Cost-benefit and discriminant validity of a stepwise dementia case-finding approach in an Asian older adult community

Background Case-finding is a recommended approach for dementia early detection in the community.Aims To investigate the discriminant validity and cost-effectiveness of a stepwise dementia case-finding approach in a Singaporean older adult community.Methods The two-phase study was conducted in the community from 2009 to 2015 in Singapore.A total of 3780 participants(age≥60 years)completed phase I(a brief cognitive screening);918 completed phase II and were included in the final analysis.In phase I,all participants were administered the Abbreviated Mental Test(AMT)and the Progressive Forgetfulness Question(PFQ).Those who screened positive on either test were invited to phase II,whereby the Mini-Mental State Examination(MMSE),Montreal Cognitive Assessment(MoCA)and a formal neuropsychological battery were administered,followed by the research diagnosis of no cognitive impairment,cognitive impairment no dementia(CIND)-Mild(≤2 impaired cognitive domains),CIND-Moderate(>2 impaired domains)or dementia.Receiver operating characteristic curve analyses were conducted for the different cognitive instruments.All discriminant indices were calculated,including sensitivity,specificity,positive and negative predictive values(NPV)and accuracy.Cost-effectiveness analysis was conducted by estimating the amount of screening time needed and the number of older adults requiring re-evaluation in two case-finding scenarios,ie,with or without preselection by the PFQ.Results The stepwise case-finding approach(preselection by the PFQ,then MMSE or MoCA or AMT)showed an excellent NPV(>99%)and accuracy(>86%)for excluding dementia-free cases.Without preselection by the PFQ,screening time for the three cognitive tools were 317.5,317.5 and 254 hours,with 159,302 and 175 screen-positive older adults involved in further evaluation.By adopting the stepwise case-finding approach,total screening time were 156.5,156.5 and 126.2 hours,which decreased by 50.7%,50.7% and 50.3% as compared with those without preselection.Furthermore,after preselection,only 98,167 and 145 screen-positive older adults required further evaluation,corresponding to a reduction of 38.4%,44.7% and 17.1% in the numbers compared with those without preselection.Conclusions A stepwise approach for dementia case-finding should be implemented in the community to minimise the time and resources needed for large-scale early detection of dementia.

基于大语言模型的糖尿病管理:潜力与展望

The increasing prevalence of diabetes has become a global public health concern in the 21st century.In 2021,it was estimated that 537 million people had diabetes,and this number is projected to reach 643 million by 2030,and 783 million by 2045[1].Such a huge burden of diabetes brings great challenges in its prevention and management,including early diagnosis,timely interventions,and regular monitoring of risk factor control and complications screening.Continuous self-care support and patient empowerment can enhance clinical and psychobehavioural outcomes[2],although these require additional resources including manpower,infrastructure(hard and technology),and finances.The emergence of digital health technologies(DHTs),especially artificial intelligence(AI),may help address these obstacles and alleviate the burden of diabetes[3].Large language models(LLMs),a generative AI that can accept image and text inputs and produce text outputs,have shown promise in various aspects of medical care.