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Interhospital Transfer of Children
Interhospital transfer of critically ill and injured children is necessitated by variation in resource availability between hospitals. Critically ill children judged in need of clinical services or expertise not locally available undergo transfer to hospitals with more appropriate resource capabilities and expertise, with the expectation that clinical outcomes of transfer will be better than nontransfer.
Significant variation both in the availability of pediatric critical care services across US hospitals1 and in child mortality among hospitals without pediatric critical care services2 suggests that interhospital transfer will remain an integral part of healthcare delivery for critically ill and injured children. Timely provision of definitive care for acute life‐threatening disease is associated with good clinical outcomes.3, 4 While prior studies have examined clinical outcomes and resource consumption among critically ill adults who underwent interhospital transfer for intensive care,59 there is scarce information regarding clinical characteristics and outcomes of interhospital transfer for critically ill and injured children.
This study was conducted to test the hypothesis that, among critically ill and injured children who undergo interhospital transfer for intensive care, children transferred after an initial hospitalization at the referring facility will have higher mortality, longer length of stay (LOS), and higher resource consumption than children transferred directly from the emergency department (ED) of the referring hospitals.
METHODS
Study Design
We conducted a secondary analysis of administrative claims data from the Michigan Medicaid program for the period January 1, 2002, to December 31, 2004. The data included all paid claims for health services rendered to enrollees in the Medicaid program. The Institutional Review Board of the University of Michigan Medical School approved the study.
Study Sample and Variable Identification
A 3‐step approach was employed to identify interhospital transfer admissions for intensive care of children. Initially, the Medicaid claims were queried to identify all hospitalizations for children 018 years who received intensive care services, using Medicare revenue codes.10 Admissions for neonatal intensive care were excluded from the analysis. The American Hospital Association Guide to the Health Care Field, a compendium of US healthcare facilities, was used to verify the presence of intensive care facilities.11, 12 Subsequently, to identify the subset of children who underwent interhospital transfer, data were queried for the presence of claims from another hospital, and the date of discharge from the referring hospital had to be the same as the date of admission to the receiving hospital intensive care unit (ICU). Finally, to ascertain the source of interhospital transfer, Medicare revenue codes and current procedural terminology (CPT) codes were used to identify claims for receipt of services at specific sites within the referring hospital; namely, the ED, ward, or the ICU. This information was used to categorize admissions into 1 of 3 pathways of interhospital transfer:
ED transferFrom the ED of the referring hospital to the ICU of the receiving hospital.
Ward transferFrom the wards of the referring hospital to the ICU of the receiving hospital.
Inter‐ICU transferFrom the ICU of the referring hospital to the ICU of the receiving hospital.
Dependent Variables
Mortality at the Receiving Hospital
This is determined by linkage to vital statistics records maintained by the Michigan Department of Community Health, Division of Vital Records and Health Statistics.
LOS at the Receiving Hospital
This is determined as the count of days of hospitalization at the receiving hospital. Of note, this includes ICU days and non‐ICU days at the receiving hospital.
Independent Variables
Source of Interhospital Transfer
The main (exposure) independent variable. Categorized into ED, ward, or inter‐ICU transfers, as described.
Patient Demographics
Age and gender.
Comorbid Illness
Determined using International Classification of Diseases, ninth revision (ICD‐9) diagnosis codes, applying methodology as described.13
Organ Dysfunction at the Referring and Receiving Hospitals
Determined using ICD‐9 diagnosis codes, applying methodology as described.14
Patient Diagnostic Categories
Eleven diagnostic categories were created based on primary admission diagnoses (Appendix A).
LOS at the Referring Hospital
Determined as the count of days of hospitalization at the referring hospital.
Receipt of Cardiopulmonary Resuscitation (CPR) on the Date of Interhospital Transfer
Determined using procedure codes.
Receipt of Medical‐Surgical Procedures at the Receiving Hospital
Identified through the use of ICD‐9 procedure codes, CPT codes, and Healthcare Common Procedure Coding System codes. The procedures are listed in Appendix B.
Statistical Analysis
Descriptive statistics were used to characterize the study sample. According to the 3 sources of interhospital transfer, patient characteristics (age, gender, presence of organ dysfunction, and comorbid illness), median LOS at the referring hospital, and receipt of CPR on the date of interhospital transfer were compared using chi‐square tests for categorical variables, and Kruskal‐Wallis tests for continuous variables. Similarly, outcome variables of in‐hospital mortality and median LOS at the receiving hospital were compared across the 3 sources of interhospital transfer. Analysis of variance was used to compare mean LOS at the receiving hospital across the 3 sources of interhospital transfer. Median (with interquartile range [IQR]) and mean (with standard deviation [SD]) values are presented to describe LOS, given skew in LOS data.
To account for potential confounding of LOS and mortality at the receiving hospital by the presence of organ dysfunction and comorbid illness1316 at the referring hospital, multivariate logistic regression and multiple linear regression analyses were conducted to estimate the odds of in‐hospital mortality and the incremental LOS, respectively, for ward and inter‐ICU transfers, compared with ED transfers. Statistical analyses were conducted using Stata 8 for windows (Stata Corporation, College Station, TX). A 2‐tailed level of 0.05 was used as the threshold for statistical significance.
RESULTS
Patient Characteristics
Of 1,643 transfer admissions for intensive care during the study period, 1022 (62%) were ED transfers, 512 (31%) were ward transfers, and 109 (7%) were inter‐ICU transfers. The average age was 2 years, with male gender (57%) predominance. Comorbid illness was present in 19% of admissions, while 11% had evidence of organ dysfunction at the referring hospital. Table 1 presents key patient demographic and clinical characteristics at the referring hospitals, by transfer source. Inter‐ICU and ward transfers were younger than ED transfers, and had a higher preponderance of comorbid illness and organ dysfunction. At the time of interhospital transfer, compared with ED transfers, the proportion of admissions with organ dysfunction (a marker of illness severity) was 3‐fold and 8‐fold higher among ward and inter‐ICU transfers, respectively.
| Transfer Source | P | |||
|---|---|---|---|---|
| Characteristics | ED (n = 1022) | Ward (n = 512) | Inter‐ICU (n = 109) | |
| ||||
| Median age in years (IQR) | 2 (09) | 1 (07) | 1 (010) | <0.01 |
| Male (%) | 57.8 | 56.2 | 47.6 | 0.13 |
| Comorbid illness (% ) | 13.1 | 25.0 | 50.5 | <0.01 |
| Pretransfer hospital length of stay (days) | ||||
| Median (IQR) | 0 | 1 (02) | 3 (18) | <0.01 |
| Mean (SD) | 0.2 (5.2) | 1.6 (4.8) | 9.7 (18.0) | <0.01 |
| Pretransfer organ dysfunction (%) | 5.5 | 14.5 | 40.4 | <0.01 |
Patterns of Transfer
The leading diagnoses among all children were respiratory disease, trauma, and neurological disease (Table 2), with some variation in diagnoses by source of interhospital transfer. For example, cardiovascular disease was the second leading diagnosis after respiratory disease among the inter‐ICU transfers, while more children with endocrine disease (predominantly diabetic ketoacidosis), traumatic injury, or drug poisoning were transferred directly from the ED, than from the ward or the ICU settings. For burn care, 80% (45/56) of all transfer admissions were direct from the ED (Table 3). The majority (78%) of children with traumatic injuries were directly transferred from the ED to the ICU, while the remainder were transferred after initial care delivered on the ward (18%) or ICU (4%) settings prior to interhospital transfer for definitive intensive trauma care. Importantly, among the inter‐ICU transfers, 104 (95%) were transferred to pediatric ICUs from referring hospitals with adult and pediatric ICU facilities, suggesting uptransfer for specialized care. Five children were transferred between hospitals with adult ICU facilities.
| Transfer Source | ||||
|---|---|---|---|---|
| Diagnostic Category (%) | Overall* (n = 1639) | ED* (n = 1018) | Ward (n = 512) | Inter‐ICU (n = 109) |
| ||||
| Respiratory disease | 35.1 | 32.8 | 41.0 | 28.4 |
| Trauma | 16.2 | 20.5 | 9.2 | 9.1 |
| Neurological disease | 12.4 | 12.5 | 12.3 | 11.9 |
| Gastrointestinal disease | 6.7 | 5.4 | 7.4 | 11.9 |
| Infectious disease | 5.8 | 4.0 | 8.4 | 10.0 |
| Endocrine disease | 5.5 | 7.9 | 1.8 | 0 |
| Drug overdose/poisoning | 5.0 | 6.4 | 2.9 | 1.8 |
| Cardiovascular disease | 4.8 | 2.8 | 6.3 | 16.5 |
| Hematologic/oncologic disease | 2.0 | 1.6 | 2.9 | 1.8 |
| Cardiac arrest | 0.2 | 0 | 0.6 | 0.9 |
| Other diagnoses | 6.2 | 5.4 | 7.2 | 7.7 |
| Transfer Source | |||||
|---|---|---|---|---|---|
| Characteristics (%) | Overall (n = 1643) | ED (n = 1022) | Ward (n = 512) | Inter‐ICU (n = 109) | P |
| Respiratory | 26.8 | 19.0 | 36.7 | 54.1 | <0.01 |
| Radiological | 21.2 | 19.5 | 20.5 | 41.3 | <0.01 |
| Vascular access | 20.0 | 15.2 | 27.0 | 33.0 | <0.01 |
| Gastrointestinal | 3.9 | 3.0 | 3.7 | 12.8 | <0.01 |
| Neurological | 3.8 | 3.2 | 3.7 | 10.1 | <0.01 |
| Cardiovascular | 3.6 | 1.8 | 4.1 | 18.4 | <0.01 |
| Burn care | 3.4 | 4.5 | 2.0 | 0 | <0.01 |
| General surgery | 3.2 | 2.1 | 4.3 | 8.3 | <0.01 |
| Dialysis | 2.6 | 2.0 | 2.5 | 8.3 | <0.01 |
| ECMO | 2.1 | 1.3 | 2.2 | 9.2 | <0.01 |
CPR was performed on the date of interhospital transfer for 23 patients (1.4% of the sample), of whom 13 (56.5%) were ward transfers, 8 (34.8%) were inter‐ICU transfers, and 2 (8.7%) were ED transfers (P < 0.02). Two‐thirds of these children did not survive subsequent hospitalization at the receiving hospitals.
Clinical Outcomes and Resource Utilization at the Receiving Hospitals
At the receiving hospitals, other than burn care, medical‐surgical procedures were performed most often among the inter‐ICU transfers. Ward transfers also had higher receipt of procedures compared with ED transfers (Table 3). The inter‐ICU and ward transfers had a higher preponderance of organ dysfunction at the receiving hospitals, compared to the ED transfers (38.5% and 29.3% versus 20.8%, P < 0.01).
Clinical outcomes at the receiving hospitals varied significantly according to the source of interhospital transfer (Table 4). Sixty‐six (4%) of patients died at the receiving hospitals. In comparison with ED transfers, unadjusted in‐hospital mortality was 2‐fold and 3‐fold higher among the ward and inter‐ICU transfers, respectively. Also, hospital LOS was significantly longer among the ward and inter‐ICU transfers than for the ED transfers.
| Transfer Source | ||||
|---|---|---|---|---|
| Characteristics | ED (n = 1022) | Ward (n = 512) | Inter‐ICU (n = 109) | P |
| ||||
| Mortality (%) | 2.8 | 5.5 | 8.3 | <0.01 |
| Length of stay (days) | ||||
| Median (IQR) | 3 (27) | 5 (312) | 13 (724) | <0.01 |
| Mean (SD) | 6.7 (10.4) | 8.5 (9.2) | 21.4 (22.9) | <0.01 |
In multivariate analyses adjusting for patient age, and the presence of comorbid illness and organ dysfunction at the referring hospital, compared with ED transfers, odds of mortality were significantly higher (odds ratio [OR], 1.76; 95% confidence interval [CI], 1.023.03) for ward transfers. Inter‐ICU transfers also had higher odds of mortality (OR, 2.07; 95% CI, 0.884.86), without achieving statistical significance. Similarly, compared with ED transfers, LOS at the receiving hospital was longer by 1.5 days (95% CI, 0.32.7 days) for ward transfers, and by 13.5 days (95% CI, 11.115.8 days) for inter‐ICU transfers.
DISCUSSION
This study is the first to highlight significant variation in clinical outcomes and resource consumption after interhospital transfer of critically ill and injured children, depending on the source of transfer. In comparison with children transferred directly from the referring hospitals' ED settings, children transferred from the referring hospitals' wards had higher mortality, while those who underwent inter‐ICU transfer had significantly higher resource consumption. In addition, ward transfers had the highest proportion of children who underwent CPR on the date of interhospital transfer, highlighting elevated severity of disease prior to transfer and an urgent need for improved understanding of pretransfer clinical care and medical decision‐making. The findings raise the possibility that more timely transfer of some patients directly from community hospital EDs to regional ICUs might improve survival and reduce resource consumption.
Although interhospital transfers are common in everyday clinical practice, there has been a knowledge gap in pediatric acute and critical care medicine regarding the clinical outcomes and resource consumption among children who undergo such transfers. Findings from the current study narrow this gap by relating triage at the referring hospitals to clinical outcomes and resource utilization at the receiving hospitals.
Certain distinct transfer patterns were observed. Most children with burn injury underwent direct transfer from the ED to the ICU; this transfer pattern may be related both to the limited availability of ICUs with burn care capability in Michigan and to the acuity of burn injuries, which often mandates immediate triage to hospitals with intensive burn care facilities. Conversely, while the majority of children with traumatic injuries were directly transferred from emergency to intensive care, over one‐fifth were transferred after initial care delivered on the ward or ICU settings prior to interhospital transfer for definitive intensive trauma care. Such imperfect regionalization of trauma care suggests further study of clinical outcomes and resource utilization among injured children is warranted. Likewise, cardiovascular disease was prominent among the inter‐ICU transfers, suggesting a clinical practice pattern of stabilization and resuscitation at the initial ICU prior to interhospital vertical or uptransfer for definitive cardiac care at the receiving hospitals.
It remains unknown whether the timing of interhospital transfer of critically ill children is a determinant of clinical outcomes. Prior studies among adults have reported higher mortality with prolonged duration of pre‐ICU care on the ward.4, 17 In the current study, ward and inter‐ICU transfers were initially hospitalized for a median of 1 and 3 days, respectively, prior to transfer. While we could not determine from administrative data what the precise triggers for interhospital transfer in this study were, it is instructive to note that ward transfers comprised more than one‐half of all children who received CPR on the date of transfer. For children who received CPR, severe clinical deterioration likely triggered transfer to hospitals with ICU facilities, but because only a minority of children received CPR overall, other triggers of transfer warrant investigation. For most of the children transferred, it seems plausible that the precipitant of transfer was likely a mismatch of their clinical status with the clinical capacities of the facilities where they were initially hospitalized. Future work should investigate if there is an association between clinical outcomes at the receiving hospitals, and both the timing of interhospital transfer and the clinical status of patients at transfer.
Importantly, compared with ED transfers, ward transfers demonstrated elevated odds of mortality after adjustment for coexisting comorbid illness, patient age, and pretransfer organ dysfunction at the referring hospital. Some possible explanations for this finding include the progression of disease while receiving care on the ward, or suboptimal access to ICU facilities due to barriers to transfer at either the referring or receiving hospitals. Importantly, progression of disease in ward settings may be detected by early identification of children at high risk of clinical deterioration on the wards of hospitals without ICU facilities, prior to cardiopulmonary arrest, because death after CPR may not be averted with subsequent ICU care.18
Various approaches to facilitate rapid and appropriate triage and reassessment of children in hospitals without ICU facilities, prior to severe clinical deterioration or need for CPR, must be investigated. These approaches might include in‐hospital measures such as the establishment of medical emergency teams to respond to clinical deterioration on the wards19 or collaborative interhospital measures such as the use of telemedicine20 or similar remote communication/triage systems to enhance communication between clinical caregivers at hospitals with ICU facilities and those in hospitals without ICU facilities. Furthermore, interhospital transfer agreements may facilitate expeditious and appropriate transfer of severely ill patients to hospitals with ICU facilities.
Access to hospitals with ICU facilities might also influence outcomes for critically ill children admitted initially to wards of hospitals without ICU facilities. Kanter2 reported significant variation in mortality among children who received care at New York hospitals without ICU facilities. Of note, 27% of statewide pediatric inpatient deaths occurred in those hospitals without ICU facilities. It appeared that, while some pediatric deaths in hospitals without ICU facilities were expected, regional variation in such mortality might have been associated with reduced access to, or poor utilization of, hospitals with ICU facilities. Barriers to interhospital transfers might include underrecognition of mismatch between patient illness severity and hospital capability at referring hospitals, or lack of capacity to accept transfers at the receiving hospitals. Further study is warranted to investigate clinical decision‐making underlying the initiation of the interhospital transfer processes, and procedural or institutional barriers that might hinder the transfer of critically ill children from hospitals without ICU facilities.
Resource consumption at the receiving hospitals, measured by hospital LOS and receipt of medical‐surgical procedures, was highest among the inter‐ICU transfers. This was an expected finding, given the high frequency of organ dysfunction among the inter‐ICU transfers, before and after interhospital transfer. These patients had the highest use of advanced and resource‐intensive technology, including continuous renal replacement therapy, extracorporeal membrane oxygenation, and cardiovascular procedures such as open‐heart surgery. In addition, the duration of hospitalization at the receiving hospital was 2 weeks longer among the inter‐ICU transfers when compared with the ED transfers. Such prolonged hospitalization has been previously associated with significantly increased resource consumption.4, 6 In the absence of physiologic data pertaining to illness severity, however, it is unknown whether this observed differential LOS by source of interhospital transfer might be attributable to both unobserved illness severity and/or extensive in‐hospital post‐ICU multidisciplinary rehabilitative care for inter‐ICU transfer patients, compared with ED transfer patients.
Our study findings need to be interpreted in light of certain limitations. Administrative claims data do not allow for assessment of the quality of hospital care, a factor that might play an important role in patient clinical outcomes. The data lacked any physiologic information that might enhance the ability to estimate patient severity of illness; the analysis used the presence of organ dysfunction at the referring hospitals as a proxy for illness severity. The use of diagnosis codebased measures of severity adjustment, as employed in the current study, however, has been reported to be comparable with clinical severity measures because of the relatively complete capture of diagnosis codes for life‐threatening conditions occurring late in the hospitalization, such as prior to interhospital transfer in the current study.2123
The absence of clinical information prevented assessment of the likelihood of in‐hospital morbidity, transport complications, and need for various therapeutic interventions after ICU care, which are also highly relevant outcomes of interhospital transfers. It is unknown if the small sample size among inter‐ICU transfers limited the ability to demonstrate a statistically significant difference in odds of mortality among inter‐ICU transfers compared with ED transfers.
Also, the identification of diagnoses and procedures was made using multiple coding instruments and is therefore susceptible to inaccuracies of detection and attribution that may have biased the findings. Study findings did not include cost, because cost data were not available for children enrolled in Medicaid managed care plans under capitated arrangements. Finally, it is unknown how generalizable the current study findings might be to children with private insurance, or to children who are uninsured.
The study findings highlight potential opportunities for future research. Further studies are warranted to identify key characteristics that differentiate children admitted to nonpediatric hospitals who are subsequently transferred to pediatric hospitals with ICU facilities versus the children who are not transferred. Also, in‐depth study of the decision‐making that underlies interhospital transfer of critically ill or injured children to hospitals with ICU facilities for advanced care after initial hospitalization is vital to improved understanding of factors that might contribute to the extensive resource consumption and mortality burden borne by these children. The existence and effectiveness of interhospital transfer agreements at the state level needs to be examined specifically as it relates to patterns and clinical outcomes of interhospital transfer of critically ill and injured children in the US.
In conclusion, in this multiyear, statewide sample among critically ill and injured children enrolled by a statewide public payer, clinical outcomes were worse and resource consumption higher, among children who underwent interhospital transfer after initial hospitalization compared with those transferred directly from referring EDs. The findings raise the possibility that more timely transfer of some patients directly from community hospital EDs to regional ICUs might improve survival and reduce resource consumption.
Efforts to improve the care of critically ill and injured children may be enhanced by improved understanding of the medical decision‐making underlying interhospital transfer; application of innovative methods to identify and ensure rapid access to clinical expertise for children initially admitted to hospitals without pediatric intensive care facilities who might subsequently require intensive care; and routine reassessment of hospitalized children to ensure effective and efficient triage and re‐triage at the ED, ward, and ICU levels of referring hospitals.
- ,,,,.A national survey of pediatric critical care resources in the United States.Pediatrics.2005;115:e382–386.
- .Regional variation in child mortality at hospitals lacking a pediatric intensive care unit.Crit Care Med.2002;30:94–99.
- ,,,,,.Direct transport to tertiary trauma centers versus transfer from lower level facilities: impact on mortality and morbidity among patients with major trauma.J Trauma.1997;43:288–296.
- ,,,.Timing of intensive care unit admission in relation to ICU outcome.Crit Care Med.1990;18:1231–1235.
- ,:Admission source to the medical intensive care unit predicts hospital death independent of APACHE II score.JAMA.1990;264:2389–2394.
- ,,,,.Accepting critically ill transfer patients: adverse effect on a referral center's outcome and benchmark measures.Ann Intern Med.2003;138:882–890.
- ,,,,,.Elective intrahospital admissions versus acute interhospital transfers to a surgical intensive care unit: cost and outcome prediction.J Trauma.1991;31:915–918.
- ,,,,.Adverse effect on a referral intensive care unit's performance of accepting patients transferred from another intensive care unit.Crit Care Med.2005;33:705–710.
- ,,,.Prognostic factors for mortality following interhospital transfers to the medical intensive care unit of a tertiary referral center.Crit Care Med.2003;31:1981–1986.
- National Government Services. Medicare UB‐04 Revenue Codes. Available at http://www.ngsmedicare.com/NGSMedicare/PartA/EducationandSupport/ToolsandMaterials/0908ub‐04.pdf. Accessed April 7,2008.
- American Hospital Association.AHA Guide to the Health Care Field.2002 ed.Chicago:American Hospital Association;2002.
- American Hospital Association.AHA Guide to the Health Care Field.2003 ed.Chicago:American Hospital Association;2003.
- ,,.Pediatric deaths attributable to complex chronic conditions: a population‐based study of Washington State, 1980–1997.Pediatrics.2000;106:205–209.
- ,,,.Importance of organ dysfunction in determining hospital outcomes in children.J Pediatr.2004;144:595–601.
- ,,, et al.Cumulative influence of organ dysfunctions and septic state on mortality of critically ill children.Am J Respir Crit Care Med.2005;171:348–353.
- ,,,,,.The epidemiology of severe sepsis in children in the United States.Am J Respir Crit Care Med.2003;167:695–701.
- ,,,.The longer patients are in hospital before intensive care admission the higher their mortality.Intensive Care Med.2004;30:1908–1913.
- ,.A prospective study of outcome of in‐patient pediatric cardiopulmonary arrest.Resuscitation.2006;71:310–318.
- ,,, et al.Effect of a rapid response team on hospital‐wide mortality and code rates outside the ICU in a children's hospital.JAMA.2007;298:2267–2274.
- ,,,,,.Use of telemedicine to provide pediatric critical care consultations to underserved rural northern California.J Pediatr.2004;144:375–380.
- ,.Risk‐adjusting acute myocardial infarction mortality: are APR‐DRGs the right tool?Health Serv Res.2000;34:1469–1489.
- ,,,,.Predicting in‐hospital deaths from coronary artery bypass graft surgery: do different severity measures give different predictions?Med Care.1998;36:28–39.
- ,,.Patient and hospital correlates of clinical outcomes and resource‐utilization in severe pediatric sepsis.Pediatrics.2007;119:487–494.
Interhospital transfer of critically ill and injured children is necessitated by variation in resource availability between hospitals. Critically ill children judged in need of clinical services or expertise not locally available undergo transfer to hospitals with more appropriate resource capabilities and expertise, with the expectation that clinical outcomes of transfer will be better than nontransfer.
Significant variation both in the availability of pediatric critical care services across US hospitals1 and in child mortality among hospitals without pediatric critical care services2 suggests that interhospital transfer will remain an integral part of healthcare delivery for critically ill and injured children. Timely provision of definitive care for acute life‐threatening disease is associated with good clinical outcomes.3, 4 While prior studies have examined clinical outcomes and resource consumption among critically ill adults who underwent interhospital transfer for intensive care,59 there is scarce information regarding clinical characteristics and outcomes of interhospital transfer for critically ill and injured children.
This study was conducted to test the hypothesis that, among critically ill and injured children who undergo interhospital transfer for intensive care, children transferred after an initial hospitalization at the referring facility will have higher mortality, longer length of stay (LOS), and higher resource consumption than children transferred directly from the emergency department (ED) of the referring hospitals.
METHODS
Study Design
We conducted a secondary analysis of administrative claims data from the Michigan Medicaid program for the period January 1, 2002, to December 31, 2004. The data included all paid claims for health services rendered to enrollees in the Medicaid program. The Institutional Review Board of the University of Michigan Medical School approved the study.
Study Sample and Variable Identification
A 3‐step approach was employed to identify interhospital transfer admissions for intensive care of children. Initially, the Medicaid claims were queried to identify all hospitalizations for children 018 years who received intensive care services, using Medicare revenue codes.10 Admissions for neonatal intensive care were excluded from the analysis. The American Hospital Association Guide to the Health Care Field, a compendium of US healthcare facilities, was used to verify the presence of intensive care facilities.11, 12 Subsequently, to identify the subset of children who underwent interhospital transfer, data were queried for the presence of claims from another hospital, and the date of discharge from the referring hospital had to be the same as the date of admission to the receiving hospital intensive care unit (ICU). Finally, to ascertain the source of interhospital transfer, Medicare revenue codes and current procedural terminology (CPT) codes were used to identify claims for receipt of services at specific sites within the referring hospital; namely, the ED, ward, or the ICU. This information was used to categorize admissions into 1 of 3 pathways of interhospital transfer:
ED transferFrom the ED of the referring hospital to the ICU of the receiving hospital.
Ward transferFrom the wards of the referring hospital to the ICU of the receiving hospital.
Inter‐ICU transferFrom the ICU of the referring hospital to the ICU of the receiving hospital.
Dependent Variables
Mortality at the Receiving Hospital
This is determined by linkage to vital statistics records maintained by the Michigan Department of Community Health, Division of Vital Records and Health Statistics.
LOS at the Receiving Hospital
This is determined as the count of days of hospitalization at the receiving hospital. Of note, this includes ICU days and non‐ICU days at the receiving hospital.
Independent Variables
Source of Interhospital Transfer
The main (exposure) independent variable. Categorized into ED, ward, or inter‐ICU transfers, as described.
Patient Demographics
Age and gender.
Comorbid Illness
Determined using International Classification of Diseases, ninth revision (ICD‐9) diagnosis codes, applying methodology as described.13
Organ Dysfunction at the Referring and Receiving Hospitals
Determined using ICD‐9 diagnosis codes, applying methodology as described.14
Patient Diagnostic Categories
Eleven diagnostic categories were created based on primary admission diagnoses (Appendix A).
LOS at the Referring Hospital
Determined as the count of days of hospitalization at the referring hospital.
Receipt of Cardiopulmonary Resuscitation (CPR) on the Date of Interhospital Transfer
Determined using procedure codes.
Receipt of Medical‐Surgical Procedures at the Receiving Hospital
Identified through the use of ICD‐9 procedure codes, CPT codes, and Healthcare Common Procedure Coding System codes. The procedures are listed in Appendix B.
Statistical Analysis
Descriptive statistics were used to characterize the study sample. According to the 3 sources of interhospital transfer, patient characteristics (age, gender, presence of organ dysfunction, and comorbid illness), median LOS at the referring hospital, and receipt of CPR on the date of interhospital transfer were compared using chi‐square tests for categorical variables, and Kruskal‐Wallis tests for continuous variables. Similarly, outcome variables of in‐hospital mortality and median LOS at the receiving hospital were compared across the 3 sources of interhospital transfer. Analysis of variance was used to compare mean LOS at the receiving hospital across the 3 sources of interhospital transfer. Median (with interquartile range [IQR]) and mean (with standard deviation [SD]) values are presented to describe LOS, given skew in LOS data.
To account for potential confounding of LOS and mortality at the receiving hospital by the presence of organ dysfunction and comorbid illness1316 at the referring hospital, multivariate logistic regression and multiple linear regression analyses were conducted to estimate the odds of in‐hospital mortality and the incremental LOS, respectively, for ward and inter‐ICU transfers, compared with ED transfers. Statistical analyses were conducted using Stata 8 for windows (Stata Corporation, College Station, TX). A 2‐tailed level of 0.05 was used as the threshold for statistical significance.
RESULTS
Patient Characteristics
Of 1,643 transfer admissions for intensive care during the study period, 1022 (62%) were ED transfers, 512 (31%) were ward transfers, and 109 (7%) were inter‐ICU transfers. The average age was 2 years, with male gender (57%) predominance. Comorbid illness was present in 19% of admissions, while 11% had evidence of organ dysfunction at the referring hospital. Table 1 presents key patient demographic and clinical characteristics at the referring hospitals, by transfer source. Inter‐ICU and ward transfers were younger than ED transfers, and had a higher preponderance of comorbid illness and organ dysfunction. At the time of interhospital transfer, compared with ED transfers, the proportion of admissions with organ dysfunction (a marker of illness severity) was 3‐fold and 8‐fold higher among ward and inter‐ICU transfers, respectively.
| Transfer Source | P | |||
|---|---|---|---|---|
| Characteristics | ED (n = 1022) | Ward (n = 512) | Inter‐ICU (n = 109) | |
| ||||
| Median age in years (IQR) | 2 (09) | 1 (07) | 1 (010) | <0.01 |
| Male (%) | 57.8 | 56.2 | 47.6 | 0.13 |
| Comorbid illness (% ) | 13.1 | 25.0 | 50.5 | <0.01 |
| Pretransfer hospital length of stay (days) | ||||
| Median (IQR) | 0 | 1 (02) | 3 (18) | <0.01 |
| Mean (SD) | 0.2 (5.2) | 1.6 (4.8) | 9.7 (18.0) | <0.01 |
| Pretransfer organ dysfunction (%) | 5.5 | 14.5 | 40.4 | <0.01 |
Patterns of Transfer
The leading diagnoses among all children were respiratory disease, trauma, and neurological disease (Table 2), with some variation in diagnoses by source of interhospital transfer. For example, cardiovascular disease was the second leading diagnosis after respiratory disease among the inter‐ICU transfers, while more children with endocrine disease (predominantly diabetic ketoacidosis), traumatic injury, or drug poisoning were transferred directly from the ED, than from the ward or the ICU settings. For burn care, 80% (45/56) of all transfer admissions were direct from the ED (Table 3). The majority (78%) of children with traumatic injuries were directly transferred from the ED to the ICU, while the remainder were transferred after initial care delivered on the ward (18%) or ICU (4%) settings prior to interhospital transfer for definitive intensive trauma care. Importantly, among the inter‐ICU transfers, 104 (95%) were transferred to pediatric ICUs from referring hospitals with adult and pediatric ICU facilities, suggesting uptransfer for specialized care. Five children were transferred between hospitals with adult ICU facilities.
| Transfer Source | ||||
|---|---|---|---|---|
| Diagnostic Category (%) | Overall* (n = 1639) | ED* (n = 1018) | Ward (n = 512) | Inter‐ICU (n = 109) |
| ||||
| Respiratory disease | 35.1 | 32.8 | 41.0 | 28.4 |
| Trauma | 16.2 | 20.5 | 9.2 | 9.1 |
| Neurological disease | 12.4 | 12.5 | 12.3 | 11.9 |
| Gastrointestinal disease | 6.7 | 5.4 | 7.4 | 11.9 |
| Infectious disease | 5.8 | 4.0 | 8.4 | 10.0 |
| Endocrine disease | 5.5 | 7.9 | 1.8 | 0 |
| Drug overdose/poisoning | 5.0 | 6.4 | 2.9 | 1.8 |
| Cardiovascular disease | 4.8 | 2.8 | 6.3 | 16.5 |
| Hematologic/oncologic disease | 2.0 | 1.6 | 2.9 | 1.8 |
| Cardiac arrest | 0.2 | 0 | 0.6 | 0.9 |
| Other diagnoses | 6.2 | 5.4 | 7.2 | 7.7 |
| Transfer Source | |||||
|---|---|---|---|---|---|
| Characteristics (%) | Overall (n = 1643) | ED (n = 1022) | Ward (n = 512) | Inter‐ICU (n = 109) | P |
| Respiratory | 26.8 | 19.0 | 36.7 | 54.1 | <0.01 |
| Radiological | 21.2 | 19.5 | 20.5 | 41.3 | <0.01 |
| Vascular access | 20.0 | 15.2 | 27.0 | 33.0 | <0.01 |
| Gastrointestinal | 3.9 | 3.0 | 3.7 | 12.8 | <0.01 |
| Neurological | 3.8 | 3.2 | 3.7 | 10.1 | <0.01 |
| Cardiovascular | 3.6 | 1.8 | 4.1 | 18.4 | <0.01 |
| Burn care | 3.4 | 4.5 | 2.0 | 0 | <0.01 |
| General surgery | 3.2 | 2.1 | 4.3 | 8.3 | <0.01 |
| Dialysis | 2.6 | 2.0 | 2.5 | 8.3 | <0.01 |
| ECMO | 2.1 | 1.3 | 2.2 | 9.2 | <0.01 |
CPR was performed on the date of interhospital transfer for 23 patients (1.4% of the sample), of whom 13 (56.5%) were ward transfers, 8 (34.8%) were inter‐ICU transfers, and 2 (8.7%) were ED transfers (P < 0.02). Two‐thirds of these children did not survive subsequent hospitalization at the receiving hospitals.
Clinical Outcomes and Resource Utilization at the Receiving Hospitals
At the receiving hospitals, other than burn care, medical‐surgical procedures were performed most often among the inter‐ICU transfers. Ward transfers also had higher receipt of procedures compared with ED transfers (Table 3). The inter‐ICU and ward transfers had a higher preponderance of organ dysfunction at the receiving hospitals, compared to the ED transfers (38.5% and 29.3% versus 20.8%, P < 0.01).
Clinical outcomes at the receiving hospitals varied significantly according to the source of interhospital transfer (Table 4). Sixty‐six (4%) of patients died at the receiving hospitals. In comparison with ED transfers, unadjusted in‐hospital mortality was 2‐fold and 3‐fold higher among the ward and inter‐ICU transfers, respectively. Also, hospital LOS was significantly longer among the ward and inter‐ICU transfers than for the ED transfers.
| Transfer Source | ||||
|---|---|---|---|---|
| Characteristics | ED (n = 1022) | Ward (n = 512) | Inter‐ICU (n = 109) | P |
| ||||
| Mortality (%) | 2.8 | 5.5 | 8.3 | <0.01 |
| Length of stay (days) | ||||
| Median (IQR) | 3 (27) | 5 (312) | 13 (724) | <0.01 |
| Mean (SD) | 6.7 (10.4) | 8.5 (9.2) | 21.4 (22.9) | <0.01 |
In multivariate analyses adjusting for patient age, and the presence of comorbid illness and organ dysfunction at the referring hospital, compared with ED transfers, odds of mortality were significantly higher (odds ratio [OR], 1.76; 95% confidence interval [CI], 1.023.03) for ward transfers. Inter‐ICU transfers also had higher odds of mortality (OR, 2.07; 95% CI, 0.884.86), without achieving statistical significance. Similarly, compared with ED transfers, LOS at the receiving hospital was longer by 1.5 days (95% CI, 0.32.7 days) for ward transfers, and by 13.5 days (95% CI, 11.115.8 days) for inter‐ICU transfers.
DISCUSSION
This study is the first to highlight significant variation in clinical outcomes and resource consumption after interhospital transfer of critically ill and injured children, depending on the source of transfer. In comparison with children transferred directly from the referring hospitals' ED settings, children transferred from the referring hospitals' wards had higher mortality, while those who underwent inter‐ICU transfer had significantly higher resource consumption. In addition, ward transfers had the highest proportion of children who underwent CPR on the date of interhospital transfer, highlighting elevated severity of disease prior to transfer and an urgent need for improved understanding of pretransfer clinical care and medical decision‐making. The findings raise the possibility that more timely transfer of some patients directly from community hospital EDs to regional ICUs might improve survival and reduce resource consumption.
Although interhospital transfers are common in everyday clinical practice, there has been a knowledge gap in pediatric acute and critical care medicine regarding the clinical outcomes and resource consumption among children who undergo such transfers. Findings from the current study narrow this gap by relating triage at the referring hospitals to clinical outcomes and resource utilization at the receiving hospitals.
Certain distinct transfer patterns were observed. Most children with burn injury underwent direct transfer from the ED to the ICU; this transfer pattern may be related both to the limited availability of ICUs with burn care capability in Michigan and to the acuity of burn injuries, which often mandates immediate triage to hospitals with intensive burn care facilities. Conversely, while the majority of children with traumatic injuries were directly transferred from emergency to intensive care, over one‐fifth were transferred after initial care delivered on the ward or ICU settings prior to interhospital transfer for definitive intensive trauma care. Such imperfect regionalization of trauma care suggests further study of clinical outcomes and resource utilization among injured children is warranted. Likewise, cardiovascular disease was prominent among the inter‐ICU transfers, suggesting a clinical practice pattern of stabilization and resuscitation at the initial ICU prior to interhospital vertical or uptransfer for definitive cardiac care at the receiving hospitals.
It remains unknown whether the timing of interhospital transfer of critically ill children is a determinant of clinical outcomes. Prior studies among adults have reported higher mortality with prolonged duration of pre‐ICU care on the ward.4, 17 In the current study, ward and inter‐ICU transfers were initially hospitalized for a median of 1 and 3 days, respectively, prior to transfer. While we could not determine from administrative data what the precise triggers for interhospital transfer in this study were, it is instructive to note that ward transfers comprised more than one‐half of all children who received CPR on the date of transfer. For children who received CPR, severe clinical deterioration likely triggered transfer to hospitals with ICU facilities, but because only a minority of children received CPR overall, other triggers of transfer warrant investigation. For most of the children transferred, it seems plausible that the precipitant of transfer was likely a mismatch of their clinical status with the clinical capacities of the facilities where they were initially hospitalized. Future work should investigate if there is an association between clinical outcomes at the receiving hospitals, and both the timing of interhospital transfer and the clinical status of patients at transfer.
Importantly, compared with ED transfers, ward transfers demonstrated elevated odds of mortality after adjustment for coexisting comorbid illness, patient age, and pretransfer organ dysfunction at the referring hospital. Some possible explanations for this finding include the progression of disease while receiving care on the ward, or suboptimal access to ICU facilities due to barriers to transfer at either the referring or receiving hospitals. Importantly, progression of disease in ward settings may be detected by early identification of children at high risk of clinical deterioration on the wards of hospitals without ICU facilities, prior to cardiopulmonary arrest, because death after CPR may not be averted with subsequent ICU care.18
Various approaches to facilitate rapid and appropriate triage and reassessment of children in hospitals without ICU facilities, prior to severe clinical deterioration or need for CPR, must be investigated. These approaches might include in‐hospital measures such as the establishment of medical emergency teams to respond to clinical deterioration on the wards19 or collaborative interhospital measures such as the use of telemedicine20 or similar remote communication/triage systems to enhance communication between clinical caregivers at hospitals with ICU facilities and those in hospitals without ICU facilities. Furthermore, interhospital transfer agreements may facilitate expeditious and appropriate transfer of severely ill patients to hospitals with ICU facilities.
Access to hospitals with ICU facilities might also influence outcomes for critically ill children admitted initially to wards of hospitals without ICU facilities. Kanter2 reported significant variation in mortality among children who received care at New York hospitals without ICU facilities. Of note, 27% of statewide pediatric inpatient deaths occurred in those hospitals without ICU facilities. It appeared that, while some pediatric deaths in hospitals without ICU facilities were expected, regional variation in such mortality might have been associated with reduced access to, or poor utilization of, hospitals with ICU facilities. Barriers to interhospital transfers might include underrecognition of mismatch between patient illness severity and hospital capability at referring hospitals, or lack of capacity to accept transfers at the receiving hospitals. Further study is warranted to investigate clinical decision‐making underlying the initiation of the interhospital transfer processes, and procedural or institutional barriers that might hinder the transfer of critically ill children from hospitals without ICU facilities.
Resource consumption at the receiving hospitals, measured by hospital LOS and receipt of medical‐surgical procedures, was highest among the inter‐ICU transfers. This was an expected finding, given the high frequency of organ dysfunction among the inter‐ICU transfers, before and after interhospital transfer. These patients had the highest use of advanced and resource‐intensive technology, including continuous renal replacement therapy, extracorporeal membrane oxygenation, and cardiovascular procedures such as open‐heart surgery. In addition, the duration of hospitalization at the receiving hospital was 2 weeks longer among the inter‐ICU transfers when compared with the ED transfers. Such prolonged hospitalization has been previously associated with significantly increased resource consumption.4, 6 In the absence of physiologic data pertaining to illness severity, however, it is unknown whether this observed differential LOS by source of interhospital transfer might be attributable to both unobserved illness severity and/or extensive in‐hospital post‐ICU multidisciplinary rehabilitative care for inter‐ICU transfer patients, compared with ED transfer patients.
Our study findings need to be interpreted in light of certain limitations. Administrative claims data do not allow for assessment of the quality of hospital care, a factor that might play an important role in patient clinical outcomes. The data lacked any physiologic information that might enhance the ability to estimate patient severity of illness; the analysis used the presence of organ dysfunction at the referring hospitals as a proxy for illness severity. The use of diagnosis codebased measures of severity adjustment, as employed in the current study, however, has been reported to be comparable with clinical severity measures because of the relatively complete capture of diagnosis codes for life‐threatening conditions occurring late in the hospitalization, such as prior to interhospital transfer in the current study.2123
The absence of clinical information prevented assessment of the likelihood of in‐hospital morbidity, transport complications, and need for various therapeutic interventions after ICU care, which are also highly relevant outcomes of interhospital transfers. It is unknown if the small sample size among inter‐ICU transfers limited the ability to demonstrate a statistically significant difference in odds of mortality among inter‐ICU transfers compared with ED transfers.
Also, the identification of diagnoses and procedures was made using multiple coding instruments and is therefore susceptible to inaccuracies of detection and attribution that may have biased the findings. Study findings did not include cost, because cost data were not available for children enrolled in Medicaid managed care plans under capitated arrangements. Finally, it is unknown how generalizable the current study findings might be to children with private insurance, or to children who are uninsured.
The study findings highlight potential opportunities for future research. Further studies are warranted to identify key characteristics that differentiate children admitted to nonpediatric hospitals who are subsequently transferred to pediatric hospitals with ICU facilities versus the children who are not transferred. Also, in‐depth study of the decision‐making that underlies interhospital transfer of critically ill or injured children to hospitals with ICU facilities for advanced care after initial hospitalization is vital to improved understanding of factors that might contribute to the extensive resource consumption and mortality burden borne by these children. The existence and effectiveness of interhospital transfer agreements at the state level needs to be examined specifically as it relates to patterns and clinical outcomes of interhospital transfer of critically ill and injured children in the US.
In conclusion, in this multiyear, statewide sample among critically ill and injured children enrolled by a statewide public payer, clinical outcomes were worse and resource consumption higher, among children who underwent interhospital transfer after initial hospitalization compared with those transferred directly from referring EDs. The findings raise the possibility that more timely transfer of some patients directly from community hospital EDs to regional ICUs might improve survival and reduce resource consumption.
Efforts to improve the care of critically ill and injured children may be enhanced by improved understanding of the medical decision‐making underlying interhospital transfer; application of innovative methods to identify and ensure rapid access to clinical expertise for children initially admitted to hospitals without pediatric intensive care facilities who might subsequently require intensive care; and routine reassessment of hospitalized children to ensure effective and efficient triage and re‐triage at the ED, ward, and ICU levels of referring hospitals.
Interhospital transfer of critically ill and injured children is necessitated by variation in resource availability between hospitals. Critically ill children judged in need of clinical services or expertise not locally available undergo transfer to hospitals with more appropriate resource capabilities and expertise, with the expectation that clinical outcomes of transfer will be better than nontransfer.
Significant variation both in the availability of pediatric critical care services across US hospitals1 and in child mortality among hospitals without pediatric critical care services2 suggests that interhospital transfer will remain an integral part of healthcare delivery for critically ill and injured children. Timely provision of definitive care for acute life‐threatening disease is associated with good clinical outcomes.3, 4 While prior studies have examined clinical outcomes and resource consumption among critically ill adults who underwent interhospital transfer for intensive care,59 there is scarce information regarding clinical characteristics and outcomes of interhospital transfer for critically ill and injured children.
This study was conducted to test the hypothesis that, among critically ill and injured children who undergo interhospital transfer for intensive care, children transferred after an initial hospitalization at the referring facility will have higher mortality, longer length of stay (LOS), and higher resource consumption than children transferred directly from the emergency department (ED) of the referring hospitals.
METHODS
Study Design
We conducted a secondary analysis of administrative claims data from the Michigan Medicaid program for the period January 1, 2002, to December 31, 2004. The data included all paid claims for health services rendered to enrollees in the Medicaid program. The Institutional Review Board of the University of Michigan Medical School approved the study.
Study Sample and Variable Identification
A 3‐step approach was employed to identify interhospital transfer admissions for intensive care of children. Initially, the Medicaid claims were queried to identify all hospitalizations for children 018 years who received intensive care services, using Medicare revenue codes.10 Admissions for neonatal intensive care were excluded from the analysis. The American Hospital Association Guide to the Health Care Field, a compendium of US healthcare facilities, was used to verify the presence of intensive care facilities.11, 12 Subsequently, to identify the subset of children who underwent interhospital transfer, data were queried for the presence of claims from another hospital, and the date of discharge from the referring hospital had to be the same as the date of admission to the receiving hospital intensive care unit (ICU). Finally, to ascertain the source of interhospital transfer, Medicare revenue codes and current procedural terminology (CPT) codes were used to identify claims for receipt of services at specific sites within the referring hospital; namely, the ED, ward, or the ICU. This information was used to categorize admissions into 1 of 3 pathways of interhospital transfer:
ED transferFrom the ED of the referring hospital to the ICU of the receiving hospital.
Ward transferFrom the wards of the referring hospital to the ICU of the receiving hospital.
Inter‐ICU transferFrom the ICU of the referring hospital to the ICU of the receiving hospital.
Dependent Variables
Mortality at the Receiving Hospital
This is determined by linkage to vital statistics records maintained by the Michigan Department of Community Health, Division of Vital Records and Health Statistics.
LOS at the Receiving Hospital
This is determined as the count of days of hospitalization at the receiving hospital. Of note, this includes ICU days and non‐ICU days at the receiving hospital.
Independent Variables
Source of Interhospital Transfer
The main (exposure) independent variable. Categorized into ED, ward, or inter‐ICU transfers, as described.
Patient Demographics
Age and gender.
Comorbid Illness
Determined using International Classification of Diseases, ninth revision (ICD‐9) diagnosis codes, applying methodology as described.13
Organ Dysfunction at the Referring and Receiving Hospitals
Determined using ICD‐9 diagnosis codes, applying methodology as described.14
Patient Diagnostic Categories
Eleven diagnostic categories were created based on primary admission diagnoses (Appendix A).
LOS at the Referring Hospital
Determined as the count of days of hospitalization at the referring hospital.
Receipt of Cardiopulmonary Resuscitation (CPR) on the Date of Interhospital Transfer
Determined using procedure codes.
Receipt of Medical‐Surgical Procedures at the Receiving Hospital
Identified through the use of ICD‐9 procedure codes, CPT codes, and Healthcare Common Procedure Coding System codes. The procedures are listed in Appendix B.
Statistical Analysis
Descriptive statistics were used to characterize the study sample. According to the 3 sources of interhospital transfer, patient characteristics (age, gender, presence of organ dysfunction, and comorbid illness), median LOS at the referring hospital, and receipt of CPR on the date of interhospital transfer were compared using chi‐square tests for categorical variables, and Kruskal‐Wallis tests for continuous variables. Similarly, outcome variables of in‐hospital mortality and median LOS at the receiving hospital were compared across the 3 sources of interhospital transfer. Analysis of variance was used to compare mean LOS at the receiving hospital across the 3 sources of interhospital transfer. Median (with interquartile range [IQR]) and mean (with standard deviation [SD]) values are presented to describe LOS, given skew in LOS data.
To account for potential confounding of LOS and mortality at the receiving hospital by the presence of organ dysfunction and comorbid illness1316 at the referring hospital, multivariate logistic regression and multiple linear regression analyses were conducted to estimate the odds of in‐hospital mortality and the incremental LOS, respectively, for ward and inter‐ICU transfers, compared with ED transfers. Statistical analyses were conducted using Stata 8 for windows (Stata Corporation, College Station, TX). A 2‐tailed level of 0.05 was used as the threshold for statistical significance.
RESULTS
Patient Characteristics
Of 1,643 transfer admissions for intensive care during the study period, 1022 (62%) were ED transfers, 512 (31%) were ward transfers, and 109 (7%) were inter‐ICU transfers. The average age was 2 years, with male gender (57%) predominance. Comorbid illness was present in 19% of admissions, while 11% had evidence of organ dysfunction at the referring hospital. Table 1 presents key patient demographic and clinical characteristics at the referring hospitals, by transfer source. Inter‐ICU and ward transfers were younger than ED transfers, and had a higher preponderance of comorbid illness and organ dysfunction. At the time of interhospital transfer, compared with ED transfers, the proportion of admissions with organ dysfunction (a marker of illness severity) was 3‐fold and 8‐fold higher among ward and inter‐ICU transfers, respectively.
| Transfer Source | P | |||
|---|---|---|---|---|
| Characteristics | ED (n = 1022) | Ward (n = 512) | Inter‐ICU (n = 109) | |
| ||||
| Median age in years (IQR) | 2 (09) | 1 (07) | 1 (010) | <0.01 |
| Male (%) | 57.8 | 56.2 | 47.6 | 0.13 |
| Comorbid illness (% ) | 13.1 | 25.0 | 50.5 | <0.01 |
| Pretransfer hospital length of stay (days) | ||||
| Median (IQR) | 0 | 1 (02) | 3 (18) | <0.01 |
| Mean (SD) | 0.2 (5.2) | 1.6 (4.8) | 9.7 (18.0) | <0.01 |
| Pretransfer organ dysfunction (%) | 5.5 | 14.5 | 40.4 | <0.01 |
Patterns of Transfer
The leading diagnoses among all children were respiratory disease, trauma, and neurological disease (Table 2), with some variation in diagnoses by source of interhospital transfer. For example, cardiovascular disease was the second leading diagnosis after respiratory disease among the inter‐ICU transfers, while more children with endocrine disease (predominantly diabetic ketoacidosis), traumatic injury, or drug poisoning were transferred directly from the ED, than from the ward or the ICU settings. For burn care, 80% (45/56) of all transfer admissions were direct from the ED (Table 3). The majority (78%) of children with traumatic injuries were directly transferred from the ED to the ICU, while the remainder were transferred after initial care delivered on the ward (18%) or ICU (4%) settings prior to interhospital transfer for definitive intensive trauma care. Importantly, among the inter‐ICU transfers, 104 (95%) were transferred to pediatric ICUs from referring hospitals with adult and pediatric ICU facilities, suggesting uptransfer for specialized care. Five children were transferred between hospitals with adult ICU facilities.
| Transfer Source | ||||
|---|---|---|---|---|
| Diagnostic Category (%) | Overall* (n = 1639) | ED* (n = 1018) | Ward (n = 512) | Inter‐ICU (n = 109) |
| ||||
| Respiratory disease | 35.1 | 32.8 | 41.0 | 28.4 |
| Trauma | 16.2 | 20.5 | 9.2 | 9.1 |
| Neurological disease | 12.4 | 12.5 | 12.3 | 11.9 |
| Gastrointestinal disease | 6.7 | 5.4 | 7.4 | 11.9 |
| Infectious disease | 5.8 | 4.0 | 8.4 | 10.0 |
| Endocrine disease | 5.5 | 7.9 | 1.8 | 0 |
| Drug overdose/poisoning | 5.0 | 6.4 | 2.9 | 1.8 |
| Cardiovascular disease | 4.8 | 2.8 | 6.3 | 16.5 |
| Hematologic/oncologic disease | 2.0 | 1.6 | 2.9 | 1.8 |
| Cardiac arrest | 0.2 | 0 | 0.6 | 0.9 |
| Other diagnoses | 6.2 | 5.4 | 7.2 | 7.7 |
| Transfer Source | |||||
|---|---|---|---|---|---|
| Characteristics (%) | Overall (n = 1643) | ED (n = 1022) | Ward (n = 512) | Inter‐ICU (n = 109) | P |
| Respiratory | 26.8 | 19.0 | 36.7 | 54.1 | <0.01 |
| Radiological | 21.2 | 19.5 | 20.5 | 41.3 | <0.01 |
| Vascular access | 20.0 | 15.2 | 27.0 | 33.0 | <0.01 |
| Gastrointestinal | 3.9 | 3.0 | 3.7 | 12.8 | <0.01 |
| Neurological | 3.8 | 3.2 | 3.7 | 10.1 | <0.01 |
| Cardiovascular | 3.6 | 1.8 | 4.1 | 18.4 | <0.01 |
| Burn care | 3.4 | 4.5 | 2.0 | 0 | <0.01 |
| General surgery | 3.2 | 2.1 | 4.3 | 8.3 | <0.01 |
| Dialysis | 2.6 | 2.0 | 2.5 | 8.3 | <0.01 |
| ECMO | 2.1 | 1.3 | 2.2 | 9.2 | <0.01 |
CPR was performed on the date of interhospital transfer for 23 patients (1.4% of the sample), of whom 13 (56.5%) were ward transfers, 8 (34.8%) were inter‐ICU transfers, and 2 (8.7%) were ED transfers (P < 0.02). Two‐thirds of these children did not survive subsequent hospitalization at the receiving hospitals.
Clinical Outcomes and Resource Utilization at the Receiving Hospitals
At the receiving hospitals, other than burn care, medical‐surgical procedures were performed most often among the inter‐ICU transfers. Ward transfers also had higher receipt of procedures compared with ED transfers (Table 3). The inter‐ICU and ward transfers had a higher preponderance of organ dysfunction at the receiving hospitals, compared to the ED transfers (38.5% and 29.3% versus 20.8%, P < 0.01).
Clinical outcomes at the receiving hospitals varied significantly according to the source of interhospital transfer (Table 4). Sixty‐six (4%) of patients died at the receiving hospitals. In comparison with ED transfers, unadjusted in‐hospital mortality was 2‐fold and 3‐fold higher among the ward and inter‐ICU transfers, respectively. Also, hospital LOS was significantly longer among the ward and inter‐ICU transfers than for the ED transfers.
| Transfer Source | ||||
|---|---|---|---|---|
| Characteristics | ED (n = 1022) | Ward (n = 512) | Inter‐ICU (n = 109) | P |
| ||||
| Mortality (%) | 2.8 | 5.5 | 8.3 | <0.01 |
| Length of stay (days) | ||||
| Median (IQR) | 3 (27) | 5 (312) | 13 (724) | <0.01 |
| Mean (SD) | 6.7 (10.4) | 8.5 (9.2) | 21.4 (22.9) | <0.01 |
In multivariate analyses adjusting for patient age, and the presence of comorbid illness and organ dysfunction at the referring hospital, compared with ED transfers, odds of mortality were significantly higher (odds ratio [OR], 1.76; 95% confidence interval [CI], 1.023.03) for ward transfers. Inter‐ICU transfers also had higher odds of mortality (OR, 2.07; 95% CI, 0.884.86), without achieving statistical significance. Similarly, compared with ED transfers, LOS at the receiving hospital was longer by 1.5 days (95% CI, 0.32.7 days) for ward transfers, and by 13.5 days (95% CI, 11.115.8 days) for inter‐ICU transfers.
DISCUSSION
This study is the first to highlight significant variation in clinical outcomes and resource consumption after interhospital transfer of critically ill and injured children, depending on the source of transfer. In comparison with children transferred directly from the referring hospitals' ED settings, children transferred from the referring hospitals' wards had higher mortality, while those who underwent inter‐ICU transfer had significantly higher resource consumption. In addition, ward transfers had the highest proportion of children who underwent CPR on the date of interhospital transfer, highlighting elevated severity of disease prior to transfer and an urgent need for improved understanding of pretransfer clinical care and medical decision‐making. The findings raise the possibility that more timely transfer of some patients directly from community hospital EDs to regional ICUs might improve survival and reduce resource consumption.
Although interhospital transfers are common in everyday clinical practice, there has been a knowledge gap in pediatric acute and critical care medicine regarding the clinical outcomes and resource consumption among children who undergo such transfers. Findings from the current study narrow this gap by relating triage at the referring hospitals to clinical outcomes and resource utilization at the receiving hospitals.
Certain distinct transfer patterns were observed. Most children with burn injury underwent direct transfer from the ED to the ICU; this transfer pattern may be related both to the limited availability of ICUs with burn care capability in Michigan and to the acuity of burn injuries, which often mandates immediate triage to hospitals with intensive burn care facilities. Conversely, while the majority of children with traumatic injuries were directly transferred from emergency to intensive care, over one‐fifth were transferred after initial care delivered on the ward or ICU settings prior to interhospital transfer for definitive intensive trauma care. Such imperfect regionalization of trauma care suggests further study of clinical outcomes and resource utilization among injured children is warranted. Likewise, cardiovascular disease was prominent among the inter‐ICU transfers, suggesting a clinical practice pattern of stabilization and resuscitation at the initial ICU prior to interhospital vertical or uptransfer for definitive cardiac care at the receiving hospitals.
It remains unknown whether the timing of interhospital transfer of critically ill children is a determinant of clinical outcomes. Prior studies among adults have reported higher mortality with prolonged duration of pre‐ICU care on the ward.4, 17 In the current study, ward and inter‐ICU transfers were initially hospitalized for a median of 1 and 3 days, respectively, prior to transfer. While we could not determine from administrative data what the precise triggers for interhospital transfer in this study were, it is instructive to note that ward transfers comprised more than one‐half of all children who received CPR on the date of transfer. For children who received CPR, severe clinical deterioration likely triggered transfer to hospitals with ICU facilities, but because only a minority of children received CPR overall, other triggers of transfer warrant investigation. For most of the children transferred, it seems plausible that the precipitant of transfer was likely a mismatch of their clinical status with the clinical capacities of the facilities where they were initially hospitalized. Future work should investigate if there is an association between clinical outcomes at the receiving hospitals, and both the timing of interhospital transfer and the clinical status of patients at transfer.
Importantly, compared with ED transfers, ward transfers demonstrated elevated odds of mortality after adjustment for coexisting comorbid illness, patient age, and pretransfer organ dysfunction at the referring hospital. Some possible explanations for this finding include the progression of disease while receiving care on the ward, or suboptimal access to ICU facilities due to barriers to transfer at either the referring or receiving hospitals. Importantly, progression of disease in ward settings may be detected by early identification of children at high risk of clinical deterioration on the wards of hospitals without ICU facilities, prior to cardiopulmonary arrest, because death after CPR may not be averted with subsequent ICU care.18
Various approaches to facilitate rapid and appropriate triage and reassessment of children in hospitals without ICU facilities, prior to severe clinical deterioration or need for CPR, must be investigated. These approaches might include in‐hospital measures such as the establishment of medical emergency teams to respond to clinical deterioration on the wards19 or collaborative interhospital measures such as the use of telemedicine20 or similar remote communication/triage systems to enhance communication between clinical caregivers at hospitals with ICU facilities and those in hospitals without ICU facilities. Furthermore, interhospital transfer agreements may facilitate expeditious and appropriate transfer of severely ill patients to hospitals with ICU facilities.
Access to hospitals with ICU facilities might also influence outcomes for critically ill children admitted initially to wards of hospitals without ICU facilities. Kanter2 reported significant variation in mortality among children who received care at New York hospitals without ICU facilities. Of note, 27% of statewide pediatric inpatient deaths occurred in those hospitals without ICU facilities. It appeared that, while some pediatric deaths in hospitals without ICU facilities were expected, regional variation in such mortality might have been associated with reduced access to, or poor utilization of, hospitals with ICU facilities. Barriers to interhospital transfers might include underrecognition of mismatch between patient illness severity and hospital capability at referring hospitals, or lack of capacity to accept transfers at the receiving hospitals. Further study is warranted to investigate clinical decision‐making underlying the initiation of the interhospital transfer processes, and procedural or institutional barriers that might hinder the transfer of critically ill children from hospitals without ICU facilities.
Resource consumption at the receiving hospitals, measured by hospital LOS and receipt of medical‐surgical procedures, was highest among the inter‐ICU transfers. This was an expected finding, given the high frequency of organ dysfunction among the inter‐ICU transfers, before and after interhospital transfer. These patients had the highest use of advanced and resource‐intensive technology, including continuous renal replacement therapy, extracorporeal membrane oxygenation, and cardiovascular procedures such as open‐heart surgery. In addition, the duration of hospitalization at the receiving hospital was 2 weeks longer among the inter‐ICU transfers when compared with the ED transfers. Such prolonged hospitalization has been previously associated with significantly increased resource consumption.4, 6 In the absence of physiologic data pertaining to illness severity, however, it is unknown whether this observed differential LOS by source of interhospital transfer might be attributable to both unobserved illness severity and/or extensive in‐hospital post‐ICU multidisciplinary rehabilitative care for inter‐ICU transfer patients, compared with ED transfer patients.
Our study findings need to be interpreted in light of certain limitations. Administrative claims data do not allow for assessment of the quality of hospital care, a factor that might play an important role in patient clinical outcomes. The data lacked any physiologic information that might enhance the ability to estimate patient severity of illness; the analysis used the presence of organ dysfunction at the referring hospitals as a proxy for illness severity. The use of diagnosis codebased measures of severity adjustment, as employed in the current study, however, has been reported to be comparable with clinical severity measures because of the relatively complete capture of diagnosis codes for life‐threatening conditions occurring late in the hospitalization, such as prior to interhospital transfer in the current study.2123
The absence of clinical information prevented assessment of the likelihood of in‐hospital morbidity, transport complications, and need for various therapeutic interventions after ICU care, which are also highly relevant outcomes of interhospital transfers. It is unknown if the small sample size among inter‐ICU transfers limited the ability to demonstrate a statistically significant difference in odds of mortality among inter‐ICU transfers compared with ED transfers.
Also, the identification of diagnoses and procedures was made using multiple coding instruments and is therefore susceptible to inaccuracies of detection and attribution that may have biased the findings. Study findings did not include cost, because cost data were not available for children enrolled in Medicaid managed care plans under capitated arrangements. Finally, it is unknown how generalizable the current study findings might be to children with private insurance, or to children who are uninsured.
The study findings highlight potential opportunities for future research. Further studies are warranted to identify key characteristics that differentiate children admitted to nonpediatric hospitals who are subsequently transferred to pediatric hospitals with ICU facilities versus the children who are not transferred. Also, in‐depth study of the decision‐making that underlies interhospital transfer of critically ill or injured children to hospitals with ICU facilities for advanced care after initial hospitalization is vital to improved understanding of factors that might contribute to the extensive resource consumption and mortality burden borne by these children. The existence and effectiveness of interhospital transfer agreements at the state level needs to be examined specifically as it relates to patterns and clinical outcomes of interhospital transfer of critically ill and injured children in the US.
In conclusion, in this multiyear, statewide sample among critically ill and injured children enrolled by a statewide public payer, clinical outcomes were worse and resource consumption higher, among children who underwent interhospital transfer after initial hospitalization compared with those transferred directly from referring EDs. The findings raise the possibility that more timely transfer of some patients directly from community hospital EDs to regional ICUs might improve survival and reduce resource consumption.
Efforts to improve the care of critically ill and injured children may be enhanced by improved understanding of the medical decision‐making underlying interhospital transfer; application of innovative methods to identify and ensure rapid access to clinical expertise for children initially admitted to hospitals without pediatric intensive care facilities who might subsequently require intensive care; and routine reassessment of hospitalized children to ensure effective and efficient triage and re‐triage at the ED, ward, and ICU levels of referring hospitals.
- ,,,,.A national survey of pediatric critical care resources in the United States.Pediatrics.2005;115:e382–386.
- .Regional variation in child mortality at hospitals lacking a pediatric intensive care unit.Crit Care Med.2002;30:94–99.
- ,,,,,.Direct transport to tertiary trauma centers versus transfer from lower level facilities: impact on mortality and morbidity among patients with major trauma.J Trauma.1997;43:288–296.
- ,,,.Timing of intensive care unit admission in relation to ICU outcome.Crit Care Med.1990;18:1231–1235.
- ,:Admission source to the medical intensive care unit predicts hospital death independent of APACHE II score.JAMA.1990;264:2389–2394.
- ,,,,.Accepting critically ill transfer patients: adverse effect on a referral center's outcome and benchmark measures.Ann Intern Med.2003;138:882–890.
- ,,,,,.Elective intrahospital admissions versus acute interhospital transfers to a surgical intensive care unit: cost and outcome prediction.J Trauma.1991;31:915–918.
- ,,,,.Adverse effect on a referral intensive care unit's performance of accepting patients transferred from another intensive care unit.Crit Care Med.2005;33:705–710.
- ,,,.Prognostic factors for mortality following interhospital transfers to the medical intensive care unit of a tertiary referral center.Crit Care Med.2003;31:1981–1986.
- National Government Services. Medicare UB‐04 Revenue Codes. Available at http://www.ngsmedicare.com/NGSMedicare/PartA/EducationandSupport/ToolsandMaterials/0908ub‐04.pdf. Accessed April 7,2008.
- American Hospital Association.AHA Guide to the Health Care Field.2002 ed.Chicago:American Hospital Association;2002.
- American Hospital Association.AHA Guide to the Health Care Field.2003 ed.Chicago:American Hospital Association;2003.
- ,,.Pediatric deaths attributable to complex chronic conditions: a population‐based study of Washington State, 1980–1997.Pediatrics.2000;106:205–209.
- ,,,.Importance of organ dysfunction in determining hospital outcomes in children.J Pediatr.2004;144:595–601.
- ,,, et al.Cumulative influence of organ dysfunctions and septic state on mortality of critically ill children.Am J Respir Crit Care Med.2005;171:348–353.
- ,,,,,.The epidemiology of severe sepsis in children in the United States.Am J Respir Crit Care Med.2003;167:695–701.
- ,,,.The longer patients are in hospital before intensive care admission the higher their mortality.Intensive Care Med.2004;30:1908–1913.
- ,.A prospective study of outcome of in‐patient pediatric cardiopulmonary arrest.Resuscitation.2006;71:310–318.
- ,,, et al.Effect of a rapid response team on hospital‐wide mortality and code rates outside the ICU in a children's hospital.JAMA.2007;298:2267–2274.
- ,,,,,.Use of telemedicine to provide pediatric critical care consultations to underserved rural northern California.J Pediatr.2004;144:375–380.
- ,.Risk‐adjusting acute myocardial infarction mortality: are APR‐DRGs the right tool?Health Serv Res.2000;34:1469–1489.
- ,,,,.Predicting in‐hospital deaths from coronary artery bypass graft surgery: do different severity measures give different predictions?Med Care.1998;36:28–39.
- ,,.Patient and hospital correlates of clinical outcomes and resource‐utilization in severe pediatric sepsis.Pediatrics.2007;119:487–494.
- ,,,,.A national survey of pediatric critical care resources in the United States.Pediatrics.2005;115:e382–386.
- .Regional variation in child mortality at hospitals lacking a pediatric intensive care unit.Crit Care Med.2002;30:94–99.
- ,,,,,.Direct transport to tertiary trauma centers versus transfer from lower level facilities: impact on mortality and morbidity among patients with major trauma.J Trauma.1997;43:288–296.
- ,,,.Timing of intensive care unit admission in relation to ICU outcome.Crit Care Med.1990;18:1231–1235.
- ,:Admission source to the medical intensive care unit predicts hospital death independent of APACHE II score.JAMA.1990;264:2389–2394.
- ,,,,.Accepting critically ill transfer patients: adverse effect on a referral center's outcome and benchmark measures.Ann Intern Med.2003;138:882–890.
- ,,,,,.Elective intrahospital admissions versus acute interhospital transfers to a surgical intensive care unit: cost and outcome prediction.J Trauma.1991;31:915–918.
- ,,,,.Adverse effect on a referral intensive care unit's performance of accepting patients transferred from another intensive care unit.Crit Care Med.2005;33:705–710.
- ,,,.Prognostic factors for mortality following interhospital transfers to the medical intensive care unit of a tertiary referral center.Crit Care Med.2003;31:1981–1986.
- National Government Services. Medicare UB‐04 Revenue Codes. Available at http://www.ngsmedicare.com/NGSMedicare/PartA/EducationandSupport/ToolsandMaterials/0908ub‐04.pdf. Accessed April 7,2008.
- American Hospital Association.AHA Guide to the Health Care Field.2002 ed.Chicago:American Hospital Association;2002.
- American Hospital Association.AHA Guide to the Health Care Field.2003 ed.Chicago:American Hospital Association;2003.
- ,,.Pediatric deaths attributable to complex chronic conditions: a population‐based study of Washington State, 1980–1997.Pediatrics.2000;106:205–209.
- ,,,.Importance of organ dysfunction in determining hospital outcomes in children.J Pediatr.2004;144:595–601.
- ,,, et al.Cumulative influence of organ dysfunctions and septic state on mortality of critically ill children.Am J Respir Crit Care Med.2005;171:348–353.
- ,,,,,.The epidemiology of severe sepsis in children in the United States.Am J Respir Crit Care Med.2003;167:695–701.
- ,,,.The longer patients are in hospital before intensive care admission the higher their mortality.Intensive Care Med.2004;30:1908–1913.
- ,.A prospective study of outcome of in‐patient pediatric cardiopulmonary arrest.Resuscitation.2006;71:310–318.
- ,,, et al.Effect of a rapid response team on hospital‐wide mortality and code rates outside the ICU in a children's hospital.JAMA.2007;298:2267–2274.
- ,,,,,.Use of telemedicine to provide pediatric critical care consultations to underserved rural northern California.J Pediatr.2004;144:375–380.
- ,.Risk‐adjusting acute myocardial infarction mortality: are APR‐DRGs the right tool?Health Serv Res.2000;34:1469–1489.
- ,,,,.Predicting in‐hospital deaths from coronary artery bypass graft surgery: do different severity measures give different predictions?Med Care.1998;36:28–39.
- ,,.Patient and hospital correlates of clinical outcomes and resource‐utilization in severe pediatric sepsis.Pediatrics.2007;119:487–494.
Copyright © 2009 Society of Hospital Medicine
Hospital Charges for Childhood Obesity
With increases in the prevalence of obesity among children and adults over the past 3 decades in the United States,13 healthcare expenditures attributed to obesity have climbed steadily, to over $100 billion in excess expenditures annually.4 Several studies have examined healthcare costs associated with obesity in adults,48 but these studies have not attempted to distinguish between excess expenditures in inpatient versus outpatient settings. In contrast, the only 2 national economic analyses of childhood obesity have focused exclusively on the inpatient setting, because the health and economic consequences of obesity among children may be most apparent in cases in which obesity is causally linked to other diagnoses (eg, type 2 diabetes mellitus, gall bladder disease) or is a comorbidity that complicates hospitalizations.9, 10
In our previous study of obesity as a comorbidity for hospitalized children, we examined the incremental charges and length‐of‐stay (LOS) for hospitalizations for the most common nonpregnancy/nonchildbirth pediatric diagnoses, comparing those coded with obesity as a secondary diagnosis versus those without.10 Using data from the Agency for Healthcare Research and Quality (AHRQ) Kid's Inpatient Database (KID) for the year 2000, we found that obesity was associated with higher charges and longer LOS for all 4 of the conditions studied (asthma, pneumonia, affective disorders, and appendicitis). Our findings regarding asthma and affective disorders echoed earlier analyses of hospitalizations for conditions clinically linked to obesity.9 However, our study was the first to demonstrate that childhood obesity is a clinically and economically significant complicating factor for conditions not thought to be linked to obesity (pneumonia, appendicitis).
For this current study, our objective was to use more recent child hospitalization data from 2003 to determine whether our prior findings of incremental charges and LOS associated with hospitalizations where obesity was coded as a secondary diagnosis compared to those where it was not, were stable over time, and whether the magnitude of differences was consistent over a period of 4 years. We hypothesized that incremental differences in hospital charges and LOS between discharges with and without obesity would be seen in the 2003 data and that the magnitude of these differences in 2003 would be similar to those in 2000. Because obesity prevalence among children increased from 2000 to 2003,11 we also hypothesized that there would be a corresponding increase in the proportion of hospitalizations with obesity as a comorbidity.
METHODS
Data Source and Sample
We analyzed data from the AHRQ KID. The KID is a nationally representative sample of annual pediatric hospital discharges. Analysis using the KID allows for improved estimates due to the discharges from community, nonrehabilitation hospitals.12 It provides data found in standard hospital discharge abstracts for more than 2 million pediatric discharges, including International Classification of Diseases, Ninth Revision, Clinical Modification (ICD‐9‐CM) codes, LOS, total hospital charges, and patient demographic information.12 Our original analysis (published in Obesity, July 2007)10 utilized data from the 2000 KID. For this analysis we utilized data from the 2003 KID (the most recent version available), and for comparison we converted the results from the 2000 study into 2003 dollars using the Consumer Price Index for Medical Care.
Using ICD‐9‐CM codes, the KID provides the principal diagnosis for each discharge, along with up to 14 secondary diagnoses. It also provides Clinical Classification Software (CCS) codes, a diagnostic categorization scheme that permits grouping of related conditions. ICD‐9‐CM codes are collapsed into a smaller number of categories that are sometimes more useful for presenting descriptive statistics than are individual ICD‐9‐CM codes or the much broader categories of Diagnosis Related Groups (DRGs). For example, all ICD‐9‐CM codes for specific types of pneumonia would be grouped together under 1 CCS code but would exclude other respiratory conditions such as pneumothorax which would generally be included in the respiratory condition DRG.12
The 2000 KID contained 2,516,833 unweighted discharges, representing 7,291,038 discharges in the population. In the 2003 KID, there were 2,984,129 unweighted discharges representing 7,409,162 discharges in the population. Our sample included all discharges for nonpregnancy‐related conditions, in children 2 years of age (due to the Centers for Disease Control definition for overweight based on body mass index [BMI] that starts at age 2 years)13, 14 to 18 years of age (2000 weighted n = 1,527,309; 2003 weighted n = 1,613,258); these numbers exclude discharges with obesity as a primary diagnosis.
Key Variables
Our outcome variables were LOS and total charges for each of the common nonpregnancy‐related principal discharge diagnoses studied. For the 2000 and 2003 KID, total charges included all hospital fees with the exception of professional fees.15
The main independent variable of interest was presence of obesity as a secondary diagnosis. Discharges were classified as either with or without obesity based on the presence of the ICD‐9‐CM code 278.0x as a secondary diagnosis (1 if yes, 0 if no). This code captures obesity unspecified (278.00), overweight (278.01), and morbid obesity (278.02). Of note, the distribution of these codes did not vary significantly between the 2 study years.
Other independent variables included sex, age (2‐5 years, 6‐10 years, 11‐14 years, and 15‐18 years), race/ethnicity (white, black, Hispanic, and other), region (Northeast, Midwest, South, and West), hospital type (based on classification by the National Association of Children's Hospitals and Related Institutions [NACHRI] as general hospital, children's unit in a general hospital, and children's hospital) and expected primary payer (Medicaid, private, and other). We chose independent variables due to their established association with our outcomes and for their patterns of association with childhood obesity.
We did not include LOS as a covariate in models of charges because obesity may have been associated with the outcome indirectly through LOS as well as directly as a main effect. To fully interpret the combination of these effects would require analyses beyond the scope of this work.
Analyses
Using CCS codes, we identified the 4 most common principal nonpregnancy‐related discharge diagnoses for children 2‐18 years old. For both 2000 and 2003 these were asthma (CCS 128), pneumonia (CCS 122), affective disorders (eg, depression and bipolar disorder) (CCS 69), and appendicitis (CCS 142). Importantly, this group of diagnoses included conditions clinically associated with obesity (asthma and affective disorders)16 and conditions not associated with obesity (pneumonia and appendicitis). Given this distinction we analyzed the 4 conditions separately.
For all discharges with these common principal diagnoses, we calculated mean LOS and mean total charges. Bivariate and multivariable analyses were conducted using simple and multiple linear regression, respectively. These analyses were designed to test the study hypothesis that obesity as a secondary diagnosis is associated with incremental economic charges and LOS. All analyses were performed on log‐transformed LOS and charge data. We included in our models those characteristics that we hypothesized were potentially related to hospital charges and LOS, based on published literature,4, 17, 18 and for that reason all covariates were retained in the final models regardless of bivariate findings. Differences in the incremental LOS and charges for 2003 versus 2000 were compared using t‐tests.
For each independent variable with missing data, we included in the analyses a category for unreported values. The KID is known to have a large number of missing data for race/ethnicity; therefore, in keeping with other studies utilizing the KID,10, 19 we also conducted multivariate analyses excluding those discharges with unreported race as a sensitivity analysis. Of note, analyses of these data excluding children of unreported race were not substantively different than those in which the unreported group was included. We present our findings including the unreported race category.
Predicted values on the log scale, for those with obesity and without obesity as a secondary diagnosis adjusted for the listed covariates, were obtained. We then back‐transformed these to their original scales and units using methods developed by Duan.20 For each principal diagnosis category, we analyzed the differences in predicted mean LOS and predicted mean total charges between discharges with and without obesity as a secondary diagnosis, adjusted for the listed covariates, using the P values obtained from the regression analyses. All results are presented in 2003 dollars.
In keeping with our earlier analysis we considered the potential influence of comorbidities other than obesity on LOS and charges. Therefore, we examined whether other comorbidities were coded more frequently among those discharges with obesity as a secondary diagnosis than those discharges without obesity. As seen in the 2000 KID, the 2003 data revealed that diabetes was more commonly seen with obesity‐related hospitalizations than with those hospitalizations without obesity. However, the proportion of discharges with obesity and diabetes was low for all of the principal diagnostic categories studied (asthma 4.8%, pneumonia 6.0%, affective disorders 4.2%, and appendicitis 1.9%). Thus, we judged the co‐occurrence of diabetes and obesity as secondary diagnoses too infrequent to be an explanatory factor for the overall incremental differences in LOS and charges.
All analyses were weighted to account for the complex probability sampling of the dataset and permit inferences regarding national hospital discharge patterns. The same sample of discharges was used to analyze LOS and charges, with the discharge weighting variable (DISCWT) used for all analyses. All results are presented as weighted data unless otherwise noted. Analyses were conducted using STATA 8.0 (Stata Corporation, College Station, TX) and SUDAAN version 9 (Research Triangle Institute, Research Triangle Park, NC).
This study was approved by the Institutional Review Board of the University of Michigan Medical School.
RESULTS
Sample Characteristics
The characteristics of the study population are presented in Table 1. In 2003, the overall proportion of nonpregnancy‐related discharges for children 2‐18 years old coded with obesity as a secondary diagnosis was 1.6%, an increase from 1.1% in 2000. Within the 4 most common nonpregnancy‐related CCS category diagnoses (asthma, pneumonia, affective disorders, and appendicitis) the proportion of discharges with obesity coded as a secondary diagnosis increased from 2000 to 2003 (Figure 1)
| Variables | Discharges | |||
|---|---|---|---|---|
| With Obesity as Secondary Diagnosis | Without Obesity | |||
| 2000 | 2003 | 2000 | 2003 | |
| Unweighted, n | 8,696 | 15,546 | 762,407 | 943,182 |
| Weighted population size | 17,672 | 25,709 | 1,509,637 | 1,587,549 |
| Age | ||||
| 2‐5 years (%) | 4.8 | 4.8 | 27.4 | 29.0 |
| 6‐10 years (%) | 14.7 | 15.9 | 22.4 | 22.3 |
| 11‐14 years (%) | 34.4 | 34.1 | 20.9 | 20.9 |
| 15‐18 years (%) | 46.1 | 45.2 | 29.3 | 27.8 |
| Sex | ||||
| Male (%) | 45.5 | 46.8 | 53.8 | 53.4 |
| Race/Ethnicity | ||||
| White (%) | 46.8 | 32.2 | 50.9 | 39.3 |
| Black (%) | 20.7 | 20.4 | 14.3 | 12.6 |
| Hispanic (%) | 14.9 | 16.8 | 14.3 | 14.4 |
| Other (%) | 4.8 | 5.0 | 5.6 | 5.6 |
| Unreported (%) | 12.8 | 25.6 | 14.9 | 28.1 |
| Payer | ||||
| Medicaid (%) | 42.6 | 46.5 | 32.1 | 36.9 |
| Private (%) | 47.1 | 42.6 | 58.0 | 53.3 |
| Other (%) | 9.4 | 10.6 | 9.4 | 9.6 |
| Unreported (%) | 0.9 | 0.3 | 0.5 | 0.2 |
| Hospital Region | ||||
| Northeast (%) | 17.3 | 15.9 | 21.7 | 18.7 |
| Midwest (%) | 24.6 | 24.8 | 20.0 | 23.3 |
| South (%) | 37.6 | 38.4 | 36.3 | 37.0 |
| West (%) | 20.5 | 20.9 | 22.0 | 21.0 |
| Hospital type | ||||
| General hospital (%) | 68.2 | 61.4 | 61.0 | 57.1 |
| Children's unit in general hospital (%) | 15.2 | 17.8 | 18.6 | 19.2 |
| Children's hospitals (%) | 14.3 | 14.9 | 17.9 | 17.6 |
| Unreported (%) | 2.3% | 5.9 | 2.5 | 6.1 |
Incremental Differences in Mean Charges Associated with Obesity
In Table 2, we present results for analyses of mean charges. Following the pattern in 2000 for all 4 of these common conditions, in 2003 the adjusted mean total hospital charges were statistically significantly higher for discharges in which obesity was listed as a secondary diagnosis, compared with those in which it was not. Moreover, the magnitude of these differences was somewhat greater in 2003 than in 2000, although it did not achieve statistical significance (P > 0.05) (Figure 2). Specifically, the difference in charges among asthma discharges with and without obesity as a comorbidity was 9% greater in 2003 than in 2000, 17% greater among pneumonia discharges, 121% greater among affective disorders, and 3% greater among appendicitis discharges.
| Adjusted Mean Charges ($) | ||||||
|---|---|---|---|---|---|---|
| 2000 | 2003 | |||||
| With Obesity | Without Obesity | Difference | With Obesity | Without Obesity | Difference | |
| ||||||
| Asthma | 8,847 | 6,884 | 1,963* | 10,589 | 8,444 | 2,145 |
| Pneumonia | 13,930 | 11,036 | 2,894* | 16,609 | 13,219 | 3,390 |
| Affective disorders | 9,446 | 8,850 | 596 | 11,942 | 10,619 | 1,323 |
| Appendicitis | 16,101 | 12,587 | 3,514 | 19,213 | 15,586 | 3,627 |
Incremental Differences in Mean LOS Associated with Obesity
Compared with those discharges without obesity coded, obesity as a secondary diagnosis was associated with a statistically significantly longer mean LOS for all four diagnoses in 2003 (Table 3). In addition, for all diagnoses except asthma, the magnitude of the difference was somewhat greater in 2003 than in 2000, although it did not reach statistical significance (P > 0.05). The greatest increase was seen with appendicitis, with the incremental difference in LOS between those with obesity and those without going from 0.17 days in 2000 to 0.83 days in 2003; an increase of over 300%.
| Adjusted Mean LOS | ||||||
|---|---|---|---|---|---|---|
| 2000 | 2003 | |||||
| With Obesity | Without Obesity | Difference | With Obesity | Without Obesity | Difference | |
| ||||||
| Asthma | 3.04 | 2.45 | 0.59* | 2.88 | 2.44 | 0.44* |
| Pneumonia | 4.26 | 3.89 | 0.37 | 4.39 | 3.83 | 0.56 |
| Affective disorders | 7.72 | 7.11 | 0.61* | 8.23 | 7.42 | 0.81* |
| Appendicitis | 3.33 | 3.16 | 0.17 | 3.91 | 3.08 | 0.83* |
DISCUSSION
Prior studies have explored the resource utilization and expenditures associated with obesity in adult populations and among obese children in the outpatient setting.48, 21 Few, however, have examined charges related to inpatient care of obese children. Our studies are the first to utilize actual charge data from a nationally representative sample to explore the economic implications of obesity among children hospitalized for common pediatric illnesses.
Our findings from this national analysis support the hypothesis that, for the 4 conditions studied, statistically significantly higher mean total hospital charges and longer mean LOS for those discharges with obesity coded as a secondary diagnosis versus those without obesity coded occurred for both 2000 and 2003, even when controlling for sex, age, race/ethnicity, region, payer, and hospital type. Our analyses also suggested that the magnitude of the incremental differences in charges from 2000 to 2003 increased somewhat, and the magnitude of the incremental differences in LOS for all of these common conditions (except asthma) is also increasing.
While these findings serve to confirm higher incremental charges and LOS associated with obesity for hospitalized children, they raise the question of why charges and LOS for children with obesity might increase at a greater rate than for those without obesity. Higher hospital charges and LOS for children with obesity coded as a secondary diagnosis may be explained by greater resource utilization due to obesity that: 1) increases the technical complexity of procedures such as surgical interventions or intravenous catheter (IV) placement;22 2) leads to greater illness severity, as has been suggested in studies of adult patients;23, 24 or 3) leads to more complications such as secondary infections.22 One explanation for the possible widening of the gap in charges during the time period studied might be that discharges coded with obesity in 2003 reflect children who were more severely obese and had a greater severity of illness, leading to higher resource utilization than those with obesity in the 2000 dataset. However, the ICD‐9‐CM code for morbid obesity (278.02) was not used more often in 2003 than in 2000. Alternatively, we suspect that between 2000 and 2003, due to an increasing awareness of the problem of childhood obesity, physicians may have become more likely to order tests or consultations specifically related to the treatment or evaluation of obesity. Other than the increase in the proportion of discharges with obesity as a comorbidity from 2000 to 2003, we were unable to explore these possible explanations with the KID datasets.
In this sample of discharges, with only 1.1% and 1.6% coded with obesity as a secondary diagnosis in 2000 and 2003, respectively, it is important to note that these discharges should not be interpreted as the prevalence of obesity in hospitalized children. Indeed, in a recent study of children hospitalized for surgical procedures at a large Midwestern tertiary care hospital, 31.6% were found to be overweight or obese.25 However, we posit that the cases coded with obesity as a secondary diagnosis in this sample represent the cases in which obesity presents a recognized factor that complicated the clinical course. Further work should explore the mechanisms by which obesity impacts the care of children hospitalized for common conditions. For example, children with obesity may require more procedures and may experience more treatment complications. These specific interventions should be the target of clinically focused analyses.
Limitations
Analyses utilizing discharge data are potentially limited by the accuracy and consistency of coding. Whether the discharges coded with obesity reflect all cases in which obesity was a complicating factor is not known. Based on the national prevalence of childhood obesity it is likely than more than 1.6% of the children hospitalized in 2003 were obese. However, whether obesity impacted the hospital course sufficiently to be included among the secondary diagnoses (as stipulated by ICD‐9‐CM guidelines for official recording)26 in more than 1.6% of cases is unknown.
This study is also limited by the inability to address the processes that might account for the consistently higher charges and longer LOS seen for those discharges coded with obesity versus those without obesity coded. The KID provides some information regarding procedures performed but cannot be reliably used to examine this aspect of patient care.27 An additional limitation of the KID data set is that it contains information about deidentified discharges. This leads to the possibility of having individual patients in the dataset with multiple hospitalizations. In this study we examined the relationship between obesity as a secondary diagnosis and incremental charges and LOS for the 4 most common clinical categories for which children are hospitalized. Findings may differ for other conditions not evaluated here. Finally, information regarding costs can only be inferred from the charge data provided by the KID. However, the ratio of charges to costs would not be expected to vary by obesity status.
CONCLUSIONS
These results extend our earlier findings of higher charges and longer LOS for pediatric discharges coded with obesity versus those without. In addition, this analysis suggests a widening gap of incremental hospital charges and LOS associated with obesity as a comorbidity for common pediatric conditions. These findings present a heightening financial imperative for further research to evaluate factors associated with greater resource utilization among obese pediatric patients.
- ,,,.Prevalence and trends in overweight among US children and adolescents, 1999–2000.JAMA.2002;288(14):1728–1732.
- ,.Epidemic increase in childhood overweight, 1986–1998.JAMA.2001;286(22):2845–2848.
- ,.Overweight children and adolescents: description, epidemiology, and demographics.Pediatrics.1998;101(Pt 2):497–504.
- ,,.National medical spending attributable to overweight and obesity: how much, and who's paying?Health Aff (Millwood).2003; (Suppl Web Exclusives):W3‐219–26.
- ,,,.The impact of obesity on rising medical spending.Health Aff (Millwood).2004;(Suppl Web Exclusives):W4‐480–6.
- ,.Current estimates of the economic cost of obesity in the United States.Obes Res.1998;6(2):97–106.
- ,,,,.Lifetime health and economic benefits of weight loss among obese persons.Am J Public Health.1999;89(10):1536–1542.
- ,,.The health and cost consequences of obesity among the future elderly.Health Aff (Millwood).2005;24(Suppl 2):W5R30–W5R41.
- ,.Economic burden of obesity in youths aged 6 to 17 years: 1979–1999.Pediatrics.2002;109(5):E81–E81.
- ,,,.Incremental hospital charges associated with obesity as a secondary diagnosis.Obesity (Silver Spring).2007;15:1895–1901.
- ,,,,,.Prevalence of overweight and obesity in the United States, 1999–2004.JAMA.2006;295(13):1549–1555.
- Healthcare Cost and Utilization Project.2005. Overview of the Kid's Inpatient Database. Available at:http://www.hcup‐us.ahrq.gov/kidoverview.jsp. Accessed December 2008.
- CDC Body Mass Index: BMI for Children and Teens. Available at: http://www.cdc.gov/nccdphp/dnpa/bmi. Accessed December2008.
- ,,,,,.Prevalence of overweight among preschool children in the United States, 1971 through 1994.Pediatrics.1997;99(4):E1.
- Healthcare Cost and Utilization Project, 2002 and 2004. Description of data elements: inpatient core file. Available at: http://www.hcup‐us.ahrq.gov/db/nation/kid/DataElements_KID_Core_2000.pdf; http://www.hcup‐us.ahrq.gov/db/nation/kid/KID_2003_CORE_Volume1_A‐L.pdf;http://www.hcup‐us. ahrq.gov/db/nation/kid/KID_2003_CORE_Volume2_M‐Z.pdf. Accessed December2008.
- .Health consequences of obesity in youth: childhood predictors of adult disease.Pediatrics.1998;101:518–525.
- ,,.Lengths of stay and costs associated with children's hospitals.Pediatrics.2005;115(4):839–844.
- ,,, et al.Health care expenditures associated with overweight and obesity among US adults: importance of age and race.Am J Public Health.2005;95(1):159–165.
- ,,,.Effects of race, insurance status, and hospital volume on perforated appendicitis in children.Pediatrics.2005;115(4):920–925.
- .Smearing estimate: a nonparametric retransformation method.J Am Stat Assoc.1983;78:605–610.
- ,,,.Resource utilization and expenditures for overweight and obese children.Arch Pediatr Adolesc Med.2007 Jan;161(1):11–14.
- ,.Appendicitis in the obese child.J Pediatr Surg.2007;42(5):857–861.
- ,.Management of the obese critically ill patient.Crit Care Clin.2001;17:187–200.
- ,,,,.Total respiratory system, lung, and chest wall mechanics in sedated‐paralyzed postoperative morbidly obese patients.Chest.1996;109:144–151.
- ,,,,.Prevalence of overweight and obesity in a U.S. pediatric surgical population.J Natl Med Assoc.2007;99(1):46–48, 50–51.
- Centers for Disease Control and Prevention. ICD‐9‐CM. Official Guidelines for Coding and Reporting. Effective April 1, 2005. Available at: http://www.cdc.gov/nchs/data/icd9/icdguide.pdf. Accessed December2008.
- ,,,.Predictors of hospital charges for children admitted with asthma.Ambul Pediatr.2006;6(1):15–20.
With increases in the prevalence of obesity among children and adults over the past 3 decades in the United States,13 healthcare expenditures attributed to obesity have climbed steadily, to over $100 billion in excess expenditures annually.4 Several studies have examined healthcare costs associated with obesity in adults,48 but these studies have not attempted to distinguish between excess expenditures in inpatient versus outpatient settings. In contrast, the only 2 national economic analyses of childhood obesity have focused exclusively on the inpatient setting, because the health and economic consequences of obesity among children may be most apparent in cases in which obesity is causally linked to other diagnoses (eg, type 2 diabetes mellitus, gall bladder disease) or is a comorbidity that complicates hospitalizations.9, 10
In our previous study of obesity as a comorbidity for hospitalized children, we examined the incremental charges and length‐of‐stay (LOS) for hospitalizations for the most common nonpregnancy/nonchildbirth pediatric diagnoses, comparing those coded with obesity as a secondary diagnosis versus those without.10 Using data from the Agency for Healthcare Research and Quality (AHRQ) Kid's Inpatient Database (KID) for the year 2000, we found that obesity was associated with higher charges and longer LOS for all 4 of the conditions studied (asthma, pneumonia, affective disorders, and appendicitis). Our findings regarding asthma and affective disorders echoed earlier analyses of hospitalizations for conditions clinically linked to obesity.9 However, our study was the first to demonstrate that childhood obesity is a clinically and economically significant complicating factor for conditions not thought to be linked to obesity (pneumonia, appendicitis).
For this current study, our objective was to use more recent child hospitalization data from 2003 to determine whether our prior findings of incremental charges and LOS associated with hospitalizations where obesity was coded as a secondary diagnosis compared to those where it was not, were stable over time, and whether the magnitude of differences was consistent over a period of 4 years. We hypothesized that incremental differences in hospital charges and LOS between discharges with and without obesity would be seen in the 2003 data and that the magnitude of these differences in 2003 would be similar to those in 2000. Because obesity prevalence among children increased from 2000 to 2003,11 we also hypothesized that there would be a corresponding increase in the proportion of hospitalizations with obesity as a comorbidity.
METHODS
Data Source and Sample
We analyzed data from the AHRQ KID. The KID is a nationally representative sample of annual pediatric hospital discharges. Analysis using the KID allows for improved estimates due to the discharges from community, nonrehabilitation hospitals.12 It provides data found in standard hospital discharge abstracts for more than 2 million pediatric discharges, including International Classification of Diseases, Ninth Revision, Clinical Modification (ICD‐9‐CM) codes, LOS, total hospital charges, and patient demographic information.12 Our original analysis (published in Obesity, July 2007)10 utilized data from the 2000 KID. For this analysis we utilized data from the 2003 KID (the most recent version available), and for comparison we converted the results from the 2000 study into 2003 dollars using the Consumer Price Index for Medical Care.
Using ICD‐9‐CM codes, the KID provides the principal diagnosis for each discharge, along with up to 14 secondary diagnoses. It also provides Clinical Classification Software (CCS) codes, a diagnostic categorization scheme that permits grouping of related conditions. ICD‐9‐CM codes are collapsed into a smaller number of categories that are sometimes more useful for presenting descriptive statistics than are individual ICD‐9‐CM codes or the much broader categories of Diagnosis Related Groups (DRGs). For example, all ICD‐9‐CM codes for specific types of pneumonia would be grouped together under 1 CCS code but would exclude other respiratory conditions such as pneumothorax which would generally be included in the respiratory condition DRG.12
The 2000 KID contained 2,516,833 unweighted discharges, representing 7,291,038 discharges in the population. In the 2003 KID, there were 2,984,129 unweighted discharges representing 7,409,162 discharges in the population. Our sample included all discharges for nonpregnancy‐related conditions, in children 2 years of age (due to the Centers for Disease Control definition for overweight based on body mass index [BMI] that starts at age 2 years)13, 14 to 18 years of age (2000 weighted n = 1,527,309; 2003 weighted n = 1,613,258); these numbers exclude discharges with obesity as a primary diagnosis.
Key Variables
Our outcome variables were LOS and total charges for each of the common nonpregnancy‐related principal discharge diagnoses studied. For the 2000 and 2003 KID, total charges included all hospital fees with the exception of professional fees.15
The main independent variable of interest was presence of obesity as a secondary diagnosis. Discharges were classified as either with or without obesity based on the presence of the ICD‐9‐CM code 278.0x as a secondary diagnosis (1 if yes, 0 if no). This code captures obesity unspecified (278.00), overweight (278.01), and morbid obesity (278.02). Of note, the distribution of these codes did not vary significantly between the 2 study years.
Other independent variables included sex, age (2‐5 years, 6‐10 years, 11‐14 years, and 15‐18 years), race/ethnicity (white, black, Hispanic, and other), region (Northeast, Midwest, South, and West), hospital type (based on classification by the National Association of Children's Hospitals and Related Institutions [NACHRI] as general hospital, children's unit in a general hospital, and children's hospital) and expected primary payer (Medicaid, private, and other). We chose independent variables due to their established association with our outcomes and for their patterns of association with childhood obesity.
We did not include LOS as a covariate in models of charges because obesity may have been associated with the outcome indirectly through LOS as well as directly as a main effect. To fully interpret the combination of these effects would require analyses beyond the scope of this work.
Analyses
Using CCS codes, we identified the 4 most common principal nonpregnancy‐related discharge diagnoses for children 2‐18 years old. For both 2000 and 2003 these were asthma (CCS 128), pneumonia (CCS 122), affective disorders (eg, depression and bipolar disorder) (CCS 69), and appendicitis (CCS 142). Importantly, this group of diagnoses included conditions clinically associated with obesity (asthma and affective disorders)16 and conditions not associated with obesity (pneumonia and appendicitis). Given this distinction we analyzed the 4 conditions separately.
For all discharges with these common principal diagnoses, we calculated mean LOS and mean total charges. Bivariate and multivariable analyses were conducted using simple and multiple linear regression, respectively. These analyses were designed to test the study hypothesis that obesity as a secondary diagnosis is associated with incremental economic charges and LOS. All analyses were performed on log‐transformed LOS and charge data. We included in our models those characteristics that we hypothesized were potentially related to hospital charges and LOS, based on published literature,4, 17, 18 and for that reason all covariates were retained in the final models regardless of bivariate findings. Differences in the incremental LOS and charges for 2003 versus 2000 were compared using t‐tests.
For each independent variable with missing data, we included in the analyses a category for unreported values. The KID is known to have a large number of missing data for race/ethnicity; therefore, in keeping with other studies utilizing the KID,10, 19 we also conducted multivariate analyses excluding those discharges with unreported race as a sensitivity analysis. Of note, analyses of these data excluding children of unreported race were not substantively different than those in which the unreported group was included. We present our findings including the unreported race category.
Predicted values on the log scale, for those with obesity and without obesity as a secondary diagnosis adjusted for the listed covariates, were obtained. We then back‐transformed these to their original scales and units using methods developed by Duan.20 For each principal diagnosis category, we analyzed the differences in predicted mean LOS and predicted mean total charges between discharges with and without obesity as a secondary diagnosis, adjusted for the listed covariates, using the P values obtained from the regression analyses. All results are presented in 2003 dollars.
In keeping with our earlier analysis we considered the potential influence of comorbidities other than obesity on LOS and charges. Therefore, we examined whether other comorbidities were coded more frequently among those discharges with obesity as a secondary diagnosis than those discharges without obesity. As seen in the 2000 KID, the 2003 data revealed that diabetes was more commonly seen with obesity‐related hospitalizations than with those hospitalizations without obesity. However, the proportion of discharges with obesity and diabetes was low for all of the principal diagnostic categories studied (asthma 4.8%, pneumonia 6.0%, affective disorders 4.2%, and appendicitis 1.9%). Thus, we judged the co‐occurrence of diabetes and obesity as secondary diagnoses too infrequent to be an explanatory factor for the overall incremental differences in LOS and charges.
All analyses were weighted to account for the complex probability sampling of the dataset and permit inferences regarding national hospital discharge patterns. The same sample of discharges was used to analyze LOS and charges, with the discharge weighting variable (DISCWT) used for all analyses. All results are presented as weighted data unless otherwise noted. Analyses were conducted using STATA 8.0 (Stata Corporation, College Station, TX) and SUDAAN version 9 (Research Triangle Institute, Research Triangle Park, NC).
This study was approved by the Institutional Review Board of the University of Michigan Medical School.
RESULTS
Sample Characteristics
The characteristics of the study population are presented in Table 1. In 2003, the overall proportion of nonpregnancy‐related discharges for children 2‐18 years old coded with obesity as a secondary diagnosis was 1.6%, an increase from 1.1% in 2000. Within the 4 most common nonpregnancy‐related CCS category diagnoses (asthma, pneumonia, affective disorders, and appendicitis) the proportion of discharges with obesity coded as a secondary diagnosis increased from 2000 to 2003 (Figure 1)
| Variables | Discharges | |||
|---|---|---|---|---|
| With Obesity as Secondary Diagnosis | Without Obesity | |||
| 2000 | 2003 | 2000 | 2003 | |
| Unweighted, n | 8,696 | 15,546 | 762,407 | 943,182 |
| Weighted population size | 17,672 | 25,709 | 1,509,637 | 1,587,549 |
| Age | ||||
| 2‐5 years (%) | 4.8 | 4.8 | 27.4 | 29.0 |
| 6‐10 years (%) | 14.7 | 15.9 | 22.4 | 22.3 |
| 11‐14 years (%) | 34.4 | 34.1 | 20.9 | 20.9 |
| 15‐18 years (%) | 46.1 | 45.2 | 29.3 | 27.8 |
| Sex | ||||
| Male (%) | 45.5 | 46.8 | 53.8 | 53.4 |
| Race/Ethnicity | ||||
| White (%) | 46.8 | 32.2 | 50.9 | 39.3 |
| Black (%) | 20.7 | 20.4 | 14.3 | 12.6 |
| Hispanic (%) | 14.9 | 16.8 | 14.3 | 14.4 |
| Other (%) | 4.8 | 5.0 | 5.6 | 5.6 |
| Unreported (%) | 12.8 | 25.6 | 14.9 | 28.1 |
| Payer | ||||
| Medicaid (%) | 42.6 | 46.5 | 32.1 | 36.9 |
| Private (%) | 47.1 | 42.6 | 58.0 | 53.3 |
| Other (%) | 9.4 | 10.6 | 9.4 | 9.6 |
| Unreported (%) | 0.9 | 0.3 | 0.5 | 0.2 |
| Hospital Region | ||||
| Northeast (%) | 17.3 | 15.9 | 21.7 | 18.7 |
| Midwest (%) | 24.6 | 24.8 | 20.0 | 23.3 |
| South (%) | 37.6 | 38.4 | 36.3 | 37.0 |
| West (%) | 20.5 | 20.9 | 22.0 | 21.0 |
| Hospital type | ||||
| General hospital (%) | 68.2 | 61.4 | 61.0 | 57.1 |
| Children's unit in general hospital (%) | 15.2 | 17.8 | 18.6 | 19.2 |
| Children's hospitals (%) | 14.3 | 14.9 | 17.9 | 17.6 |
| Unreported (%) | 2.3% | 5.9 | 2.5 | 6.1 |
Incremental Differences in Mean Charges Associated with Obesity
In Table 2, we present results for analyses of mean charges. Following the pattern in 2000 for all 4 of these common conditions, in 2003 the adjusted mean total hospital charges were statistically significantly higher for discharges in which obesity was listed as a secondary diagnosis, compared with those in which it was not. Moreover, the magnitude of these differences was somewhat greater in 2003 than in 2000, although it did not achieve statistical significance (P > 0.05) (Figure 2). Specifically, the difference in charges among asthma discharges with and without obesity as a comorbidity was 9% greater in 2003 than in 2000, 17% greater among pneumonia discharges, 121% greater among affective disorders, and 3% greater among appendicitis discharges.
| Adjusted Mean Charges ($) | ||||||
|---|---|---|---|---|---|---|
| 2000 | 2003 | |||||
| With Obesity | Without Obesity | Difference | With Obesity | Without Obesity | Difference | |
| ||||||
| Asthma | 8,847 | 6,884 | 1,963* | 10,589 | 8,444 | 2,145 |
| Pneumonia | 13,930 | 11,036 | 2,894* | 16,609 | 13,219 | 3,390 |
| Affective disorders | 9,446 | 8,850 | 596 | 11,942 | 10,619 | 1,323 |
| Appendicitis | 16,101 | 12,587 | 3,514 | 19,213 | 15,586 | 3,627 |
Incremental Differences in Mean LOS Associated with Obesity
Compared with those discharges without obesity coded, obesity as a secondary diagnosis was associated with a statistically significantly longer mean LOS for all four diagnoses in 2003 (Table 3). In addition, for all diagnoses except asthma, the magnitude of the difference was somewhat greater in 2003 than in 2000, although it did not reach statistical significance (P > 0.05). The greatest increase was seen with appendicitis, with the incremental difference in LOS between those with obesity and those without going from 0.17 days in 2000 to 0.83 days in 2003; an increase of over 300%.
| Adjusted Mean LOS | ||||||
|---|---|---|---|---|---|---|
| 2000 | 2003 | |||||
| With Obesity | Without Obesity | Difference | With Obesity | Without Obesity | Difference | |
| ||||||
| Asthma | 3.04 | 2.45 | 0.59* | 2.88 | 2.44 | 0.44* |
| Pneumonia | 4.26 | 3.89 | 0.37 | 4.39 | 3.83 | 0.56 |
| Affective disorders | 7.72 | 7.11 | 0.61* | 8.23 | 7.42 | 0.81* |
| Appendicitis | 3.33 | 3.16 | 0.17 | 3.91 | 3.08 | 0.83* |
DISCUSSION
Prior studies have explored the resource utilization and expenditures associated with obesity in adult populations and among obese children in the outpatient setting.48, 21 Few, however, have examined charges related to inpatient care of obese children. Our studies are the first to utilize actual charge data from a nationally representative sample to explore the economic implications of obesity among children hospitalized for common pediatric illnesses.
Our findings from this national analysis support the hypothesis that, for the 4 conditions studied, statistically significantly higher mean total hospital charges and longer mean LOS for those discharges with obesity coded as a secondary diagnosis versus those without obesity coded occurred for both 2000 and 2003, even when controlling for sex, age, race/ethnicity, region, payer, and hospital type. Our analyses also suggested that the magnitude of the incremental differences in charges from 2000 to 2003 increased somewhat, and the magnitude of the incremental differences in LOS for all of these common conditions (except asthma) is also increasing.
While these findings serve to confirm higher incremental charges and LOS associated with obesity for hospitalized children, they raise the question of why charges and LOS for children with obesity might increase at a greater rate than for those without obesity. Higher hospital charges and LOS for children with obesity coded as a secondary diagnosis may be explained by greater resource utilization due to obesity that: 1) increases the technical complexity of procedures such as surgical interventions or intravenous catheter (IV) placement;22 2) leads to greater illness severity, as has been suggested in studies of adult patients;23, 24 or 3) leads to more complications such as secondary infections.22 One explanation for the possible widening of the gap in charges during the time period studied might be that discharges coded with obesity in 2003 reflect children who were more severely obese and had a greater severity of illness, leading to higher resource utilization than those with obesity in the 2000 dataset. However, the ICD‐9‐CM code for morbid obesity (278.02) was not used more often in 2003 than in 2000. Alternatively, we suspect that between 2000 and 2003, due to an increasing awareness of the problem of childhood obesity, physicians may have become more likely to order tests or consultations specifically related to the treatment or evaluation of obesity. Other than the increase in the proportion of discharges with obesity as a comorbidity from 2000 to 2003, we were unable to explore these possible explanations with the KID datasets.
In this sample of discharges, with only 1.1% and 1.6% coded with obesity as a secondary diagnosis in 2000 and 2003, respectively, it is important to note that these discharges should not be interpreted as the prevalence of obesity in hospitalized children. Indeed, in a recent study of children hospitalized for surgical procedures at a large Midwestern tertiary care hospital, 31.6% were found to be overweight or obese.25 However, we posit that the cases coded with obesity as a secondary diagnosis in this sample represent the cases in which obesity presents a recognized factor that complicated the clinical course. Further work should explore the mechanisms by which obesity impacts the care of children hospitalized for common conditions. For example, children with obesity may require more procedures and may experience more treatment complications. These specific interventions should be the target of clinically focused analyses.
Limitations
Analyses utilizing discharge data are potentially limited by the accuracy and consistency of coding. Whether the discharges coded with obesity reflect all cases in which obesity was a complicating factor is not known. Based on the national prevalence of childhood obesity it is likely than more than 1.6% of the children hospitalized in 2003 were obese. However, whether obesity impacted the hospital course sufficiently to be included among the secondary diagnoses (as stipulated by ICD‐9‐CM guidelines for official recording)26 in more than 1.6% of cases is unknown.
This study is also limited by the inability to address the processes that might account for the consistently higher charges and longer LOS seen for those discharges coded with obesity versus those without obesity coded. The KID provides some information regarding procedures performed but cannot be reliably used to examine this aspect of patient care.27 An additional limitation of the KID data set is that it contains information about deidentified discharges. This leads to the possibility of having individual patients in the dataset with multiple hospitalizations. In this study we examined the relationship between obesity as a secondary diagnosis and incremental charges and LOS for the 4 most common clinical categories for which children are hospitalized. Findings may differ for other conditions not evaluated here. Finally, information regarding costs can only be inferred from the charge data provided by the KID. However, the ratio of charges to costs would not be expected to vary by obesity status.
CONCLUSIONS
These results extend our earlier findings of higher charges and longer LOS for pediatric discharges coded with obesity versus those without. In addition, this analysis suggests a widening gap of incremental hospital charges and LOS associated with obesity as a comorbidity for common pediatric conditions. These findings present a heightening financial imperative for further research to evaluate factors associated with greater resource utilization among obese pediatric patients.
With increases in the prevalence of obesity among children and adults over the past 3 decades in the United States,13 healthcare expenditures attributed to obesity have climbed steadily, to over $100 billion in excess expenditures annually.4 Several studies have examined healthcare costs associated with obesity in adults,48 but these studies have not attempted to distinguish between excess expenditures in inpatient versus outpatient settings. In contrast, the only 2 national economic analyses of childhood obesity have focused exclusively on the inpatient setting, because the health and economic consequences of obesity among children may be most apparent in cases in which obesity is causally linked to other diagnoses (eg, type 2 diabetes mellitus, gall bladder disease) or is a comorbidity that complicates hospitalizations.9, 10
In our previous study of obesity as a comorbidity for hospitalized children, we examined the incremental charges and length‐of‐stay (LOS) for hospitalizations for the most common nonpregnancy/nonchildbirth pediatric diagnoses, comparing those coded with obesity as a secondary diagnosis versus those without.10 Using data from the Agency for Healthcare Research and Quality (AHRQ) Kid's Inpatient Database (KID) for the year 2000, we found that obesity was associated with higher charges and longer LOS for all 4 of the conditions studied (asthma, pneumonia, affective disorders, and appendicitis). Our findings regarding asthma and affective disorders echoed earlier analyses of hospitalizations for conditions clinically linked to obesity.9 However, our study was the first to demonstrate that childhood obesity is a clinically and economically significant complicating factor for conditions not thought to be linked to obesity (pneumonia, appendicitis).
For this current study, our objective was to use more recent child hospitalization data from 2003 to determine whether our prior findings of incremental charges and LOS associated with hospitalizations where obesity was coded as a secondary diagnosis compared to those where it was not, were stable over time, and whether the magnitude of differences was consistent over a period of 4 years. We hypothesized that incremental differences in hospital charges and LOS between discharges with and without obesity would be seen in the 2003 data and that the magnitude of these differences in 2003 would be similar to those in 2000. Because obesity prevalence among children increased from 2000 to 2003,11 we also hypothesized that there would be a corresponding increase in the proportion of hospitalizations with obesity as a comorbidity.
METHODS
Data Source and Sample
We analyzed data from the AHRQ KID. The KID is a nationally representative sample of annual pediatric hospital discharges. Analysis using the KID allows for improved estimates due to the discharges from community, nonrehabilitation hospitals.12 It provides data found in standard hospital discharge abstracts for more than 2 million pediatric discharges, including International Classification of Diseases, Ninth Revision, Clinical Modification (ICD‐9‐CM) codes, LOS, total hospital charges, and patient demographic information.12 Our original analysis (published in Obesity, July 2007)10 utilized data from the 2000 KID. For this analysis we utilized data from the 2003 KID (the most recent version available), and for comparison we converted the results from the 2000 study into 2003 dollars using the Consumer Price Index for Medical Care.
Using ICD‐9‐CM codes, the KID provides the principal diagnosis for each discharge, along with up to 14 secondary diagnoses. It also provides Clinical Classification Software (CCS) codes, a diagnostic categorization scheme that permits grouping of related conditions. ICD‐9‐CM codes are collapsed into a smaller number of categories that are sometimes more useful for presenting descriptive statistics than are individual ICD‐9‐CM codes or the much broader categories of Diagnosis Related Groups (DRGs). For example, all ICD‐9‐CM codes for specific types of pneumonia would be grouped together under 1 CCS code but would exclude other respiratory conditions such as pneumothorax which would generally be included in the respiratory condition DRG.12
The 2000 KID contained 2,516,833 unweighted discharges, representing 7,291,038 discharges in the population. In the 2003 KID, there were 2,984,129 unweighted discharges representing 7,409,162 discharges in the population. Our sample included all discharges for nonpregnancy‐related conditions, in children 2 years of age (due to the Centers for Disease Control definition for overweight based on body mass index [BMI] that starts at age 2 years)13, 14 to 18 years of age (2000 weighted n = 1,527,309; 2003 weighted n = 1,613,258); these numbers exclude discharges with obesity as a primary diagnosis.
Key Variables
Our outcome variables were LOS and total charges for each of the common nonpregnancy‐related principal discharge diagnoses studied. For the 2000 and 2003 KID, total charges included all hospital fees with the exception of professional fees.15
The main independent variable of interest was presence of obesity as a secondary diagnosis. Discharges were classified as either with or without obesity based on the presence of the ICD‐9‐CM code 278.0x as a secondary diagnosis (1 if yes, 0 if no). This code captures obesity unspecified (278.00), overweight (278.01), and morbid obesity (278.02). Of note, the distribution of these codes did not vary significantly between the 2 study years.
Other independent variables included sex, age (2‐5 years, 6‐10 years, 11‐14 years, and 15‐18 years), race/ethnicity (white, black, Hispanic, and other), region (Northeast, Midwest, South, and West), hospital type (based on classification by the National Association of Children's Hospitals and Related Institutions [NACHRI] as general hospital, children's unit in a general hospital, and children's hospital) and expected primary payer (Medicaid, private, and other). We chose independent variables due to their established association with our outcomes and for their patterns of association with childhood obesity.
We did not include LOS as a covariate in models of charges because obesity may have been associated with the outcome indirectly through LOS as well as directly as a main effect. To fully interpret the combination of these effects would require analyses beyond the scope of this work.
Analyses
Using CCS codes, we identified the 4 most common principal nonpregnancy‐related discharge diagnoses for children 2‐18 years old. For both 2000 and 2003 these were asthma (CCS 128), pneumonia (CCS 122), affective disorders (eg, depression and bipolar disorder) (CCS 69), and appendicitis (CCS 142). Importantly, this group of diagnoses included conditions clinically associated with obesity (asthma and affective disorders)16 and conditions not associated with obesity (pneumonia and appendicitis). Given this distinction we analyzed the 4 conditions separately.
For all discharges with these common principal diagnoses, we calculated mean LOS and mean total charges. Bivariate and multivariable analyses were conducted using simple and multiple linear regression, respectively. These analyses were designed to test the study hypothesis that obesity as a secondary diagnosis is associated with incremental economic charges and LOS. All analyses were performed on log‐transformed LOS and charge data. We included in our models those characteristics that we hypothesized were potentially related to hospital charges and LOS, based on published literature,4, 17, 18 and for that reason all covariates were retained in the final models regardless of bivariate findings. Differences in the incremental LOS and charges for 2003 versus 2000 were compared using t‐tests.
For each independent variable with missing data, we included in the analyses a category for unreported values. The KID is known to have a large number of missing data for race/ethnicity; therefore, in keeping with other studies utilizing the KID,10, 19 we also conducted multivariate analyses excluding those discharges with unreported race as a sensitivity analysis. Of note, analyses of these data excluding children of unreported race were not substantively different than those in which the unreported group was included. We present our findings including the unreported race category.
Predicted values on the log scale, for those with obesity and without obesity as a secondary diagnosis adjusted for the listed covariates, were obtained. We then back‐transformed these to their original scales and units using methods developed by Duan.20 For each principal diagnosis category, we analyzed the differences in predicted mean LOS and predicted mean total charges between discharges with and without obesity as a secondary diagnosis, adjusted for the listed covariates, using the P values obtained from the regression analyses. All results are presented in 2003 dollars.
In keeping with our earlier analysis we considered the potential influence of comorbidities other than obesity on LOS and charges. Therefore, we examined whether other comorbidities were coded more frequently among those discharges with obesity as a secondary diagnosis than those discharges without obesity. As seen in the 2000 KID, the 2003 data revealed that diabetes was more commonly seen with obesity‐related hospitalizations than with those hospitalizations without obesity. However, the proportion of discharges with obesity and diabetes was low for all of the principal diagnostic categories studied (asthma 4.8%, pneumonia 6.0%, affective disorders 4.2%, and appendicitis 1.9%). Thus, we judged the co‐occurrence of diabetes and obesity as secondary diagnoses too infrequent to be an explanatory factor for the overall incremental differences in LOS and charges.
All analyses were weighted to account for the complex probability sampling of the dataset and permit inferences regarding national hospital discharge patterns. The same sample of discharges was used to analyze LOS and charges, with the discharge weighting variable (DISCWT) used for all analyses. All results are presented as weighted data unless otherwise noted. Analyses were conducted using STATA 8.0 (Stata Corporation, College Station, TX) and SUDAAN version 9 (Research Triangle Institute, Research Triangle Park, NC).
This study was approved by the Institutional Review Board of the University of Michigan Medical School.
RESULTS
Sample Characteristics
The characteristics of the study population are presented in Table 1. In 2003, the overall proportion of nonpregnancy‐related discharges for children 2‐18 years old coded with obesity as a secondary diagnosis was 1.6%, an increase from 1.1% in 2000. Within the 4 most common nonpregnancy‐related CCS category diagnoses (asthma, pneumonia, affective disorders, and appendicitis) the proportion of discharges with obesity coded as a secondary diagnosis increased from 2000 to 2003 (Figure 1)
| Variables | Discharges | |||
|---|---|---|---|---|
| With Obesity as Secondary Diagnosis | Without Obesity | |||
| 2000 | 2003 | 2000 | 2003 | |
| Unweighted, n | 8,696 | 15,546 | 762,407 | 943,182 |
| Weighted population size | 17,672 | 25,709 | 1,509,637 | 1,587,549 |
| Age | ||||
| 2‐5 years (%) | 4.8 | 4.8 | 27.4 | 29.0 |
| 6‐10 years (%) | 14.7 | 15.9 | 22.4 | 22.3 |
| 11‐14 years (%) | 34.4 | 34.1 | 20.9 | 20.9 |
| 15‐18 years (%) | 46.1 | 45.2 | 29.3 | 27.8 |
| Sex | ||||
| Male (%) | 45.5 | 46.8 | 53.8 | 53.4 |
| Race/Ethnicity | ||||
| White (%) | 46.8 | 32.2 | 50.9 | 39.3 |
| Black (%) | 20.7 | 20.4 | 14.3 | 12.6 |
| Hispanic (%) | 14.9 | 16.8 | 14.3 | 14.4 |
| Other (%) | 4.8 | 5.0 | 5.6 | 5.6 |
| Unreported (%) | 12.8 | 25.6 | 14.9 | 28.1 |
| Payer | ||||
| Medicaid (%) | 42.6 | 46.5 | 32.1 | 36.9 |
| Private (%) | 47.1 | 42.6 | 58.0 | 53.3 |
| Other (%) | 9.4 | 10.6 | 9.4 | 9.6 |
| Unreported (%) | 0.9 | 0.3 | 0.5 | 0.2 |
| Hospital Region | ||||
| Northeast (%) | 17.3 | 15.9 | 21.7 | 18.7 |
| Midwest (%) | 24.6 | 24.8 | 20.0 | 23.3 |
| South (%) | 37.6 | 38.4 | 36.3 | 37.0 |
| West (%) | 20.5 | 20.9 | 22.0 | 21.0 |
| Hospital type | ||||
| General hospital (%) | 68.2 | 61.4 | 61.0 | 57.1 |
| Children's unit in general hospital (%) | 15.2 | 17.8 | 18.6 | 19.2 |
| Children's hospitals (%) | 14.3 | 14.9 | 17.9 | 17.6 |
| Unreported (%) | 2.3% | 5.9 | 2.5 | 6.1 |
Incremental Differences in Mean Charges Associated with Obesity
In Table 2, we present results for analyses of mean charges. Following the pattern in 2000 for all 4 of these common conditions, in 2003 the adjusted mean total hospital charges were statistically significantly higher for discharges in which obesity was listed as a secondary diagnosis, compared with those in which it was not. Moreover, the magnitude of these differences was somewhat greater in 2003 than in 2000, although it did not achieve statistical significance (P > 0.05) (Figure 2). Specifically, the difference in charges among asthma discharges with and without obesity as a comorbidity was 9% greater in 2003 than in 2000, 17% greater among pneumonia discharges, 121% greater among affective disorders, and 3% greater among appendicitis discharges.
| Adjusted Mean Charges ($) | ||||||
|---|---|---|---|---|---|---|
| 2000 | 2003 | |||||
| With Obesity | Without Obesity | Difference | With Obesity | Without Obesity | Difference | |
| ||||||
| Asthma | 8,847 | 6,884 | 1,963* | 10,589 | 8,444 | 2,145 |
| Pneumonia | 13,930 | 11,036 | 2,894* | 16,609 | 13,219 | 3,390 |
| Affective disorders | 9,446 | 8,850 | 596 | 11,942 | 10,619 | 1,323 |
| Appendicitis | 16,101 | 12,587 | 3,514 | 19,213 | 15,586 | 3,627 |
Incremental Differences in Mean LOS Associated with Obesity
Compared with those discharges without obesity coded, obesity as a secondary diagnosis was associated with a statistically significantly longer mean LOS for all four diagnoses in 2003 (Table 3). In addition, for all diagnoses except asthma, the magnitude of the difference was somewhat greater in 2003 than in 2000, although it did not reach statistical significance (P > 0.05). The greatest increase was seen with appendicitis, with the incremental difference in LOS between those with obesity and those without going from 0.17 days in 2000 to 0.83 days in 2003; an increase of over 300%.
| Adjusted Mean LOS | ||||||
|---|---|---|---|---|---|---|
| 2000 | 2003 | |||||
| With Obesity | Without Obesity | Difference | With Obesity | Without Obesity | Difference | |
| ||||||
| Asthma | 3.04 | 2.45 | 0.59* | 2.88 | 2.44 | 0.44* |
| Pneumonia | 4.26 | 3.89 | 0.37 | 4.39 | 3.83 | 0.56 |
| Affective disorders | 7.72 | 7.11 | 0.61* | 8.23 | 7.42 | 0.81* |
| Appendicitis | 3.33 | 3.16 | 0.17 | 3.91 | 3.08 | 0.83* |
DISCUSSION
Prior studies have explored the resource utilization and expenditures associated with obesity in adult populations and among obese children in the outpatient setting.48, 21 Few, however, have examined charges related to inpatient care of obese children. Our studies are the first to utilize actual charge data from a nationally representative sample to explore the economic implications of obesity among children hospitalized for common pediatric illnesses.
Our findings from this national analysis support the hypothesis that, for the 4 conditions studied, statistically significantly higher mean total hospital charges and longer mean LOS for those discharges with obesity coded as a secondary diagnosis versus those without obesity coded occurred for both 2000 and 2003, even when controlling for sex, age, race/ethnicity, region, payer, and hospital type. Our analyses also suggested that the magnitude of the incremental differences in charges from 2000 to 2003 increased somewhat, and the magnitude of the incremental differences in LOS for all of these common conditions (except asthma) is also increasing.
While these findings serve to confirm higher incremental charges and LOS associated with obesity for hospitalized children, they raise the question of why charges and LOS for children with obesity might increase at a greater rate than for those without obesity. Higher hospital charges and LOS for children with obesity coded as a secondary diagnosis may be explained by greater resource utilization due to obesity that: 1) increases the technical complexity of procedures such as surgical interventions or intravenous catheter (IV) placement;22 2) leads to greater illness severity, as has been suggested in studies of adult patients;23, 24 or 3) leads to more complications such as secondary infections.22 One explanation for the possible widening of the gap in charges during the time period studied might be that discharges coded with obesity in 2003 reflect children who were more severely obese and had a greater severity of illness, leading to higher resource utilization than those with obesity in the 2000 dataset. However, the ICD‐9‐CM code for morbid obesity (278.02) was not used more often in 2003 than in 2000. Alternatively, we suspect that between 2000 and 2003, due to an increasing awareness of the problem of childhood obesity, physicians may have become more likely to order tests or consultations specifically related to the treatment or evaluation of obesity. Other than the increase in the proportion of discharges with obesity as a comorbidity from 2000 to 2003, we were unable to explore these possible explanations with the KID datasets.
In this sample of discharges, with only 1.1% and 1.6% coded with obesity as a secondary diagnosis in 2000 and 2003, respectively, it is important to note that these discharges should not be interpreted as the prevalence of obesity in hospitalized children. Indeed, in a recent study of children hospitalized for surgical procedures at a large Midwestern tertiary care hospital, 31.6% were found to be overweight or obese.25 However, we posit that the cases coded with obesity as a secondary diagnosis in this sample represent the cases in which obesity presents a recognized factor that complicated the clinical course. Further work should explore the mechanisms by which obesity impacts the care of children hospitalized for common conditions. For example, children with obesity may require more procedures and may experience more treatment complications. These specific interventions should be the target of clinically focused analyses.
Limitations
Analyses utilizing discharge data are potentially limited by the accuracy and consistency of coding. Whether the discharges coded with obesity reflect all cases in which obesity was a complicating factor is not known. Based on the national prevalence of childhood obesity it is likely than more than 1.6% of the children hospitalized in 2003 were obese. However, whether obesity impacted the hospital course sufficiently to be included among the secondary diagnoses (as stipulated by ICD‐9‐CM guidelines for official recording)26 in more than 1.6% of cases is unknown.
This study is also limited by the inability to address the processes that might account for the consistently higher charges and longer LOS seen for those discharges coded with obesity versus those without obesity coded. The KID provides some information regarding procedures performed but cannot be reliably used to examine this aspect of patient care.27 An additional limitation of the KID data set is that it contains information about deidentified discharges. This leads to the possibility of having individual patients in the dataset with multiple hospitalizations. In this study we examined the relationship between obesity as a secondary diagnosis and incremental charges and LOS for the 4 most common clinical categories for which children are hospitalized. Findings may differ for other conditions not evaluated here. Finally, information regarding costs can only be inferred from the charge data provided by the KID. However, the ratio of charges to costs would not be expected to vary by obesity status.
CONCLUSIONS
These results extend our earlier findings of higher charges and longer LOS for pediatric discharges coded with obesity versus those without. In addition, this analysis suggests a widening gap of incremental hospital charges and LOS associated with obesity as a comorbidity for common pediatric conditions. These findings present a heightening financial imperative for further research to evaluate factors associated with greater resource utilization among obese pediatric patients.
- ,,,.Prevalence and trends in overweight among US children and adolescents, 1999–2000.JAMA.2002;288(14):1728–1732.
- ,.Epidemic increase in childhood overweight, 1986–1998.JAMA.2001;286(22):2845–2848.
- ,.Overweight children and adolescents: description, epidemiology, and demographics.Pediatrics.1998;101(Pt 2):497–504.
- ,,.National medical spending attributable to overweight and obesity: how much, and who's paying?Health Aff (Millwood).2003; (Suppl Web Exclusives):W3‐219–26.
- ,,,.The impact of obesity on rising medical spending.Health Aff (Millwood).2004;(Suppl Web Exclusives):W4‐480–6.
- ,.Current estimates of the economic cost of obesity in the United States.Obes Res.1998;6(2):97–106.
- ,,,,.Lifetime health and economic benefits of weight loss among obese persons.Am J Public Health.1999;89(10):1536–1542.
- ,,.The health and cost consequences of obesity among the future elderly.Health Aff (Millwood).2005;24(Suppl 2):W5R30–W5R41.
- ,.Economic burden of obesity in youths aged 6 to 17 years: 1979–1999.Pediatrics.2002;109(5):E81–E81.
- ,,,.Incremental hospital charges associated with obesity as a secondary diagnosis.Obesity (Silver Spring).2007;15:1895–1901.
- ,,,,,.Prevalence of overweight and obesity in the United States, 1999–2004.JAMA.2006;295(13):1549–1555.
- Healthcare Cost and Utilization Project.2005. Overview of the Kid's Inpatient Database. Available at:http://www.hcup‐us.ahrq.gov/kidoverview.jsp. Accessed December 2008.
- CDC Body Mass Index: BMI for Children and Teens. Available at: http://www.cdc.gov/nccdphp/dnpa/bmi. Accessed December2008.
- ,,,,,.Prevalence of overweight among preschool children in the United States, 1971 through 1994.Pediatrics.1997;99(4):E1.
- Healthcare Cost and Utilization Project, 2002 and 2004. Description of data elements: inpatient core file. Available at: http://www.hcup‐us.ahrq.gov/db/nation/kid/DataElements_KID_Core_2000.pdf; http://www.hcup‐us.ahrq.gov/db/nation/kid/KID_2003_CORE_Volume1_A‐L.pdf;http://www.hcup‐us. ahrq.gov/db/nation/kid/KID_2003_CORE_Volume2_M‐Z.pdf. Accessed December2008.
- .Health consequences of obesity in youth: childhood predictors of adult disease.Pediatrics.1998;101:518–525.
- ,,.Lengths of stay and costs associated with children's hospitals.Pediatrics.2005;115(4):839–844.
- ,,, et al.Health care expenditures associated with overweight and obesity among US adults: importance of age and race.Am J Public Health.2005;95(1):159–165.
- ,,,.Effects of race, insurance status, and hospital volume on perforated appendicitis in children.Pediatrics.2005;115(4):920–925.
- .Smearing estimate: a nonparametric retransformation method.J Am Stat Assoc.1983;78:605–610.
- ,,,.Resource utilization and expenditures for overweight and obese children.Arch Pediatr Adolesc Med.2007 Jan;161(1):11–14.
- ,.Appendicitis in the obese child.J Pediatr Surg.2007;42(5):857–861.
- ,.Management of the obese critically ill patient.Crit Care Clin.2001;17:187–200.
- ,,,,.Total respiratory system, lung, and chest wall mechanics in sedated‐paralyzed postoperative morbidly obese patients.Chest.1996;109:144–151.
- ,,,,.Prevalence of overweight and obesity in a U.S. pediatric surgical population.J Natl Med Assoc.2007;99(1):46–48, 50–51.
- Centers for Disease Control and Prevention. ICD‐9‐CM. Official Guidelines for Coding and Reporting. Effective April 1, 2005. Available at: http://www.cdc.gov/nchs/data/icd9/icdguide.pdf. Accessed December2008.
- ,,,.Predictors of hospital charges for children admitted with asthma.Ambul Pediatr.2006;6(1):15–20.
- ,,,.Prevalence and trends in overweight among US children and adolescents, 1999–2000.JAMA.2002;288(14):1728–1732.
- ,.Epidemic increase in childhood overweight, 1986–1998.JAMA.2001;286(22):2845–2848.
- ,.Overweight children and adolescents: description, epidemiology, and demographics.Pediatrics.1998;101(Pt 2):497–504.
- ,,.National medical spending attributable to overweight and obesity: how much, and who's paying?Health Aff (Millwood).2003; (Suppl Web Exclusives):W3‐219–26.
- ,,,.The impact of obesity on rising medical spending.Health Aff (Millwood).2004;(Suppl Web Exclusives):W4‐480–6.
- ,.Current estimates of the economic cost of obesity in the United States.Obes Res.1998;6(2):97–106.
- ,,,,.Lifetime health and economic benefits of weight loss among obese persons.Am J Public Health.1999;89(10):1536–1542.
- ,,.The health and cost consequences of obesity among the future elderly.Health Aff (Millwood).2005;24(Suppl 2):W5R30–W5R41.
- ,.Economic burden of obesity in youths aged 6 to 17 years: 1979–1999.Pediatrics.2002;109(5):E81–E81.
- ,,,.Incremental hospital charges associated with obesity as a secondary diagnosis.Obesity (Silver Spring).2007;15:1895–1901.
- ,,,,,.Prevalence of overweight and obesity in the United States, 1999–2004.JAMA.2006;295(13):1549–1555.
- Healthcare Cost and Utilization Project.2005. Overview of the Kid's Inpatient Database. Available at:http://www.hcup‐us.ahrq.gov/kidoverview.jsp. Accessed December 2008.
- CDC Body Mass Index: BMI for Children and Teens. Available at: http://www.cdc.gov/nccdphp/dnpa/bmi. Accessed December2008.
- ,,,,,.Prevalence of overweight among preschool children in the United States, 1971 through 1994.Pediatrics.1997;99(4):E1.
- Healthcare Cost and Utilization Project, 2002 and 2004. Description of data elements: inpatient core file. Available at: http://www.hcup‐us.ahrq.gov/db/nation/kid/DataElements_KID_Core_2000.pdf; http://www.hcup‐us.ahrq.gov/db/nation/kid/KID_2003_CORE_Volume1_A‐L.pdf;http://www.hcup‐us. ahrq.gov/db/nation/kid/KID_2003_CORE_Volume2_M‐Z.pdf. Accessed December2008.
- .Health consequences of obesity in youth: childhood predictors of adult disease.Pediatrics.1998;101:518–525.
- ,,.Lengths of stay and costs associated with children's hospitals.Pediatrics.2005;115(4):839–844.
- ,,, et al.Health care expenditures associated with overweight and obesity among US adults: importance of age and race.Am J Public Health.2005;95(1):159–165.
- ,,,.Effects of race, insurance status, and hospital volume on perforated appendicitis in children.Pediatrics.2005;115(4):920–925.
- .Smearing estimate: a nonparametric retransformation method.J Am Stat Assoc.1983;78:605–610.
- ,,,.Resource utilization and expenditures for overweight and obese children.Arch Pediatr Adolesc Med.2007 Jan;161(1):11–14.
- ,.Appendicitis in the obese child.J Pediatr Surg.2007;42(5):857–861.
- ,.Management of the obese critically ill patient.Crit Care Clin.2001;17:187–200.
- ,,,,.Total respiratory system, lung, and chest wall mechanics in sedated‐paralyzed postoperative morbidly obese patients.Chest.1996;109:144–151.
- ,,,,.Prevalence of overweight and obesity in a U.S. pediatric surgical population.J Natl Med Assoc.2007;99(1):46–48, 50–51.
- Centers for Disease Control and Prevention. ICD‐9‐CM. Official Guidelines for Coding and Reporting. Effective April 1, 2005. Available at: http://www.cdc.gov/nchs/data/icd9/icdguide.pdf. Accessed December2008.
- ,,,.Predictors of hospital charges for children admitted with asthma.Ambul Pediatr.2006;6(1):15–20.
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