Affiliations
Center for Outcomes Research, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
Email
lorch@email.chop.edu
Given name(s)
Scott A.
Family name
Lorch
Degrees
MD, MSCE

Insurance and LOS for Children With CAP

Article Type
Changed
Mon, 05/22/2017 - 18:53
Display Headline
Health insurance and length of stay for children hospitalized with community‐acquired pneumonia

Disparities in patterns of care and outcomes for ambulatory‐care sensitive conditions remain a persistent problem for children.19 Many studies have focused on disparities in hospitalization rates and length of stay (LOS) related to asthma, however, few studies have focused on community‐acquired pneumonia (CAP) despite the fact that pneumonia is the most common, preventable, and potentially serious infection in childhood.10 Providers, payers, and families have a common interest in minimizing hospital LOS for different reasons (eg, minimizing costs, lost wages, exposure to antibiotic‐resistant bacteria), however, this interest is balanced against the potentially greater risk of readmission and adverse outcomes if LOS is inappropriately short. To date, the relationship between insurance status and LOS for CAP remains unexplored.

As in other conditions, substantial variation exists with respect to patterns of care and outcomes for children hospitalized with CAP.11 For example, children hospitalized in rural settings have a shorter LOS for pneumonia than those hospitalized in large urban settings.12 Children from racial/ethnic minorities tend to have higher rates of CAP‐associated complications, including death.11 Decades of prior studies have documented that uninsured children are less likely than insured children to make preventive care visits and obtain prescription medications, but differences in LOS or hospitalization rates between insured and uninsured children with CAP have not been studied.6, 8, 13, 14 Though imperfect, insurance status is 1 proxy for healthcare access, and current healthcare reform efforts aim to improve healthcare access and decrease socioeconomic gradients in health by increasing the number of insured American children. Nonetheless, quantifying the relationship between insurance status on LOS for children hospitalized with CAP is a first step towards understanding the influence of ambulatory care access on hospitalization for ambulatory‐care sensitive conditions.

The purpose of this study was to investigate the influence of insurance status and type on LOS for children hospitalized with CAP. In addition, we sought to determine if there were consistent trends over time in the association between insurance status and type with LOS for children hospitalized with CAP.

METHODS

Study Design and Data Source

This retrospective cross‐sectional study used data from the 1997, 2000, 2003, and 2006 Kids' Inpatient Database (KID). The KID is part of the Healthcare Cost and Utilization Project sponsored by the Agency for Healthcare Research and Quality (AHRQ). It is the only dataset on hospital use and outcomes specifically designed to study children's use of hospital services in the United States. The KID samples pediatric discharges from all community non‐rehabilitation hospitals in states participating in the Healthcare Cost and Utilization Project, using a complex stratification system, across pediatric discharge type and hospital characteristics. Community hospitals in the KID are defined as all non‐federal, short‐term, general and other specialty hospitals, including academic medical centers, obstetrics‐gynecology, otolaryngology, orthopedic, and children's hospitals. Federal hospitals, long‐term hospitals, psychiatric hospitals, alcohol/chemical dependency treatment facilities and hospitals units within institutions are excluded. Discharge‐level weights assigned to discharges within the stratum permit calculation of national estimates. Datasets, which each contain approximately 3 million discharges (unweighted), are released every 3 years beginning with 1997. The 2006 KID is the most recently available dataset and contains hospital administrative data from 38 states, representing 88.8% of the estimated US population.15 This study was considered exempt from review by the Committees for the Protection of Human Subjects at The Children's Hospital of Philadelphia.

Study Participants

Patients 18 years of age and younger were eligible for inclusion if they required hospitalization for CAP in 1997, 2000, 2003, or 2006. Using a previously validated algorithm, patients were considered as having CAP if they met 1 of 2 criteria: 1) International Classification of Diseases, 9th Revision, Clinical Modification (ICD‐9 CM) primary diagnosis code indicating pneumonia (480‐483, 485‐486), empyema (510), or pleurisy (511.0‐1, 511.9); or 2) primary diagnosis of pneumonia‐related symptom (eg, cough, fever, tachypnea) and secondary diagnosis of pneumonia, empyema or pleurisy. Pneumonia‐related symptoms included fever, respiratory abnormality unspecified, shortness of breath, tachypnea, wheezing, cough, hemoptysis, abnormal sputum, chest pain, and abnormal chest sounds.16 Because there is no specific ICD‐9 code for nosocomial pneumonia, this previously validated approach minimized such misclassification16 (eg, a child hospitalized following traumatic injury who then develops ventilator‐associated pneumonia is likely to have trauma, rather than pneumonia or a pneumonia‐related symptom, listed as the primary diagnosis). Patients with the following comorbid conditions (identified by KID data elements and ICD‐9 CM codes) were excluded as these comorbidities are characterized by risk factors not reflective of the general pediatric population: acquired and congenital immunologic disorders, malignancy, collagen vascular disease, sickle cell disease, cystic fibrosis, organ transplant, congenital heart defects, and heart failure. Patients identified as in‐hospital births were excluded to minimize the inclusion of perinatally acquired and nosocomial infections occurring in neonates. Patients with a secondary diagnosis code indicating trauma were also excluded, as a diagnosis of pneumonia in this population likely reflects nosocomial etiology. CAP‐related complications (eg, effusion, abscess; for complete list, see Supporting Appendix A in the online version of this article) were identified using ICD‐9 CM diagnosis and procedure codes. Asthma‐related hospitalizations were identified using ICD‐9 CM diagnosis code 493 in any secondary diagnosis field.

Primary Exposure

The primary exposure was insurance type, categorized as private, public, uninsured, or other (eg, Civilian Health and Medical Program Uniform Service (CHAMPUS), worker's compensation, union‐based insurance, but definition varies by state precluding categorization as purely public or private).

Primary Outcome

The primary outcome was the hospital LOS calculated in days.

Statistical Analysis

Consistent with prior work,12 subjects were characterized by age, race, sex, the presence or absence of a pneumonia‐associated complication, discharge status (discharge from hospital vs in‐hospital death), hospital type (rural, urban non‐teaching, urban teaching non‐children's, urban teaching children's), and hospital region (Northeast, Midwest, South, West). Age groups for analysis were defined as <1 year (infant), 1 to 5 years (preschool age), 6 to 11 years (school‐age), and 12 to 18 years old (adolescent). Race was recorded as a single variable (white, black, other, and missing). Patient information for race was missing from 32% of discharges in 1997, 18% in 2000, 29% in 2003, and 26% in 2006. Patients with missing race data were included to preserve the integrity of our estimates. Categorical variables were summarized by frequencies and percents. Continuous variables were summarized by mean and standard deviation values.

All analyses accounted for the complex sampling design with the survey commands included in STATA, version 10 (College Station, TX) to produce weighted estimates. To determine the adjusted impact of patient and hospital‐level characteristics in our cohort, we constructed multivariable negative binomial regression models using all available covariates for LOS because of its rightward‐skewed distribution. The negative binomial model produced an incident rate ratio (IRR) for LOS (IRR >1 indicates that the risk factor is associated with a longer length of stay). As recommended in the AHRQ technical documentation, variance estimates for each model accounted for the clustering of data at the hospital level. To address the impact of missing race data on outcome, we constructed additional multivariable negative binomial regression models while varying the underlying assumptions about race classification. In these secondary analyses, children with race coded as missing were sequentially excluded, assumed to be white, and assumed to be black. These analyses were repeated after excluding insurance from the multivariable model.

RESULTS

The more than 10.5 million children sampled (unweighted) in KID during these 4 time periods (1997, 2000, 2003, and 2006) are representative of the more than 28.9 million children hospitalized in the United States. In each of these sample years, there were approximately 150,000 children hospitalized with pneumonia across the United States (Table 1). Of those hospitalized, 23% to 28% had a concomitant diagnosis of asthma; 6% to 8% had a pneumonia‐associated complication; and mortality was <0.01% in each sample year for patients hospitalized with pneumonia. In all years, among those with racial/ethnic data, the sample population was predominantly white boys less than 6 years old. The greatest proportion of children were hospitalized in urban non‐teaching settings, and also those children living in the southern regions of the United States.

Characteristics of Children Hospitalized With Pneumonia in the United States
 1997200020032006
 N = 148,702N = 157,847N = 157,743N = 156,810
  • NOTE: Values, which represent national estimates, are listed as number (percent). Numbers across rows may not sum exactly because weighted estimates from these data are obtained using survey commands as per KIDS technical guidance.15

  • KID categorizes states into the following 4 regions: Northeast (Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont); Midwest (Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, Wisconsin); South (Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia); West (Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, Wyoming).

Race    
White56,348 (38)68,643 (44)54,903 (35)56,108 (36)
Black22,864 (15)22,580 (14)17,960 (11)18,800 (12)
Other22,203 (15)38,448 (24)39,138 (25)40,803 (26)
Missing47,287 (32)28,175 (18)45,588 (29)41,099 (26)
Age category    
<1 year43,851 (29)44,470 (28)37,798 (24)37,705 (24)
1 through 5 years75,033 (50)76,385 (48)77,530 (49)79,519 (51)
6 through 11 years19,372 (13)21,403 (14)23,126 (15)23,494 (15)
>12 years10,446 (7)15,589 (9)19,289 (12)16,092 (10)
Hospital type    
Urban non‐teaching52,756 (35)50,718 (32)52,552 (34)50,718 (32)
Rural47,910 (32)41,715 (27)39,605 (26)31,947 (21)
Urban teaching non‐children's20,378 (14)30,981 (20)28,432 (18)30,194 (20)
Urban teaching children's27,658 (19)34,021 (22)34,454 (22)41,035 (27)
Male sex83,291 (56)8,783 (56)86,034 (55)85,508 (55)
Region*    
Northeast19,750 (13)26,092 (17)23,867 (15)23,832 (15)
Midwest33,053 (22)30,706 (19)35,714 (23)35,900 (23)
South68,958 (46)68,663 (44)65,994 (42)65,460 (42)
West26,741 (18)32,385 (21)32,169 (20)31,618 (20)
Asthma26,971 (24)31,746 (28)27,729 (24)26,822 (23)
Pneumonia‐associated complication8,831 (6)11,084 (7)12,005 (8)11,724 (7)
Died334 (0.002)394 (0.002)270 (0.002)193 (0.001)
Insurance    
Private65,428 (44)73,528 (47)68,720 (44)63,997 (41)
Public68,024 (46)71,698 (45)76,779 (49)80,226 (51)
Uninsured9,922 (7)8,336 (5)6,381 (4)6,912 (4)
Other4,964 (3)4,285 (3)5,391 (3)5,283 (3)

There was little variation in the insurance status of children hospitalized with CAP between 1997 and 2006. In each of the sampled years, at least 40% of sampled children were privately insured, at least 40% were publicly insured, and approximately 5% were uninsured (Table 1). In all years, there were significant racial/ethnic disparities in insurance coverage such that whites were 4 to 6 times more likely to have private insurance than blacks, however, the large amount of missing race/ethnicity data warrant caution in interpreting this finding (Table 2; also see Supporting Information Appendix B in the online version of this article). We also found that children less than 1 year old were the most likely to be publicly insured in all years (see Supporting Appendix C in the online version of this article). There were also regional differences related to insurance coverage such that a greater proportion of children hospitalized in facilities located in the southern part of the United States were publicly insured. Notably, there were no significant differences in CAP‐associated mortality or asthma related to insurance coverage (Table 2). In 2006, CAP‐associated complications occurred in 8.5% of children with private insurance, 6.5% of children with public insurance, and 7.7% of uninsured children; the relative distribution of complications by insurance type were similar in previous years of the KID survey.

Demographic Characteristics of Children Hospitalized With Pneumonia in 2006, Stratified by Insurance Category
 PrivatePublicUninsuredOther InsuranceP
  • NOTE: Chi‐square test used to compare differences. Numbers across rows may not sum exactly because weighted estimates from these data are obtained using survey commands as per KIDS technical guidance.15 For data from other years (1997, 2000, 2003), see Supporting Appendix C in the online version of this article.

  • P < 0.001 compared with white race.

  • P < 0.001 compared with urban non‐teaching hospitals.

  • P = 0.384 compared with urban non‐teaching hospitals.

  • P = 0.004 compared with urban non‐teaching hospitals.

  • P < 0.001 compared with Northeast region.

No. of children (%)63,997 (41)80,226 (51)6,912 (4)5,283 (3) 
Male sex34,639 (41)44,140 (52)3,727 (4)2,808 (3)0.092
Race     
White30,707 (55)21,282 (38)2,241 (4)1,774 (3)<0.001
Black*5,112 (27)12,239 (65)988 (5)426 (3) 
Other11,033 (27)26,489 (65)2,112 (5)1,076 (3) 
Missing17,145 (42)20,216 (49)1,572 (4)2,007 (4) 
Age category     
<1 year10,788 (29)24,762 (65)1,164 (3)880 (3)<0.001
1 through 5 years33,664 (42)39,531 (50)3,442 (4)2,673 (3) 
6 through 11 years11,660 (50)9,684 (41)1,085 (5)1,015 (4) 
>12 years7,885 (49)6,249 (39)1,221 (8)714 (4) 
Hospital type     
Urban non‐teaching22,429 (44)24,241 (49)2,440 (5)1,555 (2)<0.001
Rural10,880 (34)18,396 (58)1,290 (4)1,109 (3) 
Urban teaching non‐children's13,130 (44)14,542 (48)1,721 (6)750 (2) 
Urban teaching children's16,591 (40)21,544 (53)1,417 (3)1,465 (4) 
Region     
Northeast12,364 (52)9,620 (40)1,466 (6)377 (2)<0.001
Midwest17,891 (50)15,573 (43)1,160 (3)1,215 (3) 
South21,479 (33)38,112 (58)3,108 (5)2,495 (4) 
West12,263 (39)16,921 (44)1,178 (5)1,195 (5) 
Asthma10,829 (41)13,923 (52)1,119 (4)866 (3)0.193
Pneumonia‐associated complication5,416 (46)5,206 (45)532 (4)556 (5)<0.001
Died66 (34)115 (60)3 (1)8 (5)0.131

After examining the general and demographic characteristics, we then examined mean LOS for all children with CAP in each sample year (Table 3). The mean LOS for children with CAP was 3.44 days in 1997, with marginal decreases in subsequent years to a mean LOS of 3.18 days in 2006. The distribution of LOS for children with CAP revealed that nearly 70% of children were hospitalized for fewer than 3 days, another 22% to 28% were hospitalized for less than 1 week, and only 3% were hospitalized for more than 1 week. This distribution did not change substantially between 1997 and 2006. Next, we compared mean LOS by insurance type and race/ethnicity in unadjusted analyses. In each sample year, publicly insured children hospitalized with CAP had significantly longer LOS than privately insured children (P < 0.001). Similarly, in all years excepting 1997, uninsured children hospitalized with CAP had significantly shorter LOS than privately insured children. There were also significant racial differences in LOS for children with CAP, such that black children had longer LOS than white children with CAP. However, the large amount of missing data for race/ethnicity limited the robustness of this finding, and subsequent sensitivity analyses demonstrated that there were no consistent racial/ethnic disparities in LOS (see Supporting Appendix B in the online version of this article). These sensitivity analyses for missing race data did not alter our primary finding of shorter LOS for uninsured versus publicly or privately insured children.

Unadjusted Length of Stay Overall and Stratified by Insurance Type and Race Category
 1997P2000P2003P2006P
  • NOTE: Values listed as mean length of stay (standard error). Wald test used to compare differences in mean length of stay with designated reference group.

Overall3.44 (0.04) 3.35 (0.05) 3.27 (0.05) 3.18 (0.04) 
Insurance type        
Private3.21 (0.04) 3.19 (0.04) 3.09 (0.04) 3.00 (0.03) 
Public3.71 (0.06)<0.0013.57 (0.06)<0.0013.44 (0.06)<0.0013.34 (0.05)<0.001
Uninsured3.18 (0.14)0.7922.92 (0.07)<0.0012.80 (0.05)<0.0012.82 (0.05)<0.001
Other3.32 (0.11)0.3193.55 (0.14)0.01343.54 (0.21)0.0373.42 (0.13)0.001
Race        
White3.31 (0.05) 3.18 (0.04) 3.19 (0.05) 3.10 (0.04) 
Black3.61 (0.08)<0.0013.32 (0.07)<0.0013.36 (0.08)<0.0013.31 (0.07)<0.001
Other3.96 (0.11)<0.0013.81 (0.09)<0.0013.67 (0.10)<0.0013.56 (0.08)<0.001
Missing3.27 (0.08)0.6453.18 (0.08)0.9262.99 (0.06)0.01342.86 (0.04)<0.001

After controlling for child age, race/ethnicity, gender, hospital type, transfer status, and presence of asthma or pneumonia‐associated complications, our multivariable analyses examining the relationship between insurance coverage and hospital LOS yielded the following results (Table 4). First, publicly insured children had significantly longer hospital stays than privately insured children, and uninsured children had significantly shorter hospital stays than privately insured children in all years except 1997. Second, children admitted with CAP at urban teaching children's hospitals had significantly longer LOS than those admitted to urban non‐teaching hospitals, and, in 2003, children admitted with CAP to rural hospitals had significantly shorter LOS than those admitted to urban non‐teaching hospitals. Third, children older than 1 year consistently had shorter hospital stays than infants less than 1 year old. Finally, though concomitant diagnosis of asthma did not consistently influence LOS, children who developed any complications had significantly longer LOS than those who did not. The cumulative impact of seemingly small differences in LOS is great. For example, in 2006, our model suggests that, for every 1000 children hospitalized with CAP in a given year, after adjusting for differences in sex, age, race, hospital‐type, region, transfer status, and diagnosis of asthma or complications, publicly insured children spend 90 to 130 more days in the hospital than privately insured children, whereas uninsured children spend between 40 to 90 fewer days in the hospital than privately insured children.

Multivariable Negative Binomial Regression Model of Factors Associated With Length of Stay
 1997200020032006
VariableIRR (95% CI)IRR (95% CI)IRR (95% CI)IRR (95% CI)
  • NOTE: All available variables included in multivariable models. KID categorizes states into the following 4 regions: Northeast (Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont); Midwest (Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, Wisconsin); South (Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia); West (Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, Wyoming).

  • Abbreviations: CI, confidence interval; IRR, incidence rate ratio.

  • Significant values are noted as follows; all other values are not significant:

  • P < 0.05;

  • P < 0.01;

  • P < 0.001.

Age category    
<1 year    
15 years0.82 (0.81, 0.84)0.83 (0.88, 0.95)0.86 (0.85, 0.88)0.87 (0.86, 0.89)
611 years0.91 (0.87, 0.95)0.91 (0.88, 0.94)0.93 (0.91, 0.95)0.93 (0.90, 0.95)
>12 years1.03 (0.99, 1.07)1.17 (1.11, 1.22)1.09 (1.06, 1.13)1.13 (1.09, 1.16)
Race    
White    
Black1.04 (0.99, 1.08)1.00 (0.95, 1.03)1.00 (0.98, 1.03)1.02 (0.98, 1.06)
Other1.09 (1.05, 1.13)1.11 (1.08, 1.15)1.09 (1.06, 1.12)1.08 (1.05, 1.11)
Missing1.00 (0.94, 1.06)1.01 (0.96, 1.06)0.95 (0.92, 0.99)*0.96 (0.93, 0.99)
Sex    
Female1.02 (0.94, 1.06)1.01 (0.99, 1.02)1.01(0.93, 100)1.01 (1.00, 1.02)
Insurance type    
Private    
Public1.13 (1.11, 1.16)1.11 (1.09, 1.14)1.11 (1.09, 1.13)1.11 (1.09, 1.13)
Uninsured1.01 (0.91, 1.11)0.93 (0.89, 0.96)0.92 (0.90, 0.96)0.94 (0.91, 0.96)
Other1.01 (0.96, 1.06)1.10 (1.03, 1.18)1.10 (1.02, 1.19)*1.07 (1.02, 1.13)
Hospital type    
Urban non‐teaching    
Rural0.98 (0.92, 1.04)0.96 (0.92, 1.00)0.97 (0.94, 1.00)0.97 (0.93, 1.00)
Urban teaching (non‐children's)0.99 (0.95, 1.04)1.06 (1.02, 1.10)1.06 (1.02, 1.10)1.03 (0.99, 1.07)
Urban teaching children's1.2 (1.14, 1.26)1.23 (1.16, 1.30)1.28 (1.21, 1.37)1.25 (1.19, 1.31)
Region    
Northeast    
Midwest0.93 (0.88, 0.98)*0.96 (0.92, 1.00)0.95 (0.91, 0.99)*0.95 (0.91, 0.99)*
South0.98 (0.94, 1.02)1.06 (1.02, 1.10)*1.04 (1.00, 1.09)1.03 (0.98, 1.08)
West0.97 (0.92, 1.01)1.22 (1.16, 1.30)*1.02 (0.97, 1.08)1.06 (1.00, 1.12)*
Transfer status    
Transfer1.35 (1.25, 1.46)1.39 (1.27, 1.52)1.31 (1.23, 1.37 )1.16 (1.10, 1.23)
Asthma0.99 (0.96, 1.03)0.97 (0.95, 0.99)0.98 (0.96, 1.00)0.98 (0.97, 1.00)*
Pneumonia Complications0.99 (0.96, 1.03)0.97 (0.95, 0.99)*0.98 (0.96, 1.0)0.98 (0.97, 1.00)*
Any complication2.20 (2.07, 2.34)2.23 (2.07, 2.40)2.22 (2.22, 2.44)2.37 (2.27, 2.47)

DISCUSSION

In this nationally representative sample selected over the past 10 years, we found that publicly insured children hospitalized with CAP have significantly longer LOS than those who are privately insured, and that, since 2000, uninsured children hospitalized with CAP have significantly shorter LOS than those who are privately insured. Though these observed differences are small, they are consistent across all 4 sampled years and, because CAP is one of the most common pediatric inpatient diagnoses, the cumulative impact of the observed differences on hospital LOS is great. Insurance status is often considered a proxy for access to preventive and ambulatory healthcare services or socioeconomic status. However, the underlying mechanisms relating insurance status to healthcare access, utilization, and ultimately, health outcomes are highly complex and difficult to elucidate.17 The observed variation in this study raises questions about the potential influence of insurance status on hospital discharge practices. Additional research is necessary to understand whether there are differences in processes of care (eg, performance of blood cultures or chest radiographs), quality of care, or other outcomes, such as readmissions, related to CAP inpatient management for children with different insurance coverage.

Apart from differences in hospital discharge practices, another possible explanation for uninsured children with CAP having shorter LOS is that these children have less severe disease than privately insured. This may occur if uninsured children with CAP are evaluated in the emergency department rather than the office setting, because emergency department providers may be more likely to admit children with CAP who lack a consistent access to ambulatory primary care services. Countering this alternative, prior studies have shown that uninsured groups are more likely to have greater disease severity than privately insured groups at the time of hospital admission.18, 19 In this study, we attempted to identify children with greater severity of disease using ICD‐9 codes for CAP‐associated complications. Though this is a relatively crude method that might lead to an underestimate of the total number of children with complications, we found that there were no significant differences in the prevalence of CAP‐associated complications between uninsured and insured groups in all sampled years.

On the other hand, uninsured patients may be released earlier by providers in order to reduce the amount of uncompensated care provided, or possibly because parents may urge providers to discharge their children, given their inability to pay forthcoming hospital bills and/or avoid further lost wages due to work absence.20, 21 In California, Bindman et al. demonstrated that decreasing the frequency of Medicaid recertification, and consequently increasing the likelihood of continuous insurance coverage, was associated with a decreased risk of hospitalization for ambulatory‐care sensitive conditions.5

We also found that children admitted to urban teaching children's hospitals with CAP had significantly longer LOS than those admitted to urban non‐teaching hospitals, whereas children in rural hospitals had significantly shorter LOS than those in urban non‐teaching hospitals in 2003. These findings are consistent with prior data from 1996 to1998 demonstrating that children admitted to rural hospitals in New York and Pennsylvania had significantly shorter LOS than large urban hospitals for 19 medical and 9 surgical conditions, including pneumonia.12 These findings may reflect underlying differences in between rural and urban hospital transfer practices, whereby rural hospitals may be more likely than urban hospitals to transfer children with relatively more severe illness to urban referral centers and retain children with less severe illness, leading to shorter LOS.12 Though our empiric understanding of differences in LOS between teaching and non‐teaching hospitals is currently limited, clinical experience supports the notion that there may be decreases in efficiency that occur in teaching hospitals, and are a result of the supervision required for care provided by trainees. It is also possible that, despite our exclusion of comorbid conditions, some children with complex or chronic medical conditions were included in this study. These children are often cared for at teaching hospitals, regardless of the primary cause for admission, and are more likely to have public insurance than other children, thus confounding the relationship between hospital type, insurance type and status, and LOS for children with CAP. The limitations of this dataset preclude further examination of this issue.

There are some limitations to this study. First, the KID data are cross‐sectional and causal inferences are limited. However, our results demonstrating that uninsured children hospitalized with CAP had shorter LOS than privately insured children were quite consistent in each sample year, suggesting that our results are a true association. Additionally, insurance status in KID is typically collected at admission, however, it is not possible to determine whether specific changes to insurance status that occurred during the hospitalization were applied to the data. The impact of this limitation would depend on the type of insurance obtained by the patient. If uninsured patients obtained public insurance, our study would underestimate the increased LOS for publicly insured patients, compared with privately insured patients, but have no effect on the difference in LOS between uninsured and privately insured patients. In the unlikely event that uninsured patients obtained private insurance, then our study would underestimate the difference for uninsured patients, compared with privately insured patients, biasing our current study results towards the null. Second, a substantial proportion of sampled children had missing data for race/ethnicity. To assess the impact of the missing race/ethnicity data on our results, we conducted sensitivity analyses and found that, though difficult to make any definitive conclusions about the relationship between race/ethnicity and LOS for children with CAP, there were no changes to our primary findings regarding differences in LOS between children with different insurance status and type. Third, KID does not include data about other unmeasured confounders (eg, parent income, parent education, regular source of care) that might be related to LOS, as well as a broad spectrum of pediatric outcomes. Serious consideration of expanding KID to include these variables is warranted. Fourth, the other category of insurance is not uniformly coded across states in the KID database. While some states use this category to classify public insurance options other than Medicare and Medicaid, other states include private insurance options in this group. Thus, it is possible that some patients with public insurance are misclassified as having other insurance. We would expect such misclassification to bias our findings towards the null hypothesis. Finally, we focused on the relationship between child health insurance status and CAP, only 1 ambulatory care‐sensitive condition. Additional research examining the relationship between insurance type and other ambulatory care‐sensitive conditions is warranted.

In summary, we found that, after multivariable adjustment, uninsured children hospitalized with community‐acquired pneumonia had significantly shorter LOS than privately insured children, and publicly insured children had a significantly longer hospital stay than privately insured children in these 4 nationally representative samples from 1997 to 2006. Current federal and state efforts to increase enrollment of children into insurance programs are a first step in reducing healthcare disparities. However, insurance coverage alone does not guarantee access to healthcare, thus, these efforts in isolation will likely be insufficient to achieve optimal health for the children of our country. As healthcare reform legislation is implemented, these findings provide hospitals and policy makers additional impetus to develop ways to achieve the ideal length of stay for every child; this ideal state will be achieved when clinical status and course, rather than nonclinical factors such as insurance type or provider's unease with ambulatory follow‐up, determine the duration of hospitalization for every child.

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  7. Chevarley FM,Owens PL,Zodet MW,Simpson LA,McCormick MC,Dougherty D.Health care for children and youth in the United States: annual report on patterns of coverage, utilization, quality, and expenditures by a county level of urban influence.Ambul Pediatr.2006;6:241264.
  8. Merenstein D,Egleston B,Diener‐West M.Lengths of stay and costs associated with children's hospitals.Pediatrics.2005;115:839844.
  9. Parker JD,Schoendorf KC.Variation in hospital discharges for ambulatory care‐sensitive conditions among children.Pediatrics.2000;106:942948.
  10. Kronman MP,Hersh AL,Feng R,Huang YS,Lee GE,Shah SS.Ambulatory visit rates and antibiotic prescribing for children with pneumonia, 1994–2007.Pediatrics.2011;127:411418.
  11. Washington EL,Shen JJ,Bell R,Coleman C,Shi L.Patterns of hospital‐based pediatric care across diverse ethnicities: the case of pneumonia.J Health Care Poor Underserved.2004;15:462473.
  12. Lorch SA,Zhang X,Rosenbaum PR,Evan‐Shoshan O,Silber JH.Equivalent lengths of stay of pediatric patients hospitalized in rural and nonrural hospitals.Pediatrics.2004;114:e400e408.
  13. Eisert S,Gabow P.Effect of Child Health Insurance Plan enrollment on the utilization of health care services by children using a public safety net system.Pediatrics.2002;110:940945.
  14. Wood PR,Smith LA,Romero D,Bradshaw P,Wise PH,Chavkin W.Relationships between welfare status, health insurance status, and health and medical care among children with asthma.Am J Public Health.2002;92:14461452.
  15. HCUP Kids' Inpatient Database (KID). Healthcare Cost and Utilization Project (HCUP), 1997, 2000, 2003, 2006. Agency for Healthcare Research and Quality. Available at: http://www.hcup‐us.ahrq.gov/kidoverview.jsp. Accessed May 17,2010.
  16. Whittle J,Fine MJ,Joyce DZ, et al.Community‐acquired pneumonia: can it be defined with claims data?Am J Med Qual.1997;12:187193.
  17. Hadley J.Sicker and poorer—the consequences of being uninsured: a review of the research on the relationship between health insurance, medical care use, health, work, and income.Med Care Res Rev.2003;60:3S75S; discussion76S–112S.
  18. McConnochie KM,Russo MJ,McBride JT,Szilagyi PG,Brooks AM,Roghmann KJ.Socioeconomic variation in asthma hospitalization: excess utilization or greater need?Pediatrics.1999;103:e75.
  19. Abdullah F,Zhang Y,Lardaro T, et al.Analysis of 23 million US hospitalizations: uninsured children have higher all‐cause in‐hospital mortality.J Public Health (Oxf).2010;32(2)236244.
  20. Heymann SJ,Earle A.The impact of welfare reform on parents' ability to care for their children's health.Am J Public Health.1999;89:502505.
  21. Smith LA,Wise PH,Wampler NS.Knowledge of welfare reform program provisions among families of children with chronic conditions.Am J Public Health.2002;92:228230.
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Disparities in patterns of care and outcomes for ambulatory‐care sensitive conditions remain a persistent problem for children.19 Many studies have focused on disparities in hospitalization rates and length of stay (LOS) related to asthma, however, few studies have focused on community‐acquired pneumonia (CAP) despite the fact that pneumonia is the most common, preventable, and potentially serious infection in childhood.10 Providers, payers, and families have a common interest in minimizing hospital LOS for different reasons (eg, minimizing costs, lost wages, exposure to antibiotic‐resistant bacteria), however, this interest is balanced against the potentially greater risk of readmission and adverse outcomes if LOS is inappropriately short. To date, the relationship between insurance status and LOS for CAP remains unexplored.

As in other conditions, substantial variation exists with respect to patterns of care and outcomes for children hospitalized with CAP.11 For example, children hospitalized in rural settings have a shorter LOS for pneumonia than those hospitalized in large urban settings.12 Children from racial/ethnic minorities tend to have higher rates of CAP‐associated complications, including death.11 Decades of prior studies have documented that uninsured children are less likely than insured children to make preventive care visits and obtain prescription medications, but differences in LOS or hospitalization rates between insured and uninsured children with CAP have not been studied.6, 8, 13, 14 Though imperfect, insurance status is 1 proxy for healthcare access, and current healthcare reform efforts aim to improve healthcare access and decrease socioeconomic gradients in health by increasing the number of insured American children. Nonetheless, quantifying the relationship between insurance status on LOS for children hospitalized with CAP is a first step towards understanding the influence of ambulatory care access on hospitalization for ambulatory‐care sensitive conditions.

The purpose of this study was to investigate the influence of insurance status and type on LOS for children hospitalized with CAP. In addition, we sought to determine if there were consistent trends over time in the association between insurance status and type with LOS for children hospitalized with CAP.

METHODS

Study Design and Data Source

This retrospective cross‐sectional study used data from the 1997, 2000, 2003, and 2006 Kids' Inpatient Database (KID). The KID is part of the Healthcare Cost and Utilization Project sponsored by the Agency for Healthcare Research and Quality (AHRQ). It is the only dataset on hospital use and outcomes specifically designed to study children's use of hospital services in the United States. The KID samples pediatric discharges from all community non‐rehabilitation hospitals in states participating in the Healthcare Cost and Utilization Project, using a complex stratification system, across pediatric discharge type and hospital characteristics. Community hospitals in the KID are defined as all non‐federal, short‐term, general and other specialty hospitals, including academic medical centers, obstetrics‐gynecology, otolaryngology, orthopedic, and children's hospitals. Federal hospitals, long‐term hospitals, psychiatric hospitals, alcohol/chemical dependency treatment facilities and hospitals units within institutions are excluded. Discharge‐level weights assigned to discharges within the stratum permit calculation of national estimates. Datasets, which each contain approximately 3 million discharges (unweighted), are released every 3 years beginning with 1997. The 2006 KID is the most recently available dataset and contains hospital administrative data from 38 states, representing 88.8% of the estimated US population.15 This study was considered exempt from review by the Committees for the Protection of Human Subjects at The Children's Hospital of Philadelphia.

Study Participants

Patients 18 years of age and younger were eligible for inclusion if they required hospitalization for CAP in 1997, 2000, 2003, or 2006. Using a previously validated algorithm, patients were considered as having CAP if they met 1 of 2 criteria: 1) International Classification of Diseases, 9th Revision, Clinical Modification (ICD‐9 CM) primary diagnosis code indicating pneumonia (480‐483, 485‐486), empyema (510), or pleurisy (511.0‐1, 511.9); or 2) primary diagnosis of pneumonia‐related symptom (eg, cough, fever, tachypnea) and secondary diagnosis of pneumonia, empyema or pleurisy. Pneumonia‐related symptoms included fever, respiratory abnormality unspecified, shortness of breath, tachypnea, wheezing, cough, hemoptysis, abnormal sputum, chest pain, and abnormal chest sounds.16 Because there is no specific ICD‐9 code for nosocomial pneumonia, this previously validated approach minimized such misclassification16 (eg, a child hospitalized following traumatic injury who then develops ventilator‐associated pneumonia is likely to have trauma, rather than pneumonia or a pneumonia‐related symptom, listed as the primary diagnosis). Patients with the following comorbid conditions (identified by KID data elements and ICD‐9 CM codes) were excluded as these comorbidities are characterized by risk factors not reflective of the general pediatric population: acquired and congenital immunologic disorders, malignancy, collagen vascular disease, sickle cell disease, cystic fibrosis, organ transplant, congenital heart defects, and heart failure. Patients identified as in‐hospital births were excluded to minimize the inclusion of perinatally acquired and nosocomial infections occurring in neonates. Patients with a secondary diagnosis code indicating trauma were also excluded, as a diagnosis of pneumonia in this population likely reflects nosocomial etiology. CAP‐related complications (eg, effusion, abscess; for complete list, see Supporting Appendix A in the online version of this article) were identified using ICD‐9 CM diagnosis and procedure codes. Asthma‐related hospitalizations were identified using ICD‐9 CM diagnosis code 493 in any secondary diagnosis field.

Primary Exposure

The primary exposure was insurance type, categorized as private, public, uninsured, or other (eg, Civilian Health and Medical Program Uniform Service (CHAMPUS), worker's compensation, union‐based insurance, but definition varies by state precluding categorization as purely public or private).

Primary Outcome

The primary outcome was the hospital LOS calculated in days.

Statistical Analysis

Consistent with prior work,12 subjects were characterized by age, race, sex, the presence or absence of a pneumonia‐associated complication, discharge status (discharge from hospital vs in‐hospital death), hospital type (rural, urban non‐teaching, urban teaching non‐children's, urban teaching children's), and hospital region (Northeast, Midwest, South, West). Age groups for analysis were defined as <1 year (infant), 1 to 5 years (preschool age), 6 to 11 years (school‐age), and 12 to 18 years old (adolescent). Race was recorded as a single variable (white, black, other, and missing). Patient information for race was missing from 32% of discharges in 1997, 18% in 2000, 29% in 2003, and 26% in 2006. Patients with missing race data were included to preserve the integrity of our estimates. Categorical variables were summarized by frequencies and percents. Continuous variables were summarized by mean and standard deviation values.

All analyses accounted for the complex sampling design with the survey commands included in STATA, version 10 (College Station, TX) to produce weighted estimates. To determine the adjusted impact of patient and hospital‐level characteristics in our cohort, we constructed multivariable negative binomial regression models using all available covariates for LOS because of its rightward‐skewed distribution. The negative binomial model produced an incident rate ratio (IRR) for LOS (IRR >1 indicates that the risk factor is associated with a longer length of stay). As recommended in the AHRQ technical documentation, variance estimates for each model accounted for the clustering of data at the hospital level. To address the impact of missing race data on outcome, we constructed additional multivariable negative binomial regression models while varying the underlying assumptions about race classification. In these secondary analyses, children with race coded as missing were sequentially excluded, assumed to be white, and assumed to be black. These analyses were repeated after excluding insurance from the multivariable model.

RESULTS

The more than 10.5 million children sampled (unweighted) in KID during these 4 time periods (1997, 2000, 2003, and 2006) are representative of the more than 28.9 million children hospitalized in the United States. In each of these sample years, there were approximately 150,000 children hospitalized with pneumonia across the United States (Table 1). Of those hospitalized, 23% to 28% had a concomitant diagnosis of asthma; 6% to 8% had a pneumonia‐associated complication; and mortality was <0.01% in each sample year for patients hospitalized with pneumonia. In all years, among those with racial/ethnic data, the sample population was predominantly white boys less than 6 years old. The greatest proportion of children were hospitalized in urban non‐teaching settings, and also those children living in the southern regions of the United States.

Characteristics of Children Hospitalized With Pneumonia in the United States
 1997200020032006
 N = 148,702N = 157,847N = 157,743N = 156,810
  • NOTE: Values, which represent national estimates, are listed as number (percent). Numbers across rows may not sum exactly because weighted estimates from these data are obtained using survey commands as per KIDS technical guidance.15

  • KID categorizes states into the following 4 regions: Northeast (Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont); Midwest (Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, Wisconsin); South (Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia); West (Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, Wyoming).

Race    
White56,348 (38)68,643 (44)54,903 (35)56,108 (36)
Black22,864 (15)22,580 (14)17,960 (11)18,800 (12)
Other22,203 (15)38,448 (24)39,138 (25)40,803 (26)
Missing47,287 (32)28,175 (18)45,588 (29)41,099 (26)
Age category    
<1 year43,851 (29)44,470 (28)37,798 (24)37,705 (24)
1 through 5 years75,033 (50)76,385 (48)77,530 (49)79,519 (51)
6 through 11 years19,372 (13)21,403 (14)23,126 (15)23,494 (15)
>12 years10,446 (7)15,589 (9)19,289 (12)16,092 (10)
Hospital type    
Urban non‐teaching52,756 (35)50,718 (32)52,552 (34)50,718 (32)
Rural47,910 (32)41,715 (27)39,605 (26)31,947 (21)
Urban teaching non‐children's20,378 (14)30,981 (20)28,432 (18)30,194 (20)
Urban teaching children's27,658 (19)34,021 (22)34,454 (22)41,035 (27)
Male sex83,291 (56)8,783 (56)86,034 (55)85,508 (55)
Region*    
Northeast19,750 (13)26,092 (17)23,867 (15)23,832 (15)
Midwest33,053 (22)30,706 (19)35,714 (23)35,900 (23)
South68,958 (46)68,663 (44)65,994 (42)65,460 (42)
West26,741 (18)32,385 (21)32,169 (20)31,618 (20)
Asthma26,971 (24)31,746 (28)27,729 (24)26,822 (23)
Pneumonia‐associated complication8,831 (6)11,084 (7)12,005 (8)11,724 (7)
Died334 (0.002)394 (0.002)270 (0.002)193 (0.001)
Insurance    
Private65,428 (44)73,528 (47)68,720 (44)63,997 (41)
Public68,024 (46)71,698 (45)76,779 (49)80,226 (51)
Uninsured9,922 (7)8,336 (5)6,381 (4)6,912 (4)
Other4,964 (3)4,285 (3)5,391 (3)5,283 (3)

There was little variation in the insurance status of children hospitalized with CAP between 1997 and 2006. In each of the sampled years, at least 40% of sampled children were privately insured, at least 40% were publicly insured, and approximately 5% were uninsured (Table 1). In all years, there were significant racial/ethnic disparities in insurance coverage such that whites were 4 to 6 times more likely to have private insurance than blacks, however, the large amount of missing race/ethnicity data warrant caution in interpreting this finding (Table 2; also see Supporting Information Appendix B in the online version of this article). We also found that children less than 1 year old were the most likely to be publicly insured in all years (see Supporting Appendix C in the online version of this article). There were also regional differences related to insurance coverage such that a greater proportion of children hospitalized in facilities located in the southern part of the United States were publicly insured. Notably, there were no significant differences in CAP‐associated mortality or asthma related to insurance coverage (Table 2). In 2006, CAP‐associated complications occurred in 8.5% of children with private insurance, 6.5% of children with public insurance, and 7.7% of uninsured children; the relative distribution of complications by insurance type were similar in previous years of the KID survey.

Demographic Characteristics of Children Hospitalized With Pneumonia in 2006, Stratified by Insurance Category
 PrivatePublicUninsuredOther InsuranceP
  • NOTE: Chi‐square test used to compare differences. Numbers across rows may not sum exactly because weighted estimates from these data are obtained using survey commands as per KIDS technical guidance.15 For data from other years (1997, 2000, 2003), see Supporting Appendix C in the online version of this article.

  • P < 0.001 compared with white race.

  • P < 0.001 compared with urban non‐teaching hospitals.

  • P = 0.384 compared with urban non‐teaching hospitals.

  • P = 0.004 compared with urban non‐teaching hospitals.

  • P < 0.001 compared with Northeast region.

No. of children (%)63,997 (41)80,226 (51)6,912 (4)5,283 (3) 
Male sex34,639 (41)44,140 (52)3,727 (4)2,808 (3)0.092
Race     
White30,707 (55)21,282 (38)2,241 (4)1,774 (3)<0.001
Black*5,112 (27)12,239 (65)988 (5)426 (3) 
Other11,033 (27)26,489 (65)2,112 (5)1,076 (3) 
Missing17,145 (42)20,216 (49)1,572 (4)2,007 (4) 
Age category     
<1 year10,788 (29)24,762 (65)1,164 (3)880 (3)<0.001
1 through 5 years33,664 (42)39,531 (50)3,442 (4)2,673 (3) 
6 through 11 years11,660 (50)9,684 (41)1,085 (5)1,015 (4) 
>12 years7,885 (49)6,249 (39)1,221 (8)714 (4) 
Hospital type     
Urban non‐teaching22,429 (44)24,241 (49)2,440 (5)1,555 (2)<0.001
Rural10,880 (34)18,396 (58)1,290 (4)1,109 (3) 
Urban teaching non‐children's13,130 (44)14,542 (48)1,721 (6)750 (2) 
Urban teaching children's16,591 (40)21,544 (53)1,417 (3)1,465 (4) 
Region     
Northeast12,364 (52)9,620 (40)1,466 (6)377 (2)<0.001
Midwest17,891 (50)15,573 (43)1,160 (3)1,215 (3) 
South21,479 (33)38,112 (58)3,108 (5)2,495 (4) 
West12,263 (39)16,921 (44)1,178 (5)1,195 (5) 
Asthma10,829 (41)13,923 (52)1,119 (4)866 (3)0.193
Pneumonia‐associated complication5,416 (46)5,206 (45)532 (4)556 (5)<0.001
Died66 (34)115 (60)3 (1)8 (5)0.131

After examining the general and demographic characteristics, we then examined mean LOS for all children with CAP in each sample year (Table 3). The mean LOS for children with CAP was 3.44 days in 1997, with marginal decreases in subsequent years to a mean LOS of 3.18 days in 2006. The distribution of LOS for children with CAP revealed that nearly 70% of children were hospitalized for fewer than 3 days, another 22% to 28% were hospitalized for less than 1 week, and only 3% were hospitalized for more than 1 week. This distribution did not change substantially between 1997 and 2006. Next, we compared mean LOS by insurance type and race/ethnicity in unadjusted analyses. In each sample year, publicly insured children hospitalized with CAP had significantly longer LOS than privately insured children (P < 0.001). Similarly, in all years excepting 1997, uninsured children hospitalized with CAP had significantly shorter LOS than privately insured children. There were also significant racial differences in LOS for children with CAP, such that black children had longer LOS than white children with CAP. However, the large amount of missing data for race/ethnicity limited the robustness of this finding, and subsequent sensitivity analyses demonstrated that there were no consistent racial/ethnic disparities in LOS (see Supporting Appendix B in the online version of this article). These sensitivity analyses for missing race data did not alter our primary finding of shorter LOS for uninsured versus publicly or privately insured children.

Unadjusted Length of Stay Overall and Stratified by Insurance Type and Race Category
 1997P2000P2003P2006P
  • NOTE: Values listed as mean length of stay (standard error). Wald test used to compare differences in mean length of stay with designated reference group.

Overall3.44 (0.04) 3.35 (0.05) 3.27 (0.05) 3.18 (0.04) 
Insurance type        
Private3.21 (0.04) 3.19 (0.04) 3.09 (0.04) 3.00 (0.03) 
Public3.71 (0.06)<0.0013.57 (0.06)<0.0013.44 (0.06)<0.0013.34 (0.05)<0.001
Uninsured3.18 (0.14)0.7922.92 (0.07)<0.0012.80 (0.05)<0.0012.82 (0.05)<0.001
Other3.32 (0.11)0.3193.55 (0.14)0.01343.54 (0.21)0.0373.42 (0.13)0.001
Race        
White3.31 (0.05) 3.18 (0.04) 3.19 (0.05) 3.10 (0.04) 
Black3.61 (0.08)<0.0013.32 (0.07)<0.0013.36 (0.08)<0.0013.31 (0.07)<0.001
Other3.96 (0.11)<0.0013.81 (0.09)<0.0013.67 (0.10)<0.0013.56 (0.08)<0.001
Missing3.27 (0.08)0.6453.18 (0.08)0.9262.99 (0.06)0.01342.86 (0.04)<0.001

After controlling for child age, race/ethnicity, gender, hospital type, transfer status, and presence of asthma or pneumonia‐associated complications, our multivariable analyses examining the relationship between insurance coverage and hospital LOS yielded the following results (Table 4). First, publicly insured children had significantly longer hospital stays than privately insured children, and uninsured children had significantly shorter hospital stays than privately insured children in all years except 1997. Second, children admitted with CAP at urban teaching children's hospitals had significantly longer LOS than those admitted to urban non‐teaching hospitals, and, in 2003, children admitted with CAP to rural hospitals had significantly shorter LOS than those admitted to urban non‐teaching hospitals. Third, children older than 1 year consistently had shorter hospital stays than infants less than 1 year old. Finally, though concomitant diagnosis of asthma did not consistently influence LOS, children who developed any complications had significantly longer LOS than those who did not. The cumulative impact of seemingly small differences in LOS is great. For example, in 2006, our model suggests that, for every 1000 children hospitalized with CAP in a given year, after adjusting for differences in sex, age, race, hospital‐type, region, transfer status, and diagnosis of asthma or complications, publicly insured children spend 90 to 130 more days in the hospital than privately insured children, whereas uninsured children spend between 40 to 90 fewer days in the hospital than privately insured children.

Multivariable Negative Binomial Regression Model of Factors Associated With Length of Stay
 1997200020032006
VariableIRR (95% CI)IRR (95% CI)IRR (95% CI)IRR (95% CI)
  • NOTE: All available variables included in multivariable models. KID categorizes states into the following 4 regions: Northeast (Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont); Midwest (Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, Wisconsin); South (Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia); West (Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, Wyoming).

  • Abbreviations: CI, confidence interval; IRR, incidence rate ratio.

  • Significant values are noted as follows; all other values are not significant:

  • P < 0.05;

  • P < 0.01;

  • P < 0.001.

Age category    
<1 year    
15 years0.82 (0.81, 0.84)0.83 (0.88, 0.95)0.86 (0.85, 0.88)0.87 (0.86, 0.89)
611 years0.91 (0.87, 0.95)0.91 (0.88, 0.94)0.93 (0.91, 0.95)0.93 (0.90, 0.95)
>12 years1.03 (0.99, 1.07)1.17 (1.11, 1.22)1.09 (1.06, 1.13)1.13 (1.09, 1.16)
Race    
White    
Black1.04 (0.99, 1.08)1.00 (0.95, 1.03)1.00 (0.98, 1.03)1.02 (0.98, 1.06)
Other1.09 (1.05, 1.13)1.11 (1.08, 1.15)1.09 (1.06, 1.12)1.08 (1.05, 1.11)
Missing1.00 (0.94, 1.06)1.01 (0.96, 1.06)0.95 (0.92, 0.99)*0.96 (0.93, 0.99)
Sex    
Female1.02 (0.94, 1.06)1.01 (0.99, 1.02)1.01(0.93, 100)1.01 (1.00, 1.02)
Insurance type    
Private    
Public1.13 (1.11, 1.16)1.11 (1.09, 1.14)1.11 (1.09, 1.13)1.11 (1.09, 1.13)
Uninsured1.01 (0.91, 1.11)0.93 (0.89, 0.96)0.92 (0.90, 0.96)0.94 (0.91, 0.96)
Other1.01 (0.96, 1.06)1.10 (1.03, 1.18)1.10 (1.02, 1.19)*1.07 (1.02, 1.13)
Hospital type    
Urban non‐teaching    
Rural0.98 (0.92, 1.04)0.96 (0.92, 1.00)0.97 (0.94, 1.00)0.97 (0.93, 1.00)
Urban teaching (non‐children's)0.99 (0.95, 1.04)1.06 (1.02, 1.10)1.06 (1.02, 1.10)1.03 (0.99, 1.07)
Urban teaching children's1.2 (1.14, 1.26)1.23 (1.16, 1.30)1.28 (1.21, 1.37)1.25 (1.19, 1.31)
Region    
Northeast    
Midwest0.93 (0.88, 0.98)*0.96 (0.92, 1.00)0.95 (0.91, 0.99)*0.95 (0.91, 0.99)*
South0.98 (0.94, 1.02)1.06 (1.02, 1.10)*1.04 (1.00, 1.09)1.03 (0.98, 1.08)
West0.97 (0.92, 1.01)1.22 (1.16, 1.30)*1.02 (0.97, 1.08)1.06 (1.00, 1.12)*
Transfer status    
Transfer1.35 (1.25, 1.46)1.39 (1.27, 1.52)1.31 (1.23, 1.37 )1.16 (1.10, 1.23)
Asthma0.99 (0.96, 1.03)0.97 (0.95, 0.99)0.98 (0.96, 1.00)0.98 (0.97, 1.00)*
Pneumonia Complications0.99 (0.96, 1.03)0.97 (0.95, 0.99)*0.98 (0.96, 1.0)0.98 (0.97, 1.00)*
Any complication2.20 (2.07, 2.34)2.23 (2.07, 2.40)2.22 (2.22, 2.44)2.37 (2.27, 2.47)

DISCUSSION

In this nationally representative sample selected over the past 10 years, we found that publicly insured children hospitalized with CAP have significantly longer LOS than those who are privately insured, and that, since 2000, uninsured children hospitalized with CAP have significantly shorter LOS than those who are privately insured. Though these observed differences are small, they are consistent across all 4 sampled years and, because CAP is one of the most common pediatric inpatient diagnoses, the cumulative impact of the observed differences on hospital LOS is great. Insurance status is often considered a proxy for access to preventive and ambulatory healthcare services or socioeconomic status. However, the underlying mechanisms relating insurance status to healthcare access, utilization, and ultimately, health outcomes are highly complex and difficult to elucidate.17 The observed variation in this study raises questions about the potential influence of insurance status on hospital discharge practices. Additional research is necessary to understand whether there are differences in processes of care (eg, performance of blood cultures or chest radiographs), quality of care, or other outcomes, such as readmissions, related to CAP inpatient management for children with different insurance coverage.

Apart from differences in hospital discharge practices, another possible explanation for uninsured children with CAP having shorter LOS is that these children have less severe disease than privately insured. This may occur if uninsured children with CAP are evaluated in the emergency department rather than the office setting, because emergency department providers may be more likely to admit children with CAP who lack a consistent access to ambulatory primary care services. Countering this alternative, prior studies have shown that uninsured groups are more likely to have greater disease severity than privately insured groups at the time of hospital admission.18, 19 In this study, we attempted to identify children with greater severity of disease using ICD‐9 codes for CAP‐associated complications. Though this is a relatively crude method that might lead to an underestimate of the total number of children with complications, we found that there were no significant differences in the prevalence of CAP‐associated complications between uninsured and insured groups in all sampled years.

On the other hand, uninsured patients may be released earlier by providers in order to reduce the amount of uncompensated care provided, or possibly because parents may urge providers to discharge their children, given their inability to pay forthcoming hospital bills and/or avoid further lost wages due to work absence.20, 21 In California, Bindman et al. demonstrated that decreasing the frequency of Medicaid recertification, and consequently increasing the likelihood of continuous insurance coverage, was associated with a decreased risk of hospitalization for ambulatory‐care sensitive conditions.5

We also found that children admitted to urban teaching children's hospitals with CAP had significantly longer LOS than those admitted to urban non‐teaching hospitals, whereas children in rural hospitals had significantly shorter LOS than those in urban non‐teaching hospitals in 2003. These findings are consistent with prior data from 1996 to1998 demonstrating that children admitted to rural hospitals in New York and Pennsylvania had significantly shorter LOS than large urban hospitals for 19 medical and 9 surgical conditions, including pneumonia.12 These findings may reflect underlying differences in between rural and urban hospital transfer practices, whereby rural hospitals may be more likely than urban hospitals to transfer children with relatively more severe illness to urban referral centers and retain children with less severe illness, leading to shorter LOS.12 Though our empiric understanding of differences in LOS between teaching and non‐teaching hospitals is currently limited, clinical experience supports the notion that there may be decreases in efficiency that occur in teaching hospitals, and are a result of the supervision required for care provided by trainees. It is also possible that, despite our exclusion of comorbid conditions, some children with complex or chronic medical conditions were included in this study. These children are often cared for at teaching hospitals, regardless of the primary cause for admission, and are more likely to have public insurance than other children, thus confounding the relationship between hospital type, insurance type and status, and LOS for children with CAP. The limitations of this dataset preclude further examination of this issue.

There are some limitations to this study. First, the KID data are cross‐sectional and causal inferences are limited. However, our results demonstrating that uninsured children hospitalized with CAP had shorter LOS than privately insured children were quite consistent in each sample year, suggesting that our results are a true association. Additionally, insurance status in KID is typically collected at admission, however, it is not possible to determine whether specific changes to insurance status that occurred during the hospitalization were applied to the data. The impact of this limitation would depend on the type of insurance obtained by the patient. If uninsured patients obtained public insurance, our study would underestimate the increased LOS for publicly insured patients, compared with privately insured patients, but have no effect on the difference in LOS between uninsured and privately insured patients. In the unlikely event that uninsured patients obtained private insurance, then our study would underestimate the difference for uninsured patients, compared with privately insured patients, biasing our current study results towards the null. Second, a substantial proportion of sampled children had missing data for race/ethnicity. To assess the impact of the missing race/ethnicity data on our results, we conducted sensitivity analyses and found that, though difficult to make any definitive conclusions about the relationship between race/ethnicity and LOS for children with CAP, there were no changes to our primary findings regarding differences in LOS between children with different insurance status and type. Third, KID does not include data about other unmeasured confounders (eg, parent income, parent education, regular source of care) that might be related to LOS, as well as a broad spectrum of pediatric outcomes. Serious consideration of expanding KID to include these variables is warranted. Fourth, the other category of insurance is not uniformly coded across states in the KID database. While some states use this category to classify public insurance options other than Medicare and Medicaid, other states include private insurance options in this group. Thus, it is possible that some patients with public insurance are misclassified as having other insurance. We would expect such misclassification to bias our findings towards the null hypothesis. Finally, we focused on the relationship between child health insurance status and CAP, only 1 ambulatory care‐sensitive condition. Additional research examining the relationship between insurance type and other ambulatory care‐sensitive conditions is warranted.

In summary, we found that, after multivariable adjustment, uninsured children hospitalized with community‐acquired pneumonia had significantly shorter LOS than privately insured children, and publicly insured children had a significantly longer hospital stay than privately insured children in these 4 nationally representative samples from 1997 to 2006. Current federal and state efforts to increase enrollment of children into insurance programs are a first step in reducing healthcare disparities. However, insurance coverage alone does not guarantee access to healthcare, thus, these efforts in isolation will likely be insufficient to achieve optimal health for the children of our country. As healthcare reform legislation is implemented, these findings provide hospitals and policy makers additional impetus to develop ways to achieve the ideal length of stay for every child; this ideal state will be achieved when clinical status and course, rather than nonclinical factors such as insurance type or provider's unease with ambulatory follow‐up, determine the duration of hospitalization for every child.

Disparities in patterns of care and outcomes for ambulatory‐care sensitive conditions remain a persistent problem for children.19 Many studies have focused on disparities in hospitalization rates and length of stay (LOS) related to asthma, however, few studies have focused on community‐acquired pneumonia (CAP) despite the fact that pneumonia is the most common, preventable, and potentially serious infection in childhood.10 Providers, payers, and families have a common interest in minimizing hospital LOS for different reasons (eg, minimizing costs, lost wages, exposure to antibiotic‐resistant bacteria), however, this interest is balanced against the potentially greater risk of readmission and adverse outcomes if LOS is inappropriately short. To date, the relationship between insurance status and LOS for CAP remains unexplored.

As in other conditions, substantial variation exists with respect to patterns of care and outcomes for children hospitalized with CAP.11 For example, children hospitalized in rural settings have a shorter LOS for pneumonia than those hospitalized in large urban settings.12 Children from racial/ethnic minorities tend to have higher rates of CAP‐associated complications, including death.11 Decades of prior studies have documented that uninsured children are less likely than insured children to make preventive care visits and obtain prescription medications, but differences in LOS or hospitalization rates between insured and uninsured children with CAP have not been studied.6, 8, 13, 14 Though imperfect, insurance status is 1 proxy for healthcare access, and current healthcare reform efforts aim to improve healthcare access and decrease socioeconomic gradients in health by increasing the number of insured American children. Nonetheless, quantifying the relationship between insurance status on LOS for children hospitalized with CAP is a first step towards understanding the influence of ambulatory care access on hospitalization for ambulatory‐care sensitive conditions.

The purpose of this study was to investigate the influence of insurance status and type on LOS for children hospitalized with CAP. In addition, we sought to determine if there were consistent trends over time in the association between insurance status and type with LOS for children hospitalized with CAP.

METHODS

Study Design and Data Source

This retrospective cross‐sectional study used data from the 1997, 2000, 2003, and 2006 Kids' Inpatient Database (KID). The KID is part of the Healthcare Cost and Utilization Project sponsored by the Agency for Healthcare Research and Quality (AHRQ). It is the only dataset on hospital use and outcomes specifically designed to study children's use of hospital services in the United States. The KID samples pediatric discharges from all community non‐rehabilitation hospitals in states participating in the Healthcare Cost and Utilization Project, using a complex stratification system, across pediatric discharge type and hospital characteristics. Community hospitals in the KID are defined as all non‐federal, short‐term, general and other specialty hospitals, including academic medical centers, obstetrics‐gynecology, otolaryngology, orthopedic, and children's hospitals. Federal hospitals, long‐term hospitals, psychiatric hospitals, alcohol/chemical dependency treatment facilities and hospitals units within institutions are excluded. Discharge‐level weights assigned to discharges within the stratum permit calculation of national estimates. Datasets, which each contain approximately 3 million discharges (unweighted), are released every 3 years beginning with 1997. The 2006 KID is the most recently available dataset and contains hospital administrative data from 38 states, representing 88.8% of the estimated US population.15 This study was considered exempt from review by the Committees for the Protection of Human Subjects at The Children's Hospital of Philadelphia.

Study Participants

Patients 18 years of age and younger were eligible for inclusion if they required hospitalization for CAP in 1997, 2000, 2003, or 2006. Using a previously validated algorithm, patients were considered as having CAP if they met 1 of 2 criteria: 1) International Classification of Diseases, 9th Revision, Clinical Modification (ICD‐9 CM) primary diagnosis code indicating pneumonia (480‐483, 485‐486), empyema (510), or pleurisy (511.0‐1, 511.9); or 2) primary diagnosis of pneumonia‐related symptom (eg, cough, fever, tachypnea) and secondary diagnosis of pneumonia, empyema or pleurisy. Pneumonia‐related symptoms included fever, respiratory abnormality unspecified, shortness of breath, tachypnea, wheezing, cough, hemoptysis, abnormal sputum, chest pain, and abnormal chest sounds.16 Because there is no specific ICD‐9 code for nosocomial pneumonia, this previously validated approach minimized such misclassification16 (eg, a child hospitalized following traumatic injury who then develops ventilator‐associated pneumonia is likely to have trauma, rather than pneumonia or a pneumonia‐related symptom, listed as the primary diagnosis). Patients with the following comorbid conditions (identified by KID data elements and ICD‐9 CM codes) were excluded as these comorbidities are characterized by risk factors not reflective of the general pediatric population: acquired and congenital immunologic disorders, malignancy, collagen vascular disease, sickle cell disease, cystic fibrosis, organ transplant, congenital heart defects, and heart failure. Patients identified as in‐hospital births were excluded to minimize the inclusion of perinatally acquired and nosocomial infections occurring in neonates. Patients with a secondary diagnosis code indicating trauma were also excluded, as a diagnosis of pneumonia in this population likely reflects nosocomial etiology. CAP‐related complications (eg, effusion, abscess; for complete list, see Supporting Appendix A in the online version of this article) were identified using ICD‐9 CM diagnosis and procedure codes. Asthma‐related hospitalizations were identified using ICD‐9 CM diagnosis code 493 in any secondary diagnosis field.

Primary Exposure

The primary exposure was insurance type, categorized as private, public, uninsured, or other (eg, Civilian Health and Medical Program Uniform Service (CHAMPUS), worker's compensation, union‐based insurance, but definition varies by state precluding categorization as purely public or private).

Primary Outcome

The primary outcome was the hospital LOS calculated in days.

Statistical Analysis

Consistent with prior work,12 subjects were characterized by age, race, sex, the presence or absence of a pneumonia‐associated complication, discharge status (discharge from hospital vs in‐hospital death), hospital type (rural, urban non‐teaching, urban teaching non‐children's, urban teaching children's), and hospital region (Northeast, Midwest, South, West). Age groups for analysis were defined as <1 year (infant), 1 to 5 years (preschool age), 6 to 11 years (school‐age), and 12 to 18 years old (adolescent). Race was recorded as a single variable (white, black, other, and missing). Patient information for race was missing from 32% of discharges in 1997, 18% in 2000, 29% in 2003, and 26% in 2006. Patients with missing race data were included to preserve the integrity of our estimates. Categorical variables were summarized by frequencies and percents. Continuous variables were summarized by mean and standard deviation values.

All analyses accounted for the complex sampling design with the survey commands included in STATA, version 10 (College Station, TX) to produce weighted estimates. To determine the adjusted impact of patient and hospital‐level characteristics in our cohort, we constructed multivariable negative binomial regression models using all available covariates for LOS because of its rightward‐skewed distribution. The negative binomial model produced an incident rate ratio (IRR) for LOS (IRR >1 indicates that the risk factor is associated with a longer length of stay). As recommended in the AHRQ technical documentation, variance estimates for each model accounted for the clustering of data at the hospital level. To address the impact of missing race data on outcome, we constructed additional multivariable negative binomial regression models while varying the underlying assumptions about race classification. In these secondary analyses, children with race coded as missing were sequentially excluded, assumed to be white, and assumed to be black. These analyses were repeated after excluding insurance from the multivariable model.

RESULTS

The more than 10.5 million children sampled (unweighted) in KID during these 4 time periods (1997, 2000, 2003, and 2006) are representative of the more than 28.9 million children hospitalized in the United States. In each of these sample years, there were approximately 150,000 children hospitalized with pneumonia across the United States (Table 1). Of those hospitalized, 23% to 28% had a concomitant diagnosis of asthma; 6% to 8% had a pneumonia‐associated complication; and mortality was <0.01% in each sample year for patients hospitalized with pneumonia. In all years, among those with racial/ethnic data, the sample population was predominantly white boys less than 6 years old. The greatest proportion of children were hospitalized in urban non‐teaching settings, and also those children living in the southern regions of the United States.

Characteristics of Children Hospitalized With Pneumonia in the United States
 1997200020032006
 N = 148,702N = 157,847N = 157,743N = 156,810
  • NOTE: Values, which represent national estimates, are listed as number (percent). Numbers across rows may not sum exactly because weighted estimates from these data are obtained using survey commands as per KIDS technical guidance.15

  • KID categorizes states into the following 4 regions: Northeast (Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont); Midwest (Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, Wisconsin); South (Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia); West (Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, Wyoming).

Race    
White56,348 (38)68,643 (44)54,903 (35)56,108 (36)
Black22,864 (15)22,580 (14)17,960 (11)18,800 (12)
Other22,203 (15)38,448 (24)39,138 (25)40,803 (26)
Missing47,287 (32)28,175 (18)45,588 (29)41,099 (26)
Age category    
<1 year43,851 (29)44,470 (28)37,798 (24)37,705 (24)
1 through 5 years75,033 (50)76,385 (48)77,530 (49)79,519 (51)
6 through 11 years19,372 (13)21,403 (14)23,126 (15)23,494 (15)
>12 years10,446 (7)15,589 (9)19,289 (12)16,092 (10)
Hospital type    
Urban non‐teaching52,756 (35)50,718 (32)52,552 (34)50,718 (32)
Rural47,910 (32)41,715 (27)39,605 (26)31,947 (21)
Urban teaching non‐children's20,378 (14)30,981 (20)28,432 (18)30,194 (20)
Urban teaching children's27,658 (19)34,021 (22)34,454 (22)41,035 (27)
Male sex83,291 (56)8,783 (56)86,034 (55)85,508 (55)
Region*    
Northeast19,750 (13)26,092 (17)23,867 (15)23,832 (15)
Midwest33,053 (22)30,706 (19)35,714 (23)35,900 (23)
South68,958 (46)68,663 (44)65,994 (42)65,460 (42)
West26,741 (18)32,385 (21)32,169 (20)31,618 (20)
Asthma26,971 (24)31,746 (28)27,729 (24)26,822 (23)
Pneumonia‐associated complication8,831 (6)11,084 (7)12,005 (8)11,724 (7)
Died334 (0.002)394 (0.002)270 (0.002)193 (0.001)
Insurance    
Private65,428 (44)73,528 (47)68,720 (44)63,997 (41)
Public68,024 (46)71,698 (45)76,779 (49)80,226 (51)
Uninsured9,922 (7)8,336 (5)6,381 (4)6,912 (4)
Other4,964 (3)4,285 (3)5,391 (3)5,283 (3)

There was little variation in the insurance status of children hospitalized with CAP between 1997 and 2006. In each of the sampled years, at least 40% of sampled children were privately insured, at least 40% were publicly insured, and approximately 5% were uninsured (Table 1). In all years, there were significant racial/ethnic disparities in insurance coverage such that whites were 4 to 6 times more likely to have private insurance than blacks, however, the large amount of missing race/ethnicity data warrant caution in interpreting this finding (Table 2; also see Supporting Information Appendix B in the online version of this article). We also found that children less than 1 year old were the most likely to be publicly insured in all years (see Supporting Appendix C in the online version of this article). There were also regional differences related to insurance coverage such that a greater proportion of children hospitalized in facilities located in the southern part of the United States were publicly insured. Notably, there were no significant differences in CAP‐associated mortality or asthma related to insurance coverage (Table 2). In 2006, CAP‐associated complications occurred in 8.5% of children with private insurance, 6.5% of children with public insurance, and 7.7% of uninsured children; the relative distribution of complications by insurance type were similar in previous years of the KID survey.

Demographic Characteristics of Children Hospitalized With Pneumonia in 2006, Stratified by Insurance Category
 PrivatePublicUninsuredOther InsuranceP
  • NOTE: Chi‐square test used to compare differences. Numbers across rows may not sum exactly because weighted estimates from these data are obtained using survey commands as per KIDS technical guidance.15 For data from other years (1997, 2000, 2003), see Supporting Appendix C in the online version of this article.

  • P < 0.001 compared with white race.

  • P < 0.001 compared with urban non‐teaching hospitals.

  • P = 0.384 compared with urban non‐teaching hospitals.

  • P = 0.004 compared with urban non‐teaching hospitals.

  • P < 0.001 compared with Northeast region.

No. of children (%)63,997 (41)80,226 (51)6,912 (4)5,283 (3) 
Male sex34,639 (41)44,140 (52)3,727 (4)2,808 (3)0.092
Race     
White30,707 (55)21,282 (38)2,241 (4)1,774 (3)<0.001
Black*5,112 (27)12,239 (65)988 (5)426 (3) 
Other11,033 (27)26,489 (65)2,112 (5)1,076 (3) 
Missing17,145 (42)20,216 (49)1,572 (4)2,007 (4) 
Age category     
<1 year10,788 (29)24,762 (65)1,164 (3)880 (3)<0.001
1 through 5 years33,664 (42)39,531 (50)3,442 (4)2,673 (3) 
6 through 11 years11,660 (50)9,684 (41)1,085 (5)1,015 (4) 
>12 years7,885 (49)6,249 (39)1,221 (8)714 (4) 
Hospital type     
Urban non‐teaching22,429 (44)24,241 (49)2,440 (5)1,555 (2)<0.001
Rural10,880 (34)18,396 (58)1,290 (4)1,109 (3) 
Urban teaching non‐children's13,130 (44)14,542 (48)1,721 (6)750 (2) 
Urban teaching children's16,591 (40)21,544 (53)1,417 (3)1,465 (4) 
Region     
Northeast12,364 (52)9,620 (40)1,466 (6)377 (2)<0.001
Midwest17,891 (50)15,573 (43)1,160 (3)1,215 (3) 
South21,479 (33)38,112 (58)3,108 (5)2,495 (4) 
West12,263 (39)16,921 (44)1,178 (5)1,195 (5) 
Asthma10,829 (41)13,923 (52)1,119 (4)866 (3)0.193
Pneumonia‐associated complication5,416 (46)5,206 (45)532 (4)556 (5)<0.001
Died66 (34)115 (60)3 (1)8 (5)0.131

After examining the general and demographic characteristics, we then examined mean LOS for all children with CAP in each sample year (Table 3). The mean LOS for children with CAP was 3.44 days in 1997, with marginal decreases in subsequent years to a mean LOS of 3.18 days in 2006. The distribution of LOS for children with CAP revealed that nearly 70% of children were hospitalized for fewer than 3 days, another 22% to 28% were hospitalized for less than 1 week, and only 3% were hospitalized for more than 1 week. This distribution did not change substantially between 1997 and 2006. Next, we compared mean LOS by insurance type and race/ethnicity in unadjusted analyses. In each sample year, publicly insured children hospitalized with CAP had significantly longer LOS than privately insured children (P < 0.001). Similarly, in all years excepting 1997, uninsured children hospitalized with CAP had significantly shorter LOS than privately insured children. There were also significant racial differences in LOS for children with CAP, such that black children had longer LOS than white children with CAP. However, the large amount of missing data for race/ethnicity limited the robustness of this finding, and subsequent sensitivity analyses demonstrated that there were no consistent racial/ethnic disparities in LOS (see Supporting Appendix B in the online version of this article). These sensitivity analyses for missing race data did not alter our primary finding of shorter LOS for uninsured versus publicly or privately insured children.

Unadjusted Length of Stay Overall and Stratified by Insurance Type and Race Category
 1997P2000P2003P2006P
  • NOTE: Values listed as mean length of stay (standard error). Wald test used to compare differences in mean length of stay with designated reference group.

Overall3.44 (0.04) 3.35 (0.05) 3.27 (0.05) 3.18 (0.04) 
Insurance type        
Private3.21 (0.04) 3.19 (0.04) 3.09 (0.04) 3.00 (0.03) 
Public3.71 (0.06)<0.0013.57 (0.06)<0.0013.44 (0.06)<0.0013.34 (0.05)<0.001
Uninsured3.18 (0.14)0.7922.92 (0.07)<0.0012.80 (0.05)<0.0012.82 (0.05)<0.001
Other3.32 (0.11)0.3193.55 (0.14)0.01343.54 (0.21)0.0373.42 (0.13)0.001
Race        
White3.31 (0.05) 3.18 (0.04) 3.19 (0.05) 3.10 (0.04) 
Black3.61 (0.08)<0.0013.32 (0.07)<0.0013.36 (0.08)<0.0013.31 (0.07)<0.001
Other3.96 (0.11)<0.0013.81 (0.09)<0.0013.67 (0.10)<0.0013.56 (0.08)<0.001
Missing3.27 (0.08)0.6453.18 (0.08)0.9262.99 (0.06)0.01342.86 (0.04)<0.001

After controlling for child age, race/ethnicity, gender, hospital type, transfer status, and presence of asthma or pneumonia‐associated complications, our multivariable analyses examining the relationship between insurance coverage and hospital LOS yielded the following results (Table 4). First, publicly insured children had significantly longer hospital stays than privately insured children, and uninsured children had significantly shorter hospital stays than privately insured children in all years except 1997. Second, children admitted with CAP at urban teaching children's hospitals had significantly longer LOS than those admitted to urban non‐teaching hospitals, and, in 2003, children admitted with CAP to rural hospitals had significantly shorter LOS than those admitted to urban non‐teaching hospitals. Third, children older than 1 year consistently had shorter hospital stays than infants less than 1 year old. Finally, though concomitant diagnosis of asthma did not consistently influence LOS, children who developed any complications had significantly longer LOS than those who did not. The cumulative impact of seemingly small differences in LOS is great. For example, in 2006, our model suggests that, for every 1000 children hospitalized with CAP in a given year, after adjusting for differences in sex, age, race, hospital‐type, region, transfer status, and diagnosis of asthma or complications, publicly insured children spend 90 to 130 more days in the hospital than privately insured children, whereas uninsured children spend between 40 to 90 fewer days in the hospital than privately insured children.

Multivariable Negative Binomial Regression Model of Factors Associated With Length of Stay
 1997200020032006
VariableIRR (95% CI)IRR (95% CI)IRR (95% CI)IRR (95% CI)
  • NOTE: All available variables included in multivariable models. KID categorizes states into the following 4 regions: Northeast (Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont); Midwest (Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, Wisconsin); South (Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia); West (Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, Wyoming).

  • Abbreviations: CI, confidence interval; IRR, incidence rate ratio.

  • Significant values are noted as follows; all other values are not significant:

  • P < 0.05;

  • P < 0.01;

  • P < 0.001.

Age category    
<1 year    
15 years0.82 (0.81, 0.84)0.83 (0.88, 0.95)0.86 (0.85, 0.88)0.87 (0.86, 0.89)
611 years0.91 (0.87, 0.95)0.91 (0.88, 0.94)0.93 (0.91, 0.95)0.93 (0.90, 0.95)
>12 years1.03 (0.99, 1.07)1.17 (1.11, 1.22)1.09 (1.06, 1.13)1.13 (1.09, 1.16)
Race    
White    
Black1.04 (0.99, 1.08)1.00 (0.95, 1.03)1.00 (0.98, 1.03)1.02 (0.98, 1.06)
Other1.09 (1.05, 1.13)1.11 (1.08, 1.15)1.09 (1.06, 1.12)1.08 (1.05, 1.11)
Missing1.00 (0.94, 1.06)1.01 (0.96, 1.06)0.95 (0.92, 0.99)*0.96 (0.93, 0.99)
Sex    
Female1.02 (0.94, 1.06)1.01 (0.99, 1.02)1.01(0.93, 100)1.01 (1.00, 1.02)
Insurance type    
Private    
Public1.13 (1.11, 1.16)1.11 (1.09, 1.14)1.11 (1.09, 1.13)1.11 (1.09, 1.13)
Uninsured1.01 (0.91, 1.11)0.93 (0.89, 0.96)0.92 (0.90, 0.96)0.94 (0.91, 0.96)
Other1.01 (0.96, 1.06)1.10 (1.03, 1.18)1.10 (1.02, 1.19)*1.07 (1.02, 1.13)
Hospital type    
Urban non‐teaching    
Rural0.98 (0.92, 1.04)0.96 (0.92, 1.00)0.97 (0.94, 1.00)0.97 (0.93, 1.00)
Urban teaching (non‐children's)0.99 (0.95, 1.04)1.06 (1.02, 1.10)1.06 (1.02, 1.10)1.03 (0.99, 1.07)
Urban teaching children's1.2 (1.14, 1.26)1.23 (1.16, 1.30)1.28 (1.21, 1.37)1.25 (1.19, 1.31)
Region    
Northeast    
Midwest0.93 (0.88, 0.98)*0.96 (0.92, 1.00)0.95 (0.91, 0.99)*0.95 (0.91, 0.99)*
South0.98 (0.94, 1.02)1.06 (1.02, 1.10)*1.04 (1.00, 1.09)1.03 (0.98, 1.08)
West0.97 (0.92, 1.01)1.22 (1.16, 1.30)*1.02 (0.97, 1.08)1.06 (1.00, 1.12)*
Transfer status    
Transfer1.35 (1.25, 1.46)1.39 (1.27, 1.52)1.31 (1.23, 1.37 )1.16 (1.10, 1.23)
Asthma0.99 (0.96, 1.03)0.97 (0.95, 0.99)0.98 (0.96, 1.00)0.98 (0.97, 1.00)*
Pneumonia Complications0.99 (0.96, 1.03)0.97 (0.95, 0.99)*0.98 (0.96, 1.0)0.98 (0.97, 1.00)*
Any complication2.20 (2.07, 2.34)2.23 (2.07, 2.40)2.22 (2.22, 2.44)2.37 (2.27, 2.47)

DISCUSSION

In this nationally representative sample selected over the past 10 years, we found that publicly insured children hospitalized with CAP have significantly longer LOS than those who are privately insured, and that, since 2000, uninsured children hospitalized with CAP have significantly shorter LOS than those who are privately insured. Though these observed differences are small, they are consistent across all 4 sampled years and, because CAP is one of the most common pediatric inpatient diagnoses, the cumulative impact of the observed differences on hospital LOS is great. Insurance status is often considered a proxy for access to preventive and ambulatory healthcare services or socioeconomic status. However, the underlying mechanisms relating insurance status to healthcare access, utilization, and ultimately, health outcomes are highly complex and difficult to elucidate.17 The observed variation in this study raises questions about the potential influence of insurance status on hospital discharge practices. Additional research is necessary to understand whether there are differences in processes of care (eg, performance of blood cultures or chest radiographs), quality of care, or other outcomes, such as readmissions, related to CAP inpatient management for children with different insurance coverage.

Apart from differences in hospital discharge practices, another possible explanation for uninsured children with CAP having shorter LOS is that these children have less severe disease than privately insured. This may occur if uninsured children with CAP are evaluated in the emergency department rather than the office setting, because emergency department providers may be more likely to admit children with CAP who lack a consistent access to ambulatory primary care services. Countering this alternative, prior studies have shown that uninsured groups are more likely to have greater disease severity than privately insured groups at the time of hospital admission.18, 19 In this study, we attempted to identify children with greater severity of disease using ICD‐9 codes for CAP‐associated complications. Though this is a relatively crude method that might lead to an underestimate of the total number of children with complications, we found that there were no significant differences in the prevalence of CAP‐associated complications between uninsured and insured groups in all sampled years.

On the other hand, uninsured patients may be released earlier by providers in order to reduce the amount of uncompensated care provided, or possibly because parents may urge providers to discharge their children, given their inability to pay forthcoming hospital bills and/or avoid further lost wages due to work absence.20, 21 In California, Bindman et al. demonstrated that decreasing the frequency of Medicaid recertification, and consequently increasing the likelihood of continuous insurance coverage, was associated with a decreased risk of hospitalization for ambulatory‐care sensitive conditions.5

We also found that children admitted to urban teaching children's hospitals with CAP had significantly longer LOS than those admitted to urban non‐teaching hospitals, whereas children in rural hospitals had significantly shorter LOS than those in urban non‐teaching hospitals in 2003. These findings are consistent with prior data from 1996 to1998 demonstrating that children admitted to rural hospitals in New York and Pennsylvania had significantly shorter LOS than large urban hospitals for 19 medical and 9 surgical conditions, including pneumonia.12 These findings may reflect underlying differences in between rural and urban hospital transfer practices, whereby rural hospitals may be more likely than urban hospitals to transfer children with relatively more severe illness to urban referral centers and retain children with less severe illness, leading to shorter LOS.12 Though our empiric understanding of differences in LOS between teaching and non‐teaching hospitals is currently limited, clinical experience supports the notion that there may be decreases in efficiency that occur in teaching hospitals, and are a result of the supervision required for care provided by trainees. It is also possible that, despite our exclusion of comorbid conditions, some children with complex or chronic medical conditions were included in this study. These children are often cared for at teaching hospitals, regardless of the primary cause for admission, and are more likely to have public insurance than other children, thus confounding the relationship between hospital type, insurance type and status, and LOS for children with CAP. The limitations of this dataset preclude further examination of this issue.

There are some limitations to this study. First, the KID data are cross‐sectional and causal inferences are limited. However, our results demonstrating that uninsured children hospitalized with CAP had shorter LOS than privately insured children were quite consistent in each sample year, suggesting that our results are a true association. Additionally, insurance status in KID is typically collected at admission, however, it is not possible to determine whether specific changes to insurance status that occurred during the hospitalization were applied to the data. The impact of this limitation would depend on the type of insurance obtained by the patient. If uninsured patients obtained public insurance, our study would underestimate the increased LOS for publicly insured patients, compared with privately insured patients, but have no effect on the difference in LOS between uninsured and privately insured patients. In the unlikely event that uninsured patients obtained private insurance, then our study would underestimate the difference for uninsured patients, compared with privately insured patients, biasing our current study results towards the null. Second, a substantial proportion of sampled children had missing data for race/ethnicity. To assess the impact of the missing race/ethnicity data on our results, we conducted sensitivity analyses and found that, though difficult to make any definitive conclusions about the relationship between race/ethnicity and LOS for children with CAP, there were no changes to our primary findings regarding differences in LOS between children with different insurance status and type. Third, KID does not include data about other unmeasured confounders (eg, parent income, parent education, regular source of care) that might be related to LOS, as well as a broad spectrum of pediatric outcomes. Serious consideration of expanding KID to include these variables is warranted. Fourth, the other category of insurance is not uniformly coded across states in the KID database. While some states use this category to classify public insurance options other than Medicare and Medicaid, other states include private insurance options in this group. Thus, it is possible that some patients with public insurance are misclassified as having other insurance. We would expect such misclassification to bias our findings towards the null hypothesis. Finally, we focused on the relationship between child health insurance status and CAP, only 1 ambulatory care‐sensitive condition. Additional research examining the relationship between insurance type and other ambulatory care‐sensitive conditions is warranted.

In summary, we found that, after multivariable adjustment, uninsured children hospitalized with community‐acquired pneumonia had significantly shorter LOS than privately insured children, and publicly insured children had a significantly longer hospital stay than privately insured children in these 4 nationally representative samples from 1997 to 2006. Current federal and state efforts to increase enrollment of children into insurance programs are a first step in reducing healthcare disparities. However, insurance coverage alone does not guarantee access to healthcare, thus, these efforts in isolation will likely be insufficient to achieve optimal health for the children of our country. As healthcare reform legislation is implemented, these findings provide hospitals and policy makers additional impetus to develop ways to achieve the ideal length of stay for every child; this ideal state will be achieved when clinical status and course, rather than nonclinical factors such as insurance type or provider's unease with ambulatory follow‐up, determine the duration of hospitalization for every child.

References
  1. Conway PH,Cnaan A,Zaoutis T,Henry BV,Grundmeier RW,Keren R.Recurrent urinary tract infections in children: risk factors and association with prophylactic antimicrobials.JAMA.2007;298:179186.
  2. Conway PH,Keren R.Factors associated with variability in outcomes for children hospitalized with urinary tract infection.J Pediatr.2009;154:789796.
  3. Shah SS,Hall M,Srivastava R,Subramony A,Levin JE.Intravenous immunoglobulin in children with streptococcal toxic shock syndrome.Clin Infect Dis.2009;49:13691376.
  4. Tieder JS,Robertson A,Garrison MM.Pediatric hospital adherence to the standard of care for acute gastroenteritis.Pediatrics.2009;124:e1081e1087.
  5. Bindman AB,Chattopadhyay A,Auerback GM.Medicaid re‐enrollment policies and children's risk of hospitalizations for ambulatory care sensitive conditions.Med Care.2008;46:10491054.
  6. Caskey RN,Davis MM.Differences associated with age, transfer status, and insurance coverage in end‐of‐life hospital care for children.J Hosp Med.2008;3:376383.
  7. Chevarley FM,Owens PL,Zodet MW,Simpson LA,McCormick MC,Dougherty D.Health care for children and youth in the United States: annual report on patterns of coverage, utilization, quality, and expenditures by a county level of urban influence.Ambul Pediatr.2006;6:241264.
  8. Merenstein D,Egleston B,Diener‐West M.Lengths of stay and costs associated with children's hospitals.Pediatrics.2005;115:839844.
  9. Parker JD,Schoendorf KC.Variation in hospital discharges for ambulatory care‐sensitive conditions among children.Pediatrics.2000;106:942948.
  10. Kronman MP,Hersh AL,Feng R,Huang YS,Lee GE,Shah SS.Ambulatory visit rates and antibiotic prescribing for children with pneumonia, 1994–2007.Pediatrics.2011;127:411418.
  11. Washington EL,Shen JJ,Bell R,Coleman C,Shi L.Patterns of hospital‐based pediatric care across diverse ethnicities: the case of pneumonia.J Health Care Poor Underserved.2004;15:462473.
  12. Lorch SA,Zhang X,Rosenbaum PR,Evan‐Shoshan O,Silber JH.Equivalent lengths of stay of pediatric patients hospitalized in rural and nonrural hospitals.Pediatrics.2004;114:e400e408.
  13. Eisert S,Gabow P.Effect of Child Health Insurance Plan enrollment on the utilization of health care services by children using a public safety net system.Pediatrics.2002;110:940945.
  14. Wood PR,Smith LA,Romero D,Bradshaw P,Wise PH,Chavkin W.Relationships between welfare status, health insurance status, and health and medical care among children with asthma.Am J Public Health.2002;92:14461452.
  15. HCUP Kids' Inpatient Database (KID). Healthcare Cost and Utilization Project (HCUP), 1997, 2000, 2003, 2006. Agency for Healthcare Research and Quality. Available at: http://www.hcup‐us.ahrq.gov/kidoverview.jsp. Accessed May 17,2010.
  16. Whittle J,Fine MJ,Joyce DZ, et al.Community‐acquired pneumonia: can it be defined with claims data?Am J Med Qual.1997;12:187193.
  17. Hadley J.Sicker and poorer—the consequences of being uninsured: a review of the research on the relationship between health insurance, medical care use, health, work, and income.Med Care Res Rev.2003;60:3S75S; discussion76S–112S.
  18. McConnochie KM,Russo MJ,McBride JT,Szilagyi PG,Brooks AM,Roghmann KJ.Socioeconomic variation in asthma hospitalization: excess utilization or greater need?Pediatrics.1999;103:e75.
  19. Abdullah F,Zhang Y,Lardaro T, et al.Analysis of 23 million US hospitalizations: uninsured children have higher all‐cause in‐hospital mortality.J Public Health (Oxf).2010;32(2)236244.
  20. Heymann SJ,Earle A.The impact of welfare reform on parents' ability to care for their children's health.Am J Public Health.1999;89:502505.
  21. Smith LA,Wise PH,Wampler NS.Knowledge of welfare reform program provisions among families of children with chronic conditions.Am J Public Health.2002;92:228230.
References
  1. Conway PH,Cnaan A,Zaoutis T,Henry BV,Grundmeier RW,Keren R.Recurrent urinary tract infections in children: risk factors and association with prophylactic antimicrobials.JAMA.2007;298:179186.
  2. Conway PH,Keren R.Factors associated with variability in outcomes for children hospitalized with urinary tract infection.J Pediatr.2009;154:789796.
  3. Shah SS,Hall M,Srivastava R,Subramony A,Levin JE.Intravenous immunoglobulin in children with streptococcal toxic shock syndrome.Clin Infect Dis.2009;49:13691376.
  4. Tieder JS,Robertson A,Garrison MM.Pediatric hospital adherence to the standard of care for acute gastroenteritis.Pediatrics.2009;124:e1081e1087.
  5. Bindman AB,Chattopadhyay A,Auerback GM.Medicaid re‐enrollment policies and children's risk of hospitalizations for ambulatory care sensitive conditions.Med Care.2008;46:10491054.
  6. Caskey RN,Davis MM.Differences associated with age, transfer status, and insurance coverage in end‐of‐life hospital care for children.J Hosp Med.2008;3:376383.
  7. Chevarley FM,Owens PL,Zodet MW,Simpson LA,McCormick MC,Dougherty D.Health care for children and youth in the United States: annual report on patterns of coverage, utilization, quality, and expenditures by a county level of urban influence.Ambul Pediatr.2006;6:241264.
  8. Merenstein D,Egleston B,Diener‐West M.Lengths of stay and costs associated with children's hospitals.Pediatrics.2005;115:839844.
  9. Parker JD,Schoendorf KC.Variation in hospital discharges for ambulatory care‐sensitive conditions among children.Pediatrics.2000;106:942948.
  10. Kronman MP,Hersh AL,Feng R,Huang YS,Lee GE,Shah SS.Ambulatory visit rates and antibiotic prescribing for children with pneumonia, 1994–2007.Pediatrics.2011;127:411418.
  11. Washington EL,Shen JJ,Bell R,Coleman C,Shi L.Patterns of hospital‐based pediatric care across diverse ethnicities: the case of pneumonia.J Health Care Poor Underserved.2004;15:462473.
  12. Lorch SA,Zhang X,Rosenbaum PR,Evan‐Shoshan O,Silber JH.Equivalent lengths of stay of pediatric patients hospitalized in rural and nonrural hospitals.Pediatrics.2004;114:e400e408.
  13. Eisert S,Gabow P.Effect of Child Health Insurance Plan enrollment on the utilization of health care services by children using a public safety net system.Pediatrics.2002;110:940945.
  14. Wood PR,Smith LA,Romero D,Bradshaw P,Wise PH,Chavkin W.Relationships between welfare status, health insurance status, and health and medical care among children with asthma.Am J Public Health.2002;92:14461452.
  15. HCUP Kids' Inpatient Database (KID). Healthcare Cost and Utilization Project (HCUP), 1997, 2000, 2003, 2006. Agency for Healthcare Research and Quality. Available at: http://www.hcup‐us.ahrq.gov/kidoverview.jsp. Accessed May 17,2010.
  16. Whittle J,Fine MJ,Joyce DZ, et al.Community‐acquired pneumonia: can it be defined with claims data?Am J Med Qual.1997;12:187193.
  17. Hadley J.Sicker and poorer—the consequences of being uninsured: a review of the research on the relationship between health insurance, medical care use, health, work, and income.Med Care Res Rev.2003;60:3S75S; discussion76S–112S.
  18. McConnochie KM,Russo MJ,McBride JT,Szilagyi PG,Brooks AM,Roghmann KJ.Socioeconomic variation in asthma hospitalization: excess utilization or greater need?Pediatrics.1999;103:e75.
  19. Abdullah F,Zhang Y,Lardaro T, et al.Analysis of 23 million US hospitalizations: uninsured children have higher all‐cause in‐hospital mortality.J Public Health (Oxf).2010;32(2)236244.
  20. Heymann SJ,Earle A.The impact of welfare reform on parents' ability to care for their children's health.Am J Public Health.1999;89:502505.
  21. Smith LA,Wise PH,Wampler NS.Knowledge of welfare reform program provisions among families of children with chronic conditions.Am J Public Health.2002;92:228230.
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MD, MPH, Division of Primary Care Pediatrics, State University of New York at Stony Brook School of Medicine, Health Sciences Center T11 020, Stony Brook, NY 11794‐8111
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Congenital Anomalies in Infant HSV

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Impact of congenital anomalies and treatment location on the outcomes of infants hospitalized with herpes simplex virus (HSV)

Herpes simplex virus (HSV) is a significant cause of pediatric hospitalization, morbidity and mortality, particularly in infants under 60 days of age, where HSV can present as meningoencephalitis, skin disease, or sepsis.14 Most prior studies use data from registries taken from single centers or a restricted group of hospitals. Thus, there is a paucity of recent, nationally‐representative information about the outcome of infants infected with HSV, especially those treated at nonteaching hospitals or with rarer comorbid conditions. The goal of this project was to determine the patient and hospital characteristics associated with worse clinical outcomes in infants under the age of 60 days admitted with HSV disease. We hypothesized that younger infants, infants with a concurrent congenital anomaly, and infants treated at non‐children's hospitals would have worse clinical outcomes. To answer these questions, we used 2003 panel data from the Healthcare Cost and Utilization Project (HCUP) Kids' Inpatient Database (KID), a nationally representative sample of inpatient hospitalizations in the United States.

Methods

Study Population and Data Collection

We conducted a retrospective population cohort study of all infants admitted at 60 days of age who were discharged with a diagnosis of HSV disease between January 1, 2003 and December 31, 2003, using the 2003 KID. The KID is a collaborative project between the Agency for Healthcare Research and Quality AHRQ and 36 states, which includes approximately 2.9 million pediatric discharge records from 3438 hospitals.5 The KID is the only national, all‐payer database of pediatric hospitalizations in the United States.

Patient Eligibility

As in prior studies,611 children were eligible for this project if they were discharged with an International Classification of Disease, ninth edition, Clinical Modification (ICD‐9CM) discharge code of 054.xx (herpes simplex virus), where xx represented any combination of one or two‐digit codes, or 771.2 (neonatal viral infection including HSV). However, the 771.2 code may also contain other perinatal infections of relatively rare frequency, such as toxoplasmosis. Thus, we also performed the same set of analyses on the cohort of children who had an 054.xx code alone. No results presented in this study changed in statistical significance when this smaller cohort of infants was examined.

Data Variables and Outcomes

Outcome Variables

We examined 2 primary clinical outcomes in this study: in‐hospital death and the occurrence of a serious complication. Complications were identified using ICD‐9CM codes from both prior work12 and examination of all diagnosis and procedure codes for eligible infants by the 2 principal investigators (Appendix). These 2 reviewers had to independently agree on the inclusion of an ICD‐9CM code as a complication. In‐hospital deaths were captured through a disposition code of 20 in the KID dataset. Length of stay (LOS) and in‐hospital costs were examined as secondary outcome measures for specific risk factors of interest.

Demographic and Comorbidity Variables

Demographic and comorbidity variables were included in the analyses to control for the increased cost, LOS, or risk of a complication that result from these factors.1315 Demographic information available in the KID included gender, age at admission, race, low birth weight infants, and insurance status. Age at admission was grouped into 4 categories: 07 days, 814 days, 1528 days, and 2960 days. Infants were classified as low birth weight if they had an ICD‐9CM code for a birth weight <2000 g (ICD‐9CM codes 765.01‐07, 765.11‐17, or 765.21‐27). We used the ICD‐9CM codes shown in the Appendix to classify various comorbid conditions. Because of the young age of the cohort, all comorbid conditions consisted of congenital anomalies that were grouped according to the involved organ system. To help classify patients by their illness severity, we used the All‐Patient Refined Diagnosis‐Related Group (APR‐DRG) severity of illness classification for each hospital admission (3M Corporation, St. Paul, MN). The APR‐DRG classification system used discharge diagnoses, procedures, and demographic information to assign patients to 4 severity of illness categories.

Hospital Characteristics

We identified the following hospital characteristics from the KID: total bed size, divided as small, medium, and large; hospital status (children's hospital vs. non‐children's hospital, teaching hospital vs. nonteaching hospital); source of admission (emergency department, clinic, other hospitals); and location (rural vs. urban). Children's hospitals were identified by the AHRQ using information from the National Association of Children's Hospitals and Related Institutions, while teaching hospital status was determined by the presence of an approved residency program and a ratio of full‐time residents to beds of 0.25 or greater.5

Statistical Analysis

All analyses accounted for the complex sampling design with the survey commands included in STATA 9.2 (Statacorp, College Station, TX) and report national estimates from the data available in the 36 surveyed states. Because of the complex sampling design, the Wald test was used to determine significant differences for each outcome in univariable analysis. Variance estimates were reported as standard errors of the mean. We constructed multivariable logistic regression models to assess the adjusted impact of patient and hospital‐level characteristics on each primary outcome measure; ie, in‐hospital death and development of a serious complication. Negative binomial models were used for our secondary outcomes, LOS and costs, because of their rightward skew. Variance estimates for each model accounted for the clustering of data at the hospital level, and data were analyzed as per the latest AHRQ statistical update.16

Results

The 2003 KID identified 1587 hospitalizations for HSV in infants admitted at an age of 60 days or less in the entire United States. These infants had a total hospital cost of $27,147,000. Of the cohort, 10% had a concurrent congenital anomaly. Most infants (73.5%) were admitted within 14 days of birth, and 15.5% were transferred from another hospital. Based on APR‐DRG criteria, 33% of the infants were classified as having a moderate risk of death, 24% as major risk, and 12.2% as extreme risk. The majority of infants were treated at non‐children's hospitals (85.3%) in urban locations (91.5%). The average LOS was 12.0 0.6 days and the average total hospital cost was $17,382 1269. After admission, 267 of the infants, or 16.8%, had at least 1 serious complication. Fifty infants died during the hospitalization included in the KID.

Risk Factor Analysis

Serious Complications

Univariable (Table 1) analysis identified several factors associated with higher rates of serious complications. Younger age at admission was associated with a higher risk of serious complications. This trend was greatest for infants admitted under 14 days of age, of which 20.2% had a serious complication, compared with 10.2% of the infants admitted between 29 and 60 days of age. Infants with any identified congenital anomaly had significantly higher rates of serious complication (41.1% vs. 14.8% for infants without a congenital anomaly). Similar findings were seen with low birth weight infants. Infants who were transferred prior to the hospitalization captured in the KID had a higher complication rate (38.7%) than infants admitted as a routine admission (15.9%) or via the emergency room (8.8%). Among hospital‐level factors, infants admitted to children's or teaching hospitals had higher rates of serious complications, although only the difference between teaching and nonteaching hospitals reached statistical significance (Table 1).

Clinical Outcomes of Infants With HSV
Patient‐Level Factors% of Cohort% with Serious Complication% Death
  • NOTE: Values are adjusted results. Bolt values signify results statistically significant at the p < 0.05 level.

  • Abbreviations: APR‐DRG, all‐patient refined diagnosis‐related group; HSV, herpes simplex virus.

  • Significant differences between groups of factors by Wald test, P < 0.01.

Age at presentation   
7 days58.421.6*4.2*
814 days15.115.83.6
1528 days16.49.72.1
2960 days10.110.20
Low birth weight   
Yes10.644.2*9.0*
No89.414.32.7
Type of insurance   
Private47.415.62.1*
Medicaid49.019.24.8
Self pay3.617.00
Race   
White52.817.73.5
Black18.917.64.2
Other28.319.24.5
Gender   
Female45.415.72.2
Male54.618.94.3
Any congenital anomaly   
Yes10.041.1*10.4*
No90.014.82.6
Admission type   
Routine62.315.9*2.8*
Emergency room22.28.81.1
Transfer from another hospital15.538.79.6
APR‐DRG risk   
Mild3.00.3*0*
Moderate33.02.00.5
Major24.024.72.3
Extreme12.285.020.8
Hospital‐level factors   
Children's hospital   
Yes14.727.06.4
No85.316.33.1
Teaching hospital   
Yes68.421.3*4.3*
No31.78.51.5
Location   
Urban91.518.0*3.6
Rural8.59.01.6
Hospital size   
Small14.119.34.2
Medium25.914.33.2
Large60.018.13.3

Many of these factors were independently associated with increased complication rates in multivariable analysis (Table 2). Infants under 7 days of age on admission (odds ratio [OR], 2.68; 95% confidence interval [CI], 1.112.47), low birth weight (OR, 5.17; 95% CI, 2.988.98), and the concurrent presence of a congenital anomaly (OR, 3.09; 95% CI, 1.805.33) were associated with higher odds of a serious complication. Site of care lost its statistical significance once our models adjusted for differences in illness severity. Insurance status, gender, and race were not associated with a change in complication rates for these infants.

Multivariable Model of Risk Factors Associated With Differences in Serious Complications or Mortality in Infants With HSV
Risk FactorSerious ComplicationMortality
Odds Ratio95% CIOdds Ratio95% CI
  • NOTE: Values are for adjusted results. Bold values signify results statistically significant at the p < 0.05 level.

  • Abbreviations: CI, confidence interval; HSV, herpes simplex virus.

  • No infant admitted between 29 and 60 days of age died in this cohort.

  • All infants died before being transferred to another hospital.

Age at admission    
7 days2.681.112.471.630.347.73
814 days1.220.403.732.150.3612.9
1428 days0.870.322.37Reference*
2960 daysReference 
Racial/ethnic status    
WhiteReferenceReference
Black0.900.451.821.300.433.89
Other0.990.571.701.190.482.99
Treatment at children's hospital2.330.836.182.590.6510.2
Treatment at teaching hospital1.710.943.121.860.566.25
Female gender0.960.631.480.280.100.82
Medicaid insurance1.510.912.501.690.634.53
Transferred from another hospital3.762.036.983.471.428.46
Transferred to another hospital1.350.672.73 
Presence of a congenital anomaly3.091.805.334.261.7610.3
Low birth weight infant5.172.988.985.331.9015.0

Death

Risk factors for higher mortality rates followed similar trends as those for the risk of a serious complication. Younger age at admission, low birth weight status, the presence of a serious complication, admission from another hospital, and treatment at a children's hospital or teaching hospital were all associated with higher mortality rates. In multivariable analysis, the concurrent presence of a congenital anomaly was associated with higher odds of death (OR, 4.26; 95% CI, 1.7610.3). The cause of increased death in infants with congenital anomalies appeared to be a higher rate of serious complications, as including serious complications in the multivariable regression model resulted in the association between congenital anomalies and death losing statistical significance (OR in revised model 1.95; 95% CI, 0.636.05). Site of care again was not associated with differences in mortality after controlling for patient case‐mix.

Concurrent Congenital Anomalies

Based on the higher complication and mortality rates seen in infants with HSV who had a concurrent congenital anomaly, we then investigated how the presence of specific congenital anomalies influenced clinical outcomes, LOS, and total hospital costs with HSV disease. Using the congenital anomaly groups listed in the Appendix, we found that congenital heart disease, central nervous system anomalies, pulmonary anomalies, and gastrointestinal anomalies were each associated with either higher rates of serious complications, longer LOS, or higher total hospital costs compared to infants without congenital anomalies (Table 3). Serious complications occurred most commonly in patients with central nervous system anomalies (55.6%) and congenital heart disease (50.8%), while infants with pulmonary anomalies had the longest LOS (37.1 10.0 days) and highest total hospital costs of all anomaly categories. The types of complications differed by the anomaly group: infants with cardiac and pulmonary anomalies had the highest rates of respiratory complications (45% and 40%, respectively), whereas those with central nervous system anomalies had the highest rates of cardiac complications (51%). Each anomaly class had a similar rate of neurological complications, between 30% and 40%.

Impact of Congenital Anomalies on the Clinical Outcomes and Health Resource Use of Infants Hospitalized With HSV
 Number*% With Serious ComplicationLOS (days)Total Hospital Costs (2003 dollars)
  • NOTE: All reported values are mean standard errors of the mean.

  • Abbreviations: HSV, herpes simplex virus; KID, Kid's Inpatient Database; LOS, length of stay.

  • Numbers of patients are national estimates derived from identified children in the KID.

  • Statistically different from infants without congenital anomalies, P < 0.05.

  • Statistically different from infants without congenital anomalies, P < 0.01.

  • Specific values could not be reported because the number of identified infants with musculoskeletal anomalies was below 10 observations.5

No congenital anomaly139114.811.3 0.615,118 1158
Type of congenital anomaly    
Congenital heart disease7350.823.5 4.646,760 9340
Central nervous system anomaly3155.615.4 3.023,962 5037
Head/neck anomaly1340.611.1 4.614,132 7860
Pulmonary anomaly1334.137.1 10.067,234 21,002
Gastrointestinal anomaly2033.521.6 4.941,207 13,878
Genitourinary anomaly1924.111.0 2.510,906 1890
Musculoskeletal anomaly    
Genetic anomaly1810.212.2 2.415,990 3808

Site of Care

Finally, we examined the LOS and costs of receiving care at a children's hospital. The data shown in Tables 1 and 2 suggest that receiving treatment at a children's hospital does not result in improved clinical outcomes for infants admitted with HSV. One potential advantage, though, is improved efficiency of care, which would result in a shorter LOS or lower costs. Using negative binomial multivariable regression models to account for differences in patient characteristics, regional variation, and insurance status, treatment at a children's hospital was associated with an 18% shorter LOS (95% CI, 1%34%) compared to non‐children's hospitals after accounting for the generally sicker infants treated at children's hospitals. Children's hospitals, though, were more expensive than non‐children's hospitals (increase of $642 per day; 95% CI, $2321052). These results remained consistent when we omitted transferred patients from the model, instead of controlling for them in the analysis.

Conclusions

There has been little prior information to guide practitioners and parents about factors that potentially influence clinical outcome of infants hospitalized with HSV in non‐children's hospitals, although over 80% of infants are managed at non‐children's hospitals. These studies also did not have the power to characterize the risk of poor clinical outcome associated with rarer clinical factors.1, 2, 6 This study, using nationally representative data, found that these rarer clinical factors and site of care may influence the outcomes of infants hospitalized with HSV, albeit in different methods. Younger age at admission and a coexisting congenital anomaly remained statistically significant predictors of worse clinical outcomes after controlling for various patient and hospital factors. Not all congenital anomalies increased the risk of death or serious complications; rather, anomalies that affected either the cardiopulmonary system or the central nervous system appeared to result in the highest increases in risk. This study also found that treatment of infants with HSV at a children's hospital was associated with a 28% shorter LOS after accounting for the sicker patients cared for by children's hospitals. This finding is in contrast to prior studies of common pediatric conditions, where there were no differences in the LOS between children's and non‐children's hospitals,17, 18 and severe sepsis, where children's hospitals had longer LOSs.19 These results confirm the importance of specific risk factors in predicting the likelihood that an infant admitted with HSV may have a poor clinical outcome. Also, these results emphasize the differences in outcomes that may occur at different types of hospitals.

This study is the first to find that certain congenital anomalies or conditions may be associated with worse clinical outcomes from HSV. There is little information in the literature to explain these findings. Those anomalies that affect the cardiopulmonary or central nervous system may either worsen the symptoms of HSV or predispose infants to have a serious complication, such as shock or respiratory failure. This finding would be similar to the increased risk of serious complications seen in infants with congenital heart disease who contract respiratory syncytial virus20 or infants with genetic syndromes who undergo heart surgery.21 Alternatively, because we do not have information on do‐not‐resuscitate status, the presence of one of these congenital anomalies may result in more withdrawal of care when an infant is infected with HSV and has a serious complication; the LOS of these children may not reflect these decisions because the decision to withdrawal care may only occur after the child's condition worsens significantly, which may happen any time during the disease course. However, this theory is less likely because we failed to find similar results with other congenital anomalies such as genetic or chromosomal syndromes. Further examination of these infants and their overall response to insults such as HSV is needed to understand how these anomalies influence the outcomes of a serious, unrelated illness.

Age upon admission was another important predictor of poor outcomes when analyzed in univariable or multivariable analysis. This result is consistent with prior work,14 which suggests that younger children are more likely to be hospitalized with either congenitally acquired HSV or systemic disease. The information contained in the KID does not allow us to determine whether young age is a risk factor for poor outcome irrespective of the clinical presentation of HSV, or whether age serves as a proxy for the appearance of more severe clinical disease. This effect of age remained present even after controlling for the higher risk of a serious complication and death in low birth weight infants. There are limited data that suggest that premature birth is an independent risk factor for worse outcomes associated with perinatal or congenital infection; 1 previous case study of Enterobacter sakazakii infections found a higher fatality rate for premature infants compared to term infants.22 This study supports these findings.

This study found that treatment at a children's hospital resulted in a 28% shorter LOS without a statistically significant difference in clinical outcomes after controlling for case‐mix differences. This finding is in contrast to prior studies of common pediatric conditions17, 18 and severe sepsis.19 There are several potential explanations for the difference in findings. For common pediatric conditions, there may be fewer variations in treatment style and less need for new diagnostic modalities that are more available at academic centers. For HSV disease, though, children's hospitals may also be more likely than non‐children's hospitals to perform polymerase‐chain reaction (PCR) testing for the diagnosis of perinatally acquired HSV, correctly identify the disorder, or receive the test results in a timely fashion. Pediatric subspecialists, such as infectious disease physicians or neurologists, are also likely to be more available at children's hospitals than at other centers. While the role of subspecialty consultation in improving outcomes for neonates with HSV is not known, improved outcomes at children's hospitals has been described for other serious conditions such as splenic injuries.23 Children's hospitals had higher daily costs than non‐children's hospitals, as has been found in other work.17, 19 Children's hospitals may be treating sicker patients, for whom we are unable to adequately adjust for their illness severity with hospital administrative data.17, 19 Also, there may be a greater use of medical tests and treatments that increase the costs of care. These costs do not include indirect costs to the families such as loss of work and travel costs. In light of the shorter LOS in children's hospitals, policy makers will need to balance the potentially higher daily costs of care with more efficient management of the disease process.

Because this study used hospital administrative records, there are a few limitations. We used ICD‐9CM diagnosis codes to identify patients, congenital anomalies, and complications. The diagnosis of some infants with HSV or less significant congenital anomalies could have been missed because clinicians either overlooked the disease or did not make the diagnosis before discharge. This form of spectrum bias would likely miss the infants with the least severe disease and make it more difficult to find the results that we found in this study.24 Prior work successfully used and validated similar ICD‐9CM codes to identify HSV cases among the different types of hospitals included in the KID.611 Our study design estimated 1587 cases of neonatal HSV in 2003. A prospective study of maternal serologic and virologic status during pregnancy estimated 480 to 2160 new cases of neonatal HSV per year.25 Thus, while miscoding is a potential limitation to our study, the overall numbers of patients in this study were similar to past annual estimates. One potential area of miscounting, though, was the inability of the KID to link the records of 16% of the identified infants with HSV whose care was transferred between hospitals. These infants may result in misleading LOS or cost information: lower for the transferring hospital, because they only kept the child a short period of time, or lower for the accepting hospital, as some of the total hospital stay is not accounted for in the KID. We accounted for this issue in 2 ways. First, we included a variable for being transferred in the multivariable models, and found no difference in any results when we omitted these patients from the analysis. Second, we performed a univariable analysis stratified by transfer status, which did not differ substantially from our main model for most variables. Accurate linkage of all the hospital records for an infant's hospital course, likely only through a mandatory reporting system for infant HSV, would help confirm the associations we identified in this study.

In conclusion, infants with congenital anomalies should be closely monitored for the development of serious complications associated with HSV, particularly those infants with congenital heart disease, pulmonary anomalies, or central nervous system anomalies. Closer investigation of the care practices that children's hospitals use in the management of infants with HSV is needed to improve the efficiency of care delivered to these infants, as HSV disease remains a significant public health problem.

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References
  1. Kimberlin DW,Lin CY,Jacobs RF, et al.Natural history of neonatal herpes simplex virus infections in the acyclovir era.Pediatrics.2001;108:223229.
  2. Whitley RJ,Kimberlin DW,Roizman B.Herpes simplex viruses.Clin Infect Dis.1998;26:541553.
  3. Arvin AM,Whitley RJ,Gutierrez KM.Herpes simplex virus infections. In: Remington JS, Wilson CB, Baker CJ, editors.Infectious Diseases of the Fetus and Newborn Infant.5th ed.Philadelphia, PA:W.B. Saunders;2001. p425446.
  4. Whitley RJ,Corey L,Arvin A, et al.Changing presentation of herpes simplex virus infection in neonates.J Infect Dis.1988;158:109116.
  5. Design of the HCUP Kids' Inpatient Database (KID), 2003. Healthcare Cost and Utilization Project (HCUP).Rockville, MD:Agency for Healthcare Research and Quality;2003. Revised January 30, 2006. Available at: http://www.hcup‐us.ahrq.gov/db/nation/kid/reports/KID_2003_Design_Edited_013006.pdf. Accessed October 2009.
  6. Whitley R,Davis EA,Suppapanya N.Incidence of neonatal herpes simplex virus infections in a managed‐care population.Sex Transm Dis.2007;34:704708.
  7. Mark KE,Kim HN,Wald A, et al.Targeted prenatal herpes simplex virus testing: can we identify women at risk of transmission to the neonate.Am J Obstet Gynecol.2006;194:408414.
  8. Szucs TD,Berger K,Fisman DN, et al.The estimated economic burden of genital herpes in the united states.BMC Infect Dis.2001;1:5.
  9. Yasmeen S,Romano PS,Schembri ME, et al.Accuracy of obstetric diagnoses and procedures in hospital discharge data.Am J Obstet Gynecol.2006;194:9921001.
  10. Gutierrez KM,Falkovitz Halpern MS,Maldonado Y, et al.The epidemiology of neonatal herpes simplex virus infections in California from 1985 to 1995.J Infect Dis.1999;180:199202.
  11. Tao G,Kassler WJ,Rein DB.Medical care expenditures for genital herpes in the United States.Sex Transm Dis.2000;27:3238.
  12. Martin GS,Mannino DM,Eaton S, et al.The epidemiology of sepsis in the United States from 1979 through 2000.N Engl J Med.2003;348:15461554.
  13. Shwartz M,Iezzoni LI,Moskowitz MA, et al.The importance of comorbidities in explaining differences in patient costs.Med Care.1996;34:767782.
  14. Yoon PW,Olney RS,Khoury MJ, et al.Contribution of birth defects and genetic diseases to pediatric hospitalizations. A population‐based study.Arch Pediatr Adolesc Med.1997;151:10961103.
  15. Silber JH,Gleeson SP,Zhao H.The influence of chronic disease on resource utilization in common acute pediatric conditions. Financial concerns for children's hospitals.Arch Pediatr Adolesc Med.1999;153:169179.
  16. Health Care Cost and Utility Project.Calculating Kids' Inpatient Database (KID) Variances. December 16, 2005. Methods Series Report # 2005‐5.Rockville, MD:Agency for Healthcare Research and Quality. Available at: http://www.hcup‐us.ahrq.gov/db/nation/kid/reports/CalculatingKIDVariances.pdf. Accessed October2009.
  17. Merenstein D,Egleston B,Diener‐West M.Lengths of stay and costs associated with children's hospitals.Pediatrics.2005;115:839844.
  18. Srivastava R,Homer CJ.Length of stay for common pediatric conditions: teaching versus nonteaching hospitals.Pediatrics.2003;112:278281.
  19. Odetola FO,Gebremariam A,Freed GL.Patient and hospital correlates of clinical outcomes and resource utilization in severe pediatric sepsis.Pediatrics.2007;119:487494.
  20. Welliver RC.Review of epidemiology and clinical risk factors for severe respiratory syncytial virus (RSV) infection.J Pediatr.2003;143:S112S117.
  21. Gaynor JW,Wernovsky G,Jarvik GP, et al.Patient characteristics are important determinants of neurodevelopmental outcome at one year of age after neonatal and infant cardiac surgery.J Thorac Cardiovasc Surg.2007;133:13441353,1353,e1341–e1343.
  22. Lai KK.Enterobacter sakazakii infections among neonates, infants, children, and adults. Case reports and a review of the literature.Medicine.2001;80:113122.
  23. Bowman SM,Zimmerman FJ,Christakis DA, et al.Hospital characteristics associated with the management of pediatric splenic injuries.JAMA.2005;294:26112617.
  24. Mulherin SA,Miller WC.Spectrum bias or spectrum effect? Subgroup variation in diagnostic test evaluation.Ann Intern Med.2002;137:598602.
  25. Brown ZA,Wald A,Morrow RA, et al.Effect of serologic status and cesarean delivery on transmission rates of herpes simplex virus from mother to infant.JAMA.2003;289:203209.
Article PDF
Issue
Journal of Hospital Medicine - 5(3)
Publications
Page Number
154-159
Legacy Keywords
children's hospital, congenital anomaly, herpes simplex virus, length of stay, newborn, pediatric hospitalizations
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Article PDF

Herpes simplex virus (HSV) is a significant cause of pediatric hospitalization, morbidity and mortality, particularly in infants under 60 days of age, where HSV can present as meningoencephalitis, skin disease, or sepsis.14 Most prior studies use data from registries taken from single centers or a restricted group of hospitals. Thus, there is a paucity of recent, nationally‐representative information about the outcome of infants infected with HSV, especially those treated at nonteaching hospitals or with rarer comorbid conditions. The goal of this project was to determine the patient and hospital characteristics associated with worse clinical outcomes in infants under the age of 60 days admitted with HSV disease. We hypothesized that younger infants, infants with a concurrent congenital anomaly, and infants treated at non‐children's hospitals would have worse clinical outcomes. To answer these questions, we used 2003 panel data from the Healthcare Cost and Utilization Project (HCUP) Kids' Inpatient Database (KID), a nationally representative sample of inpatient hospitalizations in the United States.

Methods

Study Population and Data Collection

We conducted a retrospective population cohort study of all infants admitted at 60 days of age who were discharged with a diagnosis of HSV disease between January 1, 2003 and December 31, 2003, using the 2003 KID. The KID is a collaborative project between the Agency for Healthcare Research and Quality AHRQ and 36 states, which includes approximately 2.9 million pediatric discharge records from 3438 hospitals.5 The KID is the only national, all‐payer database of pediatric hospitalizations in the United States.

Patient Eligibility

As in prior studies,611 children were eligible for this project if they were discharged with an International Classification of Disease, ninth edition, Clinical Modification (ICD‐9CM) discharge code of 054.xx (herpes simplex virus), where xx represented any combination of one or two‐digit codes, or 771.2 (neonatal viral infection including HSV). However, the 771.2 code may also contain other perinatal infections of relatively rare frequency, such as toxoplasmosis. Thus, we also performed the same set of analyses on the cohort of children who had an 054.xx code alone. No results presented in this study changed in statistical significance when this smaller cohort of infants was examined.

Data Variables and Outcomes

Outcome Variables

We examined 2 primary clinical outcomes in this study: in‐hospital death and the occurrence of a serious complication. Complications were identified using ICD‐9CM codes from both prior work12 and examination of all diagnosis and procedure codes for eligible infants by the 2 principal investigators (Appendix). These 2 reviewers had to independently agree on the inclusion of an ICD‐9CM code as a complication. In‐hospital deaths were captured through a disposition code of 20 in the KID dataset. Length of stay (LOS) and in‐hospital costs were examined as secondary outcome measures for specific risk factors of interest.

Demographic and Comorbidity Variables

Demographic and comorbidity variables were included in the analyses to control for the increased cost, LOS, or risk of a complication that result from these factors.1315 Demographic information available in the KID included gender, age at admission, race, low birth weight infants, and insurance status. Age at admission was grouped into 4 categories: 07 days, 814 days, 1528 days, and 2960 days. Infants were classified as low birth weight if they had an ICD‐9CM code for a birth weight <2000 g (ICD‐9CM codes 765.01‐07, 765.11‐17, or 765.21‐27). We used the ICD‐9CM codes shown in the Appendix to classify various comorbid conditions. Because of the young age of the cohort, all comorbid conditions consisted of congenital anomalies that were grouped according to the involved organ system. To help classify patients by their illness severity, we used the All‐Patient Refined Diagnosis‐Related Group (APR‐DRG) severity of illness classification for each hospital admission (3M Corporation, St. Paul, MN). The APR‐DRG classification system used discharge diagnoses, procedures, and demographic information to assign patients to 4 severity of illness categories.

Hospital Characteristics

We identified the following hospital characteristics from the KID: total bed size, divided as small, medium, and large; hospital status (children's hospital vs. non‐children's hospital, teaching hospital vs. nonteaching hospital); source of admission (emergency department, clinic, other hospitals); and location (rural vs. urban). Children's hospitals were identified by the AHRQ using information from the National Association of Children's Hospitals and Related Institutions, while teaching hospital status was determined by the presence of an approved residency program and a ratio of full‐time residents to beds of 0.25 or greater.5

Statistical Analysis

All analyses accounted for the complex sampling design with the survey commands included in STATA 9.2 (Statacorp, College Station, TX) and report national estimates from the data available in the 36 surveyed states. Because of the complex sampling design, the Wald test was used to determine significant differences for each outcome in univariable analysis. Variance estimates were reported as standard errors of the mean. We constructed multivariable logistic regression models to assess the adjusted impact of patient and hospital‐level characteristics on each primary outcome measure; ie, in‐hospital death and development of a serious complication. Negative binomial models were used for our secondary outcomes, LOS and costs, because of their rightward skew. Variance estimates for each model accounted for the clustering of data at the hospital level, and data were analyzed as per the latest AHRQ statistical update.16

Results

The 2003 KID identified 1587 hospitalizations for HSV in infants admitted at an age of 60 days or less in the entire United States. These infants had a total hospital cost of $27,147,000. Of the cohort, 10% had a concurrent congenital anomaly. Most infants (73.5%) were admitted within 14 days of birth, and 15.5% were transferred from another hospital. Based on APR‐DRG criteria, 33% of the infants were classified as having a moderate risk of death, 24% as major risk, and 12.2% as extreme risk. The majority of infants were treated at non‐children's hospitals (85.3%) in urban locations (91.5%). The average LOS was 12.0 0.6 days and the average total hospital cost was $17,382 1269. After admission, 267 of the infants, or 16.8%, had at least 1 serious complication. Fifty infants died during the hospitalization included in the KID.

Risk Factor Analysis

Serious Complications

Univariable (Table 1) analysis identified several factors associated with higher rates of serious complications. Younger age at admission was associated with a higher risk of serious complications. This trend was greatest for infants admitted under 14 days of age, of which 20.2% had a serious complication, compared with 10.2% of the infants admitted between 29 and 60 days of age. Infants with any identified congenital anomaly had significantly higher rates of serious complication (41.1% vs. 14.8% for infants without a congenital anomaly). Similar findings were seen with low birth weight infants. Infants who were transferred prior to the hospitalization captured in the KID had a higher complication rate (38.7%) than infants admitted as a routine admission (15.9%) or via the emergency room (8.8%). Among hospital‐level factors, infants admitted to children's or teaching hospitals had higher rates of serious complications, although only the difference between teaching and nonteaching hospitals reached statistical significance (Table 1).

Clinical Outcomes of Infants With HSV
Patient‐Level Factors% of Cohort% with Serious Complication% Death
  • NOTE: Values are adjusted results. Bolt values signify results statistically significant at the p < 0.05 level.

  • Abbreviations: APR‐DRG, all‐patient refined diagnosis‐related group; HSV, herpes simplex virus.

  • Significant differences between groups of factors by Wald test, P < 0.01.

Age at presentation   
7 days58.421.6*4.2*
814 days15.115.83.6
1528 days16.49.72.1
2960 days10.110.20
Low birth weight   
Yes10.644.2*9.0*
No89.414.32.7
Type of insurance   
Private47.415.62.1*
Medicaid49.019.24.8
Self pay3.617.00
Race   
White52.817.73.5
Black18.917.64.2
Other28.319.24.5
Gender   
Female45.415.72.2
Male54.618.94.3
Any congenital anomaly   
Yes10.041.1*10.4*
No90.014.82.6
Admission type   
Routine62.315.9*2.8*
Emergency room22.28.81.1
Transfer from another hospital15.538.79.6
APR‐DRG risk   
Mild3.00.3*0*
Moderate33.02.00.5
Major24.024.72.3
Extreme12.285.020.8
Hospital‐level factors   
Children's hospital   
Yes14.727.06.4
No85.316.33.1
Teaching hospital   
Yes68.421.3*4.3*
No31.78.51.5
Location   
Urban91.518.0*3.6
Rural8.59.01.6
Hospital size   
Small14.119.34.2
Medium25.914.33.2
Large60.018.13.3

Many of these factors were independently associated with increased complication rates in multivariable analysis (Table 2). Infants under 7 days of age on admission (odds ratio [OR], 2.68; 95% confidence interval [CI], 1.112.47), low birth weight (OR, 5.17; 95% CI, 2.988.98), and the concurrent presence of a congenital anomaly (OR, 3.09; 95% CI, 1.805.33) were associated with higher odds of a serious complication. Site of care lost its statistical significance once our models adjusted for differences in illness severity. Insurance status, gender, and race were not associated with a change in complication rates for these infants.

Multivariable Model of Risk Factors Associated With Differences in Serious Complications or Mortality in Infants With HSV
Risk FactorSerious ComplicationMortality
Odds Ratio95% CIOdds Ratio95% CI
  • NOTE: Values are for adjusted results. Bold values signify results statistically significant at the p < 0.05 level.

  • Abbreviations: CI, confidence interval; HSV, herpes simplex virus.

  • No infant admitted between 29 and 60 days of age died in this cohort.

  • All infants died before being transferred to another hospital.

Age at admission    
7 days2.681.112.471.630.347.73
814 days1.220.403.732.150.3612.9
1428 days0.870.322.37Reference*
2960 daysReference 
Racial/ethnic status    
WhiteReferenceReference
Black0.900.451.821.300.433.89
Other0.990.571.701.190.482.99
Treatment at children's hospital2.330.836.182.590.6510.2
Treatment at teaching hospital1.710.943.121.860.566.25
Female gender0.960.631.480.280.100.82
Medicaid insurance1.510.912.501.690.634.53
Transferred from another hospital3.762.036.983.471.428.46
Transferred to another hospital1.350.672.73 
Presence of a congenital anomaly3.091.805.334.261.7610.3
Low birth weight infant5.172.988.985.331.9015.0

Death

Risk factors for higher mortality rates followed similar trends as those for the risk of a serious complication. Younger age at admission, low birth weight status, the presence of a serious complication, admission from another hospital, and treatment at a children's hospital or teaching hospital were all associated with higher mortality rates. In multivariable analysis, the concurrent presence of a congenital anomaly was associated with higher odds of death (OR, 4.26; 95% CI, 1.7610.3). The cause of increased death in infants with congenital anomalies appeared to be a higher rate of serious complications, as including serious complications in the multivariable regression model resulted in the association between congenital anomalies and death losing statistical significance (OR in revised model 1.95; 95% CI, 0.636.05). Site of care again was not associated with differences in mortality after controlling for patient case‐mix.

Concurrent Congenital Anomalies

Based on the higher complication and mortality rates seen in infants with HSV who had a concurrent congenital anomaly, we then investigated how the presence of specific congenital anomalies influenced clinical outcomes, LOS, and total hospital costs with HSV disease. Using the congenital anomaly groups listed in the Appendix, we found that congenital heart disease, central nervous system anomalies, pulmonary anomalies, and gastrointestinal anomalies were each associated with either higher rates of serious complications, longer LOS, or higher total hospital costs compared to infants without congenital anomalies (Table 3). Serious complications occurred most commonly in patients with central nervous system anomalies (55.6%) and congenital heart disease (50.8%), while infants with pulmonary anomalies had the longest LOS (37.1 10.0 days) and highest total hospital costs of all anomaly categories. The types of complications differed by the anomaly group: infants with cardiac and pulmonary anomalies had the highest rates of respiratory complications (45% and 40%, respectively), whereas those with central nervous system anomalies had the highest rates of cardiac complications (51%). Each anomaly class had a similar rate of neurological complications, between 30% and 40%.

Impact of Congenital Anomalies on the Clinical Outcomes and Health Resource Use of Infants Hospitalized With HSV
 Number*% With Serious ComplicationLOS (days)Total Hospital Costs (2003 dollars)
  • NOTE: All reported values are mean standard errors of the mean.

  • Abbreviations: HSV, herpes simplex virus; KID, Kid's Inpatient Database; LOS, length of stay.

  • Numbers of patients are national estimates derived from identified children in the KID.

  • Statistically different from infants without congenital anomalies, P < 0.05.

  • Statistically different from infants without congenital anomalies, P < 0.01.

  • Specific values could not be reported because the number of identified infants with musculoskeletal anomalies was below 10 observations.5

No congenital anomaly139114.811.3 0.615,118 1158
Type of congenital anomaly    
Congenital heart disease7350.823.5 4.646,760 9340
Central nervous system anomaly3155.615.4 3.023,962 5037
Head/neck anomaly1340.611.1 4.614,132 7860
Pulmonary anomaly1334.137.1 10.067,234 21,002
Gastrointestinal anomaly2033.521.6 4.941,207 13,878
Genitourinary anomaly1924.111.0 2.510,906 1890
Musculoskeletal anomaly    
Genetic anomaly1810.212.2 2.415,990 3808

Site of Care

Finally, we examined the LOS and costs of receiving care at a children's hospital. The data shown in Tables 1 and 2 suggest that receiving treatment at a children's hospital does not result in improved clinical outcomes for infants admitted with HSV. One potential advantage, though, is improved efficiency of care, which would result in a shorter LOS or lower costs. Using negative binomial multivariable regression models to account for differences in patient characteristics, regional variation, and insurance status, treatment at a children's hospital was associated with an 18% shorter LOS (95% CI, 1%34%) compared to non‐children's hospitals after accounting for the generally sicker infants treated at children's hospitals. Children's hospitals, though, were more expensive than non‐children's hospitals (increase of $642 per day; 95% CI, $2321052). These results remained consistent when we omitted transferred patients from the model, instead of controlling for them in the analysis.

Conclusions

There has been little prior information to guide practitioners and parents about factors that potentially influence clinical outcome of infants hospitalized with HSV in non‐children's hospitals, although over 80% of infants are managed at non‐children's hospitals. These studies also did not have the power to characterize the risk of poor clinical outcome associated with rarer clinical factors.1, 2, 6 This study, using nationally representative data, found that these rarer clinical factors and site of care may influence the outcomes of infants hospitalized with HSV, albeit in different methods. Younger age at admission and a coexisting congenital anomaly remained statistically significant predictors of worse clinical outcomes after controlling for various patient and hospital factors. Not all congenital anomalies increased the risk of death or serious complications; rather, anomalies that affected either the cardiopulmonary system or the central nervous system appeared to result in the highest increases in risk. This study also found that treatment of infants with HSV at a children's hospital was associated with a 28% shorter LOS after accounting for the sicker patients cared for by children's hospitals. This finding is in contrast to prior studies of common pediatric conditions, where there were no differences in the LOS between children's and non‐children's hospitals,17, 18 and severe sepsis, where children's hospitals had longer LOSs.19 These results confirm the importance of specific risk factors in predicting the likelihood that an infant admitted with HSV may have a poor clinical outcome. Also, these results emphasize the differences in outcomes that may occur at different types of hospitals.

This study is the first to find that certain congenital anomalies or conditions may be associated with worse clinical outcomes from HSV. There is little information in the literature to explain these findings. Those anomalies that affect the cardiopulmonary or central nervous system may either worsen the symptoms of HSV or predispose infants to have a serious complication, such as shock or respiratory failure. This finding would be similar to the increased risk of serious complications seen in infants with congenital heart disease who contract respiratory syncytial virus20 or infants with genetic syndromes who undergo heart surgery.21 Alternatively, because we do not have information on do‐not‐resuscitate status, the presence of one of these congenital anomalies may result in more withdrawal of care when an infant is infected with HSV and has a serious complication; the LOS of these children may not reflect these decisions because the decision to withdrawal care may only occur after the child's condition worsens significantly, which may happen any time during the disease course. However, this theory is less likely because we failed to find similar results with other congenital anomalies such as genetic or chromosomal syndromes. Further examination of these infants and their overall response to insults such as HSV is needed to understand how these anomalies influence the outcomes of a serious, unrelated illness.

Age upon admission was another important predictor of poor outcomes when analyzed in univariable or multivariable analysis. This result is consistent with prior work,14 which suggests that younger children are more likely to be hospitalized with either congenitally acquired HSV or systemic disease. The information contained in the KID does not allow us to determine whether young age is a risk factor for poor outcome irrespective of the clinical presentation of HSV, or whether age serves as a proxy for the appearance of more severe clinical disease. This effect of age remained present even after controlling for the higher risk of a serious complication and death in low birth weight infants. There are limited data that suggest that premature birth is an independent risk factor for worse outcomes associated with perinatal or congenital infection; 1 previous case study of Enterobacter sakazakii infections found a higher fatality rate for premature infants compared to term infants.22 This study supports these findings.

This study found that treatment at a children's hospital resulted in a 28% shorter LOS without a statistically significant difference in clinical outcomes after controlling for case‐mix differences. This finding is in contrast to prior studies of common pediatric conditions17, 18 and severe sepsis.19 There are several potential explanations for the difference in findings. For common pediatric conditions, there may be fewer variations in treatment style and less need for new diagnostic modalities that are more available at academic centers. For HSV disease, though, children's hospitals may also be more likely than non‐children's hospitals to perform polymerase‐chain reaction (PCR) testing for the diagnosis of perinatally acquired HSV, correctly identify the disorder, or receive the test results in a timely fashion. Pediatric subspecialists, such as infectious disease physicians or neurologists, are also likely to be more available at children's hospitals than at other centers. While the role of subspecialty consultation in improving outcomes for neonates with HSV is not known, improved outcomes at children's hospitals has been described for other serious conditions such as splenic injuries.23 Children's hospitals had higher daily costs than non‐children's hospitals, as has been found in other work.17, 19 Children's hospitals may be treating sicker patients, for whom we are unable to adequately adjust for their illness severity with hospital administrative data.17, 19 Also, there may be a greater use of medical tests and treatments that increase the costs of care. These costs do not include indirect costs to the families such as loss of work and travel costs. In light of the shorter LOS in children's hospitals, policy makers will need to balance the potentially higher daily costs of care with more efficient management of the disease process.

Because this study used hospital administrative records, there are a few limitations. We used ICD‐9CM diagnosis codes to identify patients, congenital anomalies, and complications. The diagnosis of some infants with HSV or less significant congenital anomalies could have been missed because clinicians either overlooked the disease or did not make the diagnosis before discharge. This form of spectrum bias would likely miss the infants with the least severe disease and make it more difficult to find the results that we found in this study.24 Prior work successfully used and validated similar ICD‐9CM codes to identify HSV cases among the different types of hospitals included in the KID.611 Our study design estimated 1587 cases of neonatal HSV in 2003. A prospective study of maternal serologic and virologic status during pregnancy estimated 480 to 2160 new cases of neonatal HSV per year.25 Thus, while miscoding is a potential limitation to our study, the overall numbers of patients in this study were similar to past annual estimates. One potential area of miscounting, though, was the inability of the KID to link the records of 16% of the identified infants with HSV whose care was transferred between hospitals. These infants may result in misleading LOS or cost information: lower for the transferring hospital, because they only kept the child a short period of time, or lower for the accepting hospital, as some of the total hospital stay is not accounted for in the KID. We accounted for this issue in 2 ways. First, we included a variable for being transferred in the multivariable models, and found no difference in any results when we omitted these patients from the analysis. Second, we performed a univariable analysis stratified by transfer status, which did not differ substantially from our main model for most variables. Accurate linkage of all the hospital records for an infant's hospital course, likely only through a mandatory reporting system for infant HSV, would help confirm the associations we identified in this study.

In conclusion, infants with congenital anomalies should be closely monitored for the development of serious complications associated with HSV, particularly those infants with congenital heart disease, pulmonary anomalies, or central nervous system anomalies. Closer investigation of the care practices that children's hospitals use in the management of infants with HSV is needed to improve the efficiency of care delivered to these infants, as HSV disease remains a significant public health problem.

Herpes simplex virus (HSV) is a significant cause of pediatric hospitalization, morbidity and mortality, particularly in infants under 60 days of age, where HSV can present as meningoencephalitis, skin disease, or sepsis.14 Most prior studies use data from registries taken from single centers or a restricted group of hospitals. Thus, there is a paucity of recent, nationally‐representative information about the outcome of infants infected with HSV, especially those treated at nonteaching hospitals or with rarer comorbid conditions. The goal of this project was to determine the patient and hospital characteristics associated with worse clinical outcomes in infants under the age of 60 days admitted with HSV disease. We hypothesized that younger infants, infants with a concurrent congenital anomaly, and infants treated at non‐children's hospitals would have worse clinical outcomes. To answer these questions, we used 2003 panel data from the Healthcare Cost and Utilization Project (HCUP) Kids' Inpatient Database (KID), a nationally representative sample of inpatient hospitalizations in the United States.

Methods

Study Population and Data Collection

We conducted a retrospective population cohort study of all infants admitted at 60 days of age who were discharged with a diagnosis of HSV disease between January 1, 2003 and December 31, 2003, using the 2003 KID. The KID is a collaborative project between the Agency for Healthcare Research and Quality AHRQ and 36 states, which includes approximately 2.9 million pediatric discharge records from 3438 hospitals.5 The KID is the only national, all‐payer database of pediatric hospitalizations in the United States.

Patient Eligibility

As in prior studies,611 children were eligible for this project if they were discharged with an International Classification of Disease, ninth edition, Clinical Modification (ICD‐9CM) discharge code of 054.xx (herpes simplex virus), where xx represented any combination of one or two‐digit codes, or 771.2 (neonatal viral infection including HSV). However, the 771.2 code may also contain other perinatal infections of relatively rare frequency, such as toxoplasmosis. Thus, we also performed the same set of analyses on the cohort of children who had an 054.xx code alone. No results presented in this study changed in statistical significance when this smaller cohort of infants was examined.

Data Variables and Outcomes

Outcome Variables

We examined 2 primary clinical outcomes in this study: in‐hospital death and the occurrence of a serious complication. Complications were identified using ICD‐9CM codes from both prior work12 and examination of all diagnosis and procedure codes for eligible infants by the 2 principal investigators (Appendix). These 2 reviewers had to independently agree on the inclusion of an ICD‐9CM code as a complication. In‐hospital deaths were captured through a disposition code of 20 in the KID dataset. Length of stay (LOS) and in‐hospital costs were examined as secondary outcome measures for specific risk factors of interest.

Demographic and Comorbidity Variables

Demographic and comorbidity variables were included in the analyses to control for the increased cost, LOS, or risk of a complication that result from these factors.1315 Demographic information available in the KID included gender, age at admission, race, low birth weight infants, and insurance status. Age at admission was grouped into 4 categories: 07 days, 814 days, 1528 days, and 2960 days. Infants were classified as low birth weight if they had an ICD‐9CM code for a birth weight <2000 g (ICD‐9CM codes 765.01‐07, 765.11‐17, or 765.21‐27). We used the ICD‐9CM codes shown in the Appendix to classify various comorbid conditions. Because of the young age of the cohort, all comorbid conditions consisted of congenital anomalies that were grouped according to the involved organ system. To help classify patients by their illness severity, we used the All‐Patient Refined Diagnosis‐Related Group (APR‐DRG) severity of illness classification for each hospital admission (3M Corporation, St. Paul, MN). The APR‐DRG classification system used discharge diagnoses, procedures, and demographic information to assign patients to 4 severity of illness categories.

Hospital Characteristics

We identified the following hospital characteristics from the KID: total bed size, divided as small, medium, and large; hospital status (children's hospital vs. non‐children's hospital, teaching hospital vs. nonteaching hospital); source of admission (emergency department, clinic, other hospitals); and location (rural vs. urban). Children's hospitals were identified by the AHRQ using information from the National Association of Children's Hospitals and Related Institutions, while teaching hospital status was determined by the presence of an approved residency program and a ratio of full‐time residents to beds of 0.25 or greater.5

Statistical Analysis

All analyses accounted for the complex sampling design with the survey commands included in STATA 9.2 (Statacorp, College Station, TX) and report national estimates from the data available in the 36 surveyed states. Because of the complex sampling design, the Wald test was used to determine significant differences for each outcome in univariable analysis. Variance estimates were reported as standard errors of the mean. We constructed multivariable logistic regression models to assess the adjusted impact of patient and hospital‐level characteristics on each primary outcome measure; ie, in‐hospital death and development of a serious complication. Negative binomial models were used for our secondary outcomes, LOS and costs, because of their rightward skew. Variance estimates for each model accounted for the clustering of data at the hospital level, and data were analyzed as per the latest AHRQ statistical update.16

Results

The 2003 KID identified 1587 hospitalizations for HSV in infants admitted at an age of 60 days or less in the entire United States. These infants had a total hospital cost of $27,147,000. Of the cohort, 10% had a concurrent congenital anomaly. Most infants (73.5%) were admitted within 14 days of birth, and 15.5% were transferred from another hospital. Based on APR‐DRG criteria, 33% of the infants were classified as having a moderate risk of death, 24% as major risk, and 12.2% as extreme risk. The majority of infants were treated at non‐children's hospitals (85.3%) in urban locations (91.5%). The average LOS was 12.0 0.6 days and the average total hospital cost was $17,382 1269. After admission, 267 of the infants, or 16.8%, had at least 1 serious complication. Fifty infants died during the hospitalization included in the KID.

Risk Factor Analysis

Serious Complications

Univariable (Table 1) analysis identified several factors associated with higher rates of serious complications. Younger age at admission was associated with a higher risk of serious complications. This trend was greatest for infants admitted under 14 days of age, of which 20.2% had a serious complication, compared with 10.2% of the infants admitted between 29 and 60 days of age. Infants with any identified congenital anomaly had significantly higher rates of serious complication (41.1% vs. 14.8% for infants without a congenital anomaly). Similar findings were seen with low birth weight infants. Infants who were transferred prior to the hospitalization captured in the KID had a higher complication rate (38.7%) than infants admitted as a routine admission (15.9%) or via the emergency room (8.8%). Among hospital‐level factors, infants admitted to children's or teaching hospitals had higher rates of serious complications, although only the difference between teaching and nonteaching hospitals reached statistical significance (Table 1).

Clinical Outcomes of Infants With HSV
Patient‐Level Factors% of Cohort% with Serious Complication% Death
  • NOTE: Values are adjusted results. Bolt values signify results statistically significant at the p < 0.05 level.

  • Abbreviations: APR‐DRG, all‐patient refined diagnosis‐related group; HSV, herpes simplex virus.

  • Significant differences between groups of factors by Wald test, P < 0.01.

Age at presentation   
7 days58.421.6*4.2*
814 days15.115.83.6
1528 days16.49.72.1
2960 days10.110.20
Low birth weight   
Yes10.644.2*9.0*
No89.414.32.7
Type of insurance   
Private47.415.62.1*
Medicaid49.019.24.8
Self pay3.617.00
Race   
White52.817.73.5
Black18.917.64.2
Other28.319.24.5
Gender   
Female45.415.72.2
Male54.618.94.3
Any congenital anomaly   
Yes10.041.1*10.4*
No90.014.82.6
Admission type   
Routine62.315.9*2.8*
Emergency room22.28.81.1
Transfer from another hospital15.538.79.6
APR‐DRG risk   
Mild3.00.3*0*
Moderate33.02.00.5
Major24.024.72.3
Extreme12.285.020.8
Hospital‐level factors   
Children's hospital   
Yes14.727.06.4
No85.316.33.1
Teaching hospital   
Yes68.421.3*4.3*
No31.78.51.5
Location   
Urban91.518.0*3.6
Rural8.59.01.6
Hospital size   
Small14.119.34.2
Medium25.914.33.2
Large60.018.13.3

Many of these factors were independently associated with increased complication rates in multivariable analysis (Table 2). Infants under 7 days of age on admission (odds ratio [OR], 2.68; 95% confidence interval [CI], 1.112.47), low birth weight (OR, 5.17; 95% CI, 2.988.98), and the concurrent presence of a congenital anomaly (OR, 3.09; 95% CI, 1.805.33) were associated with higher odds of a serious complication. Site of care lost its statistical significance once our models adjusted for differences in illness severity. Insurance status, gender, and race were not associated with a change in complication rates for these infants.

Multivariable Model of Risk Factors Associated With Differences in Serious Complications or Mortality in Infants With HSV
Risk FactorSerious ComplicationMortality
Odds Ratio95% CIOdds Ratio95% CI
  • NOTE: Values are for adjusted results. Bold values signify results statistically significant at the p < 0.05 level.

  • Abbreviations: CI, confidence interval; HSV, herpes simplex virus.

  • No infant admitted between 29 and 60 days of age died in this cohort.

  • All infants died before being transferred to another hospital.

Age at admission    
7 days2.681.112.471.630.347.73
814 days1.220.403.732.150.3612.9
1428 days0.870.322.37Reference*
2960 daysReference 
Racial/ethnic status    
WhiteReferenceReference
Black0.900.451.821.300.433.89
Other0.990.571.701.190.482.99
Treatment at children's hospital2.330.836.182.590.6510.2
Treatment at teaching hospital1.710.943.121.860.566.25
Female gender0.960.631.480.280.100.82
Medicaid insurance1.510.912.501.690.634.53
Transferred from another hospital3.762.036.983.471.428.46
Transferred to another hospital1.350.672.73 
Presence of a congenital anomaly3.091.805.334.261.7610.3
Low birth weight infant5.172.988.985.331.9015.0

Death

Risk factors for higher mortality rates followed similar trends as those for the risk of a serious complication. Younger age at admission, low birth weight status, the presence of a serious complication, admission from another hospital, and treatment at a children's hospital or teaching hospital were all associated with higher mortality rates. In multivariable analysis, the concurrent presence of a congenital anomaly was associated with higher odds of death (OR, 4.26; 95% CI, 1.7610.3). The cause of increased death in infants with congenital anomalies appeared to be a higher rate of serious complications, as including serious complications in the multivariable regression model resulted in the association between congenital anomalies and death losing statistical significance (OR in revised model 1.95; 95% CI, 0.636.05). Site of care again was not associated with differences in mortality after controlling for patient case‐mix.

Concurrent Congenital Anomalies

Based on the higher complication and mortality rates seen in infants with HSV who had a concurrent congenital anomaly, we then investigated how the presence of specific congenital anomalies influenced clinical outcomes, LOS, and total hospital costs with HSV disease. Using the congenital anomaly groups listed in the Appendix, we found that congenital heart disease, central nervous system anomalies, pulmonary anomalies, and gastrointestinal anomalies were each associated with either higher rates of serious complications, longer LOS, or higher total hospital costs compared to infants without congenital anomalies (Table 3). Serious complications occurred most commonly in patients with central nervous system anomalies (55.6%) and congenital heart disease (50.8%), while infants with pulmonary anomalies had the longest LOS (37.1 10.0 days) and highest total hospital costs of all anomaly categories. The types of complications differed by the anomaly group: infants with cardiac and pulmonary anomalies had the highest rates of respiratory complications (45% and 40%, respectively), whereas those with central nervous system anomalies had the highest rates of cardiac complications (51%). Each anomaly class had a similar rate of neurological complications, between 30% and 40%.

Impact of Congenital Anomalies on the Clinical Outcomes and Health Resource Use of Infants Hospitalized With HSV
 Number*% With Serious ComplicationLOS (days)Total Hospital Costs (2003 dollars)
  • NOTE: All reported values are mean standard errors of the mean.

  • Abbreviations: HSV, herpes simplex virus; KID, Kid's Inpatient Database; LOS, length of stay.

  • Numbers of patients are national estimates derived from identified children in the KID.

  • Statistically different from infants without congenital anomalies, P < 0.05.

  • Statistically different from infants without congenital anomalies, P < 0.01.

  • Specific values could not be reported because the number of identified infants with musculoskeletal anomalies was below 10 observations.5

No congenital anomaly139114.811.3 0.615,118 1158
Type of congenital anomaly    
Congenital heart disease7350.823.5 4.646,760 9340
Central nervous system anomaly3155.615.4 3.023,962 5037
Head/neck anomaly1340.611.1 4.614,132 7860
Pulmonary anomaly1334.137.1 10.067,234 21,002
Gastrointestinal anomaly2033.521.6 4.941,207 13,878
Genitourinary anomaly1924.111.0 2.510,906 1890
Musculoskeletal anomaly    
Genetic anomaly1810.212.2 2.415,990 3808

Site of Care

Finally, we examined the LOS and costs of receiving care at a children's hospital. The data shown in Tables 1 and 2 suggest that receiving treatment at a children's hospital does not result in improved clinical outcomes for infants admitted with HSV. One potential advantage, though, is improved efficiency of care, which would result in a shorter LOS or lower costs. Using negative binomial multivariable regression models to account for differences in patient characteristics, regional variation, and insurance status, treatment at a children's hospital was associated with an 18% shorter LOS (95% CI, 1%34%) compared to non‐children's hospitals after accounting for the generally sicker infants treated at children's hospitals. Children's hospitals, though, were more expensive than non‐children's hospitals (increase of $642 per day; 95% CI, $2321052). These results remained consistent when we omitted transferred patients from the model, instead of controlling for them in the analysis.

Conclusions

There has been little prior information to guide practitioners and parents about factors that potentially influence clinical outcome of infants hospitalized with HSV in non‐children's hospitals, although over 80% of infants are managed at non‐children's hospitals. These studies also did not have the power to characterize the risk of poor clinical outcome associated with rarer clinical factors.1, 2, 6 This study, using nationally representative data, found that these rarer clinical factors and site of care may influence the outcomes of infants hospitalized with HSV, albeit in different methods. Younger age at admission and a coexisting congenital anomaly remained statistically significant predictors of worse clinical outcomes after controlling for various patient and hospital factors. Not all congenital anomalies increased the risk of death or serious complications; rather, anomalies that affected either the cardiopulmonary system or the central nervous system appeared to result in the highest increases in risk. This study also found that treatment of infants with HSV at a children's hospital was associated with a 28% shorter LOS after accounting for the sicker patients cared for by children's hospitals. This finding is in contrast to prior studies of common pediatric conditions, where there were no differences in the LOS between children's and non‐children's hospitals,17, 18 and severe sepsis, where children's hospitals had longer LOSs.19 These results confirm the importance of specific risk factors in predicting the likelihood that an infant admitted with HSV may have a poor clinical outcome. Also, these results emphasize the differences in outcomes that may occur at different types of hospitals.

This study is the first to find that certain congenital anomalies or conditions may be associated with worse clinical outcomes from HSV. There is little information in the literature to explain these findings. Those anomalies that affect the cardiopulmonary or central nervous system may either worsen the symptoms of HSV or predispose infants to have a serious complication, such as shock or respiratory failure. This finding would be similar to the increased risk of serious complications seen in infants with congenital heart disease who contract respiratory syncytial virus20 or infants with genetic syndromes who undergo heart surgery.21 Alternatively, because we do not have information on do‐not‐resuscitate status, the presence of one of these congenital anomalies may result in more withdrawal of care when an infant is infected with HSV and has a serious complication; the LOS of these children may not reflect these decisions because the decision to withdrawal care may only occur after the child's condition worsens significantly, which may happen any time during the disease course. However, this theory is less likely because we failed to find similar results with other congenital anomalies such as genetic or chromosomal syndromes. Further examination of these infants and their overall response to insults such as HSV is needed to understand how these anomalies influence the outcomes of a serious, unrelated illness.

Age upon admission was another important predictor of poor outcomes when analyzed in univariable or multivariable analysis. This result is consistent with prior work,14 which suggests that younger children are more likely to be hospitalized with either congenitally acquired HSV or systemic disease. The information contained in the KID does not allow us to determine whether young age is a risk factor for poor outcome irrespective of the clinical presentation of HSV, or whether age serves as a proxy for the appearance of more severe clinical disease. This effect of age remained present even after controlling for the higher risk of a serious complication and death in low birth weight infants. There are limited data that suggest that premature birth is an independent risk factor for worse outcomes associated with perinatal or congenital infection; 1 previous case study of Enterobacter sakazakii infections found a higher fatality rate for premature infants compared to term infants.22 This study supports these findings.

This study found that treatment at a children's hospital resulted in a 28% shorter LOS without a statistically significant difference in clinical outcomes after controlling for case‐mix differences. This finding is in contrast to prior studies of common pediatric conditions17, 18 and severe sepsis.19 There are several potential explanations for the difference in findings. For common pediatric conditions, there may be fewer variations in treatment style and less need for new diagnostic modalities that are more available at academic centers. For HSV disease, though, children's hospitals may also be more likely than non‐children's hospitals to perform polymerase‐chain reaction (PCR) testing for the diagnosis of perinatally acquired HSV, correctly identify the disorder, or receive the test results in a timely fashion. Pediatric subspecialists, such as infectious disease physicians or neurologists, are also likely to be more available at children's hospitals than at other centers. While the role of subspecialty consultation in improving outcomes for neonates with HSV is not known, improved outcomes at children's hospitals has been described for other serious conditions such as splenic injuries.23 Children's hospitals had higher daily costs than non‐children's hospitals, as has been found in other work.17, 19 Children's hospitals may be treating sicker patients, for whom we are unable to adequately adjust for their illness severity with hospital administrative data.17, 19 Also, there may be a greater use of medical tests and treatments that increase the costs of care. These costs do not include indirect costs to the families such as loss of work and travel costs. In light of the shorter LOS in children's hospitals, policy makers will need to balance the potentially higher daily costs of care with more efficient management of the disease process.

Because this study used hospital administrative records, there are a few limitations. We used ICD‐9CM diagnosis codes to identify patients, congenital anomalies, and complications. The diagnosis of some infants with HSV or less significant congenital anomalies could have been missed because clinicians either overlooked the disease or did not make the diagnosis before discharge. This form of spectrum bias would likely miss the infants with the least severe disease and make it more difficult to find the results that we found in this study.24 Prior work successfully used and validated similar ICD‐9CM codes to identify HSV cases among the different types of hospitals included in the KID.611 Our study design estimated 1587 cases of neonatal HSV in 2003. A prospective study of maternal serologic and virologic status during pregnancy estimated 480 to 2160 new cases of neonatal HSV per year.25 Thus, while miscoding is a potential limitation to our study, the overall numbers of patients in this study were similar to past annual estimates. One potential area of miscounting, though, was the inability of the KID to link the records of 16% of the identified infants with HSV whose care was transferred between hospitals. These infants may result in misleading LOS or cost information: lower for the transferring hospital, because they only kept the child a short period of time, or lower for the accepting hospital, as some of the total hospital stay is not accounted for in the KID. We accounted for this issue in 2 ways. First, we included a variable for being transferred in the multivariable models, and found no difference in any results when we omitted these patients from the analysis. Second, we performed a univariable analysis stratified by transfer status, which did not differ substantially from our main model for most variables. Accurate linkage of all the hospital records for an infant's hospital course, likely only through a mandatory reporting system for infant HSV, would help confirm the associations we identified in this study.

In conclusion, infants with congenital anomalies should be closely monitored for the development of serious complications associated with HSV, particularly those infants with congenital heart disease, pulmonary anomalies, or central nervous system anomalies. Closer investigation of the care practices that children's hospitals use in the management of infants with HSV is needed to improve the efficiency of care delivered to these infants, as HSV disease remains a significant public health problem.

References
  1. Kimberlin DW,Lin CY,Jacobs RF, et al.Natural history of neonatal herpes simplex virus infections in the acyclovir era.Pediatrics.2001;108:223229.
  2. Whitley RJ,Kimberlin DW,Roizman B.Herpes simplex viruses.Clin Infect Dis.1998;26:541553.
  3. Arvin AM,Whitley RJ,Gutierrez KM.Herpes simplex virus infections. In: Remington JS, Wilson CB, Baker CJ, editors.Infectious Diseases of the Fetus and Newborn Infant.5th ed.Philadelphia, PA:W.B. Saunders;2001. p425446.
  4. Whitley RJ,Corey L,Arvin A, et al.Changing presentation of herpes simplex virus infection in neonates.J Infect Dis.1988;158:109116.
  5. Design of the HCUP Kids' Inpatient Database (KID), 2003. Healthcare Cost and Utilization Project (HCUP).Rockville, MD:Agency for Healthcare Research and Quality;2003. Revised January 30, 2006. Available at: http://www.hcup‐us.ahrq.gov/db/nation/kid/reports/KID_2003_Design_Edited_013006.pdf. Accessed October 2009.
  6. Whitley R,Davis EA,Suppapanya N.Incidence of neonatal herpes simplex virus infections in a managed‐care population.Sex Transm Dis.2007;34:704708.
  7. Mark KE,Kim HN,Wald A, et al.Targeted prenatal herpes simplex virus testing: can we identify women at risk of transmission to the neonate.Am J Obstet Gynecol.2006;194:408414.
  8. Szucs TD,Berger K,Fisman DN, et al.The estimated economic burden of genital herpes in the united states.BMC Infect Dis.2001;1:5.
  9. Yasmeen S,Romano PS,Schembri ME, et al.Accuracy of obstetric diagnoses and procedures in hospital discharge data.Am J Obstet Gynecol.2006;194:9921001.
  10. Gutierrez KM,Falkovitz Halpern MS,Maldonado Y, et al.The epidemiology of neonatal herpes simplex virus infections in California from 1985 to 1995.J Infect Dis.1999;180:199202.
  11. Tao G,Kassler WJ,Rein DB.Medical care expenditures for genital herpes in the United States.Sex Transm Dis.2000;27:3238.
  12. Martin GS,Mannino DM,Eaton S, et al.The epidemiology of sepsis in the United States from 1979 through 2000.N Engl J Med.2003;348:15461554.
  13. Shwartz M,Iezzoni LI,Moskowitz MA, et al.The importance of comorbidities in explaining differences in patient costs.Med Care.1996;34:767782.
  14. Yoon PW,Olney RS,Khoury MJ, et al.Contribution of birth defects and genetic diseases to pediatric hospitalizations. A population‐based study.Arch Pediatr Adolesc Med.1997;151:10961103.
  15. Silber JH,Gleeson SP,Zhao H.The influence of chronic disease on resource utilization in common acute pediatric conditions. Financial concerns for children's hospitals.Arch Pediatr Adolesc Med.1999;153:169179.
  16. Health Care Cost and Utility Project.Calculating Kids' Inpatient Database (KID) Variances. December 16, 2005. Methods Series Report # 2005‐5.Rockville, MD:Agency for Healthcare Research and Quality. Available at: http://www.hcup‐us.ahrq.gov/db/nation/kid/reports/CalculatingKIDVariances.pdf. Accessed October2009.
  17. Merenstein D,Egleston B,Diener‐West M.Lengths of stay and costs associated with children's hospitals.Pediatrics.2005;115:839844.
  18. Srivastava R,Homer CJ.Length of stay for common pediatric conditions: teaching versus nonteaching hospitals.Pediatrics.2003;112:278281.
  19. Odetola FO,Gebremariam A,Freed GL.Patient and hospital correlates of clinical outcomes and resource utilization in severe pediatric sepsis.Pediatrics.2007;119:487494.
  20. Welliver RC.Review of epidemiology and clinical risk factors for severe respiratory syncytial virus (RSV) infection.J Pediatr.2003;143:S112S117.
  21. Gaynor JW,Wernovsky G,Jarvik GP, et al.Patient characteristics are important determinants of neurodevelopmental outcome at one year of age after neonatal and infant cardiac surgery.J Thorac Cardiovasc Surg.2007;133:13441353,1353,e1341–e1343.
  22. Lai KK.Enterobacter sakazakii infections among neonates, infants, children, and adults. Case reports and a review of the literature.Medicine.2001;80:113122.
  23. Bowman SM,Zimmerman FJ,Christakis DA, et al.Hospital characteristics associated with the management of pediatric splenic injuries.JAMA.2005;294:26112617.
  24. Mulherin SA,Miller WC.Spectrum bias or spectrum effect? Subgroup variation in diagnostic test evaluation.Ann Intern Med.2002;137:598602.
  25. Brown ZA,Wald A,Morrow RA, et al.Effect of serologic status and cesarean delivery on transmission rates of herpes simplex virus from mother to infant.JAMA.2003;289:203209.
References
  1. Kimberlin DW,Lin CY,Jacobs RF, et al.Natural history of neonatal herpes simplex virus infections in the acyclovir era.Pediatrics.2001;108:223229.
  2. Whitley RJ,Kimberlin DW,Roizman B.Herpes simplex viruses.Clin Infect Dis.1998;26:541553.
  3. Arvin AM,Whitley RJ,Gutierrez KM.Herpes simplex virus infections. In: Remington JS, Wilson CB, Baker CJ, editors.Infectious Diseases of the Fetus and Newborn Infant.5th ed.Philadelphia, PA:W.B. Saunders;2001. p425446.
  4. Whitley RJ,Corey L,Arvin A, et al.Changing presentation of herpes simplex virus infection in neonates.J Infect Dis.1988;158:109116.
  5. Design of the HCUP Kids' Inpatient Database (KID), 2003. Healthcare Cost and Utilization Project (HCUP).Rockville, MD:Agency for Healthcare Research and Quality;2003. Revised January 30, 2006. Available at: http://www.hcup‐us.ahrq.gov/db/nation/kid/reports/KID_2003_Design_Edited_013006.pdf. Accessed October 2009.
  6. Whitley R,Davis EA,Suppapanya N.Incidence of neonatal herpes simplex virus infections in a managed‐care population.Sex Transm Dis.2007;34:704708.
  7. Mark KE,Kim HN,Wald A, et al.Targeted prenatal herpes simplex virus testing: can we identify women at risk of transmission to the neonate.Am J Obstet Gynecol.2006;194:408414.
  8. Szucs TD,Berger K,Fisman DN, et al.The estimated economic burden of genital herpes in the united states.BMC Infect Dis.2001;1:5.
  9. Yasmeen S,Romano PS,Schembri ME, et al.Accuracy of obstetric diagnoses and procedures in hospital discharge data.Am J Obstet Gynecol.2006;194:9921001.
  10. Gutierrez KM,Falkovitz Halpern MS,Maldonado Y, et al.The epidemiology of neonatal herpes simplex virus infections in California from 1985 to 1995.J Infect Dis.1999;180:199202.
  11. Tao G,Kassler WJ,Rein DB.Medical care expenditures for genital herpes in the United States.Sex Transm Dis.2000;27:3238.
  12. Martin GS,Mannino DM,Eaton S, et al.The epidemiology of sepsis in the United States from 1979 through 2000.N Engl J Med.2003;348:15461554.
  13. Shwartz M,Iezzoni LI,Moskowitz MA, et al.The importance of comorbidities in explaining differences in patient costs.Med Care.1996;34:767782.
  14. Yoon PW,Olney RS,Khoury MJ, et al.Contribution of birth defects and genetic diseases to pediatric hospitalizations. A population‐based study.Arch Pediatr Adolesc Med.1997;151:10961103.
  15. Silber JH,Gleeson SP,Zhao H.The influence of chronic disease on resource utilization in common acute pediatric conditions. Financial concerns for children's hospitals.Arch Pediatr Adolesc Med.1999;153:169179.
  16. Health Care Cost and Utility Project.Calculating Kids' Inpatient Database (KID) Variances. December 16, 2005. Methods Series Report # 2005‐5.Rockville, MD:Agency for Healthcare Research and Quality. Available at: http://www.hcup‐us.ahrq.gov/db/nation/kid/reports/CalculatingKIDVariances.pdf. Accessed October2009.
  17. Merenstein D,Egleston B,Diener‐West M.Lengths of stay and costs associated with children's hospitals.Pediatrics.2005;115:839844.
  18. Srivastava R,Homer CJ.Length of stay for common pediatric conditions: teaching versus nonteaching hospitals.Pediatrics.2003;112:278281.
  19. Odetola FO,Gebremariam A,Freed GL.Patient and hospital correlates of clinical outcomes and resource utilization in severe pediatric sepsis.Pediatrics.2007;119:487494.
  20. Welliver RC.Review of epidemiology and clinical risk factors for severe respiratory syncytial virus (RSV) infection.J Pediatr.2003;143:S112S117.
  21. Gaynor JW,Wernovsky G,Jarvik GP, et al.Patient characteristics are important determinants of neurodevelopmental outcome at one year of age after neonatal and infant cardiac surgery.J Thorac Cardiovasc Surg.2007;133:13441353,1353,e1341–e1343.
  22. Lai KK.Enterobacter sakazakii infections among neonates, infants, children, and adults. Case reports and a review of the literature.Medicine.2001;80:113122.
  23. Bowman SM,Zimmerman FJ,Christakis DA, et al.Hospital characteristics associated with the management of pediatric splenic injuries.JAMA.2005;294:26112617.
  24. Mulherin SA,Miller WC.Spectrum bias or spectrum effect? Subgroup variation in diagnostic test evaluation.Ann Intern Med.2002;137:598602.
  25. Brown ZA,Wald A,Morrow RA, et al.Effect of serologic status and cesarean delivery on transmission rates of herpes simplex virus from mother to infant.JAMA.2003;289:203209.
Issue
Journal of Hospital Medicine - 5(3)
Issue
Journal of Hospital Medicine - 5(3)
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154-159
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154-159
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Impact of congenital anomalies and treatment location on the outcomes of infants hospitalized with herpes simplex virus (HSV)
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Impact of congenital anomalies and treatment location on the outcomes of infants hospitalized with herpes simplex virus (HSV)
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children's hospital, congenital anomaly, herpes simplex virus, length of stay, newborn, pediatric hospitalizations
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children's hospital, congenital anomaly, herpes simplex virus, length of stay, newborn, pediatric hospitalizations
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