Journal of Pediatric Psychology, Vol. 27, No. 8, 2002, pp. 739-748
© 2002 Society of Pediatric Psychology
Cognitive Functioning in Children With Sickle Cell Disease: A Meta-Analysis
1 University of South Carolina, 2 University of California, San Francisco
All correspondence should be sent to Jeffrey Schatz, Department of Psychology, University of South Carolina, Columbia, South Carolina 29208. E-mail: schatz{at}sc.edu.
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Abstract |
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Objective: To establish whether sickle cell disease (SCD) affects cognitive functioning in children with no evidence of cerebral infarction.
Methods: We conducted a meta-analysis of studies of cognition in SCD to determine the size of any statistical difference between children with SCD and controls. Methodological factors were evaluated according to the size and frequency of group differences.
Results: There were small but reliable decrements in cognitive functioning on IQ measures (4.3-point difference overall). The most methodologically rigorous studies showed a highly similar pattern. Sampling issues associated with the effect size for IQ were identified. Measures of specific abilities appear more sensitive than IQ scores to cognitive decrements in SCD.
Conclusions: SCD is associated with cognitive effects even in the absence of cerebral infarction. The causes of this cognitive decrement may include direct effects of SCD on brain function or indirect effects of chronic illness.
Key words: sickle cell disease; cognitive; neuropsychologic; meta-analysis.
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Introduction |
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The number of published reports describing cognitive functioning and potential cognitive deficits in children with sickle cell disease (SCD) has increased greatly over the past 15 years. The cognitive impairments associated with SCD that are due to cerebral vascular injury have been documented (Armstrong et al., 1996
;
Bernaudin et al., 2000;
Brown et al., 2000;
Cohen, Branch, McKie, & Adams,
1994; Craft, Schatz, Glauser,
Lee, & DeBaun, 1993;
Hariman, Griffith, Hurtig, & Keehn,
1991; Schatz et al.,
1999; Watkins et al.,
1998). The fact that stroke has a measurable and meaningful impact
on cognition is well accepted. The issue of whether children without cerebral
vascular injuries have cognitive effects related to SCD, however, is not well
established. In a recent report from the Cooperative Study of Sickle Cell
Disease (CSSCD) serial cognitive testing suggested children with SCD and
normal brain magnetic resonance imaging (MRI) exams showed declines in verbal
IQ scores over the course of a 5-year period
(Wang et al., 2001). In
addition, there were age-related declines on a test of psychomotor speed and
focused attention (coding subtest of the Wechsler scales) and mathematics
achievement. Though highly suggestive, this report is limited by the absence
of a healthy comparison group for the CSSCD data. The primary purpose of this report is to further evaluate the effects of SCD on cognitive functioning through the review and analysis of published studies. Our goal for this report is to evaluate five unresolved issues regarding the effects of sickle cell disease on cognitive functioning. Although a number of reports have examined cognitive functioning in SCD, they have reached different conclusions about cognitive effects related to the disease. It is unlikely that any two groups have identical population means, so the discrepancy across studies is probably best framed in terms of a related question: How large or meaningful are any effects of SCD on cognitive functioning?
An important difference among studies has been the use of IQ as the outcome
variable, as opposed to also examining specific areas of cognitive
functioning. IQ scores have been the most frequent method for evaluating
cognitive functioning, but there are some potential pitfalls with using IQ as
the sole outcome measure. First, most IQ tests measure only a subset of
abilities that may be relevant to cognitive functioning. For example, the
commonly used Wechsler scales at best measure three or four factors, and there
is minimal inclusion of tests tapping into important areas of cognition such
as memory and executive skills (Wechsler,
1991). Some cognitive models have at least eight cognitive factors
that may provide unique information (e.g.,
Horn & Noll, 1997).
Second, if there is a deficit in a specific area of cognitive ability, IQ
scores may underestimate its size by essentially averaging scores on
unaffected and affected skill areas. It would be useful to know how to balance
between the parsimony of IQ as a single cognitive outcome measure and the
potential increased sensitivity of multiple measures. Depending on the manner
in which the disease affects cognition, either choice could increase the
likelihood of detecting a true effect on cognition. Thus, the most appropriate
choice of the best outcome measures to evaluate cognition in children with SCD
but no cerebral infarction has not been established.
A second methodological issue evident in prior reports is the use of
different comparison groups. Predominantly, researchers have used either
siblings of the participants with SCD (typically mixed groups including both
those with normal hemoglobin and sickle cell trait) or demographically matched
peers. In the second case, there has usually been matching for at least age,
race/ethnicity, and some index of socioeconomic status (SES). It has been
suggested that the best comparison groups are siblings
(White & DeBaun, 1998)
because sibling comparison groups have greater similarity in terms of factors
such as family environment, but they have other potential limitations. For
example, not every child with SCD has a sibling within the required age range
of the study or any sibling at all. Thus, when using siblings, there are
typically fewer controls than cases. In addition, siblings are typically not
the same age as the child affected with SCD, which can pose limits for precise
matching of cases and controls according to age. Thus, there is no widely
accepted "gold standard" for a control group. The degree to which
the choice of comparison group has affected the outcomes of prior studies has
not been evaluated empirically, and such information could be useful for
interpreting prior research and guiding the design of future studies.
Another methodological issue has been the exclusionary criteria for stroke
or cerebral vascular injury. In many reports, neurologic status was determined
solely from medical history information without supporting evidence from
neuroimaging. Recently there has been increased awareness of the prevalence
and cognitive effects of "silent cerebral infarcts." Silent
cerebral infarcts are vascular injuries evident on magnetic resonance imaging
(MRI) exams in children without a history of a neurologic event. Silent
cerebral infarcts occur in approximately 15% of children with hemoglobin type
SS (HbSS) by age 12 and result in cognitive deficits
(Armstrong et al., 1996;
Bernaudin et al., 2000;
Brown et al., 2000;
Craft et al., 1993;
DeBaun et al., 1998;
Kugler et al., 1993;
Wang et al., 2001;
Watkins et al., 1998). Several
reports have suggested that the failure to exclude children with silent
infarcts from studies is the primary factor responsible for data indicating
cognitive deficits in children with SCD who do not have a history of overt
stroke (Craft et al., 1993;
White & DeBaun, 1998). The
impact of using MRI versus neurologic history as a criterion has not been
evaluated empirically. More important, the issue of whether children who are
free from silent cerebral infarcts show cognitive deficits is an unresolved
issue.
Finally, there has been mixed evidence as to whether any cognitive effects
of sickle cell disease become worse with age. Several reports have indicated
that older children with SCD show worse cognitive performance than younger
children with SCD (Brown et al.,
1993; Fowler et al.,
1988; Wang et al.,
2001), whereas other reports have specifically examined for age
effects but have not found this pattern
(Goonan et al., 1994;
Noll et al., 2001;
Steen, Xiong, Mulhern, Langston, &
Wang, 1999; Swift et al.,
1989; Wasserman, Wilimas,
Fairclough, Mulhern, & Wang, 1991). Understanding whether any
cognitive effects of SCD progress with age is important for identifying the
responsible mechanisms and planning appropriate monitoring and
interventions.
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Method |
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Identification of Prior Studies
Literature searches were conducted using MEDLINE (years 1975-2001), PsycInfo (years 1887-2001), and Dissertation Abstracts (years 1861-2001) databases using the key words "sickle cell" paired with each of the following terms: "cognitive," "neuropsychologic," "IQ," and "intelligence." Publications that included cognitive testing of a group of children with SCD but no history of stroke and a comparison group without SCD were identified. The references of each article identified were also reviewed to seek additional articles that might meet the criteria for inclusion. The following data were extracted from each study: (1) the inclusion and exclusionary criteria for each study group, (2) the number of participants and ages of each group, (3) the sickle cell disease subtypes included in the study, (4) the type of cognitive measures used and any conceptual or factorial grouping of the tests, (5) additional clinical and psychosocial data reported (e.g., hemoglobin levels, socioeconomic status [SES] variables), (6) mean and standard deviation values for each group on cognitive test variables, and (7) the presence/absence and direction of any statistical effects related to the cognitive variables. An overview of the studies included in this report is found in Table I. Most studies included only children with the HbSS subtype. For those studies with mixed subtypes within the SCD group, HbSS was the predominant subtype in the study. Only one report provided cognitive data separately for the hemoglobin type SC (HbSC) group (Midence, Graham, Acheampong, & Okuyiga, 1996
), and the sample size did not allow for a meaningful
test of potential differences between subtypes.
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Previously Unreported Data
Three additional sources of data were used for this research. Two previous
studies of cognitive functioning in children with SCD had reported data on
subtests of IQ measures but did not report the data in terms of IQ scores. In
both of these two data sets, estimated IQ scores based on short forms of the
intelligence test measures were computed
(Sattler, 1988). The first was
a report by Craft et al.
(1993) that used subtests of
the Wechsler Intelligence Scale, Revised (WISC-R;
Wechsler, 1974). The second
source was a data set that included subtests of the Differential Abilities
Scale (Elliot, 1984),
administered to 25 children with sickle cell disease but no cerebral infarcts
on MRI (Schatz, 1997) and 17
sibling controls without SCD (Schatz et
al., 1999). The SCD and sibling groups were closely matched in age
(12.2 vs. 12.1 years, respectively), grade (5.8 vs. 5.6, respectively), and
SES based on the Hollingshead Two-Factor Index of Social Position
(Hollingshead scores of 61.0 vs. 57.6, respectively, or "upper lower
class" according to the descriptive labels for the measure).
The third source was data from the standardization sample of the Wechsler
Intelligence Test for Children-Process Instrument (WISC-PI;
Kaplan, Fein, Kramer, Delis, & Moris,
1999). The WISC-PI standardization sample included a national
sample of children that had concurrently been administered the WISC-PI and a
seven-subtest version of the WISC-III. This allowed for computing a prorated
Verbal IQ (Information, Vocabulary, Arithmetic subtests), Performance IQ
(Picture Completion, Coding, Block Design subtests), and Full Scale IQ. The
Digit Span subtest was also administered to this sample. Data on
race/ethnicity, age, and maternal education were available that allowed for
extracting a demographically matched sample to children participating in the
CSSCD (see Table II;
Armstrong et al., 1996;
Wang et al., 2001). For the
WISC-III PI control group, means (standard deviations) were a Verbal IQ of
91.1 (15.6), Performance IQ of 93.1 (19.7), and Full Scale IQ of 91.8 (16.5).
The control group also showed a Digit Span scaled score of 10.0 (3.7) and a
Coding scaled score of 9.5 (4.2).
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Results |
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IQ Scores Across All Studies
Mean IQ scores were weighted according to the number of participants for each study. Cohen's d was computed as a measure of effect size (Cohen, 1988
). As shown in
Table III, the overall
difference in scores between children with SCD and comparison children was 4.3
standard score points with an effect size of d = -0.313,
t(1075) = -5.02, p < .01. These data are based on 631
children with SCD and 446 comparison children. According to conventional
definitions, this is a small effect size (r = .15;
Cohen, 1988). There was a
relationship between the effect size and the IQ score for the group with SCD
(r[16] = .54, p < .05). This relationship was not present
for the IQ score of the control groups (r[16] = -.18, ns).
There was no relationship between effect size and the number of SCD cases
(r[16] = .28, ns) or controls (r[16] = .02,
ns) or the year of publication (r[16] = .13,
ns).
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IQ Versus More Specific Cognitive Areas
Among those studies examining differences in both IQ and specific cognitive
domains, 7 of the 14 studies (50%) found differences in IQ, and 10 of 14
studies (71%) found differences in specific cognitive areas as measured by
either specific tests or domain scores (see
Table IV). Among the 10 studies
reporting specific areas of cognitive deficits, 8 of the 10 showed deficits on
tests or factors that could be broadly defined as measures of attention and
executive skills, such as the Coding and Digit Span subtests of the Wechsler
scales, or the Matching Familiar Figures test. Four of ten studies found
differences on measures of verbal or language functions, and 3 of 10 found
differences on measures of memory functions. Outcomes among the studies that
examined both IQ and specific areas of cognitive ability appeared to be
related to sample size. Studies reporting null results had a mean of 23.5
children with SCD (SD = 8.1) and 18.5 controls (SD = 7.3),
whereas those with statistical differences had a mean of 51.7 children with
SCD (SD = 36.2) and 33.6 controls (SD = 20.6). Thus, the
studies with null results had significantly fewer participants (t[12]
= -2.47, p < .05).
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For each study that looked at specific cognitive areas, power calculations
were made to determine the effect size the study was capable of detecting
(Cohen, 1988). Power was set at
.80 and alpha was set at .05 for the calculations. For the studies that found
null results, the mean effect size they were capable of detecting was
r = .43 (SD = .05). For the studies that found significant
differences, the mean effect size they were capable of detecting was
r = .34 (SD = .10). From these calculations, it is estimated
that the effect size of the specific cognitive deficits likely is of a medium
size (r value between .34 and .43).
Choice of Comparison Group
For studies using a demographically matched control group, the mean
(SD) IQ score was 90.6 (14.0), compared with 85.3 (13.1) for children
with SCD. This is a raw score difference of 5.3 points with an effect size of
d = -0.396, t(452) = -4.19, p < .01. For studies
using a sibling comparison group, the mean IQ score was 90.7 (13.0), compared
with 87.2 (14.2) for children with SCD. This is a raw score difference of 3.5
points with an effect size of d = -0.253, t(621) = -3.08,
p < .01. The difference in effect size between studies using
demographically matched controls versus those using siblings controls was not
statistically significant (z = 1.15, ns).
Exclusion of Silent Infarct Cases
For studies using neurologic history alone to exclude for cerebral
infarction, the mean IQ score was 90.2 (12.7) for controls and 87.1 (13.1) for
children with SCD. This is a raw score difference of 3.1 points with an effect
size of d = -.239, t(570) = -2.81, p < .01. For
studies using MRI to exclude for cerebral infarction, the mean IQ score was
91.2 (14.3), compared with 85.6 (14.4) for children with SCD. This is a raw
score difference of 5.6 points with an effect size of d = -0.388,
t(503) = -4.24, p < .01. The size of the discrepancy did
not differ according to use of MRI (z = 1.18, ns). The
interaction between choice of control group and use of MRI was also examined.
The most methodologically rigorous method could be considered the use of
sibling controls with MRI used to exclude silent infarct cases
(White & DeBaun, 1998).
For these choices in method there was a 5-point difference in IQ scores (91.7
vs. 86.7) between children with SCD and siblings, d = 0.356,
t(294) = 3.00, p < .01. The result using this preferred
methodology is highly similar to the effect size across all studies
(z = 0.32, ns). When comparing among these methodological
choices, the size of the IQ difference was not found to differ between any of
the combinations of methodological choices.
IQ Discrepancy by Age of Sample
The magnitude of IQ difference was evaluated according to the mean age of
the sample (see Table I).
Studies were grouped according to those with mean ages of 9 to 10 years
(n = 5), 10 to 11 years (n = 5), or 11 to 13 years
(n = 5). This grouping indicated that the difference in raw IQ points
and effect sizes increased as as the samples aged (see
Table V). For the studies with
a mean age of 9 to 10 years, there was no significant IQ difference between
children with SCD and comparison children (t[264] = 0.50,
ns). There was a significant IQ difference for samples with a mean
age of 10 to 11 years (t[303] = -2.90, p < .01) and 11 to
13 years (t[384] = -5.44, p < .01). A comparison of the
effect sizes for at each age grouping showed that the effect size for the 9-
to 10-year-old samples was smaller than for the 11- to 13-year-old samples
(z = -2.93, p < .01). There was a trend toward a smaller
difference between the 9- to 10-year-old samples than the 10- to 11-year-old
samples (z = -1.60, p < .06). The effect sizes for 10- to
11-year-old samples did not differ from the 11- to 13-year-old samples
(z = -1.30, ns).
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Discussion |
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The introduction outlined five questions that could be addressed by this meta-analysis. The following discussion clarifies how the data inform these questions and provides suggestions for research and clinical management of SCD.
Effect Size for Cognitive Functioning and Choice of Outcome
Measures
This meta-analysis indicates at least a small decrement in cognitive
functioning in children with SCD who are free from cerebral infarcts. The
analyses indicated approximately a 4- to 5-point decrement on IQ measures
compared with control groups. Including only studies with more stringent
methods (e.g., use of MRI) did not diminish the size of this IQ difference.
The effect size for IQ scores is small but within the range of other disorders
with known neuropsychological effects. The general cognitive deficit in SCD is
smaller than the IQ changes reported with acute lymphoblastic leukemia treated
with cranial radiation therapy (10 points on average;
Cousens, Waters, Said, & Stevens,
1988) or brain tumors in children treated with radiation therapy
(12-14 points on average; Mulhern,
Hancock, Fairclough, & Kun, 1992). The general cognitive
decrement with SCD, however, is comparable to those reported for patients with
early and continuously treated phenylketonuria (7 points on average;
Burgard, 2000) and long-term
survivors of bacterial meningitis (6 points on average;
Grimwood et al., 1995). In
addition, the IQ discrepancy between children with SCD that have silent
cerebral infarcts compared with those that have normal MRI is approximately
4-7 points (Armstrong et al.,
1996; Bernaudin et al.,
2000; Wang et al.,
2001).
The primary finding of a small decrement on IQ measures may also underestimate the meaningfulness of the cognitive effect. Measures of specific cognitive abilities appear to be more sensitive to the cognitive effects than IQ measures and likely represent a medium effect on cognition. It is possible, therefore, that a specific cognitive domain shows a larger effect that is attenuated when global measures are computed. There was some variability in the outcomes for tests of specific cognitive abilities, but overall the general domain of attention and executive functions seems most notably affected. These data indicate the importance of assessing specific cognitive domains when evaluating children with SCD. Outcome studies examining cognitive functioning in this population should also include measures of specific cognitive domains rather than relying solely on IQ scores.
Choice of Control Group
The use of siblings as a comparison group has been described as the
preferable control group for studying the cognitive effects of SCD
(White & DeBaun, 1998).
The data from this study indicate that, overall, studies using demographically
matched peers as a control group showed approximately a 2-point greater IQ
discrepancy than studies that used a sibling control group. This difference
was a very small effect and did not reach statistical significance with a
large number of cases and controls. There is no evidence from this empirical
review that the choice of control groups poses a meaningful bias in outcomes
for general cognitive functioning. In planning future studies, researchers
need to attend closely to the qualities of their particular sample of
comparison children, but either siblings or nonsibling peers are appropriate
choices. Researchers may consider which matching variables are most important
for their research question in choosing between these options. For example, if
developmental processes were judged as a key factor for a study, more precise
age matches could be achieved with a nonsibling case-control design; if family
environment or genetics were judged a more critical factor, a sibling control
group might be more appropriate.
There is some indication that selection of the SCD cases is a more important factor for study designs. The discrepancy between cases and controls on IQ measures was strongly related to the IQ scores for the SCD cases, but not for the controls. This may indicate a sampling bias in some of these studies. For example, researchers may have unknowingly overrecruited children with SCD who have particularly poor cognitive functioning. Recruiting a representative sample of children with SCD is likely a much more robust factor for study outcomes than the choice of siblings versus demographically matched peers.
Ruling Out Cerebral Infarction
The use of neurologic history alone to exclude children with cerebral
infarction has been a common approach in the study of cognition in SCD. These
analyses indicate that the use of MRI to exclude cases with silent infarcts
has no significant effect on the size of group IQ differences. For the age
range of children included in most studies, one would expect approximately 15%
of children with normal neurologic history to have silent cerebral infarction
and that these children would show approximately a 4-7-point decline on IQ
measures (Armstrong et al.,
1996; Bernaudin et al.,
2000). Applying these population estimates to a group of children
with SCD, one would expect the inclusion of silent infarct cases to decrease
the SCD group IQ scores by approximately 1 standard score point at most. Thus,
the use of neurologic history rather than MRI in prior studies likely had a
minimal impact on the overall group difference in IQ scores.
The greater source of error associated with failing to exclude silent
cerebral infarction may be in understanding the cause of cognitive deficits in
children free from cerebral infarcts. It is possible that the inclusion of
silent infarct cases obscures potentially meaningful relationships within the
group of children with SCD. For example, in two studies using MRI to exclude
cases with silent infarcts, a moderate to large relationship was found between
hematocrit and cognitive functioning
(Bernaudin et al., 2000;
Steen et al., 1999). In
studies using neurologic history alone, the relationship between these
variables has been smaller in size or not statistically significant
(Brown et al., 1993;
Fowler et al., 1988;
Swift et al., 1989).
Age-Related Changes in Cognitive Functioning
The analysis of age effects in this study is consistent with data from the
recent CSSCD report suggesting declines in cognitive functioning with
increasing age (Wang et al.,
2001). Although this meta-analysis has a larger sample size than
prior reports, it examined age effects in a cross-sectional manner. This
approach is subject to many potential confounds such as cohort effects across
studies or sampling biases of studies. The averaging of data across multiple
studies for each age group may not eliminate these confounds. Thus, this
finding of age effects should be viewed as only suggestive. Prospective study
of children with SCD and a control group is needed to clearly establish the
age effects found in this and other reports. In addition, the precise
relationship between age and cognitive decrements is not clear. Most study
samples have been predominantly composed of children between approximately 7
and 12 years of age. Expanding the age range to understand cognitive
functioning in preschool children and adolescents is needed. Notably, only one
study identified focused on adolescents, and no studies examined cognitive
functioning in preschool children or adults.
The indication of possible age effects over middle childhood suggests this may be an important period for routine monitoring of cognitive functioning. Baseline assessments of cognitive functioning are typically not conducted in routine clinical care of sickle cell disease. This practice may limit the detection of subtle cognitive effects. If a child with SCD shows declines in academic performance in school, a baseline assessment conducted at a younger age would allow for a more informed judgment about possible cognitive declines.
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Conclusions |
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There are at least two remaining questions related to the data in this report. First, it is not yet clear what is causing these cognitive effects in SCD. A number of potential physiological effects on brain function have been described (Brown et al., 1993
).
These potential mechanisms include an accumulation of microinfarcts that may
not be visible on standard imaging exams, chronic brain hypoxia related to
severe anemia, acute hypoxia related to physical complications such as acute
chest syndrome, and nutritional deficiencies related to high metabolic demands
(Brown et al., 1993). Among
these direct effects, some data have supported the presence of hypoxic changes
in brain tissue for children with SCD (Steen et al.,
1998,
1999).
As an alternative to more direct effects of SCD on brain functioning,
indirect effects related to social or environmental disadvantages (e.g.,
decreased learning opportunities, increased physical limitations from chronic
illness) have been cited as potential causes (e.g.,
Brown et al., 1993;
Wang et al., 2001). Indirect
effects related to social and environmental disadvantages may also be relevant
for understanding a broader range of impediments to cognitive development in
this population. For example, the demographically matched comparison groups in
this report had a grand mean IQ score of 90.7, which falls below the
population mean. Both children with SCD and the comparison group likely share
a higher number of environmental risk factors than the general population.
Developmental outcomes for children with biological risk factors (e.g.,
preterm birth, brain insults) have been shown to be more dependent on the
quality of the social environment than for children without these biological
factors (Greenberg & Crnic,
1988; Landry, Smith, Miller-Loncar, & Swank, 1997; Smith et
al., 1996; Taylor et al.,
2002). Thus, both the social context of a child with SCD and the
interaction of that context with the disease may be critical to developmental
outcomes.
More research studies are needed using methods that allow for strong causal inferences. Most studies of cognition in sickle cell disease have been cross-sectional designs. Greater use of longitudinal and experimental designs is needed. Research that manipulates potential causal factors is needed to identify effective preventative and remedial interventions. For example, interventions that reduce anemia levels (e.g., effects of blood transfusion or hydroxyurea therapy) could be useful manipulations to better show how anemia or other physical factors relate to cognitive outcomes. The study of indirect effects could be enhanced by proposing more precise models of the causal route and using path modeling rather than the more exploratory designs that have been used to date. In addition, there have been few attempts to simultaneously evaluate the relative contribution of different causes. Variables such as hematocrit levels and frequency of illness may be related, which creates confounds if these variables are examined in isolation.
The second remaining issue is the functional meaning of these cognitive
effects. We do not know if the cognitive effects are significant contributors
to problems in school functioning, vocational success, or other aspects of
quality of life. It is difficult to determine the need for intervention
without such data. Studying the effects of hydroxyurea therapy on cognitive
functioning has been proposed as one potential treatment for reducing
cognitive effects (Bernaudin et al.,
2000). Although this treatment warrants investigation, determining
the risk-to-benefit ratio for any intervention targeting cognitive functioning
should examine both cognitive test scores and functional outcome measures.
Received February 3, 2001; revision received February 11, 2002; accepted March 29, 2002
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