Measuring co-authorship and networking-adjusted scientific impact

Appraisal of the scientific impact of researchers, teams and institutions with productivity and citation metrics has major repercussions. Funding and promotion of individuals and survival of teams and institutions depend on publications and citations. In this competitive environment, the number of authors per paper is increasing and apparently some co-authors don't satisfy authorship criteria. Listing of individual contributions is still sporadic and also open to manipulation. Metrics are needed to measure the networking intensity for a single scientist or group of scientists accounting for patterns of co-authorship. Here, I define I1 for a single scientist as the number of authors who appear in at least I1 papers of the specific scientist. For a group of scientists or institution, In is defined as the number of authors who appear in at least In papers that bear the affiliation of the group or institution. I1 depends on the number of papers authored Np. The power exponent R of the relationship between I1 and Np categorizes scientists as solitary (R>2.5), nuclear (R=2.25-2.5), networked (R=2-2.25), extensively networked (R=1.75-2) or collaborators (R<1.75). R may be used to adjust for co-authorship networking the citation impact of a scientist. In similarly provides a simple measure of the effective networking size to adjust the citation impact of groups or institutions. Empirical data are provided for single scientists and institutions for the proposed metrics. Cautious adoption of adjustments for co-authorship and networking in scientific appraisals may offer incentives for more accountable co-authorship behaviour in published articles.Comment: 25 pages, 5 figure

Concurrent acute illness and comorbid conditions poorly predict antibiotic use in upper respiratory tract infections: a cross-sectional analysis

<p>Abstract</p> <p>Background</p> <p>Inappropriate antibiotic use promotes resistance. Antibiotics are generally not indicated for upper respiratory infections (URIs). Our objectives were to describe patterns of URI treatment and to identify patient and provider factors associated with antibiotic use for URIs.</p> <p>Methods</p> <p>This study was a cross-sectional analysis of medical and pharmacy claims data from the Pennsylvania Medicaid fee-for-service program database. We identified Pennsylvania Medicaid recipients with a URI office visit over a one-year period. Our outcome variable was antibiotic use within seven days after the URI visit. Study variables included URI type and presence of concurrent acute illnesses and chronic conditions. We considered the associations of each study variable with antibiotic use in a logistic regression model, stratifying by age group and adjusting for confounders.</p> <p>Results</p> <p>Among 69,936 recipients with URI, 35,786 (51.2%) received an antibiotic. In all age groups, acute sinusitis, chronic sinusitis, otitis, URI type and season were associated with antibiotic use. Except for the oldest group, physician specialty and streptococcal pharyngitis were associated with antibiotic use. History of chronic conditions was not associated with antibiotic use in any age group. In all age groups, concurrent acute illnesses and history of chronic conditions had only had fair to poor ability to distinguish patients who received an antibiotic from patients who did not.</p> <p>Conclusion</p> <p>Antibiotic prevalence for URIs was high, indicating that potentially inappropriate antibiotic utilization is occurring. Our data suggest that demographic and clinical factors are associated with antibiotic use, but additional reasons remain unexplained. Insight regarding reasons for antibiotic prescribing is needed to develop interventions to address the growing problem of antibiotic resistance.</p

A cluster randomized controlled trial of the effectiveness and cost-effectiveness of Intermediate Care Clinics for Diabetes (ICCD) : study protocol for a randomized controlled trial

Background World-wide healthcare systems are faced with an epidemic of type 2 diabetes. In the United Kingdom, clinical care is primarily provided by general practitioners (GPs) rather than hospital specialists. Intermediate care clinics for diabetes (ICCD) potentially provide a model for supporting GPs in their care of people with poorly controlled type 2 diabetes and in their management of cardiovascular risk factors. This study aims to (1) compare patients with type 2 diabetes registered with practices that have access to an ICCD service with those that have access only to usual hospital care; (2) assess the cost-effectiveness of the intervention; and (3) explore the views and experiences of patients, health professionals and other stakeholders. Methods/Design This two-arm cluster randomized controlled trial (with integral economic evaluation and qualitative study) is set in general practices in three UK Primary Care Trusts. Practices are randomized to one of two groups with patients referred to either an ICCD (intervention) or to hospital care (control). Intervention group: GP practices in the intervention arm have the opportunity to refer patients to an ICCD - a multidisciplinary team led by a specialist nurse and a diabetologist. Patients are reviewed and managed in the ICCD for a short period with a goal of improving diabetes and cardiovascular risk factor control and are then referred back to practice. or Control group: Standard GP care, with referral to secondary care as required, but no access to ICCD. Participants are adults aged 18 years or older who have type 2 diabetes that is difficult for their GPs to control. The primary outcome is the proportion of participants reaching three risk factor targets: HbA1c (≤7.0%); blood pressure (<140/80); and cholesterol (<4 mmol/l), at the end of the 18-month intervention period. The main secondary outcomes are the proportion of participants reaching individual risk factor targets and the overall 10-year risks for coronary heart disease(CHD) and stroke assessed by the United Kingdom Prospective Diabetes Study (UKPDS) risk engine. Other secondary outcomes include body mass index and waist circumference, use of medication, reported smoking, emotional adjustment, patient satisfaction and views on continuity, costs and health related quality of life. We aimed to randomize 50 practices and recruit 2,555 patients

Supporting genetics in primary care: investigating how theory can inform professional education

Evidence indicates that many barriers exist to the integration of genetic case finding into primary care. We conducted an exploratory study of the determinants of three specific behaviours related to using breast cancer genetics referral guidelines effectively: 'taking a family history', 'making a risk assessment', and 'making a referral decision'. We developed vignettes of primary care consultations with hypothetical patients, representing a wide range of genetic risk for which different referral decisions would be appropriate. We used the Theory of Planned Behavior to develop a survey instrument to capture data on behavioural intention and its predictors (attitude, subjective norm, and perceived behavioural control) for each of the three behaviours and mailed it to a sample of Canadian family physicians. We used correlation and regression analyses to explore the relationships between predictor and dependent variables. The response rate was 96/125 (77%). The predictor variables explained 38-83% of the variance in intention across the three behaviours. Family physicians' intentions were lower for 'making a risk assessment' (perceived as the most difficult) than for the other two behaviours. We illustrate how understanding psychological factors salient to behaviour can be used to tailor professional educational interventions; for example, considering the approach of behavioural rehearsal to improve confidence in skills (perceived behavioural control), or vicarious reinforcement as where participants are sceptical that genetics is consistent with their role (subjective norm)

Primary Language, Income and the Intensification of Anti-glycemic Medications in Managed Care: the (TRIAD) Study

BACKGROUND Patients who speak Spanish and/or have low socioeconomic status are at greater risk of suboptimal glycemic control. Inadequate intensification of anti-glycemic medications may partially explain this disparity. OBJECTIVE To examine the associations between primary language, income, and medication intensification. DESIGN Cohort study with 18-month follow-up. PARTICIPANTS One thousand nine hundred and thirty-nine patients with Type 2 diabetes who were not using insulin enrolled in the Translating Research into Action for Diabetes Study (TRIAD), a study of diabetes care in managed care. MEASUREMENTS Using administrative pharmacy data, we compared the odds of medication intensification for patients with baseline A1c ≥ 8%, by primary language and annual income. Covariates included age, sex, race/ethnicity, education, Charlson score, diabetes duration, baseline A1c, type of diabetes treatment, and health plan. RESULTS Overall, 42.4% of patients were taking intensified regimens at the time of follow-up. We found no difference in the odds of intensification for English speakers versus Spanish speakers. However, compared to patients with incomes 75,000 (OR 2.22, 1.53-3.24) had increased odds of intensification. This latter pattern did not differ statistically by race. CONCLUSIONS Low-income patients were less likely to receive medication intensification compared to higher-income patients, but primary language (Spanish vs. English) was not associated with differences in intensification in a managed care setting. Future studies are needed to explain the reduced rate of intensification among low income patients in managed care

Conducting High-Value Secondary Dataset Analysis: An Introductory Guide and Resources

Secondary analyses of large datasets provide a mechanism for researchers to address high impact questions that would otherwise be prohibitively expensive and time-consuming to study. This paper presents a guide to assist investigators interested in conducting secondary data analysis, including advice on the process of successful secondary data analysis as well as a brief summary of high-value datasets and online resources for researchers, including the SGIM dataset compendium (www.sgim.org/go/datasets). The same basic research principles that apply to primary data analysis apply to secondary data analysis, including the development of a clear and clinically relevant research question, study sample, appropriate measures, and a thoughtful analytic approach. A real-world case description illustrates key steps: (1) define your research topic and question; (2) select a dataset; (3) get to know your dataset; and (4) structure your analysis and presentation of findings in a way that is clinically meaningful. Secondary dataset analysis is a well-established methodology. Secondary analysis is particularly valuable for junior investigators, who have limited time and resources to demonstrate expertise and productivity

Epidemiology of Streptococcus pneumoniae and Staphylococcus aureus colonization in healthy Venezuelan children

Streptococcus pneumoniae and Staphylococcus aureus cause significant morbidity and mortality worldwide. We investigated both the colonization and co-colonization characteristics for these pathogens among 250 healthy children from 2 to 5 years of age in Merida, Venezuela, in 2007. The prevalence of S. pneumoniae colonization, S. aureus colonization, and S. pneumoniae–S. aureus co-colonization was 28%, 56%, and 16%, respectively. Pneumococcal serotypes 6B (14%), 19F (12%), 23F (12%), 15 (9%), 6A (8%), 11 (8%), 23A (6%), and 34 (6%) were the most prevalent. Non-respiratory atopy was a risk factor for S. aureus colonization (p = 0.017). Vaccine serotypes were negatively associated with preceding respiratory infection (p = 0.02) and with S. aureus colonization (p = 0.03). We observed a high prevalence of pneumococcal resistance against trimethoprim–sulfamethoxazole (40%), erythromycin (38%), and penicillin (14%). Semi-quantitative measurement of pneumococcal colonization density showed that children with young siblings and low socioeconomic status were more densely colonized (p = 0.02 and p = 0.02, respectively). In contrast, trimethoprim–sulfamethoxazole- and multidrug-resistant-pneumococci colonized children sparsely (p = 0.03 and p = 0.01, respectively). Our data form an important basis to monitor the future impact of pneumococcal vaccination on bacterial colonization, as well as to recommend a rationalized and restrictive antimicrobial use in our community