantonio ER,McKean S,Goldfinger M,Kleenfield S,Yurkofsky M,Brennan TA.Factors associated with unplanned hospital readmission among patients 65 years of age and older in a Medicare managed care plan.Am J Med.1999;107(1):1317.
  6. Krumholz HM,Parent EM,Tu N, et al.Readmission after hospitalization for congestive heart failure among Medicare beneficiaries.Arch Intern Med.1997;157(1):99104.
  7. Librero J,Peiro S,Ordinana R.Chronic comorbidity and outcomes of hospital care: length of stay, mortality and readmission at 30 and 365 days.J Clin Epidemiol.1999;52(3):171179.
  8. Rodríguez‐Artalejo F,Guallar‐Castillón P,Herrera MC, et al.Social network as a predictor of hospital readmission and mortality among older patients with heart failure.J Card Fail.2006;12:621627.
  9. Wong AW,Gan WQ,Burns J,Sin DD,van Eeden SF.Acute exacerbation of chronic obstructive pulmonary disease: influence of social factors in determining length of stay and readmission rates.Can Respir J.2008;15(7):361364.
  10. Parashar S,Rumsfeld JS,Spertus JA, et al.Time course of depression and outcome of myocardial infarction.Arch Intern Med.2006;166(18):20352043.
  11. Budpitz DS,Shebab N,Kegler SR, et al.Medication use leading to emergency department visits for adverse drug events in older adults.Ann Intern Med.2007;147(11):755765.
  12. Campbell SE,Seymour DG,Primrose WR.A systematic literature review of factors affecting outcomes in older medical patients admitted to hospital.Age Ageing.2004;33(2):110115.
  13. Kartha A,Anthony D,Manasseh CS, et al.Depression is a risk factor for rehospitalization in medical inpatients.Prim Care Companion J Clin Psychiatry.2007;9(4):256262.
  14. Almagro P,Barreiro Bienvenido ,Ochoa de Echaguen A, et al.Risk factors for hospital readmission in patients with chronic obstructive pulmonary disease.Respiration.2006;73:311317.
  15. Frasure‐Smith N,Lesperance F,Gravel G, et al.Depression and healthcare costs during the first year following myocardial infarction.J Psychosom Res.2000;48(4–5):471478.
  16. Jiang W,Alexander J,Christopher E, et al.Relationship of depression to increased risk of mortality and rehospitalization.Arch Intern Med.2001;161(15):18491856.
  17. Parashar S,Rumsfeld JS,Spertus JA, et al.Time course of depression and outcome of myocardial infarction.Arch Intern Med.2006;166:20352043.
  18. Scherer M,Herrmann‐Lingen C.Single item on positive affect is associated with 1‐year survival in consecutive medical inpatients.J Gen Hosp Psych.2009;31:813.
  19. Hasin DS,Goodwin RD,Stinson FS,Grant BF.Epidemiology of major depressive disorder: results from the National Epidemiologic Survey on Alcoholism and Related Conditions.Arch Gen Psychiatry.2005;62(10):1097106.
  20. Kroenke K,Spitzer RL,Williams JB.The PHQ‐9: Validity of a brief depression severity measure.J Gen Intern Med.2001;16:606613. [PMID:11556941]
  21. Davis TC,Long SW,Jackson RH, et al.Rapid estimate of adult literacy in medicine: a shortened screening instrument.Fam Med.1993;25:391395. [PMID:8349060]
  22. Charlson ME,Pompei P,Ales KL,MacKenzie CR.A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.J Chronic Dis.1987;40:373383. [PMID: 3558716]
  23. Rodriguez‐Artalejo F,Guallar‐Castillon P,Herrera MC, et al.Social network as a predictor of hospital readmission and mortality among older patients with heart failure.J Card Fail.2006;12(8):621627.
  24. Ashton CM,Del Junco DJ,Souchek J,Wray NP,Mansyr CL.The association between the quality of inpatient care and early readmission: a meta‐analysis of the evidence.Med Care.1997;35(10):10441059.
  25. Kronish IM,Rieckmann N,Halm FA, et al.Persistent depression affects adherence to secondary prevention behaviors after acute coronary syndromes.J Gen Intern Med.2006;21(11):11781183.
  26. Cukor D,Rosenthal DS,Jindal RM,Brown CD,Kimmel PL.Depression is an important contributor to low medication adherence in hemodialyzed patients and transplant recipients.Kidney Int.2009;75(11):12231229.
  27. Gonzalez JS,Safren SA,Delahanty LM, et al.Symptoms of depression prospectively predict poorer self‐care in patients with Type 2 diabetes.Diabet Med.2008;25(9):11021107.
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Issue
Journal of Hospital Medicine - 5(7)
Issue
Journal of Hospital Medicine - 5(7)
Page Number
378-384
Page Number
378-384
Publications
Publications
Article Type
Display Headline
Post‐discharge hospital utilization among adult medical inpatients with depressive symptoms
Display Headline
Post‐discharge hospital utilization among adult medical inpatients with depressive symptoms
Legacy Keywords
depression, hospital discharge, patient safety, readmission, rehospitalization
Legacy Keywords
depression, hospital discharge, patient safety, readmission, rehospitalization
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Department of Family Medicine, Dowling 5, Boston Medical Center, 1 BMC Place, Boston, MA 02118
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