Cancer screening research in Bangladesh: Insights from a scoping review.

This scoping review summarises the findings of research conducted on cancer screening in Bangladesh, including the prevalence, awareness, barriers, and evaluation of screening programmes, by performing a comprehensive search of electronic databases and gray literature. 25 studies that met inclusion criteria were included in the final analysis. Most of the studies were about screening for cervical cancer, were quantitative, were cross-sectional, and were conducted in hospital settings. The main challenges to screening uptake were shyness, fear, a lack of knowledge, and an inadequate understanding of the concept of screening. Visual inspection with acetic acid (VIA) was found to be a simple and cost-efficient way to detect early-stage cervical cancer. However, breast self-examination (BSE) was reported to be insufficient. Education was found to have a positive impact on cancer screening knowledge and practice, but more needs to be done to improve screening rates, such as the utilisation of media, particularly in rural areas. The results of this scoping review highlight Bangladesh's low cancer screening prevalence and uptake and suggest that targeted awareness campaigns and enhanced access to screening services are required to increase cancer screening uptake and reduce the cancer burden in Bangladesh.

A comparison of machine learning algorithms and traditional regression-based statistical modeling for predicting hypertension incidence in a Canadian population.

Risk prediction models are frequently used to identify individuals at risk of developing hypertension. This study evaluates different machine learning algorithms and compares their predictive performance with the conventional Cox proportional hazards (PH) model to predict hypertension incidence using survival data. This study analyzed 18,322 participants on 24 candidate features from the large Alberta's Tomorrow Project (ATP) to develop different prediction models. To select the top features, we applied five feature selection methods, including two filter-based: a univariate Cox p-value and C-index; two embedded-based: random survival forest and least absolute shrinkage and selection operator (Lasso); and one constraint-based: the statistically equivalent signature (SES). Five machine learning algorithms were developed to predict hypertension incidence: penalized regression Ridge, Lasso, Elastic Net (EN), random survival forest (RSF), and gradient boosting (GB), along with the conventional Cox PH model. The predictive performance of the models was assessed using C-index. The performance of machine learning algorithms was observed, similar to the conventional Cox PH model. Average C-indexes were 0.78, 0.78, 0.78, 0.76, 0.76, and 0.77 for Ridge, Lasso, EN, RSF, GB and Cox PH, respectively. Important features associated with each model were also presented. Our study findings demonstrate little predictive performance difference between machine learning algorithms and the conventional Cox PH regression model in predicting hypertension incidence. In a moderate dataset with a reasonable number of features, conventional regression-based models perform similar to machine learning algorithms with good predictive accuracy.

Development and validation of a hypertension risk prediction model and construction of a risk score in a Canadian population.

Identifying high-risk individuals for targeted intervention may prevent or delay hypertension onset. We developed a hypertension risk prediction model and subsequent risk sore among the Canadian population using measures readily available in a primary care setting. A Canadian cohort of 18,322 participants aged 35-69 years without hypertension at baseline was followed for hypertension incidence, and 625 new hypertension cases were reported. At a 2:1 ratio, the sample was randomly divided into derivation and validation sets. In the derivation sample, a Cox proportional hazard model was used to develop the model, and the model's performance was evaluated in the validation sample. Finally, a risk score table was created incorporating regression coefficients from the model. The multivariable Cox model identified age, body mass index, systolic blood pressure, diabetes, total physical activity time, and cardiovascular disease as significant risk factors (p < 0.05) of hypertension incidence. The variable sex was forced to enter the final model. Some interaction terms were identified as significant but were excluded due to their lack of incremental predictive capacity. Our model showed good discrimination (Harrel's C-statistic 0.77) and calibration (Grønnesby and Borgan test, [Formula: see text] statistic = 8.75, p = 0.07; calibration slope 1.006). A point-based score for the risks of developing hypertension was presented after 2-, 3-, 5-, and 6 years of observation. This simple, practical prediction score can reliably identify Canadian adults at high risk of developing incident hypertension in the primary care setting and facilitate discussions on modifying this risk most effectively.

Prediction of hypertension using traditional regression and machine learning models: A systematic review and meta-analysis.

OBJECTIVE: We aimed to identify existing hypertension risk prediction models developed using traditional regression-based or machine learning approaches and compare their predictive performance. METHODS: We systematically searched MEDLINE, EMBASE, Web of Science, Scopus, and the grey literature for studies predicting the risk of hypertension among the general adult population. Summary statistics from the individual studies were the C-statistic, and a random-effects meta-analysis was used to obtain pooled estimates. The predictive performance of pooled estimates was compared between traditional regression-based models and machine learning-based models. The potential sources of heterogeneity were assessed using meta-regression, and study quality was assessed using the PROBAST (Prediction model Risk Of Bias ASsessment Tool) checklist. RESULTS: Of 14,778 articles, 52 articles were selected for systematic review and 32 for meta-analysis. The overall pooled C-statistics was 0.75 [0.73-0.77] for the traditional regression-based models and 0.76 [0.72-0.79] for the machine learning-based models. High heterogeneity in C-statistic was observed. The age (p = 0.011), and sex (p = 0.044) of the participants and the number of risk factors considered in the model (p = 0.001) were identified as a source of heterogeneity in traditional regression-based models. CONCLUSION: We attempted to provide a comprehensive evaluation of hypertension risk prediction models. Many models with acceptable-to-good predictive performance were identified. Only a few models were externally validated, and the risk of bias and applicability was a concern in many studies. Overall discrimination was similar between models derived from traditional regression analysis and machine learning methods. More external validation and impact studies to implement the hypertension risk prediction model in clinical practice are required.

Hypertension prevalence and its trend in Bangladesh: evidence from a systematic review and meta-analysis.

BACKGROUND: Hypertension, itself being a major chronic condition, is one of the most significant risk factors for premature cardiovascular diseases and mortality. Hypertension is responsible for 13% of global deaths and three-quarters of the world's hypertensive population reside in low- and middle-income countries. Bangladesh is one of those countries that experiencing an epidemiological transition from communicable to non-communicable diseases, a nutritional transition from a traditional diet to process and fast food, and an increase in a sedentary lifestyle, resulting in increased hypertension prevalence. We carried out a systematic review and meta-analysis to identify existing research on hypertension prevalence in Bangladesh, summarize findings and assess its temporal change. METHODS: We searched MEDLINE, EMBASE and PubMed and relevant references to identify studies on the prevalence of hypertension in Bangladesh. We used Random-effects meta-analysis to pool the prevalence estimates and performed subgroup analyses. We assessed heterogeneity, a trend in prevalence of hypertension and publication bias in selected studies. RESULTS: Our search initially identified 735 articles and after removing duplicates, reviewing titles and abstracts, and screening full texts, 53 studies were finally selected. The studies comprised 305,432 subjects and reported overall, gender-specific, geographical location specific and criteria specific prevalence of hypertension. We identified the range of hypertension prevalence is from 1.10% to 75.0% and the overall weighted pooled prevalence of hypertension is 20.0%. An extremely high heterogeneity (I2 = 99.53%; Cochran Q-statistic p < 0.001) was observed in the prevalence of hypertension. Consequently, we performed subgroup analysis based on gender, age group and geographical location of the study participants, the cut-off level used to define hypertension, and the types of hypertension reported and presented our findings accordingly. An overall increasing trend of hypertension prevalence is also observed. CONCLUSIONS: The prevalence of hypertension is high and rising in Bangladesh. Strategies targeting prevention are required to mitigate a further increase in the prevalence and reduce the morbidity and mortality associated with it.

Knowledge mobilisation in bridging community-practice-academia-policy through meaningful engagement: systematic integrative review protocol focusing on studies conducted on health and wellness among immigrant communities.

INTRODUCTION: Though the importance of knowledge mobilisation has been established globally in health and wellness research, a certain degree of ambiguity remains regarding the meaning and extent of knowledge mobilisation activities and how they have been implemented. In this study, we aim to explore the different descriptions of knowledge mobilisation and the diverse ways mobilisation activities have been realised by different researchers working for the betterment of health and wellness of immigrant communities in their host countries. METHODS AND ANALYSIS: We aimed to conduct an integrative review to organise the available literature describing knowledge mobilisation pertaining to health and wellness in immigrant communities. We will employ a comprehensive search, using appropriate search-terms, to identify relevant literature and will qualitatively synthesise the information toward fulfilling our objectives. Specific methodological and analytical frameworks related to the integrative review process will guide each step of the process. A librarian designed the systematic search of the academic and grey literature from database inception to December 2019. The databases include MEDLINE (Ovid), Embase, PsycINFO, PubMed, CINAHL and SocINDEX. For grey literature, we will conduct searches in AHS Insite, Google, Google Scholar, OAISter and government websites. A two-stage (title-abstract and full-text) screening will be conducted, including single-citation tracking and hand search of reference lists. ETHICS AND DISSEMINATION: Ethical approval is not required for this review. We first plan to disseminate the results of our systematic review protocol through meetings with key stakeholders, followed by appropriate publications and presentations at applicable platforms. We also have opted for an integrated knowledge translation or community-engaged knowledge mobilisation approach where we have engaged with community-based citizen researchers from the inception of our research.

Community engagement with immigrant communities involving health and wellness research: a systematic review protocol towards developing a taxonomy of community engagement definitions, frameworks, and methods.

INTRODUCTION: The importance of community engagement has been established globally in health and wellness research. A certain degree of ambiguity remains, however, regarding the meaning of community engagement, which term has been used for various purposes and implemented in various forms. In this study, we aimed to explore the different definitions of community engagement, discuss the various objectives that have been proposed and uncover the diverse ways this concept has been implemented among researchers working for the betterment of the health and wellness of immigrant communities in host countries. METHODS AND ANALYSIS: Taxonomy is a process for classifying complex and multifaceted matters using logical conceptual domains and dimensions for clearer way of contextualising. We will develop a taxonomy to organise the available literature on community engagement in immigrant health and wellness research in a way that captures user knowledge and understanding of its various meanings and processes. Specific methodological and analytical frameworks for systematic review and taxonomy development will guide each step. We will conduct a comprehensive systematic search in relevant databases, from inception to December 2019, using appropriate keywords followed by snowball search (single-citation tracking, reference lists). Papers will be included if they fall within predefined inclusion criteria (seen as most likely informative on elements pertaining to community engagement) and are written in English, regardless of design (conceptual, qualitative and quantitative). Two reviewers will independently employ two-stage screening (title-abstract screening followed by screening of the full text to determine inclusion). Finally, information that helps to develop taxonomy of the concept and practice of community engagement will be abstracted and used towards taxonomy development, where different levels of stakeholder research team members will be involved. ETHICS AND DISSEMINATION: Ethical approval is not required for this systematic review. We have opted for an integrated knowledge translation or a community-engaged knowledge mobilisation approach where we are engaged with community-based citizen researchers from the inception of our programme. We plan to disseminate the results of our review through meetings with key stakeholders, followed by journal publications and presentations at applicable platforms.

Summarising and synthesising regression coefficients through systematic review and meta-analysis for improving hypertension prediction using metamodelling: protocol.

INTRODUCTION: Hypertension is one of the most common medical conditions and represents a major risk factor for heart attack, stroke, kidney disease and mortality. The risk of progression to hypertension depends on several factors, and combining these risk factors into a multivariable model for risk stratification would help to identify high-risk individuals who should be targeted for healthy behavioural changes and/or medical treatment to prevent the development of hypertension. The risk prediction models can be further improved in terms of accuracy by using a metamodel updating technique where existing hypertension prediction models can be updated by combining information available in existing models with new data. A systematic review and meta-analysis will be performed of hypertension prediction models in order to identify known risk factors for high blood pressure and to summarise the magnitude of their association with hypertension. METHODS AND ANALYSIS: MEDLINE, Embase, Web of Science, Scopus and grey literature will be systematically searched for studies predicting the risk of hypertension among the general population. The search will be based on two key concepts: hypertension and risk prediction. The summary statistics from the individual studies will be the regression coefficients of the hypertension risk prediction models, and random-effect meta-analysis will be used to obtain pooled estimates. Heterogeneity and publication bias will be assessed, along with study quality, which will be assessed using the Prediction Model Risk of Bias Assessment Tool checklist. ETHICS AND DISSEMINATION: Ethics approval is not required for this systematic review and meta-analysis. We plan to disseminate the results of our review through journal publications and presentations at applicable platforms.

Variable selection strategies and its importance in clinical prediction modelling.

Clinical prediction models are used frequently in clinical practice to identify patients who are at risk of developing an adverse outcome so that preventive measures can be initiated. A prediction model can be developed in a number of ways; however, an appropriate variable selection strategy needs to be followed in all cases. Our purpose is to introduce readers to the concept of variable selection in prediction modelling, including the importance of variable selection and variable reduction strategies. We will discuss the various variable selection techniques that can be applied during prediction model building (backward elimination, forward selection, stepwise selection and all possible subset selection), and the stopping rule/selection criteria in variable selection (p values, Akaike information criterion, Bayesian information criterion and Mallows' Cp statistic). This paper focuses on the importance of including appropriate variables, following the proper steps, and adopting the proper methods when selecting variables for prediction models.

Predicting the risk of stroke among patients with type 2 diabetes: a systematic review and meta-analysis of C-statistics.

OBJECTIVE: Stroke is a major cause of disability and death worldwide. People with diabetes are at a twofold to fivefold increased risk for stroke compared with people without diabetes. This study systematically reviews the literature on available stroke prediction models specifically developed or validated in patients with diabetes and assesses their predictive performance through meta-analysis. DESIGN: Systematic review and meta-analysis. DATA SOURCES: A detailed search was performed in MEDLINE, PubMed and EMBASE (from inception to 22 April 2019) to identify studies describing stroke prediction models. ELIGIBILITY CRITERIA: All studies that developed stroke prediction models in populations with diabetes were included. DATA EXTRACTION AND SYNTHESIS: Two reviewers independently identified eligible articles and extracted data. Random effects meta-analysis was used to obtain a pooled C-statistic. RESULTS: Our search retrieved 26 202 relevant papers and finally yielded 38 stroke prediction models, of which 34 were specifically developed for patients with diabetes and 4 were developed in general populations but validated in patients with diabetes. Among the models developed in those with diabetes, 9 reported their outcome as stroke, 23 reported their outcome as composite cardiovascular disease (CVD) where stroke was a component of the outcome and 2 did not report stroke initially as their outcome but later were validated for stroke as the outcome in other studies. C-statistics varied from 0.60 to 0.92 with a median C-statistic of 0.71 (for stroke as the outcome) and 0.70 (for stroke as part of a composite CVD outcome). Seventeen models were externally validated in diabetes populations with a pooled C-statistic of 0.68. CONCLUSIONS: Overall, the performance of these diabetes-specific stroke prediction models was not satisfactory. Research is needed to identify and incorporate new risk factors into the model to improve models' predictive ability and further external validation of the existing models in diverse population to improve generalisability.

Prevalence of cardiovascular disease among Bangladeshi adult population: a systematic review and meta-analysis of the studies.

BACKGROUND: Cardiovascular disease (CVD) is a group of conditions affecting the functioning of the heart or blood vessels and is one of the leading causes of death globally. Like other countries, CVD prevalence is also rising among the adults in Bangladesh. Epidemiological studies have shown not only a high CVD prevalence but also a significant increase in its prevalence in Bangladesh in the last few decades. To have a better understanding of the current CVD prevalence scenario, we conducted this systematic review and meta-analysis. Our objective was to assess the prevalence of CVD among the Bangladeshi adult population using evidence from the published scientific literature. METHODS: Electronic databases such as MEDLINE, Embase and PubMed were searched. We also manually checked the references of all relevant publications that describe the prevalence of CVD in Bangladeshi adults. To pool the CVD prevalence, we used random-effects meta-analysis. We assessed heterogeneity using both the formal tests and the subgroup analyses. We also assessed study quality and examined publication bias. RESULTS: We retrieved 755 potentially relevant papers through searches of electronic and gray literature, of which only 13 met inclusion criteria after the screening and were included in this review. Of the studies that met inclusion criteria, three were carried out in rural populations, five in both urban and rural populations and two in strictly urban populations. Male and female participation in the studies was almost equal. The weighted pooled prevalence of CVD was 5.0%, regardless of the types of CVD, gender and geographical location of the study participants. There was also a high heterogeneity in the observed CVD prevalence. Weighted pooled prevalence of overall CVD in the Bangladeshi population was higher in urban areas (8%) compared to rural areas (2%). However, no such difference was observed in terms of gender (3% for both males and females). The highest reported prevalence (21%) was for heart disease, while the lowest reported prevalence (1%) was for stroke. Sources of heterogeneity were often unexplained. The criteria used to assess study quality were fulfilled by only a few studies, and adequate sample size criteria was missed by almost all of them. In addition, there was evidence of small-study effects. CONCLUSION: A high CVD prevalence along with an upward trend was observed in Bangladeshi adults. Proper strategies are required for primary prevention of CVD so that a further increase can be alleviated and the morbidity and mortality associated with it can be reduced.

Prevalence of metabolic syndrome in Bangladesh: a systematic review and meta-analysis of the studies.

BACKGROUND: Metabolic syndrome (MS) is a cluster of health problems that set the stage for serious health conditions and places individuals at higher risk of cardiovascular disease, diabetes and stroke. The worldwide prevalence of MS in the adult population is on the rise and Bangladesh is no exception. According to some epidemiological study, MS is highly prevalent in Bangladesh and has increased dramatically in last few decades. To provide a clear picture of the current situation, we conducted a systematic review and meta-analysis with an objective to assess the prevalence of metabolic syndrome among the Bangladeshi population using data already published in the scientific literature. METHODS: We searched MEDLINE, EMBASE and PubMed and manually checked references of all identified relevant publications that described the prevalence of MS in Bangladesh. Random effects meta-analysis was used to pool the prevalence. Heterogeneity was explored using formal tests and subgroup analyses. Study quality and publication bias was also explored. RESULTS: Electronic and grey literature search retrieved 491 potentially relevant papers. After removing duplicates, reviewing titles and abstracts and screening full texts, 10 studies were finally selected. Most of the studies were conducted in rural populations and study participants were mostly females. The weighted pooled prevalence of metabolic syndrome regardless of gender and criteria used to define metabolic syndrome, was 30.0% with high heterogeneity observed. Weighted pooled prevalence of metabolic syndrome is higher in females (32%) compared to males (25%) though not statistically significant (p = 0.434). Prevalence was highest (37%) when Modified NCEP ATP III criteria was used to define MS, while it was lowest (20%) when WHO criteria was used. In most cases, geographical area (urban/rural) was identified as a source of heterogeneity between the studies. Most of the studies met study quality assessment criteria's except adequate sample size criteria and evidence of small study effect was also detected. CONCLUSIONS: The prevalence of metabolic syndrome is high and rising in Bangladesh. Strategies aimed at primary prevention are required to mitigate a further increase in the prevalence and for the reduction of the morbidity and mortality associated with metabolic syndrome.

Canadian Health Care System: Who Should Pay for All Medically Beneficial Treatments? A Burning Issue.

The Canadian health care system can be characterized as a mix of public and private participation, although it is often described as a publicly funded system. In Canada, "medically necessary" services are covered with public funds; however, the Canada Health Act provides no formal definition of medical necessity. The provincial and territorial health care insurance plans decide which services are medically necessary. As a result, coverage of hospital and medical services differs among provinces. Outpatient prescription drugs are not covered by public plans. The coverage for diagnostics and medications for rare diseases is also limited. Private insurance plans, often provided by employers, are an expensive solution, although coverage is not sufficient. Those who are unemployed, self-employed, or informally employed and those with rare diseases that require expensive treatments and drugs frequently are not covered by any plan and face financial difficulty paying for their prescriptions and treatments. As a result, many Canadians are struggling and facing inequality in acquiring medical services for rare diseases and outpatient prescription drugs due to an unfair Canadian health care system. This paper proposes some recommendation to make medical services more accessible and affordable to every Canadian.