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Two key policy goals in the health sector are equity and financial protection. New methods, data and powerful computers have led to a surge of interest in quantitative analysis that permits monitoring progress toward these objectives, and comparisons across countries. ADePT is a new computer program that streamlines and automates such work, ensuring that results are genuinely comparable and allowing them to be produced with a minimum of programming skills. This book provides a step-by-step guide to the use of ADePT for quantitative analysis of equity and financial protection in the health sector. It also gives the reader an accessible guide to the concepts and methods used by the software, as well as more detailed technical explanations.

Since the end of the Second World War, the international community has been focusing on reducing the number and the proportion of people who suffer from hunger. Over time it became clear that no single indicator would provide a comprehensive picture of the food security situation. Rather, a suite of indicators is necessary to describe food insecurity in all its dimensions. The demand for evidence-based policies, which brings together providers such as statistical offices and users of food security indicators including policy makers and researchers, has also been increasing. The stand-alone software, ADePT-Food Security Module (available for free downloading), was developed to produce food security indicators from food consumption data collected in household surveys. These indicators, derived at the national and subnational levels, include the consumption of calories and macronutrients, the availability of micronutrients and amino acids, the distribution of calories and the proportion of people undernourished. The book focuses on the theory, methodology, and analysis of these indicators. It has five chapters beginning with a brief overview on concepts of food security. The theory and methodology are further described in the following chapter. To help users with the interpretation of the results some examples are given in chapter 3. Chapter 4 of the book provides guidelines for the preparation of the input datasets. Finally, chapter 5 explains how to use the software. Both the software and this book are products of decades of experience in analyzing food security. This project was made possible through collaboration between FAO and the World Bank, with financial support from the European Union.

Influenza vaccination is a commonly used intervention to prevent influenza infection in healthcare workers (HCWs) and onward transmission to other staff and patients. We undertook a systematic review to synthesize the latest evidence of the direct epidemiological and economic effectiveness of seasonal influenza vaccination among HCW. We conducted a systematic search of MEDLINE/PubMed, Scopus, and Cochrane Central Register of Controlled Trials from 1980 through January 2018. All studies comparing vaccinated and non-vaccinated (i.e. placebo or non-intervention) groups of HCWs were included. Research articles that focused on only patient-related outcomes or monovalent A(H1N1)pdm09 vaccines were excluded. Two reviewers independently selected articles and extracted data. Pooled-analyses were conducted on morbidity outcomes including laboratory-confirmed influenza, influenza-like illnesses (ILI), and absenteeism. Economic studies were summarized for the characteristics of methods and findings. Thirteen articles met eligibility criteria: three articles were randomized controlled studies and ten were cohort studies. Pooled results showed a significant effect on laboratory-confirmed influenza incidence but not ILI. While the overall incidence of absenteeism was not changed by vaccine, ILI absenteeism was significantly reduced. The duration of absenteeism was also shortened by vaccination. All published economic evaluations consistently found that the immunization of HCW was cost saving based on crude estimates of avoided absenteeism by vaccination. No studies, however, comprehensively evaluated both health outcomes and costs of vaccination programs to examine cost-effectiveness. Our findings reinforced the influenza vaccine effects in reducing infection incidence and length of absenteeism. A better understanding of the incidence of absenteeism and comprehensive economic program evaluations are required to ensure the best possible management of ill HCWs and the investment in HCW immunization in increasingly constrained financial environments. These steps are fundamental to establish sustainability and cost-effectiveness of vaccination programs and underpin HCW immunization policy.

A medical specialty prediction system for remote diagnosis can reduce the unexpected costs incurred by first-visit patients who visit the wrong hospital department for their symptoms. To develop medical specialty prediction systems, several researchers have explored clinical predictive models using real medical text data. Medical text data include large amounts of information regarding patients, which increases the sequence length. Hence, a few studies have attempted to extract entities from the text as concise features and provide domain-specific knowledge for clinical text classification. However, it is still insufficient to inject them into the model effectively. Thus, we propose Entity-enhanced BERT (E-BERT), which utilizes the structural attributes of BERT for medical specialty prediction. E-BERT has an entity embedding layer and entity-aware attention to inject domain-specific knowledge and focus on relationships between medical-related entities within the sequences. Experimental results on clinical questionnaire data demonstrate the superiority of E-BERT over the other benchmark models, regardless of the input sequence length. Moreover, the visualization results for the effects of entity-aware attention prove that E-BERT effectively incorporate domain-specific knowledge and other information, enabling the capture of contextual information in the text. Finally, the robustness and applicability of the proposed method is explored by applying it to other Pre-trained Language Models. These effective medical specialty predictive model can provide practical information to first-visit patients, resulting in streamlining the diagnostic process and improving the quality of medical consultations.

‘Big data’ is massive amounts of information that can work wonders. It has become a topic of special interest for the past two decades because of a great potential that is hidden in it. Various public and private sector industries generate, store, and analyze big data with an aim to improve the services they provide. In the healthcare industry, various sources for big data include hospital records, medical records of patients, results of medical examinations, and devices that are a part of internet of things. Biomedical research also generates a significant portion of big data relevant to public healthcare. This data requires proper management and analysis in order to derive meaningful information. Otherwise, seeking solution by analyzing big data quickly becomes comparable to finding a needle in the haystack. There are various challenges associated with each step of handling big data which can only be surpassed by using high-end computing solutions for big data analysis. That is why, to provide relevant solutions for improving public health, healthcare providers are required to be fully equipped with appropriate infrastructure to systematically generate and analyze big data. An efficient management, analysis, and interpretation of big data can change the game by opening new avenues for modern healthcare. That is exactly why various industries, including the healthcare industry, are taking vigorous steps to convert this potential into better services and financial advantages. With a strong integration of biomedical and healthcare data, modern healthcare organizations can possibly revolutionize the medical therapies and personalized medicine.

In this analysis of data from a national liability insurer, 7.4% of physicians faced a malpractice claim each year, although 78% of claims did not result in payments to claimants. The authors estimate that 75 to 99% of physicians will face a malpractice claim by the age of 65. Despite tremendous interest in medical malpractice and its reform, 1 – 10 data are lacking on the proportion of physicians who face malpractice claims according to physician specialty, the size of payments according to specialty, and the cumulative incidence of being sued during the course of a physician's career. 11 – 13 A recent American Medical Association (AMA) survey of physicians showed that 5% of respondents had faced a malpractice claim during the previous year. 14 Studies estimating specialty-specific malpractice risk from actual claims are much less recent, 15 , 16 including a Florida study from 1975 through 1980 showing that 15% of medical specialists, 34% of . . .

In 2011, WHO member states signed up to the 25 × 25 initiative, a plan to cut mortality due to non-communicable diseases by 25% by 2025. However, socioeconomic factors influencing non-communicable diseases have not been included in the plan. In this study, we aimed to compare the contribution of socioeconomic status to mortality and years-of-life-lost with that of the 25 × 25 conventional risk factors. We did a multicohort study and meta-analysis with individual-level data from 48 independent prospective cohort studies with information about socioeconomic status, indexed by occupational position, 25 × 25 risk factors (high alcohol intake, physical inactivity, current smoking, hypertension, diabetes, and obesity), and mortality, for a total population of 1 751 479 (54% women) from seven high-income WHO member countries. We estimated the association of socioeconomic status and the 25 × 25 risk factors with all-cause mortality and cause-specific mortality by calculating minimally adjusted and mutually adjusted hazard ratios [HR] and 95% CIs. We also estimated the population attributable fraction and the years of life lost due to suboptimal risk factors. During 26·6 million person-years at risk (mean follow-up 13·3 years [SD 6·4 years]), 310 277 participants died. HR for the 25 × 25 risk factors and mortality varied between 1·04 (95% CI 0·98–1·11) for obesity in men and 2 ·17 (2·06–2·29) for current smoking in men. Participants with low socioeconomic status had greater mortality compared with those with high socioeconomic status (HR 1·42, 95% CI 1·38–1·45 for men; 1·34, 1·28–1·39 for women); this association remained significant in mutually adjusted models that included the 25 × 25 factors (HR 1·26, 1·21–1·32, men and women combined). The population attributable fraction was highest for smoking, followed by physical inactivity then socioeconomic status. Low socioeconomic status was associated with a 2·1-year reduction in life expectancy between ages 40 and 85 years, the corresponding years-of-life-lost were 0·5 years for high alcohol intake, 0·7 years for obesity, 3·9 years for diabetes, 1·6 years for hypertension, 2·4 years for physical inactivity, and 4·8 years for current smoking. Socioeconomic circumstances, in addition to the 25 × 25 factors, should be targeted by local and global health strategies and health risk surveillance to reduce mortality. European Commission, Swiss State Secretariat for Education, Swiss National Science Foundation, the Medical Research Council, NordForsk, Portuguese Foundation for Science and Technology.

Determinants of health - like physical activity levels and living conditions - have traditionally been the concern of public health and have not been linked closely to clinical practice. However, if standardized social and behavioral data can be incorporated into patient electronic health records (EHRs), those data can provide crucial information about factors that influence health and the effectiveness of treatment. Such information is useful for diagnosis, treatment choices, policy, health care system design, and innovations to improve health outcomes and reduce health care costs. Capturing Social and Behavioral Domains and Measures in Electronic Health Records: Phase 2 identifies domains and measures that capture the social determinants of health to inform the development of recommendations for the meaningful use of EHRs. This report is the second part of a two-part study. The Phase 1 report identified 17 domains for inclusion in EHRs. This report pinpoints 12 measures related to 11 of the initial domains and considers the implications of incorporating them into all EHRs. This book includes three chapters from the Phase 1 report in addition to the new Phase 2 material. Standardized use of EHRs that include social and behavioral domains could provide better patient care, improve population health, and enable more informative research. The recommendations of Capturing Social and Behavioral Domains and Measures in Electronic Health Records: Phase 2 will provide valuable information on which to base problem identification, clinical diagnoses, patient treatment, outcomes assessment, and population health measurement.