Published online June 1, 2006
PEDIATRICS Vol. 117 No. 6 June 2006, pp. 2006-2013 (doi:10.1542/peds.2005-2118)
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow P3Rs: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when P3Rs are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow E-mail this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My File Cabinet
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via CrossRef
Right arrow Citing Articles via ISI Web of Science (5)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Drotar, D.
Right arrow Articles by Klein, N.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Drotar, D.
Right arrow Articles by Klein, N.
Related Collections
Right arrow Premature & Newborn

The Impact of Extremely Low Birth Weight on the Families of School-Aged Children

Dennis Drotar, PhDa, Maureen Hack, MB, ChBa, Gerry Taylor, PhDa, Mark Schluchter, PhDa, Laura Andreias, MDa and Nancy Klein, PhDb

a Department of Pediatrics, Case Western Reserve University School of Medicine, Cleveland, Ohio
b Department of Education, Cleveland State University, Cleveland, Ohio


   ABSTRACT
TOP
 ABSTRACT
POPULATION AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
OBJECTIVE. The purpose of this study was to document the impact and burden of extremely low birth weight (<1000 g) and associated problems on the families of school-aged children in a controlled study. The study was also designed to document the salient predictors of individual differences of family impact within the extremely low birth weight group.

METHODS. A prospective study was completed at 8 years of a cohort of 219 children with extremely low birth weight born 1992–1995 and 176 children with normal birth weight. Measures included the following predictor variables: socioeconomic status and parent risk, birth risk, neonatal risk, neurodevelopmental outcome, impairment in adaptive abilities, and functional impact of chronic conditions. The primary outcome measure was the Impact on Family scale. A measure of family stressors and resources (the Life Stressors and Social Resources Inventory) was also obtained.

RESULTS. The primary finding was that the total family impact was greater in the extremely low birth weight group compared with controls. Moreover, the negative impact on family in specific domains was greater in the extremely low birth weight group in financial impact, caretaker burden, and familial burden. These differences were not attributable to general family stressors, socioeconomic status, child, gender, or race. Higher parent/socioeconomic risk, neurodevelopmental outcomes, and the functional impact of chronic conditions predicted greater family impact within the extremely low birth weight group, whereas birth and neonatal risk scores did not.

CONCLUSIONS. Extremely low birth weight was associated with a negative impact on families. Socioeconomic parental risk, but most especially child-related factors such as neurodevelopmental and the functional impact of chronic conditions, predicted the negative family impact within the extremely low birth weight group. Findings underscore the need to develop and test interventions to provide support for families of extremely low birth weight infants to ameliorate the burden of extremely low birth weight and associated risk factors on families.

Key Words: extremely low birth weight • prematurity • family impact • outcome • predictive • school age

Abbreviations: ELBW—extremely low birth weight • BW—birth weight • GA—gestational age • VLBW—very low birth weight • NBW—normal birth weight • SES—socioeconomic status • IOF—Impact on Family survey • LISRES—Life Stressors and Social Resource Inventory-Adult Form

Major advances in neonatal care that have taken place in the 1990s, including surfactant therapy and use of antenatal steroids that have resulted in increased survival rates for extremely low birth weight (ELBW) (eg, <1 kg) infants.1 These survivors have high rates of neonatal complications, neurodevelopmental problems, and chronic conditions,2–5 all of which would be expected to increase ongoing level of burden and stressors for the families of ELBW infants. Given the differences in survival of high-risk infants at lower birth weight (BW) and gestational age (GA), previous research on the impact of very low BW (VLBW) (eg, <1.5 kg) on families6–11 is not necessarily applicable to these recent survivors. To our knowledge, the impact of ELBW on families has not been studied in more contemporary cohorts of infants. In addition, previous studies have not measured the potential role of general family stressors and resources, which could account for the differences in family impact between ELBW and normal BW (NBW) that have been demonstrated. Moreover, to our knowledge, no previous studies have described the role of individual differences in neonatal and birth risk, neurodevelopmental outcomes, and functional effects of chronic conditions associated with ELBW in predicting family impact. Data that document the impact of risk factors on family outcomes would inform comprehensive predictive models of family impact and facilitate the development of interventions to lessen the long-term family burden and stressors of ELBW on families.

To address these gaps in scientific knowledge, we report the findings of a prospective, controlled study that involves comprehensive, objective measurement of health outcomes and general family stressors, and evaluates the impact on family of ELBW among school-aged children with such histories. Based on previous research,6–11 we hypothesized that families of children with histories of ELBW would experience greater negative impact than families of infants who were born at term. To identify salient predictors of negative family impact within the ELBW group, we tested a multifactorial, predictive model of family impact that included 5 domains of risk factors: parent/family risk, birth, neonatal, neurodevelopmental outcome, and functional impact of chronic conditions.


   POPULATION AND METHODS
TOP
 ABSTRACT
 POPULATION AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
ELBW Cohort
The study group included the survivors of the cohort of 344 ELBW children who were admitted to the NICU at Rainbow Babies and Children's Hospital, University Hospitals of Cleveland, during the 4-year period from January 1992 to December 1995. The early childhood outcome results of this cohort at 20 months corrected age and 8-year outcomes have previously been reported.5,12 Thirteen children (10 with major congenital malformations, 2 with AIDS, and 1 with tuberous sclerosis) were excluded. Of the 331 children who did not have major congenital malformations or congenital infections, 238 (72%) survived to 8 years of age, of whom 219 (92%) were followed and constitute the study population. Of the 19 children who were not examined at 8 years, 8 parents refused to participate, 4 lived out of state, and we were unable to locate 7 families.

The 219 children with ELBW had a mean BW of 810 g and mean GA of 26.4 weeks. One hundred thirty (59%) were girls and 39 (18%) multiple births. One hundred four (48%) were delivered by cesarean section and 73 (33%) of mothers received antenatal steroid therapy. Neonatal data included the presence of chronic lung disease (bronchopulmonary dysplasia), defined as an oxygen (O2) dependence at 36 weeks' corrected age,13 and the most severe cranial ultrasound abnormality during the hospital stay, defined as grade III or IV hemorrhage,14 periventricular leukomalacia, or persistent ventricular dilatation at the time of discharge home. Postnatal steroid therapy was given to 129 (59%) children to treat or prevent chronic lung disease and 93 (43%) were O2-dependent at 36 weeks' corrected age. Study children with ELBW did not differ significantly from the 19 children who were not followed at 8 years in maternal sociodemographic descriptors, birth data, or neonatal complications.

Comparison Group
A comparison group of NBW children born at term gestation (>36 weeks) was recruited at age 8 years from the same school as the ELBW child by randomly selecting a NBW child from a list of all the children in the school who were within 3 months of age and of the same race and gender as the ELBW child. Of the 219 ELBW children seen at age 8 years, matches were recruited for 176 (80%). Reasons for not finding matches for the remainder include refusal of the school principal to participate (18 children), inability to match (6 children), and repeated failure to appear for scheduled appointments (19 children).

As shown in Table 1, children with ELBW and NBW did not differ significantly with regard to their caregivers' age, marital status, education, or race. Children with ELBW were studied at a younger postnatal age than the NBW controls (8.7 ± 0.6 vs 9.2 ± 0.8 years; P < .001) because the NBW children could only be recruited after the ELBW child had been seen and their school verified so that matching could occur. However, all the school-age tests were standardized for age, and the functioning and special health care needs of school-aged children would not be expected to change in relation to this small age difference.


View this table:
[in this window]
[in a new window]
  		TABLE 1 Description of Risk Factors, Including Child Outcomes

 
Assessment of Risk Factors and Family Outcome at Age 8
As part of an overall assessment of health, growth, school-age functioning, and family adaptation, we interviewed the child's primary caregiver and legal guardian, mothers, and grandmothers (n = 206 [95%] in the ELBW group and n = 167 [96%]). The remainder of caregivers in both groups included aunts (n = 11), fathers (n = 2), cousins (n = 3), a great grandmother, and a half sister (n = 1). The children were tested at our research facility during a 2- to 3-hour assessment. The study protocol was reviewed by the institutional review board committee of University Hospitals of Cleveland, and informed consent was obtained from the parents of the children.

Risk factors were assessed in the following 5 domains: socioeconomic status (SES) and parent risk, birth risk, neonatal risk, neurodevelopmental outcome, and functional impact of chronic condition. Each of these domains were chosen because of their potential relevance to the impact of ELBW on families based on research and theory.1–12

SES and Parent Risk
Potential risk factors associated with SES included race (black race is a marker of poverty in inner-city Cleveland), parent education (<high school education), high poverty, which was defined as the percent of >30% families living below the poverty level in the census tract in which the families resided, and maternal depression, defined as a total score of ≥16, was assessed by the Center for Epidemiologic Studies Depression Scale, which is a valid screening measure.15–17

Birth and Neonatal Risk
Birth risk included the factors of BW, GA, multiple birth status, and gender. Neonatal risk was comprised of O2 dependence at 36 weeks13 and severely abnormal ultrasound.14

Neurodevelopmental Outcomes
Neurodevelopmental outcome was assessed in 3 ways. First, a complete physical neurologic examination was conducted by a pediatrician to establish the diagnosis of cerebral palsy. Second, parents were interviewed using the Vineland Adaptive Behavior Scales,18 which assess personal and social sufficiency of children in the domains of communication, daily living skills, and socialization. The Adaptive Behavior Composite was used as the primary measure of adaptive behavior. Third, psychometric testing was performed by 1 of 3 trained research assistants who were unaware as to whether the child was ELBW or NBW. The tetrad Kaufman Assessment Battery for Children was used as a measure of cognitive function.19 It included 4 subtests that form a Mental Processing Composite that has proven sensitivity to the cognitive outcomes of prematurity.20 All tests were scored on the basis of the child's postnatal age. Impairments in general cognitive ability (Mental Processing Composite) and adaptive behavior were defined in terms of standard scores on these measures that fell 2 SDs below the normative mean. One blind child was not tested and a score of 40 (3 SDs below the mean) was assigned to an additional 9 children who could not complete the measure and be validly tested, 7 because of cerebral palsy and 2 because of severe retardation/autistic-type behavior.

Functional Impact of Chronic Conditions
The risk associated with functional impact associated with ELBW was assessed by the Questionnaire for Identifying Children With Chronic Conditions.21 This parent report measure is based on the noncategorical approach and incorporates the consequences of chronic health conditions lasting ≥12 months. It has 39 question sequences divided into 3 domains: (1) functional limitations (eg, physical, cognitive emotional); (2) dependence on compensatory aids (eg, special diet, medical technology, assistive devices, and personal assistance); and (3) need for services above those routinely required by children (eg, medical services, psychologic or educational services, or other accommodations at home or in school). A chronic condition was defined as a condition lasting ≥12 months, which has either functional limitations or requires compensatory aids or services above those routinely required by children.22

Assessment of Family Outcome
Family Impact
The Impact on Family survey (IOF)23 provided a previously validated measure of the global impact of pediatric disability on the family. For each item, parents indicate the extent to which they agree with a statement regarding the impact of the child on the family. The IOF has been validated on samples of children with chronic health conditions.23 The total negative impact score served as the summary measure of family burden from this scale.

Family Stress and Resources
The Life Stressors and Social Resource Inventory-Adult Form (LISRES).24 The LISRES was used to describe a broad range of family stressors to determine if group (ELBW versus NBW) differences in family impact could be attributed to these factors. Items from the social resources subscales pertained to the degree to which friends, extended family, and spouse served as positive sources of support. Items from the stressor subscales asked about negative or conflictual interactions.

Statistical Analysis
Univariate comparisons between family impact in the ELBW and NBW groups were made using the Student's t tests for continuous variables. Regression analyses examined family impact in the ELBW and NBW groups while controlling for the influence of potential covariates. Finally, regression analyses were used to test the predictive model of risk within the ELBW group.


   RESULTS
TOP
 ABSTRACT
 POPULATION AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Comparison of Risk Factors in ELBW Versus NBW Groups
The risk factors in the various domains that were assessed are shown in Table 1. As anticipated, there were significant differences between the ELBW on a range of perinatal risk factors, neurodevelopmental outcomes, and rates of chronic conditions. On the other hand, there were no differences in SES as defined by maternal education, income, or the mean percent of families living below the poverty level of the neighborhood in which the family resided.

Analyses of Family Impact in ELBW Versus NBW Groups
The first step in the analysis was to determine the differences in family impact between the ELBW group and controls as shown in Table 2. Consistent with our hypothesis, the total family impact was greater in the ELBW group compared with controls (t = 2.68, P < .008). The direction and significance of differences (ie, greater family impact associated with ELBW) for total impact was similar for each subscale of the IOF measure with the exception of 1 subscale: disruption of planning.


View this table:
[in this window]
[in a new window]
  		TABLE 2 Impact on Family Scores for the ELBW Versus NBW Group

 
We then assessed to what extent the greater family impact associated with ELBW could be attributed to higher levels of general family stress and/or resources relative to controls. The analysis of data based on the LISRES indicated comparable levels of general (non–child-related) family stressors and resources in all 15 domains of the measure. There were nonsignificant trends for the ELBW group to report more child-related stressors (t = 1.96, P = .051) and fewer child-related resources (t = 1.95, P = .052) than the NBW group. These findings indicate that the greater family impact associated with ELBW did not reflect higher levels of family stress or resources in this group.

The final step in the analysis of differences in family impact on the ELBW versus NBW groups was to conduct regression analyses for each of the subscales of the IOF and the total impact score to control for the influence of potential covariates. For these analyses, SES, race of the caregiver, and child gender were entered first and the group (ELBW versus NBW) was entered last. Consistent with the findings for the univariate group comparisons, ELBW emerged as a significant influence on family impact (P < .02 or greater) for the analyses including total impact and 3 of the 4 subscales (financial impact, caretaker burden, and familial burden).

Analysis of Predictive Model of Risk for Family Impact Within the ELBW Sample
Having established that ELBW had a significant impact on families compared with controls, we then examined factors that predicted the impact on family within the ELBW group using a multivariate model. Based on previous research on the long-term outcomes of children with VLBW and ELBW,1–6,12–14 5 specific categories of risk, each comprised of 1 to 4 individual items, were identified. The 5 risk domains, the specific risk factors within each of the domains (each of the factors listed was given a score of 1), and the range of scores were as follows: (1) SES and parent risk (range: 0–4): less than high school education, black race, >16 on the Center for Epidemiologic Studies Depression Scale16–18 indicative of depressive symptoms, and poverty rate >30; (2) birth risk (range: 0–4): BW <750 g, GA age <27 weeks, multiple birth status, and male gender; (3) neonatal risk (range: 0–2): O2 dependence at 36 weeks13 and severely abnormal ultrasound14; (4) neurodevelopmental outcome (range: 0–3): presence of cerebral palsy, IQ <70 on the Kaufman Assessment Battery for Children,12,13 and score of <70 on the Vineland Adaptive Behavior Scales10; and (5) functional impact of chronic conditions based on the Questionnaire for Identifying Children With Chronic Conditions11 (range: 0–3): ≥1 functional limitations, ≥1 compensatory dependencies, and ≥1 services above routine.

A salient advantage of this analytic approach includes the categorization of potential risk factors in clinically relevant domains, the generation of a summary risk score within each domain, and comparison of the relative strength of prediction of risk factors across domains. Risk-factor indices such as those derived from our data have been used in similar analyses to predict cognitive development in high-risk populations in previous research.25–27 To our knowledge, such analyses have not been used to predict family impact of ELBW.

The total risk scores for each of the 5 primary domains were entered separately in a regression analysis to predict impact on the IOF. This method provided a way of analyzing the effect of each domain-specific risk score while also accounting for the effect of preceding domain-specific risk scores. The order of entry of the risk scores were as follows: SES/parent, birth risk, neonatal risk, neurodevelopmental outcome, and functional impact of chronic condition. The order of variable entry was based on temporal precedence. Factors such as parent and SES were present before the child's birth, and birth risk preceded neonatal risk in time, which was followed by neurodevelopmental outcomes, which preceded functional impact.

The results of the regression analysis are shown in Table 3. The results of the analyses were as follows: higher parent/SES risk score predicted greater family impact (P < .01). In contrast, the birth and neonatal risk scores failed to predict family impact. Risk related to the neurodevelopmental outcome and functional impact of chronic conditions associated with ELBW demonstrated the most powerful impact on families. Families of children with ELBW who had higher neurodevelopmental and functional risk demonstrated higher levels of family impact.


View this table:
[in this window]
[in a new window]
  		TABLE 3 Prediction of Impact on Family Scores in the ELBW Group From Risk-Factor Domain Scores

 

   DISCUSSION
TOP
 ABSTRACT
 POPULATION AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
Our primary finding that ELBW in a cohort of children born in the 1990s had a significant negative impact on families is, to our knowledge, the first such demonstration. Moreover, it is important to note that the clear and consistent negative impact of ELBW on families found in this study was not attributable to potential differences in general family stressors and/or resources. Previous studies that described the impact of ELBW on families did not measure family stressors or resources.

Our findings of the family impact of ELBW are consistent with that of several studies of American and Canadian children with VLBW who were born in the 1980s.6,7,10 Consistent with the present results, Cronin et al10 found that the impact was greater among families whose children with VLBW demonstrated either a functional handicap or low developmental quotient. On the other hand, the impact on family noted in the present study was more powerful and pronounced than in some previous international studies of premature and LBW infants.8–10

Our findings indicate ELBW and associated long-term problems, including the presence of chronic conditions and neurodevelopmental impairment, had a generalized impact on families in the areas that were assessed by the IOF as opposed to a selective impact on specific areas of family life (eg, financial impact, caretaker and family burden). The 1 exception was that was the families of ELBW and NBW infants had comparable scores on the Disruption of Planning subscale. One explanation for this finding is that the family structure and organization scale may be less sensitive to the impact of ELBW and associated conditions than those areas of family life that directly reflect caretaking burdens or relevant resources.

What accounted for the individual differences in negative impact of ELBW on families in the present study? The results of our multifactorial predictive model of risk suggested some answers. Antecedent parent and SES risk factors such as poverty, parental education, and maternal depression played a role. Higher poverty, less parental education, and higher maternal depression predicted greater negative family impact of ELBW on families. Among the various domains of risk factors, greater levels of impairment in neurodevelopmental outcome and functional impact of chronic conditions demonstrated the most powerful relationship with family impact. In contrast to some previous studies,7 the range of birth (eg, BW, GA, etc) and neonatal risk factors (eg, O2 dependence at 36 weeks and severely abnormal cranial ultrasound) that were assessed did not predict family outcome. These findings suggest that these birth and neonatal risk factors did not have a direct impact on families of children with ELBW who were assessed at school age. In contrast, the impact of the biologic risk factors associated with ELBW on families is experienced by families largely through the functional effects on child health, development, and day-to-day limitations of chronic conditions and impairments such as cerebral palsy. In this regard, 1 hallmark characteristic of ELBW is its eventual association with chronic conditions and disabilities in multiple developmental domains.28 Moreover, the burdens on families associated with such chronic conditions may be especially salient in the context of lower SES.

Our results are consistent with the results of a number of studies that have documented the family impact of chronic conditions,29–33 including chronic conditions that are associated with neurodevelopmental impairment.34 Chronic neurodevelopmental conditions may intensify the burden and impact on families by any and all of the following: requiring greater levels of time to interact and manage the child to enhance day-to-day functioning, necessitating increased parental time to secure services such as developmental programs and medical care, and requiring additional expenditures for medical and developmental services.

Several limitations of the present study should be considered in interpreting our findings. First, our findings were based on that of 1 neonatal tertiary care center and cannot necessarily be generalized to different centers. Moreover, our findings may not generalize to cohorts of children with histories of ELBW who are born during the later part the 1990s and 2000. Our analytic approach did not determine which individual risk factors within a specific domain accounted for the greatest impact on families. However, it should be noted the predictive model that guided the analytic plan was based on a comprehensive domain-focused cumulative (as opposed to individual risk factor) model that has received empiric support in previous research.27 Moreover, our multifactorial predictive model did capture the comprehensive range of risk factors across multiple and clinically relevant domains that are experienced by children with ELBW and their families in a large modern cohort. The analytic approach that was used in this study to evaluate the predictors of family outcome also has the advantage that it can be tested and hence replicated in any study of ELBW infants in which comprehensive data concerning comparable risk factors and outcomes are obtained.

Another potential limitation is that domains of family impact other than those assessed in this study (eg, family conflict) might be differentially sensitive to the impact of ELBW and/or risk factors. It should also be noted that the family impact of ELBW was assessed at 1 point in time: at early school age. Because the absolute level of family impact of ELBW and the predictors of such impact may change over time, our results are not necessarily generalizable to families of children with ELBW of different ages. Finally, the variance in family impact that was accounted for by the relatively small predictive model (17%). This indicates that factors other than those included in the present predictive model are also relevant predictors of family impact.5

Our findings also have several implications for future research. It is possible that the impact of ELBW on families may increase from school age to adolescence, especially for families whose children have chronic neurodevelopmental impairments with significant functional limitations. For this reason, future research should describe the longitudinal course of family impact of ELBW over time. Moreover, additional factors (eg, severe and chronic family dysfunction) that can contribute to alternative trajectories of family impact (eg, a static course of significant family impact versus a worsening of family impact over time) should be studied in future research.

Our findings may generalize to families of the increasing numbers of ELBW infants who survive with neurodevelopmental impacts and chronic conditions. However, they may not generalize to cohorts of infants with ELBW with different patterns of risk factors than were assessed here. For this reason, future research should assess the relationship of population-specific risk factors to the impact of ELBW on families. The approach to testing the influence of risk factors across several different clinically relevant domains on family impact used in this study provides a model for testing alternative predictive models of family impact among infants with ELBW.

Our present findings have potential clinical implications for interventions that are designed to reduce the family burden and impact of ELBW. For example, the predictive salience of chronic conditions and neurodevelopmental impairment or family impact underscores the importance of comprehensive interventions that address these factors. These include early identification of chronic impairments, providing necessary medical and developmental intervention to reduce the morbidity of these conditions, and support for parents to help them manage the stressors associated with chronic medical and/or neurodevelopmental problems.35–37


   ACKNOWLEDGMENTS
 
This study was supported by grants RO1 HD39756 and MO1 RR00080 from the General Clinical Research, National Institutes of Health.

We thank Miriam Curran, the project coordinator, who interviewed the parents and administered some of the questionnaires to the parents; Terry Reid, Jennifer Eppich, and Mary Morrow, all research assistants, who tested the children and interviewed and administered some of the questionnaires to the children and their parents; and Nori Mercuri-Minich and Lydia Carter, who assisted in data analysis.


   FOOTNOTES
 
Accepted Dec 21, 2005.

Address correspondence to Dennis Drotar, PhD, Division of Behavioral Pediatrics and Psychology, 11100 Euclid Ave, Mailstop 6038, Cleveland, OH 44106-6038. E-mail: dxd3{at}case.edu

The authors have indicated they have no financial relationships relevant to this article to disclose.


   REFERENCES
TOP
 ABSTRACT
 POPULATION AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. Fanaroff AA, Hack M, Walsh MC. The NICHD Neonatal Research Network: changes in practice and outcomes during the first 15 years. Semin Perinatol. 2003;27 :281 –287[CrossRef][ISI][Medline]
  2. Wilson-Costello D, Friedman H, Minich N, Fanaroff AA, Hack M. Improved survival rates with increased neurodevelopmental disability for extremely low birth weight infants in the 1990s. Pediatrics. 2005;115 :997 –1003[Abstract/Free Full Text]
  3. Vohr BR, Wright LL, Dusick AM, et al. Neurodevelopmental and functional outcomes of extremely low birth weight infants in the National Institute of Child Health and Human Development Neonatal Research Network, 1993–1994. Pediatrics. 2000;105 :1216 –1226[Abstract/Free Full Text]
  4. Emsley HCA, Wardle SP, Sims DG, Chiswick ML, D'Souza SW. Increased survival rates and deteriorating developmental outcome in 23 to 25 week old gestation infants, 1990–4 compared with 1984–9. Arch Dis Child Fetal Neonatal Ed. 1998;78 :F99 –F104[Abstract/Free Full Text]
  5. Hack M, Taylor HG, Drotar D, et al. Chronic conditions, functional limitations, and special health care needs of school-age children born extremely low birth weight in the 1990s. JAMA. 2005;294 :318 –325[Abstract/Free Full Text]
  6. Rivers A, Caron B, Hack M. Experience of families with very low birthweight children with neurologic sequelae. Clin Pediatr (Phila). 1987;26 :223 –230
  7. Taylor HG, Klein N, Minich N, Hack M. Long-term family outcomes for children with very low birth weights. Arch Pediatr Adolesc Med. 2001;155 :155 –161[Abstract/Free Full Text]
  8. Saigal S, Burrows E, Stoskopf B, Rosenbaum PL, Streiner D. Impact of extreme prematurity on families of adolescents children. J Pediatr. 2000;137 :701 –706[CrossRef][ISI][Medline]
  9. Leijon I, Finnstrom O, Sydsjo G, Wadsby M. Use of healthcare resources, family function, and socioeconomic support during the first four years after preterm birth. Arch Dis Child Fetal Neonatal Ed. 2003;88 :F415 –F420[Abstract/Free Full Text]
  10. Cronin CMG, Shapiro CR, Casiro OG, Cheang MS. The impact of very low birth-weight-infants on the family is long lasting. Arch Pediatr Adolesc Med. 1995;149 :151 –158[Abstract]
  11. Singer LT, Salvator A, Guo S, Collin M, Lilien L, Baley J. Maternal psychological distress and parenting stress after the birth of a very low-birth-weight infant. JAMA. 1999;281 :799 –805[Abstract/Free Full Text]
  12. Hack M, Wilson-Costello D, Friedman H, et al. Neurodevelopmental and predictors of < 1000 gm birth weight children born 1992–1995. Arch Pediatr Adolesc Med. 2000;154 :175 –731
  13. Shennan AT, Dunn MS, Ohlsson A, Lenox K. Abnormal pulmonary outcomes in premature infants: prediction from oxygen requirement in the neonatal period. Pediatrics. 1988;82 :527 –532[Abstract/Free Full Text]
  14. Papile LA, Burstein J, Burstein R, Koffler H. Incidence and evolution of subependymal and intraventricular hemorrhages: a study of infants with birthweights less than 1500 g. J Pediatr. 1978;92 :529 –534[CrossRef][ISI][Medline]
  15. Radloff LS. A self-report depression scale for research in the general population. Appl Psychol Meas. 1997;1 :203 –214
  16. Weissman M, Sholomskas D, Pottender M, Prusoff B, Locke BZ. Assessment of depressive symptoms in five psychiatric populations: a validation study. Am J Epidemiol. 1977;106 :385 –401
  17. Roberts RE, Vernon SE. The Center for Epidemiologic Studies Depression Scale: its use in a community sample. Am J Psychiatry. 1983;140 :41 –46[Abstract/Free Full Text]
  18. Sparrow S, Balla DA, Cicchetti DV. Vineland Adaptive Behavior Scales: Interview Edition, Survey Form Manual. A Revision of the Vineland Social Maturity Scale by E.A. Doll. Circle Pines, MN: American Guidance Service; 1984
  19. Kaufman A, Applegate B. Short forms of K-ABC Mental Processing and Achievement Scales at age 4 to 12 years for clinical and screening purposes. J Clin Child Psychol. 1988;17 :359 –369[CrossRef]
  20. Hack M, Taylor HG, Klein N, Eiben R, Schatschneider C, Mercuri-Minich N. School age outcome of a regional cohort of < 750 gm birthweight children. N Engl J Med. 1994;331 :753 –759[Abstract/Free Full Text]
  21. Stein RD, Westbrook LE, Bauman LJ. The questionnaire for identifying children with chronic conditions: A measure based on a noncategorical approach. Pediatrics. 1997;99 :513 –521[Abstract/Free Full Text]
  22. Stein RE, Bauman LJ, Westbrook LK, Coupey SM, Ireys HT. Framework for identifying children who have chronic conditions: the case for a new definition. J Pediatr. 1993;122 :342 –347[ISI][Medline]
  23. Stein R, Jessop D. PACTS Papers/AECOM: Tables Documenting the Psychometric Properties of a Measure of the Impact of a Chronic Illness on a Family. Bronx, NY: Department of Pediatrics, Albert Einstein College of Medicine; 1985
  24. Moos RH, Moos BS. Life Stressors and Social Resources Inventory: Adult Form Manual. Odessa, FL: Psychological Assessment Resources; 1994
  25. Sameroff AJ, Seifer R, Baldwin A, Baldwin C. Stability of intelligence from preschool to adolescence: The influence of social and family risk factor. Child Dev. 1993;64 :80 –97[CrossRef][ISI][Medline]
  26. Sameroff AJ, Seifer R, Barocas R, Zax M, Greenspan S. Intelligence quotient scores of 4-year-old children: Social-environmental risk factors. Pediatrics. 1987;79 :343 –350[Abstract/Free Full Text]
  27. Burchinal MR, Roberts JE, Hooper S, Zeisel SA. Cumulative risk and early cognitive development: a comparison of statistical risk models. Dev Psychol. 2000;36 :793 –807[CrossRef][ISI][Medline]
  28. van Baar AL, vanWassenaer AG, Breit JM, Dekker FW, Kok JH. Very preterm birth is associated with disabilities in multiple developmental domains. J Dev Psychol. 2005;30 :247 –255
  29. Pless IB, Pinkerton P. Chronic Childhood Disorder: Promoting Patterns of Adjustment. Chicago, IL: Yearbook Medical Publishers; 1975
  30. Silver EJ, Bauman LJ, Weiss ES. Perceived role restriction and depressive symptoms in mothers of children with chronic health conditions. J Dev Behav Pediatr. 1999;30 :362 –369
  31. Reichman N, Corman H, Noonan K. Effects of child health on parents' relationship status. Demography. 2004;41 :569 –584[CrossRef][ISI][Medline]
  32. Hausenstein EJ. The experience of distress in parents of chronically ill children: potential or likely outcome? J Clin Child Psychol. 1990;19 :356 –364[CrossRef][ISI]
  33. Jessop DJ, Riessman LK, Stein REK. Chronic childhood illness and maternal mental health. J Dev Behav Pediatr. 1989;9 :147 –158
  34. Wade SA, Taylor G, Drotar D, Stancin T, Yeates KO, Minich NM. A prospective study of long-term caregiver and family adaptation following traumatic brain injury in children. J Head Trauma Rehabil. 2002;17 :96 –111[ISI][Medline]
  35. Ireys HT, Chernoff RG, DeVet KA, Kim YJ. Maternal outcomes of a randomized controlled trial of a community-based support program for families of children with chronic illness. Arch Pediatr Adolesc Med. 2001;155 :771 –777[Abstract/Free Full Text]
  36. Stein REK, Jessop DJ. Does pediatric home care make a difference for children with chronic illness? Findings from the Pediatric Ambulatory Care Treatment Study. Pediatrics. 1984;73 :845 –853[Abstract/Free Full Text]
  37. Stein REK, Jessop DJ. Long-term effects of a pediatric home care program. Pediatrics. 1991;88 :490 –496[Abstract/Free Full Text]
PEDIATRICS (ISSN 1098-4275). ©2006 by the American Academy of Pediatrics




This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow P3Rs: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when P3Rs are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow E-mail this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My File Cabinet
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via CrossRef
Right arrow Citing Articles via ISI Web of Science (5)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Drotar, D.
Right arrow Articles by Klein, N.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Drotar, D.
Right arrow Articles by Klein, N.
Related Collections
Right arrow Premature & Newborn