Many studies published by major spinal surgery journals do not include full disclosure of researchers’ financial conflicts of interest (COIs), reports a study in Spine.

“[A] large number of physician-industry interactions…are underreported by authors publishing within the surgical spine literature,” according to the study by Jeremy D. Shaw, MD, and colleagues of University of Pittsburgh Medical Center. They propose steps to encourage more accurate disclosure of potential COIs—especially in spinal surgery, where relationships between surgeons and the biomedical device industry play a crucial role in research and innovation.

High rate of incomplete financial disclosures in spinal surgery studies

The researchers reviewed financial COIs by authors who published studies in Spine and two other major spinal surgery journals between 2014 and 2017. The analysis included nearly 40,000 authors contributing to about 6,800 research articles.

The COI disclosures for each author were compared to those in the Center for Medicare and Medicaid Services’ publicly available Open Payments Database (OPD). Established under the Affordable Care Act, the OPD requires pharmaceutical, medical device, and biological products manufacturers to report all payments to physicians of more than $10. Overall, 15.8 percent of spinal surgery authors had payments reported in the OPD.

Dr. Shaw and colleagues then compared payments reported to the OPD with the authors’ financial disclosures in the published studies. Most payments reported to the OPD were reflected in the COI statements. Of the total $1.90 billion received by authors, approximately $1.48 billion—78 percent of the total—was accurately disclosed.

“Undisclosed payments included $180 million in researcher funding and $188 million in royalties,” Dr. Shaw and colleagues write. Charitable contributions and payments from royalties/licenses were most likely to be accurately reported, while funds for research and entertainment were least likely to be disclosed.

About 77 percent of authors of

Global data networks that connect people through their devices have made it possible to create accurate short-term forecasts of new COVID-19 cases, using a method pioneered by two researchers at Sandia National Laboratories.

Jaideep Ray and Cosmin Safta used a model developed by Ray more than a decade ago to track plague epidemics using statistics. For COVID-19 they also drew upon the advice of their Sandia co-workers with expertise in modeling, mathematics and software engineering.

“I first started using this method in 2008-09. Cosmin and I adapted it in 2010 to track influenza-like illnesses,” Ray said. “When COVID-19 began to spread so rapidly, we knew we could use the same method to help forecast the outbreak.”

Ray and Safta use publicly available data from the Centers for Disease Control and Prevention, The New York Times Data Repository, Johns Hopkins University and various state departments of health. Within minutes, and without the need for high-performance computing resources, the researchers can forecast new cases in a region or nationally for the next seven to 10 days. Since April, the number of new cases have roughly followed the trends predicted by Ray and Safta.

“This method is a relatively easy and inexpensive way to get short-term forecasts about new coronavirus cases that decision-makers can use to allocate health care resources and response,” Safta explained. “This method is much easier and cheaper to do than methods that require more robust computers and manpower.”

The range of accuracy for the predictions