Triage of Persons With Tuberculosis Symptoms Using Artificial Intelligence–Based Chest Radiograph Interpretation: A Cost-Effectiveness Analysis

Background In settings without access to rapid expert radiographic interpretation, artificial intelligence (AI)–based chest radiograph (CXR) analysis can triage persons presenting with possible tuberculosis (TB) symptoms, to identify those who require additional microbiological testing. However, there is limited evidence of the cost-effectiveness of this technology as a triage tool. Methods A decision analysis model was developed to evaluate the cost-effectiveness of triage strategies with AI-based CXR analysis for patients presenting with symptoms suggestive of pulmonary TB in Karachi, Pakistan. These strategies were compared to the current standard of care using microbiological testing with smear microscopy or GeneXpert, without prior triage. Positive triage CXRs were considered to improve referral success for microbiologic testing, from 91% to 100% for eligible persons. Software diagnostic accuracy was based on a prospective field study in Karachi. Other inputs were obtained from the Pakistan TB Program. The analysis was conducted from the healthcare provider perspective, and costs were expressed in 2020 US dollars. Results Compared to upfront smear microscopy for all persons with presumptive TB, triage strategies with AI-based CXR analysis were projected to lower costs by 19%, from $23233 per 1000 persons, and avert 3%–4% disability-adjusted life-years (DALYs), from 372 DALYs. Compared to upfront GeneXpert, AI-based triage strategies lowered projected costs by 37%, from $34346 and averted 4% additional DALYs, from 369 DALYs. Reinforced follow-up for persons with positive triage CXRs but negative microbiologic tests was particularly cost-effective. Conclusions In lower-resource settings, the addition of AI-based CXR triage before microbiologic testing for persons with possible TB symptoms can reduce costs, avert additional DALYs, and improve TB detection.

STUDY OF A CATEGORY ONE AIRPORT OPERATIONAL SAFETY AND SECURITY IN ABUJA, NIGERIA

This study assesses the operational safety and security of a category one airport in Nigeria, using the Nnamdi Azikiwe International Airport, Abuja (NAIA) as the case study airport. Data were sourced using structured questionnaire and oral interviews. A total of 180 questionnaires were administered and 165 questionnaires were retrieved, amounting to a 91.7% rate of response. The structured questionnaire used aimed at identifying various safety and security parameters in the study area. This study examined the trend of the effectiveness of safety and security variable in airport operations, safety and employee’s attitude towards safety risk management using the Multicriteria Decision Analysis model to achieve this objective. The results show the weighty level, satisfaction index and effective index of the safety and security parameters used in the analysis with their score and effective index graph. Recommendation on how to improve the airport safety operations were made following policy implication on how to improve the safety management system of the airport.

Optimizing the management of patients with small renal masses in a Canadian context: A Markov decision-analysis model

Introduction: The management of patients with a small renal mass (SRM) varies significantly. The objective of this study was to determine which initial management strategy resulted in the greatest quality-adjusted life months (QALM) for an index patient with a SRM. Methods: A Markov decision analysis was used to determine the effect of 1) treating patients with a partial nephrectomy (PN); 2) active surveillance; and 3) renal mass biopsy on QALM over a 10-year horizon. All relevant health states were modelled. Biopsy sensitivity and specificity were modelled assuming an 80% prevalence of cancer using procedural pathology as the gold standard. Health state utilities were obtained from the Tufts Medical Centre Cost-Effective Analysis Registry. Deterministic sensitivity analyses were used to test key assumptions. Results: Over a 10-year time horizon for a 70-year-old male with a 2 cm SRM, the biopsy strategy resulted in 38.07 QALM, whereas treating all patients with PN resulted in 37.69 QALM and active surveillance in 36.25 QALM. The model was most sensitive to the probability that a patient would remain alive at baseline. Biopsy was the preferred strategy when sensitivity was greater than 77%. As the underlying probability of cancer increased, the threshold of renal mass biopsy sensitivity to still favor biopsy increased. Conclusions: Renal mass biopsy is the preferred initial management strategy for an index patient with a SRM to optimize QALM. When the probability of cancer is high, centers should aim for a sensitivity of at least 77% in order to consider a biopsy first strategy.

A Decision Analysis Model for the Brand Experience of Branded Apps Using Consistency Fuzzy Linguistic Preference Relations

Branded apps are not only an important platform for enterprises and customers to have real-time interactions and communicate marketing messages, but also a new business model that encourages value co-creation between the two. In order to explore the impact of branded apps on customers, this study constructs a fuzzy multi-criteria decision making (FMCDM) analysis model, and it uses consistent fuzzy linguistic preference relations (CFLPR) to set up a symmetric pairwise comparison matrix, which greatly reduces the complexity and error rate of calculations. Empirical research findings show that brand experience attributes and the influence of brand experience on customer loyalty and satisfaction can be more accurately measured. As a consequence of this study, we show that, among the brand experience facets of two retail chain branded apps, behavioral experience is the most favored, while affective experience is the least favored. Furthermore, brand attachment and active participation should be strengthened to enhance customer loyalty. Through the analytical model employed in this study, enterprises can regularly monitor changes in the brand experience preferences of branded app users and evaluate app performance to flexibly adjust mobile device-based marketing campaigns and strategies. It can also aid enterprises in using mobile devices effectively to improve customer loyalty and address the issue of diminishing brand loyalty.

Cost-effectiveness of bariatric surgery and non-surgical weight management programmes for adults with severe obesity: a decision analysis model

Objectives To determine the most cost-effective weight management programmes (WMPs) for adults, in England with severe obesity (BMI ≥ 35 kg/m2), who are more at risk of obesity related diseases. Methods An economic evaluation of five different WMPs: 1) low intensity (WMP1); 2) very low calorie diets (VLCD) added to WMP1; 3) moderate intensity (WMP2); 4) high intensity (Look AHEAD); and 5) Roux-en-Y gastric bypass (RYGB) surgery, all compared to a baseline scenario representing no WMP. We also compare a VLCD added to WMP1 vs. WMP1 alone. A microsimulation decision analysis model was used to extrapolate the impact of changes in BMI, obtained from a systematic review and meta-analysis of randomised controlled trials (RCTs) of WMPs and bariatric surgery, on long-term risks of obesity related disease, costs, quality adjusted life years (QALYs) and incremental cost-effectiveness ratios (ICERs) measured as incremental cost per QALY gained over a 30-year time horizon from a UK National Health Service (NHS) perspective. Sensitivity analyses explored the impact of long-term weight regain assumptions on results. Results RYGB was the most costly intervention but also generated the lowest incidence of obesity related disease and hence the highest QALY gains. Base case ICERs for WMP1, a VLCD added to WMP1, WMP2, Look AHEAD, and RYGB compared to no WMP were £557, £6628, £1540, £23,725 and £10,126 per QALY gained respectively. Adding a VLCD to WMP1 generated an ICER of over £121,000 per QALY compared to WMP1 alone. Sensitivity analysis found that all ICERs were sensitive to the modelled base case, five year post intervention cessation, weight regain assumption. Conclusions RYGB surgery was the most effective and cost-effective use of scarce NHS funding resources. However, where fixed healthcare budgets or patient preferences exclude surgery as an option, a standard 12 week behavioural WMP (WMP1) was the next most cost-effective intervention.