National Business Group on Health

ABSTRACTHepatitis D virus (HDV) screening rates in the United States are low. We evaluated the impact of double reflex‐based HDV testing on HDV‐related morbidity and mortality in the United States. A Screen and Treat model simulated the HDV screening cascade (decision tree) and assessed the natural history of HDV (Markov model) over a 5‐year time horizon from a United States third‐party payer perspective, for patients positive for HBV. The number of patients diagnosed with HDV and liver‐related outcomes were compared under double reflex screening (100% patients screened for HDV antibodies and HDV RNA) and current practice (11% patients screened for HDV antibodies and 25% anti‐HDV‐positive patients for HDV RNA). Implementation of HDV double reflex testing predicted a 3655% increase in patients diagnosed versus current practice (n = 7231 vs. n = 193, respectively). The number of predicted occurrences of all liver‐related outcomes over 5 years is lower with double reflex testing versus current practice (difference in numbers of events: −7% for compensated cirrhosis, −40% for decompensated cirrhosis, −23% for hepatocellular carcinoma, −34% for liver transplantation, and −32% for liver‐related deaths). Results of scenario analyses with varying HBV prevalence, treatment received, or treatment rates were similar. Simulation of double reflex testing predicted earlier detection of HDV patients, increased numbers of patients diagnosed and treated, and reduced rates of disease progression, liver‐related complications and deaths. These findings highlight the need for implementing strategies to improve HDV screening and linkage to care and treatment in the United States.Trial Registration:ClinicalTrials.gov identifier: NCT03852719

Interest is growing in the potential of artificial intelligence (AI) chatbots and large language models like OpenAI's ChatGPT and Google's Gemini, particularly in dental education. To explore dental educators' perceptions of AI chatbots and large language models, specifically their potential benefits and challenges for dental education. A global cross-sectional survey was conducted in May-June 2023 using a 31-item online-questionnaire to assess dental educators' perceptions of AI chatbots like ChatGPT and their influence on dental education. Dental educators, representing diverse backgrounds, were asked about their use of AI, its perceived impact, barriers to using chatbots, and the future role of AI in this field. 428 dental educators (survey views = 1516; response rate = 28%) with a median [25/75th percentiles] age of 45 [37, 56] and 16 [8, 25] years of experience participated, with the majority from the Americas (54%), followed by Europe (26%) and Asia (10%). Thirty-one percent of respondents already use AI tools, with 64% recognising their potential in dental education. Perception of AI's potential impact on dental education varied by region, with Africa (4[4-5]), Asia (4[4-5]), and the Americas (4[3-5]) perceiving more potential than Europe (3[3-4]). Educators stated that AI chatbots could enhance knowledge acquisition (74.3%), research (68.5%), and clinical decision-making (63.6%) but expressed concern about AI's potential to reduce human interaction (53.9%). Dental educators' chief concerns centred around the absence of clear guidelines and training for using AI chatbots. A positive yet cautious view towards AI chatbot integration in dental curricula is prevalent, underscoring the need for clear implementation guidelines.

Background: Population segmentation models are often used to stratify people into groups for care or services planning purposes. These are typically disease-specific or utilisation-based, and target high-risk patient groups. Singapore’s National Health Group (NHG) has developed a River of Life (RoL) segmentation approach stratifying its population into five segments, namely, Living Well, Living with Illness, Crisis and Complex Care, Living with Frailty and Leaving Well. Building on this approach, Yishun Health (YH), a Regional Population Health System under NHG caring for more than 300,000 residents in Northern Singapore, developed the Needs-Based Sub-Segmentation Model (NBSSM) to further stratify Yishun Zone residents who are living with illness and frailty using additional biological, psychological and social criteria. We evaluated the NBSSM’s ability to stratify residents into distinct risk-groups based on a set of person-centred outcomes. Methods: Yishun Zone residents with chronic illness and/or frailty were first identified to generate ‘Living with Illness’ and ‘Living with Frailty’ groups. These residents were further segmented into 10 sub-segments representing different needs and complexity levels, based on biological complexity i.e. number and severity of chronic diseases, as well as presence or absence of psychosocial criteria i.e. presence of mental illness and social issues. To evaluate the NBSSM, we studied its ability to stratify residents into distinct risk-groups using Charlson’s Comorbidity Index (CCI) scores as well as person-centred outcomes such as healthcare utilisation, and healthcare cost. Statistical distinction between NBSSM segments based on the above outcomes was also tested. Data from a Population Health Survey being conducted by YH, which included health behaviours and more detailed mental and social needs data, will next be integrated into the NBSSM to forecast the needs under each sub-segments to enable services planning for segment-specific interventions. Results: All 314,522 Yishun Zone residents were included for RoL segmentation, of which 53,653 residents known to YH were further sub-segmented by the NBSSM according to their biopsychosocial needs. CCI, healthcare utilisation (i.e. number of Emergency Department visits, Specialised Outpatient Clinic visits, admissions and annual bed-days), and annual total healthcare cost were found to be statistically different across the NBSSM segments (p<0.05). Residents with presence of mental health and social issues had significantly higher risk of healthcare utilization and cost. Discussion & Conclusion: Using only data that was readily available data in our electronic medical record system, the NBSSM was able to augment NHG RoL segmentation and effectively sub-segment the Yishun Zone resident population into mutually exclusive and collectively exhaustive groups with similar magnitude of risk of health needs and outcomes. NBSSM enables development of more targeted and aligned clinical governance and leadership of segment-specific interventions through clearer assignment of segment-specific accountability for population outcomes. Integrating comprehensive needs assessment data from the YH Population Health Survey will enable more coherent health system and services planning. In conclusion, needs-based segmentation models are more person-centred than disease-specified or utilization-only segmentation models. NBSSM enhances our health system’s system transformation and service planning capabilities to direct targeted interventions to meet needs and improve population health outcomes.

Introduction/AimsTrials incorporating placebo‐to‐active treatment crossover are encouraged in fatal conditions like amyotrophic lateral sclerosis (ALS) but may underestimate active treatment survival benefit. Here, we apply methods for modeling survival without crossover, including the rank‐preserving structural failure time model (RPSFTM), to data from the CENTAUR trial of sodium phenylbutyrate and taurursodiol (PB and TURSO) in ALS incorporating both randomized placebo‐controlled and open‐label extension (OLE) phases.MethodsIntent‐to‐treat (ITT) and RPSFTM survival analyses were performed with final data at a July 2020 cutoff date. Analyses of subgroups based on randomized treatment and OLE phase participation were also performed.ResultsHazard ratios (95% confidence intervals) of death for PB and TURSO versus participants initially on placebo were 0.57 (0.35–0.92) on ITT analysis and 0.39 (0.17–0.88) in the primary on‐treatment RPSFTM analysis (p = .023). Median ITT survival duration for PB and TURSO (25.8 mo) was 6.9 mo longer than placebo (18.9 mo) on ITT analysis and 10.6 mo longer than the median RPSFTM‐adjusted survival duration for placebo (15.2 mo). Median survival duration was 18.8 mo longer in the PB and TURSO–randomized subgroup who continued into the OLE phase versus the placebo‐randomized subgroup who did not continue into the OLE phase (p < .0001), although OLE phase selection bias may have potentially confounded these results.DiscussionSimilar to the prespecified ITT analysis, post hoc analyses adjusting for treatment crossover in CENTAUR showed a significant survival benefit for PB and TURSO. Such methods may provide clinical context for observed survival outcomes in future ALS crossover trials.

Hepatocellular carcinoma (HCC) is a serious complication of hepatitis C virus (HCV) infection. Sustained virologic response (SVR) for HCV is associated with a reduction in cirrhosis, HCC and mortality and their associated costs. Japanese HCV patients are older with higher prevalence of HCC. Here we used a decision-analytic Markov model to estimate the economic benefit of HCV cure by reducing HCC and DCC burden in Japan. A cohort of 10000 HCV genotype 1b (GT1b) Japanese patients was modelled with a hybrid decision tree and Markov state-transition model capturing natural history of HCV over a lifetime horizon. Treatment options were approved all-oral direct-acting anti-virals (DAAs) vs no treatment. Treatment efficacy was based on clinical trials and transition rates and costs obtained from Japan-specific data. Cases of HCC, decompensated cirrhosis (DCC) and quality-adjusted life years (QALYs) were projected for patients treated with DAAs vs NT. QALYs were monetized using a willingness-to-pay threshold of ¥4-to-¥6 million. Incremental savings with treatment were calculated by adding the projected cost of complications avoided to the monetized gains in QALYs. The model showed that DAA treatment vs no treatment, reduces 2057 cases of HCC and 1478 cases of decompensated cirrhosis and saves ¥850446.73 and ¥338229.90 per patient (ppt). Additionally, treatment can lead to additional 2.64 QALYs gained per patient. The indirect economic gains associated with treatment-related QALY improvements were ¥10576000, ¥13220000 and ¥15864000 ppt (willingness-to-pay thresholds of ¥4 million, ¥5 million and ¥6 million). Total economic savings of treatment with DAAs (vs no treatment) was ¥7526372.63, ¥10170372.63 and ¥12814372.63, at these different willingness-to-pay thresholds. In conclusion treatment of HCV GT1b with all-oral DAAs in Japan can lead to significant direct and indirect savings related to avoidance of HCC and DCC.

Concern over the need for effective and accessible healthcare for individuals with advanced chronic illness has drawn attention to the significant gaps in our knowledge of palliative medicine. To advance our understanding of this field, community-based participatory research (CBPR) is proposed as a tool for future research initiatives. This paper offers a rationale for how CBPR may be employed to address specific gaps in palliative care research. Several examples where this approach has been used previously are described, and potential obstacles to implementing this research method are delineated. Despite challenges to incorporating CBPR to palliative care research, this approach holds substantial potential to advance our current understanding of the field and promote sensitivity for future programs, practices and policies.

In the present paper, a computer-aided approach&#13;\nto fault observability investigation of linear analog circuits is developed. The method is based on sensitivity investigation of the test characteristics in the frequency domain. The test frequencies are selected maximizing the sensitivity of the magnitude of the test characteristics. Applying postprocessing of the simulation results using macrodefinitions in the graphical analyzer Probe, a fault observability investigation of the circuit is performed. A number of sensitivity measures are defined in Probe for observability investigation of multiple faults using pre-defined macrodefinitions. The sensitivity of S-parameters is obtained in order to investigate the fault observability at RF.

An aging population and extended longevity are increasing the number of older people needing informal and family support. At the same time, women, the traditional caregivers, have entered the work force in record numbers. Consequently, concerns about how to care for dependent family members have become workplace issues. In response to the needs of employees who care for family members, employers have produced an array of policies, benefits, and programs, including flexible work schedules and information and referral services. Although these programs are a valuable complement to community services and government initiatives, relatively few employers have recognized the potential effects of caregiving on absenteeism, productivity, and turnover; even fewer have responded with workplace programs directed to the needs of their caregiving employees. To fill the gap, the government is considering mandating employee benefits, such as leave time for family illness. Community services are increasingly being directed to the needs of older people and their caregivers.