Improving primary care through information. A Wonca keynote paper.

Information from health care encounters across the entire health care spectrum, when consistently collected, analysed and applied can provide a clearer picture of patients' history as well as current and future needs through a better understanding of their morbidity burden and health care experiences. It can facilitate clinical activity to target limited resources to those patients most in need through risk adjustment mechanisms that consider the morbidity burden of populations, and it can help target quality improvement and cost saving activities in the right places. It can also open the door to a new chapter of evidence-based medicine around multi-morbidity. In summary, it can support a better integrated health system where primary care can provide continuous, coordinated, and comprehensive person-centred care to those who could benefit most. This paper explores the potential uses of information collected in electronic health records (EHRs) to inform case-mix and predictive modelling, as well as the associated challenges, with a particular focus on their application to primary care

Primary care priorities in addressing health equity: summary of the WONCA 2013 health equity workshop

BACKGROUND: Research consistently shows that gaps in health and health care persist, and are even widening. While the strength of a country’s primary health care system and its primary care attributes significantly improves populations’ health and reduces inequity (differences in health and health care that are unfair and unjust), many areas, such as inequity reduction through the provision of health promotion and preventive services, are not explicitly addressed by general practice. Substantiating the role of primary care in reducing inequity as well as establishing educational training programs geared towards health inequity reduction and improvement of the health and health care of underserved populations are needed. METHODS: This paper summarizes the work performed at the World WONCA (World Organization of National Colleges and Academies of Family Medicine) 2013 Meetings’ Health Equity Workshop which aimed to explore how a better understanding of health inequities could enable primary care providers (PCPs)/general practitioners (GPs) to adopt strategies that could improve health outcomes through the delivery of primary health care. It explored the development of a health equity curriculum and opened a discussion on the future and potential impact of health equity training among GPs. RESULTS: A survey completed by workshop participants on the current and expected levels of primary care participation in various inequity reduction activities showed that promoting access (availability and coverage) to primary care services was the most important priority. Assessment of the gaps between current and preferred priorities showed that to bridge expectations and actual performance, the following should be the focus of governments and health care systems: forming cross-national collaborations; incorporating health equity and cultural competency training in medical education; and, engaging in initiation of advocacy programs that involve major stakeholders in equity promotion policy making as well as promoting research on health equity. CONCLUSIONS: This workshop formed the basis for the establishment of WONCA’s Health Equity Special Interest Group, set up in early 2014, aiming to bring the essential experience, skills and perspective of interested GPs around the world to address differences in health that are unfair, unjust, unnecessary but avoidable

Boundary Layer Transition Flight Experiment Overview and In-Situ Measurements

In support of the Boundary Layer Transition Flight Experiment (BLTFE) Project, a manufactured protuberance tile was installed on the port wing of Space Shuttle Orbiter Discovery for the flights of STS-119 and STS-128. Additional instrumentation was also installed in order to obtain more spatially resolved measurements downstream of the protuberance. This paper provides an overview of the BLTFE Project, including the project history, organizations involved, and motivations for the flight experiment. Significant efforts were made to place the protuberance at an appropriate location on the Orbiter and to design the protuberance to withstand the expected environments. Efforts were also extended to understand the as-fabricated shape of the protuberance and the thermal protection system tile configuration surrounding the protuberance. A high-level overview of the in-situ flight data is presented, along with a summary of the comparisons between pre- and post-flight analysis predictions and flight data. Comparisons show that predictions for boundary layer transition onset time closely match the flight data, while predicted temperatures were significantly higher than observed flight temperatures

Assessing socioeconomic health care utilization inequity in Israel: impact of alternative approaches to morbidity adjustment

<p/> <p>Background</p> <p>The ability to accurately detect differential resource use between persons of different socioeconomic status relies on the accuracy of health-needs adjustment measures. This study tests different approaches to morbidity adjustment in explanation of health care utilization inequity.</p> <p>Methods</p> <p>A representative sample was selected of 10 percent (~270,000) adult enrolees of Clalit Health Services, Israel's largest health care organization. The Johns-Hopkins University Adjusted Clinical Groups^®were used to assess each person's overall morbidity burden based on one year's (2009) diagnostic information. The odds of above average health care resource use (primary care visits, specialty visits, diagnostic tests, or hospitalizations) were tested using multivariate logistic regression models, separately adjusting for levels of health-need using data on age and gender, comorbidity (using the Charlson Comorbidity Index), or morbidity burden (using the Adjusted Clinical Groups). Model fit was assessed using tests of the Area Under the Receiver Operating Characteristics Curve and the Akaike Information Criteria.</p> <p>Results</p> <p>Low socioeconomic status was associated with higher morbidity burden (1.5-fold difference). Adjusting for health needs using age and gender or the Charlson index, persons of low socioeconomic status had greater odds of above average resource use for all types of services examined (primary care and specialist visits, diagnostic tests, or hospitalizations). In contrast, after adjustment for overall morbidity burden (using Adjusted Clinical Groups), low socioeconomic status was no longer associated with greater odds of specialty care or diagnostic tests (OR: 0.95, CI: 0.94-0.99; and OR: 0.91, CI: 0.86-0.96, for specialty visits and diagnostic respectively). Tests of model fit showed that adjustment using the comprehensive morbidity burden measure provided a better fit than age and gender or the Charlson Index.</p> <p>Conclusions</p> <p>Identification of socioeconomic differences in health care utilization is an important step in disparity reduction efforts. Adjustment for health-needs using a comprehensive morbidity burden diagnoses-based measure, this study showed relative underutilization in use of specialist and diagnostic services, and thus allowed for identification of inequity in health resources use, which could not be detected with less comprehensive forms of health-needs adjustments.</p

A framework for human microbiome research

A variety of microbial communities and their genes (the microbiome) exist throughout the human body, with fundamental roles in human health and disease. The National Institutes of Health (NIH)-funded Human Microbiome Project Consortium has established a population-scale framework to develop metagenomic protocols, resulting in a broad range of quality-controlled resources and data including standardized methods for creating, processing and interpreting distinct types of high-throughput metagenomic data available to the scientific community. Here we present resources from a population of 242 healthy adults sampled at 15 or 18 body sites up to three times, which have generated 5,177 microbial taxonomic profiles from 16S ribosomal RNA genes and over 3.5 terabases of metagenomic sequence so far. In parallel, approximately 800 reference strains isolated from the human body have been sequenced. Collectively, these data represent the largest resource describing the abundance and variety of the human microbiome, while providing a framework for current and future studies

Structure, function and diversity of the healthy human microbiome

Author Posting. © The Authors, 2012. This article is posted here by permission of Nature Publishing Group. The definitive version was published in Nature 486 (2012): 207-214, doi:10.1038/nature11234.Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analysed the largest cohort and set of distinct, clinically relevant body habitats so far. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology and translational applications of the human microbiome.This research was supported in part by National Institutes of Health grants U54HG004969 to B.W.B.; U54HG003273 to R.A.G.; U54HG004973 to R.A.G., S.K.H. and J.F.P.; U54HG003067 to E.S.Lander; U54AI084844 to K.E.N.; N01AI30071 to R.L.Strausberg; U54HG004968 to G.M.W.; U01HG004866 to O.R.W.; U54HG003079 to R.K.W.; R01HG005969 to C.H.; R01HG004872 to R.K.; R01HG004885 to M.P.; R01HG005975 to P.D.S.; R01HG004908 to Y.Y.; R01HG004900 to M.K.Cho and P. Sankar; R01HG005171 to D.E.H.; R01HG004853 to A.L.M.; R01HG004856 to R.R.; R01HG004877 to R.R.S. and R.F.; R01HG005172 to P. Spicer.; R01HG004857 to M.P.; R01HG004906 to T.M.S.; R21HG005811 to E.A.V.; M.J.B. was supported by UH2AR057506; G.A.B. was supported by UH2AI083263 and UH3AI083263 (G.A.B., C. N. Cornelissen, L. K. Eaves and J. F. Strauss); S.M.H. was supported by UH3DK083993 (V. B. Young, E. B. Chang, F. Meyer, T. M. S., M. L. Sogin, J. M. Tiedje); K.P.R. was supported by UH2DK083990 (J. V.); J.A.S. and H.H.K. were supported by UH2AR057504 and UH3AR057504 (J.A.S.); DP2OD001500 to K.M.A.; N01HG62088 to the Coriell Institute for Medical Research; U01DE016937 to F.E.D.; S.K.H. was supported by RC1DE0202098 and R01DE021574 (S.K.H. and H. Li); J.I. was supported by R21CA139193 (J.I. and D. S. Michaud); K.P.L. was supported by P30DE020751 (D. J. Smith); Army Research Office grant W911NF-11-1-0473 to C.H.; National Science Foundation grants NSF DBI-1053486 to C.H. and NSF IIS-0812111 to M.P.; The Office of Science of the US Department of Energy under Contract No. DE-AC02-05CH11231 for P.S. C.; LANL Laboratory-Directed Research and Development grant 20100034DR and the US Defense Threat Reduction Agency grants B104153I and B084531I to P.S.C.; Research Foundation - Flanders (FWO) grant to K.F. and J.Raes; R.K. is an HHMI Early Career Scientist; Gordon&BettyMoore Foundation funding and institutional funding fromthe J. David Gladstone Institutes to K.S.P.; A.M.S. was supported by fellowships provided by the Rackham Graduate School and the NIH Molecular Mechanisms in Microbial Pathogenesis Training Grant T32AI007528; a Crohn’s and Colitis Foundation of Canada Grant in Aid of Research to E.A.V.; 2010 IBM Faculty Award to K.C.W.; analysis of the HMPdata was performed using National Energy Research Scientific Computing resources, the BluBioU Computational Resource at Rice University

Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same regio