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The purpose of this study is to compare the compositions of federally funded surgical research between 2003 and 2013, and to assess differences in funding trends between surgery and other medical specialties. The National Institutes of Health (NIH) Research Portfolio Online Reporting Tool database was queried for grants within core surgical disciplines during 2003 and 2013. Funding was categorized by award type, methodology, and discipline. Application success rates for surgery and 5 nonsurgical departments were trended over time. Inflation-adjusted NIH funding for surgical research decreased 19% from $270M in 2003 to $219M in 2013, with a shift from R-awards to U-awards. Proportional funding to outcomes research almost tripled, while translational research diminished. Nonsurgical departments have increased NIH application volume over the last 10 years; however, surgery’s application volume has been stagnant. To preserve surgery’s role in innovative research, new efforts are needed to incentivize an increase in application volume. •We queried NIH Research Portfolio Online Reporting Tool to categorize surgical research funding.•Trends in research methodology and subject area (2003 and 2013) were examined.•Comparisons were made between surgery and nonsurgical departments.•Increasing NIH application rate should be a priority for all academic surgeons.

To provide recommendations for the selection of comparators for randomized controlled trials of health-related behavioral interventions. The National Institutes of Health Office of Behavioral and Social Science Research convened an expert panel to critically review the literature on control or comparison groups for behavioral trials and to develop strategies for improving comparator choices and for resolving controversies and disagreements about comparators. The panel developed a Pragmatic Model for Comparator Selection in Health-Related Behavioral Trials. The model indicates that the optimal comparator is the one that best serves the primary purpose of the trial but that the optimal comparator's limitations and barriers to its use must also be taken into account. We developed best practice recommendations for the selection of comparators for health-related behavioral trials. Use of the Pragmatic Model for Comparator Selection in Health-Related Behavioral Trials can improve the comparator selection process and help resolve disagreements about comparator choices.

Vannevar Bush enshrined the ‘basic’ and ‘applied’ research dichotomy on which much of science policy is still built 75 years later. However, it is time to assess whether this vision for science best serves the purposes of medical research and physician-scientists in the 21st century.

The Cures Act that President Obama has signed into law will provide the National Institutes of Health with critical tools and resources to advance biomedical research across the spectrum from basic, curiosity-driven studies to advanced trials of promising therapies. The Cures Act, formally known as H.R. 34 or the 21st Century Cures Act, 1 passed overwhelmingly in the U.S. House of Representatives and Senate in the waning days of the 114th Congress and was signed into law by President Barack Obama on December 13, 2016. Weighing in at nearly 1000 pages, this bipartisan bill is the product of years of hard work by Republican and Democratic lawmakers, in collaboration with a broad array of diverse stakeholders. As with any landmark piece of legislation, the complex negotiations leading up to its passage were challenging and intense. But the final provisions are . . .

In an era of severe funding constraints for public health research, more efficient means of conducting research will be needed if scientific progress is to continue. At present major funders, such as the National Institutes of Health, do not provide specific instructions to grant authors or to reviewers regarding the cost efficiency of the research that they conduct. Doing so could potentially allow more research to be funded within current budgetary constraints and reduce waste. I describe how a blinded randomized trial was conducted for$275 000 by completely automating the consent and data collection processes. The study used the participants’ own computer equipment, relied on big data for outcomes, and outsourced some costly tasks, potentially saving $ 1 million in research costs.

Context: The responsiveness of NIH (National Institutes of Health) funding to disease burden is a long-standing issue of policy interest. Previous analyses of this issue have been hindered by data constraints, have not specified channels through which the NIH funding process could be responsive to disease considerations, and have not examined differences across NIH institutes and centers. Methods: We collected data from the NIH's new RCDC (Research, Condition, and Disease Categorization) database on funding for 107 diseases in 2008 and linked these to data on deaths and hospitalizations for these diseases. We used RCDC data and information from another NIH database—RePORTER—to determine institute-specific funding for these diseases and also funding by award type. We used these data to examine the overall responsiveness of NIH funding to disease burden, within-institute responsiveness, and the responsiveness of different types of NIH awards. Findings: Overall, we found a strong and statistically significant relationship between NIH funding and deaths and hospitalizations associated with a disease. We detected some evidence that more \"applied\" grant mechanisms—in particular, funding for clinical trials—are more responsive than other types of funding. We also found evidence of differences across institutes in their extent of responsiveness. Conclusions: Overall, the data suggest that NIH funding is responsive to the two measures of disease burden. More applied grant mechanisms also may serve as \"safety valves\" in the allocation process, allowing Congress, disease advocacy groups, and others to apply pressure to address particular health priorities in a more fine-grained way than is possible through investigator-initiated \"basic\" research grants alone.

Background Ideally, the distribution of research funding for different types of cancer should be equitable with respect to the societal burden each type of cancer imposes. These burdens can be estimated in a variety of ways; “Years of Life Lost” (YLL) measures the severity of death in regard to the age it occurs, \"Disability-Adjusted Life-Years\" (DALY) estimates the effects of non-lethal disabilities incurred by disease and economic metrics focus on the losses to tax revenue, productivity or direct medical expenses. We compared research funding from the National Cancer Institute (NCI) to a variety of burden metrics for the most common types of cancer to identify mismatches between spending and societal burden. Methods Research funding levels were obtained from the NCI website and information for societal health and economic burdens were collected from government databases and published reports. We calculated the funding levels per unit burden for a wide range of different cancers and burden metrics and compared these values to identify discrepancies. Results Our analysis reveals a considerable mismatch between funding levels and burden. Some cancers are funded at levels far higher than their relative burden suggests (breast cancer, prostate cancer, and leukemia) while other cancers appear underfunded (bladder, esophageal, liver, oral, pancreatic, stomach, and uterine cancers). Conclusions These discrepancies indicate that an improved method of health care research funding allocation should be investigated to better match funding levels to societal burden.

Background The National Institutes of Health (NIH) Research Task Force (RTF) on Research Standards for Chronic Low Back Pain impact score is a composite measure of Patient Reported Outcomes Measurement Information System (PROMIS) pain intensity, pain interference and physical function. PROMIS surveys are available in short-form and computer adaptive testing (CAT) formats. Minimal important change (MIC) can be estimated to determine if between-group differences are large enough to be important. To date, three anchor-based estimates of impact score MIC ranging from 3 to 7.5 have been published, and all were based on data collected using PROMIS short-form surveys. None used CAT versions of PROMIS surveys. Methods Secondary analysis of data collected during the conduct of two randomized clinical trials of 6-week courses of nonpharmacological pain therapies. Research subjects were US active-duty service members referred to an interdisciplinary pain management center. Impact score was assessed at the beginning and end of treatment. The Patient Global Impression of Change (PGIC) questionnaire was administered at the end of treatment and asked respondents to report their status compared to the start of treatment using a 7-item categorical scale ranging from very much improved to very much worse. A PGIC response of “much” or “very much” improved defined important improvement. Receiver operating characteristic (ROC) curve analysis and predictive logistic regression models were used to estimate MIC for the full combined sample and stratified by study sample and baseline impact score. Measures of individual statistical change were also computed. Results Overall, a decrease of 3 points in impact score was the estimated MIC (2.5 for ROC analysis and 3.4 for predictive modeling approach). Larger decreases in impact score were needed for participants with moderate and severe baseline pain impact to report important improvement. Thresholds for individual statistically significant change ranged from 6 to 14. Conclusions Using data collected with CAT surveys, we calculated an MIC of 3 points for the NIH RTF impact score, and estimates ranged from 1.3 to 7.2 depending on the baseline impact score and statistical approach used. These findings are consistent with previous MIC estimates that were based on non-adaptive short form surveys and have implications for improving the accuracy of pain treatment response assessment. Registry information Trial registration. ClinicalTrials.gov. Registry numbers: NCT03297905 (registered 9/29/17) and NCT04656340 (registered 11/30/20). Link to full applications: https://classic.clinicaltrials.gov/ct2/show/NCT03297905?titles=Determinants+of+Optimal+Dosage%26cntry=US%26draw=2%26rank=1 ; https://classic.clinicaltrials.gov/ct2/show/results/NCT04656340?titles=Complementary+and+Integrative+pain+therapies+and+functional+restoration+%28IMPPPORT%29%26draw=2%26rank=1 . Patient enrollment dates: SMART: 17 March 2021, prospectively registered; IMPPPORT: 9 December 2015, retrospectively registered.

The genome project leader reflects on his 12 years at the helm of a juggernaut biomedical agency, and what lies ahead. The genome project leader reflects on his 12 years at the helm of a juggernaut biomedical agency, and what lies ahead. Close-up portrait of Francis Collins

This study evaluated whether there were improvements in the number of departmental National Institutes of Health (NIH) training grants and the academic productivity of departmental chairs in terms of NIH research funding and PubMed-cited publications when compared to chairs of the same departments in 2006. Each chair was identified from the Society of Academic Associations of Academic Anesthesiology & Perioperative Medicine's Association of Academic Anesthesiology Chairs and entered into the NIH Research Portfolio Online Reporting Tools (RePORTER), PubMed, SCOPUS, and the National Provider Identifier Registry. The number and funding amounts of training grants awarded to the department in 2010, 2015, and 2020 were obtained as well as the department's national ranking and total dollar amount for NIH funding in 2020. For the current chair cohort, total publications and m-quotient (h-index corrected for active research years) were recorded along with each chair's history of NIH grant funding. These data were compared to a previous study of anesthesiology chairs that reviewed funding and publications through 2006. We analyzed data from 100 academic departments of anesthesiology and compared their scholarly activity relative to data gathered in 2006. In 2020, 52 of 100 departments of anesthesiology had evidence of NIH funding. There were not statistically significant (P > 0.05) differences in grants funding obtained by chairs between 2006 and 2020 with the exception that more chairs in 2006 had program or center grants. Median publications for chairs significantly increased from 35 in 2006 to 55 in 2021 (IRR = 1.5, 95% CI = 1.2–2.0, P = 0.003). Nineteen percent of chairs were female, which did not significantly differ from the proportion of women in the 2006 paper (15%, χ2 = 0.57, df = 1, P = 0.452). Of the male chairs, 90% were professors whereas 63% of female chairs were professors (χ2 = 8.8, df = 1, P = 0.003). Female chairs had fewer publications than male chairs (IRR = 1.8, 95% CI = 1.2–1.8, P = 0.002); however, m-quotients were not significantly different between men and women (P = 0.602). When compared to 2006, department of anesthesiology chairs had more publications in 2021; however, NIH funding rates remained unchanged. The specialty had 19% female chairs, and those chairs had fewer publications than their male counterparts, though sex differences were attenuated using metrics that account for disparities in career length. •Anesthesiology academic chairs had more PubMed-cited publications in 2021 than 2006.•Female department chairs had fewer publications than their male counterparts.•Chairs of NIH-ranked vs. unranked departments had more NIH funding and publications.•Percentage of female anesthesiology chairs remains unchanged over the last 15 years.•The proportion of departments with training grants has increased since 1995.