The global estimate of premature cardiovascular mortality: a systematic review and meta-analysis of age-standardized mortality rate.

BACKGROUND: Cardiovascular disease (CVD) is a significant cause of premature mortality worldwide, with a growing burden in recent years. Despite this, there is a lack of comprehensive meta-analyses that quantify the extent of premature CVD mortality. Study addressed this gap by estimating the pooled age-standardized mortality rate (ASMR) of premature CVD mortality. METHODS: We conducted a systematic review of published CVD mortality studies that reported ASMR as an indicator for premature mortality measurement. All English articles published as of October 2022 were searched in four electronic databases: PubMed, Scopus, Web of Science (WoS), and the Cochrane Central Register of Controlled Trials (CENTRAL). We computed pooled estimates of ASMR using random-effects meta-analysis. We assessed heterogeneity from the selected studies using the I2 statistic. Subgroup analyses and meta regression analysis was performed based on sex, main CVD types, income country level, study time and age group. The analysis was performed using R software with the "meta" and "metafor" packages. RESULTS: A total of 15 studies met the inclusion criteria. The estimated global ASMR for premature mortality from total CVD was 96.04 per 100,000 people (95% CI: 67.18, 137.31). Subgroup analysis by specific CVD types revealed a higher ASMR for ischemic heart disease (ASMR = 15.57, 95% CI: 11.27, 21.5) compared to stroke (ASMR = 12.36, 95% CI: 8.09, 18.91). Sex-specific differences were also observed, with higher ASMRs for males (37.50, 95% CI: 23.69, 59.37) than females (15.75, 95% CI: 9.61, 25.81). Middle-income countries had a significantly higher ASMR (90.58, 95% CI: 56.40, 145.48) compared to high-income countries (21.42, 95% CI: 15.63, 29.37). Stratifying by age group indicated that the age groups of 20-64 years and 30-74 years had a higher ASMR than the age group of 0-74 years. Our multivariable meta-regression model suggested significant differences in the adjusted ASMR estimates for all covariates except study time. CONCLUSIONS: This meta-analysis synthesized a comprehensive estimate of the worldwide burden of premature CVD mortality. Our findings underscore the continued burden of premature CVD mortality, particularly in middle-income countries. Addressing this issue requires targeted interventions to mitigate the high risk of premature CVD mortality in these vulnerable populations.

Risk Factors for COVID-19 Mortality in Malaysia.

Background: Understanding the risks of COVID-19 mortality helps in the planning and prevention of the disease. This study aimed to determine the risk factors for COVID-19 mortality in Malaysia. Methods: Secondary online data provided by the Ministry of Health, Malaysia and Malaysia's national COVID-19 immunisation programme were used: i) COVID-19 deaths data; ii) vaccination coverage data and iii) population estimate data. Quasi-Poisson regression was performed to determine the risk factors for COVID-19 mortality. Results: Four risk factors were identified: i) vaccination status (partial versus unvaccinated, incidence rate ratio [IRR]: 0.59; 95% CI: 0.54, 0.64; complete versus unvaccinated, IRR: 0.50; 95% CI: 0.45, 0.56; booster versus unvaccinated, IRR: 0.13; 95% CI: 0.05, 0.26); ii) age group (19 years old-59 years old versus above 60 years old, IRR: 0.90; 95% CI: 0.84, 0.97; 13 years old-18 years old versus above 60 years old, IRR: 0.09; 95% CI: 0.04, 0.19; 6 years old-12 years old versus above 60 years old, IRR: 0.09; 95% CI: 0.03, 0.22; below 5 years old versus above 60 years old, IRR: 0.11; 95% CI: 0.04, 0.23); iii) gender (male versus female, IRR: 1.23; 95% CI: 1.14, 1.32) and iv) comorbidity (yes versus no, IRR: 2.13; 95% CI: 1.96, 2.32). Conclusion: This study highlighted the risk factors for COVID-19 mortality and the benefit of COVID-19 vaccination, especially of booster vaccination, in reducing the risk of COVID-19 mortality in Malaysia.

Modelling excess mortality among breast cancer patients in the North East Region of Peninsular Malaysia, 2007-2011: a population-based study.

BACKGROUND: Measurement of breast cancer burden and identification of its influencing factors help in the development of public health policy and strategy against the disease. This study aimed to examine the variability of the excess mortality of female breast cancer patients in the North East Region of Peninsular Malaysia. METHODS: This retrospective cohort study was conducted using breast cancer data from the Kelantan Cancer Registry between 2007 and 2011, and Kelantan general population mortality data. The breast cancer cases were followed up for 5 years until 2016. Out of 598 cases, 549 cases met the study criteria and were included in the analysis. Modelling of excess mortality was conducted using Poisson regression. RESULTS: Excess mortality of breast cancer varied according to age group (50 years old and below vs above 50 years old, Adj. EHR: 1.47; 95% CI: 1.31, 4.09; P = 0.004), ethnicity (Malay vs non-Malay, Adj. EHR: 2.31; 95% CI: 1.11, 1.96; P = 0.008), and stage (stage III and IV vs. stage I and II, Adj. EHR: 5.75; 95% CI: 4.24, 7.81; P < 0.001). CONCLUSIONS: Public health policy and strategy aim to improve cancer survival should focus more on patients presented at age below 50 years old, Malay ethnicity, and at a later stage.

A Bibliometric Analysis of Stroke Caregiver Research from 1989 to 2022.

Many stroke survivors suffer with varying degrees of disability and require assistance. Family members commonly act as informal caregivers, caring for these stroke survivors and ensuring care adherence. However, many caregivers reported a poor quality of life and physical and psychological distress. Due to these issues, multiple studies have been conducted to understand the experience of caregivers, the outcomes of caregiving, and interventional studies among caregivers. This study aims to explore the intellectual landscape of studies on stroke caregivers using bibliometric analysis. Studies with "stroke" and "caregiver" terms in the title were extracted from the Web of Sciences (WOS) database. The resulting publications were analysed using the 'bibliometrix' package in R. There were 678 publications analysed, dating from 1989 to 2022. The USA has the highest number of publications (28.6%), followed by China (12.1%) and Canada (6.1%). The most productive institution, journal and author were The University of Toronto (9.5%), 'Topics in Stroke Rehabilitation' journal (5.8%) and Tamilyn Bakas (3.1%), respectively. Co-occurrences keywords analysis revealed mainstream research on stroke survivors, burden, quality of life, depression, care, and rehabilitation, reflecting the timeless hotspot in the field. This bibliometric analysis helps us understand the current state of stroke caregiver research and its recent developments. This study can be used to evaluate research policies and promote international cooperation.

Developing a Supplementary Diagnostic Tool for Breast Cancer Risk Estimation Using Ensemble Transfer Learning.

Breast cancer is the most prevalent cancer worldwide. Thus, it is necessary to improve the efficiency of the medical workflow of the disease. Therefore, this study aims to develop a supplementary diagnostic tool for radiologists using ensemble transfer learning and digital mammograms. The digital mammograms and their associated information were collected from the department of radiology and pathology at Hospital Universiti Sains Malaysia. Thirteen pre-trained networks were selected and tested in this study. ResNet101V2 and ResNet152 had the highest mean PR-AUC, MobileNetV3Small and ResNet152 had the highest mean precision, ResNet101 had the highest mean F1 score, and ResNet152 and ResNet152V2 had the highest mean Youden J index. Subsequently, three ensemble models were developed using the top three pre-trained networks whose ranking was based on PR-AUC values, precision, and F1 scores. The final ensemble model, which consisted of Resnet101, Resnet152, and ResNet50V2, had a mean precision value, F1 score, and Youden J index of 0.82, 0.68, and 0.12, respectively. Additionally, the final model demonstrated balanced performance across mammographic density. In conclusion, this study demonstrates the good performance of ensemble transfer learning and digital mammograms in breast cancer risk estimation. This model can be utilised as a supplementary diagnostic tool for radiologists, thus reducing their workloads and further improving the medical workflow in the screening and diagnosis of breast cancer.

Premature mortality and years of potential life lost from cardiovascular diseases: Protocol of a systematic review and meta-analysis.

INTRODUCTION: Despite the burden of cardiovascular disease (CVD) continuing to increase globally, no comprehensive meta-analyses have been conducted quantifying premature CVD mortality. This paper reports the protocol for a systematic review and meta-analysis to derive updated estimates of premature CVD mortality. METHODS AND EXPECTED OUTPUTS: This review will include the studies that reported premature CVD mortality based on standard premature mortality indicators, including years of life lost (YLL), age standardized mortality rate (ASMR) or standardised mortality ratio (SMR). PUBMED, Scopus, Web of Science (WoS), CINAHL, and Cochrane Central Register of Controlled Trials (CENTRAL) will be used as the literature databases. The study selection as well as the evaluation of the quality of the included articles will be done independently by two reviewers. Pooled estimates of YLL, ASMR, and SMR will be computed by applying random-effects meta-analysis. Heterogeneity among selected studies will be assessed using the I2 statistic and Q statistic with associated p-values. A funnel plot analysis and Egger's test will be conducted to assess the potential impact of publication bias. Depending on data availability, we propose to conduct subgroup analyses by sex, geographic location, main CVD types, and study time. Reporting of our findings will follow the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines. CONCLUSION: Our meta-analysis will provide a comprehensive synthesis of the available evidence on premature CVD mortality, which is a major public health concern worldwide. The results of this meta-analysis will have important implications for clinical practice and public health policy, providing insights into strategies to prevent and manage premature CVD mortality. TRIAL REGISTRATION: Systematic review registration: PROSPERO CRD42021288415. https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021288415.

The burden of premature mortality from cardiovascular diseases: A systematic review of years of life lost.

INTRODUCTION: Premature mortality refers to deaths that occur before the expected age of death in a given population. Years of life lost (YLL) is a standard parameter that is frequently used to quantify some component of an "avoidable" mortality burden. OBJECTIVE: To identify the studies on premature cardiovascular disease (CVD) mortality and synthesise their findings on YLL based on the regional area, main CVD types, sex, and study time. METHOD: We conducted a systematic review of published CVD mortality studies that reported YLL as an indicator for premature mortality measurement. A literature search for eligible studies was conducted in five electronic databases: PubMed, Scopus, Web of Science (WoS), and the Cochrane Central Register of Controlled Trials (CENTRAL). The Newcastle-Ottawa Scale was used to assess the quality of the included studies. The synthesis of YLL was grouped into years of potential life lost (YPLL) and standard expected years of life lost (SEYLL) using descriptive analysis. These subgroups were further divided into WHO (World Health Organization) regions, study time, CVD type, and sex to reduce the effect of heterogeneity between studies. RESULTS: Forty studies met the inclusion criteria for this review. Of these, 17 studies reported premature CVD mortality using YPLL, and the remaining 23 studies calculated SEYLL. The selected studies represent all WHO regions except for the Eastern Mediterranean. The overall median YPLL and SEYLL rates per 100,000 population were 594.2 and 1357.0, respectively. The YPLL rate and SEYLL rate demonstrated low levels in high-income countries, including Switzerland, Belgium, Spain, Slovenia, the USA, and South Korea, and a high rate in middle-income countries (including Brazil, India, South Africa, and Serbia). Over the past three decades (1990-2022), there has been a slight increase in the YPLL rate and the SEYLL rate for overall CVD and ischemic heart disease but a slight decrease in the SEYLL rate for cerebrovascular disease. The SEYLL rate for overall CVD demonstrated a notable increase in the Western Pacific region, while the European region has experienced a decline and the American region has nearly reached a plateau. In regard to sex, the male showed a higher median YPLL rate and median SEYLL rate than the female, where the rate in males substantially increased after three decades. CONCLUSION: Estimates from both the YPLL and SEYLL indicators indicate that premature CVD mortality continues to be a major burden for middle-income countries. The pattern of the YLL rate does not appear to have lessened over the past three decades, particularly for men. It is vitally necessary to develop and execute strategies and activities to lessen this mortality gap. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42021288415.

Diagnostic Accuracy of Machine Learning Models on Mammography in Breast Cancer Classification: A Meta-Analysis.

In this meta-analysis, we aimed to estimate the diagnostic accuracy of machine learning models on digital mammograms and tomosynthesis in breast cancer classification and to assess the factors affecting its diagnostic accuracy. We searched for related studies in Web of Science, Scopus, PubMed, Google Scholar and Embase. The studies were screened in two stages to exclude the unrelated studies and duplicates. Finally, 36 studies containing 68 machine learning models were included in this meta-analysis. The area under the curve (AUC), hierarchical summary receiver operating characteristics (HSROC) curve, pooled sensitivity and pooled specificity were estimated using a bivariate Reitsma model. Overall AUC, pooled sensitivity and pooled specificity were 0.90 (95% CI: 0.85-0.90), 0.83 (95% CI: 0.78-0.87) and 0.84 (95% CI: 0.81-0.87), respectively. Additionally, the three significant covariates identified in this study were country (p = 0.003), source (p = 0.002) and classifier (p = 0.016). The type of data covariate was not statistically significant (p = 0.121). Additionally, Deeks' linear regression test indicated that there exists a publication bias in the included studies (p = 0.002). Thus, the results should be interpreted with caution.

Top 100 Most-Cited Publications on Breast Cancer and Machine Learning Research: A Bibliometric Analysis.

BACKGROUND: Rapid advancement in computing technology and digital information leads to the possible use of machine learning on breast cancer. OBJECTIVE: This study aimed to evaluate the research output of the top 100 publications and further identify a research theme of breast cancer and machine-learning studies. METHODS: Databases of Scopus and Web of Science were used to extract the top 100 publications. These publications were filtered based on the total citation of each paper. Additionally, a bibliometric analysis was applied to the top 100 publications. RESULTS: The top 100 publications were published between 1993 and 2019. The most productive author was Giger ML, and the top two institutions were the University of Chicago and the National University of Singapore. The most active countries were the USA, Germany, and China. Ten clusters were identified as both basic and specialised themes of breast cancer and machine learning. CONCLUSION: Various countries demonstrated comparable interest in breast cancer and machine-learning research. A few Asian countries, such as China, India and Singapore, were listed in the top 10 countries based on the total citation. Additionally, the use of deep learning and breast imaging data was trending in the past 10 years in the field of breast cancer and machine-learning research.

A Global Bibliometric Analysis on Antibiotic-Resistant Active Pulmonary Tuberculosis over the Last 25 Years (1996-2020).

BACKGROUND: Tuberculosis (TB) is still a leading global cause of mortality and an increasingly crucial problem in fighting TB is antibiotic resistance. We aimed to conduct a bibliometric analysis on the articles of the past 25 years on antibiotic-resistant active pulmonary TB. METHODS: Appropriate keywords were combined using the Boolean and wildcard operators and searched in Scopus database for articles published between 1996 and 2020 in English language. For all the bibliometric analyses, the Bibliometrix package in RStudio and Biblioshiny web apps were used. We identified the publication and citation trends, topmost cited documents, most productive authors, countries and institutions and most influential journals and funding agencies. We constructed collaborative networks of countries and co-citations. In addition, we developed a Three-Fields plot and a Thematic Map to explore different publication themes. RESULTS: We included 7024 articles (88.9% research articles) and a persistently increasing publication and citation trends were evident throughout the past 25 years. Boehme 2010 was the most cited paper (1609 times cited), Stefan Niemann was the most productive author (86 papers), and 'International Journal of Tuberculosis and Lung Disease' was the leading journal. Centers for Disease Control and Prevention was the top contributing institution (3.7% papers) and both US- and UK-based funders were leading. The most productive countries were the USA, India, the UK, South Africa, and China and most of the collaborations took place between the USA, the UK, and South Africa. CONCLUSION: Undoubtedly, researchers and funders from the USA dominated followed by the UK in most of the fields in antibiotic-resistant TB research. The outcomes of antibiotic-resistant TB research would be more productive and translational if researchers from low- or middle-income countries (especially from Africa, South America and Asia) with high research productivity and TB burden could be in collaboration with high-income countries exhibiting low TB burden.

Factors Influencing Mammographic Density in Asian Women: A Retrospective Cohort Study in the Northeast Region of Peninsular Malaysia.

Mammographic density is a significant risk factor for breast cancer. In this study, we identified the risk factors of mammographic density in Asian women and quantified the impact of breast density on the severity of breast cancer. We collected data from Hospital Universiti Sains Malaysia, a research- and university-based hospital located in Kelantan, Malaysia. Multivariable logistic regression was performed to analyse the data. Five significant factors were found to be associated with mammographic density: age (OR: 0.94; 95% CI: 0.92, 0.96), number of children (OR: 0.88; 95% CI: 0.81, 0.96), body mass index (OR: 0.88; 95% CI: 0.85, 0.92), menopause status (yes vs. no, OR: 0.59; 95% CI: 0.42, 0.82), and BI-RADS classification (2 vs. 1, OR: 1.87; 95% CI: 1.22, 2.84; 3 vs. 1, OR: 3.25; 95% CI: 1.86, 5.66; 4 vs. 1, OR: 3.75; 95% CI: 1.88, 7.46; 5 vs. 1, OR: 2.46; 95% CI: 1.21, 5.02; 6 vs. 1, OR: 2.50; 95% CI: 0.65, 9.56). Similarly, the average predicted probabilities were higher among BI-RADS 3 and 4 classified women. Understanding mammographic density and its influencing factors aids in accurately assessing and screening dense breast women.

Bibliometric Analysis of Global Research Activity on Premature Mortality.

Premature mortality is defined as death that occurs before the average age of death for a particular population. Although premature mortality is a public health problem globally, the literature indicates no bibliometric studies that have made a holistic evaluation of the publications on this issue. This study aims to explore the characteristics of the publications on premature mortality in terms of the number of publications, citations, countries, collaboration, and the author's productivity and to further identify the trending keyword and relevant research topics. All the articles related to premature mortality data were retrieved from the Web of Science (WOS) database using the search terms "premature death," "premature mortality," or "years of life loss." The retrieved articles were downloaded in a BibTeX format file. A Bibliometrix package from R software was used to perform bibliometric analyses. A total of 1060 original research articles and reviews have been published since 1971, with a total of 5499 contributing authors. The number of publications has increased substantially in the past decade. The annual percentage growth rate of publications is 5.08%. The United States is the leading country in this area of research with the highest number of publications (n = 280), the highest total citation (17,378), and the most activity in collaboration. Our thematic map suggests that the cluster for cardiovascular disease became the main research domain in this field, while the cluster for air pollution is an important topic for future research. Additionally, neurodegeneration is another cluster of research that should be developed further and connected with premature mortality. These bibliometric findings hopefully will help scholars better understand the global overview of premature mortality and provide information for potential collaborators, with the information promising attractive areas for future research.

Over-the-Counter Breast Cancer Classification Using Machine Learning and Patient Registration Records.

This study aims to determine the feasibility of machine learning (ML) and patient registration record to be utilised to develop an over-the-counter (OTC) screening model for breast cancer risk estimation. Data were retrospectively collected from women who came to the Hospital Universiti Sains Malaysia, Malaysia for breast-related problems. Eight ML models were used: k-nearest neighbour (kNN), elastic-net logistic regression, multivariate adaptive regression splines, artificial neural network, partial least square, random forest, support vector machine (SVM), and extreme gradient boosting. Features utilised for the development of the screening models were limited to information in the patient registration form. The final model was evaluated in terms of performance across a mammographic density. Additionally, the feature importance of the final model was assessed using the model agnostic approach. kNN had the highest Youden J index, precision, and PR-AUC, while SVM had the highest F2 score. The kNN model was selected as the final model. The model had a balanced performance in terms of sensitivity, specificity, and PR-AUC across the mammographic density groups. The most important feature was the age at examination. In conclusion, this study showed that ML and patient registration information are feasible to be used as the OTC screening model for breast cancer.

Net survival differences of breast cancer between stages at diagnosis and age groups in the east coast region of West Malaysia: a retrospective cohort study.

OBJECTIVE: Estimation of the net survival of breast cancer helps in assessing breast cancer burden at a population level. Thus, this study aims to estimate the net survival of breast cancer at different cancer staging and age at diagnosis in the east coast region of West Malaysia. SETTING: Kelantan, Malaysia. PARTICIPANTS: All breast cancer cases diagnosed in 2007 and 2011 identified from Kelantan Cancer Registry. DESIGN: This retrospective cohort study used a relative survival approach to estimate the net survival of patients with breast cancer. Thus, two data were needed; breast cancer data from Kelantan Cancer Registry and general population mortality data for Kelantan population. PRIMARY AND SECONDARY OUTCOME MEASURES: Net survival according to stage and age group at diagnosis at 1, 3 and 5 years following diagnosis. RESULTS: The highest net survival was observed among stage I and II breast cancer cases, while the lowest net survival was observed among stage IV breast cancer cases. In term of age at diagnosis, breast cancer cases aged 65 and older had the best net survival compared with the other age groups. CONCLUSION: The age at diagnosis had a minimal impact on the net survival compared with the stage at diagnosis. The finding of this study is applicable to other populations with similar breast cancer profile.

Measuring Time-Varying Effective Reproduction Numbers for COVID-19 and Their Relationship with Movement Control Order in Malaysia.

To curb the spread of SARS-CoV-2 virus (COVID-19) in Malaysia, the government imposed a nationwide movement control order (MCO) from 18 March 2020 to 3 May 2020. It was enforced in four phases (i.e., MCO 1, MCO 2, MCO 3 and MCO 4). In this paper, we propose an initiative to assess the impact of MCO by using time-varying reproduction number (Rt). We used data from the Johns Hopkins University Centre for Systems Science and Engineering Coronavirus repository. Day 1 was taken from the first assumed local transmission of COVID-19. We estimated Rt by using the EpiEstim package and plotted the epidemic curve and Rt. Then, we extracted the mean Rt at day 1, day 5 and day 10 for all MCO phases and compared the differences. The Rt values peaked around day 43, which was shortly before the start of MCO 1. The means for Rt at day 1, day 5, and day 10 for all MCOs ranged between 0.665 and 1.147. The average Rt gradually decreased in MCO 1 and MCO 2. Although spikes in the number of confirmed cases were observed when restrictions were gradually relaxed in the later MCO phases, the situation remained under control with Rt values being stabilised to below unity level (Rt value less than one).