Journal of Pediatric Psychology Advance Access published online on June 3, 2008
Journal of Pediatric Psychology, doi:10.1093/jpepsy/jsn051
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The Association of Psychiatric Diagnoses, Health Service Use, and Expenditures in Children with Obesity-related Health Conditions
1Department of Clinical and Health Psychology, 2Department of Health Services Research, Management and Policy, University of Florida, 3Department of Pediatrics and Public Health, Children's Hospital, and 4University of Florida Center for Medicaid and the Uninsured, University of Florida
All correspondence concerning this article should be addressed to David M. Janicke, PhD, Department of Clinical and Health Psychology, PO Box 100165, University of Florida, Gainesville, FL 32610, USA. E-mail: djanicke{at}phhp.ufl.edu
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Abstract |
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Objective To examine the association of psychiatric diagnoses and use of health care services in children with obesity-related health conditions. Method A retrospective, longitudinal design was used to examine Medicaid claims data. The data set consisted of 13,688 youth diagnosed with type 2 diabetes, metabolic syndrome, dyslipidemia, or obesity. Results The presence of any type of psychiatric diagnosis was associated with higher health service use. In particular, the presence of an internalizing diagnosis was more consistently associated with higher service use than the presence of an externalizing diagnosis. Children with both an externalizing and internalizing disorder diagnosis had greater service use than children with a diagnosis in only one of these categories. Conclusions These data highlight a subgroup of children with obesity-related health conditions who are at greater risk for higher health service use, and the need for further research on the association between psychiatric diagnosis and health service use.
Key words: children; diabetes; dyslipidemia; health service use; metabolic syndrome; obesity; psychiatric diagnosis.
Pediatric obesity is a major public health issue. Over 33% of children and adolescents in the US are now overweight or at-risk for overweight (Ogden et al., 2006
). Overweight children are at elevated risk for a number of negative health conditions including type 2 diabetes (Fagot-Campagna et al., 2000; Glaser, 1999), metabolic syndrome (Weiss et al., 2004), and cardiovascular risk factors such as dyslipidemia (Field, Cook, & Gillman, 2005; Freedman, Dietz, Srinivasan, & Berenson, 1999). These deleterious health conditions are much more common in overweight children relative to their nonoverweight peers (American Diabetes Association – Consensus Statement, 2003; Duncan, Li, & Zhou, 2004; de Ferranti, Gauvreau, Ludwig, Newburger, & Rifai, 2006; Fagot-Campagna et al., 2000; Mei et al., 2007). Unfortunately, the prevalence of these obesity-related health conditions in children continue to increase (Duncan et al., 2004; Fagot-Campagna et al., 2000)
While the health consequences of obesity are substantial, the economic effects of obesity on the health care system are also significant. Obesity-associated hospital expenditures (adjusted for inflation) for children increased by 3-fold from 1979–1981 to 1997–1999 (Wang & Dietz, 2002). Given the increasing prevalence of negative health conditions associated with overweight status, it is not surprising that overweight children incur greater health care expenditures than their nonoverweight counterparts (Hampl, Carroll, Simon, & Sharma, 2007).
Studies with general pediatric populations provide evidence that social, emotional, and behavioral factors are associated with higher health services use and expenditures (Janicke, Finney, & Riley, 2001; Lavigne et al., 1998). Unfortunately, rates of psychiatric diagnosis appear to be greater in children who are overweight or have obesity-related health conditions relative to children with other chronic health conditions (Hesketh, Wake & Waters, 2004; Janicke, Harman, Kelleher, Zhang, in press; Sjoberg, Nilsson, & Leppert, 2005). While data on causal mechanisms are not conclusive, it is likely that the relationship between psychiatric diagnosis and obesity-related health conditions is bidirectional. In some children, being overweight or having an obesity-related health condition can lead to greater psychosocial distress and psychiatric diagnosis (Mustillo et al., 2003). Alternatively, these same social, emotional, and behavior problems can have a negative impact on the behaviors related to establishing or maintaining a healthy weight status (i.e., dietary intake and physical activity) (Gray, Janicke, Ingerski, & Silverstein, 2008; Storch et al., 2007). Such behaviors contribute to increased weight gain and a greater likelihood that health services will be used to treat obesity-related conditions.
While the adult literature provides some evidence that psychosocial distress is related to greater health care expenditures in those with an obesity-related health condition (Kalsekar et al., 2006), there are no data examining the relationship between psychosocial distress and health service use in children who are overweight or have obesity-related health conditions. As the rates of obesity and related health conditions continue to rise, a relationship between increased expenditures and psychosocial distress could signal the potential for an expansion in health service use and expenditures for segments of the pediatric population, placing an even greater burden on the health care system. Understanding specific diagnostic and demographic factors related to greater service use within this population could have important implications for health service providers, researchers, and policymakers.
The purpose of this study is to determine: (a) in this sample of children with an obesity-related condition, do those with a psychiatric diagnosis have greater nonpsychiatric health services use and expenditures than those without a psychiatric diagnosis; and (b) if category of psychiatric disorder, age, gender, and race are related to greater health services use and expenditures for children with obesity-related health conditions. It is hypothesized that for children with an obesity-related diagnosis, the presence of a psychiatric diagnosis will be related to greater nonpsychiatric service use and expenditures than the absence of a psychiatric diagnosis.
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Methods |
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Claims data from the State of Florida Agency for Health Care Administration Medicaid database, 2001 through 2005, were used in this analysis. The claims database included outpatient and inpatient facility claims, medical/physician claims, and pharmacy claims. All data extracted for analysis were from records for children between the ages of 5.0 and 18.0 years at the time of the corresponding claim. Children were included in this analysis if they were enrolled in Medicaid at any point during the 4-year retrospective period. Only claims for services paid for by Medicaid are included in the analyses.
The International Classification of Disease Ninth Revision (ICD-9) was used to identify and select children with obesity-related health conditions. Only data for children with one of four predefined obesity-related conditions were included in this analysis: type 2 diabetes mellitus, metabolic syndrome, dyslipidemia, and obese/morbidly obese (Table I). These specific medical conditions were selected as a large number of children who receive these diagnoses are overweight or at-risk for overweight (American Diabetes Association, 2003; de Ferranti et al., 2006; Duncan et al., 2004; Fagot-Campagna et al., 2000; Mei et al., 2007). Weight and height data were not available from the Medicaid claims database. We chose not to focus only on children with an ICD-9 code for obesity because the use of this code is often underutilized by medical professionals (Dilley et al., 2007; Hampl et al., 2007).
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Children with a psychiatric condition were identified using the ICD-9 codes from the claims data (Table I). Psychiatric conditions were grouped into three categories: internalizing disorders (i.e., emotional disorders such as depression and anxiety), externalizing disorders (i.e., behavioral disorders such as oppositional defiant disorder or attention-deficit disorder), and other psychiatric disorders. For purposes of this study, a child was considered to have a psychiatric condition if a psychiatric ICD-9 diagnosis on any of their medical claims records was present during the 4-year retrospective period.
State Assistance Category
To be enrolled in Medicaid, each child must qualify under at least one State Assistance Category (SAC). A large number of children qualify based on the category of "supplemental security income (SSI)." In the state of Florida, children are eligible for SSI if they are disabled, as defined by the following criteria: (a) a condition that results in marked or severe functional limitation; and (b) can be expected to result in death or can be expected to last for a continuous period of at least 12 months. Other common SACs through which children in this study qualified for Medicaid include TANF (Temporary Assistance for Needy Families), SOBRA CHILD, and Qualified Medicare Beneficiaries.
Dependent Variables
It is likely that children with a psychiatric diagnosis have additional health care service use for visits specifically addressing the psychiatric condition. This difference in utilization for psychiatric services would present a bias in expenditure and volume analyses as children without a psychiatric condition would be less likely to have used psychiatric services. Thus, only nonpsychiatric health services were included in the analyses. Psychiatric service use was defined as any claim associated with an ICD-9 psychiatric diagnostic code, while nonpsychiatric service referred to claims without an ICD-9 psychiatric diagnostic code. To control for length of enrollment in the Medicaid Program, all volume and expenditure variables were initially totaled across the 4-year period. For each volume and expenditure category, the accumulated totals for each child were then divided by the number of years that the child was enrolled in the Medicaid program. For each child, this created a mean annual value in each volume and expenditure category that was used for analysis, with the exception of inpatient length of stay, the description of which is noted in the following paragraph.
Utilization Data
Total outpatient visits, emergency department (ED) visits, pharmacy claims, and inpatient length of stay that accrued during the 4-year period were calculated from the claims data. ED visits were the combined total of outpatient and inpatient ED visits. The inpatient length of stay variable was created using the beginning and end date of service values for inpatient care. The number of accumulated inpatient days was divided by the number of inpatient admissions to determine the average length of stay for each child.
Medical Expenditures
Expenditures accrued during the 4-year period were calculated from the claims data. Expenditure data were accumulated across the 4-year time span to calculate total outpatient expenditures, total inpatient expenditures, total medical/physician expenditures, and total pharmacy expenditures. An additional variable, total medical expenditures, was the sum of these four expenditure categories. As noted previously, all expenditure variables were converted to mean annual expenditures for analysis. All expenditures were adjusted to 2005 dollars using the Medical Care Consumer Price Index.
Statistical Analysis
Cost Analyses
Separate analysis of covariance (ANCOVA) tests, controlling for child age and SSI, was used to examine differences in medical expenditures incurred by children with and without a psychiatric diagnosis. Differences were examined for total expenditures, as well as subcategories of expenditures. In addition, independent sample t-tests were used to compare the total average expenditures per year across four groups of psychiatric diagnoses: children with only an internalizing diagnosis, those with only an externalizing diagnosis, those with only a diagnosis in the "other psychiatric diagnosis" category, and those with both an internalizing diagnosis and an externalizing diagnosis. The p-value was set at.0125 (.05/4) to adjust for multiple comparisons in the subcategories of psychiatric diagnosis.
Gamma regression analyses were used to predict total nonpsychiatric expenditures for those children with expenditures greater than zero. As expenditure data tend to be highly skewed, ordinary least squares regression often produces biased results. Instead, we used gamma regression which is a nonlinear regression model that directly accounts for the skewed distribution, produces unbiased estimates, and does not require predictions to be retransformed (Manning, Basu, & Mullahy, 2004). The analysis was conducted for the combined category of "any psychiatric diagnosis," as well as for subcategories of psychiatric diagnosis including internalizing, externalizing, and "other psychiatric" diagnosis. Other predictors included in this analysis were race, age, gender, SAC (SSI vs. non-SSI assistance code), and each of the four obesity-related health conditions.
Volume Analyses
ANCOVA tests, controlling for child age and SSI, were also used to examine differences in the volume of health services use between children with and without any psychiatric diagnosis across the four health services use categories, including outpatient visits, ED visits, inpatient length of stay, and pharmacy claims.
Second, negative binomial regression was used to examine the impact of psychiatric diagnoses and other factors on the volume of nonpsychiatric health care services used in each of the four service categories. Negative binomial regressions are useful for analyzing count data, which tend to not meet the assumptions necessary for ordinary least squares regression (Gardner, Mulvey, & Shaw, 1995). Negative binomial regression does not rely on the assumption that the mean of the count variable is equal to the variance. A negative binomial regression model also allows the coefficients to be presented in terms of incidence rate ratios (IRR), which is simply the ratio of two incidence rates based on alternative values of the independent variable. For instance, an IRR of 1.15 for an indicator variable for type 2 diabetes would indicate that the incidence rate for the event in question (e.g., number of physician visits) was 15% greater for individuals with type 2 diabetes compared to individuals without type 2 diabetes. These analyses control for the influence of the other predictor variables included in the model on the dependent variable. Other predictor variables of service use included in this analysis were race, age, gender, SSI, and each of the four obesity-related health conditions.
For race, the "other" category consisted of Asian Americans, children of mixed race, and those who were categorized as "other" on the Medicaid database. There were limited numbers of observations within any specific category of "other race;" thus, we combined them together so we would not lose those observations. Analyses were conducted using Stata Statistical Software.
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Results |
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Sample Characteristics
The final data set contained 13,688 children and adolescents. The mean age of children in the entire sample was 10.5 years (SD = 3.0). Girls made up 52% of the sample. The sample was racially diverse with large percentages of Caucasian (28.7%), African American (25.4%), and Hispanic (33.8%) children. The most frequent medical diagnosis was dyslipidemia (37.1%), followed by metabolic syndrome (29.4%), type 2 diabetes (27.8%), and obese/morbidly obese (17.2%); these categories were not mutually exclusive as many children had more than one of these diagnoses. Of the 13,688 youth, 36.2% (n = 4,952) had at least one psychiatric diagnosis over the past 4 years. Of these 4,592 children, 70.2% (n = 3,476) had an internalizing diagnosis and 72.2% (n = 3,575) had an externalizing diagnosis. However, the number of children in each category was much less when looking at those who had a diagnosis in only that category; 25.4% (n = 1,260) had only an internalizing diagnosis, 22.5% (n = 1,116) had only an externalizing diagnosis, and 29.3% (n = 1,452) had both an externalizing and an internalizing diagnosis only. Table II displays the number of diagnoses in each of these psychiatric categories in isolation.
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Independent sample t-tests found that children with a psychiatric diagnosis were older (M = 10.66; SD = 2.9) than children without a psychiatric diagnosis (M = 10.38; SD = 3.0) (p <.001). In addition, more children with a psychiatric diagnosis received SSI (42.6%) relative to children without a psychiatric diagnosis (11.2%) (
= 746; p <.001).
Expenditures for Nonpsychiatric Health Service Use
ANCOVA analyses showed that, compared to children without a psychiatric diagnosis, children with psychiatric diagnosis had significantly greater yearly total expenditures than children without a psychiatric diagnosis (M = $4,431, SD = $13,963 vs. M = $2,598, SD = $10,170; p =.02). A breakdown of total expenditures into subcategories showed that, relative to their peers without a psychiatric diagnosis, children with a psychiatric diagnosis also had higher average outpatient facilities expenditures (M = $408, SD = 848 vs. M = $302, SD = 819; p =.01), and medical/physician expenditures (M = $2,598, SD = 9,986 vs. M = $1,323, SD = 6,932; p <.001). Total pharmacy costs did not differ between children with and without a psychiatric diagnosis (M = $376, SD = 1,105 vs. M = $276, SD = 3,474; p =.65).
Given the distinct nature of different psychiatric diagnoses, a series of comparisons were also run to examine the differences in total nonpsychiatric expenditures per year across psychiatric diagnostic categories (Table II). Children with both an internalizing and externalizing disorder had higher total expenditures than children with only an internalizing disorder, who in turn had higher expenditures than children with only an externalizing disorder. Children with only an externalizing disorder diagnosis had roughly the same total expenditures as children without any psychiatric diagnosis. Children with a diagnosis only in the "other psychiatric diagnosis" category had total expenditures that were over $10,000 higher those children with only internalizing or externalizing disorders, but these differences did not reach statistical significance due to the large variance in total expenditures for the "other psychiatric diagnosis" category.
Gamma regression analysis was used to determine factors related to higher total expenditures for children that incurred at least some expenditure. Children with a psychiatric diagnosis were significantly more likely to have greater yearly total expenditures [p <.001; β coefficient (β) =.366; confidence interval (CI) =.331–.402], relative to those who do not have a psychiatric diagnosis. Males were more likely to have lower average total expenditures than females (p <.001; β = –.168; CI = –.201 to –.135), while Caucasian youth were more likely to have greater expenditures than African American youth (p <.001; β = –.145; CI = –.190 to –.010). Increased age was associated with higher total expenditures (p <.001; β =.028; CI =.209–.034).
A separate set of gamma regression analyses were conducted to examine the relationships between expenditures and the three categories of psychiatric diagnosis. Table III displays the factors associated with total, outpatient, inpatient, and pharmacy expenditures, with psychiatric diagnosis broken down into internalizing disorder, externalizing disorder, and "other" psychiatric disorder. Relative to children without an internalizing diagnosis, children with an internalizing diagnosis were significantly more likely to have greater total yearly expenditures, outpatient expenditures, and pharmacy expenditures. Similar relationships were found between these expenditures categories and "other psychiatric" diagnosis category. There was no consistent relationship between those with and without an externalizing disorder across expenditures categories.
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Volume of Nonpsychiatric Health Care Service Use
ANCOVA analyses showed that, compared to children without a psychiatric diagnosis, children with a psychiatric diagnosis had greater average yearly outpatient visits (M =.95, SD = 3.5 vs. M = 1.45, SD = 3.2; p <.0125), pharmacy claims (M = 4.3, SD = 6.2 vs. M = 2.9, SD = 5.7; p <.001), and ED visits (M = 0.8, SD = 1.1 vs. M = 0.56, SD = 0.9; p <.001).
Negative binomial regression analysis providing incident rate ratios showed that children with an obesity-related health condition who also had a psychiatric diagnosis incurred 18% more nonpsychiatric outpatient visits per year (IRR = 1.18; CI = 1.13–1.23; p <.001) than those who did not have a psychiatric diagnosis. Similar differences were seen between those with and without any type of psychiatric diagnosis with regards to average inpatient length of stay (IRR = 1.22; CI = 1.06–1.40; p =.005), pharmacy claims (IRR = 1.35; CI = 1.28–1.42; p <.001), and ED visits (IRR = 1.16; CI = 1.11–1.22; p <.001). Males had less use across all categories than females. African American and Hispanic youth had less use than Caucasian children across all categories, with the exception that African American children had a longer average inpatient length of stay (IRR = 1.39; CI = 1.16–1.67; p <.001) relative to Caucasian children.
As with the expenditure analysis, a second set of regression analysis was conducted using categories of psychiatric diagnosis (internalizing, externalizing, or "other psychiatric" diagnosis) to examine factors related to higher average annual utilization of nonpsychiatric health care services. Table IV shows that across all service use categories, the presence of any internalizing diagnosis was related to greater nonpsychiatric service use. The presence of an externalizing disorder was related to shorter inpatient length of stay and less ED use, but greater pharmacy claims.
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Discussion |
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A strong association exists between psychiatric diagnoses and increased health service expenditures and use for children with obesity-related health conditions. While similar relationships have been noted in the general pediatric population (Hampl et al., 2007
; Janicke et al., 2001; Janicke & Finney, 2000), this study is the first to report specifically on children with obesity-related chronic health conditions. For children with an obesity-related health condition, those with a comorbid psychiatric diagnosis had 35% greater outpatient costs, 50% higher inpatient costs, 96% higher medical/physician costs, and 70% higher total costs per year for nonpsychiatric-related health care services than those without a comorbid psychiatric diagnosis. While examination of the impact of psychiatric diagnosis as a single category is enlightening, it ignores the heterogeneous nature of psychiatric conditions. Breaking down psychiatric diagnosis into diagnostic categories provides more information to inform assessment, intervention research and practice, and policy. Separate examination of internalizing and externalizing diagnostic categories shows a strong association between the presence of an internalizing disorder diagnosis and higher nonpsychiatric expenditures. On the other hand, the impact of externalizing diagnosis was mixed, with higher expenditures than children without a psychiatric diagnosis in some services categories, but not all. Not surprisingly, children with both an externalizing and externalizing disorder diagnosis had higher total expenditures than children with a diagnosis in only one of these two categories.
Children with a diagnosis in the "other psychiatric diagnosis" category had greater odds of service use and expenditures than children with both an externalizing and internalizing diagnoses. Although the difference in total expenditures between the "other psychiatric diagnosis" category and internalizing–externalizing categories did not reach statistical significance, those with a diagnosis in the "other psychiatric diagnosis" category had average yearly expenditures that were $10,000 higher than children with both an internalizing and externalizing diagnosis. Much of this difference appears due to increased use of inpatient services. Post hoc inspection of the data showed that most of the expenses for children in the "other"psychiatric diagnosis category were associated with three diagnoses: paranoia, acute reaction to stress, and unspecified emotional disturbance. Thus, these costs may be due to the need for hospitalization associated with more severe forms of psychopathology, which may be related to poorer overall health status.
Unfortunately, no definitive conclusions can be drawn as to the reasons for these relationships. Certainly, a "discovery effect" may have impacted this association, as those with a higher frequency of health visits have more chances to be identified as suffering from a psychiatric condition. However, it is highly likely that multiple and bi-directional factors contribute to the associations observed in this study. Internalizing disorder diagnoses are associated with increased somatic symptoms, which can lead to parents and children seeking care not recognized as being related to psychosocial distress. Moreover, internalizing disorders such as depression and anxiety are associated with behaviors that are not conducive to healthy lifestyles or present as barriers to healthy lifestyle habits (i.e., limited motivation for change, high levels of sedentary activity, disrupted eating patterns, and low self-esteem) (Gray et al., 2008). Ultimately, these behaviors and barriers may contribute to higher weight, poorer health status, and increased medical expenditures. Alternatively, obesity and obesity-related health conditions may lead to greater psychosocial distress and psychiatric diagnosis (Mustillo et al., 2003). A problematic cycle may develop, whereby an obesity-related health condition contributes to increased distress, which can impact healthy lifestyle behaviors, which then contribute to further declines in health status. For example, many overweight children are subjected to negative stigma and problematic peer interactions, which can contribute to negative mood and low self-esteem, and may ultimately impede their willingness to engage in health promoting activities due to fear of future victimization and humiliation (Gray et al., 2008; Pearce, Boergers, & Prinstein, 2002; Storch et al., 2007). Clearly, this is speculation and more research in required to examine these causal mechanisms.
Not surprisingly, a diagnosis of type 2 diabetes was related to higher IRRs, indicating greater service use than the other obesity related diagnoses. Health service use and expenditures also increased with age, which is in contrast to the pattern of decreasing health service use with increasing age observed in the general pediatric population (Janicke & Finney, 2000). However, this is not surprising as it reflects the growing need for medical intervention as health status declines or as medical complications develop in these youth. Moreover, the presence of psychiatric diagnosis increased with age, likely the result of greater opportunities to receive such a diagnosis. Caucasian children used more total and outpatient services than non-Caucasian populations, which is similar to what has been reported in the general pediatric population (Janicke & Finney, 2000). We are uncertain as to the reason for this difference, given the similar access to health care services for families in this study. It may be that African American and Caucasian families hold different, culturally based views of well-being and trust regarding medicine's ability to meet their needs that ultimately impact service use (Blendon et al., 1995). Finally, while research examining the association with gender and health service use in the general pediatric population is inconclusive (Janicke & Finney, 2000), in this sample girls exhibited greater service use than boys across categories.
These results should be interpreted with several limitations in mind. First, defining diagnosis of a psychiatric condition based on ICD-9 codes does not convey information on the degree or length of psychosocial impairment. The use of these codes represents a conservative definition in that many children with psychiatric conditions go undiagnosed in claims data even when treated with psychiatric drugs. Moreover, psychiatric diagnosis seems to be a less effective predictor of health service use than dimensional estimates of behavior and emotional problems (Lavigne et al., 1998). However, specificity in psychiatric diagnosis across broad categories is high. Second, this study did not control for other comorbidities, or other factors that impact health status, weight status, and psychopathology, such as socioeconomic status and parental psychosocial distress. Since this sample comes from a Medicaid population, the number of children from lower social economic status backgrounds relative to the general population is most-likely overrepresented. Finally, we choose to exclude psychiatric service use from our analyses. Psychiatric visits reflect a real burden on the health care system, which is not reflected in these results. It is likely that if these costs were included, the reported differences between those with and without a psychiatric diagnosis would be even greater than noted here.
The relationships noted here have significant implications for policy decisions and medical providers. Recent data suggest that overweight children incur greater health care costs than their nonoverweight counterparts (Hampl et al., 2007). This study identifies a subgroup of children with obesity-related health conditions who appear at even greater risk for increased health service expenditures. Given the alarming growth of pediatric obesity and related health conditions (Ogden et al., 2006), as well as the strong association between these conditions and psychiatric distress (Hesketh et al., 2004; Janicke et al., 2006; Mustillo et al., 2003; Sjoberg et al., 2005) indications are that this subgroup will only expand, which highlights the potential for increased demands on an already burdened health care system.
This study contributes to the limited body of literature examining health service use and related expenditures in youth with obesity-related health conditions, adding a specific emphasis on the impact of psychiatric diagnosis on service use. Our data highlight the importance of thorough assessment of psychosocial functioning, including the presence of social and emotional problems in children with obesity-related health conditions. Further research is needed to examine if improvement in overweight status and psychosocial functioning are associated with reductions in medical expenditures and heath care service use. As the relationships examined in the study are based on obesity-related health conditions, longitudinal research examining the association between actual weight status, psychosocial functioning, and health service expenditures in children are needed. Previous research has demonstrated that a variety of parent and family factors not assessed in this study are associated with health care use in the general pediatric population. Given that obesity runs in families (Whitaker, Wright, Pepe, Seidel, & Dietz, 1997), determining if and how child weight status interacts with these additional parent factors to impact health service use may further inform policy and prevention practices that can address care for the entire family unit. As the number of youth diagnosed with obesity-related conditions increases, it will be vital to further expand our understanding of factors that impact child health status and service use to establish efficient and cost-effective health service policy and practice.
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This research was funded by a contract from the Florida Agency for Health Care Administration to the University of Florida Center for Medicaid and the Uninsured.
Conflicts of interest: None declared.
Received February 12, 2008; revision received May 3, 2008; accepted May 3, 2008
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