Developing predictive models of health literacy.

IntroductionLow health literacy (LHL) remains a formidable barrier to improving health care quality and outcomes. Given the lack of precision of single demographic characteristics to predict health literacy, and the administrative burden and inability of existing health literacy measures to estimate health literacy at a population level, LHL is largely unaddressed in public health and clinical practice. To help overcome these limitations, we developed two models to estimate health literacy.MethodsWe analyzed data from the 2003 National Assessment of Adult Literacy (NAAL), using linear regression to predict mean health literacy scores and probit regression to predict the probability of an individual having 'above basic' proficiency. Predictors included gender, age, race/ethnicity, educational attainment, poverty status, marital status, language spoken in the home, metropolitan statistical area (MSA) and length of time in U.S.ResultsAll variables except MSA were statistically significant, with lower educational attainment being the strongest predictor. Our linear regression model and the probit model accounted for about 30% and 21% of the variance in health literacy scores, respectively, nearly twice as much as the variance accounted for by either education or poverty alone.ConclusionsMultivariable models permit a more accurate estimation of health literacy than single predictors. Further, such models can be applied to readily available administrative or census data to produce estimates of average health literacy and identify communities that would benefit most from appropriate, targeted interventions in the clinical setting to address poor quality care and outcomes related to LHL

Up for Review

We study whether organizations that reward individual performance should give autonomy or should control how managers evaluate their subordinates. The normal way to establish control is to formalize the evaluations, so that managers cannot choose when and how to evaluate. We argue that organizations face a trade-off because formalization helps reduce biases but also introduces rigidities. Using linked employer-employee data, we study the link between formal performance appraisals and firm financial performance.Jaime Ortega acknowledges financial support from the Spanish Ministry of Economics, Industry and Competitiveness (grants ECO2012-33308 and ECO2015- 2 69615-R). Rocio Bonet acknowledges financial support from the European Commission grant WSCA-Grant Agreement No. 239217-7FP-People-IRG

Developing predictive models of health literacy.

IntroductionLow health literacy (LHL) remains a formidable barrier to improving health care quality and outcomes. Given the lack of precision of single demographic characteristics to predict health literacy, and the administrative burden and inability of existing health literacy measures to estimate health literacy at a population level, LHL is largely unaddressed in public health and clinical practice. To help overcome these limitations, we developed two models to estimate health literacy.MethodsWe analyzed data from the 2003 National Assessment of Adult Literacy (NAAL), using linear regression to predict mean health literacy scores and probit regression to predict the probability of an individual having 'above basic' proficiency. Predictors included gender, age, race/ethnicity, educational attainment, poverty status, marital status, language spoken in the home, metropolitan statistical area (MSA) and length of time in U.S.ResultsAll variables except MSA were statistically significant, with lower educational attainment being the strongest predictor. Our linear regression model and the probit model accounted for about 30% and 21% of the variance in health literacy scores, respectively, nearly twice as much as the variance accounted for by either education or poverty alone.ConclusionsMultivariable models permit a more accurate estimation of health literacy than single predictors. Further, such models can be applied to readily available administrative or census data to produce estimates of average health literacy and identify communities that would benefit most from appropriate, targeted interventions in the clinical setting to address poor quality care and outcomes related to LHL

The Vanderbilt Petexbatun Regional Archaeological Project 1989–1994: Overview, History, and Major Results of a Multidisciplinary Study of the Classic Maya collapse

The background, research design, structure, personnel, and history of investigations of the Vanderbilt Petexbatun Archaeological Project are summarized and critiqued. The major findings of each of the dozen subprojects of this multidisciplinary investigation of Maya civilization in the southwestern Peten region of Guatemala are reviewed. Subproject results include important new evidence on Classic Maya history, warfare, ecology, nutrition, cave ritual, social organization, and trade. These are summarized with particular emphasis on the implications of the Petexbatun findings for theories of the decline of southern lowland Maya civilization at the end of the Classic period