Lost in Translation: Piloting a Novel Framework to Assess the Challenges in Translating Scientific Uncertainty From Empirical Findings to WHO Policy Statements.

BACKGROUND:Calls for evidence-informed public health policy, with implicit promises of greater program effectiveness, have intensified recently. The methods to produce such policies are not self-evident, requiring a conciliation of values and norms between policy-makers and evidence producers. In particular, the translation of uncertainty from empirical research findings, particularly issues of statistical variability and generalizability, is a persistent challenge because of the incremental nature of research and the iterative cycle of advancing knowledge and implementation. This paper aims to assess how the concept of uncertainty is considered and acknowledged in World Health Organization (WHO) policy recommendations and guidelines. METHODS:We selected four WHO policy statements published between 2008-2013 regarding maternal and child nutrient supplementation, infant feeding, heat action plans, and malaria control to represent topics with a spectrum of available evidence bases. Each of these four statements was analyzed using a novel framework to assess the treatment of statistical variability and generalizability. RESULTS:WHO currently provides substantial guidance on addressing statistical variability through GRADE (Grading of Recommendations Assessment, Development, and Evaluation) ratings for precision and consistency in their guideline documents. Accordingly, our analysis showed that policy-informing questions were addressed by systematic reviews and representations of statistical variability (eg, with numeric confidence intervals). In contrast, the presentation of contextual or "background" evidence regarding etiology or disease burden showed little consideration for this variability. Moreover, generalizability or "indirectness" was uniformly neglected, with little explicit consideration of study settings or subgroups. CONCLUSION:In this paper, we found that non-uniform treatment of statistical variability and generalizability factors that may contribute to uncertainty regarding recommendations were neglected, including the state of evidence informing background questions (prevalence, mechanisms, or burden or distributions of health problems) and little assessment of generalizability, alternate interventions, and additional outcomes not captured by systematic review. These other factors often form a basis for providing policy recommendations, particularly in the absence of a strong evidence base for intervention effects. Consequently, they should also be subject to stringent and systematic evaluation criteria. We suggest that more effort is needed to systematically acknowledge (1) when evidence is missing, conflicting, or equivocal, (2) what normative considerations were also employed, and (3) how additional evidence may be accrued

Consenting to health record linkage: evidence from a multi-purpose longitudinal survey of a general population

Background: The British Household Panel Survey (BHPS) is the first long-running UK longitudinal survey with a non-medical focus and a sample covering the whole age range to have asked for permission to link to a range of administrative health records. This study determines whether informed consent led to selection bias and reflects on the value of the BHPS linked with health records for epidemiological research. Methods. Multivariate logistical regression is used, with whether the respondent gave consent to data linkage or not as the dependent variable. Independent variables were entered as four blocks; (i) a set of standard demographics likely to be found in most health registration data, (ii) a broader set of socio-economic characteristics, (iii) a set of indicators of health conditions and (iv) information about the use of health services. Results: Participants aged 16-24, males and those living in England were more likely to consent. Consent is not biased with respect to socio-economic characteristics or health. Recent users of GP services are underrepresented among consenters. Conclusions: Whilst data could only be linked for a minority of BHPS participants, the BHPS offers a great range of information on people's life histories, their attitudes and behaviours making it an invaluable source for epidemiological research. © 2012 Knies et al; licensee BioMed Central Ltd

The implications of outcome truncation in reproductive medicine RCTs: a simulation platform for trialists and simulation study.

From Europe PMC via Jisc Publications RouterHistory: ppub 2021-08-01, epub 2021-08-06Publication status: PublishedFunder: Wellcome Trust; Grant(s): 204796/Z/16/ZBackgroundRandomised controlled trials in reproductive medicine are often subject to outcome truncation, where the study outcomes are only defined in a subset of the randomised cohort. Examples include birthweight (measurable only in the subgroup of participants who give birth) and miscarriage (which can only occur in participants who become pregnant). These outcomes are typically analysed by making a comparison between treatment arms within the subgroup (for example, comparing birthweights in the subgroup who gave birth or miscarriages in the subgroup who became pregnant). However, this approach does not represent a randomised comparison when treatment influences the probability of being observed (i.e. survival). The practical implications of this for the design and interpretation of reproductive trials are unclear however.MethodsWe developed a simulation platform to investigate the implications of outcome truncation for reproductive medicine trials. We used this to perform a simulation study, in which we considered the bias, type 1 error, coverage, and precision of standard statistical analyses for truncated continuous and binary outcomes. Simulation settings were informed by published assisted reproduction trials.ResultsIncreasing treatment effect on the intermediate variable, strength of confounding between the intermediate and outcome variables, and the presence of an interaction between treatment and confounder were found to adversely affect performance. However, within parameter ranges we would consider to be more realistic, the adverse effects were generally not drastic. For binary outcomes, the study highlighted that outcome truncation could cause separation in smaller studies, where none or all of the participants in a study arm experience the outcome event. This was found to have severe consequences for inferences.ConclusionWe have provided a simulation platform that can be used by researchers in the design and interpretation of reproductive medicine trials subject to outcome truncation and have used this to conduct a simulation study. The study highlights several key factors which trialists in the field should consider carefully to protect against erroneous inferences. Standard analyses of truncated binary outcomes in small studies may be highly biassed, and it remains to identify suitable approaches for analysing data in this context

Power spectra of TASEPs with a localized slow site

The totally asymmetric simple exclusion process (TASEP) with a localized defect is revisited in this article with attention paid to the power spectra of the particle occupancy N(t). Intrigued by the oscillatory behaviors in the power spectra of an ordinary TASEP in high/low density phase(HD/LD) observed by Adams et al. (2007 Phys. Rev. Lett. 99 020601), we introduce a single slow site with hopping rate q<1 to the system. As the power spectrum contains time-correlation information of the particle occupancy of the system, we are particularly interested in how the defect affects fluctuation in particle number of the left and right subsystems as well as that of the entire system. Exploiting Monte Carlo simulations, we observe the disappearance of oscillations when the defect is located at the center of the system. When the defect is off center, oscillations are restored. To explore the origin of such phenomenon, we use a linearized Langevin equation to calculate the power spectrum for the sublattices and the whole lattice. We provide insights into the interactions between the sublattices coupled through the defect site for both simulation and analytical results.Comment: 16 pages, 6 figures; v2: Minor revision

Algoriphagus machipongonensis sp. nov., co-isolated with a colonial choanoflagellate

A Gram-negative, non-motile, non-spore-forming bacterial strain, PR1[superscript T], was isolated from a mud core sample containing colonial choanoflagellates near Hog Island, Virginia, USA. Strain PR1[superscript T] grew optimally at 30 °C and with 3 % (w/v) NaCl. Strain PR1[superscript T] contained MK-7 as the major menaquinone as well as carotenoids but lacked pigments of the flexirubin-type. The predominant fatty acids were iso-C15 : 0 (29.4 %), iso-C17 : 1ω9c (18.5 %) and summed feature 3 (C16 : 1ω6c and/or C16 : 1ω7c; 11.3 %). The major polar lipids detected in strain PR1[superscript T] were phosphatidylethanolamine, an unknown phospholipid, an aminophospholipid, an aminolipid and two lipids of unknown character. The DNA G+C content was 38.7 mol%. Phylogenetic analysis based on 16S rRNA gene sequences revealed that strain PR1[superscript T] fell within the cluster comprising the genus Algoriphagus and was most closely related to Algoriphagus halophilus JC 2051[superscript T] (95.4 % sequence similarity) and Algoriphagus lutimaris S1-3[superscript T] (95.3 % sequence similarity). The 16S rRNA gene sequence similarity between strain PR1[superscript T] and the type strains of other species of the genus Algoriphagus were in the range 91–95 %. Differential phenotypic properties and phylogenetic and genetic distinctiveness of strain PR1[superscript T] demonstrated that this strain was distinct from other members of the genus Algoriphagus, including its closest relative, A. halophilus. Based on phenotypic, chemotaxonomic, phylogenetic and genomic data, strain PR1[superscript T] should be placed in the genus Algoriphagus as a representative of a novel species, for which the name Algoriphagus machipongonensis sp. nov. is proposed. The type strain is PR1[superscript T] ( = ATCC BAA-2233[superscript T]  = DSM 24695[superscript T]).Gordon and Betty Moore Foundation (Investigator Award (581))National Institutes of Health (U.S.) (NIH National Research Service Award and Fellowship grant (5F32GM086054))United States. National Aeronautics and Space Administration (NASA Astrobiology Institute (NNA08CN84A

Lost in Translation: Piloting a Novel Framework to Assess the Challenges in Translating Scientific Uncertainty From Empirical Findings to WHO Policy Statements

Background: Calls for evidence-informed public health policy, with implicit promises of greater program effectiveness, have intensified recently. The methods to produce such policies are not self-evident, requiring a conciliation of values and norms between policy-makers and evidence producers. In particular, the translation of uncertainty from empirical research findings, particularly issues of statistical variability and generalizability, is a persistent challenge because of the incremental nature of research and the iterative cycle of advancing knowledge and implementation. This paper aims to assess how the concept of uncertainty is considered and acknowledged in World Health Organization (WHO) policy recommendations and guidelines. Methods: We selected four WHO policy statements published between 2008-2013 regarding maternal and child nutrient supplementation, infant feeding, heat action plans, and malaria control to represent topics with a spectrum of available evidence bases. Each of these four statements was analyzed using a novel framework to assess the treatment of statistical variability and generalizability. Results: WHO currently provides substantial guidance on addressing statistical variability through GRADE (Grading of Recommendations Assessment, Development, and Evaluation) ratings for precision and consistency in their guideline documents. Accordingly, our analysis showed that policy-informing questions were addressed by systematic reviews and representations of statistical variability (eg, with numeric confidence intervals). In contrast, the presentation of contextual or “background” evidence regarding etiology or disease burden showed little consideration for this variability. Moreover, generalizability or “indirectness” was uniformly neglected, with little explicit consideration of study settings or subgroups. Conclusion: In this paper, we found that non-uniform treatment of statistical variability and generalizability factors that may contribute to uncertainty regarding recommendations were neglected, including the state of evidence informing background questions (prevalence, mechanisms, or burden or distributions of health problems) and little assessment of generalizability, alternate interventions, and additional outcomes not captured by systematic review. These other factors often form a basis for providing policy recommendations, particularly in the absence of a strong evidence base for intervention effects. Consequently, they should also be subject to stringent and systematic evaluation criteria. We suggest that more effort is needed to systematically acknowledge (1) when evidence is missing, conflicting, or equivocal, (2) what normative considerations were also employed, and (3) how additional evidence may be accrued