- Email: [email protected]
Identifying at-risk children for early intervention services: Lessons from the infant health and development program
Identifying at-risk children for early intervention services: Lessons from the Infant Health and Development Program Russell S. Kirby, PhD, MS, Mark E. S w a n s o n , MD, MPH, Kelly J. K e l l e h e r , MD, MPH, R o b e r t H. B r a d l e y , PhD, a n d Patrick H. C a s e y , MD From the Center for Ambulatory Research and Education, Department of Pediatrics, University of Arkansas for Medical Sciences, University of Arkansas University Affiliated Program, and Center for Research on Teaching and Learning, University of Arkansas-Little Rock
A U.S. law mandates early intervention services for infants and young children who have, or are at risk for, developmental problems. Participating slates must develop definitions for identifying infants and young children at risk for developmental problems. To assess the sensitivity, specificity, and positive predictive value of some commonly identified risk factors, we examined the definitions proposed by five states. Data on risk factors and 36-month developmental outcomes were obtained from follow-up participants in the Infant Health and Development Program, a mullisite, collaborative prospective intervention program involving 985 low birth weight preterm infants. Few individual rlsk factors proposed by these stales were associated with poor developmental outcomes. Characleristics with positive predictive values greater than 30% were highly specific but tended to involve few cases. Risk factors with positive predictive values greater than 50%, such as hypothyroidism, occurred infrequently (<6%) in this sample. When state definitions for at-risk children were examined in composite, each definition yielded a positive predictive value of 25% to 35%, with poor specificities ranging from 42% to 40%. These data on tow birth weight infants have implications for the design and funding of population-based early intervention programs, and suggest that more careful clinical and longitudinal research Is necessary before appropriate definitions can be promulgated for identifying children in need of early intervention services. (J PEDIATR1993;422:680-6)
Supported by grants from the Robert Wood Johnson Foundation to the Department of Pediatrics, Stanford University; The Frank Porter Graham Child Development Center, University of North Carolina; and the eight participating universities. Additional support was provided to the Department of Pediatrics, Stanford University, from the Pew Charitable Trusts; from the Bureau of Maternal and Child Health and Resources Development, Health Resources Services Administration, U.S. Public Health Service, Department of Health and Human Services (grant No. MCJ060515); and from the Stanford Center for the Study of Families, Children, and Youth. Presented in part at the meeting of the Southern Society for Pediatric Research, New Orleans, La., Jan. 29-31, 1992. Submitted for publication Oct. 5, 1992; accepted Dec. 16, 1992. Reprint requests: Russell S. Kirby, PhD, MS, Department of Pediatrics/CARE Slot 512, University of Arkansas for Medical Sciences, 4301 W. Markham St. Little Rock, AR 72205. Copyright 9 1993 by Mosby-Year Book, Inc. 0022-3476/93/$1.00 + .10 9/20/45046 680
Public Law 99-457 is a U.S. federal law that mandates participating states to provide early intervention services for infants and young children who have, or are at risk for, developmental problems.l, 2 More than previous laws addressing developmental problems, such as the Education for the IHDP LBW PL PPV \
Infant Health and Development Program Low birth weight Public Law Positive predictive value
Handicapped Act (PL 94-142), PL 99-457 focuses on young children at risk because of biologic or medical factors) Pediatricians are in a prime position to identify and follow high-risk children because they are usually the primary professional contact for families before the children reach school age.
The Journal of Pediatrics Volume 122, Number 5, Part 1
As part of their federal mandate for eligibility determination, participating states are required to develop criteria to identify children at risk for an adverse developmental outcome. In this respect, the law poses a considerable challenge because definitive risk factors for poor developmental outcomes have not 1Seen elaborated. 4a2 The cost-effectiveness of intervention has not been determined for all conditions and all types of intervention, and the costs of overidentification or underidentifieation of children at risk are significant. 13 Failure to intervene properly with children at high risk may present the educational system with students who need more intensive services, both in regular and special education; however, the provision of services to too many children may stress the personnel and resources of state systems. Thus the accurate identification of young children most likely to have poor developmental outcomes has critical implications for children, families, and service systems. The Infant Health and Development Program provides an opportunity to examine the performance of proposed state criteria for identification of at-risk infants and young children. The IHDP is a national collaborative, randomized clinical trial conducted at eight medical schools by using a large sample of low birth weight infants followed from birth until 3 years of age. 14 A broad range of medical, developmental, parental, and family data were collected, and they allow investigators to study the relationship between risk factors (biologic and environmental) and outcomes at 3 years of age. The purpose of this article is to examine how well eligibility criteria from five representative states predicted poor developmental outcome at 3 years of age in the IHDP cohort. METHODS Sample populatlon. The IHDP cohort consists of inborn, preterm, LBW infants enrolled at eight participating sites; uniform sampling criteria were used. All data were collected under a standard protocol at every site. The sample and subsequent procedures have been described but will be reviewed briefly here. 14 Every infant (N = 4551) born at each of the eight participating sites with a birth weight --<2500 gm and a gestational age _<37 weeks during the 9-month enrollment period was screened for inclusion in IHDP. Infants who lived outside the catchment area were excluded (n = 1524), as were those discharged outside the recruitment period (n = 431). Infants who exceeded 37 weeks of corrected gestational age were excluded (n = 604), as were infants who died within the first 48 hours of life (n = 233). Triplets, quadruplets, and the twin of an ineligible child were excluded (n = 53), and only one of each pair of eligible twins was included in analyses (n = 105). Maternal exclusions were for maternal drug or alcohol abuse (n = 51), inability to communicate adequately in English
Kirby et al.
68 1
(n = 108), and maternal report of psychiatric hospitalization (n = 6). Infant medical exclusions included hospitalization >60 days after 40 weeks of gestational corrected age (n = 9), oxygen support for >90 days (n = 19), severe neurologie abnormality (n = 3), and severe sensory deficit (n = 2) or chromosomal-multiple anomaly syndromes (n = 25). Additionally, the parents of 305 infants refused consent, and 47 families refused group assignment. A total of 985 infants were included in the research program. The research design was for one third of the infants enrolled in IHDP to be randomly assigned to the intervention track, which consisted of a center-based educational component, home visits, and parental group meetings. Interventiontrack children were excluded from this analysis because the intention was to assess the association between risk factors and developmental outcomes in children who did not participate in a comprehensive early intervention program. There were 608 children in the follow-up group; data on 36-month developmental outcomes were available for 562 of the subjects. Risk factors. The IHDP data collection forms obtained at birth, in early infancy, and at 12 and 24 months of age were reviewed to identify variables that measure the risk factors for PL 99-457 proposed for each of five states (Hawaii, Massachusetts, North Carolina, South Dakota, and Washington) selected because they represented differing approaches to definitions of eligibility for early intervention services. A list of the specific conditions identified for each state is available from the authors. Risk factors were grouped into the following three broad categories: (1) established risk, (2) biologic risk, and (3) environmental risk. 15q7 Established risk factors are those known to cause developmental disabilities in children; they include chromosomal abnormalities and structural or metabolic defects that lead to developmental delays. Biologic risk factors are diseases, health conditions, and markers of health care utilization that have been associated with developmental disorders and delays. These include measures of perinatal outcome (extremely low birth weight, intraventricular hemorrhage, respiratory distress syndrome, extended neonatal hospitalization) and maternal reproductive characteristics (drug abuse, infection). In a sense, the entire IHDP sample could be considered at biologic risk because all participants had LBW when born prematurely. Environmental risk factors are measures of the sociocultural milieux in which the child lives; they include socioeconomic status measures, household composition, psychosocial'characteristics of the primary care giver, and level of social support. Measures were available for almost all risk factors for PL 99-457. The proportion of all criteria for each state that could be obtained in the IHDP datasets is shown in Table I. Most missing criteria related to family characteristics, health status or psychosocial conditions, IHDP exclusion criteria,
682
Kirby et al.
The Journal of Pediatrics May 1993
Table I. Number of proposed state criteria available in IHDP dataset Slate
Total
Excluded*
Unavailable
North Carolina 37 2 9 Hawaii. 22 I 5 Massachusetts 24 1 2 South Dakota 19 0 2 Washington 17 0 3 *Excludedcriteriaare risk factorsthat resultedin exclusionfrom the IHDP sample. Examplesincludechromosomalabnormalitiesincompatiblewith normal intellectualdevelopment,severecongenitalanomalies,and known maternal drug use during pregnancy.
or conditions not widely appreciated at the time the IHDP was initiated (fetal alcohol syndrome, acquired immunodeficiency syndrome). Data on parental substance abuse were limited to known maternal use of drugs during pregnancy; most maternal drinkers or drug users were excluded. Because this study was a controlled intervention trial, all participants received well-child care, thus eliminating the risk factor proposed for North Carolina of no well-child care by 6 months of age. Outcomes. Poor developmental outcomes at 36 months of gestation-corrected age were identified from the IHDP 36month neurodevelopmental and health examinations, interval health interviews, and developmental testing. The following conditions were defined as poor 36-month developmental outcomes: Stanford-Binet Intelligence Quotient less than 70, required use of a hearing aid, diagnosed severe eye condition (optic atrophy, buphthalmos), abnormal clinical neurologic status, cerebral palsy, diagnosed severe neurologic disease (other neuromuscular abnormalities, congenital abnormalities of the head and brain, severe attention deficit disorders, and seizure disorders), and severe congenital anomaly not resulting in exclusion from the sample. Of the 608 children in the IHDP follow-up group, 46 lacked data for the 36-month assessment and were excluded. There were 119 children with an intelligence quotient less than 70 at 36 months. A total of 58 children had at least one of the other defined conditions; 29 of them also had an intelligence quotient less than 70. Thus the total number with poor 36-month developmental outcomes was 148, or 26.3% of the follow-up group. Analyses. The sensitivity, specificity, and positive predictive values of eligibility criteria for early intervention programs in each of the five states were calculated by using data measuring poor 36-month developmental outcomes. 18 Criteria were evaluated individually and collectively according to the eligibility definitions for each state. Composite analyses were run twice, first with all risk criteria included and second with any risk criteria for birth weight, gestation, and duration of hospital stay excluded, because by definition the
IHDP sample was a LBW, preterm sample, with most participants qualifying as "at risk" under these criteria. RESULTS Table II lists selected risk criteria identified by one or more of the states, together with the percentage of the total IHDP follow-up group with that condition. The sensitivity, specificity, and PPV for a poor developmental outcome at 36 months of age are also included (the full table of criteria identified by one or more states is available from the authors). In general, those conditions with a high degree of sensitivity were also common in the sample and have a relatively low degree of specificity. In contrast, none of the conditions with specificity greater than 95% occurred among more than 5% of the sample. Most of these high-specificity but rare conditions were "established risk factors," characterized by low sensitivity, very high specificity, and high PPVs. These risk factors were present in relatively few study subjects. Biologic risk factors had low sensitivity, although higher than for established risk factors. Specificity was generally high, but PPVs were lower than for established risk factors. An exception was neonatal hospitalization for more than 5 days. Five of six study subjects met this criterion because of the nature of the sample, with sensitivity of 87.8% and specificity of 17.6%. Environmental risk factors occurred infrequently (<20% of study subjects) and had low sensitivity, relatively high specificity, and low PPVs. Family income and marital status risk factors occurred frequently and were neither sensitive nor specific. Few individual environmental risk factors proposed by these states were associated with poor 36-month developmental outcomes in this sample. Characteristics with PPVs >30% were highly specific but tended to involve few eases. Risk factors with PPVs >50% (abnormal clinical neurologic impression, neonatal seizures, meningitis, hypothyroidism, musculoskeletal anomalies, other genetic and metabolic conditions) occurred infrequently (<6%) in this sample. Composite criteria for each State were analyzed to assess overall performance of the state systems (Table III). When state definitions for at-risk children were examined in composite, state criteria yielded PPVs of 28% to 34%, with sensitivity ranging from 96% (North Carolina) to 76% (South Dakota) and specificity from 12% (Hawaii) to 40% (Massachusetts). When composite criteria were analyzed with birth weight, gestation, and hospitalization indicators excluded, PPVs improved slightly; sensitivity ranged from 94% (North Carolina) to 75% (South Dakota) and specificity from 17% (North Carolina) to 47% (Massachusetts). DISCUSSION The value of early intervention services for children with, or at risk for, developmental problems in the first 3 years of life, and for their families and society, has been pointed out
The Journal of Pediatrics Folume 122, Number 5, Part 1
Kirby et al.
683
Table II, Developmental outcome at 36 months for selected risk criteria from I H D P follow-up data ( n = 562)*
Established risk Hypothyroidism, any severity Acyanotic congenital heart disease Neonatal seizures Meningitis (neonatal) Biologic risk Intraventricular hemorrhage: grade I l l / I V in neonatal period Severe respiratory distress syndrome/ hyaline membrane disease Birth asphyxia Newborn hospitalization >5 days Environmental risk Mother <17 years old Mother unmarried at child's birth No prenatal care Family income <$10,O00/yr Chronic maternal illness
Infants
Sensitivity
Specificity
PPV
(%)
(%)
(%)
(%)
0.4 1.4 2.1 0.7
0.7 1.4 6.1 2.0
99.8 98.6 99.3 99.8
50.0 25.0 75.0 75.0
3.2
7.4
98.3
61 .i
20.3
29.7
83.1
38.6
7.5 83.8
12.2 87.8
94.2 17.6
42.9 27.6
5.2 50.2
4.1 61.5
94.4 53.9
20.7 32.3
4.1 32.4 13.9
3.4 43.2 16.2
95.7 7 ! .5 87.0
21.7 35.2 30.8
*A total of 46 cases with missing 36-month assessment.
Table III. Likelihood of 3 6 - m o n t h composite poor outcome based on I H D P follow-up data (n = 562)* Infants
Sensitivity
Specificity
PPV
68.7 37.5 14.2 48.4
77.0 59.5 21.6 57.4
34.3 70.3 88.4 54.8
29.5 41.7 40.0 31.3
73.7 51.1 88.1 39.3
87.2 68.9 95.9 52.7
31.2 55.3 14.7 65.5
31.2 35.5 28.7 35.3
90.0 66.7 88.3 65.5 83.8
94.6 86.5 95.9 75.7 90.5
11.6 40.3 14.5 38.2 18.6
27.7 34.1 28.6 30.4 28.5
(%)
Biologic risks'i Hawaii Massachusetts North Carolina: established high risk South Dakota Environmental riskst Hawaii Massachusetts (3+) North Carolina: potential high risk South Dakota (3+) Combined risk factors Hawaii Massachusetts North Carolina South Dakota Washington
(%)
(%)
(%)
*A total of 46 cases with missing 36-month assessment. tThese classificationsare presented as proposed by each state. Washington did not differentiate risk factors into biologic and environmental categories. North Carolina differentiated into established and potential risk factors, ~hieh have been grouped for comparative purposeswith biologicand environmentalrisk factors from the other states.
frequently. 19-22 Services for identified children are likely to result in e n h a n c e d academic achievement, l a n g u a g e , growth, and social skills. Such services also m a y reduce family stress, institutionalization, later special education placement, a n d societal dependency. Eventually these prog r a m s could reduce societal and public costs t h r o u g h lower special education and health care costs, combined with increased tax revenues from productive workers.
Public Law 99-457 provides states with a framework to organize and coordinate a comprehensive program of early intervention services for very young children. T h e first step in this enabling legislation is defining the target population of children with, or at risk for, developmental problems. Overall, we found t h a t the eligibility criteria employed by the states were sensitive but not specific; t h a t is, they identified almost all the children with, or at risk for, poor devel-
684
Kirby et al.
opmental outcomes at 3 years of age, but they also identified many children with normal outcomes (false-positive results). States included for this study used three sets of factors to Classify children for services. First, every state identified a small group of children with "established" problems, early metabolic or neurologic insults that lead with some certainty to disability or long-term needs. Because these defined disorders occur rarely in any population, only a few children with established needs were identified. The second general category of risk factors consisted of biologic or medical risk factors. A core group of these factors, which included intraventricular hemorrhage, birth asphyxia, and hypoglycemia, were all consistently associated with a poor outcome. Although specific i n predicting a poor outcome, these conditions again identified a very small group of children. The other biologic variables, which focused on birth weight, prematurity, and hospitalization history, were very common in this population. Therefore the use of these criteria captured a large number of children (the measure was sensitive), but the specificity and PPV were very low because many children who met these criteria did not have poor outcomes. Environmental variables are the third category of risk factors. These factors were also sensitive but not specific when used as risk factors to identify children with, or at risk for, developmental problems. Most of the children with poor outcomes had at least one of the environmental risk factors identified by the states; however, even more of the children without poor outcomes had o n e o r more of these factors. Therefore the majority of children identified by states employing individual environmental variables will not have a poor outcome even without services. The IHDP sample consisted of LBW preterm infants born to residents of defined catchment areas. Infants with severe birth defects, extended hospitalizations during the neonatal period, and neurosensorydeficits were excluded because these subjects would be unlikely to benefit from the expensive intervention program. Exclusion of these cases had a minimal effect on our findings. If one assumed that all infants excluded from the IHDP sample because of medical exclusions were included in the follow-up group, and that all these infants had poor developmental outcomes at 36 months of age, then, when the Massachusetts composite risk criteria are used, the results differ only slightly from the initial analysis: sensitivity 90% compared with 86%; specificity 40% (unchanged); and PPV 43% compared with 34%. However, our findings may not be generalizable to the population of infants and toddlers because the follow-up sample was limited to LBW preterm infants whose mothers were not known to have abused drugs or alcohol during pregnancy, had no self-report of psychiatric
The Journal of Pediatrics May 1993
hospitalization, and could communicate effectively in English. In all states studied, criteria based on birth weight, gestation, or duration of neonatal hospitalization figured prominently as biologic or medical risk factors. To assess the effect of these indicators on the overall utility of composite state criteria in identifying children at risk for poor developmental outcome, we performed composite analyses with these factors included and excluded. Results did not differ markedly between the two analyses. The largest difference was found for Washington State, where sensitivity rose from 84% without birth weight, gestation, or hospitalization risk factors to 90% when these factors were included, whereas specificity declined from 31% to 19% and PPV changed from 30% to 29%. In the general population these conditions may be useful indicators of need for early intervention services; however, they contribute little to the identification of LBW preterm infants with poor developmental outcomes at 36 months of age. Kochanek and Buka z3 performed a similar analysis on a different sample with limited criteria. Infants resident in nine target Rhode Island communities were eligible for part!cipation in a two-stage screening program. In level 1, infants were classified as at risk, not at risk, or possibly at risk on the basis of the presence or absence of established conditions, child characteristics, parent demographics, and parent characteristics. Of 3363 infants screened, 29% were at risk. Of these, only 5% had established risk conditions. More than half of the infants at risk were identified on the basis of parent demographics. This study emphasizes the importance of both child and family factors in the developmental screening program. However, the results of Kochanek and Buka are based on assessments of infants; they did not assess outcomes at 36 months of age. Their study shows the importance of using combinations of risk factors for child and parent characteristics, as confirmed in our study. The established risk conditions mentioned above will identify a small but needy group of children. The degree to which these children benefit from early intervention services remains to be seen. 19 Moreover, a core group of other biologic risk factors (severe intraventricular hemorrhage, postneonatal seizures, existing severe cfironic illness at 12 months of age, and infection involving the central nervous system) Will also identify a select group of high-risk infants who are likely to have poor outcomes. The remainder of the biologic and all of the environmental criteria are highly sensitive but not specific (a broad "net"). States such as Massachusetts and South Dakota have made some inroads into the problem ofoveridentification by requiring multiple environmental factors. When the composite analyses were run for Massachusetts by using the criterion of one or more high-risk conditions compared with three or more, the fol-
The Journal of Pediatrics Volume 122, Nttmber 5, Part 1
lowing differences were observed: sensitivity, 98% versus 86%; specificity, 10% versus 40%; and PPV, 28% versus 34%. Similar findings were obtained for South Dakota, and in both cases the results are similar to those for Hawaii, North Carolina, and Washington. Both states improved the specificity and PPV 6f their risk criteria, but sensitivity decreased slightly. Similar strategies could be employed with the less-specific biologic variables. A middle ground that employs risk factors that are highly sensitive in identifying children who should be monitored and periodically reassessed may be ari attractive alternative for early intervention programs. The classification of many children with environmental risk factors likely to have normal outcomes as eligible for early intervention services has important implications for state policy makers. As an example, the cost of providing services to young children is growing rapidly. The problems in evaluating the costs and benefits of early intervention services have recently been reviewed. 24 Costs vary with the severity of the child's condition and the family's financial and emotional resources. Early estimates of the cost of a year's services to a young child and family appeared to be reasonable. For example, North Carolina reported average costs of $1117 per child served in 1985 and 1986. 21 More recent analyses indicate that costs are likely much higher and should be estimated from risk-based groupings because so much of the cost depends on the intensity of services. Florida estimated costs for a year's services of between $1500 per child in the lowest-risk group to S 15,000 per child in the highest-risk group. 22 With many states facing budget cutbacks, it is unlikely that administrators will choose electively to serve large groups of children who are not likely to have poor developmental outcomes, especially because states must also serve children with no risk factors who are identified as having developmental delay in the first 3 years of life. The limited availability of trained care providers also should give pause to those proposing the "wide net" approach to serving young children at risk. Many states and localities have few services to offer already-identified children with developmental disabilities. Because case-finding services increase the number of referrals, some services are likely to be overloaded and poorly responsive to those children most in need, particularly in poor, rural states. Nevertheless, the growing body of literature on the costeffectiveness of early intervention services for developmentally impaired or at-risk children likely will encourage many states to provide services for children identified with highly sensitive composite criteria sets like the ones examined in this studyJ 4 Although some authors dispute the adequacy of the cost-effectiveness research on early intervention, most studies have demonstrated significant societal and publicsector cost savings. 21,22'24 Unfortunately, these studies
Kirby et al.
685
have not analyzed their findings across the various risk levels to determine whether savings occur predominantly with the most or least impaired. It is possible that all families would benefit from the development of an individualized family service plan, which is the core concept of PL 99-457. Even with these alternative strategies, the proposed criteria for identification of children qualifying for services will continue to include a large group of children who will not have poor outcomes. Although our results show remarkable consistency across states, the population chosen to study these criteria and the methods employed indicate the need for caution in the interpretation of these data. The study sample consisted of only LBW preterm infants who had no major neurologic problems at birth and no extended need for oxygen; this population could be considered at higher risk than other samples. Thus we expect all the risk factors to be more common in our sample, and we assume that as the probability of these factors decreases in the general population, their PPV will also decrease. Moreover, we have included only those outcomes at 3 years of age that clearly qualify a child for special services. States may wish/to consider interventions for other children with lesser impairments. Finally, this examination of state identification criteria is largely theoretical in its assumption that far too many children will qualify for services, because the states lack adequate providers and case-finding services to begin to qualify all the eligible children. We have examined the effects of multiple risk criteria in only the most cursory manner. More research with the use of a wide range of indicators and predictors of perinatal and acquired risk factors for poor developmental outcomes is needed to determine the most effective and efficacious criteria for early intervention services. We conclude that the majority of children identified by the proposed criteria for early identification of high-risk infants will not have poor outcomes even without intervention services. States must carefully weigh the consequences to their budgets and to already overused early intervention services if current criteria are employed. Our data on LBW preterm infants suggest that more careful clinical and longitudinal research is necessary before appropriate definitions can be promulgated for identifying children in need of early intervention services. REFERENCES 1. Palfrey JS, Singer J, Walker D, Butler J. Early identification of children's special needs: a study of five metropolitan communities. J PEDIATR 1987;111:651-9. 2. Ramey CT, Stedman DJ, Borders-Patterson A, Mengel W. Predicting school failure from information available at birth. Am J Ment Defic [now Am J Ment Retard] 1978;82:525-34. 3. DeGraw C, Edell D, Ellers B, et al. Public Law 99-457: new
686
4.
5.
6.
7.
8.
9. 10.
! 1.
12. 13.
14.
Kirby et aL
opportunities to serve young children with special needs. J PeDIATR 1988;113:971-4. Ellenberg JH, Nelson KB. Cluster of perinatal events identifying infants at high risk for death or disability. J PEDIATR 1988;113:546-52. Finkelstein NW, Ramey CT. Information from birth certificates as a risk index for educational handicap. Am 3 Ment Defie [now Am J Ment Retard] 1980;84:546-52. Fried PA, Watkinson B. 36- and 48-month neurobehavioral follow-up of children prenatally exposed to marijuana, cigarettes, and alcohol. J Dev Behav Pediatr 1990;11:49-58. Low JA, Galbraith RS, Muir DW, et al. The predictive significance of biologic risk factors for deficits in children of a highrisk population. Am J Obstet Gynecol 1983;145:1059-68. MeCarton CM, Vaughn HG Jr, Golden ~.R. Can neurobehavioral outcomes be predicted by perinatal variables? In: Vietz PM, Vaughn HG Jr, eds. Early identification of infants with developmental disabilities. Philadelphia: Grune and Stratton, 1988:9-40. Rantakallio P, yon Wendt L. Risk factors for mental retardation. Arch Dis Child 1985;60:946-52. Stanley F J, English DR. Prevalence of and risk factors for cerebral palsy in a total population cohort of low birth weight (<2000 gm) infants. Dev Med Child Neurol 1986;28:559-68. Gordon BN, Jens KG. A conceptual model for tracking highrisk infants and making early service decisions. J Dev Behav Pediatr 1988;9:279-86. Cooke RWI. Cerebral palsy in very low birth weight infants. Arch Dis Child 1990;65:201-6. Ramey CT, Bryant DM, Suarez TM. Early intervention: why, for whom, how, and at what cost? Clin Perinatol 1990;17:4755. The Infant Health and Development Program. Enhancing the outcomes of low birth weight, premature infants: a multisite, randomized trial. JAMA 1990;263:3035-42.
The Journal of Pediatrics May 1993
15. Berman C, Biro P, Ferichel ES, eds. Keeping track: tracking systems for high-risk infants and young children. 2nd ed. Washington, D.C.: National Center for Clinical Infant Programs, 1989. 16. Blackman J. Warning signals: basic criteria for screening and tracking high-risk infants and toddlers. Washington, D.C.: National Center for Clinical Infant Programs, 1986. 17. Bee ttL, Barnard KE, Eyres SJ, et al. Prediction of IQ and language skill from perinatal status, child performance, family characteristics and mother-infant interaction. Child Dev 1982;53:1134-56. 18. Last JM, ed. A dictionary of epidemiology. 2rid ed. New York: Oxford University Press, 1988:119-20. 19. Guralnick MJ. The next generation of research on the effectiveness of early intervention. Except Child 1991;58:174-83. 20. Ramey CT, Bryant DM, Wasik BH, Sparling J J, Fendt KH, LaVange LM. Infant health and development program for low birth weight, premature infants: program elements, family participation, and child intelligence. Pediatrics 1992;89:45465. 21. North Carolina Interagency Coordinating Council. Early intervention in North Carolina. Report to the governor for 1990. Raleigh, North Carolina: North Carolina lnteragency Coordinating Council, January 1991. 22. Florida's cost/implementation study for Public Law 99-457, part H, infants and toddlers. Phase II findings. Tallahassee, Florida: Center for Prevention and Early Intervention Policy, Florida State University, 1991. 23. KochanekTT, BukaSL. Using biologic and ecologic factors to identify vulnerable infants and toddlers. Infants and Young Children 1991;4:11-25. 24. Barnett WS, Escobar CM. The economics of early intervention for handicapped children: what do we really know? Journal of the Division for Early Childhood 1988;12:169-81.
A U.S. law mandates early intervention services for infants and young children who have, or are at risk for, developmental problems. Participating slates must develop definitions for identifying infants and young children at risk for developmental problems. To assess the sensitivity, specificity, and positive predictive value of some commonly identified risk factors, we examined the definitions proposed by five states. Data on risk factors and 36-month developmental outcomes were obtained from follow-up participants in the Infant Health and Development Program, a mullisite, collaborative prospective intervention program involving 985 low birth weight preterm infants. Few individual rlsk factors proposed by these stales were associated with poor developmental outcomes. Characleristics with positive predictive values greater than 30% were highly specific but tended to involve few cases. Risk factors with positive predictive values greater than 50%, such as hypothyroidism, occurred infrequently (<6%) in this sample. When state definitions for at-risk children were examined in composite, each definition yielded a positive predictive value of 25% to 35%, with poor specificities ranging from 42% to 40%. These data on tow birth weight infants have implications for the design and funding of population-based early intervention programs, and suggest that more careful clinical and longitudinal research Is necessary before appropriate definitions can be promulgated for identifying children in need of early intervention services. (J PEDIATR1993;422:680-6)
Supported by grants from the Robert Wood Johnson Foundation to the Department of Pediatrics, Stanford University; The Frank Porter Graham Child Development Center, University of North Carolina; and the eight participating universities. Additional support was provided to the Department of Pediatrics, Stanford University, from the Pew Charitable Trusts; from the Bureau of Maternal and Child Health and Resources Development, Health Resources Services Administration, U.S. Public Health Service, Department of Health and Human Services (grant No. MCJ060515); and from the Stanford Center for the Study of Families, Children, and Youth. Presented in part at the meeting of the Southern Society for Pediatric Research, New Orleans, La., Jan. 29-31, 1992. Submitted for publication Oct. 5, 1992; accepted Dec. 16, 1992. Reprint requests: Russell S. Kirby, PhD, MS, Department of Pediatrics/CARE Slot 512, University of Arkansas for Medical Sciences, 4301 W. Markham St. Little Rock, AR 72205. Copyright 9 1993 by Mosby-Year Book, Inc. 0022-3476/93/$1.00 + .10 9/20/45046 680
Public Law 99-457 is a U.S. federal law that mandates participating states to provide early intervention services for infants and young children who have, or are at risk for, developmental problems.l, 2 More than previous laws addressing developmental problems, such as the Education for the IHDP LBW PL PPV \
Infant Health and Development Program Low birth weight Public Law Positive predictive value
Handicapped Act (PL 94-142), PL 99-457 focuses on young children at risk because of biologic or medical factors) Pediatricians are in a prime position to identify and follow high-risk children because they are usually the primary professional contact for families before the children reach school age.
The Journal of Pediatrics Volume 122, Number 5, Part 1
As part of their federal mandate for eligibility determination, participating states are required to develop criteria to identify children at risk for an adverse developmental outcome. In this respect, the law poses a considerable challenge because definitive risk factors for poor developmental outcomes have not 1Seen elaborated. 4a2 The cost-effectiveness of intervention has not been determined for all conditions and all types of intervention, and the costs of overidentification or underidentifieation of children at risk are significant. 13 Failure to intervene properly with children at high risk may present the educational system with students who need more intensive services, both in regular and special education; however, the provision of services to too many children may stress the personnel and resources of state systems. Thus the accurate identification of young children most likely to have poor developmental outcomes has critical implications for children, families, and service systems. The Infant Health and Development Program provides an opportunity to examine the performance of proposed state criteria for identification of at-risk infants and young children. The IHDP is a national collaborative, randomized clinical trial conducted at eight medical schools by using a large sample of low birth weight infants followed from birth until 3 years of age. 14 A broad range of medical, developmental, parental, and family data were collected, and they allow investigators to study the relationship between risk factors (biologic and environmental) and outcomes at 3 years of age. The purpose of this article is to examine how well eligibility criteria from five representative states predicted poor developmental outcome at 3 years of age in the IHDP cohort. METHODS Sample populatlon. The IHDP cohort consists of inborn, preterm, LBW infants enrolled at eight participating sites; uniform sampling criteria were used. All data were collected under a standard protocol at every site. The sample and subsequent procedures have been described but will be reviewed briefly here. 14 Every infant (N = 4551) born at each of the eight participating sites with a birth weight --<2500 gm and a gestational age _<37 weeks during the 9-month enrollment period was screened for inclusion in IHDP. Infants who lived outside the catchment area were excluded (n = 1524), as were those discharged outside the recruitment period (n = 431). Infants who exceeded 37 weeks of corrected gestational age were excluded (n = 604), as were infants who died within the first 48 hours of life (n = 233). Triplets, quadruplets, and the twin of an ineligible child were excluded (n = 53), and only one of each pair of eligible twins was included in analyses (n = 105). Maternal exclusions were for maternal drug or alcohol abuse (n = 51), inability to communicate adequately in English
Kirby et al.
68 1
(n = 108), and maternal report of psychiatric hospitalization (n = 6). Infant medical exclusions included hospitalization >60 days after 40 weeks of gestational corrected age (n = 9), oxygen support for >90 days (n = 19), severe neurologie abnormality (n = 3), and severe sensory deficit (n = 2) or chromosomal-multiple anomaly syndromes (n = 25). Additionally, the parents of 305 infants refused consent, and 47 families refused group assignment. A total of 985 infants were included in the research program. The research design was for one third of the infants enrolled in IHDP to be randomly assigned to the intervention track, which consisted of a center-based educational component, home visits, and parental group meetings. Interventiontrack children were excluded from this analysis because the intention was to assess the association between risk factors and developmental outcomes in children who did not participate in a comprehensive early intervention program. There were 608 children in the follow-up group; data on 36-month developmental outcomes were available for 562 of the subjects. Risk factors. The IHDP data collection forms obtained at birth, in early infancy, and at 12 and 24 months of age were reviewed to identify variables that measure the risk factors for PL 99-457 proposed for each of five states (Hawaii, Massachusetts, North Carolina, South Dakota, and Washington) selected because they represented differing approaches to definitions of eligibility for early intervention services. A list of the specific conditions identified for each state is available from the authors. Risk factors were grouped into the following three broad categories: (1) established risk, (2) biologic risk, and (3) environmental risk. 15q7 Established risk factors are those known to cause developmental disabilities in children; they include chromosomal abnormalities and structural or metabolic defects that lead to developmental delays. Biologic risk factors are diseases, health conditions, and markers of health care utilization that have been associated with developmental disorders and delays. These include measures of perinatal outcome (extremely low birth weight, intraventricular hemorrhage, respiratory distress syndrome, extended neonatal hospitalization) and maternal reproductive characteristics (drug abuse, infection). In a sense, the entire IHDP sample could be considered at biologic risk because all participants had LBW when born prematurely. Environmental risk factors are measures of the sociocultural milieux in which the child lives; they include socioeconomic status measures, household composition, psychosocial'characteristics of the primary care giver, and level of social support. Measures were available for almost all risk factors for PL 99-457. The proportion of all criteria for each state that could be obtained in the IHDP datasets is shown in Table I. Most missing criteria related to family characteristics, health status or psychosocial conditions, IHDP exclusion criteria,
682
Kirby et al.
The Journal of Pediatrics May 1993
Table I. Number of proposed state criteria available in IHDP dataset Slate
Total
Excluded*
Unavailable
North Carolina 37 2 9 Hawaii. 22 I 5 Massachusetts 24 1 2 South Dakota 19 0 2 Washington 17 0 3 *Excludedcriteriaare risk factorsthat resultedin exclusionfrom the IHDP sample. Examplesincludechromosomalabnormalitiesincompatiblewith normal intellectualdevelopment,severecongenitalanomalies,and known maternal drug use during pregnancy.
or conditions not widely appreciated at the time the IHDP was initiated (fetal alcohol syndrome, acquired immunodeficiency syndrome). Data on parental substance abuse were limited to known maternal use of drugs during pregnancy; most maternal drinkers or drug users were excluded. Because this study was a controlled intervention trial, all participants received well-child care, thus eliminating the risk factor proposed for North Carolina of no well-child care by 6 months of age. Outcomes. Poor developmental outcomes at 36 months of gestation-corrected age were identified from the IHDP 36month neurodevelopmental and health examinations, interval health interviews, and developmental testing. The following conditions were defined as poor 36-month developmental outcomes: Stanford-Binet Intelligence Quotient less than 70, required use of a hearing aid, diagnosed severe eye condition (optic atrophy, buphthalmos), abnormal clinical neurologic status, cerebral palsy, diagnosed severe neurologic disease (other neuromuscular abnormalities, congenital abnormalities of the head and brain, severe attention deficit disorders, and seizure disorders), and severe congenital anomaly not resulting in exclusion from the sample. Of the 608 children in the IHDP follow-up group, 46 lacked data for the 36-month assessment and were excluded. There were 119 children with an intelligence quotient less than 70 at 36 months. A total of 58 children had at least one of the other defined conditions; 29 of them also had an intelligence quotient less than 70. Thus the total number with poor 36-month developmental outcomes was 148, or 26.3% of the follow-up group. Analyses. The sensitivity, specificity, and positive predictive values of eligibility criteria for early intervention programs in each of the five states were calculated by using data measuring poor 36-month developmental outcomes. 18 Criteria were evaluated individually and collectively according to the eligibility definitions for each state. Composite analyses were run twice, first with all risk criteria included and second with any risk criteria for birth weight, gestation, and duration of hospital stay excluded, because by definition the
IHDP sample was a LBW, preterm sample, with most participants qualifying as "at risk" under these criteria. RESULTS Table II lists selected risk criteria identified by one or more of the states, together with the percentage of the total IHDP follow-up group with that condition. The sensitivity, specificity, and PPV for a poor developmental outcome at 36 months of age are also included (the full table of criteria identified by one or more states is available from the authors). In general, those conditions with a high degree of sensitivity were also common in the sample and have a relatively low degree of specificity. In contrast, none of the conditions with specificity greater than 95% occurred among more than 5% of the sample. Most of these high-specificity but rare conditions were "established risk factors," characterized by low sensitivity, very high specificity, and high PPVs. These risk factors were present in relatively few study subjects. Biologic risk factors had low sensitivity, although higher than for established risk factors. Specificity was generally high, but PPVs were lower than for established risk factors. An exception was neonatal hospitalization for more than 5 days. Five of six study subjects met this criterion because of the nature of the sample, with sensitivity of 87.8% and specificity of 17.6%. Environmental risk factors occurred infrequently (<20% of study subjects) and had low sensitivity, relatively high specificity, and low PPVs. Family income and marital status risk factors occurred frequently and were neither sensitive nor specific. Few individual environmental risk factors proposed by these states were associated with poor 36-month developmental outcomes in this sample. Characteristics with PPVs >30% were highly specific but tended to involve few eases. Risk factors with PPVs >50% (abnormal clinical neurologic impression, neonatal seizures, meningitis, hypothyroidism, musculoskeletal anomalies, other genetic and metabolic conditions) occurred infrequently (<6%) in this sample. Composite criteria for each State were analyzed to assess overall performance of the state systems (Table III). When state definitions for at-risk children were examined in composite, state criteria yielded PPVs of 28% to 34%, with sensitivity ranging from 96% (North Carolina) to 76% (South Dakota) and specificity from 12% (Hawaii) to 40% (Massachusetts). When composite criteria were analyzed with birth weight, gestation, and hospitalization indicators excluded, PPVs improved slightly; sensitivity ranged from 94% (North Carolina) to 75% (South Dakota) and specificity from 17% (North Carolina) to 47% (Massachusetts). DISCUSSION The value of early intervention services for children with, or at risk for, developmental problems in the first 3 years of life, and for their families and society, has been pointed out
The Journal of Pediatrics Folume 122, Number 5, Part 1
Kirby et al.
683
Table II, Developmental outcome at 36 months for selected risk criteria from I H D P follow-up data ( n = 562)*
Established risk Hypothyroidism, any severity Acyanotic congenital heart disease Neonatal seizures Meningitis (neonatal) Biologic risk Intraventricular hemorrhage: grade I l l / I V in neonatal period Severe respiratory distress syndrome/ hyaline membrane disease Birth asphyxia Newborn hospitalization >5 days Environmental risk Mother <17 years old Mother unmarried at child's birth No prenatal care Family income <$10,O00/yr Chronic maternal illness
Infants
Sensitivity
Specificity
PPV
(%)
(%)
(%)
(%)
0.4 1.4 2.1 0.7
0.7 1.4 6.1 2.0
99.8 98.6 99.3 99.8
50.0 25.0 75.0 75.0
3.2
7.4
98.3
61 .i
20.3
29.7
83.1
38.6
7.5 83.8
12.2 87.8
94.2 17.6
42.9 27.6
5.2 50.2
4.1 61.5
94.4 53.9
20.7 32.3
4.1 32.4 13.9
3.4 43.2 16.2
95.7 7 ! .5 87.0
21.7 35.2 30.8
*A total of 46 cases with missing 36-month assessment.
Table III. Likelihood of 3 6 - m o n t h composite poor outcome based on I H D P follow-up data (n = 562)* Infants
Sensitivity
Specificity
PPV
68.7 37.5 14.2 48.4
77.0 59.5 21.6 57.4
34.3 70.3 88.4 54.8
29.5 41.7 40.0 31.3
73.7 51.1 88.1 39.3
87.2 68.9 95.9 52.7
31.2 55.3 14.7 65.5
31.2 35.5 28.7 35.3
90.0 66.7 88.3 65.5 83.8
94.6 86.5 95.9 75.7 90.5
11.6 40.3 14.5 38.2 18.6
27.7 34.1 28.6 30.4 28.5
(%)
Biologic risks'i Hawaii Massachusetts North Carolina: established high risk South Dakota Environmental riskst Hawaii Massachusetts (3+) North Carolina: potential high risk South Dakota (3+) Combined risk factors Hawaii Massachusetts North Carolina South Dakota Washington
(%)
(%)
(%)
*A total of 46 cases with missing 36-month assessment. tThese classificationsare presented as proposed by each state. Washington did not differentiate risk factors into biologic and environmental categories. North Carolina differentiated into established and potential risk factors, ~hieh have been grouped for comparative purposeswith biologicand environmentalrisk factors from the other states.
frequently. 19-22 Services for identified children are likely to result in e n h a n c e d academic achievement, l a n g u a g e , growth, and social skills. Such services also m a y reduce family stress, institutionalization, later special education placement, a n d societal dependency. Eventually these prog r a m s could reduce societal and public costs t h r o u g h lower special education and health care costs, combined with increased tax revenues from productive workers.
Public Law 99-457 provides states with a framework to organize and coordinate a comprehensive program of early intervention services for very young children. T h e first step in this enabling legislation is defining the target population of children with, or at risk for, developmental problems. Overall, we found t h a t the eligibility criteria employed by the states were sensitive but not specific; t h a t is, they identified almost all the children with, or at risk for, poor devel-
684
Kirby et al.
opmental outcomes at 3 years of age, but they also identified many children with normal outcomes (false-positive results). States included for this study used three sets of factors to Classify children for services. First, every state identified a small group of children with "established" problems, early metabolic or neurologic insults that lead with some certainty to disability or long-term needs. Because these defined disorders occur rarely in any population, only a few children with established needs were identified. The second general category of risk factors consisted of biologic or medical risk factors. A core group of these factors, which included intraventricular hemorrhage, birth asphyxia, and hypoglycemia, were all consistently associated with a poor outcome. Although specific i n predicting a poor outcome, these conditions again identified a very small group of children. The other biologic variables, which focused on birth weight, prematurity, and hospitalization history, were very common in this population. Therefore the use of these criteria captured a large number of children (the measure was sensitive), but the specificity and PPV were very low because many children who met these criteria did not have poor outcomes. Environmental variables are the third category of risk factors. These factors were also sensitive but not specific when used as risk factors to identify children with, or at risk for, developmental problems. Most of the children with poor outcomes had at least one of the environmental risk factors identified by the states; however, even more of the children without poor outcomes had o n e o r more of these factors. Therefore the majority of children identified by states employing individual environmental variables will not have a poor outcome even without services. The IHDP sample consisted of LBW preterm infants born to residents of defined catchment areas. Infants with severe birth defects, extended hospitalizations during the neonatal period, and neurosensorydeficits were excluded because these subjects would be unlikely to benefit from the expensive intervention program. Exclusion of these cases had a minimal effect on our findings. If one assumed that all infants excluded from the IHDP sample because of medical exclusions were included in the follow-up group, and that all these infants had poor developmental outcomes at 36 months of age, then, when the Massachusetts composite risk criteria are used, the results differ only slightly from the initial analysis: sensitivity 90% compared with 86%; specificity 40% (unchanged); and PPV 43% compared with 34%. However, our findings may not be generalizable to the population of infants and toddlers because the follow-up sample was limited to LBW preterm infants whose mothers were not known to have abused drugs or alcohol during pregnancy, had no self-report of psychiatric
The Journal of Pediatrics May 1993
hospitalization, and could communicate effectively in English. In all states studied, criteria based on birth weight, gestation, or duration of neonatal hospitalization figured prominently as biologic or medical risk factors. To assess the effect of these indicators on the overall utility of composite state criteria in identifying children at risk for poor developmental outcome, we performed composite analyses with these factors included and excluded. Results did not differ markedly between the two analyses. The largest difference was found for Washington State, where sensitivity rose from 84% without birth weight, gestation, or hospitalization risk factors to 90% when these factors were included, whereas specificity declined from 31% to 19% and PPV changed from 30% to 29%. In the general population these conditions may be useful indicators of need for early intervention services; however, they contribute little to the identification of LBW preterm infants with poor developmental outcomes at 36 months of age. Kochanek and Buka z3 performed a similar analysis on a different sample with limited criteria. Infants resident in nine target Rhode Island communities were eligible for part!cipation in a two-stage screening program. In level 1, infants were classified as at risk, not at risk, or possibly at risk on the basis of the presence or absence of established conditions, child characteristics, parent demographics, and parent characteristics. Of 3363 infants screened, 29% were at risk. Of these, only 5% had established risk conditions. More than half of the infants at risk were identified on the basis of parent demographics. This study emphasizes the importance of both child and family factors in the developmental screening program. However, the results of Kochanek and Buka are based on assessments of infants; they did not assess outcomes at 36 months of age. Their study shows the importance of using combinations of risk factors for child and parent characteristics, as confirmed in our study. The established risk conditions mentioned above will identify a small but needy group of children. The degree to which these children benefit from early intervention services remains to be seen. 19 Moreover, a core group of other biologic risk factors (severe intraventricular hemorrhage, postneonatal seizures, existing severe cfironic illness at 12 months of age, and infection involving the central nervous system) Will also identify a select group of high-risk infants who are likely to have poor outcomes. The remainder of the biologic and all of the environmental criteria are highly sensitive but not specific (a broad "net"). States such as Massachusetts and South Dakota have made some inroads into the problem ofoveridentification by requiring multiple environmental factors. When the composite analyses were run for Massachusetts by using the criterion of one or more high-risk conditions compared with three or more, the fol-
The Journal of Pediatrics Volume 122, Nttmber 5, Part 1
lowing differences were observed: sensitivity, 98% versus 86%; specificity, 10% versus 40%; and PPV, 28% versus 34%. Similar findings were obtained for South Dakota, and in both cases the results are similar to those for Hawaii, North Carolina, and Washington. Both states improved the specificity and PPV 6f their risk criteria, but sensitivity decreased slightly. Similar strategies could be employed with the less-specific biologic variables. A middle ground that employs risk factors that are highly sensitive in identifying children who should be monitored and periodically reassessed may be ari attractive alternative for early intervention programs. The classification of many children with environmental risk factors likely to have normal outcomes as eligible for early intervention services has important implications for state policy makers. As an example, the cost of providing services to young children is growing rapidly. The problems in evaluating the costs and benefits of early intervention services have recently been reviewed. 24 Costs vary with the severity of the child's condition and the family's financial and emotional resources. Early estimates of the cost of a year's services to a young child and family appeared to be reasonable. For example, North Carolina reported average costs of $1117 per child served in 1985 and 1986. 21 More recent analyses indicate that costs are likely much higher and should be estimated from risk-based groupings because so much of the cost depends on the intensity of services. Florida estimated costs for a year's services of between $1500 per child in the lowest-risk group to S 15,000 per child in the highest-risk group. 22 With many states facing budget cutbacks, it is unlikely that administrators will choose electively to serve large groups of children who are not likely to have poor developmental outcomes, especially because states must also serve children with no risk factors who are identified as having developmental delay in the first 3 years of life. The limited availability of trained care providers also should give pause to those proposing the "wide net" approach to serving young children at risk. Many states and localities have few services to offer already-identified children with developmental disabilities. Because case-finding services increase the number of referrals, some services are likely to be overloaded and poorly responsive to those children most in need, particularly in poor, rural states. Nevertheless, the growing body of literature on the costeffectiveness of early intervention services for developmentally impaired or at-risk children likely will encourage many states to provide services for children identified with highly sensitive composite criteria sets like the ones examined in this studyJ 4 Although some authors dispute the adequacy of the cost-effectiveness research on early intervention, most studies have demonstrated significant societal and publicsector cost savings. 21,22'24 Unfortunately, these studies
Kirby et al.
685
have not analyzed their findings across the various risk levels to determine whether savings occur predominantly with the most or least impaired. It is possible that all families would benefit from the development of an individualized family service plan, which is the core concept of PL 99-457. Even with these alternative strategies, the proposed criteria for identification of children qualifying for services will continue to include a large group of children who will not have poor outcomes. Although our results show remarkable consistency across states, the population chosen to study these criteria and the methods employed indicate the need for caution in the interpretation of these data. The study sample consisted of only LBW preterm infants who had no major neurologic problems at birth and no extended need for oxygen; this population could be considered at higher risk than other samples. Thus we expect all the risk factors to be more common in our sample, and we assume that as the probability of these factors decreases in the general population, their PPV will also decrease. Moreover, we have included only those outcomes at 3 years of age that clearly qualify a child for special services. States may wish/to consider interventions for other children with lesser impairments. Finally, this examination of state identification criteria is largely theoretical in its assumption that far too many children will qualify for services, because the states lack adequate providers and case-finding services to begin to qualify all the eligible children. We have examined the effects of multiple risk criteria in only the most cursory manner. More research with the use of a wide range of indicators and predictors of perinatal and acquired risk factors for poor developmental outcomes is needed to determine the most effective and efficacious criteria for early intervention services. We conclude that the majority of children identified by the proposed criteria for early identification of high-risk infants will not have poor outcomes even without intervention services. States must carefully weigh the consequences to their budgets and to already overused early intervention services if current criteria are employed. Our data on LBW preterm infants suggest that more careful clinical and longitudinal research is necessary before appropriate definitions can be promulgated for identifying children in need of early intervention services. REFERENCES 1. Palfrey JS, Singer J, Walker D, Butler J. Early identification of children's special needs: a study of five metropolitan communities. J PEDIATR 1987;111:651-9. 2. Ramey CT, Stedman DJ, Borders-Patterson A, Mengel W. Predicting school failure from information available at birth. Am J Ment Defic [now Am J Ment Retard] 1978;82:525-34. 3. DeGraw C, Edell D, Ellers B, et al. Public Law 99-457: new
686
4.
5.
6.
7.
8.
9. 10.
! 1.
12. 13.
14.
Kirby et aL
opportunities to serve young children with special needs. J PeDIATR 1988;113:971-4. Ellenberg JH, Nelson KB. Cluster of perinatal events identifying infants at high risk for death or disability. J PEDIATR 1988;113:546-52. Finkelstein NW, Ramey CT. Information from birth certificates as a risk index for educational handicap. Am 3 Ment Defie [now Am J Ment Retard] 1980;84:546-52. Fried PA, Watkinson B. 36- and 48-month neurobehavioral follow-up of children prenatally exposed to marijuana, cigarettes, and alcohol. J Dev Behav Pediatr 1990;11:49-58. Low JA, Galbraith RS, Muir DW, et al. The predictive significance of biologic risk factors for deficits in children of a highrisk population. Am J Obstet Gynecol 1983;145:1059-68. MeCarton CM, Vaughn HG Jr, Golden ~.R. Can neurobehavioral outcomes be predicted by perinatal variables? In: Vietz PM, Vaughn HG Jr, eds. Early identification of infants with developmental disabilities. Philadelphia: Grune and Stratton, 1988:9-40. Rantakallio P, yon Wendt L. Risk factors for mental retardation. Arch Dis Child 1985;60:946-52. Stanley F J, English DR. Prevalence of and risk factors for cerebral palsy in a total population cohort of low birth weight (<2000 gm) infants. Dev Med Child Neurol 1986;28:559-68. Gordon BN, Jens KG. A conceptual model for tracking highrisk infants and making early service decisions. J Dev Behav Pediatr 1988;9:279-86. Cooke RWI. Cerebral palsy in very low birth weight infants. Arch Dis Child 1990;65:201-6. Ramey CT, Bryant DM, Suarez TM. Early intervention: why, for whom, how, and at what cost? Clin Perinatol 1990;17:4755. The Infant Health and Development Program. Enhancing the outcomes of low birth weight, premature infants: a multisite, randomized trial. JAMA 1990;263:3035-42.
The Journal of Pediatrics May 1993
15. Berman C, Biro P, Ferichel ES, eds. Keeping track: tracking systems for high-risk infants and young children. 2nd ed. Washington, D.C.: National Center for Clinical Infant Programs, 1989. 16. Blackman J. Warning signals: basic criteria for screening and tracking high-risk infants and toddlers. Washington, D.C.: National Center for Clinical Infant Programs, 1986. 17. Bee ttL, Barnard KE, Eyres SJ, et al. Prediction of IQ and language skill from perinatal status, child performance, family characteristics and mother-infant interaction. Child Dev 1982;53:1134-56. 18. Last JM, ed. A dictionary of epidemiology. 2rid ed. New York: Oxford University Press, 1988:119-20. 19. Guralnick MJ. The next generation of research on the effectiveness of early intervention. Except Child 1991;58:174-83. 20. Ramey CT, Bryant DM, Wasik BH, Sparling J J, Fendt KH, LaVange LM. Infant health and development program for low birth weight, premature infants: program elements, family participation, and child intelligence. Pediatrics 1992;89:45465. 21. North Carolina Interagency Coordinating Council. Early intervention in North Carolina. Report to the governor for 1990. Raleigh, North Carolina: North Carolina lnteragency Coordinating Council, January 1991. 22. Florida's cost/implementation study for Public Law 99-457, part H, infants and toddlers. Phase II findings. Tallahassee, Florida: Center for Prevention and Early Intervention Policy, Florida State University, 1991. 23. KochanekTT, BukaSL. Using biologic and ecologic factors to identify vulnerable infants and toddlers. Infants and Young Children 1991;4:11-25. 24. Barnett WS, Escobar CM. The economics of early intervention for handicapped children: what do we really know? Journal of the Division for Early Childhood 1988;12:169-81.