Bonnie T. Zima, MD, MPH

Suicide is a leading cause of death among 10- to 34-year-olds in the United States.^1,2 During the past two decades, the youth suicide death rate has risen by 24%, and more than 5,000 young people die from suicide each year.³ Suicide ideation (SI) and suicide attempts (SAs) are well-established risk factors for suicide death and a source of morbidity for patients and families. One-third of youth with SI attempt suicide at some point in their lifetime.⁴ Approximately 11% of youth SAs result in suicide death, and 2% of youth who attempt suicide subsequently go on to die from suicide after recovering from a prior SA.⁵ More than 60,000 youth are hospitalized for SI or SA each year,⁶ and young people hospitalized for SA are at high short-term risk of repeat SA and suicide death.⁷ Hospitals need strategies for measuring the quality of SI and SA hospitalizations, monitoring postdischarge outcomes, and identifying the patients at the highest risk of poor outcomes. Readmissions are a useful hospital quality measure that can indicate re-emergence of SI, repeat SA, or inadequate community-based mental health treatment, and interventions designed for patients with readmissions can potentially avert morbidity or mortality.

 

The National Committee on Quality Assurance recommends measurement of quality metrics for 30-day mental health follow-up after psychiatric hospitalizations, 30-day readmissions after adult (but not pediatric) psychiatric hospitalizations, and 30-day readmissions in pediatric medical and surgical hospitalizations. Readmission measures are not consistently used to evaluate pediatric psychiatric hospitalizations, and psychiatric quality measures are not used to evaluate medical or surgical hospitalizations for SA. Recent research has investigated transfers to postacute care,⁸ readmission prevalence, variation in hospital readmission performance, and risk factors for readmissions after pediatric psychiatric hospitalizations.^9–11 However, no national study has investigated 30-day readmissions in youth hospitalized specifically for SI or SA.

To inform hospital quality measurement and improve hospital and postdischarge care for youth at risk of suicide, more information is needed about the characteristics of and the risk factors for readmissions after index SI/SA hospitalization. To address this knowledge gap, among SI/SA hospitalizations in 6- to 17-year-olds, we examined (1) unplanned 30-day readmissions and characteristics of hospitalizations by 30-day readmission status; (2) patient, hospital, and regional characteristics associated with 30-day readmissions; and (3) characteristics of 30-day readmissions.

METHODS

Study Design and Data Source

We conducted a national, retrospective cohort study of hospitalizations for patients aged 6-17 years using the Agency for Healthcare Research and Quality (AHRQ) 2013 and 2014 Nationwide Readmissions Database (NRD). The combined 2013-2014 NRD includes administrative data from a nationally representative sample of 29 million hospitalizations in 22 states, accounting for 49.3% of all US hospitalizations, and is weighted for national projections. The NRD includes hospital information, patient demographic information, and International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis, procedure, and external cause of injury codes (E-codes). The database includes one primary diagnosis, up to 24 additional diagnoses, and up to 4 E-codes for each hospitalization. The NRD includes information about hospitalizations in acute-care general hospitals (including their psychiatric units) but not from specialty psychiatric hospitals. The database also includes de-identified, verified patient linkage numbers so that patients can be tracked across multiple hospitalizations at the same institution or different institutions within the same state. This study was considered to be exempt from review by the Children’s Hospital of Philadelphia Institutional Review Board.

Sample

We identified a sample of 181,575 hospitalizations for SI (n = 119,037) or SA (n = 62,538) among 6- to 17-year-olds between January 1, 2013, and December 31, 2014 (Figure). We included children as young as 6 years because validated methods exist to identify SI and SA in this age group,¹² and because suicide deaths have recently increased among younger children.³ We excluded patients aged 18 years and older from this study since delivery of mental health services differs for adults.¹³ To create the sample, we first identified all hospitalizations of patients aged 6-17 years. We then used a validated algorithm relying on ICD-9-CM diagnosis codes for poisonings and E-codes for self-injury (E950-959) to identify hospitalizations related to SA.¹² Because E-code completeness varies among states,¹⁴ we also used the combination of having both a diagnosis code for injury (800-999) and an ICD-9-CM code for SI (V62.84) as a proxy for SA. Among hospitalizations without SA, we identified hospitalizations with SI using the ICD-9-CM code for SI (V62.84) in any position.

 

We identified 133,516 index hospitalizations with complete data at risk for an unplanned readmission. Because NRD data cannot be linked between calendar years, we limited the study time period for each calendar year to 10 months. We excluded hospitalizations in January because the full 30-day time frame to determine whether a hospitalization had occurred in the preceding 30 days, a known risk factor for readmissions in other samples,¹⁵ was not available. We excluded index hospitalizations occurring in December, because the full 30-day time frame to ascertain readmissions was not available. We excluded hospitalizations resulting in death, since these are not at risk for readmission, and hospitalizations resulting in transfer, since the timing of discharge to the community was not known. Readmission hospitalizations were eligible to be included as index hospitalizations if they met sample inclusion criteria. The final sample for readmission analyses included 95,354 SI hospitalizations and 38,162 SA hospitalizations (Figure).

Primary Outcome

The primary outcome was any unplanned, all-cause readmission within 30 days of index hospitalization for SI or SA. Among 30-day readmissions, we examined readmission timing, whether the readmission was to the same hospital or a different hospital, length of stay, and indication for readmission (medical/surgical or psychiatric, and presence of SI or SA diagnoses). Planned readmissions were identified using measure specifications endorsed by the AHRQ and the National Quality Forum¹⁶ and excluded from measurement.

Among index hospitalizations for SI, we specifically examined 30-day readmissions for subsequent SA, since one objective of hospitalization for SI is to prevent progression to SA or death. We could not identify hospitalizations for repeat SA after index hospitalization for SA, because diagnosis codes did not differentiate between readmission for complications of index SA and readmission for repeat SA.

Independent Variables

We analyzed demographic, clinical, and hospital factors associated with readmissions in other samples.^17–20 Demographic characteristics included patient gender and age, urban or rural residence, payer, and median national income quartile for a patient’s ZIP code. Race and ethnicity data are not available in the NRD.

Clinical characteristics included hospitalization in the 30-days preceding the index hospitalization, index hospitalization length of stay, and admission via the emergency department (ED) versus direct admission. A patient’s chronic condition profile was determined using index hospitalization diagnosis codes. Complex chronic conditions (eg, cancer, cystic fibrosis) were identified using a classification system used in several prior studies of hospital administrative datasets,²¹ and other noncomplex chronic medical conditions (eg, asthma, obesity) were identified using the Healthcare Cost and Utilization Project (HCUP) chronic condition indicator system.²² Psychiatric conditions (eg, anxiety disorders, substance abuse, autism) were identified and categorized using a classification system used in studies of hospital administrative datasets.²³ The number of psychiatric conditions was determined by counting the number of psychiatric condition categories in which a patient had a diagnosis. SA was categorized as having lower risk of death (eg, medication overdose, injury from cutting or piercing) or higher risk of death (eg, hanging, suffocation, or firearm injury).²⁴

Because of known temporal trends in SI and SA,^25,26 the month and year of admission were included as covariates. Hospital characteristics included teaching hospital and children’s hospital designations.

 

Statistical Analysis

We compared descriptive, summary statistics for characteristics of index hospitalizations with and without a 30-day readmission using Rao-Scott chi-square tests. In multivariable analyses, we derived logistic regression models to measure the associations of patient, hospital, and temporal factors with 30-day hospital readmissions. Analyses were conducted in SAS PROC SURVEYLOGISTIC and were weighted to achieve national estimates, clustered by sample stratum and hospital to account for the complex survey design,²⁷ clustered by patients to account for multiple index visits per patient, and adjusted for clinical, demographic, and hospital characteristics. SAS version 9.4 (SAS Institute, Cary, North Carolina) was used for all analyses. All tests were two-sided, and a P value <.05 was considered as statistically significant.

RESULTS

Sample Characteristics

In the weighted analyses, we identified 133,516 hospitalizations in acute-care hospitals for SI or SA, and 8.5% (n = 11,375) of hospitalizations had at least one unplanned 30-day readmission to an acute-care hospital. Unweighted, the sample included 37,683 patients and 42,198 hospitalizations. Among all patients represented in the sample, 90.5% had only a single SI or SA hospitalization, 7.7% had two hospitalizations, and 1.8% had >2 hospitalizations in one year.

Table 1 summarizes the sample characteristics and displays the demographic, clinical, and hospital characteristics of index hospitalizations by the 30-day readmission status. Patients represented in the index hospitalizations were 64.9% female, 3.6% were aged 6-9 years, 40.1% were aged 10-14 years, and 56.3% were aged 15-17 years. Nearly half of the patients (49.1%) used public insurance. Nearly half (44.9%) lived in metropolitan areas with >1 million residents, 36.1% lived in metropolitan areas with 50,000 to 1 million residents, and 14.7% lived in rural areas.

Median length of stay for the index hospitalization was 5 days (interquartile range [IQR] 3-7). Nearly one-third (32.3%) of patients had a noncomplex chronic medical condition, 7.8% had a complex chronic medical condition, and 98.1% had a psychiatric condition. The most common psychiatric conditions were depressive disorders (60.0%) and anxiety disorders (42.2%). More than half (55.0%) of the patients had >2 psychiatric conditions. Most hospitalizations in the sample had SI only (71.4%). Among patients with SA, 81.0% had a lower lethality mechanism of injury and 19.0% had a higher lethality mechanism.

Patients experiencing a readmission were more likely to be 10-14 years old and use public insurance than patients without a readmission (P < .001 for both). For clinical characteristics, patients with a readmission were more likely to have longer index hospital stays (6 vs. 5 days), >2 psychiatric conditions (SI vs. SA), a prior admission in the 30 days preceding the index hospitalization, and admission via the ED (vs. direct admission) (P < .001 for all).

Association of Patient and Hospital Characteristics with Readmissions

Table 2 displays the patient and hospital characteristics associated with readmissions. Among demographic characteristics, 10- to 14-year-old patients had higher odds of readmission (odds ratio [OR]: 1.18, 95% confidence interval [CI]: 1.07-1.29) than 15- to 17-year-old patients. Having public insurance was associated with higher odds of readmission (OR: 1.14, 95% CI: 1.04-1.25). We found no differences in readmission rates based on sex, urban or rural location, or patient’s ZIP code income quartile.

 

Among clinical characteristics, hospitalizations with an admission for SI or SA in the preceding 30 days, meaning that the index hospitalization itself was a readmission, had the strongest association with readmissions (OR: 3.14, 95% CI: 2.73-3.61). In addition, patients admitted via the ED for the index hospitalization had higher odds of readmission (OR: 1.25, 95% CI: 1.15-1.36). Chronic psychiatric conditions associated with higher odds of readmission included psychotic disorders (OR: 1.39, 95% CI: 1.16-1.67) and bipolar disorder (OR: 1.27, 95% CI: 1.13-1.44).

Characteristics of 30-day Readmissions

Table 3 displays the characteristics of readmissions after SI compared to that after SA. Among the combined sample of 11,375 30-day readmissions, 34.1% occurred within 7 days, and 65.9% in 8-30 days. Eleven percent of patients with any readmission had more than one readmission within 30 days. Among readmissions, 94.5% were for a psychiatric problem and 5.5% were for a medical or surgical problem. A total of 43.9% had a diagnosis of SI and 19.5% a diagnosis of SA. Readmissions were more likely to occur at a different hospital after SI than after SA (48.1% vs. 31.3%, P < .001). Medical and surgical indications for readmission were less common after SI than after SA (4.4% vs. 8.7%, P < .001). Only 1.2% of SI hospitalizations had a readmission for SA within 30 days. Of these cases, 55.6% were aged 15-17 years, 43.3% were aged 10-14 years, and 1.1% were aged 6-9 years; 73.1% of the patients were female, and 49.1% used public insurance.

DISCUSSION

SI and SA in children and adolescents are substantial public health problems associated with significant hospital resource utilization. In 2013 and 2014, there were 181,575 pediatric acute-care hospitalizations for SI or SA, accounting for 9.5% of all hospitalizations in 6- to 17-year-old patients nationally. Among acute-care SI and SA hospitalizations, 8.5% had a readmission to an acute-care hospital within 30 days. The study data source did not include psychiatric specialty hospitals, and the number of index hospitalizations is likely substantially higher when psychiatric specialty hospitalizations are included. Readmissions may also be higher if patients were readmitted to psychiatric specialty hospitals after discharge from acute-care hospitals. The strongest risk factor for unplanned 30-day readmissions was a previous hospitalization in the 30 days before the index admission, likely a marker for severity or complexity of psychiatric illness. Other characteristics associated with higher odds of readmission were bipolar disorder, psychotic disorders, and age 10-14 years. More than one-third of readmissions occurred within the first 7 days after hospital discharge. The prevalence of SI and SA hospitalizations and readmissions was similar to findings in previous analyses of mental health hospitalizations.^10,28

A patient’s psychiatric illness type and severity, as evidenced by the need for frequent repeat hospitalizations, was highly associated with the risk of 30-day readmission. Any hospitalization in the 30 days preceding the index hospitalization, whether for SI/SA or for another problem, was a strong risk factor for readmissions. We suspect that prior SI/SA hospitalizations reflect a patient’s chronic elevated risk for suicide. Prior hospitalizations not for SI or SA could be hospitalizations for mental illness exacerbations that increase the risk of SI or SA, eg, bipolar disorder with acute mania, or they could represent physical health problems. Chronic physical health problems are a known risk factor for SI and SA.²⁹

A knowledge of those characteristics that increase the readmission risk can inform future resource allocation, research, and policy in several ways. First, longer hospital stays could mitigate readmission risk in some patients with severe psychiatric illness. European studies in older adolescents and adults show that for severe psychiatric illness, a longer hospital stay is associated with a lower risk of hospital readmission.^15,30 Second, better access to intensive community-based mental health (MH) services, including evidence-based psychotherapy and medication management, improves symptoms in young people.³¹ Access to these services likely affects the risk of hospital readmission. We found that readmission risk was highest in 10- to 14-year-olds. Taken in the context of existing evidence that suicide rates are rising in younger patients,^1,3 our findings suggest that particular attention to community services for younger patients is needed. Third, care coordination could help patients access beneficial services to reduce readmissions and improve other outcomes. Enhanced discharge care coordination reduced suicide deaths in high-risk populations in Europe³² and Japan³³ and improved attendance at mental health follow-up after pediatric ED discharge in a small United States sample.³⁴ Given that one-third of readmissions occurred within seven days, care coordination designed to ensure access to ambulatory services in the immediate postdischarge period may be particularly beneficial.

We found that ZIP code income quartile was not associated with readmissions. We suspect that poverty is not as closely correlated with MH hospitalization outcomes as it is with physical health hospitalization outcomes for several reasons. Medicaid insurance historically has more robust coverage of mental health services than some private insurance plans, which might offset some of the risk of poor mental health outcomes associated with poverty. Low-income families are eligible to use social services, and families accessing social services might have more opportunities to become familiar with community mental health programs. Further, the expectation of high achievement found in some higher income families is associated with MH problems in children and adolescents.³⁵ Therefore, being in a higher income quartile might not be as protective against poor mental health outcomes as it is against poor physical health outcomes.

Although the NRD provides a rich source of readmission data across hospitals nationally, several limitations are inherent to this administrative dataset. First, data from specialty psychiatric hospitals were not included in the NRD. The study underestimates the total number of index hospitalizations and readmissions, since index SI/SA hospitalizations at psychiatric hospitals are not included, and readmissions are not included if they occurred at specialty psychiatric hospitals. Second, because data cannot be linked between calendar years, we excluded January and December hospitalizations, and findings might not generalize to hospitalizations in January and December. Seasonal trends in SI/SA hospitalizations are known to occur.³⁶ Third, race, ethnicity, primary language, gender identity, and sexual orientation are not available in the NRD, and we could not examine the association of these characteristics with the likelihood of readmissions. Fourth, we did not have information about pre- or posthospitalization insurance enrollment or outpatient services that could affect the risk of readmission. Nevertheless, this study offers information on the characteristics of readmissions after hospitalizations for SI and SA in a large nationally representative sample of youth, and the findings can inform resource planning to prevent suicides.

 

CONCLUSION

Hospital readmissions are common in patients with SI and SA, and patients with a recent previous hospitalization have the highest risk of readmission. More than one-third of readmissions after SI or SA occurred within the first seven days. Due to the dearth of mental health services in the community, hospitals offer an important safety net for youth experiencing acute suicidal crises. Strategies to improve the continuum of care for patients at risk of suicide that solely focus on reducing readmissions are not likely to benefit patients. However, readmissions can identify opportunities for improving hospital discharge processes and outpatient services. Future research and clinical innovation to investigate and improve hospital discharge planning and access to community mental health services is likely to benefit patients and could reduce 30-day hospital readmissions.

Acknowledgments

The authors thank John Lawlor for his assistance with the analysis.

Disclosures

The authors have no potential conflicts of interest to disclose.

Funding

Dr. Zima received funding from the Behavioral Health Centers of Excellence for California (SB852).

Suicide is a leading cause of death among 10- to 34-year-olds in the United States.^1,2 During the past two decades, the youth suicide death rate has risen by 24%, and more than 5,000 young people die from suicide each year.³ Suicide ideation (SI) and suicide attempts (SAs) are well-established risk factors for suicide death and a source of morbidity for patients and families. One-third of youth with SI attempt suicide at some point in their lifetime.⁴ Approximately 11% of youth SAs result in suicide death, and 2% of youth who attempt suicide subsequently go on to die from suicide after recovering from a prior SA.⁵ More than 60,000 youth are hospitalized for SI or SA each year,⁶ and young people hospitalized for SA are at high short-term risk of repeat SA and suicide death.⁷ Hospitals need strategies for measuring the quality of SI and SA hospitalizations, monitoring postdischarge outcomes, and identifying the patients at the highest risk of poor outcomes. Readmissions are a useful hospital quality measure that can indicate re-emergence of SI, repeat SA, or inadequate community-based mental health treatment, and interventions designed for patients with readmissions can potentially avert morbidity or mortality.

 

The National Committee on Quality Assurance recommends measurement of quality metrics for 30-day mental health follow-up after psychiatric hospitalizations, 30-day readmissions after adult (but not pediatric) psychiatric hospitalizations, and 30-day readmissions in pediatric medical and surgical hospitalizations. Readmission measures are not consistently used to evaluate pediatric psychiatric hospitalizations, and psychiatric quality measures are not used to evaluate medical or surgical hospitalizations for SA. Recent research has investigated transfers to postacute care,⁸ readmission prevalence, variation in hospital readmission performance, and risk factors for readmissions after pediatric psychiatric hospitalizations.^9–11 However, no national study has investigated 30-day readmissions in youth hospitalized specifically for SI or SA.

To inform hospital quality measurement and improve hospital and postdischarge care for youth at risk of suicide, more information is needed about the characteristics of and the risk factors for readmissions after index SI/SA hospitalization. To address this knowledge gap, among SI/SA hospitalizations in 6- to 17-year-olds, we examined (1) unplanned 30-day readmissions and characteristics of hospitalizations by 30-day readmission status; (2) patient, hospital, and regional characteristics associated with 30-day readmissions; and (3) characteristics of 30-day readmissions.

METHODS

Study Design and Data Source

We conducted a national, retrospective cohort study of hospitalizations for patients aged 6-17 years using the Agency for Healthcare Research and Quality (AHRQ) 2013 and 2014 Nationwide Readmissions Database (NRD). The combined 2013-2014 NRD includes administrative data from a nationally representative sample of 29 million hospitalizations in 22 states, accounting for 49.3% of all US hospitalizations, and is weighted for national projections. The NRD includes hospital information, patient demographic information, and International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis, procedure, and external cause of injury codes (E-codes). The database includes one primary diagnosis, up to 24 additional diagnoses, and up to 4 E-codes for each hospitalization. The NRD includes information about hospitalizations in acute-care general hospitals (including their psychiatric units) but not from specialty psychiatric hospitals. The database also includes de-identified, verified patient linkage numbers so that patients can be tracked across multiple hospitalizations at the same institution or different institutions within the same state. This study was considered to be exempt from review by the Children’s Hospital of Philadelphia Institutional Review Board.

Sample

We identified a sample of 181,575 hospitalizations for SI (n = 119,037) or SA (n = 62,538) among 6- to 17-year-olds between January 1, 2013, and December 31, 2014 (Figure). We included children as young as 6 years because validated methods exist to identify SI and SA in this age group,¹² and because suicide deaths have recently increased among younger children.³ We excluded patients aged 18 years and older from this study since delivery of mental health services differs for adults.¹³ To create the sample, we first identified all hospitalizations of patients aged 6-17 years. We then used a validated algorithm relying on ICD-9-CM diagnosis codes for poisonings and E-codes for self-injury (E950-959) to identify hospitalizations related to SA.¹² Because E-code completeness varies among states,¹⁴ we also used the combination of having both a diagnosis code for injury (800-999) and an ICD-9-CM code for SI (V62.84) as a proxy for SA. Among hospitalizations without SA, we identified hospitalizations with SI using the ICD-9-CM code for SI (V62.84) in any position.

 

We identified 133,516 index hospitalizations with complete data at risk for an unplanned readmission. Because NRD data cannot be linked between calendar years, we limited the study time period for each calendar year to 10 months. We excluded hospitalizations in January because the full 30-day time frame to determine whether a hospitalization had occurred in the preceding 30 days, a known risk factor for readmissions in other samples,¹⁵ was not available. We excluded index hospitalizations occurring in December, because the full 30-day time frame to ascertain readmissions was not available. We excluded hospitalizations resulting in death, since these are not at risk for readmission, and hospitalizations resulting in transfer, since the timing of discharge to the community was not known. Readmission hospitalizations were eligible to be included as index hospitalizations if they met sample inclusion criteria. The final sample for readmission analyses included 95,354 SI hospitalizations and 38,162 SA hospitalizations (Figure).

Primary Outcome

The primary outcome was any unplanned, all-cause readmission within 30 days of index hospitalization for SI or SA. Among 30-day readmissions, we examined readmission timing, whether the readmission was to the same hospital or a different hospital, length of stay, and indication for readmission (medical/surgical or psychiatric, and presence of SI or SA diagnoses). Planned readmissions were identified using measure specifications endorsed by the AHRQ and the National Quality Forum¹⁶ and excluded from measurement.

Among index hospitalizations for SI, we specifically examined 30-day readmissions for subsequent SA, since one objective of hospitalization for SI is to prevent progression to SA or death. We could not identify hospitalizations for repeat SA after index hospitalization for SA, because diagnosis codes did not differentiate between readmission for complications of index SA and readmission for repeat SA.

Independent Variables

We analyzed demographic, clinical, and hospital factors associated with readmissions in other samples.^17–20 Demographic characteristics included patient gender and age, urban or rural residence, payer, and median national income quartile for a patient’s ZIP code. Race and ethnicity data are not available in the NRD.

Clinical characteristics included hospitalization in the 30-days preceding the index hospitalization, index hospitalization length of stay, and admission via the emergency department (ED) versus direct admission. A patient’s chronic condition profile was determined using index hospitalization diagnosis codes. Complex chronic conditions (eg, cancer, cystic fibrosis) were identified using a classification system used in several prior studies of hospital administrative datasets,²¹ and other noncomplex chronic medical conditions (eg, asthma, obesity) were identified using the Healthcare Cost and Utilization Project (HCUP) chronic condition indicator system.²² Psychiatric conditions (eg, anxiety disorders, substance abuse, autism) were identified and categorized using a classification system used in studies of hospital administrative datasets.²³ The number of psychiatric conditions was determined by counting the number of psychiatric condition categories in which a patient had a diagnosis. SA was categorized as having lower risk of death (eg, medication overdose, injury from cutting or piercing) or higher risk of death (eg, hanging, suffocation, or firearm injury).²⁴

Because of known temporal trends in SI and SA,^25,26 the month and year of admission were included as covariates. Hospital characteristics included teaching hospital and children’s hospital designations.

 

Statistical Analysis

We compared descriptive, summary statistics for characteristics of index hospitalizations with and without a 30-day readmission using Rao-Scott chi-square tests. In multivariable analyses, we derived logistic regression models to measure the associations of patient, hospital, and temporal factors with 30-day hospital readmissions. Analyses were conducted in SAS PROC SURVEYLOGISTIC and were weighted to achieve national estimates, clustered by sample stratum and hospital to account for the complex survey design,²⁷ clustered by patients to account for multiple index visits per patient, and adjusted for clinical, demographic, and hospital characteristics. SAS version 9.4 (SAS Institute, Cary, North Carolina) was used for all analyses. All tests were two-sided, and a P value <.05 was considered as statistically significant.

RESULTS

Sample Characteristics

In the weighted analyses, we identified 133,516 hospitalizations in acute-care hospitals for SI or SA, and 8.5% (n = 11,375) of hospitalizations had at least one unplanned 30-day readmission to an acute-care hospital. Unweighted, the sample included 37,683 patients and 42,198 hospitalizations. Among all patients represented in the sample, 90.5% had only a single SI or SA hospitalization, 7.7% had two hospitalizations, and 1.8% had >2 hospitalizations in one year.

Table 1 summarizes the sample characteristics and displays the demographic, clinical, and hospital characteristics of index hospitalizations by the 30-day readmission status. Patients represented in the index hospitalizations were 64.9% female, 3.6% were aged 6-9 years, 40.1% were aged 10-14 years, and 56.3% were aged 15-17 years. Nearly half of the patients (49.1%) used public insurance. Nearly half (44.9%) lived in metropolitan areas with >1 million residents, 36.1% lived in metropolitan areas with 50,000 to 1 million residents, and 14.7% lived in rural areas.

Median length of stay for the index hospitalization was 5 days (interquartile range [IQR] 3-7). Nearly one-third (32.3%) of patients had a noncomplex chronic medical condition, 7.8% had a complex chronic medical condition, and 98.1% had a psychiatric condition. The most common psychiatric conditions were depressive disorders (60.0%) and anxiety disorders (42.2%). More than half (55.0%) of the patients had >2 psychiatric conditions. Most hospitalizations in the sample had SI only (71.4%). Among patients with SA, 81.0% had a lower lethality mechanism of injury and 19.0% had a higher lethality mechanism.

Patients experiencing a readmission were more likely to be 10-14 years old and use public insurance than patients without a readmission (P < .001 for both). For clinical characteristics, patients with a readmission were more likely to have longer index hospital stays (6 vs. 5 days), >2 psychiatric conditions (SI vs. SA), a prior admission in the 30 days preceding the index hospitalization, and admission via the ED (vs. direct admission) (P < .001 for all).

Association of Patient and Hospital Characteristics with Readmissions

Table 2 displays the patient and hospital characteristics associated with readmissions. Among demographic characteristics, 10- to 14-year-old patients had higher odds of readmission (odds ratio [OR]: 1.18, 95% confidence interval [CI]: 1.07-1.29) than 15- to 17-year-old patients. Having public insurance was associated with higher odds of readmission (OR: 1.14, 95% CI: 1.04-1.25). We found no differences in readmission rates based on sex, urban or rural location, or patient’s ZIP code income quartile.

 

Among clinical characteristics, hospitalizations with an admission for SI or SA in the preceding 30 days, meaning that the index hospitalization itself was a readmission, had the strongest association with readmissions (OR: 3.14, 95% CI: 2.73-3.61). In addition, patients admitted via the ED for the index hospitalization had higher odds of readmission (OR: 1.25, 95% CI: 1.15-1.36). Chronic psychiatric conditions associated with higher odds of readmission included psychotic disorders (OR: 1.39, 95% CI: 1.16-1.67) and bipolar disorder (OR: 1.27, 95% CI: 1.13-1.44).

Characteristics of 30-day Readmissions

Table 3 displays the characteristics of readmissions after SI compared to that after SA. Among the combined sample of 11,375 30-day readmissions, 34.1% occurred within 7 days, and 65.9% in 8-30 days. Eleven percent of patients with any readmission had more than one readmission within 30 days. Among readmissions, 94.5% were for a psychiatric problem and 5.5% were for a medical or surgical problem. A total of 43.9% had a diagnosis of SI and 19.5% a diagnosis of SA. Readmissions were more likely to occur at a different hospital after SI than after SA (48.1% vs. 31.3%, P < .001). Medical and surgical indications for readmission were less common after SI than after SA (4.4% vs. 8.7%, P < .001). Only 1.2% of SI hospitalizations had a readmission for SA within 30 days. Of these cases, 55.6% were aged 15-17 years, 43.3% were aged 10-14 years, and 1.1% were aged 6-9 years; 73.1% of the patients were female, and 49.1% used public insurance.

DISCUSSION

SI and SA in children and adolescents are substantial public health problems associated with significant hospital resource utilization. In 2013 and 2014, there were 181,575 pediatric acute-care hospitalizations for SI or SA, accounting for 9.5% of all hospitalizations in 6- to 17-year-old patients nationally. Among acute-care SI and SA hospitalizations, 8.5% had a readmission to an acute-care hospital within 30 days. The study data source did not include psychiatric specialty hospitals, and the number of index hospitalizations is likely substantially higher when psychiatric specialty hospitalizations are included. Readmissions may also be higher if patients were readmitted to psychiatric specialty hospitals after discharge from acute-care hospitals. The strongest risk factor for unplanned 30-day readmissions was a previous hospitalization in the 30 days before the index admission, likely a marker for severity or complexity of psychiatric illness. Other characteristics associated with higher odds of readmission were bipolar disorder, psychotic disorders, and age 10-14 years. More than one-third of readmissions occurred within the first 7 days after hospital discharge. The prevalence of SI and SA hospitalizations and readmissions was similar to findings in previous analyses of mental health hospitalizations.^10,28

A patient’s psychiatric illness type and severity, as evidenced by the need for frequent repeat hospitalizations, was highly associated with the risk of 30-day readmission. Any hospitalization in the 30 days preceding the index hospitalization, whether for SI/SA or for another problem, was a strong risk factor for readmissions. We suspect that prior SI/SA hospitalizations reflect a patient’s chronic elevated risk for suicide. Prior hospitalizations not for SI or SA could be hospitalizations for mental illness exacerbations that increase the risk of SI or SA, eg, bipolar disorder with acute mania, or they could represent physical health problems. Chronic physical health problems are a known risk factor for SI and SA.²⁹

A knowledge of those characteristics that increase the readmission risk can inform future resource allocation, research, and policy in several ways. First, longer hospital stays could mitigate readmission risk in some patients with severe psychiatric illness. European studies in older adolescents and adults show that for severe psychiatric illness, a longer hospital stay is associated with a lower risk of hospital readmission.^15,30 Second, better access to intensive community-based mental health (MH) services, including evidence-based psychotherapy and medication management, improves symptoms in young people.³¹ Access to these services likely affects the risk of hospital readmission. We found that readmission risk was highest in 10- to 14-year-olds. Taken in the context of existing evidence that suicide rates are rising in younger patients,^1,3 our findings suggest that particular attention to community services for younger patients is needed. Third, care coordination could help patients access beneficial services to reduce readmissions and improve other outcomes. Enhanced discharge care coordination reduced suicide deaths in high-risk populations in Europe³² and Japan³³ and improved attendance at mental health follow-up after pediatric ED discharge in a small United States sample.³⁴ Given that one-third of readmissions occurred within seven days, care coordination designed to ensure access to ambulatory services in the immediate postdischarge period may be particularly beneficial.

We found that ZIP code income quartile was not associated with readmissions. We suspect that poverty is not as closely correlated with MH hospitalization outcomes as it is with physical health hospitalization outcomes for several reasons. Medicaid insurance historically has more robust coverage of mental health services than some private insurance plans, which might offset some of the risk of poor mental health outcomes associated with poverty. Low-income families are eligible to use social services, and families accessing social services might have more opportunities to become familiar with community mental health programs. Further, the expectation of high achievement found in some higher income families is associated with MH problems in children and adolescents.³⁵ Therefore, being in a higher income quartile might not be as protective against poor mental health outcomes as it is against poor physical health outcomes.

Although the NRD provides a rich source of readmission data across hospitals nationally, several limitations are inherent to this administrative dataset. First, data from specialty psychiatric hospitals were not included in the NRD. The study underestimates the total number of index hospitalizations and readmissions, since index SI/SA hospitalizations at psychiatric hospitals are not included, and readmissions are not included if they occurred at specialty psychiatric hospitals. Second, because data cannot be linked between calendar years, we excluded January and December hospitalizations, and findings might not generalize to hospitalizations in January and December. Seasonal trends in SI/SA hospitalizations are known to occur.³⁶ Third, race, ethnicity, primary language, gender identity, and sexual orientation are not available in the NRD, and we could not examine the association of these characteristics with the likelihood of readmissions. Fourth, we did not have information about pre- or posthospitalization insurance enrollment or outpatient services that could affect the risk of readmission. Nevertheless, this study offers information on the characteristics of readmissions after hospitalizations for SI and SA in a large nationally representative sample of youth, and the findings can inform resource planning to prevent suicides.

 

CONCLUSION

Hospital readmissions are common in patients with SI and SA, and patients with a recent previous hospitalization have the highest risk of readmission. More than one-third of readmissions after SI or SA occurred within the first seven days. Due to the dearth of mental health services in the community, hospitals offer an important safety net for youth experiencing acute suicidal crises. Strategies to improve the continuum of care for patients at risk of suicide that solely focus on reducing readmissions are not likely to benefit patients. However, readmissions can identify opportunities for improving hospital discharge processes and outpatient services. Future research and clinical innovation to investigate and improve hospital discharge planning and access to community mental health services is likely to benefit patients and could reduce 30-day hospital readmissions.

Acknowledgments

The authors thank John Lawlor for his assistance with the analysis.

Disclosures

The authors have no potential conflicts of interest to disclose.

Funding

Dr. Zima received funding from the Behavioral Health Centers of Excellence for California (SB852).

Suicide is a leading cause of death among 10- to 34-year-olds in the United States.^1,2 During the past two decades, the youth suicide death rate has risen by 24%, and more than 5,000 young people die from suicide each year.³ Suicide ideation (SI) and suicide attempts (SAs) are well-established risk factors for suicide death and a source of morbidity for patients and families. One-third of youth with SI attempt suicide at some point in their lifetime.⁴ Approximately 11% of youth SAs result in suicide death, and 2% of youth who attempt suicide subsequently go on to die from suicide after recovering from a prior SA.⁵ More than 60,000 youth are hospitalized for SI or SA each year,⁶ and young people hospitalized for SA are at high short-term risk of repeat SA and suicide death.⁷ Hospitals need strategies for measuring the quality of SI and SA hospitalizations, monitoring postdischarge outcomes, and identifying the patients at the highest risk of poor outcomes. Readmissions are a useful hospital quality measure that can indicate re-emergence of SI, repeat SA, or inadequate community-based mental health treatment, and interventions designed for patients with readmissions can potentially avert morbidity or mortality.

 

The National Committee on Quality Assurance recommends measurement of quality metrics for 30-day mental health follow-up after psychiatric hospitalizations, 30-day readmissions after adult (but not pediatric) psychiatric hospitalizations, and 30-day readmissions in pediatric medical and surgical hospitalizations. Readmission measures are not consistently used to evaluate pediatric psychiatric hospitalizations, and psychiatric quality measures are not used to evaluate medical or surgical hospitalizations for SA. Recent research has investigated transfers to postacute care,⁸ readmission prevalence, variation in hospital readmission performance, and risk factors for readmissions after pediatric psychiatric hospitalizations.^9–11 However, no national study has investigated 30-day readmissions in youth hospitalized specifically for SI or SA.

To inform hospital quality measurement and improve hospital and postdischarge care for youth at risk of suicide, more information is needed about the characteristics of and the risk factors for readmissions after index SI/SA hospitalization. To address this knowledge gap, among SI/SA hospitalizations in 6- to 17-year-olds, we examined (1) unplanned 30-day readmissions and characteristics of hospitalizations by 30-day readmission status; (2) patient, hospital, and regional characteristics associated with 30-day readmissions; and (3) characteristics of 30-day readmissions.

METHODS

Study Design and Data Source

We conducted a national, retrospective cohort study of hospitalizations for patients aged 6-17 years using the Agency for Healthcare Research and Quality (AHRQ) 2013 and 2014 Nationwide Readmissions Database (NRD). The combined 2013-2014 NRD includes administrative data from a nationally representative sample of 29 million hospitalizations in 22 states, accounting for 49.3% of all US hospitalizations, and is weighted for national projections. The NRD includes hospital information, patient demographic information, and International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis, procedure, and external cause of injury codes (E-codes). The database includes one primary diagnosis, up to 24 additional diagnoses, and up to 4 E-codes for each hospitalization. The NRD includes information about hospitalizations in acute-care general hospitals (including their psychiatric units) but not from specialty psychiatric hospitals. The database also includes de-identified, verified patient linkage numbers so that patients can be tracked across multiple hospitalizations at the same institution or different institutions within the same state. This study was considered to be exempt from review by the Children’s Hospital of Philadelphia Institutional Review Board.

Sample

We identified a sample of 181,575 hospitalizations for SI (n = 119,037) or SA (n = 62,538) among 6- to 17-year-olds between January 1, 2013, and December 31, 2014 (Figure). We included children as young as 6 years because validated methods exist to identify SI and SA in this age group,¹² and because suicide deaths have recently increased among younger children.³ We excluded patients aged 18 years and older from this study since delivery of mental health services differs for adults.¹³ To create the sample, we first identified all hospitalizations of patients aged 6-17 years. We then used a validated algorithm relying on ICD-9-CM diagnosis codes for poisonings and E-codes for self-injury (E950-959) to identify hospitalizations related to SA.¹² Because E-code completeness varies among states,¹⁴ we also used the combination of having both a diagnosis code for injury (800-999) and an ICD-9-CM code for SI (V62.84) as a proxy for SA. Among hospitalizations without SA, we identified hospitalizations with SI using the ICD-9-CM code for SI (V62.84) in any position.

 

We identified 133,516 index hospitalizations with complete data at risk for an unplanned readmission. Because NRD data cannot be linked between calendar years, we limited the study time period for each calendar year to 10 months. We excluded hospitalizations in January because the full 30-day time frame to determine whether a hospitalization had occurred in the preceding 30 days, a known risk factor for readmissions in other samples,¹⁵ was not available. We excluded index hospitalizations occurring in December, because the full 30-day time frame to ascertain readmissions was not available. We excluded hospitalizations resulting in death, since these are not at risk for readmission, and hospitalizations resulting in transfer, since the timing of discharge to the community was not known. Readmission hospitalizations were eligible to be included as index hospitalizations if they met sample inclusion criteria. The final sample for readmission analyses included 95,354 SI hospitalizations and 38,162 SA hospitalizations (Figure).

Primary Outcome

The primary outcome was any unplanned, all-cause readmission within 30 days of index hospitalization for SI or SA. Among 30-day readmissions, we examined readmission timing, whether the readmission was to the same hospital or a different hospital, length of stay, and indication for readmission (medical/surgical or psychiatric, and presence of SI or SA diagnoses). Planned readmissions were identified using measure specifications endorsed by the AHRQ and the National Quality Forum¹⁶ and excluded from measurement.

Among index hospitalizations for SI, we specifically examined 30-day readmissions for subsequent SA, since one objective of hospitalization for SI is to prevent progression to SA or death. We could not identify hospitalizations for repeat SA after index hospitalization for SA, because diagnosis codes did not differentiate between readmission for complications of index SA and readmission for repeat SA.

Independent Variables

We analyzed demographic, clinical, and hospital factors associated with readmissions in other samples.^17–20 Demographic characteristics included patient gender and age, urban or rural residence, payer, and median national income quartile for a patient’s ZIP code. Race and ethnicity data are not available in the NRD.

Clinical characteristics included hospitalization in the 30-days preceding the index hospitalization, index hospitalization length of stay, and admission via the emergency department (ED) versus direct admission. A patient’s chronic condition profile was determined using index hospitalization diagnosis codes. Complex chronic conditions (eg, cancer, cystic fibrosis) were identified using a classification system used in several prior studies of hospital administrative datasets,²¹ and other noncomplex chronic medical conditions (eg, asthma, obesity) were identified using the Healthcare Cost and Utilization Project (HCUP) chronic condition indicator system.²² Psychiatric conditions (eg, anxiety disorders, substance abuse, autism) were identified and categorized using a classification system used in studies of hospital administrative datasets.²³ The number of psychiatric conditions was determined by counting the number of psychiatric condition categories in which a patient had a diagnosis. SA was categorized as having lower risk of death (eg, medication overdose, injury from cutting or piercing) or higher risk of death (eg, hanging, suffocation, or firearm injury).²⁴

Because of known temporal trends in SI and SA,^25,26 the month and year of admission were included as covariates. Hospital characteristics included teaching hospital and children’s hospital designations.

 

Statistical Analysis

We compared descriptive, summary statistics for characteristics of index hospitalizations with and without a 30-day readmission using Rao-Scott chi-square tests. In multivariable analyses, we derived logistic regression models to measure the associations of patient, hospital, and temporal factors with 30-day hospital readmissions. Analyses were conducted in SAS PROC SURVEYLOGISTIC and were weighted to achieve national estimates, clustered by sample stratum and hospital to account for the complex survey design,²⁷ clustered by patients to account for multiple index visits per patient, and adjusted for clinical, demographic, and hospital characteristics. SAS version 9.4 (SAS Institute, Cary, North Carolina) was used for all analyses. All tests were two-sided, and a P value <.05 was considered as statistically significant.

RESULTS

Sample Characteristics

In the weighted analyses, we identified 133,516 hospitalizations in acute-care hospitals for SI or SA, and 8.5% (n = 11,375) of hospitalizations had at least one unplanned 30-day readmission to an acute-care hospital. Unweighted, the sample included 37,683 patients and 42,198 hospitalizations. Among all patients represented in the sample, 90.5% had only a single SI or SA hospitalization, 7.7% had two hospitalizations, and 1.8% had >2 hospitalizations in one year.

Table 1 summarizes the sample characteristics and displays the demographic, clinical, and hospital characteristics of index hospitalizations by the 30-day readmission status. Patients represented in the index hospitalizations were 64.9% female, 3.6% were aged 6-9 years, 40.1% were aged 10-14 years, and 56.3% were aged 15-17 years. Nearly half of the patients (49.1%) used public insurance. Nearly half (44.9%) lived in metropolitan areas with >1 million residents, 36.1% lived in metropolitan areas with 50,000 to 1 million residents, and 14.7% lived in rural areas.

Median length of stay for the index hospitalization was 5 days (interquartile range [IQR] 3-7). Nearly one-third (32.3%) of patients had a noncomplex chronic medical condition, 7.8% had a complex chronic medical condition, and 98.1% had a psychiatric condition. The most common psychiatric conditions were depressive disorders (60.0%) and anxiety disorders (42.2%). More than half (55.0%) of the patients had >2 psychiatric conditions. Most hospitalizations in the sample had SI only (71.4%). Among patients with SA, 81.0% had a lower lethality mechanism of injury and 19.0% had a higher lethality mechanism.

Patients experiencing a readmission were more likely to be 10-14 years old and use public insurance than patients without a readmission (P < .001 for both). For clinical characteristics, patients with a readmission were more likely to have longer index hospital stays (6 vs. 5 days), >2 psychiatric conditions (SI vs. SA), a prior admission in the 30 days preceding the index hospitalization, and admission via the ED (vs. direct admission) (P < .001 for all).

Association of Patient and Hospital Characteristics with Readmissions

Table 2 displays the patient and hospital characteristics associated with readmissions. Among demographic characteristics, 10- to 14-year-old patients had higher odds of readmission (odds ratio [OR]: 1.18, 95% confidence interval [CI]: 1.07-1.29) than 15- to 17-year-old patients. Having public insurance was associated with higher odds of readmission (OR: 1.14, 95% CI: 1.04-1.25). We found no differences in readmission rates based on sex, urban or rural location, or patient’s ZIP code income quartile.

 

Among clinical characteristics, hospitalizations with an admission for SI or SA in the preceding 30 days, meaning that the index hospitalization itself was a readmission, had the strongest association with readmissions (OR: 3.14, 95% CI: 2.73-3.61). In addition, patients admitted via the ED for the index hospitalization had higher odds of readmission (OR: 1.25, 95% CI: 1.15-1.36). Chronic psychiatric conditions associated with higher odds of readmission included psychotic disorders (OR: 1.39, 95% CI: 1.16-1.67) and bipolar disorder (OR: 1.27, 95% CI: 1.13-1.44).

Characteristics of 30-day Readmissions

Table 3 displays the characteristics of readmissions after SI compared to that after SA. Among the combined sample of 11,375 30-day readmissions, 34.1% occurred within 7 days, and 65.9% in 8-30 days. Eleven percent of patients with any readmission had more than one readmission within 30 days. Among readmissions, 94.5% were for a psychiatric problem and 5.5% were for a medical or surgical problem. A total of 43.9% had a diagnosis of SI and 19.5% a diagnosis of SA. Readmissions were more likely to occur at a different hospital after SI than after SA (48.1% vs. 31.3%, P < .001). Medical and surgical indications for readmission were less common after SI than after SA (4.4% vs. 8.7%, P < .001). Only 1.2% of SI hospitalizations had a readmission for SA within 30 days. Of these cases, 55.6% were aged 15-17 years, 43.3% were aged 10-14 years, and 1.1% were aged 6-9 years; 73.1% of the patients were female, and 49.1% used public insurance.

DISCUSSION

SI and SA in children and adolescents are substantial public health problems associated with significant hospital resource utilization. In 2013 and 2014, there were 181,575 pediatric acute-care hospitalizations for SI or SA, accounting for 9.5% of all hospitalizations in 6- to 17-year-old patients nationally. Among acute-care SI and SA hospitalizations, 8.5% had a readmission to an acute-care hospital within 30 days. The study data source did not include psychiatric specialty hospitals, and the number of index hospitalizations is likely substantially higher when psychiatric specialty hospitalizations are included. Readmissions may also be higher if patients were readmitted to psychiatric specialty hospitals after discharge from acute-care hospitals. The strongest risk factor for unplanned 30-day readmissions was a previous hospitalization in the 30 days before the index admission, likely a marker for severity or complexity of psychiatric illness. Other characteristics associated with higher odds of readmission were bipolar disorder, psychotic disorders, and age 10-14 years. More than one-third of readmissions occurred within the first 7 days after hospital discharge. The prevalence of SI and SA hospitalizations and readmissions was similar to findings in previous analyses of mental health hospitalizations.^10,28

A patient’s psychiatric illness type and severity, as evidenced by the need for frequent repeat hospitalizations, was highly associated with the risk of 30-day readmission. Any hospitalization in the 30 days preceding the index hospitalization, whether for SI/SA or for another problem, was a strong risk factor for readmissions. We suspect that prior SI/SA hospitalizations reflect a patient’s chronic elevated risk for suicide. Prior hospitalizations not for SI or SA could be hospitalizations for mental illness exacerbations that increase the risk of SI or SA, eg, bipolar disorder with acute mania, or they could represent physical health problems. Chronic physical health problems are a known risk factor for SI and SA.²⁹

A knowledge of those characteristics that increase the readmission risk can inform future resource allocation, research, and policy in several ways. First, longer hospital stays could mitigate readmission risk in some patients with severe psychiatric illness. European studies in older adolescents and adults show that for severe psychiatric illness, a longer hospital stay is associated with a lower risk of hospital readmission.^15,30 Second, better access to intensive community-based mental health (MH) services, including evidence-based psychotherapy and medication management, improves symptoms in young people.³¹ Access to these services likely affects the risk of hospital readmission. We found that readmission risk was highest in 10- to 14-year-olds. Taken in the context of existing evidence that suicide rates are rising in younger patients,^1,3 our findings suggest that particular attention to community services for younger patients is needed. Third, care coordination could help patients access beneficial services to reduce readmissions and improve other outcomes. Enhanced discharge care coordination reduced suicide deaths in high-risk populations in Europe³² and Japan³³ and improved attendance at mental health follow-up after pediatric ED discharge in a small United States sample.³⁴ Given that one-third of readmissions occurred within seven days, care coordination designed to ensure access to ambulatory services in the immediate postdischarge period may be particularly beneficial.

We found that ZIP code income quartile was not associated with readmissions. We suspect that poverty is not as closely correlated with MH hospitalization outcomes as it is with physical health hospitalization outcomes for several reasons. Medicaid insurance historically has more robust coverage of mental health services than some private insurance plans, which might offset some of the risk of poor mental health outcomes associated with poverty. Low-income families are eligible to use social services, and families accessing social services might have more opportunities to become familiar with community mental health programs. Further, the expectation of high achievement found in some higher income families is associated with MH problems in children and adolescents.³⁵ Therefore, being in a higher income quartile might not be as protective against poor mental health outcomes as it is against poor physical health outcomes.

Although the NRD provides a rich source of readmission data across hospitals nationally, several limitations are inherent to this administrative dataset. First, data from specialty psychiatric hospitals were not included in the NRD. The study underestimates the total number of index hospitalizations and readmissions, since index SI/SA hospitalizations at psychiatric hospitals are not included, and readmissions are not included if they occurred at specialty psychiatric hospitals. Second, because data cannot be linked between calendar years, we excluded January and December hospitalizations, and findings might not generalize to hospitalizations in January and December. Seasonal trends in SI/SA hospitalizations are known to occur.³⁶ Third, race, ethnicity, primary language, gender identity, and sexual orientation are not available in the NRD, and we could not examine the association of these characteristics with the likelihood of readmissions. Fourth, we did not have information about pre- or posthospitalization insurance enrollment or outpatient services that could affect the risk of readmission. Nevertheless, this study offers information on the characteristics of readmissions after hospitalizations for SI and SA in a large nationally representative sample of youth, and the findings can inform resource planning to prevent suicides.

 

CONCLUSION

Hospital readmissions are common in patients with SI and SA, and patients with a recent previous hospitalization have the highest risk of readmission. More than one-third of readmissions after SI or SA occurred within the first seven days. Due to the dearth of mental health services in the community, hospitals offer an important safety net for youth experiencing acute suicidal crises. Strategies to improve the continuum of care for patients at risk of suicide that solely focus on reducing readmissions are not likely to benefit patients. However, readmissions can identify opportunities for improving hospital discharge processes and outpatient services. Future research and clinical innovation to investigate and improve hospital discharge planning and access to community mental health services is likely to benefit patients and could reduce 30-day hospital readmissions.

Acknowledgments

The authors thank John Lawlor for his assistance with the analysis.

Disclosures

The authors have no potential conflicts of interest to disclose.

Funding

Dr. Zima received funding from the Behavioral Health Centers of Excellence for California (SB852).

Readmission prevention is a focus of national efforts to improve the quality of hospital care for children.^1-5 Several factors contribute to the risk of readmission for hospitalized children, including age, race or ethnicity, payer, and the type and number of comorbid health conditions.^6-9 Mental health conditions (MHCs) are a prevalent comorbidity in children hospitalized for physical health reasons that could influence their postdischarge health and safety.

MHCs are increasingly common in children hospitalized for physical health indications; a comorbid MHC is currently present in 10% to 25% of hospitalized children ages 3 years and older.^10,11 Hospital length of stay (LOS) and cost are higher in children with an MHC.^12,13 Increased resource use may occur because MHCs can impede hospital treatment effectiveness and the child’s recovery from physical illness. MHCs are associated with a lower adherence with medications^14-16 and a lower ability to cope with health events and problems.^17-19 In adults, MHCs are a well-established risk factor for hospital readmission for a variety of physical health conditions.^20-24 Although the influence of MHCs on readmissions in children has not been extensively investigated, higher readmission rates have been reported in adolescents hospitalized for diabetes with an MHC compared with those with no MHC.^25,26

To our knowledge, no large studies have examined the relationship between the presence of a comorbid MHC and hospital readmissions in children or adolescents hospitalized for a broad array of medical or procedure conditions. Therefore, we conducted this study to (1) assess the likelihood of 30-day hospital readmission in children with versus without MHC who were hospitalized for one of 10 medical or 10 procedure conditions, and (2) to assess which MHCs are associated with the highest likelihood of hospital readmission.

METHODS

Study Design and Setting

We conducted a national, retrospective cohort study of index hospitalizations for children ages 3 to 21 years who were discharged from January 1, 2013, to November 30, 2013, in the Agency for Healthcare Research and Quality’s (AHRQ) Nationwide Readmissions Database (NRD). Admissions occurring in December 2013 were excluded because they did not have a 30-day timeframe available for readmission measurement. The 2013 NRD includes administrative data for a nationally representative sample of 14 million hospitalizations in 21 states, accounting for 49% of all US hospitalizations and weighted to represent 35.6 million hospitalizations. The database includes deidentified, verified patient linkage numbers so that patients can be tracked across multiple hospitalizations at the same institution or different institutions within a state. The NRD includes hospital information, patient demographic information, and the International Classification of Diseases, 9th Revision-Clinical Modification (ICD-9-CM) discharge diagnoses and procedures, with 1 primary diagnosis and up to 24 additional fields for comorbid diagnoses. This study was approved for exemption by the Children’s Hospital of Philadelphia Institutional Review Board.

Index Admissions

We used the methods described below to create a study cohort of the 10 medical and 10 procedure index admissions associated with the highest volume (ie, the greatest absolute number) of 30-day hospital readmissions. Conditions with a high volume of readmissions were chosen in an effort to identify conditions in which readmission-prevention interventions had the greatest potential to reduce the absolute number of readmissions. We first categorized index hospitalizations for medical and procedure conditions by using the All Patient Refined Diagnosis Related Groups (APR-DRGs; 3M Health Information Systems, Wallingford, CT).²⁷ APR-DRGs use all diagnosis and/or procedure ICD-9-CM codes registered for a hospital discharge to assign 1 reason that best explains the need for hospitalization. We then excluded obstetric hospitalizations, psychiatric hospitalizations, and hospitalizations resulting in death or transfer from being considered as index admissions. Afterwards, we ranked each APR-DRG index hospitalization by the total number of 30-day hospital readmissions that occurred afterward and selected the 10 medical and 10 procedure index admissions with the highest number of readmissions. The APR-DRG index admissions are listed in Figures 1 and 2. For the APR-DRG “digestive system diagnoses,” the most common diagnosis was constipation, and we refer to that category as “constipation.” The most common diagnosis for the APR-DRG called “other operating room procedure for neoplasm” was tumor biopsy, and we refer to that category as “tumor biopsy.”

 

Main Outcome Measure

The primary study outcome was unplanned, all-cause readmission to any hospital within 30 days of index hospitalization. All-cause readmissions include any hospitalization for the same or different condition as the index admission, including conditions not eligible to be considered as index admissions (obstetric, psychiatric, and hospitalizations resulting in death or transfer). Planned readmissions, identified by using pediatric-specific measure specifications endorsed by AHRQ and the National Quality Forum,²⁸ were excluded from measurement. For index admissions with multiple 30-day readmissions, only the first readmission was counted. Each readmission was treated as an index admission.

Main Independent Variable

The main independent variable was the presence of an MHC documented during the index hospitalization. MHCs were identified and classified into diagnosis categories derived from the AHRQ Chronic Condition Indicator system by using ICD-9-CM codes.²⁹ MHC categories included anxiety disorders, attention-deficit/hyperactivity disorder (ADHD), autism, depression, and substance abuse. Less common MHCs included bipolar disorder, schizophrenia, disruptive behavior disorders, somatoform disorders, and eating disorders. These conditions are included in the group with any MHC, but we did not calculate the adjusted odds ratios (AORs) of readmission for these conditions. Children were identified as having multiple MHCs if they had more than 1 MHC.

Other Characteristics of Index Hospitalizations

A priori, we selected for analysis the known demographic, clinical, and hospital factors associated with the risk of readmission.^20-24 The demographic characteristics included patient age, gender, payer category, urban or rural residence, and the median income quartile for a patient’s ZIP code. The hospital characteristics included location, ownership, and teaching hospital designation. The clinical characteristics included the number of chronic conditions³⁰ and indicators for the presence of a complex chronic condition in each of 12 organ systems.³¹

Statistical Analysis

We calculated descriptive summary statistics for the characteristics of index hospitalizations. We compared characteristics in index admissions of children with versus without MHC by using Wilcoxon Rank-Sum tests for continuous variables and Wald χ² tests for categorical variables. In the multivariable analysis, we derived logistic regression models to assess the relationship of 30-day hospital readmission with each type of MHC, adjusting for index admission demographic, hospital, and clinical characteristics. MHCs were modeled as binary indicator variables with the presence of any MHC, more than 1 MHC, or each of 5 MHC categories (anxiety disorders, ADHD, autism, depression, substance abuse) compared with no MHC. Four types of logistic regression models were derived (1) for the combined sample of all 10 index medical admissions with each MHC category versus no MHC as a primary predictor, (2) for each medical index admission with any MHC versus no MHC as the primary predictor, (3) for the combined sample of all 10 index procedure admissions with each MHC category versus no MHC as a primary predictor, and (4) for each procedure index admission with any MHC versus no MHC as the primary predictor. All analyses were weighted to achieve national estimates and clustered by hospital by using AHRQ-recommended survey procedures. SAS version 9.4 (SAS Institute, Cary, NC) was used for all analyses. All tests were two-sided, and a P < .05 was
considered statistically significant.

RESULTS

Study Population

The study sample included 471,057 index hospitalizations, including 346,960 medical and 124,097 procedure admissions (Table). The selected hospitalizations accounted for 39.6% of all index hospitalizations and 40.7% of all unplanned 30-day readmissions for patients ages 3 to <21 years in 2013. For all medical and procedure index admissions combined, median age at index admission was 12 years (interquartile range [IQR], 7-17); 49.2% used public insurance, and 74.0% were from urban teaching hospitals. Median LOS was 2 days (IQR, 1-4; Table).

Across all index admissions, 16.3% were for children with an MHC. Overall, children with MHCs were older and more likely to have a chronic³⁰ or complex chronic³¹ physical health condition than children with no MHCs (Table).

Index Medical Admissions, Mental Health Conditions, and Hospital Readmission

The 10 index medical hospitalizations with the most readmissions for children ages 3 to 20 years were asthma, chemotherapy, constipation, diabetes, gastroenteritis, inflammatory bowel disease, neutropenia, pneumonia, seizure, and sickle cell crisis. Across all index medical hospitalizations, 17.5% were for patients with an MHC (Figure 1). Of index medical admissions with any MHC, 26.3% had ADHD, 22.9% had an anxiety disorder, 14.9% had autism, 18.3% had depression, and 30.9% had substance abuse. Among all admissions with MHCs, 28.9% had 2 or more MHCs.

Index Medical Admissions Combined

For all index medical hospitalizations combined, 17.0% (n = 59,138) had an unplanned, 30-day hospital readmission. The rate of 30-day hospital readmissions was higher with versus without an MHC (17.5 vs 16.8%; P < .001). In a multivariable analysis, presence of an MHC was associated with a higher likelihood of hospital readmission following an index medical admission (AOR, 1.23; 95% confidence interval [CI], 1.19-1.26); Figure 1). All MHCs except autism and ADHD had a higher likelihood of readmission (Figure 3). 

Specific Index Medical Admissions

For specific index medical admissions, the rate of 30-day hospital readmission ranged from 2.9% for asthma to 74.3% for chemotherapy. For 8 of the 10 specific index medical hospitalizations (all aside from chemotherapy and pneumonia), an MHC was associated with higher adjusted odds of 30-day readmission (AOR range, 1.10-1.70; Figure 1). In pneumonia index admissions, having an MHC was associated with lower odds of readmission compared with having no MHC (AOR, 0.82; 95% CI, 0.69-0.97; Figure 1).

 

Index Procedure Admissions, Mental Health Conditions, and Hospital Readmission

The 10 index procedure hospitalizations with the most readmissions for children ages 3 to 20 years were appendectomy, bone marrow transplant, bowel procedures, craniotomy, knee procedures, respiratory and chest procedures, spinal fusion, tumor biopsy, urinary tract procedures, and ventricular shunt procedures. Across all index procedure hospitalizations, 13.1% were for patients with an MHC (Figure 2). Of index procedure admissions with any MHC, 35.8% had substance abuse, 33.5% had ADHD, 19.8% had an anxiety disorder, 12.2% had depression, 9.9% had autism, and 20.9% had more than 1 MHC.

Index Procedure Admissions Combined

For all index procedure hospitalizations combined, 6.2% (n = 7632) had an unplanned, 30-day hospital readmission. The rate of 30-day hospital readmissions was significantly higher with versus without an MHC (7.2 vs 5.1%; P < .001). In a multivariable analysis, MHCs were associated with a higher likelihood of hospital readmission following an index procedure admission (AOR, 1.24; 95% CI, 1.15-1.33; Figure 2). Among common MHCs, only anxiety disorders were not associated with higher odds of readmission (AOR, 1.06; 95% CI, 0.92-1.22; Figure 3). 

Specific Index Procedure Admissions

For specific index procedure admissions, the rate of 30-day hospital readmission ranged from 2.2% for knee procedures to 33.6% for tumor biopsy. For 3 (ie, urinary tract, ventricular shunt, and bowel procedures) of the 10 specific index procedure hospitalizations, having an MHC was associated with higher adjusted odds of 30-day readmission (AOR range, 1.38-2.27; Figure 2).

In total, adjusting for sociodemographic, clinical, and hospital characteristics, MHCs were associated with an additional 2501 medical readmissions and 217 procedure readmissions beyond what would have been expected if MHCs were not associated with readmissions.

DISCUSSION

MHCs are common among pediatric hospitalizations with the highest volume of readmissions; MHCs were present in approximately 1 in 5 medical and 1 in 7 procedure index hospitalizations. Across medical and procedure admissions, the adjusted likelihood of unplanned, all-cause 30-day readmission was 25% higher for children with versus without an MHC. The readmission likelihood varied by the type of medical or procedure admission and by the type of MHC. MHCs had the strongest associations with readmissions following hospitalization for diabetes and urinary tract procedures. The MHC categories associated with the highest readmission likelihood were depression, substance abuse, and multiple MHCs.

The current study complements existing literature by helping establish MHCs as a prevalent and important risk factor for hospital readmission in children. Estimates of the prevalence of MHCs in hospitalized children are between 10% and 25%,^10,11,32 and prevalence has increased by as much as 160% over the last 10 years.²⁹ Prior investigations have found that children with an MHC tend to stay longer in the hospital compared with children with no MHC.³² Results from the present study suggest that children with MHCs also experience more inpatient days because of rehospitalizations. Subsequent investigations should strive to understand the mechanisms in the hospital, community, and family environment that are responsible for the increased inpatient utilization in children with MHCs. Understanding how the receipt of mental health services before, during, and after hospitalization influences readmissions could help identify opportunities for practice improvement. Families report the need for better coordination of their child’s medical and mental health care,³³ and opportunities exist to improve attendance at mental health visits after acute care encounters.³⁴ Among adults, interventions that address posthospital access to mental healthcare have prevented readmissions.³⁵

Depression was associated with an increased risk of readmission in medical and procedure hospitalizations. As a well-known risk factor for readmission in adult patients,²¹ depression can adversely affect and exacerbate the physical health recovery of patients experiencing acute and chronic illnesses.^14,36,37 Depression is considered a modifiable contributor that, when controlled, may help lower readmission risk. Optimal adherence with behavior and medication treatment for depression is associated with a lower risk of unplanned 30-day readmissions.^14-16,19 Emerging evidence demonstrates how multifaceted, psychosocial approaches can improve patients’ adherence with depression treatment plans.³⁸ Increased attention to depression in hospitalized children may uncover new ways to manage symptoms as children transition from hospital to home.

Other MHCs were associated with a different risk of readmission among medical and procedure hospitalizations. For example, ADHD or autism documented during index hospitalization was associated with an increased risk of readmission following procedure hospitalizations and a decreased risk following medical hospitalizations. Perhaps children with ADHD or autism who exhibit hyperactive, impulsive, or repetitive behaviors^39,40 are at risk for disrupting their postprocedure wound healing, nutrition recovery, or pain tolerance, which might contribute to increased readmission risk.

MHCs were associated with different readmission risks across specific types of medical or procedure hospitalizations. For example, among medical conditions, the association of readmissions with MHCs was highest for diabetes, which is consistent with prior research.²⁶ Factors that might mediate this relationship include changes in diet and appetite, difficulty with diabetes care plan adherence, and intentional nonadherence as a form of self-harm. Similarly, a higher risk of readmission in chronic medical conditions like asthma, constipation, and sickle cell disease might be mediated by difficulty adhering to medical plans or managing exacerbations at home. In contrast, MHCs had no association with readmission following chemotherapy. In our clinical experience, readmissions following chemotherapy are driven by physiologic problems, such as thrombocytopenia, fever, and/or neutropenia. MHCs might have limited influence over those health issues. For procedure hospitalizations, MHCs had 1 of the strongest associations with ventricular shunt procedures. We hypothesize that MHCs might lead some children to experience general health symptoms that might be associated with shunt malfunction (eg, fatigue, headache, behavior change), which could lead to an increased risk of readmission to evaluate for shunt malfunction. Conversely, we found no relationship between MHCs and readmissions following appendectomy. For appendectomy, MHCs might have limited influence over the development of postsurgical complications (eg, wound infection or ileus). Future research to better elucidate mediators of increased risk of readmission associated with MHCs in certain medical and procedure conditions could help explain these relationships and identify possible future intervention targets to prevent readmissions.

This study has several limitations. The administrative data are not positioned to discover the mechanisms by which MHCs are associated with a higher likelihood of readmission. We used hospital ICD-9-CM codes to identify patients with MHCs. Other methods using more clinically rich data (eg, chart review, prescription medications, etc.) may be preferable to identify patients with MHCs. Although the use of ICD-9-CM codes may have sufficient specificity, some hospitalized children may have an MHC that is not coded. Patients identified by using diagnosis codes could represent patients with a higher severity of illness, patients using medications, or patients whose outpatient records are accessible to make the hospital team aware of the MHC. If documentation of MHCs during hospitalization represents a higher severity of illness, findings may not extrapolate to lower-severity MHCs. As hospitals transition from ICD-9 -CM to ICD-10 coding, and health systems develop more integrated inpatient and outpatient EHRs, diagnostic specificity may improve. We could not analyze the relationships with several potential confounders and explanatory variables that may be related both to the likelihood of having an MHC and the risk of readmission, including medication administration, psychiatric consultation, and parent mental health. Postdischarge health services, including access to a medical home or a usual source of mental healthcare and measures of medication adherence, were not available in the NRD.

Despite these limitations, the current study underscores the importance of MHCs in hospitalized children upon discharge. As subsequent investigations uncover the key drivers explaining the influence of MHCs on hospital readmission risk, hospitals and their local outpatient and community practices may find it useful to consider MHCs when (1) developing contingency plans and establishing follow-up care at discharge,⁴¹ (2) exploring opportunities of care integration between mental and physical health care professionals, and (3) devising strategies to reduce hospital readmissions among populations of children.

 

CONCLUSIONS

MHCs are prevalent in hospitalized children and are associated with an increased risk of 30-day, unplanned hospital readmission. Future readmission prevention efforts may uncover new ways to improve children’s transitions from hospital to home by investigating strategies to address their MHCs.

Acknowledgments

The authors thank Donjo Lau and Troy Richardson for their assistance with the analysis.

Disclosures

Dr. Doupnik was supported by a Ruth L. Kirschstein National Research Service Award institutional training grant (T32-HP010026), funded by the National Institutes of Health. Dr. Zima was supported by the Behavioral Health Centers of Excellence for California (SB852). Dr. Bardach was supported by the National Institute of Child Health and Human Development (K23-HD065836). Dr. Berry was supported by the Agency for Healthcare Research and Quality (R21 HS023092-01). The authors have no financial relationships relevant to this article to disclose. The authors have no potential conflicts of interest to disclose. Dr. Doupnik led the study design and analysis and drafted the initial manuscript. Mr. Lawlor performed the data analysis. Dr. Hall provided statistical consultation. All authors participated in the design of the study, interpretation of the data, revised the manuscript for key intellectual content, and all authors read and approved the final manuscript.

Readmission prevention is a focus of national efforts to improve the quality of hospital care for children.^1-5 Several factors contribute to the risk of readmission for hospitalized children, including age, race or ethnicity, payer, and the type and number of comorbid health conditions.^6-9 Mental health conditions (MHCs) are a prevalent comorbidity in children hospitalized for physical health reasons that could influence their postdischarge health and safety.

MHCs are increasingly common in children hospitalized for physical health indications; a comorbid MHC is currently present in 10% to 25% of hospitalized children ages 3 years and older.^10,11 Hospital length of stay (LOS) and cost are higher in children with an MHC.^12,13 Increased resource use may occur because MHCs can impede hospital treatment effectiveness and the child’s recovery from physical illness. MHCs are associated with a lower adherence with medications^14-16 and a lower ability to cope with health events and problems.^17-19 In adults, MHCs are a well-established risk factor for hospital readmission for a variety of physical health conditions.^20-24 Although the influence of MHCs on readmissions in children has not been extensively investigated, higher readmission rates have been reported in adolescents hospitalized for diabetes with an MHC compared with those with no MHC.^25,26

To our knowledge, no large studies have examined the relationship between the presence of a comorbid MHC and hospital readmissions in children or adolescents hospitalized for a broad array of medical or procedure conditions. Therefore, we conducted this study to (1) assess the likelihood of 30-day hospital readmission in children with versus without MHC who were hospitalized for one of 10 medical or 10 procedure conditions, and (2) to assess which MHCs are associated with the highest likelihood of hospital readmission.

METHODS

Study Design and Setting

We conducted a national, retrospective cohort study of index hospitalizations for children ages 3 to 21 years who were discharged from January 1, 2013, to November 30, 2013, in the Agency for Healthcare Research and Quality’s (AHRQ) Nationwide Readmissions Database (NRD). Admissions occurring in December 2013 were excluded because they did not have a 30-day timeframe available for readmission measurement. The 2013 NRD includes administrative data for a nationally representative sample of 14 million hospitalizations in 21 states, accounting for 49% of all US hospitalizations and weighted to represent 35.6 million hospitalizations. The database includes deidentified, verified patient linkage numbers so that patients can be tracked across multiple hospitalizations at the same institution or different institutions within a state. The NRD includes hospital information, patient demographic information, and the International Classification of Diseases, 9th Revision-Clinical Modification (ICD-9-CM) discharge diagnoses and procedures, with 1 primary diagnosis and up to 24 additional fields for comorbid diagnoses. This study was approved for exemption by the Children’s Hospital of Philadelphia Institutional Review Board.

Index Admissions

We used the methods described below to create a study cohort of the 10 medical and 10 procedure index admissions associated with the highest volume (ie, the greatest absolute number) of 30-day hospital readmissions. Conditions with a high volume of readmissions were chosen in an effort to identify conditions in which readmission-prevention interventions had the greatest potential to reduce the absolute number of readmissions. We first categorized index hospitalizations for medical and procedure conditions by using the All Patient Refined Diagnosis Related Groups (APR-DRGs; 3M Health Information Systems, Wallingford, CT).²⁷ APR-DRGs use all diagnosis and/or procedure ICD-9-CM codes registered for a hospital discharge to assign 1 reason that best explains the need for hospitalization. We then excluded obstetric hospitalizations, psychiatric hospitalizations, and hospitalizations resulting in death or transfer from being considered as index admissions. Afterwards, we ranked each APR-DRG index hospitalization by the total number of 30-day hospital readmissions that occurred afterward and selected the 10 medical and 10 procedure index admissions with the highest number of readmissions. The APR-DRG index admissions are listed in Figures 1 and 2. For the APR-DRG “digestive system diagnoses,” the most common diagnosis was constipation, and we refer to that category as “constipation.” The most common diagnosis for the APR-DRG called “other operating room procedure for neoplasm” was tumor biopsy, and we refer to that category as “tumor biopsy.”

 

Main Outcome Measure

The primary study outcome was unplanned, all-cause readmission to any hospital within 30 days of index hospitalization. All-cause readmissions include any hospitalization for the same or different condition as the index admission, including conditions not eligible to be considered as index admissions (obstetric, psychiatric, and hospitalizations resulting in death or transfer). Planned readmissions, identified by using pediatric-specific measure specifications endorsed by AHRQ and the National Quality Forum,²⁸ were excluded from measurement. For index admissions with multiple 30-day readmissions, only the first readmission was counted. Each readmission was treated as an index admission.

Main Independent Variable

The main independent variable was the presence of an MHC documented during the index hospitalization. MHCs were identified and classified into diagnosis categories derived from the AHRQ Chronic Condition Indicator system by using ICD-9-CM codes.²⁹ MHC categories included anxiety disorders, attention-deficit/hyperactivity disorder (ADHD), autism, depression, and substance abuse. Less common MHCs included bipolar disorder, schizophrenia, disruptive behavior disorders, somatoform disorders, and eating disorders. These conditions are included in the group with any MHC, but we did not calculate the adjusted odds ratios (AORs) of readmission for these conditions. Children were identified as having multiple MHCs if they had more than 1 MHC.

Other Characteristics of Index Hospitalizations

A priori, we selected for analysis the known demographic, clinical, and hospital factors associated with the risk of readmission.^20-24 The demographic characteristics included patient age, gender, payer category, urban or rural residence, and the median income quartile for a patient’s ZIP code. The hospital characteristics included location, ownership, and teaching hospital designation. The clinical characteristics included the number of chronic conditions³⁰ and indicators for the presence of a complex chronic condition in each of 12 organ systems.³¹

Statistical Analysis

We calculated descriptive summary statistics for the characteristics of index hospitalizations. We compared characteristics in index admissions of children with versus without MHC by using Wilcoxon Rank-Sum tests for continuous variables and Wald χ² tests for categorical variables. In the multivariable analysis, we derived logistic regression models to assess the relationship of 30-day hospital readmission with each type of MHC, adjusting for index admission demographic, hospital, and clinical characteristics. MHCs were modeled as binary indicator variables with the presence of any MHC, more than 1 MHC, or each of 5 MHC categories (anxiety disorders, ADHD, autism, depression, substance abuse) compared with no MHC. Four types of logistic regression models were derived (1) for the combined sample of all 10 index medical admissions with each MHC category versus no MHC as a primary predictor, (2) for each medical index admission with any MHC versus no MHC as the primary predictor, (3) for the combined sample of all 10 index procedure admissions with each MHC category versus no MHC as a primary predictor, and (4) for each procedure index admission with any MHC versus no MHC as the primary predictor. All analyses were weighted to achieve national estimates and clustered by hospital by using AHRQ-recommended survey procedures. SAS version 9.4 (SAS Institute, Cary, NC) was used for all analyses. All tests were two-sided, and a P < .05 was
considered statistically significant.

RESULTS

Study Population

The study sample included 471,057 index hospitalizations, including 346,960 medical and 124,097 procedure admissions (Table). The selected hospitalizations accounted for 39.6% of all index hospitalizations and 40.7% of all unplanned 30-day readmissions for patients ages 3 to <21 years in 2013. For all medical and procedure index admissions combined, median age at index admission was 12 years (interquartile range [IQR], 7-17); 49.2% used public insurance, and 74.0% were from urban teaching hospitals. Median LOS was 2 days (IQR, 1-4; Table).

Across all index admissions, 16.3% were for children with an MHC. Overall, children with MHCs were older and more likely to have a chronic³⁰ or complex chronic³¹ physical health condition than children with no MHCs (Table).

Index Medical Admissions, Mental Health Conditions, and Hospital Readmission

The 10 index medical hospitalizations with the most readmissions for children ages 3 to 20 years were asthma, chemotherapy, constipation, diabetes, gastroenteritis, inflammatory bowel disease, neutropenia, pneumonia, seizure, and sickle cell crisis. Across all index medical hospitalizations, 17.5% were for patients with an MHC (Figure 1). Of index medical admissions with any MHC, 26.3% had ADHD, 22.9% had an anxiety disorder, 14.9% had autism, 18.3% had depression, and 30.9% had substance abuse. Among all admissions with MHCs, 28.9% had 2 or more MHCs.

Index Medical Admissions Combined

For all index medical hospitalizations combined, 17.0% (n = 59,138) had an unplanned, 30-day hospital readmission. The rate of 30-day hospital readmissions was higher with versus without an MHC (17.5 vs 16.8%; P < .001). In a multivariable analysis, presence of an MHC was associated with a higher likelihood of hospital readmission following an index medical admission (AOR, 1.23; 95% confidence interval [CI], 1.19-1.26); Figure 1). All MHCs except autism and ADHD had a higher likelihood of readmission (Figure 3). 

Specific Index Medical Admissions

For specific index medical admissions, the rate of 30-day hospital readmission ranged from 2.9% for asthma to 74.3% for chemotherapy. For 8 of the 10 specific index medical hospitalizations (all aside from chemotherapy and pneumonia), an MHC was associated with higher adjusted odds of 30-day readmission (AOR range, 1.10-1.70; Figure 1). In pneumonia index admissions, having an MHC was associated with lower odds of readmission compared with having no MHC (AOR, 0.82; 95% CI, 0.69-0.97; Figure 1).

 

Index Procedure Admissions, Mental Health Conditions, and Hospital Readmission

The 10 index procedure hospitalizations with the most readmissions for children ages 3 to 20 years were appendectomy, bone marrow transplant, bowel procedures, craniotomy, knee procedures, respiratory and chest procedures, spinal fusion, tumor biopsy, urinary tract procedures, and ventricular shunt procedures. Across all index procedure hospitalizations, 13.1% were for patients with an MHC (Figure 2). Of index procedure admissions with any MHC, 35.8% had substance abuse, 33.5% had ADHD, 19.8% had an anxiety disorder, 12.2% had depression, 9.9% had autism, and 20.9% had more than 1 MHC.

Index Procedure Admissions Combined

For all index procedure hospitalizations combined, 6.2% (n = 7632) had an unplanned, 30-day hospital readmission. The rate of 30-day hospital readmissions was significantly higher with versus without an MHC (7.2 vs 5.1%; P < .001). In a multivariable analysis, MHCs were associated with a higher likelihood of hospital readmission following an index procedure admission (AOR, 1.24; 95% CI, 1.15-1.33; Figure 2). Among common MHCs, only anxiety disorders were not associated with higher odds of readmission (AOR, 1.06; 95% CI, 0.92-1.22; Figure 3). 

Specific Index Procedure Admissions

For specific index procedure admissions, the rate of 30-day hospital readmission ranged from 2.2% for knee procedures to 33.6% for tumor biopsy. For 3 (ie, urinary tract, ventricular shunt, and bowel procedures) of the 10 specific index procedure hospitalizations, having an MHC was associated with higher adjusted odds of 30-day readmission (AOR range, 1.38-2.27; Figure 2).

In total, adjusting for sociodemographic, clinical, and hospital characteristics, MHCs were associated with an additional 2501 medical readmissions and 217 procedure readmissions beyond what would have been expected if MHCs were not associated with readmissions.

DISCUSSION

MHCs are common among pediatric hospitalizations with the highest volume of readmissions; MHCs were present in approximately 1 in 5 medical and 1 in 7 procedure index hospitalizations. Across medical and procedure admissions, the adjusted likelihood of unplanned, all-cause 30-day readmission was 25% higher for children with versus without an MHC. The readmission likelihood varied by the type of medical or procedure admission and by the type of MHC. MHCs had the strongest associations with readmissions following hospitalization for diabetes and urinary tract procedures. The MHC categories associated with the highest readmission likelihood were depression, substance abuse, and multiple MHCs.

The current study complements existing literature by helping establish MHCs as a prevalent and important risk factor for hospital readmission in children. Estimates of the prevalence of MHCs in hospitalized children are between 10% and 25%,^10,11,32 and prevalence has increased by as much as 160% over the last 10 years.²⁹ Prior investigations have found that children with an MHC tend to stay longer in the hospital compared with children with no MHC.³² Results from the present study suggest that children with MHCs also experience more inpatient days because of rehospitalizations. Subsequent investigations should strive to understand the mechanisms in the hospital, community, and family environment that are responsible for the increased inpatient utilization in children with MHCs. Understanding how the receipt of mental health services before, during, and after hospitalization influences readmissions could help identify opportunities for practice improvement. Families report the need for better coordination of their child’s medical and mental health care,³³ and opportunities exist to improve attendance at mental health visits after acute care encounters.³⁴ Among adults, interventions that address posthospital access to mental healthcare have prevented readmissions.³⁵

Depression was associated with an increased risk of readmission in medical and procedure hospitalizations. As a well-known risk factor for readmission in adult patients,²¹ depression can adversely affect and exacerbate the physical health recovery of patients experiencing acute and chronic illnesses.^14,36,37 Depression is considered a modifiable contributor that, when controlled, may help lower readmission risk. Optimal adherence with behavior and medication treatment for depression is associated with a lower risk of unplanned 30-day readmissions.^14-16,19 Emerging evidence demonstrates how multifaceted, psychosocial approaches can improve patients’ adherence with depression treatment plans.³⁸ Increased attention to depression in hospitalized children may uncover new ways to manage symptoms as children transition from hospital to home.

Other MHCs were associated with a different risk of readmission among medical and procedure hospitalizations. For example, ADHD or autism documented during index hospitalization was associated with an increased risk of readmission following procedure hospitalizations and a decreased risk following medical hospitalizations. Perhaps children with ADHD or autism who exhibit hyperactive, impulsive, or repetitive behaviors^39,40 are at risk for disrupting their postprocedure wound healing, nutrition recovery, or pain tolerance, which might contribute to increased readmission risk.

MHCs were associated with different readmission risks across specific types of medical or procedure hospitalizations. For example, among medical conditions, the association of readmissions with MHCs was highest for diabetes, which is consistent with prior research.²⁶ Factors that might mediate this relationship include changes in diet and appetite, difficulty with diabetes care plan adherence, and intentional nonadherence as a form of self-harm. Similarly, a higher risk of readmission in chronic medical conditions like asthma, constipation, and sickle cell disease might be mediated by difficulty adhering to medical plans or managing exacerbations at home. In contrast, MHCs had no association with readmission following chemotherapy. In our clinical experience, readmissions following chemotherapy are driven by physiologic problems, such as thrombocytopenia, fever, and/or neutropenia. MHCs might have limited influence over those health issues. For procedure hospitalizations, MHCs had 1 of the strongest associations with ventricular shunt procedures. We hypothesize that MHCs might lead some children to experience general health symptoms that might be associated with shunt malfunction (eg, fatigue, headache, behavior change), which could lead to an increased risk of readmission to evaluate for shunt malfunction. Conversely, we found no relationship between MHCs and readmissions following appendectomy. For appendectomy, MHCs might have limited influence over the development of postsurgical complications (eg, wound infection or ileus). Future research to better elucidate mediators of increased risk of readmission associated with MHCs in certain medical and procedure conditions could help explain these relationships and identify possible future intervention targets to prevent readmissions.

This study has several limitations. The administrative data are not positioned to discover the mechanisms by which MHCs are associated with a higher likelihood of readmission. We used hospital ICD-9-CM codes to identify patients with MHCs. Other methods using more clinically rich data (eg, chart review, prescription medications, etc.) may be preferable to identify patients with MHCs. Although the use of ICD-9-CM codes may have sufficient specificity, some hospitalized children may have an MHC that is not coded. Patients identified by using diagnosis codes could represent patients with a higher severity of illness, patients using medications, or patients whose outpatient records are accessible to make the hospital team aware of the MHC. If documentation of MHCs during hospitalization represents a higher severity of illness, findings may not extrapolate to lower-severity MHCs. As hospitals transition from ICD-9 -CM to ICD-10 coding, and health systems develop more integrated inpatient and outpatient EHRs, diagnostic specificity may improve. We could not analyze the relationships with several potential confounders and explanatory variables that may be related both to the likelihood of having an MHC and the risk of readmission, including medication administration, psychiatric consultation, and parent mental health. Postdischarge health services, including access to a medical home or a usual source of mental healthcare and measures of medication adherence, were not available in the NRD.

Despite these limitations, the current study underscores the importance of MHCs in hospitalized children upon discharge. As subsequent investigations uncover the key drivers explaining the influence of MHCs on hospital readmission risk, hospitals and their local outpatient and community practices may find it useful to consider MHCs when (1) developing contingency plans and establishing follow-up care at discharge,⁴¹ (2) exploring opportunities of care integration between mental and physical health care professionals, and (3) devising strategies to reduce hospital readmissions among populations of children.

 

CONCLUSIONS

MHCs are prevalent in hospitalized children and are associated with an increased risk of 30-day, unplanned hospital readmission. Future readmission prevention efforts may uncover new ways to improve children’s transitions from hospital to home by investigating strategies to address their MHCs.

Acknowledgments

The authors thank Donjo Lau and Troy Richardson for their assistance with the analysis.

Disclosures

Dr. Doupnik was supported by a Ruth L. Kirschstein National Research Service Award institutional training grant (T32-HP010026), funded by the National Institutes of Health. Dr. Zima was supported by the Behavioral Health Centers of Excellence for California (SB852). Dr. Bardach was supported by the National Institute of Child Health and Human Development (K23-HD065836). Dr. Berry was supported by the Agency for Healthcare Research and Quality (R21 HS023092-01). The authors have no financial relationships relevant to this article to disclose. The authors have no potential conflicts of interest to disclose. Dr. Doupnik led the study design and analysis and drafted the initial manuscript. Mr. Lawlor performed the data analysis. Dr. Hall provided statistical consultation. All authors participated in the design of the study, interpretation of the data, revised the manuscript for key intellectual content, and all authors read and approved the final manuscript.

Readmission prevention is a focus of national efforts to improve the quality of hospital care for children.^1-5 Several factors contribute to the risk of readmission for hospitalized children, including age, race or ethnicity, payer, and the type and number of comorbid health conditions.^6-9 Mental health conditions (MHCs) are a prevalent comorbidity in children hospitalized for physical health reasons that could influence their postdischarge health and safety.

MHCs are increasingly common in children hospitalized for physical health indications; a comorbid MHC is currently present in 10% to 25% of hospitalized children ages 3 years and older.^10,11 Hospital length of stay (LOS) and cost are higher in children with an MHC.^12,13 Increased resource use may occur because MHCs can impede hospital treatment effectiveness and the child’s recovery from physical illness. MHCs are associated with a lower adherence with medications^14-16 and a lower ability to cope with health events and problems.^17-19 In adults, MHCs are a well-established risk factor for hospital readmission for a variety of physical health conditions.^20-24 Although the influence of MHCs on readmissions in children has not been extensively investigated, higher readmission rates have been reported in adolescents hospitalized for diabetes with an MHC compared with those with no MHC.^25,26

To our knowledge, no large studies have examined the relationship between the presence of a comorbid MHC and hospital readmissions in children or adolescents hospitalized for a broad array of medical or procedure conditions. Therefore, we conducted this study to (1) assess the likelihood of 30-day hospital readmission in children with versus without MHC who were hospitalized for one of 10 medical or 10 procedure conditions, and (2) to assess which MHCs are associated with the highest likelihood of hospital readmission.

METHODS

Study Design and Setting

We conducted a national, retrospective cohort study of index hospitalizations for children ages 3 to 21 years who were discharged from January 1, 2013, to November 30, 2013, in the Agency for Healthcare Research and Quality’s (AHRQ) Nationwide Readmissions Database (NRD). Admissions occurring in December 2013 were excluded because they did not have a 30-day timeframe available for readmission measurement. The 2013 NRD includes administrative data for a nationally representative sample of 14 million hospitalizations in 21 states, accounting for 49% of all US hospitalizations and weighted to represent 35.6 million hospitalizations. The database includes deidentified, verified patient linkage numbers so that patients can be tracked across multiple hospitalizations at the same institution or different institutions within a state. The NRD includes hospital information, patient demographic information, and the International Classification of Diseases, 9th Revision-Clinical Modification (ICD-9-CM) discharge diagnoses and procedures, with 1 primary diagnosis and up to 24 additional fields for comorbid diagnoses. This study was approved for exemption by the Children’s Hospital of Philadelphia Institutional Review Board.

Index Admissions

We used the methods described below to create a study cohort of the 10 medical and 10 procedure index admissions associated with the highest volume (ie, the greatest absolute number) of 30-day hospital readmissions. Conditions with a high volume of readmissions were chosen in an effort to identify conditions in which readmission-prevention interventions had the greatest potential to reduce the absolute number of readmissions. We first categorized index hospitalizations for medical and procedure conditions by using the All Patient Refined Diagnosis Related Groups (APR-DRGs; 3M Health Information Systems, Wallingford, CT).²⁷ APR-DRGs use all diagnosis and/or procedure ICD-9-CM codes registered for a hospital discharge to assign 1 reason that best explains the need for hospitalization. We then excluded obstetric hospitalizations, psychiatric hospitalizations, and hospitalizations resulting in death or transfer from being considered as index admissions. Afterwards, we ranked each APR-DRG index hospitalization by the total number of 30-day hospital readmissions that occurred afterward and selected the 10 medical and 10 procedure index admissions with the highest number of readmissions. The APR-DRG index admissions are listed in Figures 1 and 2. For the APR-DRG “digestive system diagnoses,” the most common diagnosis was constipation, and we refer to that category as “constipation.” The most common diagnosis for the APR-DRG called “other operating room procedure for neoplasm” was tumor biopsy, and we refer to that category as “tumor biopsy.”

 

Main Outcome Measure

The primary study outcome was unplanned, all-cause readmission to any hospital within 30 days of index hospitalization. All-cause readmissions include any hospitalization for the same or different condition as the index admission, including conditions not eligible to be considered as index admissions (obstetric, psychiatric, and hospitalizations resulting in death or transfer). Planned readmissions, identified by using pediatric-specific measure specifications endorsed by AHRQ and the National Quality Forum,²⁸ were excluded from measurement. For index admissions with multiple 30-day readmissions, only the first readmission was counted. Each readmission was treated as an index admission.

Main Independent Variable

The main independent variable was the presence of an MHC documented during the index hospitalization. MHCs were identified and classified into diagnosis categories derived from the AHRQ Chronic Condition Indicator system by using ICD-9-CM codes.²⁹ MHC categories included anxiety disorders, attention-deficit/hyperactivity disorder (ADHD), autism, depression, and substance abuse. Less common MHCs included bipolar disorder, schizophrenia, disruptive behavior disorders, somatoform disorders, and eating disorders. These conditions are included in the group with any MHC, but we did not calculate the adjusted odds ratios (AORs) of readmission for these conditions. Children were identified as having multiple MHCs if they had more than 1 MHC.

Other Characteristics of Index Hospitalizations

A priori, we selected for analysis the known demographic, clinical, and hospital factors associated with the risk of readmission.^20-24 The demographic characteristics included patient age, gender, payer category, urban or rural residence, and the median income quartile for a patient’s ZIP code. The hospital characteristics included location, ownership, and teaching hospital designation. The clinical characteristics included the number of chronic conditions³⁰ and indicators for the presence of a complex chronic condition in each of 12 organ systems.³¹

Statistical Analysis

We calculated descriptive summary statistics for the characteristics of index hospitalizations. We compared characteristics in index admissions of children with versus without MHC by using Wilcoxon Rank-Sum tests for continuous variables and Wald χ² tests for categorical variables. In the multivariable analysis, we derived logistic regression models to assess the relationship of 30-day hospital readmission with each type of MHC, adjusting for index admission demographic, hospital, and clinical characteristics. MHCs were modeled as binary indicator variables with the presence of any MHC, more than 1 MHC, or each of 5 MHC categories (anxiety disorders, ADHD, autism, depression, substance abuse) compared with no MHC. Four types of logistic regression models were derived (1) for the combined sample of all 10 index medical admissions with each MHC category versus no MHC as a primary predictor, (2) for each medical index admission with any MHC versus no MHC as the primary predictor, (3) for the combined sample of all 10 index procedure admissions with each MHC category versus no MHC as a primary predictor, and (4) for each procedure index admission with any MHC versus no MHC as the primary predictor. All analyses were weighted to achieve national estimates and clustered by hospital by using AHRQ-recommended survey procedures. SAS version 9.4 (SAS Institute, Cary, NC) was used for all analyses. All tests were two-sided, and a P < .05 was
considered statistically significant.

RESULTS

Study Population

The study sample included 471,057 index hospitalizations, including 346,960 medical and 124,097 procedure admissions (Table). The selected hospitalizations accounted for 39.6% of all index hospitalizations and 40.7% of all unplanned 30-day readmissions for patients ages 3 to <21 years in 2013. For all medical and procedure index admissions combined, median age at index admission was 12 years (interquartile range [IQR], 7-17); 49.2% used public insurance, and 74.0% were from urban teaching hospitals. Median LOS was 2 days (IQR, 1-4; Table).

Across all index admissions, 16.3% were for children with an MHC. Overall, children with MHCs were older and more likely to have a chronic³⁰ or complex chronic³¹ physical health condition than children with no MHCs (Table).

Index Medical Admissions, Mental Health Conditions, and Hospital Readmission

The 10 index medical hospitalizations with the most readmissions for children ages 3 to 20 years were asthma, chemotherapy, constipation, diabetes, gastroenteritis, inflammatory bowel disease, neutropenia, pneumonia, seizure, and sickle cell crisis. Across all index medical hospitalizations, 17.5% were for patients with an MHC (Figure 1). Of index medical admissions with any MHC, 26.3% had ADHD, 22.9% had an anxiety disorder, 14.9% had autism, 18.3% had depression, and 30.9% had substance abuse. Among all admissions with MHCs, 28.9% had 2 or more MHCs.

Index Medical Admissions Combined

For all index medical hospitalizations combined, 17.0% (n = 59,138) had an unplanned, 30-day hospital readmission. The rate of 30-day hospital readmissions was higher with versus without an MHC (17.5 vs 16.8%; P < .001). In a multivariable analysis, presence of an MHC was associated with a higher likelihood of hospital readmission following an index medical admission (AOR, 1.23; 95% confidence interval [CI], 1.19-1.26); Figure 1). All MHCs except autism and ADHD had a higher likelihood of readmission (Figure 3). 

Specific Index Medical Admissions

For specific index medical admissions, the rate of 30-day hospital readmission ranged from 2.9% for asthma to 74.3% for chemotherapy. For 8 of the 10 specific index medical hospitalizations (all aside from chemotherapy and pneumonia), an MHC was associated with higher adjusted odds of 30-day readmission (AOR range, 1.10-1.70; Figure 1). In pneumonia index admissions, having an MHC was associated with lower odds of readmission compared with having no MHC (AOR, 0.82; 95% CI, 0.69-0.97; Figure 1).

 

Index Procedure Admissions, Mental Health Conditions, and Hospital Readmission

The 10 index procedure hospitalizations with the most readmissions for children ages 3 to 20 years were appendectomy, bone marrow transplant, bowel procedures, craniotomy, knee procedures, respiratory and chest procedures, spinal fusion, tumor biopsy, urinary tract procedures, and ventricular shunt procedures. Across all index procedure hospitalizations, 13.1% were for patients with an MHC (Figure 2). Of index procedure admissions with any MHC, 35.8% had substance abuse, 33.5% had ADHD, 19.8% had an anxiety disorder, 12.2% had depression, 9.9% had autism, and 20.9% had more than 1 MHC.

Index Procedure Admissions Combined

For all index procedure hospitalizations combined, 6.2% (n = 7632) had an unplanned, 30-day hospital readmission. The rate of 30-day hospital readmissions was significantly higher with versus without an MHC (7.2 vs 5.1%; P < .001). In a multivariable analysis, MHCs were associated with a higher likelihood of hospital readmission following an index procedure admission (AOR, 1.24; 95% CI, 1.15-1.33; Figure 2). Among common MHCs, only anxiety disorders were not associated with higher odds of readmission (AOR, 1.06; 95% CI, 0.92-1.22; Figure 3). 

Specific Index Procedure Admissions

For specific index procedure admissions, the rate of 30-day hospital readmission ranged from 2.2% for knee procedures to 33.6% for tumor biopsy. For 3 (ie, urinary tract, ventricular shunt, and bowel procedures) of the 10 specific index procedure hospitalizations, having an MHC was associated with higher adjusted odds of 30-day readmission (AOR range, 1.38-2.27; Figure 2).

In total, adjusting for sociodemographic, clinical, and hospital characteristics, MHCs were associated with an additional 2501 medical readmissions and 217 procedure readmissions beyond what would have been expected if MHCs were not associated with readmissions.

DISCUSSION

MHCs are common among pediatric hospitalizations with the highest volume of readmissions; MHCs were present in approximately 1 in 5 medical and 1 in 7 procedure index hospitalizations. Across medical and procedure admissions, the adjusted likelihood of unplanned, all-cause 30-day readmission was 25% higher for children with versus without an MHC. The readmission likelihood varied by the type of medical or procedure admission and by the type of MHC. MHCs had the strongest associations with readmissions following hospitalization for diabetes and urinary tract procedures. The MHC categories associated with the highest readmission likelihood were depression, substance abuse, and multiple MHCs.

The current study complements existing literature by helping establish MHCs as a prevalent and important risk factor for hospital readmission in children. Estimates of the prevalence of MHCs in hospitalized children are between 10% and 25%,^10,11,32 and prevalence has increased by as much as 160% over the last 10 years.²⁹ Prior investigations have found that children with an MHC tend to stay longer in the hospital compared with children with no MHC.³² Results from the present study suggest that children with MHCs also experience more inpatient days because of rehospitalizations. Subsequent investigations should strive to understand the mechanisms in the hospital, community, and family environment that are responsible for the increased inpatient utilization in children with MHCs. Understanding how the receipt of mental health services before, during, and after hospitalization influences readmissions could help identify opportunities for practice improvement. Families report the need for better coordination of their child’s medical and mental health care,³³ and opportunities exist to improve attendance at mental health visits after acute care encounters.³⁴ Among adults, interventions that address posthospital access to mental healthcare have prevented readmissions.³⁵

Depression was associated with an increased risk of readmission in medical and procedure hospitalizations. As a well-known risk factor for readmission in adult patients,²¹ depression can adversely affect and exacerbate the physical health recovery of patients experiencing acute and chronic illnesses.^14,36,37 Depression is considered a modifiable contributor that, when controlled, may help lower readmission risk. Optimal adherence with behavior and medication treatment for depression is associated with a lower risk of unplanned 30-day readmissions.^14-16,19 Emerging evidence demonstrates how multifaceted, psychosocial approaches can improve patients’ adherence with depression treatment plans.³⁸ Increased attention to depression in hospitalized children may uncover new ways to manage symptoms as children transition from hospital to home.

Other MHCs were associated with a different risk of readmission among medical and procedure hospitalizations. For example, ADHD or autism documented during index hospitalization was associated with an increased risk of readmission following procedure hospitalizations and a decreased risk following medical hospitalizations. Perhaps children with ADHD or autism who exhibit hyperactive, impulsive, or repetitive behaviors^39,40 are at risk for disrupting their postprocedure wound healing, nutrition recovery, or pain tolerance, which might contribute to increased readmission risk.

MHCs were associated with different readmission risks across specific types of medical or procedure hospitalizations. For example, among medical conditions, the association of readmissions with MHCs was highest for diabetes, which is consistent with prior research.²⁶ Factors that might mediate this relationship include changes in diet and appetite, difficulty with diabetes care plan adherence, and intentional nonadherence as a form of self-harm. Similarly, a higher risk of readmission in chronic medical conditions like asthma, constipation, and sickle cell disease might be mediated by difficulty adhering to medical plans or managing exacerbations at home. In contrast, MHCs had no association with readmission following chemotherapy. In our clinical experience, readmissions following chemotherapy are driven by physiologic problems, such as thrombocytopenia, fever, and/or neutropenia. MHCs might have limited influence over those health issues. For procedure hospitalizations, MHCs had 1 of the strongest associations with ventricular shunt procedures. We hypothesize that MHCs might lead some children to experience general health symptoms that might be associated with shunt malfunction (eg, fatigue, headache, behavior change), which could lead to an increased risk of readmission to evaluate for shunt malfunction. Conversely, we found no relationship between MHCs and readmissions following appendectomy. For appendectomy, MHCs might have limited influence over the development of postsurgical complications (eg, wound infection or ileus). Future research to better elucidate mediators of increased risk of readmission associated with MHCs in certain medical and procedure conditions could help explain these relationships and identify possible future intervention targets to prevent readmissions.

This study has several limitations. The administrative data are not positioned to discover the mechanisms by which MHCs are associated with a higher likelihood of readmission. We used hospital ICD-9-CM codes to identify patients with MHCs. Other methods using more clinically rich data (eg, chart review, prescription medications, etc.) may be preferable to identify patients with MHCs. Although the use of ICD-9-CM codes may have sufficient specificity, some hospitalized children may have an MHC that is not coded. Patients identified by using diagnosis codes could represent patients with a higher severity of illness, patients using medications, or patients whose outpatient records are accessible to make the hospital team aware of the MHC. If documentation of MHCs during hospitalization represents a higher severity of illness, findings may not extrapolate to lower-severity MHCs. As hospitals transition from ICD-9 -CM to ICD-10 coding, and health systems develop more integrated inpatient and outpatient EHRs, diagnostic specificity may improve. We could not analyze the relationships with several potential confounders and explanatory variables that may be related both to the likelihood of having an MHC and the risk of readmission, including medication administration, psychiatric consultation, and parent mental health. Postdischarge health services, including access to a medical home or a usual source of mental healthcare and measures of medication adherence, were not available in the NRD.

Despite these limitations, the current study underscores the importance of MHCs in hospitalized children upon discharge. As subsequent investigations uncover the key drivers explaining the influence of MHCs on hospital readmission risk, hospitals and their local outpatient and community practices may find it useful to consider MHCs when (1) developing contingency plans and establishing follow-up care at discharge,⁴¹ (2) exploring opportunities of care integration between mental and physical health care professionals, and (3) devising strategies to reduce hospital readmissions among populations of children.

 

CONCLUSIONS

MHCs are prevalent in hospitalized children and are associated with an increased risk of 30-day, unplanned hospital readmission. Future readmission prevention efforts may uncover new ways to improve children’s transitions from hospital to home by investigating strategies to address their MHCs.

Acknowledgments

The authors thank Donjo Lau and Troy Richardson for their assistance with the analysis.

Disclosures

Dr. Doupnik was supported by a Ruth L. Kirschstein National Research Service Award institutional training grant (T32-HP010026), funded by the National Institutes of Health. Dr. Zima was supported by the Behavioral Health Centers of Excellence for California (SB852). Dr. Bardach was supported by the National Institute of Child Health and Human Development (K23-HD065836). Dr. Berry was supported by the Agency for Healthcare Research and Quality (R21 HS023092-01). The authors have no financial relationships relevant to this article to disclose. The authors have no potential conflicts of interest to disclose. Dr. Doupnik led the study design and analysis and drafted the initial manuscript. Mr. Lawlor performed the data analysis. Dr. Hall provided statistical consultation. All authors participated in the design of the study, interpretation of the data, revised the manuscript for key intellectual content, and all authors read and approved the final manuscript.