Translation validation of a new back pain screening questionnaire (the STarT Back Screening Tool) in French

Background: Low back pain (LBP) is a major public health problem and the identification of individuals at risk of persistent LBP poses substantial challenges to clinical management. The STarT Back questionnaire is a validated nine-item patient self-report questionnaire that classifies patients with LBP at low, medium or high-risk of poor prognosis for persistent non-specific LBP. The objective of this study was to translate and cross-culturally adapt the English version of the STarT Back questionnaire into French. Methods: The translation was performed using best practice translation guidelines. The following phases were performed: contact with the STarT Back questionnaire developers, initial translations (English into French), synthesis, back translations, expert committee review, test of the pre-final version on 44 individuals with LBP, final version. Results: The linguistic translation required minor semantic alterations. The participants interviewed indicated that all items of the questionnaire were globally clear and comprehensible. However, 6 subjects (14%) wondered if two questions were related to back pain or general health. After discussion within the expert committee and with the developer of the STarT Back tool, it was decided to modify the questionnaire and to add a reference to back pain in these two questions. Conclusions: The French version of the STarT Back questionnaire has been shown to be comprehensible and adapted to the French speaking general population. Investigations are now required to test the psychometric properties (reliability, internal and external validity, responsiveness) of this translated version of the questionnaire

Genetic Modification of the Association between Peripubertal Dioxin Exposure and Pubertal Onset in a Cohort of Russian Boys

Background: Exposure to dioxins has been associated with delayed pubertal onset in both epidemiologic and animal studies. Whether genetic polymorphisms may modify this association is currently unknown. Identifying such genes could provide insight into mechanistic pathways. This is one of the first studies to assess genetic susceptibility to dioxins. Objectives: We evaluated whether common polymorphisms in genes affecting either molecular responses to dioxin exposure or pubertal onset influence the association between peripubertal serum dioxin concentration and male pubertal onset. Methods: In this prospective cohort of Russian adolescent boys (n = 392), we assessed gene–environment interactions for 337 tagging single-nucleotide polymorphisms (SNPs) from 46 candidate genes and two intergenic regions. Dioxins were measured in the boys’ serum at age 8–9 years. Pubertal onset was based on testicular volume and on genitalia staging. Statistical approaches for controlling for multiple testing were used, both with and without prescreening for marginal genetic associations. Results: After accounting for multiple testing, two tag SNPs in the glucocorticoid receptor (GR/NR3C1) gene and one in the estrogen receptor-α (ESR1) gene were significant (q < 0.2) modifiers of the association between peripubertal serum dioxin concentration and male pubertal onset defined by genitalia staging, although not by testicular volume. The results were sensitive to whether multiple comparison adjustment was applied to all gene–environment tests or only to those with marginal genetic associations. Conclusions: Common genetic polymorphisms in the glucocorticoid receptor and estrogen receptor-α genes may modify the association between peripubertal serum dioxin concentration and pubertal onset. Further studies are warranted to confirm these findings

Serum Concentrations of Organochlorine Pesticides and Growth among Russian Boys

Background: Limited human data suggest an association of organochlorine pesticides (OCPs) with adverse effects on children’s growth

Temporal trends in serum concentrations of polychlorinated dioxins, furans, and PCBs among adult women living in Chapaevsk, Russia: a longitudinal study from 2000 to 2009

<p>Abstract</p> <p>Background</p> <p>The present study assessed the temporal trend in serum concentrations of polychlorinated dibenzo-<it>p</it>-dioxins, dibenzofurans, and biphenyls (PCBs) among residents of a Russian town where levels of these chemicals are elevated due to prior industrial activity.</p> <p>Methods</p> <p>Two serum samples were collected from eight adult women (in 2000 and 2009), and analyzed with gas chromatography-high-resolution mass spectrometry.</p> <p>Results</p> <p>The average total toxic equivalency (TEQ) decreased by 30% (from 36 to 25 pg/g lipid), and the average sum of PCB congeners decreased by 19% (from 291 to 211 ng/g lipid). Total TEQs decreased for seven of the eight women, and the sum of PCBs decreased for six of eight women. During this nine year period, larger decreases in serum TEQs and PCBs were found in women with greater increases in body mass index.</p> <p>Conclusions</p> <p>This study provides suggestive evidence that average serum concentrations of dioxins, furans, and PCBs are decreasing over time among residents of this town.</p

Considerations for pooling real-world data as a comparator cohort to a single arm trial: a simulation study on assessment of heterogeneity

Abstract Background Novel precision medicine therapeutics target increasingly granular, genomically-defined populations. Rare sub-groups make it challenging to study within a clinical trial or single real-world data (RWD) source; therefore, pooling from disparate sources of RWD may be required for feasibility. Heterogeneity assessment for pooled data is particularly complex when contrasting a pooled real-world comparator cohort (rwCC) with a single-arm clinical trial (SAT), because the individual comparisons are not independent as all compare a rwCC to the same SAT. Our objective was to develop a methodological framework for pooling RWD focused on the rwCC use case, and simulate novel approaches of heterogeneity assessment, especially for small datasets. Methods We present a framework with the following steps: pre-specification, assessment of dataset eligibility, and outcome analyses (including assessment of outcome heterogeneity). We then simulated heterogeneity assessments for a binary response outcome in a SAT compared to two rwCCs, using standard methods for meta-analysis, and an Adjusted Cochran’s Q test, and directly comparing the individual participant data (IPD) from the rwCCs. Results We found identical power to detect a true difference for the adjusted Cochran’s Q test and the IPD method, with both approaches superior to a standard Cochran’s Q test. When assessing the impact of heterogeneity in the null scenario of no difference between the SAT and rwCCs, a lack of statistical power led to Type 1 error inflation. Similarly, in the alternative scenario of a true difference between SAT and rwCCs, we found substantial Type 2 error, with underpowered heterogeneity testing leading to underestimation of the treatment effect. Conclusions We developed a methodological framework for pooling RWD sources in the context of designing a rwCC for a SAT. When testing for heterogeneity during this process, the adjusted Cochran’s Q test matches the statistical power of IPD heterogeneity testing. Limitations of quantitative heterogeneity testing in protecting against Type 1 or Type 2 error indicate these tests are best used descriptively, and after careful selection of datasets based on clinical/data considerations. We hope these findings will facilitate the rigorous pooling of RWD to unlock insights to benefit oncology patients

Big Data and Disease Prevention: From Quantified Self to Quantified Communities.

Big data is often discussed in the context of improving medical care, but it also has a less appreciated but equally important role to play in preventing disease. Big data can facilitate action on the modifiable risk factors that contribute to a large fraction of the chronic disease burden, such as physical activity, diet, tobacco use, and exposure to pollution. It can do so by facilitating the discovery of risk factors for disease at population, subpopulation, and individual levels, and by improving the effectiveness of interventions to help people achieve healthier behaviors in healthier environments. In this article, we describe new sources of big data in population health, explore their applications, and present two case studies illustrating how big data can be leveraged for prevention. We also discuss the many implementation obstacles that must be overcome before this vision can become a reality