Julio Frenk, Lincoln Chen, Zulfiqar A Bhutta, Jordan J. Cohen et al.
Jeremy Grimshaw, Liz Shirran, Ruth Thomas, G Mowatt et al.
BACKGROUND: Increasing recognition of the failure to translate research findings into practice has led to greater awareness of the importance of using active dissemination and implementation strategies. Although there is a growing body of research evidence about the effectiveness of different strategies, this is not easily accessible to policy makers and professionals. OBJECTIVES: To identify, appraise, and synthesize systematic reviews of professional educational or quality assurance interventions to improve quality of care. RESEARCH DESIGN: An overview was made of systematic reviews of professional behavior change interventions published between 1966 and 1998. RESULTS: Forty-one reviews were identified covering a wide range of interventions and behaviors. In general, passive approaches are generally ineffective and unlikely to result in behavior change. Most other interventions are effective under some circumstances; none are effective under all circumstances. Promising approaches include educational outreach (for prescribing) and reminders. Multifaceted interventions targeting different barriers to change are more likely to be effective than single interventions. CONCLUSIONS: Although the current evidence base is incomplete, it provides valuable insights into the likely effectiveness of different interventions. Future quality improvement or educational activities should be informed by the findings of systematic reviews of professional behavior change interventions.
Helen Noble, Joanna Smith
Evaluating the quality of research is essential if findings are to be utilised in practice and incorporated into care delivery. In a previous article we explored 'bias' across research designs and outlined strategies to minimise bias. Concepts such as reliability, validity and generalisability typically associated with quantitative research and alternative terminology will be compared in relation to their application to qualitative research. In addition, some of the strategies adopted by qualitative researchers to enhance the credibility of their research are outlined.
Lincoln Chen, Timothy Evans, Sudhir Anand, Jo Ivey Boufford et al.
In this analysis of the global workforce, the Joint Learning Initiative-a consortium of more than 100 health leaders-proposes that mobilisation and strengthening of human resources for health, neglected yet critical, is central to combating health crises in some of the world's poorest countries and for building sustainable health systems in all countries. Nearly all countries are challenged by worker shortage, skill mix imbalance, maldistribution, negative work environment, and weak knowledge base. Especially in the poorest countries, the workforce is under assault by HIV/AIDS, out-migration, and inadequate investment. Effective country strategies should be backed by international reinforcement. Ultimately, the crisis in human resources is a shared problem requiring shared responsibility for cooperative action. Alliances for action are recommended to strengthen the performance of all existing actors while expanding space and energy for fresh actors.
Greg Irving, Ana Luísa Neves, Hajira Dambha‐Miller, Ai Oishi et al.
OBJECTIVE: To describe the average primary care physician consultation length in economically developed and low-income/middle-income countries, and to examine the relationship between consultation length and organisational-level economic, and health outcomes. DESIGN AND OUTCOME MEASURES: This is a systematic review of published and grey literature in English, Chinese, Japanese, Spanish, Portuguese and Russian languages from 1946 to 2016, for articles reporting on primary care physician consultation lengths. Data were extracted and analysed for quality, and linear regression models were constructed to examine the relationship between consultation length and health service outcomes. RESULTS: One hundred and seventy nine studies were identified from 111 publications covering 28 570 712 consultations in 67 countries. Average consultation length differed across the world, ranging from 48 s in Bangladesh to 22.5 min in Sweden. We found that 18 countries representing about 50% of the global population spend 5 min or less with their primary care physicians. We also found significant associations between consultation length and healthcare spending per capita, admissions to hospital with ambulatory sensitive conditions such as diabetes, primary care physician density, physician efficiency and physician satisfaction. CONCLUSION: There are international variations in consultation length, and it is concerning that a large proportion of the global population have only a few minutes with their primary care physicians. Such a short consultation length is likely to adversely affect patient healthcare and physician workload and stress.
Tahmina Begum
Communication between patients and health professionals is seen as the core clinical function in building a therapeutic doctor-patient relationship, which is the heart and art of the medicine. Patients satisfaction is strongly influenced by the quality of the communication that occurs. Effective communication is the basis of mutual understanding and trust. This paper aims to raise awareness on the important issues involved in doctor-patient and inter-professional communication among the medical professionals.J Bangladesh Coll Phys Surg 2014; 32: 84-88
Christopher J L Murray, Cristiana Abbafati, Kaja Abbas, Mohammad Hossein Abbasi et al.
The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a rules-based synthesis of the available evidence on levels and trends in health outcomes, a diverse set of risk factors, and health system responses. GBD 2019 covered 204 countries and territories, as well as first administrative level disaggregations for 22 countries, from 1990 to 2019. Because GBD is highly standardised and comprehensive, spanning both fatal and non-fatal outcomes, and uses a mutually exclusive and collectively exhaustive list of hierarchical disease and injury causes, the study provides a powerful basis for detailed and broad insights on global health trends and emerging challenges. GBD 2019 incorporates data from 281 586 sources and provides more than 3·5 billion estimates of health outcome and health system measures of interest for global, national, and subnational policy dialogue. All GBD estimates are publicly available and adhere to the Guidelines on Accurate and Transparent Health Estimate Reporting. From this vast amount of information, five key insights that are important for health, social, and economic development strategies have been distilled. These insights are subject to the many limitations outlined in each of the component GBD capstone papers.
Gilles R. Dagenais, Darryl P. Leong, Sumathy Rangarajan, Fernando Laņas et al.
BACKGROUND To our knowledge, no previous study has prospectively documented the incidence of common diseases and related mortality in high-income countries (HICs), middle-income countries (MICs), and low-income countries (LICs) with standardised approaches. Such information is key to developing global and context-specific health strategies. In our analysis of the Prospective Urban Rural Epidemiology (PURE) study, we aimed to evaluate differences in the incidence of common diseases, related hospital admissions, and related mortality in a large contemporary cohort of adults from 21 HICs, MICs, and LICs across five continents by use of standardised approaches. METHODS The PURE study is a prospective, population-based cohort study of individuals aged 35-70 years who have been enrolled from 21 countries across five continents. The key outcomes were the incidence of fatal and non-fatal cardiovascular diseases, cancers, injuries, respiratory diseases, and hospital admissions, and we calculated the age-standardised and sex-standardised incidence of these events per 1000 person-years. FINDINGS This analysis assesses the incidence of events in 162 534 participants who were enrolled in the first two phases of the PURE core study, between Jan 6, 2005, and Dec 4, 2016, and who were assessed for a median of 9·5 years (IQR 8·5-10·9). During follow-up, 11 307 (7·0%) participants died, 9329 (5·7%) participants had cardiovascular disease, 5151 (3·2%) participants had a cancer, 4386 (2·7%) participants had injuries requiring hospital admission, 2911 (1·8%) participants had pneumonia, and 1830 (1·1%) participants had chronic obstructive pulmonary disease (COPD). Cardiovascular disease occurred more often in LICs (7·1 cases per 1000 person-years) and in MICs (6·8 cases per 1000 person-years) than in HICs (4·3 cases per 1000 person-years). However, incident cancers, injuries, COPD, and pneumonia were most common in HICs and least common in LICs. Overall mortality rates in LICs (13·3 deaths per 1000 person-years) were double those in MICs (6·9 deaths per 1000 person-years) and four times higher than in HICs (3·4 deaths per 1000 person-years). This pattern of the highest mortality in LICs and the lowest in HICs was observed for all causes of death except cancer, where mortality was similar across country income levels. Cardiovascular disease was the most common cause of deaths overall (40%) but accounted for only 23% of deaths in HICs (vs 41% in MICs and 43% in LICs), despite more cardiovascular disease risk factors (as judged by INTERHEART risk scores) in HICs and the fewest such risk factors in LICs. The ratio of deaths from cardiovascular disease to those from cancer was 0·4 in HICs, 1·3 in MICs, and 3·0 in LICs, and four upper-MICs (Argentina, Chile, Turkey, and Poland) showed ratios similar to the HICs. Rates of first hospital admission and cardiovascular disease medication use were lowest in LICs and highest in HICs. INTERPRETATION Among adults aged 35-70 years, cardiovascular disease is the major cause of mortality globally. However, in HICs and some upper-MICs, deaths from cancer are now more common than those from cardiovascular disease, indicating a transition in the predominant causes of deaths in middle-age. As cardiovascular disease decreases in many countries, mortality from cancer will probably become the leading cause of death. The high mortality in poorer countries is not related to risk factors, but it might be related to poorer access to health care. FUNDING Full funding sources are listed at the end of the paper (see Acknowledgments).
Md. Kamrul Hasan, Md. Ashraful Alam, Dola Das, Eklas Hossain et al.
Diabetes, also known as chronic illness, is a group of metabolic diseases due to a high level of sugar in the blood over a long period. The risk factor and severity of diabetes can be reduced significantly if the precise early prediction is possible. The robust and accurate prediction of diabetes is highly challenging due to the limited number of labeled data and also the presence of outliers (or missing values) in the diabetes datasets. In this literature, we are proposing a robust framework for diabetes prediction where the outlier rejection, filling the missing values, data standardization, feature selection, K-fold cross-validation, and different Machine Learning (ML) classifiers (k-nearest Neighbour, Decision Trees, Random Forest, AdaBoost, Naive Bayes, and XGBoost) and Multilayer Perceptron (MLP) were employed. The weighted ensembling of different ML models is also proposed, in this literature, to improve the prediction of diabetes where the weights are estimated from the corresponding Area Under ROC Curve (AUC) of the ML model. AUC is chosen as the performance metric, which is then maximized during hyperparameter tuning using the grid search technique. All the experiments, in this literature, were conducted under the same experimental conditions using the Pima Indian Diabetes Dataset. From all the extensive experiments, our proposed ensembling classifier is the best performing classifier with the sensitivity, specificity, false omission rate, diagnostic odds ratio, and AUC as 0.789, 0.934, 0.092, 66.234, and 0.950 respectively which outperforms the state-of-the-art results by 2.00 % in AUC. Our proposed framework for the diabetes prediction outperforms the other methods discussed in the article. It can also provide better results on the same dataset which can lead to better performance in diabetes prediction. Our source code for diabetes prediction is made publicly available.
Pronab Ghosh, Sami Azam, Mirjam Jonkman, Asif Karim et al.
Cardiovascular diseases (CVD) are among the most common serious illnesses affecting human health. CVDs may be prevented or mitigated by early diagnosis, and this may reduce mortality rates. Identifying risk factors using machine learning models is a promising approach. We would like to propose a model that incorporates different methods to achieve effective prediction of heart disease. For our proposed model to be successful, we have used efficient Data Collection, Data Pre-processing and Data Transformation methods to create accurate information for the training model. We have used a combined dataset (Cleveland, Long Beach VA, Switzerland, Hungarian and Stat log). Suitable features are selected by using the Relief, and Least Absolute Shrinkage and Selection Operator (LASSO) techniques. New hybrid classifiers like Decision Tree Bagging Method (DTBM), Random Forest Bagging Method (RFBM), K-Nearest Neighbors Bagging Method (KNNBM), AdaBoost Boosting Method (ABBM), and Gradient Boosting Boosting Method (GBBM) are developed by integrating the traditional classifiers with bagging and boosting methods, which are used in the training process. We have also instrumented some machine learning algorithms to calculate the Accuracy (ACC), Sensitivity (SEN), Error Rate, Precision (PRE) and F1 Score (F1) of our model, along with the Negative Predictive Value (NPR), False Positive Rate (FPR), and False Negative Rate (FNR). The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy while using RFBM and Relief feature selection methods (99.05%).
Mohammad Shafenoor Amin, Yin Kia Chiam, Kasturi Dewi Varathan
Jena Hamadani, Mohammed Imrul Hasan, Andrew Baldi, Sheikh Jamal Hossain et al.
BACKGROUND: Stay-at-home orders (lockdowns) have been deployed globally to control COVID-19 transmission, and might impair economic conditions and mental health, and exacerbate risk of food insecurity and intimate partner violence. The effect of lockdowns in low-income and middle-income countries must be understood to ensure safe deployment of these interventions in less affluent settings. We aimed to determine the immediate impact of COVID-19 lockdown orders on women and their families in rural Bangladesh. METHODS: An interrupted time series was used to compare data collected from families in Rupganj upazila, rural Bangladesh (randomly selected from participants in a randomised controlled trial), on income, food security, and mental health a median of 1 year and 2 years before the COVID-19 pandemic to data collected during the lockdown. We also assessed women's experiences of intimate partner violence during the pandemic. RESULTS: Between May 19 and June 18, 2020, we randomly selected and invited the mothers of 3016 children to participate in the study, 2424 of whom provided consent. 2414 (99·9%, 95% CI 99·6-99·9) of 2417 mothers were aware of, and adhering to, the stay-at-home advice. 2321 (96·0%, 95·2-96·7) of 2417 mothers reported a reduction in paid work for the family. Median monthly family income fell from US$212 at baseline to $59 during lockdown, and the proportion of families earning less than $1·90 per day rose from five (0·2%, 0·0-0·5) of 2422 to 992 (47·3%, 45·2-49·5) of 2096 (p<0·0001 comparing baseline with lockdown period). Before the pandemic, 136 (5·6%, 4·7-6·6) of 2420 and 65 (2·7%, 2·1-3·4) of 2420 families experienced moderate and severe food insecurity, respectively. This increased to 881 (36·5%, 34·5-38·4) of 2417 and 371 (15·3%, 13·9-16·8) of 2417 during the lockdown; the number of families experiencing any level of food insecurity increased by 51·7% (48·1-55·4; p<0·0001). Mothers' depression and anxiety symptoms increased during the lockdown. Among women experiencing emotional or moderate physical violence, over half reported it had increased since the lockdown. INTERPRETATION: COVID-19 lockdowns present significant economic, psychosocial, and physical risks to the wellbeing of women and their families across economic strata in rural Bangladesh. Beyond supporting only the most socioeconomically deprived, support is needed for all affected families. FUNDING: National Health and Medical Research Council, Australia.
Md. Mamun Ali, Bikash Kumar Paul, Kawsar Ahmed, Francis M. Bui et al.
Machine learning and data mining-based approaches to prediction and detection of heart disease would be of great clinical utility, but are highly challenging to develop. In most countries there is a lack of cardiovascular expertise and a significant rate of incorrectly diagnosed cases which could be addressed by developing accurate and efficient early-stage heart disease prediction by analytical support of clinical decision-making with digital patient records. This study aimed to identify machine learning classifiers with the highest accuracy for such diagnostic purposes. Several supervised machine-learning algorithms were applied and compared for performance and accuracy in heart disease prediction. Feature importance scores for each feature were estimated for all applied algorithms except MLP and KNN. All the features were ranked based on the importance score to find those giving high heart disease predictions. This study found that using a heart disease dataset collected from Kaggle three-classification based on k-nearest neighbor (KNN), decision tree (DT) and random forests (RF) algorithms the RF method achieved 100% accuracy along with 100% sensitivity and specificity. Thus, we found that a relatively simple supervised machine learning algorithm can be used to make heart disease predictions with very high accuracy and excellent potential utility.
John Hoddinott, Yisehac Yohannes, Hoddinott, John, Yohannes, Yisehac
Household food security is an important measure of well-being. Although it may not encapsulate all dimensions of poverty, the inability of households to obtain access to enough food for an active, healthy life is surely an important component of their poverty. Accordingly, devising an appropriate measure of food security outcomes is useful in order to identify the food insecure, assess the severity of their food shortfall, characterize the nature of their insecurity (for example, seasonal versus chronic), predict who is most at risk of future hunger, monitor changes in circumstances, and assess the impact of interventions. However, obtaining detailed data on food security status—such as 24-hour recall data on caloric intakes—can be time consuming and expensive and require a high level of technical skill both in data collection and analysis. This paper examines whether an alternative indicator, dietary diversity, defined as the number of unique foods consumed over a given period of time, provides information on household food security. It draws on data from 10 countries (India, the Philippines, Mozambique, Mexico, Bangladesh, Egypt, Mali, Malawi, Ghana, and Kenya) that encompass both poor and middle-income countries, rural and urban sectors, data collected in different seasons, and data on calories acquisition obtained using two different methods. The paper uses linear regression techniques to investigate the magnitude of the association between dietary diversity and food security. An appendix compiles the results of using methods such as correlation coefficients, contingency tables, and receiver operator curves. We find that a 1 percent increase in dietary diversity is associated with a 1 percent increase in per capita consumption, a 0.7 percent increase in total per capita caloric availability, a 0.5 percent increase in household per capita daily caloric availability from staples, and a 1.4 percent increase in household per capita daily caloric availability from nonstaples. These associations, which are found in both rural and urban areas and across seasons, do not depend on the method used to assess these associations, nor when using the number of unique food groups consumed is the measure of dietary diversity. There is an association between dietary diversity and food access at the individual level, although the magnitude of this association is considerably weaker than that between dietary diversity and food access. Looking across all samples, the magnitude of the association between dietary diversity and caloric availability at the household level increases with the mean level of caloric availability. Accordingly, dietary diversity would appear to show promise as a means of measuring food security and monitoring changes and impact, particularly when resources available for such measurement are scarce.
Victoria Miller, Salim Yusuf, Clara K Chow, Mahshid Dehghan et al.
BACKGROUND: Several international guidelines recommend the consumption of two servings of fruits and three servings of vegetables per day, but their intake is thought to be low worldwide. We aimed to determine the extent to which such low intake is related to availability and affordability. METHODS: We assessed fruit and vegetable consumption using data from country-specific, validated semi-quantitative food frequency questionnaires in the Prospective Urban Rural Epidemiology (PURE) study, which enrolled participants from communities in 18 countries between Jan 1, 2003, and Dec 31, 2013. We documented household income data from participants in these communities; we also recorded the diversity and non-sale prices of fruits and vegetables from grocery stores and market places between Jan 1, 2009, and Dec 31, 2013. We determined the cost of fruits and vegetables relative to income per household member. Linear random effects models, adjusting for the clustering of households within communities, were used to assess mean fruit and vegetable intake by their relative cost. FINDINGS: Of 143 305 participants who reported plausible energy intake in the food frequency questionnaire, mean fruit and vegetable intake was 3·76 servings (95% CI 3·66-3·86) per day. Mean daily consumption was 2·14 servings (1·93-2·36) in low-income countries (LICs), 3·17 servings (2·99-3·35) in lower-middle-income countries (LMICs), 4·31 servings (4·09-4·53) in upper-middle-income countries (UMICs), and 5·42 servings (5·13-5·71) in high-income countries (HICs). In 130 402 participants who had household income data available, the cost of two servings of fruits and three servings of vegetables per day per individual accounted for 51·97% (95% CI 46·06-57·88) of household income in LICs, 18·10% (14·53-21·68) in LMICs, 15·87% (11·51-20·23) in UMICs, and 1·85% (-3·90 to 7·59) in HICs (ptrend=0·0001). In all regions, a higher percentage of income to meet the guidelines was required in rural areas than in urban areas (p<0·0001 for each pairwise comparison). Fruit and vegetable consumption among individuals decreased as the relative cost increased (ptrend=0·00040). INTERPRETATION: The consumption of fruit and vegetables is low worldwide, particularly in LICs, and this is associated with low affordability. Policies worldwide should enhance the availability and affordability of fruits and vegetables. FUNDING: Population Health Research Institute, the Canadian Institutes of Health Research, Heart and Stroke Foundation of Ontario, AstraZeneca (Canada), Sanofi-Aventis (France and Canada), Boehringer Ingelheim (Germany and Canada), Servier, GlaxoSmithKline, Novartis, King Pharma, and national or local organisations in participating countries.
Afrina Yasmin, Sadia Tasneem, Kaniz Fatema
Marketers are faced with new challenges and opportunities within this digital age. Digital marketing is the utilization of electronic media by the marketers to promote the products or services into the market. The main objective of digital marketing is attracting customers and allowing them to interact with the brand through digital media. This article focuses on the importance of digital marketing for both marketers and consumers. We examine the effect of digital marketing on the firms’ sales. Additionally the differences between traditional marketing and digital marketing in this paper are presented. This study has described various forms of digital marketing, effectiveness of it and the impact it has on firm’s sales. The examined sample consists of one hundred fifty firms and fifty executives which have been randomly selected to prove the effectiveness of digital marketing. Collected data has been analyzed with the help of various statistical tools and techniques.
Md. Maniruzzaman, Md. Jahanur Rahman, Benojir Ahammed, Md. Menhazul Abedin
Background and objectives Diabetes is a chronic disease characterized by high blood sugar. It may cause many complicated disease like stroke, kidney failure, heart attack, etc. About 422 million people were affected by diabetes disease in worldwide in 2014. The figure will be reached 642 million in 2040. The main objective of this study is to develop a machine learning (ML)-based system for predicting diabetic patients. Materials and methods Logistic regression (LR) is used to identify the risk factors for diabetes disease based on p value and odds ratio (OR). We have adopted four classifiers like naïve Bayes (NB), decision tree (DT), Adaboost (AB), and random forest (RF) to predict the diabetic patients. Three types of partition protocols (K2, K5, and K10) have also adopted and repeated these protocols into 20 trails. Performances of these classifiers are evaluated using accuracy (ACC) and area under the curve (AUC). Results We have used diabetes dataset, conducted in 2009-2012, derived from the National Health and Nutrition Examination Survey. The dataset consists of 6561 respondents with 657 diabetic and 5904 controls. LR model demonstrates that 7 factors out of 14 as age, education, BMI, systolic BP, diastolic BP, direct cholesterol, and total cholesterol are the risk factors for diabetes. The overall ACC of ML-based system is 90.62% . The combination of LR-based feature selection and RF-based classifier gives 94.25% ACC and 0.95 AUC for K10 protocol. Conclusion The combination of LR and RF-based classifier performs better. This combination will be very helpful for predicting diabetic patients.
Dina Balabanova, Anne Mills, Lesong Conteh, Baktygul Akkazieva et al.
In 1985, the Rockefeller Foundation published Good health at low cost to discuss why some countries or regions achieve better health and social outcomes than do others at a similar level of income and to show the role of political will and socially progressive policies. 25 years on, the Good Health at Low Cost project revisited these places but looked anew at Bangladesh, Ethiopia, Kyrgyzstan, Thailand, and the Indian state of Tamil Nadu, which have all either achieved substantial improvements in health or access to services or implemented innovative health policies relative to their neighbours. A series of comparative case studies (2009-11) looked at how and why each region accomplished these changes. Attributes of success included good governance and political commitment, effective bureaucracies that preserve institutional memory and can learn from experience, and the ability to innovate and adapt to resource limitations. Furthermore, the capacity to respond to population needs and build resilience into health systems in the face of political unrest, economic crises, and natural disasters was important. Transport infrastructure, female empowerment, and education also played a part. Health systems are complex and no simple recipe exists for success. Yet in the countries and regions studied, progress has been assisted by institutional stability, with continuity of reforms despite political and economic turmoil, learning lessons from experience, seizing windows of opportunity, and ensuring sensitivity to context. These experiences show that improvements in health can still be achieved in countries with relatively few resources, though strategic investment is necessary to address new challenges such as complex chronic diseases and growing population expectations.
Paramjit Gill, Aparna Shankar, Terry Quirke, Nick Freemantle
Overcoming language barriers to health care is a global challenge. There is great linguistic diversity in the major cities in the UK with more than 300 languages, excluding dialects, spoken by children in London alone. However, there is dearth of data on the number of non-English speakers for planning effective interpreting services. The aim was to estimate the number of people requiring language support amongst the minority ethnic communities in England. Secondary analysis of national representative sample of subjects recruited to the Health Surveys for England 1999 and 2004. 298,432 individuals from the four main minority ethnic communities (Indian, Pakistani, Bangladeshi and Chinese) who may be unable to communicate effectively with a health professional. This represents 2,520,885 general practice consultations per year where interpreting services might be required. Effective interpreting services are required to improve access and health outcomes of non-English speakers and thereby facilitate a reduction in health inequalities.