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Background: Cognitive impairment without dementia is associated with increased risk for disability, increased health care costs, and progression to dementia. There are no population-based prevalence estimates of this condition in the United States. Objective: To estimate the prevalence of cognitive impairment without dementia in the United States and determine longitudinal cognitive and mortality outcomes. Design: Longitudinal study from July 2001 to March 2005. Setting: In-home assessment for cognitive impairment. Participants: Participants in ADAMS (Aging, Demographics, and Memory Study) who were age 71 years or older drawn from the nationally representative HRS (Health and Retirement Study). Of 1770 selected individuals, 856 completed initial assessment, and of 241 selected individuals, 180 completed 16- to 18-month follow-up assessment. Measurements: Assessments, including neuropsychological testing, neurologic examination, and clinical and medical history, were used to assign a diagnosis of normal cognition, cognitive impairment without dementia, or dementia. National prevalence rates were estimated by using a population-weighted sample. Results: In 2002, an estimated 5.4 million people (22.2%) in the United States age 71 years or older had cognitive impairment without dementia. Prominent subtypes included prodromal Alzheimer disease (8.2%) and cerebrovascular disease (5.7%). Among participants who completed follow-up assessments, 11.7% with cognitive impairment without dementia progressed to dementia annually, whereas those with subtypes of prodromal Alzheimer disease and stroke progressed at annual rates of 17% to 20%. The annual death rate was 8% among those with cognitive impairment without dementia and almost 15% among those with cognitive impairment due to medical conditions. Limitations: Only 56% of the nondeceased target sample completed the initial assessment. Population sampling weights were derived to adjust for at least some of the potential bias due to nonresponse and attrition. Conclusion: Cognitive impairment without dementia is more prevalent in the United States than dementia, and its subtypes vary in prevalence and outcomes.

Recently, researchers have proposed a variety of new methods for employing exploratory data mining algorithms to address missing data. Two promising classes of missing data methods take advantage of classification and regression trees and random forests. A first method uses the predicted probabilities of response (vs. non-response) generated by a CART analysis to create inverse probability weights. This method has been shown to perform well in prior simulations when nonresponse was generated by tree-based structures, even under low sample sizes. A second method uses the values falling in terminal nodes of CART trees to generate multiple imputations. In prior studies, these methods performed well at estimating main effects and interactions in regression models when sample sizes were large ( N = 1000 ), but their performance was not evaluated under small sample conditions. In the present research, we assess the performance of CART-based weights and CART-based imputations under low sample sizes ( N = 125 or 250) and nonnormality when missing data are generated by smooth functions (linear, quadratic, cubic, interactive). Results suggest that random forest weights excel under low sample sizes, regardless of nonnormality, whereas CART multiple imputation is more efficient with larger samples ( N = 500 or 1000). [ABSTRACT FROM AUTHOR]

Background Educational policy makers and test critics often assert that standardized test scores are strongly influenced by factors beyond individual differences in academic achievement such as family income and wealth. Unfortunately, few empirical studies consider the simultaneous and related influences of family income, parental education, and high school achievement on college admissions test scores.Focus Of Study This research was animated by the nagging question of the association of family income with SAT performance. For example, is the relationship between family income and SAT performance non-linear? Does the relationship differ markedly by race? More importantly, how strong are the effects of poverty on SAT performance?Research Design This study is a secondary analysis of a large national sample of Black and White college-bound high school students who took the SAT in 2003 (N = 781,437).Data Collection and Analysis Employing data from the College Board's Student Descriptive Questionnaire, this study used structural equation modeling (SEM) to estimate the effects of family income on SAT scores for Black and White examinees accounting for the simultaneous effects of parental education and high school achievement.Findings/Results Results suggest the effects of family income on SAT scores, though relatively modest in contrasts to high school achievement, are substantial, non-linear, and nearly twice as large for Black students. Moreover, the unstandardized direct effect of high school achievement on SAT performance is not enough to address the substantial effects of poverty for Black students.Conclusions/Recommendations The findings are discussed with respect to social inequality and educational opportunity in college admissions.

Abstract:In this paper, we model gender differences in cognitive ability in China using a new sample of middle-aged and older Chinese respondents. Modeled after the American Health and Retirement Study (HRS), the CHARLS Pilot survey respondents are 45 years and older in two quite distinct provinces—Zhejiang, a high-growth industrialized province on the East Coast, and Gansu, a largely agricultural and poor province in the West—in a sense new and old China. Our cognition measures proxy for two different dimensions of adult cognition—episodic memory and intact mental status. On both measures, Chinese women score much lower than do Chinese men, a gender difference that grows among older Chinese cohorts. We relate both these cognition scores to schooling, urban residence, family and community levels of economic resources, and height. We find that cognition is more closely related to mean community resources than to family resources, especially for women, suggesting that in traditional poor Chinese communities there are strong economic incentives to favor boys at the expense of girls. We also find that these gender differences in cognitive ability have been steadily decreasing across birth cohorts as the economy of China grew rapidly. Among cohorts of young adults in China, there is no longer any gender disparity in cognitive ability. This parallels the situation in the United States where cognition scores of adult women actually exceed those of adult men.

AbstractObjective:To develop and validate a latent model of health outcomes among persons with spinal cord injury.Methods:Survey data were collected at a large specialty hospital in the southeastern USA from 1,388 adult participants with traumatic spinal cord injury of at least 1 year's duration. Multiple indicators of health outcomes were used, including general health ratings, days adversely affected by poor health and poor mental health, treatments and hospitalizations, depressive symptoms, symptoms of illness or infection (eg, sweats, chills, fever), and multiple individual conditions (eg, pressure ulcers, subsequent injuries, fractures, contractures).Results:We performed exploratory factor analysis on half of the sample and confirmatory factor analysis on the other. A 6-factor solution was the best overall solution, because there was an excellent fit with the exploratory factor analysis (root mean square error of approximation = 0.042) and acceptable fit with the confirmatory factor analysis (root mean square error of approximation = 0.065). Four of the factors were types of secondary conditions, including symptoms of illness or infection, orthopedic conditions, pressure ulcers, and subsequent injuries. The 2 remaining factors reflected global health and treatment. Gender, race-ethnicity, age, injury severity, and years of education were all significantly related to at least 1 factor dimension, indicating variations in health outcomes related to these characteristics.Conclusion:Identification of the 6 factors represents an improvement over the utilization of multiple individual indicators, because composite scores generated from multiple individual indicators provide more informative and stable outcome scores than utilization of single indicators.J Spinal Cord Med. February 2010; 33(1): 22–32

AbstractBackground/Objective:To develop a latent behavioral model by identifying and confirming the factor structure of health behaviors of people with spinal cord injury (SCI) and their relationships with biographic, injury, and educational characteristics.Research Design:Survey data were collected from 1,388 adults with traumatic SCI of at least 1 year duration.Main Outcome Measures:Selection of health behaviors was based on a bidimensional behavioral risk model. Behaviors were measured by core item sets from the Behavioral Risk Factor Surveillance System and supplemented by an alcohol screening measure, select fitness proxies, and the SCI Health Survey.Results:Latent variable structural equation modeling was used to identify underlying factors and their relationship with participant characteristics. Seven specific factors were identified by exploratory factor analysis and were cross-validated using confirmatory factor analysis. They included: (a) healthy nutrition, (b) unhealthy nutrition, (c) fitness, (d) smoking, (e) alcohol use, (f) psychotropic prescription medications, and (g) SCI healthy activities. Two higher-order dimensions were also identified, including a risk dimension (b, d, e) and a protective dimension (a, c, g). Participant characteristics were associated with the domains. For instance, participants with the most severe injuries scored lower on smoking and alcohol but higher on psychotropic medications; age was positively correlated with healthy nutrition and negatively correlated with alcohol and tobacco use but also negatively correlated with fitness.Conclusion:Behaviors can be meaningfully combined into underlying dimensions to more efficiently use them as predictors of secondary conditions.

OBJECTIVE To examine neuropsychological measures among normal individuals that predict time to subsequent cognitive decline. DESIGN Cognitive performance, as measured by 6 neuropsychological tests, was examined at baseline. Participants were followed up for approximately 5 years. Cox proportional hazards models were used to evaluate the neuropsychological measures at baseline that predicted time to progression from normal cognition to mild impairment. Comparable data also examined time to progression from mild impairment to a diagnosis of Alzheimer disease. SETTING Community volunteer-based sample examined at a medical institution. PARTICIPANTS One hundred and seven individuals who were cognitively normal and 235 individuals with mild cognitive impairment at baseline. MAIN OUTCOME MEASURES Time to progression from normal cognition to mild impairment and time to progression from mild impairment to a diagnosis of Alzheimer disease. RESULTS The risk of progressing from normal to mild impairment was considerably greater among those with lower scores on tests of episodic memory (eg, hazard ratio for a 1-SD decrease in the California Verbal Learning Test, 0.55; P<.001). Normal individuals who carried at least 1 copy of the apolipoprotein E ε2 allele were less likely to develop cognitive impairments over time than individuals with no ε2 allele (hazard ratio for presence of allele, 0.13; P = .006). Measures of both episodic memory and executive function were significant predictors of time to progression from mild impairment to a clinical diagnosis of Alzheimer disease (eg, hazard ratio for a 1-SD decrease in California Verbal Learning Test score, 0.67; P = .005; hazard ratio for a 1-SD increase in the time to complete part B of the Trail Making test, 1.40; P = .007). Among individuals with mild impairments, the apolipoprotein E ε4 allele increased risk for Alzheimer disease in a dose-dependent manner; however, this effect was not significant within the context of multivariable models. CONCLUSIONS Episodic memory performance among normal individuals predicts time to progression to mild impairment while apolipoprotein E ε2 status is associated with lower risk of cognitive decline among normal individuals. Tests of both episodic memory and executive function are predictors of time to progression from mild impairment to a clinical diagnosis of Alzheimer disease.Arch Neurol. 2007;64:862-871 --

AbstractIn a recent article McArdle and Prescott (2005) showed how simultaneous estimation of the bio-metric parameters can be easily programmed using current mixed-effects modeling programs (e.g., SAS PROC MIXED). This article extends these concepts to deal with mixed-effect modeling of longitudinal twin data. The biometric basis of a polynomial growth curve model was used by Vandenberg and Falkner (1965) and this general class of longitudinal models was represented in structural equation form as a latent curve model by McArdle (1986). The new mixed-effects modeling approach presented here makes it easy to analyze longitudinal growth-decline models with biometric components based on standard maximum likelihood estimation and standard indices of goodness-of-fit (i.e., ?2, df, ?a). The validity of this approach is first checked by the creation of simulated longitudinal twin data followed by numerical analysis using different computer programs (i.e., Mplus, Mx, MIXED, NLMIXED). The practical utility of this approach is examined through the application of these techniques to real longitudinal data from the Swedish Adoption/Twin Study of Aging (Pedersen et al., 2002). This approach generally allows researchers to explore the genetic and nongenetic basis of the latent status and latent changes in longitudinal scores in the absence of measurement error. These results show the mixed-effects approach easily accounts for complex patterns of incomplete longitudinal or twin pair data. The results also show this approach easily allows a variety of complex latent basis curves, such as the use of age-at-testing instead of wave-of-testing. Natural extensions of this mixed-effects longitudinal approach include more intensive studies of the available data, the analysis of categorical longitudinal data, and mixtures of latent growth-survival/ frailty models.

We use a statistical model that combines longitudinal and survival analyses to estimate the influence of level and change in cognition on age at death in old and very old individuals. Data are from the Berlin Aging Study, in which an initial sample of 516 elderly individuals with an age range of 70 to 103 years was assessed up to 11 times across a period of up to 13 years. Four cognitive ability domains were assessed by two variables each: perceptual speed (Digit Letter and Identical Pictures), episodic memory (Paired Associates and Memory for Text), fluency (Categories and Word Beginnings), and verbal knowledge (Vocabulary and Spot-a-Word). Longitudinal models on cognition controlled for dementia diagnosis and retest effects, while survival models on age at death controlled for age, sex, socioeconomic status, sensory and motor performance, and broad personality characteristics. Results indicate: (1) Individual differences in the level of and in the linear change in performance are present for all cognitive variables; (2) when analyzed independently of cognitive performance, all covariates, except broad personality factors, predict survival; (3) when cognitive performance is accounted for, age, sex, and motor performance do predict survival, while socioeconomic status and broad personality factors do not, and sensory performance does only at times; (4) when cognitive variables are analyzed independently of each other, both level and change in speed and fluency, as well as level in memory and knowledge predict survival; (5) when all cognitive variables are analyzed simultaneously using a two-stage procedure, none of them is significantly associated to survival. In agreement with others, our findings suggest that survival is related to cognitive development in old and very old age in a relatively global, rather than ability-specific, manner.

AbstractWe describe methods to fit structured latent growth curves to data from MZ and DZ twins. The well-known Gompertz, logistic and exponential curves may be written as a function of three components ? asymptote, initial value, and rate of change. These components are allowed to vary and covary within individuals in a structured latent growth model. Such models are highly economical, requiring a small number of parameters to describe covariation across many occasions of measurement. We extend these methods to analyse longitudinal data from MZ and DZ twins and focus on the estimation of genetic and environmental variation and covariation in each of the asymptote, initial and rate of growth factors. For illustration, the models are fitted to longitudinal Bayley Infant Mental Development Scale data published by McArdle (1986). In these data, all three components of growth appear strongly familial with the majority of variance associated with the shared environment; differences between the models were not great. Occasion-specific residual factors not associated with the curve components account for approximately 40% of variance of which a significant proportion is additive genetic. Though the growth curve model fit less well than some others, they make restrictive, falsifiable predictions about the mean, variance and twin covariance of other (not yet measured) occasions of measurement. Twin Research (2000) 3, 165?177.

AbstractThe estimation of genetic effects from twin studies usually relies upon the equal environment assumption ? that monozygous (MZ) and dizygous (DZ) twin pairs experience equal similarity of their environments from prenatal experiences through adulthood. However, the sharing of a chorion may make a subset of identical twins more similar, or in some cases, more different, than twins that do not share a chorion. Recent studies suggest monochorionic MZ twins resemble one another more than dichorionic MZ twins in cognitive abilities, personality, and risk for psychiatric disorder. To the extent that prenatal environment affects these characteristics, the traditional twin method will yield biased estimates of genetic and environmental influences. We develop models for quantifying this bias and estimating the influence of chorion type on estimates of heritability.