Expressions 1990

https://openspace.dmacc.edu/expressions/1012/thumbnail.jp

Personality factors in the Long Life Family Study

OBJECTIVES. To evaluate personality profiles of Long Life Family Study participants relative to population norms and offspring of centenarians from the New England Centenarian Study. METHOD. Personality domains of agreeableness, conscientiousness, extraversion, neuroticism, and openness were assessed with the NEO Five-Factor Inventory in 4,937 participants from the Long Life Family Study (mean age 70 years). A linear mixed model of age and gender was implemented adjusting for other covariates. RESULTS. A significant age trend was found in all five personality domains. On average, the offspring generation of long-lived families scored low in neuroticism, high in extraversion, and within average values for the other three domains. Older participants tended to score higher in neuroticism and lower in the other domains compared with younger participants, but the estimated scores generally remained within average population values. No significant differences were found between long-lived family members and their spouses. DISCUSSION. Personality factors and more specifically low neuroticism and high extraversion may be important for achieving extreme old age. In addition, personality scores of family members were not significantly different from those of their spouses, suggesting that environmental factors may play a significant role in addition to genetic factors

Implementation and results of an integrated data quality assurance protocol in a randomized controlled trial in Uttar Pradesh, India

Background: There are few published standards or methodological guidelines for integrating Data Quality Assurance (DQA) protocols into large-scale health systems research trials, especially in resource-limited settings. The BetterBirth Trial is a matched-pair, cluster-randomized controlled trial (RCT) of the BetterBirth Program, which seeks to improve quality of facility-based deliveries and reduce 7-day maternal and neonatal mortality and maternal morbidity in Uttar Pradesh, India. In the trial, over 6300 deliveries were observed and over 153,000 mother-baby pairs across 120 study sites were followed to assess health outcomes. We designed and implemented a robust and integrated DQA system to sustain high-quality data throughout the trial. Methods: We designed the Data Quality Monitoring and Improvement System (DQMIS) to reinforce six dimensions of data quality: accuracy, reliability, timeliness, completeness, precision, and integrity. The DQMIS was comprised of five functional components: 1) a monitoring and evaluation team to support the system; 2) a DQA protocol, including data collection audits and targets, rapid data feedback, and supportive supervision; 3) training; 4) standard operating procedures for data collection; and 5) an electronic data collection and reporting system. Routine audits by supervisors included double data entry, simultaneous delivery observations, and review of recorded calls to patients. Data feedback reports identified errors automatically, facilitating supportive supervision through a continuous quality improvement model. Results: The five functional components of the DQMIS successfully reinforced data reliability, timeliness, completeness, precision, and integrity. The DQMIS also resulted in 98.33% accuracy across all data collection activities in the trial. All data collection activities demonstrated improvement in accuracy throughout implementation. Data collectors demonstrated a statistically significant (p = 0.0004) increase in accuracy throughout consecutive audits. The DQMIS was successful, despite an increase from 20 to 130 data collectors. Conclusions: In the absence of widely disseminated data quality methods and standards for large RCT interventions in limited-resource settings, we developed an integrated DQA system, combining auditing, rapid data feedback, and supportive supervision, which ensured high-quality data and could serve as a model for future health systems research trials. Future efforts should focus on standardization of DQA processes for health systems research. Trial Registration ClinicalTrials.gov identifier, NCT02148952. Registered on 13 February 2014

LETTER TO THE EDITOR Mutations in progranulin explain atypical phenotypes with variants in MAPT

doi:10.1093/brain/awl289 Mutations in presenilin-1 (PSEN1) cause autosomal dominant Alzheimer’s disease and mutations in MAPT cause the familial tauopathy Frontotemporal dementia linked to chromosome 17 (FTDP-17). However, there have been reports of mutations in PSEN1 and MAPT associated with cases of FTD with ubiquitin-positive tau-negative inclusion pathology. Here, we demonstrate that the MAPT variants are almost certainly rare benign polymorphisms as all of these cases harbour mutations in Progranulin (PGRN). Mutations in PGRN were recently shown to cause ubiquitin-positive FTDP-17

Implementation and results of an integrated data quality assurance protocol in a randomized controlled trial in Uttar Pradesh, India

This dataset contains data referenced in the publication "Implementation and results of an integrated data quality assurance protocol in a randomized controlled trial in Uttar Pradesh, India"