PEDIATRICS Vol. 122 No. 2 August 2008, pp. 347-359 (doi:10.1542/peds.2007-1406)
ARTICLE |
Who is at Risk for Special Health Care Needs: Findings From the National Survey of Children's Health
a Institute for Health Policy Studies
b Departments of Pediatrics
c Medicine, University of California, San Francisco, California
d National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, Maryland
e Robert Wood Johnson Clinical Scholars Program, Yale University, New Haven, Connecticut
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ABSTRACT |
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 TOP ABSTRACT METHODSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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OBJECTIVE. A conceptual model of risk factors for special health care needs in childhood was presented previously. This article uses that conceptual model to identify candidate variables for an exploratory empirical examination of the effects of factors that may increase or decrease the risk of developing a special health care need.
METHODS. The National Survey of Children's Health was used for our analysis (N = 102 353). We used multilevel and multivariate analysis methods. We examined risk factors for special health care needs generally and for specific physical, developmental, behavioral, and emotional conditions cooccurring with special health care needs. Risk factors were grouped into 6 major domains, namely, predisposing characteristics, genetic endowment, physical environment, social environment, health-influencing behavior, and health care system characteristics. We examined preschool-aged and school-aged children separately.
RESULTS. Significant associations were found in 5 of 6 domains studied (no variables in the health care systems characteristics were significant). Individual variables found to decrease or to increase significantly the odds of experiencing special health care needs were expressed at the child level (eg, age and gender), family level (eg, family structure and family conflict), and neighborhood level (eg, perception of supportiveness of the neighborhood).
CONCLUSIONS. This analysis is the first to consider empirically a range of risk factors for special health care needs, using a population health model. Although provisional, the results of our analysis can help us to begin thinking about which characteristics of the child, family, and community are worthy of further exploration. Some of the variables we found to be significantly associated with special health care needs, such as age and ethnicity, are immutable. However, we found a number of significant correlates (ie, possible risk factors) that may be amenable to public health interventions, including breastfeeding practices, exposure to secondhand smoke, family closeness, and neighborhood cohesion.
Key Words: children with special health care needs • risk factors • outcomes • epidemiology • National Survey of Children's Health
Abbreviations: CSHCN—children with special health care needs • ADHD—attention-deficit/hyperactivity disorder • NSCH—National Survey of Children's Health • OR—odds ratio
Children with special health care needs (CSHCN) are an important and highly vulnerable subset of the child population. For planning purposes, the Maternal and Child Health Bureau defines CSHCN as "children who have or are at increased risk for a chronic physical, developmental, behavioral, or emotional condition and who also require health and related services of a type or amount beyond that required by children generally [italics added]."1 By incorporating the term "at increased risk," this planning definition specifically recognizes the importance of prevention. Operationally, a modified version of the definition without the aforementioned component has been used. In fact, the primary tool for identifying CSHCN in research projects and national surveys, the CSHCN screener, identifies only children with existing special health care needs. Consequently, no published studies have empirically addressed the concept of at-risk status (ie, at risk of both a chronic condition and the need for services), and children at risk of special health care needs have not been included in empirical studies of CSHCN conducted to date.2–10
A previous article presented a conceptualization of the population at risk for special health care needs.11 That conceptual model posits that risk is related to 6 major domains (predisposing characteristics, genetic endowment, physical environment, social environment, health-influencing behavior, and health care system characteristics) operating at multiple levels (child, family, community, and societal levels), as shown in Fig 1. This article uses that conceptual model to identify candidate variables for an exploratory empirical examination of the effects of factors that may increase or decrease the risk of developing a special health care need. This initial analysis is intended to stimulate discussion among researchers, advocates, and policymakers regarding the concept and application of risk for childhood special health care needs.
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METHODS |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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Data Source
The 2003 National Survey of Children's Health (NSCH) was used for our analysis.12 The NSCH was a random-digit-dialed, population-based, telephone survey sponsored by the Maternal and Child Health Bureau and conducted by the National Center for Health Statistics, using the State and Local Area Integrated Telephone Survey mechanism. Interviews with 102 353 households with children <18 years of age began in January 2003 and were completed by July 2004. Interviews were completed for 68.8% of selected households that were confirmed to include age-eligible children; the overall response rate was 55.3%, which reflects both the interview completion rate and the rate at which the randomly generated telephone numbers were successfully screened as eligible (ie, reaching households with children) or not eligible. The detailed NSCH questionnaire was administered for 1 child in each household with an age-eligible child. If >1 child was present, then 1 child was selected at random to be the subject of the interview. The parent or guardian who knew the most about the health and health care of the selected child served as the respondent for the interview. Follow-up telephone calls were made as necessary to contact this parent or guardian.
The survey was designed to provide representative state and national data on children that could be used to characterize their health status, the types of services they need and use, and the strengths and shortcomings of the health care system. Importantly for our purposes, the NSCH included the CSHCN Screener, a multiple-item survey tool for determining the presence of a special health care need.13 The screening questions were used to establish the dependent variable for our analysis, that is, the presence or absence of a special health care need. The NSCH questionnaire also included a checklist of childhood chronic conditions and extensive information on the social and demographic characteristics of the child, family, and neighborhood. Because the NSCH data set included approximately equal numbers of children from each state, with information on the zip code of residence, it was possible to incorporate state- and county-level contextual variables.
Analytic Approach
The conceptual model of risk for special health care needs that was used to guide our empirical analysis was based on 5 key points derived from the literature.14–23 Those points also guided the development and application of the analytic models presented here. First, determinants of health have been demonstrated to include 6 major domains, namely, predisposing characteristics, genetic endowment, physical environment, social environment, health-influencing behaviors, and health care system characteristics. We operationalized the previously described conceptual model11 by identifying variables that represented each of these major domains, using the NSCH and other sources of contextual data at the state and county levels. Our final empirical models incorporated 5 of these domains; none of the health care system characteristics was significant in the final models.
For some domains, only proxy measures were available. Genetic endowment was our most problematic domain; we used parental overall health and parental mental health as proxy measures of genetic endowment. Although we recognize that these proxy measures are far from optimal, we thought that inclusion of surrogate measures of genetic endowment was important, given the recent advances in our understanding of the genetic contributions to chronic conditions. For example, recent reports estimated that 
70% of the variance in the development of asthma24–26 and at least 30% to 40% of the variance in the development of attention-deficit/hyperactivity disorder (ADHD)27–29 can be attributed to genetic (gene alone or gene-environment interaction) influences. Parental overall health and parental mental health were the best measures of genetic endowment available to us in the NSCH.
Second, the conceptual model recognizes that the relative importance of each of these domains in contributing to the presence of a chronic condition and a resultant special health care need is likely to vary across the major chronic conditions experienced by children. We address this point in the analyses presented here by separately modeling several major chronic condition groups, categorized as physical, developmental, emotional, and behavioral. These conditions and condition groups were selected to be representative of the broad range of conditions that underlie special health care needs, using the Maternal and Child Health Bureau definition; they include hay fever or any kind of respiratory allergy; asthma; eczema or any kind of skin allergy; stuttering, stammering, or speech problems; learning disabilities; conduct or behavioral problems; frequent or severe headaches; ADHD; and depression and anxiety problems. It should be noted that condition data were collected independently of the child's CSHCN status. Therefore, we label them as cooccurring conditions. In addition, some of the conditions were assessed by using a 12-month recall period, whereas others were identified as having ever been present (Table 1). The latter conditions are those that are typically considered chronic regardless of onset.
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Third, all of the domains can be conceptualized as acting at the child, family, community, or societal level. To test this, we conducted multilevel analyses to model simultaneously variations in risk of special health care needs at the individual level and at the contextual level of counties and states. We estimated a 3-level null model by using multilevel modeling methods (MIXED procedure in SAS 9.1.3; SAS Institute, Cary, NC) to examine the extent to which variation in the presence of a special health care need was explained at the levels of the individual, county, and state. Our analysis revealed that only 0.05% of the variation in special health care needs could be explained by county- and state-level characteristics. Therefore, we decided to use only individual-level data in our analyses.
Fourth, the previously described conceptual model recognizes the presence of a complex interplay of causal factors influencing the development of chronic conditions and associated special health care needs. Causality can be expressed directly and indirectly, and causal forces can be mediated by other factors. Our present analysis presents a simplified model of causality and assumes that causality is unidirectional. We excluded certain variables with obvious bidirectional causality, such as the child's health care utilization.
Fifth, the conceptual model incorporates a temporal aspect of the development of special health care needs. We were unable to incorporate the temporal aspect directly in our present analysis because of the cross-sectional nature of our data set. However, to account for the difference in developmental status across childhood, we conducted separate analyses for children <6 years of age (N = 33 322) and 6 to 17 years of age (N = 69 031).
Estimation Methods
We fitted logistic regression models for the presence or absence of a special health care need (coded 1 if present or 0 otherwise) and for the presence or absence of particular chronic conditions cooccurring with a special health care need (coded 1 if both were present or 0 otherwise). In this article, we present 16 different equations, using the chronic conditions in Tables 1 and 2 as outcome variables and the other variables shown in Table 1 as independent variables. To determine which independent variables to include in each equation, we first tested the bivariate relationship between each independent variable and the presence or absence of a special health care need (or the presence or absence of a particular chronic condition cooccurring with a special health care need). All variables with statistically significant bivariate relationships (based on the Wald F test) were then included in multivariate logistic regression analyses. Because the likelihood of multicolinearity was high among some of the independent variables, we used backward elimination to refine the final models. At each step of this process, the independent variable that was least significant, that is, the variable whose main effect (Wald F) was associated with the largest P value above .05, was removed and the model was refitted. Only variables with statistically significant (at the .05 level) main effects remain in the final models presented in Tables 3 and 4. Variables with >2 categories (eg, race/ethnicity and Census region) could have statistically significant main effects but have some categories that were not significantly different from each other; no attempt was made to achieve more-parsimonious models by reducing the number of categories within an independent variable.
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It should be noted that this procedure does not provide a direct empirical estimation of the conceptual model; rather, it is an exploratory technique to identify a set of variables in the NSCH that reflect the domains in the conceptual model and that are most parsimonious in predicting the presence or absence of a special health care need. Although our results are expressed as odds ratios (ORs), we use them as proximate measures of risk for developing a special health care need.
All analyses were performed by using sampling weights provided for the NSCH. These weights produce estimates that represent the noninstitutionalized population of children in the United States. Logistic regression analyses were performed by using SUDAAN 9.0 (Research Triangle Institute, Research Triangle Park, NC), which accounts for the complex sample design in the estimation of confidence intervals and P values. Only results significant at <.05 are discussed in the text.
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RESULTS |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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Prevalence of Special Health Care Needs and Cooccurring Chronic Conditions
On the basis of NSCH data, the prevalence of special health care needs was 11.1% in the preschool age group of 0 to 5 years and 20.8% in the school age group of 6 to 17 years. The 2 most prevalent conditions found among CSHCN in both age groups were asthma and hay fever/respiratory allergies (Table 2). For preschool-aged children, the next most frequent conditions cooccurring with special health care needs were eczema/skin allergies, speech problems, learning disabilities, and behavioral/conduct problems. For school-aged children, other common conditions cooccurring with special health care needs were ADHD, learning disabilities, depression/anxiety, behavioral/conduct problems, eczema, and speech problems. Some CSHCN did not have any of the conditions examined (22.4% in the preschool age group and 11.2% in the school age group) but were reported to have a chronic medical, behavioral, or other health condition as part of the CSHCN screening. Multiple conditions were observed for 40.5% of preschool-aged CSHCN and 60.7% of school-aged CSHCN.
Risk Factors Related to Special Health Care Needs
Our description of the empirical findings concerning risk factors for special health care needs is organized according to predisposing characteristics, genetic endowment, physical environment, social environment, and health-influencing behavior. These factors operate at the child, family, and neighborhood levels. The regression results, presented as ORs, are shown in Table 3 for the 0- to 5-year age group and in Table 4 for the 6- to 17-year age group. For each model, these tables also indicate the proportion of variance in the presence (or absence) of special health care needs associated with the risk and protective factors in the model.30 Our description of the results includes only risk factors that were significant in the multivariate models.
Predisposing Characteristics
The significant predictors for CSHCN included age, ethnicity, and gender. We found that risk factors for special health care needs varied according to age group, with some factors being more important for preschool-aged children and others for school-aged children. For example, non-Hispanic black children had higher odds of experiencing special health care needs than did non-Hispanic white children in the younger age group. In contrast, among older children, non-Hispanic white children had the highest odds of having a special health care need. In both age groups, boys had higher odds than girls of having a special health care need cooccurring with a physical, developmental, behavioral/conduct, or emotional condition (ORs: 1.34–3.33). The difference was greatest for behavioral/conduct problems; relative to girls, the odds that boys had a special health care need cooccurring with a behavioral/conduct problem were 
3 times as high (OR for 0-5-year-old children: 3.33; OR for 6-17-year-old children: 2.63).
Genetic Endowment
Within the genetic endowment domain, parental health status was an important predictor of the child's special health care need status. Relative to children of parents reporting at least good physical health, children of parents reporting poor/fair physical health were more likely (ORs: 1.39–3.01) to have a special health care need cooccurring with physical, developmental, or behavioral/conduct special health care needs, in both age groups. Furthermore, children of parents reporting poor/fair mental health status had higher odds (ORs: 1.46–2.39) of experiencing a special health care need cooccurring with all conditions studied, with the exception of asthma for both age groups and learning disability for preschool-aged children.
Physical Environment
We found 3 significant variables in the physical environment domain, but only for school-aged children. Living in the West region was associated with lower odds of having a special health care need. Having a smoker in the household was associated with higher odds of experiencing a special health care need cooccurring with severe headaches or developmental, behavioral/conduct, or emotional conditions (ORs: 1.20–1.63). Living in a metropolitan area was associated with higher odds of a special health care need cooccurring with eczema (OR: 1.39).
Social Environment
Many factors in the social environment domain were significantly associated with special health care needs for both age groups. These factors are expressed at the family and community levels.
Living in a 2-parent household was associated with lower odds of experiencing special health care needs cooccurring with severe headaches, learning disabilities, behavioral/conduct problems, or emotional conditions in school-aged children. Preschool-aged children living in a 2-parent household had lower odds of experiencing a special health care need cooccurring with hay fever/respiratory allergies or behavioral/conduct problems.
Positive family relationships seemed to convey a protective effect for special health care needs cooccurring with nonphysical conditions. Having a close parent-child relationship and dealing with family conflicts in a calm manner were both associated with lower odds of having a special health care need cooccurring with behavioral/conduct or emotional conditions among school-aged children. Specifically, children in this age group who lacked a close relationship with their parents had much higher odds of having a special health care need cooccurring with a behavioral/conduct problem (OR: 4.49). Similarly, children with parents who had not met any of the children's friends had higher odds of having a special health care need generally (OR: 2.52) and cooccurring with developmental, behavioral, or emotional conditions. Interestingly, the number of days that parents read to their preschool-aged children was associated with higher odds of special health care needs overall and co-occurring with hay fever/respiratory allergies, asthma, and eczema/skin allergies (ORs: 1.30–1.45).
Even for preschool-aged children, dealing with family conflicts through arguing or shouting was associated with higher odds of having a special health care need cooccurring with a behavioral/conduct problem (OR: 1.54). For this younger group, spending fewer days during the week eating together as a family also was associated with higher odds of experiencing a special health care need cooccurring with a learning disability (OR: 1.48).
Community and neighborhood variables also were significantly related to the presence of special health care needs. Living in supportive neighborhoods decreased the odds of having special health care needs in both age groups. For school-aged children, supportive neighborhoods were associated with lower odds of experiencing special health care needs cooccurring with developmental, behavioral/conduct, or emotional conditions. Hay fever/respiratory allergy cooccurring with a special health care need was the only condition not related to living in a supportive neighborhood. For the younger age group, living in supportive neighborhoods reduced the odds of special health care needs cooccurring with asthma, eczema, or learning disabilities.
Having English as the primary language of the household was associated with higher odds of having a special health care need in both age groups (OR for 0-5-year-old children: 1.62; OR for 6-17-year-old children: 3.10). Also, college attendance by an adult household member was associated with higher odds of having special health care needs cooccurring with physical conditions (hay fever/respiratory allergies or eczema /skin allergies for younger children and hay fever/respiratory allergies, asthma, or eczema/skin allergies for older children).
Other social environmental factors associated with having special health care needs were parental employment and poverty status. Parental unemployment was associated with higher odds of having special health care needs in both age groups. Poverty status was associated with higher odds of having special health care needs cooccurring with asthma, developmental problems, or behavioral/conduct problems among school-aged children.
Health-Influencing Behavior
In the domain of health-influencing behavior, breastfeeding appears to have a protective effect for younger children. Children who were breastfed had lower odds of having a special health care need cooccurring with asthma, speech problems, or behavioral/conduct problems. Information about breastfeeding was not available for older children. Among school-aged children, having adequate sleep was associated with lower odds of having a special health care need cooccurring with any of the physical, behavioral, and emotional conditions studied.
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DISCUSSION |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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The empirical analysis presented here was designed to provide researchers, advocates, and policymakers with new information on the factors associated with special health care needs. Although our results are provisional for the reasons discussed below, they represent an important step toward identifying factors that may place children at risk for the development of special health care needs and toward the eventual development of policies to reduce the number of children developing special health care needs.
Our intention was to identify factors operating at the child level, family level, neighborhood level, and higher levels of geographic aggregation that either place children at risk of or protect them from developing special health care needs. Several important findings emerged from our analysis. First, we found that a large number of variables were correlated with increased risk of special health care needs. Significant associations were found in 5 of 6 domains identified in our conceptual model of risk, namely, predisposing characteristics, genetic endowment, physical environment, social environment, and health-influencing behavior. This central finding suggests that multifaceted interventions might be most effective in reducing risk.
Second, we found that very little of the variation in risk of special health care needs could be accounted for by factors operating at the levels of counties and states. The variables explaining risk seemed to operate primarily at the child, family, and neighborhood levels. If these results are confirmed in other studies, then they could have significant implications for preventive policies. In particular, they suggest that the focus of prevention may best be aimed at the child, family, and neighborhood levels, rather than the state or county level. This is not to say that state or county health policies are not important, but their impact, whatever it may be, is felt at lower levels of aggregation, at the child, family, and neighborhood levels.
Third, we found an interaction between the child's age and the factors that predict special health care needs. That is, the factors that put children at elevated risk or protect them were found to vary according to age. Our models also demonstrated higher predictive power for school-aged children, compared with their preschool-aged counterparts, as indicated by the greater proportion of variance accounted for by the predictor variables in the models for school-aged children. This may be the result of the types of conditions that are prevalent at different ages, the effect of cumulative exposure to risk factors, or other developmental characteristics of children that emerge as they grow. The 2 age groups shared some common risk factors, including depressed parental physical and mental health, male gender, residence in unsupportive neighborhoods, and parental unemployment.
Fourth, we found that the factors that predict special health care needs varied according to type of condition. That is, the relative importance of predisposing characteristics, genetic endowment, physical and social environments, and health-influencing behaviors varied from condition to condition.
Findings on the effect of race and ethnicity varied between age groups. Whereas Hispanic children were found to be at lower risk than non-Hispanic white children in both age groups, black children and children in the other-race category were at lower risk than non-Hispanic white children in the older age group but at greater risk in the younger age group. Other research demonstrated that Hispanic families are less likely to use medications for their children31; because use of medications is 1 criterion for identification as having a special health care need in the CSHCN screener, this could explain some of the decreased risk among Hispanic children. It is also possible that lack of familiarity with available services contributes to the lower rates of special health care needs among Hispanic and older non-Hispanic black children.
Our proxy variables for an individual's genetic endowment, that is, parental health and mental health, were highly significant for virtually every condition cooccurring with a special health care need. These variables may be capturing other constructs, such as the physical environment or an individual's perception of illness. We also recognize that reverse causation may be at work in this association, because the stress of having a child who has a chronic condition cooccurring with a special health care need may worsen a parent's health. As we continue to gain knowledge about the effects of an individual's genetic endowment on risk status, measurement of this important domain should improve.
We found some counterintuitive findings for preschool-aged children, including an increased likelihood of developing a special health care need when parents regularly read to the child. In fact, regular reading may be a consequence rather than a cause of special needs. That is, children who experience disabilities and needs may receive more attention from their parents. With cross-sectional data, however, it is impossible to distinguish causality.
Other risk factors were associated only with certain conditions or types of conditions; specifically, a number of risk factors from the social environment were found to be associated with special health care needs cooccurring with developmental, behavioral, and emotional conditions in older children but not with special health care needs cooccurring with physical conditions. These risk factors included the closeness between a child and his or her parents, methods of settling disagreements in the family, and neighborhood safety. This indicates that, although analyses looking at all CSHCN as a group can be appropriate, it is also important to examine specific conditions separately.
Our findings reveal that 2 areas in which existing public health efforts are targeted at improving the health of children, that is, breastfeeding and secondhand smoke exposure, are also important predictors of special health care need status. For preschool-aged children, we found that breastfeeding was associated with a protective effect. For school-aged children, we found that secondhand smoke exposure was associated with increased risk of special health care needs.
Although family-level variables were not highly predictive of special health care needs in younger children, we found strong family effects for school-aged children. A constellation of factors related to the closeness and supportiveness of the family emerged from our analysis of the 6- to 17-year-old population. Specifically, we found that parents' knowledge of their child's friends, a close relationship between parent and child, and a calm manner of settling arguments and disagreements by the parents were all associated with reduced likelihood of special health care needs for children in this age group. These findings suggest that family behavior and dynamics may be important factors to consider in future research aimed at preventing special health care needs.
We also found that living in a supportive neighborhood was associated with a protective effect for both preschool-aged and older children. These findings suggest that safe and supportive neighborhoods may confer important health benefits to children, in addition to improving the overall quality of life for residents. Longitudinal studies of the impact of safe and supportive neighborhoods on child development might incorporate assessment of their effects on the presence of special health care needs to corroborate our cross-sectional findings.
Among older children, we found that speaking English at home and a parental college education were associated with increased rates of special health care needs. These relationships deserve further study, to determine whether these 2 variables are linked in a causal manner to development of special health care needs. Alternatively, parents who speak English at home may be better able to navigate the medical and social systems, to pursue diagnoses for their children, and to obtain services.
Our analysis is subject to several limitations. First, our ability to measure adequately all 6 relevant domains of risk factors for population health was limited. For some domains, such as the social environment, we had a large number of measures. For others, such as genetic environment, we had few measures and even those were proxy indicators. Second, no adjustments to the 
levels were made to account for multiple comparisons in these exploratory analyses. The likelihood of type I errors may therefore be inflated. Relationships identified in these analyses should be confirmed with additional research. Third, we did not test any interactions. Given the large number of variables, a direct test of all possible interactions would have been nearly impossible. Fourth, our model assumes a very simple, 1-way causality and does not take into account the possibility of feedback loops or reverse causality. We excluded variables (such as health care utilization) that had obvious 2-way causal directionality. Because we used cross-sectional data in this empirical analysis, we cannot make causal conclusions. We made the assumption that we can treat ORs as indicators of risk. Accurate determination of how the independent variables are related to risk requires longitudinal data and a probabilistic model. These limitations reduce our ability to draw strong conclusions concerning which variables influence risk for special health care needs. Our results should be viewed as provisional and subject to confirmation by using other data sets.
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CONCLUSIONS |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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The analysis presented here is the first to consider empirically a range of risk factors for special health care needs, using a population health model. The strengths of our analysis include a broad population health perspective, a large-scale data set with a complement of important variables, and the consideration of contextual indicators. The weaknesses include simplified causal assumptions, the use of cross-sectional data, and a limited set of indicator variables for some domains. These limitations suggest avenues for future research on risk factors for special health care needs, including use of longitudinal data, such as might be generated from the new National Children's Study (www.nationalchildrensstudy.gov), use of more-complex statistical modeling methods that can incorporate more-realistic assumptions of causality, and collection of better indicators for some domains, such as genetic endowment. The provisional results of our analysis can help us to begin thinking about which characteristics of the child, family, and community are worthy of further exploration. Some of the variables we found to be significant predictors, such as age and ethnicity, are immutable. However, we found a number of significant predictors that may be amenable to public health interventions, including breastfeeding practices, exposure to secondhand smoke, family closeness, and neighborhood cohesion.
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ACKNOWLEDGMENTS |
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This work was supported by the Maternal and Child Health Bureau (grant 1R40MC03619).
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FOOTNOTES |
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Accepted Nov 15, 2007.
Address correspondence to Paul W. Newacheck, DrPH, Institute for Health Policy Studies, University of California, San Francisco, 3333 California St, Suite 265, San Francisco, CA 94118. E-msail: E-mail: paul.newacheck{at}ucsf.edu
The opinions expressed in this article are the authors' and do not necessarily reflect the views or policies of the institutions with which the authors are affiliated or the funding agency.
The authors have indicated they have no financial relationships relevant to this article to disclose.
| What's Known on This Subject Very little is known about the risk factors for special health care needs.
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| What This Study Adds This is the first study to examine risk and protective factors for special health care needs. Although preliminary, it provides important insights into possible risk and protective factors.
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