Frank Jenkins

The Interactions of Vocabulary, Phonemic Awareness, Decoding, and Reading Comprehension

ABSTRACT The authors used data from a large, national sample to examine the interaction of various literacy measures among young children with disabilities. Using structural equation modeling, they examined the relationships among measures of phonemic awareness, decoding, vocabulary, and reading comprehension. Child and family factors, including sex, severity of disability, race/ethnicity, household income, and mothers education were used as covariates. The model supported the notion of 2 unique paths to reading comprehension, one through decoding and a second through vocabulary.

Home Literacy Environment and Its Role in the Achievement of Preschoolers With Disabilities

The purpose of this study was to examine the relationship between the home literacy environment of a nationally representative sample of preschoolers with disabilities and their subsequent receptive vocabulary and reading comprehension skills using data from the Pre-Elementary Education Longitudinal Study. Results from linear regressions indicated that only a small amount of the total variance in children’s receptive language and passage comprehension skills was explained by the home literacy environment. However, the home literacy environment of 3- to 5-year-olds with less severe disabilities was a significant predictor of scores on a test of receptive vocabulary and reading comprehension in later years. The home literacy environment was not a significant predictor of receptive vocabulary or reading comprehension for children with moderate to severe disabilities.

Novel Methods and Data Sources for Surveillance of State-Level Diabetes and Prediabetes Prevalence

States bear substantial responsibility for addressing the rising rates of diabetes and prediabetes in the United States. However, accurate state-level estimates of diabetes and prediabetes prevalence that include undiagnosed cases have been impossible to produce with traditional sources of state-level data. Various new and nontraditional sources for estimating state-level prevalence are now available. These include surveys with expanded samples that can support state-level estimation in some states and administrative and clinical data from insurance claims and electronic health records. These sources pose methodologic challenges because they typically cover partial, sometimes nonrandom subpopulations; they do not always use the same measurements for all individuals; and they use different and limited sets of variables for case finding and adjustment. We present an approach for adjusting new and nontraditional data sources for diabetes surveillance that addresses these limitations, and we present the results of our proposed approach for 2 states (Alabama and California) as a proof of concept. The method reweights surveys and other data sources with population undercoverage to make them more representative of state populations, and it adjusts for nonrandom use of laboratory testing in clinically generated data sets. These enhanced diabetes and prediabetes prevalence estimates can be used to better understand the total burden of diabetes and prediabetes at the state level and to guide policies and programs designed to prevent and control these chronic diseases.

State-level estimation of diabetes and prediabetes prevalence: Combining national and local survey data and clinical data.

Many statisticians and policy researchers are interested in using data generated through the normal delivery of health care services, rather than carefully designed and implemented population-representative surveys, to estimate disease prevalence. These larger databases allow for the estimation of smaller geographies, for example, states, at potentially lower expense. However, these health care records frequently do not cover all of the population of interest and may not collect some covariates that are important for accurate estimation. In a recent paper, the authors have described how to adjust for the incomplete coverage of administrative claims data and electronic health records at the state or local level. This article illustrates how to adjust and combine multiple data sets, namely, national surveys, state-level surveys, claims data, and electronic health record data, to improve estimates of diabetes and prediabetes prevalence, along with the estimates of the methods accuracy. We demonstrate and validate the method using data from three jurisdictions (Alabama, California, and New York City). This method can be applied more generally to other areas and other data sources.