Abstract
Objective To compare academic achievement in children with oral-facial clefts (OFC) with their unaffected siblings. Methods 256 children with OFC were identified from the Iowa Registry for Congenital and Inherited Disorders, and 387 unaffected siblings were identified from birth certificates. These data were linked to Iowa Testing Programs achievement data. We compared academic achievement in children with OFC with their unaffected siblings using linear regression models, adjusted for potential confounders. In post hoc analyses, we explored modifiers of siblings’ academic performance. Results Achievement scores were similar between children with OFC and their siblings. Children with cleft palate only were significantly more likely to use special education than their unaffected siblings. Siblings’ academic achievement was inversely related to distance in birth order and age from the affected child. Conclusion Children with OFC and their siblings received similar achievement scores. Younger siblings, in particular, may share a vulnerability to poor academic outcomes.
Keywords: academic achievement, orofacial cleft, sibling
Previous studies have found that children with isolated oral-facial clefts (OFC) have elevated risk for learning problems, with proposed mechanisms including differences in brain morphology or function, social stigmatization, and “downstream” effects of early impairment in speech and language (Richman, McCoy, Conrad, & Nopoulos, 2012). With regard to biological mechanisms, several authors have proposed that the embryological events that result in clefting also affect early brain development. Studies by Nopoulos and colleagues (Nopoulos, Langbehn, Canady, Magnotta, & Richman, 2007; Nopoulos et al., 2002; van der Plas, Conrad, Canady, Richman, & Nopoulos, 2010) provide support for this hypothesis, with differences revealed in brain structure in individuals with OFC compared to unaffected controls. Further, these authors have found associations between regions of structural difference and neurobehavioral outcomes (Nopoulos et al., 2010; Shriver, Canady, Richman, Andreasen, & Nopoulos, 2006). Based on the social psychology literature related to facial attractiveness (Langlois et al., 2000), it has also been proposed and there is some evidence to suggest that clefting affects the perceptions and expectations of teachers, which can in turn have a deleterious effect on achievement (Richman, 1978). Recent studies have focused on the long-term effects of speech and language deficits associated with clefting, which may interfere with the development of early reading and related academic skills (Chapman, 2011; Conrad, McCoy, Devolder, Richman, & Nopoulos, 2014). Finally, although not well-studied, demographic and maternal health factors that place infants at risk for clefting (e.g., low socioeconomic status, maternal nutrition, exposures to alcohol or tobacco, genetic vulnerability) might also affect development and academic achievement.
To date, most of the research showing an association between OFC and academic achievement has relied on small clinic samples compared to test norms or unaffected control groups (Broder, Richman, & Matheson, 1998; Collett, Stott-Miller, Kapp-Simon, Cunningham, & Speltz, 2010; Conrad, McCoy, Devolder, Richman, & Nopoulos, 2014). Three recent population-based studies of children with OFC compared with unaffected peers have corroborated the findings from earlier clinical investigations (Persson, Becker, & Svensson, 2012; Wehby et al., 2014; Yazdy, Autry, Honein, & Frias, 2008). In the most recent of these studies, we compared ∼600 children with OFC with a comparison group of nearly 2,000 unaffected classmates (Wehby et al., 2014). Across all grade levels from elementary through high school, children with OFC had lower scores on measures of reading, math, language, science, and social studies. These group differences were only slightly reduced when controlling for parental education, race/ethnicity, and prenatal exposures to alcohol or tobacco. As is true in most population-based studies, there were potential confounding variables that we could not address in this study. For example, parental education was used to control for socioeconomic status (SES); however, parental education alone may not capture qualities of the home environment that can affect academic achievement such as parents’ investment in their child’s education and home activities to facilitate early learning (McGrath et al., 2007; Phillips & Lonigan, 2009; Wehby, McCarthy, Castilla, & Murray, 2012).
In the current study, we used unaffected siblings as a comparison group to examine the influence of selected shared and unshared features of the home environment. We identified children with OFC from the Iowa Registry for Congenital and Inherited Disorders (IRCID) and siblings from birth certificates, and obtained birth record and academic achievement data. Academic achievement was assessed by the Iowa Testing Programs (ITP), which routinely tracks academic progress for all children in the state of Iowa.
We tested the hypothesis that children with OFC would perform lower on academic achievement tests than their siblings. Among unaffected siblings, we also evaluated factors that may uniquely affect the achievement of siblings of a child with OFC, factors usually assumed to be null in a sibling comparison model. For example, the academic performance of unaffected siblings could be compromised by the diversion of parents’ attention and resources away from the sibling and toward the affected child. We examined this possibility in post hoc analyses of achievement in relation to the differences in birth order and age between the child with OFC and his or her sibling, assuming that siblings born subsequent to the affected child and younger in age would be more impacted than siblings born before the affected child and more distant in age. We also examined whether achievement of unaffected siblings was influenced by the sex match between siblings and affected children (same or opposite) and cleft type (lip only, lip and palate, palate only).
Methods
Study Population
The study population consisted of 516 children born with isolated OFC (i.e., without other major birth defects or syndromes) in Iowa from January 1, 1989, and December 31, 2003, and their biological siblings.1 Children with OFC were identified from the IRCID, which conducts active surveillance of birth defects using systematic review of medical records from hospitals and clinics in Iowa and in neighboring states that serve Iowa residents. The sample included children with isolated cleft lip only (CL), cleft lip and palate (CLP), and cleft palate only (CP). Thirteen children with OFC were included in the registry but died prior to school entry. Data for children with OFC were linked with data from the ITP as described below. Of the entire eligible population of children with oral clefts (N = 503), 410 (81.5%) were found in the ITP database.
Biological (half and full) siblings of children with OFC were identified from birth certificate files. Of all children with clefts, 65% (n = 280) had ≥1 sibling in the study.
Demographic and Prenatal Exposure Data
Iowa birth certificate data were used to capture demographic and prenatal exposure data for children with OFC and unaffected siblings. For each live birth, a birth certificate worksheet is filled out and submitted to the Iowa Department of Public Health. Data collected for the worksheet are taken from the maternal and infant medical records and, if needed, from maternal self-reports for data not available in the medical record (e.g., tobacco use during pregnancy). Data entered on the worksheet are usually reviewed by the mother.
Academic Achievement
Academic achievement was measured using the Iowa Tests of Basic Skills (ITBS; Hoover, Dunbar, & Frisbie, 2003) for children in grades kindergarten through 8, and the Iowa Tests of Educational Development (ITED; Forsyth, Ansley, Feldt, & Alnot, 2003) for high school students. The ITBS and ITED are standardized tests with nationally representative norms and excellent psychometrics (Forsyth, Ansley, Feldt, & Alnot, 2003; Hoover, Dunbar, & Frisbie, 2003). The tests include multiple-choice questions with four to five response options. These tests are administered to virtually all students in Iowa and are among the most commonly used tests of academic achievement nationwide. The ITBS and ITED are regularly updated; three versions of the ITBS and ITED were in use during the testing years included in the study (1990 through 2010). The main dependent measures drawn from these test batteries included students' standard scores on Reading, Language, Mathematics, Science, Social Studies, Sources of Information (e.g., use of maps and diagrams), and Overall Composite. Standard scores are commonly used developmental scores from the ITBS/ITED tests that are based on an interval scale and national norms and capture variation in achievement over time, which is appropriate given the longitudinal data we analyze. Finally, we examined the proportion of cases versus siblings placed in special education services as recorded by Iowa schools at the time of ITBS/ITED assessment; data on special education were first provided in 2001 and are available for all school years thereafter.
Data linkages merging ITP, IRCID, and birth certificate data were performed by an independent staff, and the study investigators had access only to the anonymous merged data (see Wehby et al., 2014). All data linkages and study procedures were approved by the University of Iowa Institutional Review Board and the Iowa Department of Public Health.
Study Sample
Our analytical sample consisted of child-grade observations; the unit of the analysis was the child’s available achievement test score in each grade. To improve generalizability and data quality, we restricted our analyses to ITP data through 2010, as the tests and scoring changed significantly in 2011 (last year available at the time of this analysis). We also excluded scores for children in kindergarten, grade 1, and grade 12, as these grades had limited testing of students. Child’s age in the sample ranged from 7 to 19 years. After these exclusions, our analytical sample consisted of 256 children with OFC (101 CL; 50 CP; 105 CLP) contributing 1,564 child-grade observations, and 387 unaffected siblings contributing 2,342 child-grade observations. Because some participants were missing data for one or more academic achievement outcomes, the final sample varied across measures, ranging from 155 to 245 cases (965–1,549 child-grade observations) and 208 to 368 siblings (1,490–2,313 child-grade observations). In the few cases of multiple tests for a grade (<2% of child-grade observations), such as due to repeating a grade, only the first tests were retained in the analysis, as they more accurately reflect the initial achievement of the child at that grade.
Statistical Analysis
Affected Children Versus Unaffected Siblings
We used separate linear regression analyses to compare children with OFC with their siblings on each academic achievement domain. The models included family-fixed effects to ensure that each affected child was compared only with his/her siblings. We employed ordinary least squares (OLS) regression, as the test scores were continuous and close to being normally distributed. OLS also allows inclusion of family-fixed effects so that only within-family variation—by comparing affected children only with their own unaffected siblings and not with other siblings—can be used to estimate differences between affected children and siblings. The model compares the affected child with all his/her siblings within the family, and the estimated OLS coefficient is based on averaging differences between an affected child and unaffected siblings within each family (in the case of multiple siblings) and then averaging the within-family differences across all families in the sample. All analyses were adjusted for sex and age of the child, grade level, and test version and years that the test had been in use (as scores may vary as a function of how long a new version has been in use). We evaluated group differences first for all children with OFC and then by cleft type (i.e., cleft of the lip only, palate only, and lip and palate).
To address the threat from time-variant household confounding variables that are not captured by the family-fixed effects, we estimated additional models that added conceptually relevant covariates that can vary between siblings and relate to both cleft risk and academic achievement. In addition to the covariates noted above, these analyses included maternal reports of prenatal smoking and alcohol consumption, and parental age and maternal marital status at the time of a sibling’s birth. All variables were represented by dummy variables including multiple categories for age. To preserve the sample size for the second specification to be as close as possible to the first one, we represented observations with missing data on covariates by dummy variables instead of excluding them except for marital status, which had only few observations missing. The regression models were estimated using OLS, and standard errors were clustered at the family unit level. We initially estimated the models by combining all grades to get an average difference in educational achievement. We then repeated these regression analyses, stratified by school level (elementary, middle, and high school).
Analyses Within Unaffected Siblings
In post hoc analyses among unaffected siblings only, we estimated models that regressed sibling academic achievement outcomes on key indicators including the difference in birth years between sibling and affected child (i.e., difference in age), an indicator for sibling and affected child of same sex (compared with opposite sex), and indicators for cleft type (CL and CP versus CLP). These analyses adjusted for birth order, which has been found to correlate with academic achievement (Hotz & Pantano, 2013). Other covariates included sibling sex and dummy variables representing sibling birth year, maternal marital status and age at sibling birth and paternal age at sibling birth, maternal use of alcohol and tobacco during pregnancy, grade-level, test-version dummies, and years since test version was first developed. In an alternative model, we replaced the difference in birth years by the difference in birth order. Covariates in this latter model were identical to those used for difference in birth years.
Results
Sample Description
Demographic characteristics and achievement test data are summarized separately for children with OFC and their siblings in Table I. Children in both groups were predominately male (61% OFC, 53% siblings). Forty-one percent of the children with OFC had CLP, 39.5% had CL, and 19.5% had CP. In both groups, most mothers were married, white/non-Hispanic, and between the ages of 20–35 at the time of the child’s birth. Maternal smoking and alcohol consumption during pregnancy were similar in both groups. Note that because in some cases multiple siblings were included per affected child, family demographics are slightly different for the two groups. The ratio of child-grade observations of affected children (overall and by cleft type) to unaffected siblings was fairly comparable across school-levels (see Supplementary Table S1).
Table I.
Demographic and Clinical Characteristics for Children With Oral Clefts and Unaffected Siblings
| Variable | Children with oral clefts | Unaffected siblings |
|---|---|---|
| N (%) | N (%) | |
| Cleft lip only | ||
| Yes | 101 (39.5) | |
| No | 155 (60.5) | |
| Cleft palate only | ||
| Yes | 50 (19.5) | |
| No | 206 (80.5) | |
| Cleft lip with palate | ||
| Yes | 105 (41.0) | |
| No | 151 (59.0) | |
| Male | ||
| Yes | 156 (60.9) | 206 (53.2) |
| No | 100 (39.1) | 181 (46.8) |
| Mother married | ||
| Yes | 195 (76.5) | 279 (72.3) |
| No | 60 (23.5) | 107 (27.7) |
| Missing | 1 (0.4) | 1 (0.3) |
| Maternal race/ethnicity | ||
| Non-White or Hispanic | 13 (5.1) | 29 (7.5) |
| White/Non-Hispanic | 242 (94.5) | 356 (92.0) |
| Missing | 1 (0.4) | 2 (0.5) |
| Maternal age | ||
| <20 years | 26 (10.1) | 43 (11.1) |
| 20–35 years | 205 (80.1) | 314 (81.1) |
| >35 years | 25 (9.8) | 30 (7.8) |
| Father’s age | ||
| <20 years | 8 (3.1) | 14 (3.6) |
| 20–35 years | 172 (67.2) | 259 (66.9) |
| >35 years | 44 (17.2) | 53 (13.7) |
| Missing | 32 (12.5) | 61 (15.8) |
| Maternal education | ||
| <High School | 34 (13.3) | 62 (16.0) |
| High school–Some college | 180 (70.3) | 269 (69.5) |
| ≥College | 39 (15.2) | 51 (13.2) |
| Missing | 3 (1.2) | 5 (1.3) |
| Father’s education | ||
| <High School | 9 (3.5) | 22 (5.7) |
| High school–Some college | 154 (60.2) | 207 (53.5) |
| ≥College | 36 (14.1) | 51 (13.1) |
| Missing | 57 (22.2) | 107 (27.7) |
| Maternal smoking | ||
| Yes | 61 (23.8) | 84 (21.7) |
| No | 195 (76.2) | 299 (77.3) |
| Missing | 0 | 4 (1.0) |
| Maternal alcohol consumption | ||
| Yes | 8 (3.1) | 15 (3.9) |
| No | 247 (96.5) | 366 (94.6) |
| Missing | 1 (0.4) | 6 (1.5) |
| Total observations | 256 | 387 |
Note. Because some affected children had more than one sibling in the analysis, the overall percentages (e.g., for parental education) are not exactly identical between these two groups.
The majority of the tests (∼90%) were based on the most recent test version (A/B) in the study period used between 2001 and 2010 (Supplementary Table S2). The means of the test standard scores across the various test areas and Composite are summarized in Table II, and ranged from ∼222 to 235 for children with OFC, and from 222 to 234 for siblings. The rate of using special education services ranged from ∼16% among siblings to ∼18% among affected children.
Table II.
Descriptive Statistics for Academic Achievement Test Scores for Children With Oral Clefts and Unaffected Siblings
| Variable | Children with oral clefts | Unaffected siblings |
|---|---|---|
| Mean (SD) or % | ||
| Reading | 222.07 (42.11) | 222.22 (42.16) |
| Language | 225.63 (46.49) | 226.54 (45.92) |
| Math | 228.35 (44.77) | 227.63 (44.32) |
| Science | 234.59 (48.02) | 234.48 (47.78) |
| Social studies | 229.52 (46.67) | 229.70 (46.33) |
| Sources of information | 227.46 (45.11) | 228.66 (43.00) |
| Composite total | 227.88 (43.80) | 229.03 (42.83) |
| Special education | ||
| Yes | 17.9 | 16.1 |
| No | 82.1 | 83.9 |
Note. Means of standard scores are reported. Standard scores vary by grade level (which is adjusted for in all the multivariate models reported in the tables below), with a mean of 168 for children in the second grade to a mean of 275 for eleventh graders. The estimates are based on cases and siblings with complete data on each variable. The sample of cases with complete data for the variables ranged from 155 to 245 (965–1549 child-grade observations). The number of siblings with complete data ranged from 208 to 368 (1490–2313 child-grade observations).
Differences in Academic Achievement
Differences in academic achievement between children with OFC and their siblings were small and statistically nonsignificant for all academic achievement domains, with few differences in unadjusted versus adjusted analyses (Table III). There were also no statistically significant differences in test scores when adjusting for cleft type (Table IV). However, on average, children with CL tended to have higher scores than unaffected siblings, while children with CP and CLP had lower scores than their siblings. Children with CP were significantly more likely to use special education services by about 12 percentage-points than their unaffected siblings. This difference increased and remained significant in adjusted analyses. In contrast, children with CL were less likely to use special education than unaffected siblings. Findings were similar by grade level (Supplementary Table S3).
Table III.
Differences in Academic Achievement Test Scores Between Children With Oral Clefts and Their Unaffected Siblings
| Case group | Reading | Language | Math | Science | Social studies | Sources of information | Composite | Special education |
|---|---|---|---|---|---|---|---|---|
| Basic regression specification |
||||||||
| N | 4,129 | 3,605 | 4,356 | 4,164 | 3,006 | 2,965 | 2,749 | 3,929 |
| Any cleft | 0.32 | 0.56 | 0.80 | 1.80 | 0.57 | 0.34 | 0.37 | 0.002 |
| β(SE) | (1.62) | (2.04) | (1.57) | (1.85) | (2.19) | (1.94) | (1.99) | (0.023) |
| Expanded regression specification |
||||||||
| N | 4,125 | 3,601 | 4,350 | 4,158 | 3,006 | 2,964 | 2,749 | 3,923 |
| Any cleft | 0.56 | 1.49 | 1.33 | 2.02 | 1.22 | 0.92 | 1.10 | −0.003 |
| β(SE) | (1.56) | (1.88) | (1.50) | (1.78) | (2.09) | (1.77) | (1.83) | (0.022) |
Note. Two separate regression models (with the same sample size) were estimated for each outcome. The basic regression models are adjusted for child age, sex, and grade level; test version and years test had been in use. In addition to these variables, the expanded regression models were adjusted for maternal reports of prenatal smoking and alcohol use; parental age; and mother’s marital status.
*p < .1; **p < .05; ***p < .01.
Table IV.
Differences Between Children With Clefts and Their Unaffected Siblings in Academic Achievement Test Scores by Cleft Type (Cleft Lip Only, Cleft Palate Only, Cleft Lip and Palate)
| Case group | Reading | Language | Math | Science | Social studies | Sources of information | Composite | Special education |
|---|---|---|---|---|---|---|---|---|
| Basic regression specification |
||||||||
| N | 4,129 | 3,605 | 4,356 | 4,164 | 3,006 | 2,965 | 2,749 | 3,929 |
| Cleft lip only | 2.00 | 3.43 | 2.98 | 3.33 | 5.22* | 2.39 | 2.45 | −0.051* |
| β(SE) | (2.27) | (3.23) | (2.33) | (2.81) | (3.59) | (3.34) | (3.34) | (0.026) |
| Cleft palate only | −2.59 | −4.59 | 0.06 | 1.79 | −4.06 | −3.87 | −2.78 | 0.12** |
| β(SE) | (4.13) | (4.40) | (3.59) | (4.51) | (5.08) | (4.29) | (4.37) | (0.049) |
| Cleft lip with palate | −0.11 | −0.02 | −1.09 | 0.23 | −2.19 | 0.15 | −0.32 | 0.004 |
| β(SE) | (2.61) | (3.10) | (2.57) | (2.90) | (2.96) | (2.52) | (2.76) | (0.043) |
| Expanded regression specification |
||||||||
| N | 4,125 | 3,601 | 4,350 | 4,158 | 3,006 | 2,964 | 2,749 | 3,923 |
| Cleft lip only | 2.17 | 3.87 | 3.01 | 3.32 | 5.88* | 2.87 | 2.96 | −0.055** |
| β(SE) | (2.11) | (2.85) | (2.20) | (2.69) | (3.42) | (2.92) | (2.99) | (0.025) |
| Cleft palate only | −3.84 | −6.17* | −0.11 | 1.08 | −6.14 | −5.81* | −4.34 | 0.13** |
| β(SE) | (3.94) | (3.99) | (3.35) | (4.23) | (4.60) | (3.50) | (3.65) | (0.052) |
| Cleft lip with palate | 0.85 | 2.56 | 0.24 | 1.08 | −0.31 | 2.17 | 1.83 | −0.009 |
| β(SE) | (2.54) | (2.97) | (2.53) | (2.81) | (2.85) | (2.45) | (2.60) | (0.042) |
Note. Two separate regression models (with the same sample size) were estimated for each outcome. The basic regression models are adjusted for child age, sex, and grade level; test version and years test had been in use. In addition to these variables, the expanded regression models were adjusted for maternal reports of prenatal smoking and alcohol use; parental age; and mother’s marital status.
*p < .1; **p < .05; ***p < .01.
Post Hoc Analyses Among Siblings
Among siblings, academic achievement was inversely associated with the time (in years) between the sibling and the affected child (Table V). In all domains, children who were older than an affected child had better academic achievement than those who were born after an affected child even after controlling for birth order. Similarly, younger siblings were more likely to use special education services by ∼2 percentage-points per year; effects estimated were linear and represented changes in outcome per one-year increase in birth year difference. Findings were similar when examining difference in birth order, again showing that younger siblings scored worse than older siblings on most domains; estimated effects were linear and represented changes in outcome per one-unit increase in birth order difference. There were no differences in academic achievement based on whether the sibling was the same or opposite sex of the affected child. Finally, siblings of a child with CLP tended to have lower reading and language scores than siblings of a child with CL, and were more likely to use special education than the siblings of a child with CP.
Table V.
Modifiers of Academic Achievement Test Scores Among Unaffected Siblings Only
| Unaffected siblings | Reading | Language | Math | Science | Social studies | Sources of information | Composite | Special education |
|---|---|---|---|---|---|---|---|---|
| Same-sex siblings model |
||||||||
| N | 2,188 | 1,939 | 2,303 | 2,199 | 1,608 | 1,603 | 1,482 | 2,084 |
| Birth year of sibling | −1.32*** | −1.00** | −0.63* | −1.70*** | −1.03* | −0.73 | −0.85* | 0.02*** |
| β(SE) | (0.42) | (0.44) | (0.41) | (0.50) | (0.55) | (0.50) | (0.49) | (0.005) |
| Same sex | −0.21 | −3.55 | 0.008 | 2.21 | 1.69 | 1.62 | 1.00 | 0.027 |
| β(SE) | (2.80) | (3.32) | (2.73) | (3.02) | (3.21) | (2.86) | (2.94) | (0.039) |
| Cleft lip only | 5.72** | 8.57** | 0.57 | 2.90 | 1.88 | 3.69 | 4.78 | −0.045 |
| β(SE) | (2.91) | (3.62) | (2.95) | (3.37) | (3.52) | (3.27) | (3.35) | (0.039) |
| Cleft palate only | 0.66 | 4.14 | −2.91 | −3.14 | −2.22 | 3.87 | 2.04 | −0.12*** |
| β(SE) | (3.83) | (4.53) | (3.52) | (4.06) | (4.76) | (3.91) | (4.17) | (0.044) |
| Birth order model |
||||||||
| N | 2,254 | 1,978 | 2,373 | 2,264 | 1,640 | 1,634 | 1,510 | 2,152 |
| Birth order of siblinga | −3.35*** | −3.87*** | −1.88* | −4.29*** | −2.41* | −2.99*** | −2.47** | 0.06*** |
| β(SE) | (1.27) | (1.45) | (1.24) | (1.31) | (1.38) | (1.20) | (1.27) | (0.015) |
Note. Cleft lip with cleft palate is the reference/omitted category.
aWe do not show the results for the sex of siblings and cleft types of affected children when replacing the difference in birth year between the unaffected sibling and case by the difference in birth order model because the results are the same.
*p < .1; **p < .05; ***p < .01.
Discussion
In this population-based sample of children with OFC, we found statistically nonsignificant differences in academic achievement between affected children and their unaffected siblings, except for greater use of special education among children with CP. The differences in test scores were small in magnitude (i.e., <.05 SD for all outcomes), making it unlikely that the lack of significance was due to statistical power issues. Although differences as a function of cleft type were not statistically significant, children with CL tended to perform as well as or better than unaffected siblings, while those with CP and CLP performed slightly worse than siblings.
The lack of differences between children with OFC relative to their siblings stands in contrast to the results of our earlier study, which compared children with OFC with their unaffected classmates (Wehby et al., 2014), and other studies that have compared children with OFC with the general population (Persson, Becker, & Svensson, 2012; Yazdy, Autry, Honein, & Frias, 2008). In the current study, scores for children with OFC and their siblings were lower than those observed among unaffected classmates in our earlier study (Wehby et al., 2014) and the proportion of children receiving special education services was higher than the national average of ∼14% (U.S. Department of Education, National Center for Education Statistics, 2013). This may indicate that previously reported deficits in academic achievement among children with OFC are related to characteristics shared by unaffected family members, such as a genetic vulnerability to learning problems or qualities of the family environment that are associated with academic achievement. We have not found that differences in academic achievement from classmates are explained by demographic confounders or prenatal exposures (Wehby et al., 2014), nor are we aware of other studies indicating that these variables account for differences in achievement. However, there may be subtle family environmental qualities that influence children with OFC as well as their siblings. For example, in noncleft samples, several groups have investigated characteristics of the home learning environment that influence reading acquisition, such as time spent in shared reading, children’s books available in the home, and parents’ own enjoyment of reading (Hart et al., 2009; Wehby, McCarthy, Castilla, & Murray, 2012; van der Leij et al., 2013). Although we are not aware of comparable studies in families of children with OFC, the home learning environment may differ between affected and unaffected families. This would suggest that early parent-focused interventions might be especially useful for this population, potentially benefiting children with clefts and their unaffected siblings.
To further identify factors that uniquely affect academic achievement of siblings of children with OFC, our study is the first to consider factors such as birth order, distance in age from the sibling with a cleft, and cleft type. Our analyses of the unaffected sibling group revealed that children who were older than the affected child tended to show better academic achievement than those who were younger. This effect was linear and present for both differences in birth order and age. These findings are not accounted for by birth order alone, as this was included as a covariate in both models. Sibling achievement might therefore be affected by the events that surround the care of an affected child with a cleft. In early childhood, these events include surgery to correct a cleft lip and cleft palate, and the stress of early feeding difficulties. In school-aged children with clefts, families may be preoccupied with speech and language interventions and services to address dental needs. Further, although findings are mixed, some studies suggest that behavior problems (e.g., inattention/hyperactivity, Wehby, McCarthy, Castilla, & Murray, 2012; separation anxiety, Tyler, Wehby, Robbins, & Damiano, 2013) are more common in children with OFC. All of these may divert parents’ attention and financial resources, and may be most detrimental to children born after the affected child. Our finding that siblings of children with CLP scored lower than siblings of children with CL in some domains is consistent with this hypothesis, as CLP requires more intensive services and longer-term follow-up (e.g., palate repair, higher risk of problems with feeding and language). Although we are not aware of research on the topic, there are many other conditions in which such an effect might be observed. For example, neurodevelopmental disorders or medical conditions that require intensive long-term care might be expected to divert parent and family resources away from siblings to an even greater extent than observed in this study of children with OFC, and further study might inform our understanding of the broader effects of these conditions on the family.
Our study is limited, to some extent, by the reliance on broad/school-wide measures of academic achievement. Although the ITBS/ITED are well-established measures, they are not individualized to identify specific neuropsychological deficits and targets for future intervention. Further, aside from cleft type, our study did not include data on clinical characteristics that may be relevant to the academic performance of both affected children and unaffected siblings. For example, we did not have access to clinical information on comorbid medical or developmental conditions that may affect learning (e.g., neurodevelopmental disorders, such as attention deficit hyperactivity disorder; hearing impairment; other medical conditions), on children’s special education classification and services received, or on interventions received outside of a formal special education program that might attenuate learning problems.
Conclusions
Our research has indicated that children with OFC have lower academic performance than classmates in all subject areas across multiple grade levels (Wehby et al., 2014), but they show roughly equivalent academic achievement to their unaffected siblings. The similar functioning levels of affected children and their unaffected siblings may be due to shared genetic or family environmental factors, or to unshared family processes that favor the child with cleft (e.g., differential parent attention, diversion of resources away from the unaffected sibling). Processes related to the latter pathway have been rarely studied in the cleft population, and further research with more detailed measurement of family processes is recommended.
Supplementary Data
Supplementary data can be found at: http://www.jpepsy.oxfordjournals.org/
Acknowledgments
The authors thank Bill Budelier, Kwame Nyarko, Nichole Nidey, and Julee Bormet for their assistance with the study and the Iowa Department of Public Health for providing access to data and approving data mergers.
Funding
This work was supported by the National Institute of Dental and Craniofacial Research (grant 1 R03 DE022094).
Conflicts of interest: None declared.
Footnotes
1 Prior to 1989, birth certificate data did not include parent names, making it impossible to identify siblings. We have therefore restricted this analysis to children with clefts and siblings born in 1989 or later.
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