Exploring Genetic Numeracy Skills in a Sample of U.S. University Students.

Misconceptions concerning numerical genetic risk exist even within educated populations. To more fully characterize and understand the extent of these risk misunderstandings, which have large potential impact on clinical care, we analyzed the responses from 2,576 students enrolled at 2 Southwestern universities using the PGRID tool, a 138-item web-based survey comprising measures of understanding of genetics, genetic disease, and genetic risk. The primary purpose of this study was to characterize the intersection of risk perception and knowledge, termed genetic numeracy (GN). Additionally, we identify sociodemographic factors that might shape varying levels of GN skills within the study sample and explore the impact of GN on genetic testing intentions using both the Marascuilo procedure and logistic regression analysis. Despite having some college coursework or at least one college degree, most respondents lacked high-level aptitude in understanding genetic inheritance risk, especially with respect to recessive disorders. Prior education about genetics and biology, as well as exposure to biomedical models of genetics, was associated with higher GN levels; exposure to popular media models of genetics was inversely associated with higher GN levels. Differing GN levels affects genetic testing intentions. GN will become more relevant as genetic testing is increasingly incorporated into general clinical care.

Explanatory Models of Genetics and Genetic Risk among a Selected Group of Students.

This exploratory qualitative study focuses on how college students conceptualize genetics and genetic risk, concepts essential for genetic literacy (GL) and genetic numeracy (GN), components of overall health literacy (HL). HL is dependent on both the background knowledge and culture of a patient, and lower HL is linked to increased morbidity and mortality for a number of chronic health conditions (e.g., diabetes and cancer). A purposive sample of 86 students from three Southwestern universities participated in eight focus groups. The sample ranged in age from 18 to 54 years, and comprised primarily of female (67.4%), single (74.4%), and non-White (57%) participants, none of whom were genetics/biology majors. A holistic-content approach revealed broad categories concerning participants' explanatory models (EMs) of genetics and genetic risk. Participants' EMs were grounded in highly contextualized narratives that only partially overlapped with biomedical models. While higher education levels should be associated with predominately knowledge-based EM of genetic risk, this study shows that even in well-educated populations cultural factors can dominate. Study findings reveal gaps in how this sample of young adults obtains, processes, and understands genetic/genomic concepts. Future studies should assess how individuals with low GL and GN obtain and process genetics and genetic risk information and incorporate this information into health decision making. Future work should also address the interaction of communication between health educators, providers, and genetic counselors, to increase patient understanding of genetic risk.

A comparison of human and machine translation of health promotion materials for public health practice: time, costs, and quality.

CONTEXT: Most local public health departments serve limited English proficiency groups but lack sufficient resources to translate the health promotion materials that they produce into different languages. Machine translation (MT) with human postediting could fill this gap and work toward decreasing health disparities among non-English speakers. OBJECTIVES: (1) To identify the time and costs associated with human translation (HT) of public health documents, (2) determine the time necessary for human postediting of MT, and (3) compare the quality of postedited MT and HT. DESIGN: A quality comparison of 25 MT and HT documents was performed with public health translators. The public health professionals involved were queried about the workflow, costs, and time for HT of 11 English public health documents over a 20-month period. Three recently translated documents of similar size and topic were then machine translated, the time for human postediting was recorded, and a blind quality analysis was performed. SETTING: Seattle/King County, Washington. PARTICIPANTS: Public health professionals. MAIN OUTCOME MEASURES: (1) Estimated times for various HT tasks; (2) observed postediting times for MT documents; (3) actual costs for HT; and (4) comparison of quality ratings for HT and MT. RESULTS: Human translation via local health department methods took 17 hours to 6 days. While HT postediting words per minute ranged from 1.58 to 5.88, MT plus human postediting words per minute ranged from 10 to 30. The cost of HT ranged from $130 to $1220; MT required no additional costs. A quality comparison by bilingual public health professionals showed that MT and HT were equivalently preferred. CONCLUSIONS: MT with human postediting can reduce the time and costs of translating public health materials while maintaining quality similar to HT. In conjunction with postediting, MT could greatly improve the availability of multilingual public health materials.

Methods for using data abstracted from medical charts to impute longitudinal missing data in a clinical trial.

OBJECTIVE: To describe a method for imputing missing follow-up blood pressure data in a clinical hypertension trial using blood pressures abstracted from medical charts. METHODS: We tested a two-step method. In the first, a longitudinal mixed-effects model was estimated on blood pressures abstracted from medical charts. In the second, the patient-specific fitted values from this model at follow-up were used to impute blood pressures missing at follow-up in the trial. Simulations that imposed alternative missing data mechanisms on observed trial data were used to compare this approach to imputation approaches that do not incorporate data from charts. RESULTS: For data that are missing at random, incorporating the fitted values from chart-based longitudinal models leads to estimates of the trial-based blood pressures that are unbiased and have lower mean squared deviation than do blood pressures imputed without the chart-based data. For data that are missing not at random, incorporating fitted values ameliorates but does not eliminate the inherent missing data bias. CONCLUSIONS: Incorporating chart data into an imputation algorithm via the use of longitudinal mixed-effects model is an efficient way to impute longitudinal data that are missing from a randomized trial.