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Background and objective: Asthma control can be assessed with the Asthma Control Test (ACT) and a score of 20 or higher indicates good asthma control. Patients pay for their consultation and treatment in the fee-for-service primary healthcare system in Singapore. We hypothesized that achieving asthma control would result in lower asthma costs through reduced acute exacerbations, fewer physician consultations and lower lost productivity. The study compared the healthcare costs of patients who achieved asthma control and those with suboptimal asthma control based on ACT scores. Factors influencing asthma control and healthcare expenditure over time were also examined. Methods: A total of 736 patients were enrolled into an asthma care programme in two polyclinics during 2008 and 2013. Direct costs of asthma management were derived from the frequency of polyclinic consultations, medication costs and hospitalization. Indirect costs were estimated from lost workdays due to exacerbations. The generalized estimating equation (GEE) approach was used to longitudinally model the factors associated with total healthcare expenditure. Results: Patients with asthma control spent S$48 (US $36) more per doctor visit on asthma drugs (P < 0.01) but incurred S$65 (US$48) less per doctor visit in total costs (P < 0.01) than those with suboptimal asthma control. The savings from achieving asthma control for obese patients were greater than for normal-weight patients (S$42 or the equivalent of US$31; P < 0.05). Conclusion: Optimal asthma control was associated with reduced healthcare costs. An effective treatment regimen should also consider other modifiable factors such as weight control to achieve asthma control and eventually reduce asthma costs.

Background and objective: Bronchial thermoplasty (BT) has been shown to be effective at reducing asthma exacerbations and improving asthma control for patients with severe persistent asthma but it is also expensive. Evidence on its cost-effectiveness is limited and inconclusive. In this study, we aim to evaluate the incremental cost-effectiveness of BT combined with optimized asthma therapy (BT-OAT) relative to OAT for difficult-to-treat and severe asthma patients in Singapore, and to provide a general framework for determining BT's cost-effectiveness in other healthcare settings. Methods: We developed a Markov model to estimate the costs and quality-adjusted life years (QALYs) gained with BT-OAT versus OAT from the societal and health system perspectives. The model was populated using Singapore-specific costs and transition probabilities and utilities from the literature. Sensitivity analyses were conducted to identify the main factors determining cost-effectiveness of BT-OAT. Results: BT-OAT is not cost-effective relative to OAT over a 5-year time horizon with an incremental costeffectiveness ratio (ICER) of $US138 889 per QALY from the societal perspective and $US139 041 per QALY from the health system perspective. The cost-effectiveness of BT-OAT largely depends on a combination of the cost of the BT procedure and the cost of asthma-related hospitalizations and emergency department (ED) visits. Conclusion: Based on established thresholds for costeffectiveness, BT-OAT is not cost-effective compared with OAT in Singapore. Given its current clinical efficacy, BT-OAT is most likely to be cost-effective in a setting where the cost of BT procedure is low and costs of hospitalization and ED visits are high.

Background Current guidelines for mammography screening for breast cancer vary across agencies, especially for women aged 40–49. Using artificial Intelligence (AI) to read mammography images has been shown to predict breast cancer risk with higher accuracy than alternative approaches including polygenic risk scores (PRS), raising the question whether AI-based screening is more cost-effective than screening based on PRS or existing guidelines. This study provides the first evidence to shed light on this important question. Methods This study is a model-based economic evaluation. We used a hybrid decision tree/microsimulation model to compare the cost-effectiveness of eight strategies of mammography screening for women aged 40–49 (screening beyond age 50 follows existing guidelines). Six of these strategies were defined by combinations of risk prediction approaches (AI, PRS or family history) and screening frequency for low-risk women (no screening or biennial screening). The other two strategies involved annual screening for all women and no screening, respectively. Data used to populate the model were sourced from the published literature. Results Risk prediction using AI followed by no screening for low-risk women is the most cost-effective strategy. It dominates (i.e., costs more and generates fewer quality adjusted life years (QALYs)) strategies for risk prediction using PRS followed by no screening or biennial screening for low-risk women, risk prediction using AI or family history followed by biennial screening for low-risk women, and annual screening for all women. It also extendedly dominates (i.e., achieves higher QALYs at a lower incremental cost per QALY) the strategy for risk prediction using family history followed by no screening for low-risk women. Meanwhile, it is cost-effective versus no screening, with an incremental cost-effectiveness ratio of $23,755 per QALY gained. Conclusions Risk prediction using AI followed by no breast cancer screening for low-risk women is the most cost-effective strategy. This finding can be explained by AI’s ability to identify high-risk women more accurately than PRS and family history (which reduces the possibility of delayed breast cancer diagnosis) and fewer false-positive diagnoses from not screening low-risk women.

Our proposed algorithm-driven testing strategy for MODY is not yet cost-effective based on established benchmarks. However, as genetic testing prices continue to fall, this strategy is likely to become cost-effective in the near future.