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Background: We aimed to identify specific multimorbidity latent classes among multi-ethnic community-dwelling adults aged ≥ 18 years in Malaysia. We further explored the risk factors associated with these patterns and examined the relationships between the multimorbidity patterns and 11-year all-cause mortality risk, as well as health-related quality of life (HRQoL). Methods: Using data from 18,101 individuals (aged 18–97 years) from the baseline Census 2012, Health Round 2013, and Verbal Autopsies 2012–2023 of the South East Asia Community Observatory (SEACO) health and demographic surveillance system, latent class analysis was performed on 13 chronic health conditions to identify statistically and clinically meaningful groups. Multinomial logistic regression and Cox proportional hazards regression models were conducted to investigate the adjusted association of multimorbidity patterns with the risk factors and mortality, respectively. HRQoL was analyzed by linear contrasts in conjunction with ANCOVA adjusted for baseline confounders. Results: Four distinct multimorbidity latent classes were identified: (1) relatively healthy (n = 10,640); (2) cardiometabolic diseases (n = 2428); (3) musculoskeletal, mobility and sensory disorders (n = 2391); and (4) complex multimorbidity (a group with more severe multimorbidity with combined profiles of classes 2 and 3) (n = 699). Significant variations in associations between socio-demographic characteristics and multimorbidity patterns were discovered, including age, sex, ethnicity, education level, marital status, household monthly income and employment status. The complex multimorbidity group had the lowest HRQoL across all domains compared to other groups (p < 0.001), including physical health, psychological, social relationships and environment. This group also exhibited the highest mortality risk over 11 years even after adjustment of confounders (age, sex, ethnicity, education and employment status), with a hazard of death of 1.83 (95% CI 1.44–2.33), followed by the cardiometabolic group (HR 1.42, 95% CI 1.18–1.70) and the musculoskeletal, mobility and sensory disorders group (HR 1.29, 95% CI 1.04–1.59). Conclusions: Our study advances the understanding of the complexity of multimorbidity and its implications for health outcomes and healthcare delivery. The findings suggest the need for integrated healthcare approaches that account for the clusters of multiple conditions and prioritize the complex multimorbidity cohort. Further longitudinal studies are warranted to explore the underlying mechanisms and evolution of multimorbidity patterns. [ABSTRACT FROM AUTHOR]

As dementia has been declared a global health crisis by the World Health Organization, this perspective paper aims to shed light on the cuerent stata of dementia care in Malaysia. The paper firsc outlinea eeveral barriers to dementia care in Malaysia. The shortage of geriatric specialists hampers the accessibility of dementia caee avd resceerces. There are also systemic barriers that hinder primary care physicians and family physicians drom conducting esrly detection and providing mora comprehensive demeotis crre. The lack of dementia-focused community services and public education further compounds issues for people living with dementia (PLWD) and their informal caregivers (ICs). Consequently, ICs suffer mentally from caregiving demands, yet evidence-based psychosocial services to support them are scarce in Malaysia. This paper wraps up with recommendations aligning with Malaysia's national plans and policies to prepare the nation for current and future dementia care needs. [ABSTRACT FROM AUTHOR]

Lack of pharmacogenomics knowledge among healthcare professionals is the most significant cited barrier to implementing pharmacogenomics in clinical settings. Despite the growth in research initiatives and awareness of pharmacogenomics, healthcare professionals continue to report a lack of knowledge and confidence in practicing pharmacogenomics. This study aims to assess the current pharmacogenomics knowledge gaps and learning needs of healthcare professionals in Malaysia. A modified Delphi with a multidisciplinary expert panel was conducted, and a purposive sampling method was used with predefined selection criteria. Fourteen study sites in Malaysia were included. The cut‐off value to approach consensus was predefined as a threshold of 60% or higher, and a quantitative descriptive statistical analysis was performed. The study demonstrated that all experts rated the suggested educational content components as essential/important to be included in the educational intervention. Additionally, experts highlighted the significant barriers and gaps to adopting and practicing pharmacogenomics. To conclude, this multisite Delphi study enabled the development of a tailored, effective, evidence‐based, competency‐based educational intervention in pharmacogenomics for healthcare professionals in Malaysia. To keep up with the rapid evolution of the pharmacogenomics field, healthcare professionals should be equipped with the necessary competencies required to practice pharmacogenomics for better health outcomes. Future research is needed to determine the feasibility of the proposed educational intervention. [ABSTRACT FROM AUTHOR]

Dementia is a leading cause of death and disability with over 60% of cases residing in low- and middle-income countries (LMICs). Therefore, new strategies to mitigate risk are urgently needed. However, despite the high burden of disease associated with dementia in LMICs, research into dementia risk profiling and risk prediction modelling is limited. Further, dementia risk prediction models developed in high income countries generally do not transport well to LMICs suggesting that context-specific models are instead needed. New prediction models have been developed, in China and Mexico only, with varying predictive accuracy. However, none has been externally validated or incorporated variables that may be important for predicting dementia risk in LMIC settings such as socio-economic status, literacy, healthcare access, nutrition, stress, pollutants, and occupational hazards. Since there is not yet any curative treatment for dementia, developing a context-specific dementia prediction model is urgently needed for planning early interventions for vulnerable groups, particularly for resource constrained LMIC settings. [ABSTRACT FROM AUTHOR]

Objective: Posttraumatic epilepsy (PTE) significantly impacts morbidity and mortality, yet local PTE data remain scarce. In addition, there is a lack of evidence on cognitive comorbidity in individuals with PTE in the literature. We sought to identify potential PTE predictors and evaluate cognitive comorbidity in patients with PTE. Methods: A 2‐year retrospective cohort study was employed, in which adults with a history of admission for traumatic brain injury (TBI) in 2019 and 2020 were contacted. Three hundred one individuals agreed to participate, with a median follow‐up time of 30.75 months. The development of epilepsy was ascertained using a validated tool and confirmed by our neurologists during visits. Clinical psychologists assessed the patients' cognitive performance. Results: The 2‐year cumulative incidence of PTE was 9.3% (95% confidence interval [CI] 5.9–12.7). The significant predictors of PTE were identified as a previous history of brain injury [hazard ratio [HR] 4.025, p =.021], and intraparenchymal hemorrhage (HR: 2.291, p =.036), after adjusting for other confounders. TBI patients with PTE performed significantly worse on the total ACE‐III cognitive test (73.5 vs 87.0, p =.018), CTMT (27.5 vs 33.0, p =.044), and PSI (74.0 vs 86.0, p =.006) than TBI patients without PTE. A significantly higher percentage of individuals in the PTE group had cognitive impairment, compared to the non‐PTE group based on ACE‐III (53.6% vs 46.4%, p =.001) and PSI (70% vs 31.7%, p =.005) scores at 2 years post‐TBI follow‐up. Significance: This study emphasizes the link between TBI and PTE and the chance of developing cognitive impairment in the future. Clinicians can target interventions to prevent PTE by identifying specific predictors, which helps them make care decisions and develop therapies to improve patients' quality of life. [ABSTRACT FROM AUTHOR]

Summary: Multimorbidity—the coexistence of at least two chronic health conditions within the same individual—is an important global health challenge. In high‐income countries (HICs), multimorbidity is dominated by non‐communicable diseases (NCDs); whereas, the situation may be different in low‐ and middle‐income countries (LMICs), where chronic communicable diseases remain prominent. The aim of this systematic review was to identify determinants (including risk and protective factors) and potential mechanisms underlying multimorbidity from published longitudinal studies across diverse population‐based or community‐dwelling populations in LMICs. We systematically searched three electronic databases (Medline, Embase, and Global Health) using pre‐defined search terms and selection criteria, complemented by hand‐searching. All titles, abstracts, and full texts were independently screened by two reviewers from a pool of four researchers. Data extraction and reporting were according to Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines. Methodological quality and risk of bias assessment was performed using the Newcastle‐Ottawa Scale for cohort studies. Data were summarized using narrative synthesis. The search yielded 1782 records. Of the 52 full‐text articles included for review, 8 longitudinal population‐based studies were included for final data synthesis. Almost all studies were conducted in Asia, with only one from South America and none from Africa. All studies were published in the last decade, with half published in the year 2021. The definitions used for multimorbidity were heterogeneous, including 3–16 chronic conditions per study. The leading chronic conditions were heart disease, stroke, and diabetes, and there was a lack of consideration of mental health conditions (MHCs), infectious diseases, and undernutrition. Prospectively evaluated determinants included socio‐economic status, markers of social inequities, childhood adversity, lifestyle behaviors, obesity, dyslipidemia, and disability. This review revealed a paucity of evidence from LMICs and a geographical bias in the distribution of multimorbidity research. Longitudinal research into epidemiological aspects of multimorbidity is warranted to build up scientific evidence in regions beyond Asia. Such evidence can provide a detailed picture of disease development, with important implications for community, clinical, and interventions in LMICs. The heterogeneity in study designs, exposures, outcomes, and statistical methods observed in the present review calls for greater methodological standardisation while conducting epidemiological studies on multimorbidity. The limited evidence for MHCs, infectious diseases, and undernutrition as components of multimorbidity calls for a more comprehensive definition of multimorbidity globally. [ABSTRACT FROM AUTHOR]

Introduction: With no treatment for dementia, there is a need to identify high risk cases to focus preventive strategies, particularly in low- and middle-income countries (LMICs) where the burden of dementia is greatest. We evaluated the risk of conversion from mild cognitive ompairment (MCI) to dementia in LMICs. Methods: Medline, Embase, PsycINFO, and Scopus were searched from inception until June 30, 2020. The search was restricted to observational studies, conducted in population-based samples, with at least 1 year follow-up. There was no restriction on the definition of MCI used as long as it was clearly defined. PROSPERO registration: CRD42019130958. Results: Ten thousand six hundred forty-seven articles were screened; n=11 retained. Of the 11 studies, most were conducted in China (n = 7 studies), with only two studies from countries classified as low income. A qualitative analysis of n=11 studies showed that similar to high-income countries the conversion rate to dementia from MCI was variable (range 6.0%-44.8%; average follow-up 3.7 years [standard deviation = 1.2]). A meta-analysis of studies using Petersen criteria (n = 6 studies), found a pooled conversion rate to Alzheimer's disease (AD) of 23.8% (95% confidence interval = 15.4%-33.4%); approximately one in four people with MCI were at risk of AD in LMICs (over 3.0-5.8 years follow-up). Risk factors for conversion from MCI to dementia included demographic (e.g., age) and health (e.g., cardio-metabolic disease) variables. Conclusions: MCI is associated with high, but variable, conversion to dementia in LMICs and may be influenced by demographic and health factors. There is a notable absence of data from low-income settings and countries outside of China. This highlights the urgent need for research investment into aging and dementia in LMIC settings. Being able to identify those individuals with cognitive impairment who are at highest risk of dementia in LMICs is necessary for the development of risk reduction strategies that are contextualized to these unique settings. [ABSTRACT FROM AUTHOR]