Exploring the merits of research performance measures that comply with the San Francisco Declaration on Research Assessment and strategies to overcome barriers of adoption: qualitative interviews with administrators and researchers.

BACKGROUND: In prior research, we identified and prioritized ten measures to assess research performance that comply with the San Francisco Declaration on Research Assessment, a principle adopted worldwide that discourages metrics-based assessment. Given the shift away from assessment based on Journal Impact Factor, we explored potential barriers to implementing and adopting the prioritized measures. METHODS: We identified administrators and researchers across six research institutes, conducted telephone interviews with consenting participants, and used qualitative description and inductive content analysis to derive themes. RESULTS: We interviewed 18 participants: 6 administrators (research institute business managers and directors) and 12 researchers (7 on appointment committees) who varied by career stage (2 early, 5 mid, 5 late). Participants appreciated that the measures were similar to those currently in use, comprehensive, relevant across disciplines, and generated using a rigorous process. They also said the reporting template was easy to understand and use. In contrast, a few administrators thought the measures were not relevant across disciplines. A few participants said it would be time-consuming and difficult to prepare narratives when reporting the measures, and several thought that it would be difficult to objectively evaluate researchers from a different discipline without considerable effort to read their work. Strategies viewed as necessary to overcome barriers and support implementation of the measures included high-level endorsement of the measures, an official launch accompanied by a multi-pronged communication strategy, training for both researchers and evaluators, administrative support or automated reporting for researchers, guidance for evaluators, and sharing of approaches across research institutes. CONCLUSIONS: While participants identified many strengths of the measures, they also identified a few limitations and offered corresponding strategies to address the barriers that we will apply at our organization. Ongoing work is needed to develop a framework to help evaluators translate the measures into an overall assessment. Given little prior research that identified research assessment measures and strategies to support adoption of those measures, this research may be of interest to other organizations that assess the quality and impact of research.

Online Improvement of Condition-Baesd Maintenance Policy via Monte Carlo Tree Search.

Often in manufacturing systems, scenarios arise where the demand for maintenance exceeds the capacity of maintenance resources. This results in the problem of allocating the limited resources among machines competing for them. This maintenance scheduling problem can be formulated as a Markov decision process (MDP) with the goal of finding the optimal dynamic maintenance action given the current system state. However, as the system becomes more complex, solving an MDP suffers from the curse of dimensionality. To overcome this issue, we propose a two-stage approach that first optimizes a static condition-based maintenance (CBM) policy using a genetic algorithm (GA) and then improves the policy online via Monte Carlo tree search (MCTS). The static policy significantly reduces the state space of the online problem by allowing us to ignore machines that are not sufficiently degraded. Furthermore, we formulate MCTS to seek a maintenance schedule that maximizes the long-term production volume of the system to reconcile the conflict between maintenance and production objectives. We demonstrate that the resulting online policy is an improvement over the static CBM policy found by GA. Note to Practitioners­: This article proposes a method of scheduling maintenance in complex manufacturing systems in scenarios where there is frequent competition for maintenance resources. We use a condition-based maintenance policy that prescribes maintenance actions based on a machine's current health. However, when several machines are due for maintenance, a maintenance technician must choose between multiple competing jobs. While a common approach is to establish rules that dictate how maintenance jobs should be prioritized, such as the first-in, first-out rule, the goal of this work is to improve upon static policies in real time. We do this by strategically evaluating sequences of maintenance actions and playing out many "what-if" scenarios to see how the system will behave in the future. Implementation of the proposed method relies on the construction of a simulation model of the target system. This model is capable of retrieving the current state of the physical system, including the degradation state of machines, the availability of maintenance resources, and the distribution of parts throughout buffers in the system. We present several simulation experiments that demonstrate the improvement in system performance that our approach provides. Future work will aim to improve the efficiency of maintenance prioritization through online learning as well as more accurately identify manufacturing system configurations that will yield the greatest benefit of these methods.

Epidemiology of COVID-19 vs. influenza: Differential failure of COVID-19 mitigation among Hispanics, Cook County Health, Illinois.

BACKGROUND: During the early phases of the COVID-19 pandemic in the U.S., African-American or Hispanic communities were disproportionately impacted. To better understand the epidemiology and relative effects of COVID-19 among hospitalized Hispanic patients, we compared individual and census-tract level characteristics of patients diagnosed with COVID-19 to those diagnosed with influenza, another viral infection with respiratory transmission. We evaluated temporal changes in epidemiology related to a shelter-in-place mandate. METHODS: We evaluated patients hospitalized at Cook County Health, the safety-net health system for the Chicago metropolitan area. Among self-identified hospitalized Hispanic patients, we compared those with influenza (2019-2020 season) to COVID-19 infection during March 16, 2020-May 11, 2020. We used multivariable analysis to identify differences in individual and census-tract level characteristics between the two groups. RESULTS: Relative to non-Hispanic blacks and whites, COVID-19 rapidly increased among Hispanics during promotion of social-distancing policies. Whereas non-Hispanic blacks were more likely to be hospitalized for influenza, Hispanic patients predominated among COVID-19 infections (40% relative increase compared to influenza). In the comparative analysis of influenza and COVID-19, Hispanic patients with COVID-19 were more likely to reside in census tracts with higher proportions of residents with the following characteristics: Hispanic; no high school diploma; non-US citizen; limited English speaking ability; employed in manufacturing or construction; and overcrowding. By multivariable analysis, Hispanic patients hospitalized with COVID-19 compared to those with influenza were more likely to be male (adjusted OR = 1.8; 95% CI 1.1 to 2.9), obese (aOR = 2.5; 95% CI 1.5 to 4.2), or reside in a census tract with ≥40% of residents without a high-school diploma (aOR = 2.5; 95% CI 1.3 to 4.8). CONCLUSIONS: The rapid and disproportionate increase in COVID-19 hospitalizations among Hispanics after the shelter-in-place mandate indicates that public health strategies were inadequate in protecting this population-in particular, for those residing in neighborhoods with lower levels of educational attainment.

An effective model for natural selection in promoters.

We have produced an evolutionary model for promoters, analogous to the commonly used synonymous/nonsynonymous mutation models for protein-coding sequences. Although our model, called Sunflower, relies on some simple assumptions, it captures enough of the biology of transcription factor action to show clear correlation with other biological features. Sunflower predicts a binding profile of transcription factors to DNA sequences, in which different factors compete for the same potential binding sites. The parametrized model simultaneously estimates a continuous measurement of binding occupancy across the genomic sequence for each factor. We can then introduce a localized mutation, rerun the binding model, and record the difference in binding profiles. A single mutation can alter interactions both upstream and downstream of its position due to potential overlapping binding sites, and our statistic captures this domino effect. Over evolutionary time, we observe a clear excess of low-scoring mutations fixed in promoters, consistent with most changes being neutral. However, this is not consistent across all promoters, and some promoters show more rapid divergence. This divergence often occurs in the presence of relatively constant protein-coding divergence. Interestingly, different classes of promoters show different sensitivity to mutations, with phosphorylation-related genes having promoters inherently more sensitive to mutations than immune genes. Although there have previously been a number of models attempting to handle transcription factor binding, Sunflower provides a richer biological model, incorporating weak binding sites and the possibility of competition. The results show the first clear correlations between such a model and evolutionary processes.

A cost-effectiveness analysis of the arthroplasty options for displaced femoral neck fractures in the active, healthy, elderly population.

This study was performed to explore the cost-effectiveness of total hip arthroplasty (THA) compared with hemiarthroplasty (HEMI) in the treatment of displaced femoral neck fractures in active otherwise healthy older patients in whom the optimum treatment is believed to be an arthroplasty procedure. A Markov decision model was used to determine whether THA or HEMI was most cost-effective for the management of a displaced femoral neck fracture in this patient population. Total hip arthroplasty was associated with an average cost $3000 more than HEMI, and the average quality-adjusted life year gain was 1.53. The incremental cost-effectiveness ratio associated with the THA treatment strategy is $1960 per quality-adjusted life year. Currently available data support the use of THA as the more cost-effective treatment strategy in this specific population. The increased upfront cost appears to be offset by the improved functional results when compared with HEMI in this select patient group.

Cost-effectiveness analysis of the treatment for intermediate risk endometrial cancer: postoperative brachytherapy vs. observation.

OBJECTIVE: To compare the survival, morbidity, and cost of treating women with intermediate risk endometrial cancer with postoperative vaginal cuff brachytherapy versus observation followed by treatment for vaginal recurrence. METHODS: A cost-effectiveness analysis was performed comparing two treatment strategies for intermediate risk endometrial cancer (Stage IC, IG3, II-tumors limited to the uterus with greater than 50% myometrial invasion or poor differentiation or cervical metastasis). All patients undergo hysterectomy, oophorectomy, and lymphadenectomy: strategy 1-postoperative vaginal cuff brachytherapy, strategy 2-observation. Strategy 2 patients who develop vaginal recurrence undergo diagnostic work-up followed by teletherapy and brachytherapy. All six principles of cost-effectiveness analysis were employed. Importantly, actual payer costs were evaluated, not charges. RESULTS: Although the treatment for vaginal cuff recurrence is expensive, since only 8% of patients develop a vaginal recurrence, there was a 31% decreased cost by not treating patients with postoperative low-dose rate brachytherapy (strategy 2). Also, although the complication rate for teletherapy is greater than brachytherapy, since only 8% of patients develop a vaginal recurrence and require teletherapy, projected complication rates for the two strategies are similar. Survival would be decreased 3% by withholding postoperative brachytherapy (strategy 2). With postoperative high-dose rate brachytherapy (strategy 1), the cost per life saved would be 38,764 US dollars. CONCLUSION: Using a cost-effectiveness analysis, we have shown that withholding postoperative brachytherapy for patients with intermediate risk endometrial cancer results in a 31% decrease in cost, has a similar radiation complication rate, and results in a 3% decrease in survival.