From a social—ecological perspective, 12 ,13 the contextual environment in which children with cognitive delays grow and develop may contribute to their risk for behavior problems.
A number of studies have shown signiﬁcant associations between community and neigh- borhood socioeconomic factors and young and early school-aged children’s mental health, internalizing and externalizing behavior problems, 14and criminal, delinquent, and violent behaviors. 15 —17 It is possible that young children with cognitive delays who are prone to health and developmental difﬁculties are es- pecially sensitive to their community’s social and economic characteristics. However, to our knowledge, the relationship between commu- nity factors and behavior problems has not yet been examined among children with cognitive delays.
Therefore, the purpose of this study was to investigate the relationships among cognitive de- lay, community factors, and behavior problemsin early childhood by using a national sample of US children. We focused on very early childhood as it is a critical period for the development of behavior problems among children with cognitive delays. 6We expected to observe signiﬁcant dis- parities in behavior problems between 4-year-old children with and without cognitive delays. Fur- thermore, we hypothesized that children with cognitive delays living in adverse community environments would be particularly vulnerable to developing behavior problems. To our knowl- edge, this is theﬁrst study to investigate the role of community factors on behavior problems among young children with cognitive delays. METHODS We drew data from the Early Childhood Longitudinal Study, Birth Cohort (ECLS-B), a nationally representative cohort of nearly 10 700 children born in 2001 and their parents. 18The ECLS-B selected a probability sample of the approximately 4 millionchildren born in 2001, with oversampling of children from minority groups, twins, and children born at low and very low birth weights, based on registered births from the National Center for Health Statistics vital statistics system. The sampling frame excluded children born to mothers aged younger than 15 years or who were adopted or died before the initial collection wave. For the present study, we used data from theﬁrst 3 waves of data collection, which occurred from 2001 to 2005 when the children were approximately 9 months, 24 months, and 4 years of age.
We obtained restricted ECLS-B data by approval from the Institute for Education Sci- ences Data Security Ofﬁce of the US Department of Education, National Center for Education Statistics (NCES). In accordance with NCES guidelines, we rounded all reported unweighted sample sizes to the nearest 50. 19 Of the original cohort, approximately 8950 children completed a cognitive assessment at 9 and 24 months. Our study sample included Objectives.We investigated relationships among cognitive delay, community factors, and behavior problems over 2 years in early childhood with a national sample of US families.
Methods.Data were from 3 waves of the Early Childhood Longitudinal Study, Birth Cohort (2001–2005; n = 7650). We de ned cognitive delay as the lowest 10% of mental scores from the Bayley Short Form–Research Edition, administered at 9 and 24 months. At 24 months, we classi ed children as typically developing or as having resolved, newly developed, or persistent cognitive delays. Behavior was measured at age 4 years with the Preschool and Kindergarten Behavior Scales (range = 0–36). Community factors included perceived neighborhood safety and an index of county disadvantage.
Results.Behavior scores at age 4 years (mean = 12.4; SD = 4.9) were higher among children with resolved (Β= 0.70; SE = 0.20), newly developed (Β= 1.92; SE = 0.25), and persistent (Β= 2.96; SE = 0.41) cognitive delays than for typically developing children. The interaction between county disadvantage and cognitive delay status was statistically signi cant (P< .01), suggesting that county disadvantage was particularly detrimental for children with persistent delays.
Conclusions.The community context may provide an opportunity for public health interventions to improve the behavioral health of children with cognitive delays. (Am J Public Health.2014;104:2114–2121. doi:10.2105/AJPH.2014.302119) RESEARCH AND PRACTICE 2114|Research and Practice|Peer Reviewed|Cheng et al.American Journal of Public Health|November 2014, Vol 104, No. 11 7650 of these children with complete covariate data who remained in the ECLS-B for the 4-year wave.
Measures Cognitive delay.We deﬁned cognitive delay at ages 9 months and 24 months by using scores from the mental scale of the Bayley Short Form—Research Edition (BSF-R), 19 a screening instrument comprising a subset of items from the revised Bayley Scales of Infant Development (BSID-II). 20 The NCES selected BSF-R items from the BSID-II by using Item Response Theory modeling to approximate children’s performance on the full BSID-II and to facilitate comparisons of BSF-R scores to BSID-II scores. The ECLS-B dataﬁle included estimated BSID-II scores (predicted number of correct item responses), derived from the BSF-R. Item Response Theory reliability co- efﬁcients for the BSF-R mental scale were 0.81 and 0.88, respectively, at 9 and 24 months. 21 The NCES also converted children’s raw scores to age-normed T scores (mean = 50; SD = 10) by standardizing the raw scores relative to the weighted ECLS-B sample. For these scores, the age at administration for preterm infants was recorded as their chro- nological age minus the number of weeks preterm.
We considered children falling within the lowest 10th percentile of these age-normed scores to have cognitive delays at ages 9 months and 24 months, consistent with pre- vious research. 6,22 We further classiﬁed chil- dren as having resolved, newly developed, or persistent cognitive delays on the basis of the results of the BSF-R at 9 months and 24 months.
Child behavior.The ECLS-B assessed child behavior with the Preschool and Kindergarten Behavior Scales—Second Edition (PKBS-2), 23 a norm-referenced, standardized instrument designed to evaluate the social skills and problem behaviors of children aged 3 to 6 years. The NCES administered 24 items from the original 42-item PKBS-2 scale during the 4-year collection wave, which asked mothers to report the frequencies of child behaviors observed in the previous 3 months. 24 We employed a principal—component analysis to select 9 of these items that loaded on a single construct representing behavior problems atage 4 years. These items asked how often the child was 1. physically aggressive, 2. angry, 3. impulsive, 4. overly active, 5. paid attention (reverse coded), 6. had temper tantrums, 7. had difﬁculty concentrating, 8. annoyed other children, and 9. destroyed things.
We added responses to these items to create a single variable with higher scores indicating worse child behavior (range = 0—36).
County disadvantage.We created a county disadvantage index (CDI) by using a principal— component analysis of 5 county-level variables available from the 2000 Decennial Census, 25 including percentages of 1. families living in poverty, 2. households with income higher than the state median, 3. women with a bachelor’s degree or more, 4. single mothers, and 5. mothers of young children who were unemployed.
We reverse-coded variables for the percent- ages of households with income higher than the state median and women with a bachelor’s degree or more, and then standardized the 5 variables following procedures of principal— component analysis. 26We created the CDI from the average of the items weighted by theiritem loadings (mean = 0; SD = 1; and Cronbach a= 0.92; Table 1). We based our choice of measures and analytic approach to construct the CDI on the work of McManus et al., who developed and employed this index to examine social function 27and health-related quality of life 28among children born with very low birth weight. We subsequently linked the CDI to data on individual children on the basis of their residential zip codes at age 4 years.
Perceived neighborhood safety.During the 24-month parent interview, mothers were asked“Do you consider your neighborhood as very safe from crime, fairly safe, fairly unsafe, or very unsafe?”We compared participants who responded that their neighborhood was “fairly unsafe”or“very unsafe”from crime to those who responded“very safe”and“fairly safe.” Birth and family characteristics.We obtained data on birth events from the birth certiﬁcates of each child, including birth weight in grams, plurality (whether the child was a singleton or twin or higher-order birth), and whether the child had any congenital anomaly (e.g., spina biﬁda or Down syndrome).
To assess child demographic and family factors, we used data from the 24-month parent interview, during which mothers pro- vided information about the child’s gender and race/ethnicity (non-Hispanic White, non- Hispanic Black, non-Hispanic other race, or Hispanic), family structure (single- vs 2-parent household), children in the household other than the index child (0, 1, or 2 or more), TABLE 1—Results of Principal–Component Analysis of County Disadvantage Characteristics:
2000 US Census and the Early Childhood Longitudinal Study, Birth Cohort, 2001–2005 Characteristic Mean (SD) Range Item Loadings Families living in poverty, % 9.0 (4.4) 1.6–35.4 0.90 Households with incomes higher than the state median, a% 48.7 (10.3) 20.5–79.4 0.83 Women with a bachelor’s degree or more, a% 23.6 (9.1) 5.8–56.9 0.73 Single mothers, % 6.9 (1.9) 1.7–19.3 0.73 Mothers of young children who were unemployed, % 4.6 (2.2) 0.0–23.6 0.83 County Disadvantage Index b,c 0 (1) –2.5–4.5 0.92 Note.The sample size was n = 7650. Unweighted sample size rounded to the nearest 50 in accordance with National Center for Education Statistics guidelines. aReverse coded.bThe County Disadvantage Index was created by summing each standardized county characteristic weighted by its item loading. Higher scores indicate more county disadvantage.
RESEARCH AND PRACTICE November 2014, Vol 104, No. 11|American Journal of Public HealthCheng et al.|Peer Reviewed|Research and Practice|2115 socioeconomic status (SES), and US region of residence (Northeast, Midwest, South, or West).
We deﬁned SES by using quintiles of a composite index generated by NCES that incorporated parental education, labor force participation, and occupation. 19 We also created a variable to designate whether the family moved outside the reported zip code between the 24-month and 4-year data waves (i.e., moved = 1; did not move = 0).
Analyses We conducted all analyses with SAS version 9.2 (SAS Institute, Cary, NC). We obtained means and percentages to describe the sample characteristics with appropriate weights to ac- count for the complex sampling design of the ECLS-B. We used weightedv 2statistics and ttest results to examine differences in charac- teristics across cognitive delay subcategories (i.e., resolved, newly developed, and persistent cognitive delay vs typically developing).
We performed hierarchical linear model- ing 29 with a random intercept via PROC MIXED to estimate the effect of cognitive delay and community factors on child behavior scores. With this approach, we were able to estimate average differences in child behavior between counties, while accounting for un- measured similarity (i.e., nonindependence) among individuals living in the same county. 30 There were 987 counties included in our multilevel models, 17% of which comprised more than 10 children. Each model reported the estimatedﬁxed effect for each covariate with robust standard errors and the estimated between-county variability in the outcome.
We performed these analyses unweighted, as a sensitivity analysis with weighted generalized estimating equations yielded almost identical results (data not shown).
Theﬁrst (null) model described the overall county-level variability in child behavior. The second model added cognitive delay status to determine the inﬂuences of cognitive delay to explaining variances in child behavior on county and child levels; the third model ad- justed for covariates. We also reran the models removing perceived neighborhood safety con- cerns; this did not inﬂuence ourﬁndings (data not shown) so we presented results from the full model. In the fourth model we added interaction terms between children’s cognitivedelay status and community factors to deter- mine effect modiﬁcation in the relationship between cognitive delay status and child be- havior by county disadvantage and perceived neighborhood safety. RESULTS Table 2 presents the weighted distribution of child and family characteristics for the full sample and within cognitive delay subgroups (typically developing and resolved, newly de- veloped, and persistent cognitive delays). At age 4 years, the overall mean problem behav- ior (PKBS-2) score for the sample was 12.4 (SD = 4.9). Mean mother-reported PKBS-2 scores were higher among children with cog- nitive delays than among typically developing children. In terms of neighborhood factors, 8.0% of children lived in neighborhoods where parents perceived safety concerns. Children with newly developed cognitive delays were the most likely to live in neighborhoods with perceived safety concerns (13.8% vs 7.6% of typically developing children). The CDI scores were higher among children with cognitive delays than among typically developing children.
Ourﬁrst mixed model (Table 3) revealed statistically signiﬁcant between-county vari- ability in child problem behavior scores at age 4 years (r 2= 0.80; SE = 0.18). In the second model, cognitive delay status was signiﬁcantly associated with worse child behavior. Mean behavior scores at age 4 years were signiﬁ- cantly higher among children who had re- solved (Β= 0.70; SE = 0.20), newly developed (Β= 1.92; SE = 0.25), and persistent cognitive delays (Β= 2.96; SE = 0.41) at age 24 months, relative to typically developing children. These differences attenuated, but remained statistically signiﬁcant for children with newly developed and persistent cognitive delays after we adjusted for covariates (model 3). Perceived neighborhood safety concerns were independently associated with higher behavior scores in the adjusted model (Β=1.07; SE=0.22).
In the fourth model, the interaction between the CDI and cognitive delay status was statis- tically signiﬁcant (P< .01), suggesting that county disadvantage was particularly detri- mental for children with persistent cognitive delays (Β= 1.05; SE = 0.41). Upon closer ex- amination, we observed a county disadvantagegradient in behavior, whereby children with persistent cognitive delays living in the most disadvantaged counties fared worst (Β= 2.64; SE = 0.68 for children with persistent cognitive delays living in the most disadvantaged counties vs typically developing children living in advantaged counties; Figure 1).
The interaction between cognitive delay status and perceived safety was not statistically signiﬁcant (P= .21), indicating that living in neighborhoods with perceived safety concerns was associated with worse behavior regardless of whether or not children had cognitive delays (data not shown). DISCUSSION To our knowledge, this is theﬁrst study to investigate the relationships among cognitive delay, community factors, and behavior prob- lems in very early childhood. By capitalizing on data from a large, national cohort of US children, we found that children who met our criteria for cognitive delay at age 24 months had worse behavior at age 4 years than their typically developing peers, independent of birth, sociodemographic, and family factors.
This result is consistent with previous studies demonstrating disparities in behavior problems between children with and without cognitive delays before school entry. 6,31 Our results add to the literature by suggesting that these dif- ferences may be inﬂuenced by children’s con- textual environments.
We found that children with persistent cognitive delays living in disadvantaged counties had worse behavior than their peers in more advantaged counties, even after we accounted for individual and family-level characteristics. Several mechanisms may ex- plain the pathways by which county disadvan- tage inﬂuenced child behavior. Children with persistent cognitive delays who live in disad- vantaged counties may be more vulnerable to environmental stressors (e.g., discrimination or social isolation) or negative social processes (e.g., low social cohesion, capital, or control) than their peers, which contribute to worse behavior. 15Children with cognitive delays growing up in disadvantaged counties may also have limited access to resources that stimulate learning and promote positive development, such as specialized early intervention services RESEARCH AND PRACTICE 2116|Research and Practice|Peer Reviewed|Cheng et al.American Journal of Public Health|November 2014, Vol 104, No. 11 or high-quality schools, medical facilities, and child care. 15 It is also possible that parental stress or increased family conﬂict associated with livingin a disadvantaged community may be height- ened among families of children with cognitive delays. For example, Kohen et al. 32 found that living in a socioeconomically deprived ordisorganized neighborhood was associated with maternal depression and family dysfunc- tion; these factors were subsequently associ- ated with negative parenting practices (e.g., low TABLE 2—Bivariate Analysis of Cohort Characteristics by Children’s Cognitive Delay Status at Age 24 Months: National Estimates From the Early Childhood Longitudinal Study, Birth Cohort, United States, 2001–2005 Cognitive Delay Status at 24 Months CharacteristicTotal, % or Mean (SD)Typically Developing, % or Mean (SD)Resolved, % or Mean (SD)Newly Developed, % or Mean (SD)Persistent, % or Mean (SD)P Total, unweighted 7650 5900 850 650 250 Perceived neighborhood safety Very or fairly safe from crime 92.0 92.4 94.0 86.2 90.0 < .001 Fairly or very unsafe from crime 8.0 7.6 6.0 13.8 10.0 Plurality Singleton birth 96.8 97.3 93.0 95.8 93.0 < .001 Twin or triplet 3.2 2.7 7.0 4.2 7.0 Congenital anomaly Yes 1.4 1.3 2.3 0.7 2.9 .167 No 98.6 98.7 97.7 99.3 97.1 Child gender Male 51.3 49.1 53.8 66.3 77.1 < .001 Female 48.7 50.9 46.2 33.7 22.9 Child race/ethnicity Non-Hispanic White 55.6 58.1 55.3 33.7 39.3 < .001 Non-Hispanic Black 13.2 12.4 13.7 18.0 26.1 Non-Hispanic other race 6.9 6.7 7.2 7.7 11.8 Hispanic 24.3 22.8 23.8 40.6 22.8 Family structure Single-parent household 19.5 18.4 21.1 25.5 35.7 < .001 Two-parent household 80.5 81.6 79.9 74.5 64.3 Other children in the household 0 33.0 33.8 23.5 36.9 23.4 < .001 1 37.8 38.3 38.1 32.4 34.7 ‡2 29.2 27.9 38.4 30.7 41.9 Socioeconomic status First quintile (lowest) 18.9 17.4 21.8 30.4 25.6 < .001 Second quintile 19.5 18.8 18.3 27.2 22.7 Third quintile 19.9 20.0 18.9 17.6 25.8 Fourth quintile 20.7 21.3 21.4 15.0 16.0 Fifth quintile (highest) 21.1 22.5 19.6 9.9 9.9 US region of residence Northeast 16.0 16.1 15.5 15.1 15.8 .449 Midwest 22.7 23.1 20.1 20.8 20.7 South 38.1 37.6 37.1 42.8 47.4 West 23.2 23.1 27.4 21.1 16.0 Moved between data waves Yes 33.2 32.7 32.7 39.8 31.8 .023 No 66.8 67.3 67.3 60.2 68.2 Continued RESEARCH AND PRACTICE November 2014, Vol 104, No. 11|American Journal of Public HealthCheng et al.|Peer Reviewed|Research and Practice|2117 stimulation, low consistency, and high punitive behavior) and adverse child behavior and cognition. 32 Future work should investigate these potential mechanisms.
Although we expected that perceived neighborhood safety would also modify the relationship between cognitive delay status andbehavior problems, we instead found that perceived safety concerns were associated with worse child behavior regardless of cognitive delay status. This is consistent with past re- search reporting substantial negative conse- quences of exposure to community violence on the psychological well-being of children andadolescents. 33 Therefore, those who are de- veloping interventions aiming to ameliorate behavior problems in young children may wish to consider the inﬂuence of neighborhood safety and develop strategies to address it.
Consistent with our previous study in this same birth cohort, 6we found that young children with cognitive delays have worse behavior than their typically developing peers.
It is estimated that between 30% and 50% of children with cognitive delays develop comorbid mental health disorders, a risk nearly 3 times that of typically developing children. 1 The dual experience of cognitive delay and behavior problems has substantial short- and long-term implications for children, families, and society at large. For children with cognitive delays, these impacts include failure of resi- dential placement 34 and reduced vocational, educational, and social opportunities. 8Behav- ior problems among children with cognitive delays are also associated with higher levels of parental stress, 35and increased costs of care, 36 and have a substantial impact on families’ decisions regarding children’s residential placement. 10 Behavior problems in young children with cognitive delays are likely to persist if left untreated, as evidence suggests only small de- clines in behavior problems through school age and adolescence into adulthood. 7,8 More re- search into the factors underlying this persis- tent and costly disparity would aid in the design and implementation of intervention services, yet exploration of factors contributing to the psychosocial development of individuals with cognitive delays has typically been limited to condition-related (e.g., severity) or individual- level (e.g., age and gender) characteristics. 1 The present study, although not addressing causality, demonstrates the importance of TABLE 2—Continued Birth weight, grams 3321.6 (561.9) 3354.3 (536.3) 3128.7 (680.1) 3262.8 (562.2) 2958.3 (759.2) < .001 PKBS-2 score, age 4 y 12.4 (4.9) 12.1 (4.8) 12.7 (5.1) 14.2 (5.3) 14.7 (5.2) < .001 County Disadvantage Index a 0 (1) –0.01 (0.9) 0 (0.9) 0.05 (0.9) 0.16 (0.9) < .001 Note.PKBS-2 = Preschool and Kindergarten Behavior Scales–Second Edition. Weighted estimates. Percentages may not sum to 100 because of rounding. Unweighted sample sizes were rounded to the nearest 50 in accordance with National Center for Education Statistics guidelines. Cognitive delay was de ned by the 10th percentile of the Bayley Short Form–Research Edition mental scale at ages 9 months or 24 months, categorized as resolved (cognitive delay detected at 9 months but not at 24 months), newly developed (cognitive delay detected at 24 months but not at 9 months), persistent (cognitive delay detected at 9 months and 24 months), versus typically developing.Pvalues denote statistical signi cance of differences in characteristics across cognitive delay subcategories. aThe County Disadvantage Index sums the standardized variables, weighted by their factor loadings.
TABLE 3—Fixed and Random Effects for a Series of Linear Mixed Multilevel Random Intercept Models of Child Behavior at Age 4 Years Among Children With and Without Cognitive Delay: Data From the Early Childhood Longitudinal Study, Birth Cohort, United States, 2001–2005 Variable Model 1, B (SE) Model 2, B (SE) Model 3, B (SE) Model 4, B (SE) Intercept 12.66*** (0.08) 12.32*** (0.08) 10.21*** (0.36) 10.20*** (0.36) Cognitive delay status a Resolved 0.70** (0.20) 0.36 (0.19) 0.37 (0.19) Newly developed 1.92*** (0.25) 1.19*** (0.25) 1.18*** (0.24) Persistent 2.96*** (0.41) 1.94*** (0.40) 1.70*** (0.38) Typically developing (Ref) Perceived neighborhood safety Very or fairly safe from crime (Ref) Fairly or very unsafe from crime 1.07*** (0.22) 1.08*** (0.22) Birth weight, thousands of grams –0.39*** (0.08) –0.39*** (0.08) Plurality Singleton birth (Ref) Twin or triplet –0.30 (0.20) –0.31 (0.20) Congenital anomaly Yes 0.28 (0.40) 0.26 (0.40) No (Ref) Child gender Male 1.96*** (0.13) 1.96*** (0.13) Female (Ref) Child race/ethnicity Non-Hispanic White (Ref) Non-Hispanic Black –0.20 (0.23) –0.21 (0.23) Non-Hispanic other race –0.41* (0.16) –0.40** (0.16) Hispanic –0.45** (0.18) –0.46** (0.18) Continued RESEARCH AND PRACTICE 2118|Research and Practice|Peer Reviewed|Cheng et al.American Journal of Public Health|November 2014, Vol 104, No. 11 investigating a wider range of social and envi- ronmental factors that may place young chil- dren with cognitive delays at risk.
Strengths and Limitations Strengths of this study include the use of a large, national cohort of US children and well-validated measures of early cognition and child behavior.
We also acknowledge several potential lim- itations. First, we assessed child behavior and county disadvantage cross-sectionally, limitingcausal inference. Second, we relied on maternal report of child behavior, which has the poten- tial to introduce bias. We also could not rule out the potential for reverse causality. It may be that parents with children with more behavior problems were more likely to perceive safety threats in their neighborhoods. Also, we have not fully accounted for potential selection into communities (i.e., that selection into neighbor- hoods and counties may not be random or independent from the outcome), which may have led to overestimation of effects. 15,37 The ECLS-B did not ask participants to report on physical features of their neighborhoods (e.g., recreational resources or quality of hous- ing) or other social factors (e.g., social cohesion or norms) that may have inﬂuenced the re- lationships identiﬁed in this study. 38 In addition, our CDI may have encompassed large areas that do not necessarily correspond to self-deﬁned neighborhood boundaries.
Future work exploring additional community mechanisms and more granular units of geo- graphic analyses is needed. However, although a county is larger than what is typically thought of as a“neighborhood,”it may capture at least some variation in the social, physical, economic, and service environments in which people live. Counties are also the primary administrative divisions of most states and may thus be the appropriate choice when public health action is being considered.
Ourﬁndings suggest that pediatric health care and social service providers may need to be aware of the potential inﬂuence of county disadvantage on the behavioral development of children with cognitive delay. Behavioral assessments of children with cognitive delays might need to include assessments of their community contexts. In highlighting the po- tential importance of county disadvantage to understanding disparities in behavior problems between young children with and without cognitive delays, our results also point to new opportunities for intervention. Programs to improve behavioral outcomes among young children with cognitive delays may be en- hanced by incorporating strategies to address their social environments.
Although there is limited experimental re- search on neighborhood health effects, 38 such interventions may be guided by evidence from the Moving to Opportunity program, which found that adolescent girls who moved from high-poverty to low-poverty neighborhoods had fewer mental health problems and arrests for violent crime than girls who stayed in high-poverty neighborhoods. 17,39 Findings from the present study lend support to further exploring such public health interventions and their effects on different subgroups (e.g., chil- dren with cognitive delays). Efforts to reduce county disadvantage may ultimately provide beneﬁt to children with cognitive delays. TABLE 3—Continued Family structure Single-parent household 0.74*** (0.17) 0.74*** (0.17) Two-parent household (Ref) Number of other children in the household 0 (Ref) 1 0.63*** (0.15) 0.64*** (0.15) ‡2 0.63*** (0.16) 0.63*** (0.16) Socioeconomic status First quintile (lowest) 2.06*** (0.22) 2.07*** (0.22) Second quintile 1.51*** (0.19) 1.53*** (0.19) Third quintile 1.24*** (0.19) 1.27*** (0.19) Fourth quintile 0.84*** (0.16) 0.85*** (0.16) Fifth quintile (highest; Ref) US region of residence Northeast (Ref) Midwest 1.26*** (0.25) 1.26*** (0.25) South 0.91** (0.24) 0.91*** (0.24) West 0.61** (0.25) 0.61** (0.25) Moved between data waves Yes 0.13 (0.13) 0.13 (0.13) No (Ref) County disadvantage b 0.03 (0.07) –0.05 (0.07) County disadvantage·cognitive delay status Disadvantage·resolved 0.31 (0.17) Disadvantage·newly developed 0.13 (0.25) Disadvantage·persistent 1.05** (0.41) Disadvantage·typically developing (Ref) Random effects Between-county variance 0.80*** (0.18) 0.77*** (0.18) 0.42** (0.12) 0.40*** (0.12) Residual variance 27.3*** (0.45) 26.8*** (0.44) 25.0*** (0.41) 25.0*** (0.41) Note.The dependent variable in all models was child behavior, measured with the Preschool and Kindergarten Behavior Scales–Second Edition. Higher scores on the scale indicate worse behavior. aCognitive delay was de ned by the 10th percentile of the Bayley Short Form–Research Edition mental scale at ages 9 months or 24 months, categorized as resolved (cognitive delay detected at 9 months but not at 24 months), newly developed (cognitive delay detected at 24 months but not at 9 months), persistent (cognitive delay detected at 9 months and 24 months), versus typically developing.
bThe County Disadvantage Index sums the standardized variables, weighted by their factor loadings.
*P£.05; **P£.01; ***P£.001. RESEARCH AND PRACTICE November 2014, Vol 104, No. 11|American Journal of Public HealthCheng et al.|Peer Reviewed|Research and Practice|2119 Conclusions We examined the inﬂuence of cognitive delays and community factors on behavior problems in very early childhood by using a national sample of US children. Our results suggest that disparities in behavior problems between young children with and without cognitive delays may be inﬂuenced by the contextual environment within which these children reside. Further inquiry into speciﬁc county and neighborhood mechanisms con- tributing to behavior problems among young children with cognitive delays is needed. Nev- ertheless, our descriptiveﬁndings suggest that these contexts may provide opportunities for the design and implementation of public health interventions to improve the behavioral health of young children with cognitive delays. j About the AuthorsErika R. Cheng is with Harvard Medical School and Massachusetts General Hospital for Children, Division of General Academic Pediatrics, Center for Child and Ado- lescent Health Research and Policy, Boston, MA. Hyojun Park, Stephanie A. Robert, and Mari Palta are with Department of Population Health Sciences, University of Wisconsin School of Medicine and Public Health, Madison.
Whitney P. Witt is with Behavioral Health and Quality Research, Truven Health Analytics, Bethesda, MD.Correspondence should be sent to Erika R. Cheng, PhD, MPA, Harvard Medical School and Division of General Academic Pediatrics, Center for Child and Adolescent Health Research and Policy, Massachusetts General Hos- pital for Children, 100 Cambridge St, 1570-B5, Boston, MA 02114 (e-mail: firstname.lastname@example.org). Reprints can be ordered at http://www.ajph.org by clicking the “Reprints”link.
This article was accepted June 5, 2014. ContributorsE. R. Cheng conceptualized and designed the study, conducted the analyses, drafted the initial article, and approved theﬁnal article as submitted. H. Park assisted with the study design, conducted the analyses, and contributed to the interpretation of the data. S. A. Robert critically reviewed and revised the article, contributed to the interpretation of the data, and approved theﬁnal article as submitted. M. Palta assisted with the study design, helped interpret the data, critically reviewed and revised the article, and approved theﬁnal article as submitted. W. P. Witt assisted with the study design, acquisition of data, interpretation of data, and drafting of the article; critically reviewed and revised the article; and approved theﬁnal article as submitted.
AcknowledgmentsThis research was made possible by a Science and Medicine Graduate Research Scholars Fellowship from the University of Wisconsin in the College of Agriculture and Life Sciences and the School of Medicine and Public Health, and a dissertation grant from The New York Community Trust, Fahs-Beck Fund for Research and Experimentation, awarded to E. R. Cheng. E. R. Cheng was additionally supported by a grant from the Eunice Kennedy Shriver National Institute of Child Healthand Human Development Research Training in Prevention and Care of Chronic Illness in Childhood (T32HD075727-01; principal investigator: J. A. Finkelstein).
The authors wish to thank Bridget B. Catlin, PhD, Julie Poehlmann, PhD, John Mullahy, PhD, and the anony- mous reviewers for their comments, suggestions, and assistance during the preparation of this article. Human Participant ProtectionThe University of Wisconsin—Madison Health Sciences institutional review board considered this study exempt from review because the data had been previously collected and de-identiﬁed.
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