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. Author manuscript; available in PMC: 2021 Jul 1.
Published in final edited form as: Drug Alcohol Depend. 2020 Apr 27;212:107987. doi: 10.1016/j.drugalcdep.2020.107987

Length of hospitalization and hospital readmissions among patients with substance use disorders in New York City, NY USA

Tawandra L Rowell-Cunsolo 1, Jianfang Liu 1, Gloria Hu 2, Elaine Larson 1,2
PMCID: PMC7293944  NIHMSID: NIHMS1589511  PMID: 32388493

Abstract

Introduction.

In the United States, the number of patients with substance use disorders is steadily increasing. Individuals with a substance use disorder may be more likely to experience negative hospital outcomes, including lengthier hospital stays and frequent readmissions, which is extremely costly to patients and the government. While there are established associations between substance use disorder and hospital readmissions, impact on other outcomes such as length of stay remain unclear. We assessed whether hospital admissions diagnosed with substance use disorders experienced longer hospital stays and readmissions compared to patients without a substance use disorder diagnosis.

Methods.

This is a retrospective study of hospital readmissions rates in three hospitals in the New York City, New York area. Data were examined over a 10-year period (from 2007 to 2016), (n = 768,219). We used multilevel multivariable regression models to compare the hospital length of stay, time-to-hospital readmission, and 30-day hospital readmission among admissions with substance use disorder compared to admissions without the disorder.

Results.

As compared to those who did not having substance use disorder, those with substance use disorder had longer hospital LOS (Regression coefficient (b) =1.24; 95% CI: 1.15–1.33), were more likely to experience hospital readmission at any point in time (HR= 1.24; 95% CI: 1.22–1.25), and more likely to have a 30-day hospital readmission (RR= 1.16; 95% CI: 1.13–1.19).

Conclusions.

Hospital settings could potentially serve as useful venues for substance use-related interventions and could benefit from strong coordination with outpatient providers and more targeted discharge planning.

Keywords: substance use disorder, patients, hospitalization

1. Introduction

Over 20 million adults are diagnosed with substance use disorders (SUDs) in the United States (Hedden, 2015). SUDs cost the country $740 billion annually in costs related to crime, lost work productivity and health care (National Institute on Drug Abuse, 2020). The health care costs associated with SUD include overutilization of emergency rooms (McGeary and French, 2000), associated healthcare (National Institute on Drug Abuse, 2020), and premature mortality (Chen et al., 2018).

As they experience a variety of barriers to seeing healthcare providers on a regular basis (Neale et al., 2008), SUD-diagnosed individuals may experience more frequent hospital readmissions and lengthier hospital stays upon admission. Previous literature has consistently demonstrated an association between SUDs and readmission rates. For example, a large cohort study of patients at an urban, safety-net academic hospital in the United States indicated that the incidence rate of rehospitalization was significantly higher in patients with an SUD diagnosis than those without (Walley et al., 2012). In a separate study of Medicaid patients in the United States, SUD diagnosis was significantly associated with readmission as well as psychotic illness and multiple comorbidities (Mark et al., 2015), and a study of the National Readmission Database identified elevated 30-day readmission rates among those with diagnoses of opioid abuse or dependence (Gupta et al., 2018). Higher rates of hospitalization among those with SUD have also been documented in settings outside the United States (Bimerew et al., 2007; Lin et al., 2007).

Although readmissions among patients with SUDs are elevated compared to patients without SUD diagnoses, findings have been mixed regarding the effect of SUD on length of stay (LOS). In a national study of hospital utilization, SUD was not found to be associated with LOS among patients with psychiatric disorders including SUDs (Bressi et al., 2006), and a study in a large academic hospital similarly failed to find an association between SUD and LOS (Masters et al., 2014). Conversely, a study of the National Readmission Database demonstrated significantly longer LOS among patients diagnosed with SUDs (Wancata et al., 2001), and reduced function and psychological well-being associated with SUDs have been found to be positively associated with LOS in other settings (Lyketsos et al., 2002). While it has been hypothesized that short LOS among patients with SUD may be attributable to non-compliance with treatment or patient-provider friction (Bressi et al., 2006), the relationship and mechanisms through which SUD might impact LOS is not well understood.

Lengthy hospitalizations and readmissions may be avoidable (van Walraven et al., 2011), and currently represent a significant financial burden on the healthcare system (Gryczynski et al., 2016). In addition to cost, there are additional risks associated with lengthy hospitalization including increased susceptibility to hospital-acquired infections (Schimmel, 2003). While there are established associations between SUD and hospital readmission, impact on other outcomes such as LOS remain unclear. To address this gap in the literature, we aim to examine whether the relationship between SUD diagnosis and readmissions remains significant when testing in a different patient population, and how SUD diagnosis relates to hospital LOS within one of the largest health care systems in the United States.

Further, external factors affecting the association between SUD and higher health care utilization are not well understood. Because patients with SUDs often have other co-occurring conditions (Brooner et al., 1997; Wu et al., 2018), the period during hospitalization may be an opportune time to offer substance use treatment services in addition to other clinical services being provided. Examining factors associated with negative hospital outcomes is crucial for identifying populations particularly at risk for high health care utilization and can inform discharge planning and developing programs and interventions designed to improve health outcomes among this population (Kansagara et al., 2011). In addition to examining the relationship between SUD and health care utilization in New York City, this study contributes to existing literature by identifying populations particularly at risk for readmissions and increased LOS.

2. Materials and Methods

2.1. Participants

This was a secondary data analysis of data from all adult (≥18 years) inpatient hospital discharges between January 1st, 2007 and December 31st, 2016 from a large hospital system in the New York City, New York metropolitan area. The hospital system includes a community hospital and two tertiary/quaternary care hospitals. All study data, including patients’ demographic information, inpatient admission date, discharge date and status, and International Classification of Disease, ninth revision, Clinical Modification (ICD-9-CM) codes were extracted retrospectively from the institution’s clinical data warehouse server to an encrypted network. Institutional review boards at the study institutions approved and monitored the study protocol.

2.2. Measures

Study outcomes.

There were three outcome variables in this study: hospital LOS, time-to-hospital readmission (readmitted to the hospital system within the study period), and 30-day hospital readmission (dichotomized: hospital readmission within 30 days of discharge vs. no hospital readmission within 30 days of discharge). The main independent variable was SUD (dichotomized: SUD positive vs. SUD negative) extracted from ICD-9-CM codes for each admission [see list of ICD9-CM codes for SUD in the Appendix].

Patient characteristics.

Patient characteristics including age, race, gender, inpatient admission date and discharge date, ICD-9-CM codes, intensive care unit (ICU) stay, and insurance plan were extracted from hospital discharge data.

Charlson/Deyo age-comorbidity score.

The modified and enhanced version of the ICD-9-CM coding algorithm proposed by Quan et al. (2005) was used to generate the validated Charlson/Deyo age-comorbidity score, which represents a patient’s clinical age-comorbidity score (Quan et al., 2005). It is measured as a weighted summation of 17 categories of comorbid illnesses and the patient’s age point: beginning at age 40, one point is added for each 10-year increase in age. The Charlson/Deyo age-comorbidity score ranges from 0 to 38, and it is a covariate when assessing the relationship between SUD and the three outcomes of interest. Mental Illness. A binary variable indicating patients’ mental illness status (positive vs. negative) was also created from ICD-9-CM codes for each admission [Please see the Appendix for a list of ICD9-CM codes for Mental Illness].

2.3. Data analysis

Descriptive statistics were used to characterize the sample characteristics. Frequency, mean with standard deviation or median with interquartile range (IQR) was calculated for the total sample and the SUD-positive and SUD-negative groups separately. Chi-square analysis for categorical variables or independent-samples t-test for continuous variables were used to assess if sample characteristics or outcome measures were the same between the SUD positive and negative groups. Three outcome measures were included in this study: hospital length of stay (LOS), time-to-hospital readmission, and 30-day hospital readmission (binary: yes vs. no).

We built multilevel, multivariable regression models to test the association between outcome measures and independent variables, which include SUD diagnosis status (binary: positive vs. negative), Charlson/Deyo age-comorbidity score, mental illness status, gender, ICU stay and insurance plan (Medicaid vs. Medicare), controlling for potential covariates (calendar year and race). Given the large sample size, linear regression was used to examine hospital LOS (Schmidt and Finan, 2018). Regression coefficients (B) and associated 95% confidence intervals (CIs) were calculated to assess the strength and direction of relationships in linear regression model.

A Cox regression model was used to assess the relationship between independent variables and time-to-hospital readmission. Proportional hazard assumption was checked using Schoenfeld residuals’ plot. Hazard ratio (HR) and associated 95% CIs were calculated to assess the strength and direction of relationship for time-to-hospital readmission. Because the prevalence rate of the binary outcome 30-day readmission was higher than 10%, we used a Cox regression model with time as a constant variable to estimate the risk ratio (RR) (Barros and Hirakata, 2003; Diaz-Quijano, 2012). We did not use traditional logistic regression model for 30-day readmission to estimate odds ratio as odds ratio tends to be inflated when the outcome event is not rare. RRs and associated 95% CIs were calculated to assess the strength and direction of relationship for 30-day readmission. For all three outcomes, we used the multi-level version of regression models to account for the clustering effect of individual patients with multiple admissions (424,261 patients with 768,219 admissions for LOS and time-to-hospital readmission and 421,291 patients with 762,024 admissions for 30-day readmission). The sample size for binary outcome 30-day readmission was slightly lower than the other two outcomes to allow for sufficient follow-up time for each admission. Data analysis was conducted in Version 9.4 of the SAS System for Windows software.

3. Results

A total of 768,219 adult hospital admissions were recorded from 2007–2016 (see Table 1 for sample characteristics). While the vast majority of admissions did not have an SUD (94.73%; n=727,739); 5.27% (n=40,480) had been diagnosed with an SUD. The average age of the admission was 57.39 years old (SD = 19.77) with those who had an SUD being significantly younger (mean=46.85 years; SD=14.44) than those who did not have a SUD (mean=57.98 years; SD=19.86) (p<0.001). Slightly over half (55.58%) of the sample were female and about a third was White (31.22%). The largest proportion of admissions identified Medicare (45.23%) as their primary insurance type; 23.06% used Medicaid.

Table 1.

Sample demographic characteristics, hospital admission attributes, length of stay and readmissions outcomes (n=768,219 admissions)

Total sample (n=768,219 admissions, 100%) Positive SUD (n=40,480 admissions) Negative SUD (n=727,739 admissions) P
Sociodemographic factors
Sex
 Male 341265 (44.42) 26635 (65.80) 314630 (43.23) <0.0001
 Female 426954 (55.58) 13845 (34.20) 413109 (56.77)
Age, Mean (SD) 57.39 (19.77) 46.85 (14.44) 57.98 (19.86)
Race
 White Non-Hispanic 239809 (31.22) 8796 (21.74) 231013 (31.74) <0.0001
 Black Non-Hispanic 76577 (9.97) 8505 (21.01) 68072 (9.35)
 Hispanic 32216 (4.19) 2406 (5.94) 29810 (4.10)
 Asian 15815 (2.06) 135 (0.33) 15680 (2.15)
 Other 130678 (17.01) 9972 (24.63) 120706 (16.59)
 Unknown 273124 (35.55) 10666 (26.35) 262458 (36.06)
Insurance Plan
 Medicare 347500 (45.23) 9057 (22.37) 338443 (46.51) <0.0001
 Medicaid 177126 (23.06) 24109 (59.56) 153017 (21.03)
 Self-pay 12739 (1.66) 1180 (2.92) 11559 (1.59)
 All other 230854 (30.05) 6134 (15.15) 224720 (30.88)
Mental illness 234751 (30.56) 40204 (99.32) 194547 (26.74) <0.0001
Charlson/Deyo age-comorbidity score, 4.05 (3.19) 3.26 (3.11) 4.09 (3.19) <0.0001
Mean (SD)
Hospital Admission Attributes
ICU stay 88689 (11.65) 3937 (9.73) 84752 (11.65)
Admission year <0.001
 2007 75736 (9.86) 4380 (10.82) 71356 (9.81)
 2008 73260 (9.54) 4145 (10.24) 69115 (9.50)
 2009 78806 (10.26) 4408 (10.89) 74398 (10.22)
 2010 81731 (10.64) 4827 (11.92) 76904 (10.57)
 2011 81435 (10.60) 4574 (11.3) 76861 (10.56)
 2012 79476 (10.35) 4277 (10.57) 75199 (10.33)
 2013 79380 (10.33) 4354 (10.76) 75026 (10.31)
 2014 68026 (8.86) 3674 (9.08) 64352 (8.84)
 2015 75605 (9.84) 2192 (5.42) 73413 (10.09)
 2016 74764 (9.73) 3649 (9.01) 71115 (9.77)
Length of Stay and Readmissions Outcomes
Hospital length of stay
 Median (Q1–Q3) 4.0 (3.0–8.0) 6.0 (3.0–10.0) 4.0 (3.0–8.0) <0.001
 Mean (SD) 7.03 (9.05) 9.05 (11.12) 6.92 (8.91)
Hospital readmission 347493 (45.23) 22051 (54.47) 325442 (44.72) <0.001
30-day Hospital readmission 92470 (12.13) 5925 (14.64) 81005 (11.13) <0.001
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*

p-value from Chi-square analysis for categorical variables or independent-sample t-test for continuous variables

The average Charlson/Deyo age-comorbidity score among all admissions was 4.05 (SD = 3.19). Almost all SUD-positive admissions had a mental illness (99.32%), compared to about a quarter (26.74%) of the SUD-negative admissions. The median hospital LOS for the total sample, SUD-positive sample, and SUD-negative sample was four days (IQR = 3–8; Mean=7.0; SD=9.1), six days (IQR = 3–10; Mean=9.1; SD=11.1), and four days (IQR = 3–8 days; Mean=6.9 days; SD=8.9), respectively. Overall, the hospital readmission rate was 45.23% and the SUD-positive sample had a higher hospital readmission rate than the SUD negative sample (54.47% vs. 44.72%; p-value<0.001). The 30-day hospital readmission rate for the overall sample was 12.13% and it was higher in the SUD-positive sample than in the SUD-negative sample (14.64% vs. 11.13%; p-value<0.001).

Table 2 summarizes the associations between hospital LOS and independent variables, SUD diagnosis status (positive vs. negative), Charlson/Deyo age-comorbidity score, gender, and ICU stay and finance plan (Medicaid vs. Medicare) controlling for other potential covariates. The hospital LOS of those with SUD was about one day longer than that of those without an SUD (B=1.24; 95%CI: 1.15–1.33). Admissions with higher Charlson/Deyo age-comorbidity scores had longer LOS (B=0.33; 95%CI: 0.32–0.33). Lengthier LOS was also evidenced among males (B=0.09; 95%CI: 0.05–0.13); those who had an ICU stay (B=8.27; 95% CI: 8.21–8.33); those whose insurance plan was Medicaid, as compared to those with Medicare (B=0.87; 95%CI: 0.81–0.93); and those with a mental illness (B=1.33; 95% CI: 1.28–1.37).

Table 2.

Multilevel linear regression model assessing relationship between hospital length of stay and independent variables (n=424,261 patients with 768,219 admissions)

Regression Coefficient(B) 95% CI Lower Bound 95% CI Upper Bound P
Positive SUD diagnosis 1.24 1.15 1.33 <0.0001
Charlson/Deyo age-comorbidity score 0.33 0.32 0.33 <0.0001
Sex (Male vs. Female) 0.09 0.05 0.13 <0.0001
ICU stay (vs. non-ICU stay) 8.27 8.21 8.33 <0.0001
Medicaid admission (vs. Medicare admission) 0.87 0.81 0.93 <0.0001
Other (vs. Medicare admission) −0.23 −0.29 −0.18 <0.0001
Self-pay (vs. Medicare admission) 0.32 0.17 0.48 <0.0001
Mental illness (positive vs. negative) 1.33 1.28 1.37 <0.0001
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*

model controls for effects of race, time trends, and hospital of admission

Table 3 summarizes the association between time-to-hospital readmission and independent variables. For those with an SUD, the hazard of hospital readmission increased by about a quarter (HR= 1.24; 95%CI: 1.22–1.25). Readmission at any point in time was also more likely among those with greater Charlson/Deyo age-comorbidity scores (HR=1.07; 95%CI: 1.07–1.08) and males (HR=1.02 95%CI; 1.02–1.03). Those who had an ICU stay were less likely to have hospital readmission at any point in time (HR=0.91; 95%CI: 0.90–0.92); additionally, hazard of hospital readmission was higher in those with mental illness (HR=1.07; 95%CI: 1.06–1.08). There was no difference between those insured with Medicaid and those with Medicare.

Table 3.

Hazard ratio and associated 95% CI from multilevel cox regression model assessing time-to-readmission outcome (n=424,261 patients with 768,219 admissions)

Hazard Ratio 95% CI Lower Bound 95% CI Upper Bound P
Positive SUD diagnosis 1.24 1.22 1.25 <0.0001
Charlson/Deyo age-comorbidity score 1.07 1.07 1.08 <0.0001
Sex (Male vs. Female) 1.02 1.02 1.03 <0.0001
ICU stay (vs. non-ICU stay) 0.91 0.90 0.92 <0.0001
Medicaid admission (vs. Medicare admission) 1.00 0.99 1.01 0.7291
other(vs. Medicare admission) 0.71 0.70 0.72 <0.0001
Self pay(vs. Medicare admission) 0.49 0.48 0.51 <0.0001
Mental illness(positive vs. negative) 1.07 1.06 1.08 <0.0001
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*

model controls for effects of race, time trends, and hospital of admission

The relationship between 30-day hospital readmission and independent variables are presented in Table 4. Those with positive SUD were 16% more likely to have 30-day hospital readmission (RR= 1.16; 95%CI: 1.13–1.19). The relationships between other independent variables and 30-day hospital readmission are similar to those between these variables and the time-to-hospital readmission outcome, with the exception of insurance type (Medicaid vs. Medicare): those with Medicaid were more likely to have 30-day readmission compared to those with Medicare (RR=1.18; 95%CI: 1.16–1.21).

Table 4.

Relative Risk and associated 95% CI from multilevel cox regression model with time as a constant variable assessing 30-day readmission outcome (n=421,291 patients with 762,024 admissions)

Relative Risk 95% CI Lower Bound 95% CI Upper Bound P
Positive SUD diagnosis 1.16 1.13 1.19 <.0001
Charlson/Deyo age-comorbidity score 1.11 1.11 1.11 <.0001
Sex (Male vs. Female) 1.13 1.12 1.15 <.0001
ICU stay (vs. non-ICU stay) 1.04 1.02 1.06 <.0001
Medicaid admission (vs. Medicare admission) 1.18 1.16 1.21 <.0001
other(vs. Medicare admission) 0.90 0.89 0.92 <.0001
Self pay(vs. Medicare admission) 0.49 0.48 0.51 <.0001
Mental illness(positive vs. negative) 1.04 1.03 1.06 <.0001
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*

model controls for effects of race, time trends, and hospital of admission

4. Discussion

Length of stay

While we found that SUD diagnoses are associated with increased length of hospitalization, findings from previous studies of the association between SUD and length of hospitalization are mixed. Our findings are consistent with two major studies that have identified SUD as a risk factor for increased LOS (Lyketsos et al., 2002; Wancata et al., 2001). In particular, our results are supported by Wancata and colleagues’ finding that the association between substance use and LOS is specific and stronger than the association between LOS and other psychiatric comorbidities.

However, some studies have shown that SUD is not associated with length of hospitalization, or is associated with shorter LOS (Bressi et al., 2006; Masters et al., 2014; Xafenias et al., 2008). We propose a variety of explanations for these findings; for example, the average LOS among SUD users may be brought down by short-term, acute hospitalizations for detoxification or other drug-related events. Other proposed explanations include factors related to the patient-physician relationship or patient characteristics, such as treatment noncompliance or tension between patients and providers. We would like to note, however, that these factors may differ by context, as hospital admissions and patient-provider relationships can vary markedly in different areas. Programs that integrate substance use treatment into other aspects of clinical care may be suitable for patients with SUD. Mounting evidence suggests that addiction consultation services can potentially increase provider knowledge in addiction treatment and management (Weinstein et al., 2018) and improve quality of care among SUD-diagnosed individuals (Nordeck et al., 2018). Addiction consultation services can also be used to facilitate successful linkage to addiction treatment post-discharge (Trowbridge et al., 2017).

30-day readmission

Thirty-day readmission rates were also more frequent among SUD admissions. Our findings are consistent with previous research demonstrating higher 12-month hospital readmission rates among patients with drug dependence (Smith et al., 2015). Because 30-day readmission is often seen as a result of inadequate care during hospitalization (Jencks et al., 2009), and research suggests that SUD patients may receive an inferior quality of care compared to their non-SUD counterparts (Mitchell et al., 2009), our findings additionally highlight the importance of promoting access and engagement in adequate outpatient services for substance use treatment. Using a range of modalities to deliver evidence-based treatment may help address accessibility challenges for SUD-diagnosed patients post-discharge, resulting in improved health outcomes. For instance, telemedicine interventions have shown great promise in effectively delivering addiction support services and improving treatment retention among individuals with SUD (Eibl et al., 2017; Lin et al., 2019). Since effective substance use treatment reportedly reduces the likelihood of experiencing hospitalization (Barnett and Swindle, 1997), such efforts could be useful in reducing costs associated with frequent hospital visits among SUD patients.

Our study findings further highlight the need to reduce LOS and readmissions among SUD-diagnosed admissions. A comprehensive approach to treatment may require more intensive discharge support or case management services that address key community and environmental factors impacting recovery (Smith et al., 2004). An evidence-based approach such as Shared Decision Making, which emphasizes the importance of patient preferences, may be considered to promote discussion and acceptance of more specialized treatment options for SUD patients being discharged (Friedrichs et al., 2016).

Contributions to the literature

While our findings build on existing research on SUD and hospital outcomes, they make several new contributions to the existing body of research in this area. First, the findings provide information on a substantial number of SUD admissions within an urban setting. Over the data collection period, rates of overdose in New York City underwent a period of decline, followed by a period of rapid increase (Nolan et al., 2018); our results were able to document SUD admissions and hospitalization-related outcomes during this period of considerable change. Our research also helps lay the foundation for future investigations to inform the most effective evidence-based interventions to improve health outcomes among SUD-diagnosed admissions.

4.1. Limitations

This study has several limitations. First, our definition of SUD was based on ICD-9-CM codes, which has some limitations. SUD might be undetected or underreported, especially if SUD is not the primary reason for hospital admission, and given time constraints of hospital staff. Therefore, our data may have inadvertently overlooked some admissions with SUDs.

Further, SUD might be differentially reported depending on method of ascertainment- for example, if an SUD diagnosis was based on self-report or visual exam. Our data also only include admissions from three hospital systems in the New York City area and may not be generalizable to admissions to hospitals outside of this network. Finally, our dataset did not include variables on factors associated with hospitalization among this population, including frequency of drug use and overdose history.

Conclusions

Our research has documented significantly longer LOS and more hospital readmissions among patients with an SUD compared to those without an SUD over a 10-year period. These findings suggest that hospital settings could potentially serve as useful venues for substance use-related interventions and could benefit from strong coordination with outpatient providers and more targeted discharge planning. Such planning should include both individual and family-based interventions, which have been shown to successfully engage SUD-diagnosed individuals (Smith and Meyers, 2007; Smith et al., 2004). Because they are a captive audience during hospitalization, patients with SUDs may benefit from medical services that are thoroughly integrated with evidence-based substance use treatment.

Supplementary Material

1
2

Highlights.

  • Retrospective study of three hospitals in the NYC area over a 10-year period

  • Admissions with SUD had longer hospital stays and higher readmission rates

  • Hospital settings could serve as useful venues for substance use interventions

Role of funding source

This research was supported by a grant from the Agency for Healthcare Research and Quality (PI: Larson; R01HS024915). Dr. Rowell-Cunsolo was supported by the National Institute on Drug Abuse (PI: Rowell-Cunsolo; K01DA036411).

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Conflict of interest

No conflict declared.

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