Doxycycline post-exposure prophylaxis (PEP) has been shown to be efficacious for the prevention of bacterial sexually transmitted infections, but resistance implications for Neisseria gonorrhoeae remain unknown. We aimed to use a mathematical model to investigate the anticipated impact of doxycycline PEP on the burden of gonorrhoea and antimicrobial resistance dynamics in men who have sex with men (MSM) in the USA.
Medicare Advantage (MA) plans are increasingly enrolling veterans. Because MA plans receive full capitated payments regardless of whether or not veterans use Medicare services, the federal government can incur substantial duplicative, wasteful spending if veterans in MA plans predominantly seek care through the Veterans Health Administration (VHA) system. The recent growth of MA plans that disproportionately enroll veterans could further exacerbate such wasteful spending. Using national data, we found that veterans increasingly enrolled in MA between 2016 and 2022, including in a growing number of MA plans in which 20 percent or more of the enrollees were veterans. Notably, about one in five VHA enrollees in these high-veteran MA plans did not incur any Medicare services paid by MA within a given year-a rate 2.5 times that of VHA enrollees in other MA plans and 5.7 times that of the general MA population. Meanwhile, VHA enrollees in high-veteran MA plans were significantly more likely to receive VHA-funded care. In 2020, the Centers for Medicare and Medicaid Services paid more than $1.32 billion to MA plans for VHA enrollees who did not use any Medicare services, with 19.1 percent going to high-veteran MA plans.
Background: Implementation science (IS) could accelerate progress toward achieving health equity goals. However, the lack of attention to the outer setting where interventions are implemented limits applicability and generalizability of findings to different populations, settings, and time periods. We developed a data resource to assess outer setting across seven centers funded by the National Cancer Institute’s IS Centers in Cancer Control (ISC3) Network Program.
Methods: Our Data Resource captures seven key environments, including: (1) food; (2) physical; (3) economic; (4) social; (5) health care; (6) cancer behavioral and screening; and (7) cancer-related policy. Data were obtained from public sources including the US Census and American Community Survey. We present medians and interquartile ranges based on the distribution of all counties in the US, all ISC3 centers, and within each Center for twelve selected measures. Distributions of each factor are compared with the national estimate using single sample sign tests.
Conclusions: Our results indicate that the outer setting varies across Centers and often differs from the national level. These findings demonstrate the importance of assessing the contextual environment in which interventions are implemented and suggest potential implications for intervention generalizability and scalability.
Citation: Warner ET, Huguet N, Fredericks M, Gundersen D, Nederveld A, Brown MC, Houston TK, Davis KL, Mazzucca S, Rendle KA, Emmons KM. Advancing health equity through implementation science: Identifying and examining measures of the outer setting. Social Science & Medicine. 2023;331:116095. doi:10.1016/j.socscimed.2023.116095
Artificial intelligence (AI) and machine learning (ML) systems are increasingly used in medicine to improve clinical decision-making and healthcare delivery. In gastroenterology and hepatology, studies have explored a myriad of opportunities for AI/ML applications which are already making the transition to bedside. Despite these advances, there is a risk that biases and health inequities can be introduced or exacerbated by these technologies. If unrecognised, these technologies could generate or worsen systematic racial, ethnic and sex disparities when deployed on a large scale. There are several mechanisms through which AI/ML could contribute to health inequities in gastroenterology and hepatology, including diagnosis of oesophageal cancer, management of inflammatory bowel disease (IBD), liver transplantation, colorectal cancer screening and many others. This review adapts a framework for ethical AI/ML development and application to gastroenterology and hepatology such that clinical practice is advanced while minimising bias and optimising health equity.
Citation: Uche-Anya E, Anyane-Yeboa A, Berzin T, Ghassemi M, May F. Artificial intelligence in gastroenterology and hepatology: how to advance clinical practice while ensuring health equity. Gut. 2022; 71:1909-1915. Published 2022 Sep. http://dx.doi.org/10.1136/gutjnl-2021-326271
Qualitative methods are critical for implementation science as they generate opportunities to examine complexity and include a diversity of perspectives. However, it can be a challenge to identify the approach that will provide the best fit for achieving a given set of practice-driven research needs. After all, implementation scientists must find a balance between speed and rigor, reliance on existing frameworks and new discoveries, and inclusion of insider and outsider perspectives. This paper offers guidance on taking a pragmatic approach to analysis, which entails strategically combining and borrowing from established qualitative approaches to meet a study’s needs, typically with guidance from an existing framework and with explicit research and practice change goals.
Section 1 offers a series of practical questions to guide the development of a pragmatic analytic approach. These include examining the balance of inductive and deductive procedures, the extent to which insider or outsider perspectives are privileged, study requirements related to data and products that support scientific advancement and practice change, and strategic resource allocation. This is followed by an introduction to three approaches commonly considered for implementation science projects: grounded theory, framework analysis, and interpretive phenomenological analysis, highlighting core analytic procedures that may be borrowed for a pragmatic approach. Section 2 addresses opportunities to ensure and communicate rigor of pragmatic analytic approaches. Section 3 provides an illustrative example from the team’s work, highlighting how a pragmatic analytic approach was designed and executed and the diversity of research and practice products generated.
As qualitative inquiry gains prominence in implementation science, it is critical to take advantage of qualitative methods’ diversity and flexibility. This paper furthers the conversation regarding how to strategically mix and match components of established qualitative approaches to meet the analytic needs of implementation science projects, thereby supporting high-impact research and improved opportunities to create practice change.
Citation: Ramanadhan S, Revette AC, Lee RM, Aveling EL. Pragmatic approaches to analyzing qualitative data for implementation science: an introduction. Implementation Science Communications. 2021; 2:70. Published 2021 Jun 29. https://doi.org/10.1186/s43058-021-00174-1
Objective: This study aimed to estimate the 10-year cost-effectiveness of school-based BMI report cards, a commonly implemented program for childhood obesity prevention in the US where student BMI is reported to parents/guardians by letter with nutrition and physical activity resources, for students in grades 3 to 7.
Methods: A microsimulation model, using data inputs from evidence reviews on health impacts and costs, estimated: how many students would be reached if the 15 states currently measuring student BMI (but not reporting to parents/guardians) implemented BMI report cards from 2023 to 2032; how many cases of childhood obesity would be prevented; expected changes in childhood obesity prevalence; and costs to society.
Conclusions: School-based BMI report cards are not cost-effective childhood obesity interventions. Deimplementation should be considered to free up resources for implementing effective programs..
Citation: Poole MK, Gortmaker SL, Barrett JL, McCulloch SM, Rimm EB, Emmons KM, Ward ZJ, Kenney EL. The societal costs and health impacts on obesity of BMI report cards in US schools. Obesity. 2023;31(8):2110-2118. doi:10.1002/oby.23788