Vitamin D is essential for maintaining serum calcium levels, ensuring sufficient bone mineralization, immunomodulatory properties, and a protective effect on the cardiovascular system, renal disease, cancer, as well as in pregnancy. Vitamin D deficiency is prevalent worldwide, and it is not related to a country’s development index. However, the data on vitamin D deficiencies are primarily taken from out-of-date, small-scale studies on target age groups or specific diseases, rather than from large-scale, population-based surveys. In Malaysia, for the past 16 years, studies were conducted involving adult men and women, pregnant women, postmenopausal women, adolescent, and children especially with specific diseases such as spina bifida, epilepsy, chronic liver disease, and atopic dermatitis. Only a few large surveys were conducted involving children and adolescents. Across the specific target population studied, vitamin D deficiency and insufficiency were seen particularly among females, Indians, and those of Malay ethnicity. This is related to widely known causes of vitamin D deficiency such as skin type (melanin) and sun avoidant lifestyles that include covering clothes, largely practiced by Malay Muslims in Malaysia. Other related causes or the high-risk groups are breastfed infants, the elderly, the obese, those on medications, and those characterized by fat malabsorption and geophysical factors. Vitamin D deficiency can be managed with pharmacological or non-pharmacological approaches, depending on the severity. The objective is to raise serum vitamin D to a normal level, hence, relieving the symptoms and reducing the adverse health outcomes. Despite no clear guidelines in treating vitamin D deficiency in Malaysia, this condition can be prevented with taking adequate vitamin D in food resources, sun exposure, or supplementation. Special attention should be given to high-risk groups including infants, obese patients, and the elderly.
Background Similar to other coronaviruses, COVID-19 is transmitted mainly by droplets and is highly transmissible through close proximity or physical contact with an infected person. Countries across the globe have implemented public health control measures to prevent onwards transmission and reduce burden on health care settings. Social or physical distancing was found to be one of appropriate measure based on previous experience with epidemic and pandemic contagious diseases. This study aims to review the latest evidence of the impact of social or physical distancing implemented during COVID-19 pandemic towards COVID-19 and other related infectious disease transmission. Methodology The study uses PRISMA review protocol and formulation of research question was based on PICO. The selected databases include Ovid MEDLINE and Scopus. Thorough identification, screening and eligibility process were done, revealed selected 8 articles. The articles then ranked in quality through MMAT. Results A total of eight papers included in this analysis. Five studies (USA, Canada, South Korea and the United Kingdom) showed physical distancing had resulted in a reduction in Covid-19 transmission. In comparison, three other studies (Australia, South Korea and Finland) showed a similar decline on other infectious diseases (Human Immunodeficiency Virus (HIV), other sexually transmitted infections (STI), Influenza, Respiratory Syncytial Virus (RSV) and Vaccine-Preventive Disease (VPD). The degree of the distancing policy implemented differ between strict and lenient, with both result in effectiveness in reducing transmission of infectious disease. Conclusion Physical or social distancing may come in the form of extreme or lenient measure in effectively containing contagious disease like COVID-19, however the stricter the measure will give more proportionate impact towards the economy, education, mental health issues, morbidity and mortality of non-COVID-19 diseases. Since we need this measure to ensure the reduction of infectious diseases transmission in order to help flattening the curve which allow much needed time for healthcare system to prepare adequately to response, ‘Precision physical distancing” can be implemented which will have more benefit towards the survival of the community as a whole.
BACKGROUND: Multiple studies have been conducted on the level of knowledge, attitude, and preventive practices (KAP) towards leptospirosis, descriptively, analytically pertaining to its relationship and also associated factors such as sociodemographic and economic factors. Over the years, different community settings and sampling frames were applied. AIM: The goal of this review is to identify available literature evidence on the community's knowledge, attitudes, and behaviours about leptospirosis, taking into account variations and similarities in techniques, tools, and data analysis. METHODS: A literature search was undertaken using the electronic databases PubMed, Scopus, Web of Science, and Ovid. Open access articles produced between 2011 and 2021 were analysed, with an emphasis on community's KAP. RESULTS: Eight articles met the inclusion benchmarks. The relationship between knowledge, attitude and preventive practices is not congruent. However, most studies showed that good knowledge is attributed to good attitude, but attitude does not necessarily contribute to good practice. Socio-demographic factors such as educational level, ethnicity, age, income and geographical location (distance to the river) have an influence on knowledge, attitude and practice. CONCLUSION: More KAP studies with standardised methodology and questionnaires regarding leptospirosis are required in order to formulate effective, sustainable and replicable health program interventions to prevent the community from leptospirosis infection and fatality. In the future, more qualitative studies should be done to further investigate and combine with quantitative studies to form prediction modelling.
BACKGROUND: Dengue fever outbreaks have been an important public health issue causing high morbidity and mortality, and serious economic effects, particularly in Asia. Control strategies are a challenge to be implemented due to a variety of factors. However, new approaches such as Wolbachia-infected Aedes aegypti have been shown to successfully lowering the life spans of the mosquito, eggs resistance, and disease transmission capabilities. Field trials are still on-going, and there are data to support its benefit in a large population. This systematic review aims to determine the current progress and impact of using Wolbachia in curbing dengue cases in high dengue case locations worldwide. METHODOLOGY: The study uses the Preferred Reporting Items for Systematic reviews and Meta-Analyses review protocol, while the formulation of the research question was based on population of interest, comparison, and outcome. The selected databases include Web of Science, Scopus, PubMed, SAGE, and EBSCOhost. A thorough identification, screening, and included process were done and the results retrieved four articles. These articles were then ranked based on quality using mixed methods appraisal tool. RESULTS: A total of four articles were included from 2019 and 2020 reports in both dengue- and non-dengue-endemic settings. In this review, comparisons in terms of the hierarchy of the study design, community engagement and acceptance, Wolbachia-infected A. aegypti deployment, entomological outcome, and epidemiological outcomes were detailed. All four studies showed a decrease in dengue incidence in Wolbachia-intervention populations. CONCLUSION: Wolbachia programs have been shown to be an effective method in combating dengue diseases. Strong community engagement and involvement from multidisciplinary teams are important factors to ensure the effectiveness and good outcomes of the program.
Introduction: The increasing prevalence of type 2 Diabetes Mellitus (DM) can be done from identifying those with prediabetes and offer early interventions by utilising prescreening diagnostic tools. Machine learning algorithms and big data mining approaches have been postulated for predictive disease modelling in hospital and clinical settings. Aim: This review was aimed at outlining the relative performance accuracies in predicting prediabetes conditions in different machine learning algorithms. Materials and Methods: A systematic literature search was conducted at Universiti of Kebangsaan Malaysia Kuala Lumpur, based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) review protocol, and the research question was formulated based on the keywords of "Prediabetes" (Population), "Internet of Things" and "prediction model" (Intervention) and "screening" and "risk" (Outcome). International Prospective Register of Systematic Reviews (PROSPERO) registration (CRD42021264947) was done and databases were screened on 10th-24th June 2021 via Web of Science, Scopus, PubMed, Ovid and EBSCOhost. Inclusion criteria was English language prediction studies published between 2011-2021. Review articles, editorials, proceedings, commentary articles and articles not focusing on prediabetes were excluded. The quality of the articles was ranked via the Prediction Model Risk of Bias Assessment Tool (PROBAST). Results: Five articles included were published in 2014-2021. The sample sizes ranged from 570 to 24,331 participants. Three studies (South Korea, United State of America (USA), Japan) suggested the applicability of the screening score prediction models for use in clinical settings related to personalised risk assessment and targeted interventions, with the predictors used being suitable for either the clinic or hospital. The simplicity of gender, age, Body Mass Index (BMI), blood pressure and waist circumference as predictors suggested that they can be utilised by the community. Conclusion: This review highlights the fact that the heterogeneity of the population used and validation issues may affect generalisation. Future studies should address these concerns to guide advocacy among healthcare providers in clinical practice as well as in data and expertise sharing for developing and validating urgently needed prediabetic prediction models.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.