5
Measuring Influences on Children’s Health

A comprehensive system to monitor children’s health would contain an inclusive, continuing assessment and monitoring of the range of influences on children’s health, including children’s biology and behavior, social environments (family, community, culture, and discrimination), physical environments, and services and policy contexts. Development of such a system requires careful long-term consideration of which influences are important, how they are being measured, how to improve their measurement, and what additional measures might result in important benefits to children’s health.

We begin this chapter with an overview of current issues and challenges in measuring the multiple influences on children’s health. We then outline the current approach and particular challenges of measuring each of the influences identified in Chapter 3 and then discuss how the gaps in measurement of each influence might be improved, including potential future opportunities in light of advances in research methods. Many of the methodological problems and practical obstacles in measuring various health influences are the same as those in developing and implementing measures of health. These areas of overlap are not repeated here, although commonalities are briefly noted. Many of the current surveys that capture data on influences were mentioned in the previous chapter and are outlined in Appendixes A and B; descriptive information regarding specific surveys is not repeated here. Appendix B lists various data elements for the influences outlined in this report that are captured by 12 of the major national surveys.

For some types of influences discussed below, there is ample evidence of the effect they have on children’s health. The challenge is to ensure their adequacy in



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Children’s Health, The Nation’s Wealth: Assessing and Improving Child Health 5 Measuring Influences on Children’s Health A comprehensive system to monitor children’s health would contain an inclusive, continuing assessment and monitoring of the range of influences on children’s health, including children’s biology and behavior, social environments (family, community, culture, and discrimination), physical environments, and services and policy contexts. Development of such a system requires careful long-term consideration of which influences are important, how they are being measured, how to improve their measurement, and what additional measures might result in important benefits to children’s health. We begin this chapter with an overview of current issues and challenges in measuring the multiple influences on children’s health. We then outline the current approach and particular challenges of measuring each of the influences identified in Chapter 3 and then discuss how the gaps in measurement of each influence might be improved, including potential future opportunities in light of advances in research methods. Many of the methodological problems and practical obstacles in measuring various health influences are the same as those in developing and implementing measures of health. These areas of overlap are not repeated here, although commonalities are briefly noted. Many of the current surveys that capture data on influences were mentioned in the previous chapter and are outlined in Appendixes A and B; descriptive information regarding specific surveys is not repeated here. Appendix B lists various data elements for the influences outlined in this report that are captured by 12 of the major national surveys. For some types of influences discussed below, there is ample evidence of the effect they have on children’s health. The challenge is to ensure their adequacy in

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Children’s Health, The Nation’s Wealth: Assessing and Improving Child Health date collection efforts so that differences across time and among subpopulations can be effectively monitored. For others, although there is evidence that they influence health, the challenge is to develop more adequate means of understanding the nature of their influences. In these instances, attention needs to be focused on using data collection to facilitate studies of the way in which they operation on populations and subpopulations. OVERVIEW The measurement of many influences poses methodological challenges that must be considered and systematically addressed in future research, surveys and evaluation studies. For such factors as biological influences on children’s health, invasive medical tests may be necessary and raise potential ethical questions about risk-benefit ratios of specific assessment procedures. In other cases, the need for highly personal information raises confidentiality concerns and concerns about unintended consequences of shared information. In still other instances, such measures as policy influences may require aggregation across governmental units and agencies. Several overall issues must be considered to improve the measurement of influences on children’s health. First, how do various influences interact with one another over time to affect health? Specific influences may set in motion a chain reaction, unleashing other biological and behavioral processes than can cascade toward a specific outcome (final common pathway) or a range of potential outcomes (multiple pathways). Since each interaction in such a cascade is potentially a point to monitor and intervene, understanding and measuring such effects become important methodological challenges. As a specific developmental stage or sensitive period, exposure to a specific influence can unleash a cascade of effects with significant short- and long-term impacts, whereas the same exposure at a different stage may have a muted or minimal effect. Another challenge is how to understand and model the effect of multiple influences for policy purposes. For example, when a child is exposed to multiple adverse influences at the biological, behavioral, family, and community levels, are these factors simply additive, or are they multiplicative (Rutter, 1994; Werner, 1993)? The most effective prevention and intervention strategies may target high-risk groups (i.e., those affected by multiple risk factors), rather than using strategies that address single risk factors. For policy purposes, which children may be most at risk for later adverse outcomes, and which may be most in need of special assistance? Aggregation of data on influences at the individual, family, and community levels is complicated (Small and Supple, 2001) and prone to errors in the application of statistical techniques, drawing appropriate causal inferences, and estimating the relative size of influences’ effects. Apart from biomarkers, the physical environment, family demography, and

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Children’s Health, The Nation’s Wealth: Assessing and Improving Child Health results from formal medical evaluations, almost all influences require the subjective reports of people (often parents reporting for children) who must describe their perception of the presence or absence, severity, and duration of a particular health influence. Such perceptions tend to differ from person to person, raising important concerns about the validity of any single source of information, particularly when policy decisions (such as the distribution of resources) are to be based on such information. Despite the fact that parents from different cultural backgrounds must complete these surveys, there are often insufficient data demonstrating that survey items are accurately understood by parents across different cultural contexts, and surveys are not consistently offered in multiple languages. While this challenge poses daunting obstacles to the interpretability of survey findings across cultures, new translation methods have been developed and described that may facilitate more valid responses across cultural groups (Erkut, Alacron et al., 1999b). Another concept implicit in the committee’s conceptual approach is the important role of both positive and negative influences on health. If health trajectories are to be modified, then health measurement at a population level needs to clearly account for the presence and effect of influences, their direct and indirect relationship to each other, and to the health outcome of interest. For example, if substance abuse during adolescence is the outcome of interest, a conceptually driven and integrated health measurement strategy would measure and account for the effect of adverse influences on drug use (e.g., peer influences, school performance, lack of adequate parental supervision) as well as protective factors (e.g., mentoring relationships and educational and economic support). Despite knowledge that adverse health influences often disproportionately fall on some population subgroups more than others, systematic collection of health care data on subpopulations at a local, state, or national level is episodic. Surveys rarely provide enough information to develop a comprehensive picture of the health of young children, or to understand the role of various influences during early childhood, or to assess their receipt of appropriate personal or public health services or the effect of health care on their health. While there has been recent increasing emphasis on the importance of early childhood, as well as considerable focus on adolescence, there has not been the same kind of focus on health influences in the intervening years. MEASURING BIOLOGICAL INFLUENCES The range of biological influences on children’s health are assessed using “biomarkers,” which are indicators signaling events in biological systems or samples (for review, see National Research Council, 1989). There are three categories of biomarkers: biomarkers of exposure, biomarkers of effect, and biomarkers of susceptibility (see Figure 5-1). The markers fall along the time course from exposure (e.g., prenatal exposure to alcohol) to health outcome (e.g.,

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Children’s Health, The Nation’s Wealth: Assessing and Improving Child Health FIGURE 5-1 The three types of biomarkers. NOTE: The three categories of biomarkers are biomarkers of exposure, biomarkers of effect, and biomarkers of susceptibility. The boxes represent the different steps in the progression from exposures to a health outcome. The solid arrows depict the rate of change from one stage to the next; the time of progression from one stage to the next is highly variable. The nested ovals represent the areas where biomarkers of exposure, effects, and susceptibility may be found and show their overlapping nature (adapted from Committee on Biological Markers of the National Research Council, 1987). fetal alcohol syndrome). In general, biomarkers of exposure are nearer in time to the exposure (i.e., they are designed to detect exposure rather than the effect of exposure), while biomarkers of effect are generally nearer in time to the outcome (i.e., they are designed to detect the effect of exposure or the effect on health). The time course of moving from exposure to outcome is not continuous. For example, an internal dose can occur quickly after an acute exposure, while a biological effect may take decades (e.g., exposure to radioactive material and the development of thyroid cancer or exposure to asbestos and lung cancer). Biomarkers of susceptibility can mark increased vulnerability at any of the steps between exposure and outcome. Biological factors that influence health, such as genotypes for functionally important genetic polymorphisms, variations in gene expression, and biochemical measures that reflect body stores or internal doses of environmental exposures, are useful biomarkers. Thus, the concept of biomarkers provides an organizational framework for considering existing indicators and the potential influence of the biological environment. We use this framework in considering current assessments of biological influences, as well as assessment gaps. Identification and

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Children’s Health, The Nation’s Wealth: Assessing and Improving Child Health selection of particular biomarkers for a specific research or public health purpose would need to include careful consideration of such factors as the ease of collecting a particular biological specimen and the cost of biological analyses. Biomarkers of Susceptibility Biomarkers of susceptibility include such factors as biological measures of health and genes. A child’s current health as reflected in his or her level of immunity or level of cortisol production may also serve as a valuable indicator of biological susceptibility. Under certain circumstances, age can serve as a proxy for developmental susceptibility, such as the age of risk for sudden infant death syndrome (infants) or testicular cancer (adolescents). The composition of ages in a population, obtainable from the U.S. Census Bureau, can be used as an indirect indicator of susceptibility in the population for specific age-related health conditions. However, most measures of biological susceptibility require some form of biological assay. As discussed in Chapter 3, polymorphisms (variations from person to person in a gene’s molecular structure) in certain genes may impart susceptibility to certain environmental exposures. A biomarker of susceptibility for the individual would be the specific genotype of that gene, while the biomarker of susceptibility for the population would be the frequency of that genotype. An example of an existing database for genes of susceptibility includes newborn screens. Newborn screen testing varies from state to state, but most states include screening for hyperphenylalaninemia (PKU), hypothyroidism, classical galactosemia, and hemoglobinopathies.1 While congenital hypothyroidism is not always caused by a genetic polymorphism, the screen identifies cases that are genetic in origin. The newborn screen is the only universal population-based database in the United States for children’s genetic susceptibilities. Biomarkers of Exposure There are several measurement activities for internal dose/body stores/body burdens. Two major programs are being conducted by the Centers for Disease Control and Prevention (CDC): the biomonitoring program and the National Health and Nutrition Examination Survey (NHANES) survey. The process of expanding biomonitoring capability to select state laboratories is currently under way (Federal Register, Vol. 68, No. 64/Thursday, April 3, 2003/Notices p. 16287). 1   A listing of the tests done in each state, as well as the summary results of the screening, can be found at the following web site: http://genes-r-us.uthscsa.edu.

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Children’s Health, The Nation’s Wealth: Assessing and Improving Child Health Biomonitoring is the direct measurement of environmental chemicals, their primary metabolites, or their reaction products in people—usually in blood or urine specimens. The CDC Division of Laboratory Services has developed methods to measure 200 substances in blood or urine, including but not limited to polychlorinated biphenyls, dioxins, furans, the persistent organic pollutants, DDT and its metabolite DDE, nonpersistent organic pesticides and their metabolites, polyaromatic hydrocarbon metabolites, phthalate metabolites, metals (e.g., lead), volatile organic compounds, and phytoestrogens.2 The NHANES survey also includes information about the health and diet of people in the United States. There are both questionnaire and laboratory measurements on a survey of 4,800 children younger than age 19 over a 2-year cycle. Laboratory measurements include iron status, vitamin stores and folate levels, and indicators of specific infections such as viral hepatitis. Biomarkers of Effects There are few measurement activities related to biomarkers of effects that are not measurements of health. For example, the NHANES survey measures a few, such as physical growth, biomarkers of inflammation and bone density, and liver, kidney, and respiratory function. NHANES also measures immunization status by measuring antibody levels as a result of immunization. While patterns of changes in gene expression may be a sensitive and specific biomarker of effect, no current population-based measurement activities of gene expression are currently taking place, except in clinical settings for research purposes. Challenges in Measuring Biological Influences Several methodological issues are of concern in measuring biological influences. First, obtaining biological samples from fetuses and children is difficult. Samples for biomarkers must be obtained ethically, non-invasively and with a minimum of pain, and be acceptable to both child and parent. Table 5-1 provides examples of types of samples with their advantages and disadvantages. Although it may soon be possible to determine multiple polymorphisms in individuals, the ethical issues in doing so are complex. Guidelines on the ethics of this testing have been proposed (Bakhtiar and Nelson, 2001). Second, validating a biomarker as a true measure of a biological influence on health is difficult and time-consuming. A number of steps are necessary, includ 2   The current national status report of population exposure levels (from the CDC’s 2002 NHANES) for 116 of these chemicals can be found on the CDC web site: http://www.cdc.gov/exposurereport/.

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Children’s Health, The Nation’s Wealth: Assessing and Improving Child Health TABLE 5-1 Appropriate Biological Samples in Which to Measure Biomarkers Indicating Fetal/Pediatric/Adult Exposure/Effect Samples Advantages Disadvantages Adult/Pediatric Urine Large sample size Requires cooperation, difficult in young children Hair May indicate timing of exposure Requires cooperation, may not be desirable, requires special analytical techniques Blood Battery of biomarkers may be used Invasive, painful, difficult to obtain in young children, amount of blood limited in young children Breath Easy to obtain large quantities Requires special equipment, technology is limited, requires cooperation Saliva Easy to obtain Requires cooperation, sample size limited Transdermal Easy to obtain Requires special equipment, technology is limited, requires cooperation Nails Easy to obtain, may indicate timing of exposure Requires cooperation, may be difficult in young child, sample size limited Newborna Cord blood Large volume available, discarded sample, battery of biomarkers may be used Narrow window of opportunity to collect, single time point for measurement Placenta Large sample size, discarded sample Narrow window of opportunity to collect Umbilical cord Large sample size, discarded sample Narrow window of opportunity to collect Amniotic fluid Large sample size, discarded sample Difficult to collect, narrow window of opportunity to collect Urine Concentrates metabolites, discarded sample Difficult to collect Hair May indicate timing of exposure May not be available, may not be acceptable to parent Breath Easy to obtain Requires special equipment, technology is limited Saliva Easy to obtain Small sample Nails May indicate timing of exposure Extremely difficult to obtain, invasive Transdermal Easy to obtain Requires special equipment, technology is limited Meconium Easy to obtain, may indicate timing of exposure, discarded sample None aObviously, biomarkers measured in newborn samples only indicate fetal exposure retrospectively.

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Children’s Health, The Nation’s Wealth: Assessing and Improving Child Health ing (1) developing and validating a biomarker to identify a chemical or biochemical exposure or exposure effect; (2) selecting the biological sample (e.g., blood, breath, or urine) to measure the biomarker; (3) addressing ethical, practical, and cost-related obstacles in actually obtaining the sample; (4) developing a method for analytical quantification of the biomarker in the specific sample (addressing how much biomarker can be recovered from the tissue sample, how much variation exists in recovery of the marker between samples, biomarker stability in the chosen sample, etc.); and (5) ascertaining biomarker sensitivity and specificity to exposure or effect. Validation of a marker also depends on its expected use. Although biological markers observed well before the onset of disease may have little value for predicting the later occurrence of disease, they may be more useful for identifying exposed populations for long-term follow-up. Examples of biological samples appropriate for biomarker determination are hair, saliva, blood, urine, breath, umbilical cord, umbilical cord blood, placenta, stool (including the first stools passed by a newborn, called meconium), and toenails. Addressing Gaps in Measuring Biological Influences The importance of biomarkers has been insufficiently appreciated in assessing children’s health and its influences. Biomarkers may be useful even beyond measuring the effect of chemical or environmental agent exposure. For example, biomarkers might be developed that could indicate environmental interactions with the other spheres of influences. This is relevant for all aspects of health measurement, because for any influence to affect physical health or well-being, it must be translated through the child’s internal biological environment. Such biological events could potentially be measured. While biomarkers have been associated mainly with toxic events and poor outcomes, biomarkers of positive influences and positive effects could be developed. When biomarkers of exposure and effect are collected, most often they are collected at the same time in the same person. Yet the effect of a particular exposure often does not occur until later and sometimes a long time after the exposure. Without longitudinal studies, the possibility of understanding the cause-effect linkage is lost, and the effect may be attributed incorrectly. Thus, the opportunity to develop high-impact health policies is lost. Another methodological gap is the paucity of biomarkers when the exposure does not result in systemic absorption. Two examples are the respiratory system and the skin. While air pollution can be measured and quantified, indicators of dose to the airways or the biologically effective dose have not been developed. The absence of valid indicators may obscure the linkage of exposures to effects on health. Thus, the influence on occurrence of asthma or other important respiratory diseases of some elements in air pollution remains controversial. Development of new biomarkers using breath or nasal secretions may potentially be use-

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Children’s Health, The Nation’s Wealth: Assessing and Improving Child Health ful in this area. Where biomarkers have been developed, such as the collection of urinary and salivary samples for cotinine levels, as biomarkers of environmental tobacco smoke, they have been very useful. Finally, current biomarker methods are based mainly on analyzing one biomarker at a time. For biomarkers for which a battery of tests and an algorithm have been established, sensitivity and specificity improve, thus indicating that systems or arrays of biomarkers may have far more potential than isolated single measurement biomarkers. For gene expression alteration/biomarkers, further application of systems biology approaches with pattern identification/informatics technology are likely to be fruitful. A rapidly developing technology used for complex pattern recognition is the electronic nose. Inspired by the ability of dogs to determine complex patterns of odors, current testing on the device has been done on classifying bacteria or fungi by detecting their odors (such as identifying women with Type II diabetes by urine odor—(Mohamed et al., 2002). It is possible to imagine that this technology might be useful in measuring volatile biomarkers from skin (e.g., those emitted by melanomas and detected by dogs) (Church and Williams, 2001). Currently, NHANES limits its biomarker assessment to children old enough to tolerate the drawing of blood. Smaller children are subjected to fewer laboratory assessments due to the smaller sizes of their blood samples. Development of more sensitive laboratory techniques using noninvasive biological samples is needed. Biomarkers in exhaled breath, urine, and saliva may prove very useful for this age group. Current examples of the usefulness of these techniques include using breath carbon monoxide levels to predict neonatal jaundice (Smith et al., 1984) and urine toxicology for parental substance use. Similarly, the development of programs, such as the newborn blood screen, could be extended to meconium, cord blood, cord, and placenta, which now are typically discarded. While some measures of infection are currently taken (e.g., rubella, herpes), development of biomarkers for emerging infectious diseases such as West Nile virus, Lyme disease, or hantavirus warrant additional research. Prior research has shown a correlation between the formation of chemical modifications of DNA (DNA-adduct) formation and health effects, yet current measurement activities do not include these genotoxic changes. MEASURING BEHAVIORAL INFLUENCES Given the central role of children’s behavior on their health, whether by active participation in health promotion or disease intervention efforts or by behaviors that increase the risk for poor health, a systematic strategy for assessing and monitoring such health influences is critical. However, apart from youth, parent, or teacher reports and limited use of urine or hair tests to detect the use of illicit drugs, there are no concrete or fully objective tests for the presence of such behaviors. Moreover, infants and young children pose especially difficult mea-

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Children’s Health, The Nation’s Wealth: Assessing and Improving Child Health surement challenges, because they do not have the capacity to report on their moods or cognitions. To fully understand the relationship between children’s behaviors and health across regions and populations, optimal measurement strategies in most cases require (1) reliance on multiple informants (single-informant data on youth behavior are usually incomplete and should be used with caution), including reliable observational data about the behaviors of infants and young children; (2) combining measures of behavior across informants and settings; (3) demonstration that the behaviors are not simply normal variations; and (4) demonstration that the behavior is in fact related to adverse health consequences. Multi-informant reports are not always needed, but the validity and adequacy of single-informant data should be scrutinized during the planning and execution of studies of children’s behavioral influences. Moreover, because children’s behavior is constantly changing, measures must be sufficiently sensitive to detect such changes, as well as able to detect relevant differences in the timing, duration, and intensity of behavior influences on health. To what extent do studies take into account these factors? Data regarding child and youth risk behaviors are gathered routinely from a number of national surveys (see examples below), some consisting of one-time investigator-initiated (even longitudinal) projects, and others consisting of programmatic efforts to collect such information regularly. However, across the broad range of studies listed in Appendixes A and B, most do not meet the requirements outlined above. As an example, in the National Health Interview Survey (NHIS), four questions from a single informant (parent-caretaker) are asked about children’s risk behaviors. Similar limitations are found in most other national surveys, with the notable exceptions of the Youth Risk Behavior Survey (YRBS) and the current NHANES study, which devote significant time to interviewing children in major behavioral areas related to adverse outcomes (e.g., substance use). In the current NHANES survey, multi-informant interviews are conducted using a well-validated instrument (the Diagnostic Interview Schedule for Children—Shaffer et al., 1996; Jensen et al., 1995). However, for the NHANES study, valid determinations and differences within and across any single geographic policy region (such as a city, county, or state) are not possible, given the sampling frame and sample sizes for this particular survey, rendering the study inadequate for adapting regional policies to variations in regional behavioral influences on health. The YRBS, which attempts to track 10 high-risk youth behaviors, based on representative samples of entire classrooms within schools within states, has modest promise for policy and planning purposes, although the data are self-reports. While innovative, this methodology is largely dependent on the states’ own resources to implement the surveys and, in any given year of the survey, as many as 50 percent of states may not have valid or presumably generalizable data. Moreover, rates of specific high-risk behaviors are solely dependent on youths’ self-reports (using a pencil and paper survey measure administered in classroom group settings); are often much higher than those found in more in-depth, meth-

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Children’s Health, The Nation’s Wealth: Assessing and Improving Child Health odologically rigorous surveys; and systematically miss school dropouts and youth not at school that day or in alternative placement settings. Publicly available data are reported every 2 years. Under conditions of complete implementation and ideal circumstances, representative classroom data could be obtained from states concerning these high-risk behaviors among high school students. This survey also assesses exercise and positive health behaviors. However, due to the modest levels of funding and lack of centralized control of assessment and sampling procedures, the survey relies on each state to conduct and follow-up the data collection procedures. This produces great unevenness in actual survey execution. Several other recurring national surveys offer highly relevant information in very specific, targeted areas. For example, the Substance Abuse and Mental Health Services Administration’s National Household Survey on Drug Use and Health (NHSDUH) assesses information relevant to the prevalence, patterns, and consequences of drug and alcohol use among individuals age 12 and older, as well as family environment and parenting practices or perceptions that might influence substance use practices among youth. The Monitoring The Future study (funded by the National Institute on Drug Abuse, conducted by the Institute for Social Research at the University of Michigan) assesses substance use, other behaviors, attitudes, and values of 50,000 U.S. secondary school students, college students, and young adults; periodic follow-up questionnaires are mailed to a sample of each graduating class for several years after study entrance. Another source of behavioral data on younger children is the National Labor Survey on Youth, which continues to follow the children of women in the original cohort. This survey includes the child Behavior Problems Index, but the early rounds of the survey primarily include children born to young mothers. Data regarding youth behavior and its implications for health are sometimes available from investigator-initiated surveys. For example, the National Longitudinal Survey of Adolescent Health (ADD-Health) began in 1994–1995 with a sample of 7th- through 12th-grade schools. Interviews were attempted with the more than 100,000 students attending these schools, with three follow-up personal interviews conducted with a random one-fifth of these students. Health-related behaviors have been relatively well measured in each survey wave through questionnaire responses. Absent from current efforts to measure children’s behavioral influences is consideration of their attitudes, beliefs, expectations, and cultural factors that shape decisions to seek health care or engage in health promotion or illness prevention activities. For example, “local” instruments have been developed by researchers exploring in a cross-sectional and prospective fashion the relative roles of parents’ and peers’ perceptions and risk involvement on risk and protective behaviors among adolescents (Stanton, Li, Galbraith et al., 2000; Cottrell, Li, Harris et al., 2003). As noted in Chapter 3, substantial evidence indicates that these factors exert major influences on youths’ health behaviors and subsequent health, whether related to their health behavior choices, tobacco/alcohol/

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Children’s Health, The Nation’s Wealth: Assessing and Improving Child Health community health infrastructure on which many parents rely (Fawcett, Pain, Francisco, and Vliet, 1993). The community service system includes a range of child development, behavioral, and mental health services and centers; programs to address the needs of children with learning disabilities and behavioral problems; health education programs provided through public health departments; educational services; nutritional services; and other programs provided by public health systems and communities (Halfon, Inkelas, Wood, and Schuster, 2001). At present, there is no systematic measurement of the effectiveness and efficiency of these community service systems or their capacity to meet the service needs of their communities and provide services acceptable to parents. There are also major gaps in understanding the delivery of health services and the potential effect of these services on special populations. For example, although the number of children in foster care has increased dramatically over the past two decades and the high prevalence of mental health conditions in this population is solidly documented, there has been little focus on the accessibility and appropriateness of health services provided to children in foster care. It is not known whether health care providers and local health care systems are capable of providing mental health and developmental services to this high-risk group of children, the degree of continuity in the services provided, or whether the services are actually effective in addressing each child’s particular needs (Rubin et al., 2004; Simms, Dubowitz, and Szilagyi, 2000; Horwitz, Simms, and Farrington, 1994; Takayama, Bergman, and Connell, 1994; Halfon, Berkowitz, and Klee, 1992; Halfon and Klee, 1987). Such measures would not only be helpful in specifying the burden of illness in this high-risk group of children, but in better understanding whether local and state authorities responsible for ensuring the well-being of children in foster care are actually meeting their legal and morel responsibilities. A similar case can be made for a number of other special populations. For example, while the Report of the Surgeon General’s Conference on Children’s Mental Health (U.S. Public Health Service, 2000) documented the increase in mental health needs and the widening gaps in unmet needs for services, there is very little information at the federal, state, or local level on the affect of preventive, treatment, or rehabilitation services on children with mental health problems or on monitoring of the extent to which gaps are being closed. It is also important to consider how services may be arranged and delivered based on population health needs. In keeping with the concept of health that was adopted for this report, we consider the services not only to prevent and treat diseases, conditions, and impairments, but also to prevent the effect of adverse influences and promote optimal health. The latter factors are especially important for children with serious ongoing health conditions. While only a minority of children have such conditions, they use a disproportionate amount of personal health care services and their medical expenses account for a substantial portion of health care expenditures (Ireys and Perry, 1999).

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Children’s Health, The Nation’s Wealth: Assessing and Improving Child Health Addressing Gaps in Measuring Services Better questions about access to and use of services are required to obtain more adequate information about types of care needed and received. This requires an understanding of the relative importance of primary care and specialty care, to improve the quality of decisions on personnel training, resource distribution, and financing and organization of services. A continuing imperative is to improve methods and measures for all services. Clinical measures of quality that are based on evidence from various types of research studies are developing at a relatively rapid pace, but efforts to develop measures of health services performance are not. Recent research has demonstrated that the quality of systems for delivering primary care for children can be assessed using criteria that are widely accepted as constituting good primary care. These are based on characteristics including accessibility for first-contact care, person-focused care over time, comprehensiveness of care, and coordination of care when people have to be seen elsewhere (Cassady et al., 2000; Starfield et al., 1998). However, there has been little movement to incorporate primary care measures into existing data collection efforts. Moreover, there has been no effort to develop ways of conceptualizing and assessing the adequacy of specialty care services. Recent research is showing the variable nature of need for specialty services, including the need for advice and guidance, confirmation of initial opinion, and need for definitive interventions that can be provided only at the specialty level (Forrest, Glade, Baker, Bocian, Kang, and Starfield, 1999; Forrest, Rebok, Riley, Starfield, Green, Robertson, and Tambor, 2001) Both national and international data indicate great variability in referral rates from primary care to specialists and from one cultural context to another (Forrest, Majeed, Weiner, Carroll, and Bindman, 2002a). Although much of this variability can be attributed to age and case-mix differences, considerable variability remains even after controlling for these characteristics. Moreover, there is consistent and robust evidence of gaps in coordination of care, even though better coordination has been demonstrated to improve at least some aspects of the results of care (Forrest, Glade, Baker, von Schrader, and Starfield, 2000). Thus, for policy-related measures to be available and adequate, policy makers need to encourage and support efforts to develop criteria for referral and then to develop evidence-based guidelines to monitor rates of referral in different areas and in different population subgroups to ensure the most effective and equitable use of health services personnel and resources. To capture the performance of both the personal health care system and the public health system, allow systematic assessment across the range of different performance attributes, and consider disparities in the distribution of services across various populations, an integrated measurement system should adopt a broad set of performance categories. As illustrated in Table 5-2, these categories include the effectiveness, efficiency, availability, appropriateness, capability, safety,

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Children’s Health, The Nation’s Wealth: Assessing and Improving Child Health continuity, acceptability, coordination, and equity of services. Such an approach would build on work in other countries, for example Australia, where broader measures are used to assess population effects as well as individual effects. The table suggests how each attribute might be operationalized using a specific health service—developmental assessments—as an example. Although data would come from health service encounters and parents’ perception regarding services, the committee has not laid out the specific data collection necessary. These same attributes and a similar kind of matrix could be constructed to assess the performance of other services, including other health care services, such as prenatal care, primary services to children, and specialty services. A growing body of scientific evidence highlights the importance of the early years and experiences, developmental supports, and services that children receive. National and local research studies have highlighted gaps in the availability and quality of existing early childhood health services (Bethell, Peck, and Schor, 2001b; Bethell, Reuland, Halfon, and Schor, 2004). The federal Maternal and Child Health Bureau (MCHB) has launched a State Early Childhood Comprehensive Systems Initiative. Through this initiative, specific states are starting to improve the availability of health, early intervention, education, and family support services. There is very little information on the performance of these emerging early childhood service delivery systems and their effect on children’s health. Measurement of the effect of services on children also plays an important role in the context of other influences, especially when identifying the most effective and efficient intervention points along the pathway to health. Depending on the performance of a service, it may act to improve or modify health and reduce disparities that are due to social and economic differences or to environmental exposures. Understanding the potential effect of a service on populations requires understanding the variation in the performance of the service (e.g., effectiveness, efficiency, availability, and appropriateness) across populations, and the impact on variation in health. Performance is affected by factors intrinsic to the services delivery system and the context in which they operate. It is important to understand how its performance is affected by factors intrinsic to the service delivery system, geographic variations in the delivery of services, and a range of other potentially interacting factors. Accurate measurement of services and evaluation of their effect on children is important in order to partition the effects of the availability and delivery of services and their unique contribution to health outcomes in relation to the other influences on health. POLICY There are relatively few efforts to assess the effect of policy changes on health, particularly children’s health. In rare cases (e.g., welfare reform, residential mobility programs, health insurance), random assignment evaluation studies have been mounted. Occasionally, rigorous longitudinal designs have been imple-

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Children’s Health, The Nation’s Wealth: Assessing and Improving Child Health TABLE 5-2 An Integrated Service System Performance Approach (Population-Level Developmental Assessment Examplea) Performance Attributes Construct Effectiveness Care/service intervention or action achieves desired results at individual, family, and community levels Efficiency Achieving desired results with most cost-effective use of resources Availability Ability of clients/patients to obtain care/service at the right place and right time, based on needs and is equitable Appropriateness Care/service provided is relevant to client/patient needs and based on established standards Capability Self-assessment of skill to conduct appropriate risk assessment Safety Potential risks of an intervention or the environment are avoided or minimized Continuity Ability to provide uninterrupted care/service across programs, practitioners, organizations, and levels of care/service over time Acceptability Care/service provided meets expectations of client, community, providers, and paying organizations Coordination Different aspects of care are connected seamlessly Equity Absence of systematic differences across population subgroups aAt national level, if we want to measure how care of children can impact development. mented to assess the effect of specific policies. For the most part, laws are passed and regulations written without specification of the aspects of health that are likely to be affected, the mechanisms by which that is likely to occur, or funding for rigorous evaluations. As a general rule, evaluations of the effect of new policies on children’s health, including not only health policies, but also most environmental, education, welfare, and other social policy, come from academic research studies conducted after the policy is implemented. The kinds of data systems that are the focus of the committee’s report can be used to provide a limited assessment of certain policies. In general, however, there is little activity in the United States to measure the effect of policies on children’s health. The United Kingdom, Canada, and Australia have gone several steps further by developing approaches that attempt to assess policy effects more systematically and comprehensively. Data collection by agencies such as the National Center for Health Statistics and ongoing surveys by the MCHB and the Agency for Healthcare Research and

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Children’s Health, The Nation’s Wealth: Assessing and Improving Child Health Measurement Strategies Example Measures Providers are using appropriate screening/ surveillance measures to detect problems Rates of children receiving indicated assessments with appropriate instruments Estimating costs of interventions Average expenditure per child identified Parents report services are available and they can have problem assessed Rates of children actually reaching indicated services Rates of services based on American Academy of Pediatrics guidelines Proportion of children appropriately identified 1. Routine periodic survey of provider about knowledge, skill, and tracking needs 1. Self-assessment of skill to conduct appropriate risk assessment 2. Community health service—asset mapping of developmental services in community 2. Adequate referral services Monitoring of avoidance of unsafe or unwarranted interventions Rates of initiation of inappropriate modes of therapy Same provider/practice conducting assessment Rates of children with regular person conducting assessment Assess parent satisfaction with assessment and its results Satisfaction with care by ethnicity, income, and practice type Spectrum of care provided without conflict in management strategies (e.g., drug Rates of conflicting advice or incompatible advice and/or management incompatibilities) Population-based monitoring of all aspects of health system performance No differences in access to or receipt of individual services Quality have unexplored potential for assessing the effects of policy changes on children’s health outcomes. For example, state trends in health insurance coverage using the Census Bureau’s CPS could be expanded to assess the effect of Medicaid or SCHIP policy changes on enrollment. Available national surveys such as the NHIS and the Medical Expenditure Panel Surveys could potentially provide insight into effects on access and utilization. They do not provide the state-level estimates necessary to monitor such programs as Medicaid and SCHIP, which are under state jurisdiction. National data systems provide very few data to assess whether policy changes designed to affect enrollment, access, or utilization of health care services actually result in changes in children’s health outcomes. The best attempts that have been made to examine the effect of changes in Medicaid on children’s health have been done using very long time frames and very gross and narrow measures of health outcomes, such as infant mortality (Currie and Gruber, 1996b). Many health and other social policies are focused on reducing disparities in

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Children’s Health, The Nation’s Wealth: Assessing and Improving Child Health access, utilization, and health outcomes based on such social factors as differences in income, race, ethnicity, and gender. Even when relatively good data can be collected on the outcome of interest—such as infant mortality—and overall trends in that outcome accurately measured, existing data systems do not include sufficient other variables to test what accounts for the observed trends. Moreover, existing data also may not be able to differentiate between overall and subgroup trends. For instance, even though infant mortality rates have decreased for all ethnic and racial groups, disparities between whites and blacks have actually increased. This indicates that the influence on the absolute trends is likely to be different from the influence on the disparities trend (Wise, 2003). Because one of the two goals of Healthy People 2010 is to eliminate disparities, it is important to develop data systems that can measure the effect of policies on health outcome disparities. A success story in assessing the effect of a major health policy is the national Back to Sleep campaign (Wise, 2003). Because the outcome of interest is infant mortality and because there is a specific long-standing data collection system for this outcome, it has been possible to monitor the effect on sudden infant death syndrome (SIDS) specifically from the time that this national Back to Sleep campaign was introduced (American Academy of Pediatrics Task Force on Infant Position and SIDS, 1992; Pollack and Frohna, 2002; Lesko, Corwin, Vezina, Hunt, Mandel et al., 1998). Using infant mortality data, which contain some information on social class, it was shown that the Back to Sleep educational initiative dramatically reduced mortality rates due to SIDS, but also increased social disparities (Wise, 2003). Research studies were required to demonstrate that the effect of this new information and educational program has a bigger uptake and adoption by wealthier and more educated families. This was an important source of information for national, state, and local policy makers and programs interested in making midcourse corrections in their Back to Sleep campaign. Measurement of the effect of policies related to the physical environment can be done at several different levels, including the monitoring of air, water and food quality, biomonitoring, and health effects. However, for environmental policy changes, the use of multiple indicators, as shown in Figure 5-1, allows rapid assessment of changes in influences by measuring environmental indices and biomarkers of exposure. These can then be correlated over time with changes in biomarkers of early and late effects, and finally with indicators of health. If a “significant risk” rather than an “actual harm” standard prevails in environmental policy (as it did for leaded gasoline), then biomarkers of exposure, while an indirect measure of children’s health, could be used to document significant risk. The presence of an environmental influence for which there is evidence of likely harm, as measured using biomarkers, can then be used to guide environmental policy decisions. While Healthy People 2010 provides a possible framework for evaluating the effect of some influences on health, including policy changes, its structure does

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Children’s Health, The Nation’s Wealth: Assessing and Improving Child Health not permit examination of changes (improvements or decrements) in children’s health from a dynamic perspective, as conceptualized in this report. Healthy People 2010 provides a large number of indicators that reflect particular aspects of health—for example, behaviors influencing health, mental health, injuries, and vaccination status—but it does not offer a model for assessing the interaction or accumulation of these indicators in children or groups of children. There has been little work on how indicators of health can be combined to form a composite of health at the individual or population level or to profile health and changes in health at the population level across the group of indicators. Similarly, developmental concepts (e.g., rates of change in health potential over time) are not incorporated in the 2010 goals or objectives. The effects of policy changes cannot be adequately assessed without tracking the way these changes and their consequences affect children’s developmental trajectories. A number of other countries are ahead of the United States in monitoring both health overall and children’s health in particular. In England, Canada, and Australia, major efforts have been undertaken to monitor the health of children over time. Although health was conceptualized in conventional ways using such indicators as mortality and morbidity, rather than in a dynamic manner, a recent effort carried out in the Canadian province of Manitoba (Manitoba Centre for Health Policy, 2002) provides lessons for future U.S. efforts. Data were organized by regions of the province, with each region characterized by an overall measure of health—premature mortality—and the areas ranked from high to low. These rankings were similar to the areas’ ranking by socioeconomic status measures, reflecting worse health in more socially deprived areas. Areas were also ranked on the basis of mortality rates, adolescent reproductive health, acute and chronic conditions, and injury rates and these rankings were compared against the overall rankings. In this way, areas that performed better or worse than expected given their overall health status (i.e., premature mortality) were identified, making it possible to link particular policies (including those relating to health services) in different areas to level of performance on various health indicators. This is an important example of using ongoing data collection for monitoring children’s health. It represents public commitment to children and demonstrates the feasibility of implementing such an effort on a large scale. In Vancouver, British Columbia, Canada, a major initiative has been launched to measure and link measurement of children’s health, development, and educational achievement for all school-age children to all existing programs and policies that affect these outcomes. Through the nationally sponsored Canadian Human Early Learning Partnership, this pilot project in Vancouver has mapped differences in children’s health and social outcomes at the neighborhood level and related those differences to the availability and delivery of different health education and social service programs (Hertzman, McLean, Kohen et al., 2002). While representing a step toward a more extensive, ongoing, and integrated data system to measure and monitor the longitudinal health and edu-

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Children’s Health, The Nation’s Wealth: Assessing and Improving Child Health cational trajectories of children, this initial effort aggregates health, human services, and neighborhood data and links them at the level of the individual child and neighborhood. The framework used in Vancouver to collect and report data on children provides an opportunity to monitor continuously the effects of a range of policy changes.7 Challenges in Measuring Policy Measuring the effect of policies on children poses extraordinary challenges, given the many other influences that concurrently affect children’s lives. Few attempts have been successfully mounted in this regard, particularly in areas in which the policy is not explicitly targeted toward children. As discussed previously, use of ongoing data collection for this purpose is exceptionally rare, but are noteworthy for the quality and effect of the information they provide. One of the major challenges has to do with the frequently changing nature of the policy environment. Policies put in place during one administration may be accompanied by attempts to evaluate its effect, only to have that program changed or eliminated by the next administration. The most convincing studies of policy effects involve random assignment to an experimental and control group. This is not only expensive and at times difficult to implement, but it can also be difficult to justify ethically, especially when a given policy is enacted with the purpose of benefiting children. Nonetheless, without such studies, the best intentions of policy makers can have untoward effects. Promising alternative strategies rely on the natural experiments provided by changes in national or state policies over time (e.g., Currie and Gruber, 1996a) and ongoing data collection. In this case, sharp policy changes from one administration to the next or from one state to another aid evaluators, since they can then look for health care and health changes surrounding the policy changes. These evaluation studies require consistent and representative measurement of children’s health and other demographic as well as policy conditions before and after the changes of interest. Studies based on trends in state-specific policies benefit greatly from consistent information across states and time regarding exactly what state policies have been implemented. Assessments of policy effects—indeed, the design of policies themselves—are limited by conceptualizations of what constitutes health for children. Current policy perspectives continue to focus largely on diseases and illnesses and health services relevant to those diagnoses rather than on facilitating healthy development. To embrace a more dynamic view of children’s health, policy approaches need to consider health in a developmental context, focusing on facilitating well- 7   For additional information, see http://www.earlylearning.ubc.ca/CHILD/.

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Children’s Health, The Nation’s Wealth: Assessing and Improving Child Health ness and health potential. This broader conceptualization will require consideration of not only biological factors, but also the range of behavioral, family, neighborhood, community, and system influences on children’s health. A good example is the Sampson, Raudenbush, and Earls (1997) analysis of the importance of a community’s “collective efficacy,” discussed in Chapter 3. Understanding the factors that enhance collective efficacy would allow a community to develop policies intended to improve it, thereby improving the healthy development of its children. Evidence about the importance of particular influences on health usually comes from studies of the relative risk of particular influences on a specific health outcome. The information provides a numerical estimate of the extent to which exposure to an influence increases the likelihood that a particular health outcome will occur, compared with the situation when the influence is not present. Clinical decision makers usually rely heavily on such evidence to justify interventions to reduce exposure to such influences in individuals. In contrast, policy makers concerned with population health are more appropriately interested in attributable risk, that is, the extent to which different influences contribute to health outcomes. This is important for making policy decisions about which influences are most likely to improve health outcomes in the population. Such evaluations have much more potential to contribute to rational decisions about the most effective strategies to improve health. The challenge for such evaluations, however, is to include multiple types of influences as well as their interactions, in order to avoid attributing more benefit to certain types than to others. For example, McGinnis and Foege (1993) reviewed studies of the effect of certain behaviors on subsequent death and concluded that the combined behaviors accounted for 50 percent of deaths. The study was relatively unusual in examining attributable risk and an important model for needed studies, although it did not include a full range of influences. Moreover, it did not assess the interactions among the various types of influences, thus raising the likely possibility that behavioral factors were a result of, confounded by, or interacted with other types of influences that were not studied. Their report, for example, explicitly recognized the importance of appropriate health services (in the form of primary care) and socioeconomic characteristics, but it did not consider the effect of these services on the prevalence of the behaviors. Where there is evidence that certain exposures are likely to cause ill effects, the wise course of action is to avoid such exposures, especially for children. Ill effects experienced during childhood alter future health. Policies that limit the release of noxious chemicals or other agents and the building of safe schools, houses, roadways, and cities can be expected to maximize the potential for good health, both of children and the adults they become.

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Children’s Health, The Nation’s Wealth: Assessing and Improving Child Health Addressing Challenges in Measuring Policy Monitoring policy effects on child health has not been a national priority. While existing laws require that environmental impact statements be developed when new roads, bridges, or dams are built, there is no such requirement to monitor the effect of labor, health, housing, energy, or transportation policies on children’s health. Yet as noted earlier, such policies can exert important yet unintended and unanticipated effects on children’s health, sometimes positive and sometimes negative. Given strong evidence that children’s health sets the stage for life-long health, assessing the effects of policy on children’s health should be given much more attention. Existing ongoing data systems have several limitations as a tool for assessing policy effects, including their limited focus on particular diseases, the relative lack of longitudinal data, and the inability to link data across systems. Given the latitude afforded to states to implement policy, there is also a need for better tracking of state-specific policy implementation from one year to the next. Approaches being undertaken in Canada, England, and Australia provide valuable models for the United States. These models should be considered as new approaches to measuring children’s health are developed. CONCLUSION Additional well-designed research and evaluation that address the challenges articulated throughout this chapter are needed to fully understand the range of influences and the interactions between them. The conceptual basis of many studies of children’s health would be improved by the simultaneous study of at least one factor from each important category of influences known to be associated with health—an exception is the assessment of social class. In this way, studies can avoid the most egregious biases from failing to include variables that influence health and that interact in powerful ways with variables that have been included. A prototype of such a study is that of Lantz et al. (1998), which included both behavioral as well as social factors in the analysis of a national dataset. This was a study of adults; similar studies are warranted for children. No single survey collects data on all influences on children’s health in a comprehensive manner; it would be both financially and methodologically onerous to do so. Ensuring that the portfolio of surveys collects at least some data on multiple salient influences and improving the comprehensiveness of individual surveys drawing on the content of existing surveys should be priorities as research continues to elucidate the dynamism of health and its influences. Surveys focused on child outcomes other than health would profit from paying more attention to health outcomes and influences. For example, education-focused datasets often provide rich information on the child’s readiness for literacy, family access to resources, and school quality but lack data on the child’s biomedical markers and

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Children’s Health, The Nation’s Wealth: Assessing and Improving Child Health family health care seeking and health care access. Similarly, surveys focused on health outcomes have comparatively little information on such influences as family and communities variables. Over time, a comprehensive continuous measurement system should be informed and evolve based on knowledge gained from continued research and expanded data collection in existing surveys.