Health equity audits: a systematic review of the effectiveness.

OBJECTIVES: The purpose of this systematic review is to explore whether health equity audits (HEAs) are effective in improving the equity of service provision and reducing health inequalities. DESIGN: Three databases (Ovid Medline, Embase, Web of Science) and grey literature (Opengrey, Google Scholar) were systematically searched for articles published after 2000, reporting on the effectiveness of HEA. Title and abstracts were screened according to an eligibility criteria to identify studies which included a full audit cycle (eg, initial equity analysis, service changes and review). Data were extracted from studies meeting the eligibility criteria after full text review and risk of bias assessed using the ROBINS-I tool. RESULTS: The search strategy identified 596 articles. Fifteen records were reviewed in full text and three records were included in final review. An additional HEA report was identified through contact with an author. Three different HEAs were included from one peer-reviewed journal article, two published reports and one unpublished report (n=4 records on n=3 HEAs). This included 102 851 participants and over 148 practices/pharmacies (information was not recorded for all records). One study reviewed health equity impacts of HEA implementation in key indicators for coronary heart disease, type 2 diabetes and chronic obstructive pulmonary disease. Two HEAs explored Stop Smoking Services on programme access and equity. All reported some degree of reduction in health inequalities compared with prior HEA implementation. However, impact of HEA implementation compared with other concurrent programmes and initiatives was unclear. All included studies were judged to have moderate to serious risk of bias. CONCLUSIONS: There is an urgent need to identify effective interventions to address health inequalities. While HEAs are recommended, we only identified limited weak evidence to support their use. More evidence is needed to explore whether HEA implementation can reduce inequalities and which factors are influencing effectiveness. TRIAL REGISTRATION NUMBER: The study was registered prior to its conduction in PROSPERO (CRD 42020218642)

Using a discrete choice experiment involving cost to value a classification system measuring the quality of life impact of self-management for diabetes

Objective: This paper describes the use of a novel approach in health valuation of a discrete choice experiment (DCE) including a cost attribute to value a recently developed classification system for measuring the quality of life impact (both health and treatment experience) of self-management for diabetes. Methods: A large online survey was conducted using DCE with cost on UK respondents from the general population (n=1,497) and individuals with diabetes (n=405). The data was modelled using a conditional logit model with robust standard errors. The marginal rate of substitution (MRS) was used to generate willingness to pay estimates for every state defined by the classification system. Robustness of results was assessed by including interaction effects for household income. Results: There were some logical inconsistencies and insignificant coefficients for the milder levels of some attributes. There were some differences in the rank ordering of different attributes for the general population and diabetes patients. The willingness to pay to avoid the most severe state was £1,118.53 per month for the general population and £2,356.02 per month for the diabetes patient population. The results were largely robust. Conclusion: Health and self-management can be valued in a single classification system using DCE with cost. The MRS for key attributes can be used to inform cost-benefit analysis of self-management interventions in diabetes using results from clinical studies where this new classification system has been applied. The method shows promise, but found large willingness to pay estimates exceeding the cost levels used in the survey

Within the fold: assessing differential expression measures and reproducibility in microarray assays

BACKGROUND: 'Fold-change' cutoffs have been widely used in microarray assays to identify genes that are differentially expressed between query and reference samples. More accurate measures of differential expression and effective data-normalization strategies are required to identify high-confidence sets of genes with biologically meaningful changes in transcription. Further, the analysis of a large number of expression profiles is facilitated by a common reference sample, the construction of which must be carefully addressed. RESULTS: We carried out a series of 'self-self' hybridizations in which aliquots of the same RNA sample were labeled separately with Cy3 and Cy5 fluorescent dyes and co-hybridized to the same microarray. From this, we can analyze the intensity-dependent behavior of microarray data, define a statistically significant measure of differential expression that exploits the structure of the fluorescent signals, and measure the inherent reproducibility of the technique. We also devised a simple procedure for identifying and eliminating low-quality data for replicates within and between slides. We examine the properties required of a universal reference RNA sample and show how pooling a small number of samples with a diverse representation of expressed genes can outperform more complex mixtures as a reference sample. CONCLUSION: Analysis of cell-line samples can identify systematic structure in measured gene-expression levels. A general procedure for analyzing cDNA microarray data is proposed and validated. We show that pooled reference samples should be based not only on the expression of individual genes in each cell line but also on the expression levels of genes within cell lines

Eight-Dimensional Mid-Infrared/Optical Bayesian Quasar Selection

We explore the multidimensional, multiwavelength selection of quasars from mid-IR (MIR) plus optical data, specifically from Spitzer-IRAC and the Sloan Digital Sky Survey (SDSS). We apply modern statistical techniques to combined Spitzer MIR and SDSS optical data, allowing up to 8-D color selection of quasars. Using a Bayesian selection method, we catalog 5546 quasar candidates to an 8.0 um depth of 56 uJy over an area of ~24 sq. deg; ~70% of these candidates are not identified by applying the same Bayesian algorithm to 4-color SDSS optical data alone. Our selection recovers 97.7% of known type 1 quasars in this area and greatly improves the effectiveness of identifying 3.5<z<5 quasars. Even using only the two shortest wavelength IRAC bandpasses, it is possible to use our Bayesian techniques to select quasars with 97% completeness and as little as 10% contamination. This sample has a photometric redshift accuracy of 93.6% (Delta Z +/-0.3), remaining roughly constant when the two reddest MIR bands are excluded. While our methods are designed to find type 1 (unobscured) quasars, as many as 1200 of the objects are type 2 (obscured) quasar candidates. Coupling deep optical imaging data with deep mid-IR data could enable selection of quasars in significant numbers past the peak of the quasar luminosity function (QLF) to at least z~4. Such a sample would constrain the shape of the QLF and enable quasar clustering studies over the largest range of redshift and luminosity to date, yielding significant gains in our understanding of quasars and the evolution of galaxies.Comment: 49 pages, 14 figures, 7 tables. AJ, accepte

Estimating a preference-based single index measuring the quality of life impact of self-management for diabetes

Objective. Self-management is becoming increasingly important in diabetes but is neglected in conventional preference-based measures. The objective of this paper was to generate health state utility values for a novel classification system measuring the quality-of-life impact of self-management for diabetes, which can be used to generate quality-adjusted life years (QALYs). Methods. A large online survey was conducted using a discrete choice experiment (DCE), with duration as an additional attribute, on members of the UK general population (n = 1,493) to elicit values for health (social limitations, mood, vitality, hypoglycaemia) and non-health (stress, hassle, control, support) aspects of self-management in diabetes. The data were modelled using a conditional fixed-effects logit model and utility estimates were anchored on the one to zero (full health to dead) scale. Results. The model produced significant and consistent coefficients, with one logical inconsistency and 3 insignificant coefficients for the milder levels of some attributes. The anchored utilities ranged from 1 for the best state to −0.029 for the worst state (meaning worse than dead) defined by the classification system. Conclusion. The results presented here can potentially be used to generate utility values capturing the day to day impact of interventions in diabetes on both health and self-management. These utility values can potentially be used to generate QALYs for economic models of the cost-effectiveness of interventions in diabetes

The scale of population structure in Arabidopsis thaliana

The population structure of an organism reflects its evolutionary history and influences its evolutionary trajectory. It constrains the combination of genetic diversity and reveals patterns of past gene flow. Understanding it is a prerequisite for detecting genomic regions under selection, predicting the effect of population disturbances, or modeling gene flow. This paper examines the detailed global population structure of Arabidopsis thaliana. Using a set of 5,707 plants collected from around the globe and genotyped at 149 SNPs, we show that while A. thaliana as a species self-fertilizes 97% of the time, there is considerable variation among local groups. This level of outcrossing greatly limits observed heterozygosity but is sufficient to generate considerable local haplotypic diversity. We also find that in its native Eurasian range A. thaliana exhibits continuous isolation by distance at every geographic scale without natural breaks corresponding to classical notions of populations. By contrast, in North America, where it exists as an exotic species, A. thaliana exhibits little or no population structure at a continental scale but local isolation by distance that extends hundreds of km. This suggests a pattern for the development of isolation by distance that can establish itself shortly after an organism fills a new habitat range. It also raises questions about the general applicability of many standard population genetics models. Any model based on discrete clusters of interchangeable individuals will be an uneasy fit to organisms like A. thaliana which exhibit continuous isolation by distance on many scales

Explaining Cold-Pulse Dynamics in Tokamak Plasmas Using Local Turbulent Transport Models

A long-standing enigma in plasma transport has been resolved by modeling of cold-pulse experiments conducted on the Alcator C-Mod tokamak. Controlled edge cooling of fusion plasmas triggers core electron heating on time scales faster than an energy confinement time, which has long been interpreted as strong evidence of nonlocal transport. This Letter shows that the steady-state profiles, the cold-pulse rise time, and disappearance at higher density as measured in these experiments are successfully captured by a recent local quasilinear turbulent transport model, demonstrating that the existence of nonlocal transport phenomena is not necessary for explaining the behavior and time scales of cold-pulse experiments in tokamak plasmas.United States. Department of Energy (Award DE-FC02-99ER54512)United States. Department of Energy (Grant DESC0014264