IntroductionThis study aims to establish the Knowledge, Attitudes and Practices (KAP) of the general population (people with and without diabetes) towards diabetes. The study will examine (a) recognition and understanding of causes, prevention and treatment strategies of diabetes; (b) identify the knowledge gaps and behavioural patterns that may hamper diabetes prevention and control; (c) stigma towards and stigma perceived by people with diabetes and (d) awareness of anti-diabetes campaigns.Methods and analysisThe study is a nationwide, cross-sectional study of Singapore’s general population aged 18 years and above (n=3000), comprising Chinese, Malay, Indian and other ethnic groups, who can understand English, Chinese, Malay or Tamil language. The sample was derived using a disproportionate stratified sampling using age and ethnicity. The proportion of respondents in each ethnic group (Chinese, Malay and Indian) was set to approximately 30%, while the proportion of respondents in each age group was set around 20% in order to ensure a sufficient sample size. The respondents will be administered questionnaires on diabetes KAP, stigma towards diabetes, lifestyle, diet and awareness of local anti-diabetes campaigns. The analysis will include descriptive statistics and multiple logistic and linear regression analyses to determine the socio-demographic correlates of correct recognition of diabetes, help-seeking preferences, as well as overall knowledge and attitudes among those with and without diabetes. We will consider a p value ≤0.05 as significant.Ethics and disseminationThis study protocol has been reviewed by the Institutional Research Review Committee and the National Healthcare Group Domain Specific Review Board (NHG DSRB Ref 2018/00430). The results of the study will be shared with policymakers and other stakeholders. There will be a local mass media briefing to disseminate the findings online, in print and on television and radio. The results will be published in peer-reviewed journals and presented in scientific meetings.

Understanding the impact of age on vaccinations is essential for the design and delivery of vaccines against SARS-CoV-2. Here, we present findings from a comprehensive analysis of multiple compartments of the memory immune response in 312 individuals vaccinated with the BNT162b2 SARS-CoV-2 mRNA vaccine. Two vaccine doses induce high antibody and T cell responses in most individuals. However, antibody recognition of the Spike protein of the Delta and Omicron variants is less efficient than that of the ancestral Wuhan strain. Age-stratified analyses identify a group of low antibody responders where individuals ≥60 years are overrepresented. Waning of the antibody and cellular responses is observed in 30% of the vaccinees after 6 months. However, age does not influence the waning of these responses. Taken together, while individuals ≥60 years old take longer to acquire vaccine-induced immunity, they develop more sustained acquired immunity at 6 months post-vaccination. A third dose strongly boosts the low antibody responses in the older individuals against the ancestral Wuhan strain, Delta and Omicron variants.

Background: Multimorbidity presents a key challenge to healthcare systems globally. However, heterogeneity in the definition of multimorbidity and design of epidemiological studies results in difficulty in comparing multimorbidity studies. This scoping review aimed to describe multimorbidity prevalence in studies using large datasets and report the differences in multimorbidity definition and study design. Methods: We conducted a systematic search of MEDLINE, EMBASE, and CINAHL databases to identify large epidemiological studies on multimorbidity. We used the Preferred Reporting Items for Systematic Reviews and Meta-analysis Extension for Scoping Reviews (PRISMA-ScR) protocol for reporting the results. Results: Twenty articles were identified. We found two key definitions of multimorbidity: at least two (MM2+) or at least three (MM3+) chronic conditions. The prevalence of multimorbidity MM2+ ranged from 15.3% to 93.1%, and 11.8% to 89.7% in MM3+. The number of chronic conditions used by the articles ranged from 15 to 147, which were organized into 21 body system categories. There were seventeen cross-sectional studies and three retrospective cohort studies, and four diagnosis coding systems were used. Conclusions: We found a wide range in reported prevalence, definition, and conduct of multimorbidity studies. Obtaining consensus in these areas will facilitate better understanding of the magnitude and epidemiology of multimorbidity.

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ObjectivesThere are multiple instruments for measuring multimorbidity. The main objective of this systematic review was to provide a list of instruments that are suitable for use in studies aiming to measure the association of a specific outcome with different levels of multimorbidity as the main independent variable in community-dwelling individuals. The secondary objective was to provide details of the requirements, strengths and limitations of these instruments, and the chosen outcomes.MethodsWe conducted the review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PROSPERO registration number: CRD42018105297). We searched MEDLINE, Embase and CINAHL electronic databases published in English and manually searched the Journal of Comorbidity between 1 January 2010 and 23 October 2020 inclusive. Studies also had to select adult patients from primary care or general population and had at least one specified outcome variable. Two authors screened the titles, abstracts and full texts independently. Disagreements were resolved with a third author. The modified Newcastle-Ottawa Scale was used for quality assessment.ResultsNinety-six studies were identified, with 69 of them rated to have a low risk of bias. In total, 33 unique instruments were described. Disease Count and weighted indices like Charlson Comorbidity Index were commonly used. Other approaches included pharmaceutical-based instruments. Disease Count was the common instrument used for measuring all three essential core outcomes of multimorbidity research: mortality, mental health and quality of life. There was a rise in the development of novel weighted indices by using prognostic models. The data obtained for measuring multimorbidity were from sources including medical records, patient self-reports and large administrative databases.ConclusionsWe listed the details of 33 instruments for measuring the level of multimorbidity as a resource for investigators interested in the measurement of multimorbidity for its association with or prediction of a specific outcome.