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In order to solve the issues of obtaining the basic topographic data in the comprehensive management planning and design of small watershed, we established the Qilongwan small watershed digital twin baseplate model, including SAR satellite remote sensing, tilt photography and LiDAR. We extracted the longitudinal section of the main channel, the boundary of the two terrace plots, and the depression degree of the 10 woodland sample areas in the model, and then compared the accuracy test with the manual interpretation or measured results. The results show that the extraction accuracy of lidar as the data source, tilt photography as the second of the data source, and SAR satellite re- mote sensing as the data source. In view of the advantages and disadvantages of the three data source models, the multi-source data fusion can be carried out according to the actual work needs to improve the application effect of digital twin technology in the planning and design of small watershed comprehensive management. In the small watershed of Qilongwan, the shikan terrace plot is selected for SAR satellite remote sensing and tilt photography data fusion, and the popular science exhibition hall is selected for tilt photography and lidar data fusion, which has achieved good results. [ABSTRACT FROM AUTHOR]

In recent years, there has been growing interest in recommendation systems, which is matched by their widespread adoption across various sectors. This can be attributed to their effectiveness in reducing an avalanche of data into individualized information that is meaningful, relevant, and can easily be absorbed by a single person. Several studies have recently navigated the landscape of recommendation systems, attending to their approaches, challenges, and applications, as well as the evaluation metrics necessary for effective implementation. This systematic review investigates the understudied aspects of recommendation systems, including the data input into the systems and their features or outputs. The data in (input) and data out (features) are both diverse and vary significantly from not just one application domain to another, but also from one application use case to another, which is a distinction that has not been thoroughly addressed in the past. In addition, this study explores several application domains, providing a comprehensive breakdown of the categorical data consumed by these systems and the features, or outputs, of these systems. Without focusing on any particular journals or their rankings, this study collects and reviews articles on recommendation systems published from 2018 to April 2024, in four top-tier research repositories, including IEEE Xplore Digital Library, Springer Link, ACM Digital Library, and Google Scholar. [ABSTRACT FROM AUTHOR]

Behavioral health needs continue rise in the United States and constitute a key driver of health care utilization, costs, and outcomes. Social workers provide a wide range of services across health, behavioral health, and community settings, and while this heterogeneity in practice benefits care delivery, it complicates health workforce analyses. This analysis compares five commonly used national data sources and details similarities and differences in their estimates of the number, type, and practice characteristics of social workers. The analysis suggests that estimates vary significantly between data sets ranging from 282,425 to 1,022,859 social workers; as well as yield different findings of types of social workers in the United States, depending on the data set used. These differences have the potential to shape how researchers and policy makers assess the adequacy of the social work workforce and identify solutions to address the nation's behavioral health and social care needs. [ABSTRACT FROM AUTHOR]

Integration is a hallmark of mixed methods research and is constantly evolving. Joint displays have emerged as an effective method of integration at the analysis and interpretation level, facilitating visual comparison of quantitative and qualitative findings to present inferences greater than the quantitative and qualitative findings alone. Although literature reviews and concept analyses frequently precede mixed methods investigations, these are not commonly included in the integration process. To advance mixed methods research, we present a novel method of integrating data sources including rigorous literature reviews using joint display, that builds on the concept of creating a sum that is greater than quantitative and qualitative findings alone, with examples from two research studies. [ABSTRACT FROM AUTHOR]

Abstract Background Utilizing regional health data goes hand in hand with challenges: can they be used for health planning, are they applicable to the relevant topics? The study explores current data utilization and needs of stakeholders working in regional health services planning. Methods We conducted 16 semi-structured expert-interviews with stakeholders of regional health planning in Brandenburg, a federal state in the north-east of Germany, by telephone or online-meeting tools between 05/2022 and 03/2023. The data were analysed according to qualitative content analysis. Results Utilization of data sources depends on individual knowledge and personnel resources instead of being guided by standardized procedures. Interviewees primarily use internal data; some use many different platforms, studies and reports. Regional health-related data are used for reliable health planning, to prepare resolutions, draft contracts, but also for events and requests from policy makers or the press. Challenges exist in terms of availability, awareness, and acceptance of the data, perceived applicability, the ability to use it and the utilization itself. Many regional health planners indicated they would appreciate a regional integrated cross-organizational data source if the benefits for health planning outweighed the efforts. Discussion Actors in health planning primarily utilized their own data for planning; additional data sources are not available or the level of aggregation is too high, not known by them or are often not used due to a lack of time. A standardized regional monitoring would require the definition of indicators as well as the strengthening of cross-sectoral planning.

Background: Utilizing regional health data goes hand in hand with challenges: can they be used for health planning, are they applicable to the relevant topics? The study explores current data utilization and needs of stakeholders working in regional health services planning. Methods: We conducted 16 semi-structured expert-interviews with stakeholders of regional health planning in Brandenburg, a federal state in the north-east of Germany, by telephone or online-meeting tools between 05/2022 and 03/2023. The data were analysed according to qualitative content analysis. Results: Utilization of data sources depends on individual knowledge and personnel resources instead of being guided by standardized procedures. Interviewees primarily use internal data; some use many different platforms, studies and reports. Regional health-related data are used for reliable health planning, to prepare resolutions, draft contracts, but also for events and requests from policy makers or the press. Challenges exist in terms of availability, awareness, and acceptance of the data, perceived applicability, the ability to use it and the utilization itself. Many regional health planners indicated they would appreciate a regional integrated cross-organizational data source if the benefits for health planning outweighed the efforts. Discussion: Actors in health planning primarily utilized their own data for planning; additional data sources are not available or the level of aggregation is too high, not known by them or are often not used due to a lack of time. A standardized regional monitoring would require the definition of indicators as well as the strengthening of cross-sectoral planning. [ABSTRACT FROM AUTHOR]

The occurrence of natural disasters, accelerated by climate change, has become a continuous menace to the environment and consequently impacts the socioeconomic well-being of people. Flood events are natural disasters resulting from excessive rainfall duration, intensity, and snow melt. Flood disaster management systems that are machine learning-based have been increasingly suggested and applied to forestall the impacts of floods on the environment in terms of monitoring and warning. This study aims to critically review various studies conducted on flood management systems to identify applicable data sources and machine learning models. The study applied Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to source data from an academic database using some selected keywords, which were identified for the review process after filtering a total number of forty-two pertinent research papers was used. The review identified different combinations of flood data, flood management techniques, flood models, application of machine learning in flood predictions, optimization techniques, data processing techniques, and evaluation techniques. The study concluded that a standard approach should be applied in building robust and efficient flood disaster management systems. Lastly, informed future research directions on using machine learning for flood prediction and susceptibility mapping are provided. [ABSTRACT FROM AUTHOR]

Research in the field of History of Education has experienced a remarkable increase in recent decades. Resulting publications are referenced in generalist databases that do not catalogue academic works according to the specific characteristics of History of Education. Seeking to give response to this bibliographic gap, we are developing a database catered for historians of education that aims to map out present, past, and future research. Conceived within the framework of Digital Humanities/Digital History, Hecumen is being designed, with the aid of Artificial Intelligence, as an open access finding aid that permits (1) conducting specific and multilevel complex engine searches, (2) having a panoramic view of publications; (3) mapping out relevant/missing areas of research, and, ultimately, (4) keeping up to date with the research produced by historians of education. This paper presents, contextualises, and problematises Hecumen – a digital tool that will facilitate and boost History of Education research. [ABSTRACT FROM AUTHOR]

Integrating multiple data source technologies is essential for organizations to respond to highly dynamic market needs. Although physical data integration systems have been considered to have better query processing systems, they pose higher implementation and maintenance costs. Meanwhile, virtual data integration has become an alternative topic that is increasingly attracting the attention of researchers in the current era of big data. Various data integration methodologies have been developed and used in various domains, processing heterogeneous data using various approaches. This review article aims to provide an overview of heterogeneous data integration research focusing on methodology and approaches. It surveys existing publications, highlighting key trends, challenges, and open research topics. The main findings are: (i) Research has been conducted in various domains. However, most focus on big data rather than specific study domains; (ii) researchers primarily focus on semantics challenges, and (iii) gaps still need to be addressed and related to integration issues involving semantics and unstructured data formats that must be thoroughly addressed. Furthermore, considering elements of cutting-edge technology, such as machine learning and data integration, about privacy concerns provides a chance for additional investigation. Finally, we provide insight into the potential for a broader review of integration challenges based on case studies.

In Root Shock: How Tearing Up City Neighborhoods Hurts America and What We Can Do About It, Mindy Fullilove analyses how the US government’s urban renewal policy destroyed multiple communities across the country. Fullilove intended to include an appendix discussing situation analysis, the research method she used to study root shock. This paper takes up the task of that missing appendix. Situation analysis is a flexible and accessible way to study complex social phenomena or events. The goal is to describe how macro-political, social, and economic structures influence micro-level events, processes, and decision-making. In this paper, we define situation analysis and offer a guide to the method, detailing the phases of data collection and analysis: identifying ‘what happened’ and the people involved; documenting a variety of perspectives on the events of the situation; and setting events and perspectives within an embedding context. We conclude with a discussion of the unique insights gained when this approach is applied in policy studies. [ABSTRACT FROM AUTHOR]

This paper presents a conceptual framework that accurately represents the current and future perspectives of data-driven companies in tourism by means of an analysis of the data sources used in the data-driven tourism research literature, as well as the research topics to which they are applied. For this purpose, a bibliometric analysis of data-driven tourism research is carried out. The framework of the study is all tourism-related publications whose research was based on data sources during the period 1982–2020. The results show some of the basic bibliometric performance indicators and the maps of science. The main themes of research interest are identified, and the conceptual evolution is obtained based on these maps. Three major thematic areas are identified: tourism research topics, information sources, and data analysis techniques. Based on these three thematic areas, the conceptual model of data architecture and processes of a data-driven organization in the tourism sector are obtained. An additional qualitative analysis of the three thematic areas is performed. [ABSTRACT FROM AUTHOR]

the article reviews and substantiates research methods and data sources on the dynamics of the spatial structure of the largest urban agglomerations in Russia. The object of the study is modern methods for studying urban agglomerations based on new data sources. A case study of the number two urban agglomeration in Russia—St. Petersburg urban agglomeration—shows that interdisciplinary synthesis of socioeconomic geography, regional economics, urban studies, geoinformatics and cartography, land management, and variety of data sources (mobile network operators' data, tax statistics, housing construction, satellite observations, retail chain activity, and road networks), as well as modern GIS equipment, make it possible to evaluate this structure, its changes, and fluctuations. The main objective of the study is to critically rethink the methods of studying the spatial structure of one of the largest urban agglomerations in Russia that developed in the turbulent period between 2014 and 2022. Using a deductive approach, the authors inventoried the available methods for studying urban agglomerations and traditional data sources and obtained updated methods and new sources. Next, the advantages and disadvantages of each group of methods are identified. Using bibliographic analysis, the authors identified the limitations and possibilities for empirical content (availability of specific data sources). Based on their own critical analysis, the authors offer a final expert assessment of the applicability and usefulness of the methods specifically for the St. Petersburg urban agglomeration. The authors' contribution lies in the adaptation of modern groups of methods for studying the spatial structure of cities to study the considered urban agglomeration, taking into account the local specifics. It is expected that development of a modern methodology for studying the spatial structure of the St. Petersburg urban agglomeration, based on a symbiosis of modern methods and data sources, will contribute to studying Russia's largest urban agglomerations. [ABSTRACT FROM AUTHOR]

Federated learning (FL) has emerged as one of the de-facto privacy-preserving paradigms that can effectively work with decentralized data sources (e.g., hospitals) without acquiring any private data. Recently, applications of FL have vastly expanded into multiple domains, particularly the medical domain, and FL is becoming one of the mainstream technologies of the near future. In this study, we provide insights into FL fundamental concepts (e.g., the difference from centralized learning, functions of clients and servers, workflows, and nature of data), architecture and applications in the general medical domain, synergies with emerging technologies, key challenges (medical domain), and potential research prospects. We discuss major taxonomies of the FL systems and enlist technical factors in the FL ecosystem that are the foundation of many adversarial attacks on these systems. We also highlight the promising applications of FL in the medical domain by taking the recent COVID-19 pandemic as an application use case. We highlight potential research and development trajectories to further enhance the persuasiveness of this emerging paradigm from the technical point of view. We aim to concisely present the progress of FL up to the present in the medical domain including COVID-19 and to suggest future research trajectories in this area. [ABSTRACT FROM AUTHOR]

Purpose: An adolescent health information system is a relevantly new concept, especially in humanitarian settings. This article aims to map the available adolescent health indicators collected in selected humanitarian settings, identify the available data sources, and determine the alignment between these indicators and the draft list of priority indicators for adolescent health measurement recommended by the Global Action for the Measurement of Adolescent Health Advisory Group. Methods: We selected five countries experiencing humanitarian crises- Myanmar, Nigeria, Palestine, Ukraine, and Yemen. We identified the adolescent health indicators collected in each country using document analysis and a purposive sampling approach. We reviewed the primary population-based surveys used to gather adolescent health data and noted the most recent year each survey was conducted. The identified indicators were then categorized by measurement domains and specific areas of adolescent health. Results: The Multiple Indicator Cluster Survey and Demographic Health Survey were conducted in all five countries selected, but three out of five countries have not administered either within the last five years. Yemen and Palestine only included married women in their sample sizes, and no one younger than 15 was interviewed. Indicators most commonly assess reproductive health, tobacco use, and adolescent fertility. Limited data was found on younger adolescents, males, water, sanitation, hygiene, disability, and nutrition indicators. Discussion: Adolescent health information in humanitarian crises requires more frequent surveys, including all adolescent age groups, and unique data collection methodologies. The current surveys used to measure adolescent health indicators have limited ability to be inclusive to all adolescents. It is important to establish a list of priority indicators deemed essential in humanitarian settings and relevant ways to collect them. [ABSTRACT FROM AUTHOR]

The presence of chemical contaminants in food is often unavoidable and associated with many adverse health effects. Exposure assessment is the essential element of an overall risk assessment process. While the specific purpose of the exposure assessment process can vary, the main goal is to provide a foundation for health-protective decisions. In recent years, there have been significant advances in exposure assessment methodologies and procedures, subsequently contributing to an increased complexity of the process. This paper aims to provide a generalized, simplified, and practical road map for exposure assessment, pointing to the pros and cons of different methods and challenges that occur while performing this type of study.

Data ingestion tools are critical component of Data Lake. Existing data ingestion tools face challenges of handling large variety, formats, sources of data. There exists void for unified data ingestion interface to handle the above research problems. This study proposes an innovative and integrated framework for data ingestion in a data lake, addressing the challenges posed by heterogeneous data sources, formats, and metadata management. The framework comprises three novel modules: First Unified Data Integration Connectors (UDIC), which provide seamless connectivity and data retrieval capabilities from diverse sources including databases, data warehouses, file systems, cloud storage, and APIs; Second, Adaptive Data Variety Transformation (ADVT), a module that intelligently handles the transformation and processing of structured, semi-structured, and unstructured data types, ensuring efficient ingestion into the data lake; and third, Intelligent Metadata Management (IMM), a module that captures, stores, and manages metadata associated with the ingested data, offering advanced search, discovery, and enrichment functionalities. Comparative study corroborates features offered by the service with existing data ingestion tools to evaluate the novelty and significance of the study. Performance validation shows varying ingestion latencies across different data types: approximately 148.1 microseconds per record for structured data, 234.2 microseconds per record for semi-structured data, 65.6 microseconds per kilobyte (KB) for video data, and 42.7 microseconds per KB for image data. These results underscore the importance of considering data structure and size in optimizing ingestion processes. Overall, this research aims to revolutionize data ingestion in data lake environments by providing a unified solution for handling diverse data sources, formats, and metadata management.

Traditional assessment indexes could not fully describe offshore wind resources, for the meteorological properties of offshore are more complex than onshore. As a result, the uncertainty of offshore wind power projects would be increased and final economic benefits would be affected. Therefore, a study on offshore wind resource assessment is carried out, including three processes of “studying data sources, conducting multi-dimensional indexes system and proposing an offshore wind resource assessment method based on analytic hierarchy process (AHP)”. First, measured wind data and two kinds of reanalysis data are used to analyze the characteristics and reliability of data sources. Second, indexes such as effective wind speed occurrence, affluent level occurrence, coefficient of variation, neutral state occurrence have been proposed to depict availability, richness, and stability of offshore wind resources, respectively. Combined with existing parameters (wind power density, dominant wind direction occurrence, water depth, distance to coast), a multi-dimensional indexes system has been built and on this basis, an offshore wind energy potential assessment method has been proposed. Furthermore, the proposed method is verified by the annual energy production of five offshore wind turbines and practical operating data of four offshore wind farms in China. This study also compares the ranking results of the AHP model to two multi-criteria decision making (MCDM) models including weighted aggregated sum product assessment (WASPAS) and multi-attribute ideal real comparative analysis (MAIRCA). Results show the proposed method gains well in practical engineering applications, where the economic score values have been considered based on the offshore reasonable utilization hours of the whole life cycle in China.

In recent years, there has been growing interest in recommendation systems, which is matched by their widespread adoption across various sectors. This can be attributed to their effectiveness in reducing an avalanche of data into individualized information that is meaningful, relevant, and can easily be absorbed by a single person. Several studies have recently navigated the landscape of recommendation systems, attending to their approaches, challenges, and applications, as well as the evaluation metrics necessary for effective implementation. This systematic review investigates the understudied aspects of recommendation systems, including the data input into the systems and their features or outputs. The data in (input) and data out (features) are both diverse and vary significantly from not just one application domain to another, but also from one application use case to another, which is a distinction that has not been thoroughly addressed in the past. In addition, this study explores several application domains, providing a comprehensive breakdown of the categorical data consumed by these systems and the features, or outputs, of these systems. Without focusing on any particular journals or their rankings, this study collects and reviews articles on recommendation systems published from 2018 to April 2024, in four top-tier research repositories, including IEEE Xplore Digital Library, Springer Link, ACM Digital Library, and Google Scholar.

Lake thermal dynamics have been considerably impacted by climate change, with potential adverse effects on aquatic ecosystems. To better understand the potential impacts of future climate change on lake thermal dynamics and related processes, the use of mathematical models is essential. In this study, we provide a comprehensive review of lake water temperature modeling. We begin by discussing the physical concepts that regulate thermal dynamics in lakes, which serve as a primer for the description of process‐based models. We then provide an overview of different sources of observational water temperature data, including in situ monitoring and satellite Earth observations, used in the field of lake water temperature modeling. We classify and review the various lake water temperature models available, and then discuss model performance, including commonly used performance metrics and optimization methods. Finally, we analyze emerging modeling approaches, including forecasting, digital twins, combining process‐based modeling with deep learning, evaluating structural model differences through ensemble modeling, adapted water management, and coupling of climate and lake models. This review is aimed at a diverse group of professionals working in the fields of limnology and hydrology, including ecologists, biologists, physicists, engineers, and remote sensing researchers from the private and public sectors who are interested in understanding lake water temperature modeling and its potential applications. Plain Language Summary: Lake thermal dynamics are fundamental in controlling mixing processes and have significant implications for biological and geochemical processes. Consequently, the impacts of climate change on these dynamics can have severe consequences for the health of lakes and their aquatic ecosystems. In this context, mathematical models are essential for understanding the potential effects of future climate change on lake thermal dynamics and related processes. This manuscript offers a comprehensive review of lake water temperature modeling. It covers the fundamental physical concepts that govern thermal dynamics in lakes and provides an overview of various sources of observational water temperature data, including in situ monitoring and satellite data used in these models. The study evaluates different types of lake water temperature models, including statistical, process‐based, and hybrid models. It explores emerging modeling approaches such as forecasting, digital twins, combining process‐based modeling with deep learning, ensemble modeling, and climate‐lake models coupling. Model performance is also discussed, highlighting suggested evaluation metrics and providing a comprehensive analysis of the state‐of‐the‐art optimization methods to assess model accuracy. This review targets researchers in limnology, hydrology, ecology, biology, physics, engineering, and remote sensing from the private and public sectors interested in lake water temperature modeling and its applications. Key Points: Lake thermal dynamics are central in shaping mixing processes and the health of aquatic ecosystems, and climate change alters these dynamicsMathematical models are essential to understand past and project future climate change impacts on lake thermal dynamicsThis study reviews lake water temperature modeling, covering concepts, data sources, and model evaluation for applications across disciplines [ABSTRACT FROM AUTHOR]

The presence of chemical contaminants in food is often unavoidable and associated with many adverse health effects. Exposure assessment is the essential element of an overall risk assessment process. While the specific purpose of the exposure assessment process can vary, the main goal is to provide a foundation for health-protective decisions. In recent years, there have been significant advances in exposure assessment methodologies and procedures, subsequently contributing to an increased complexity of the process. This paper aims to provide a generalized, simplified, and practical road map for exposure assessment, pointing to the pros and cons of different methods and challenges that occur while performing this type of study. [ABSTRACT FROM AUTHOR]

Abstract Background Policymakers in sub-Saharan Africa (SSA) face challenging decisions regarding the allocation of health resources. Economic evaluations can help decision makers to determine which health interventions should be funded and or included in their benefits package. A major problem is whether the evaluations incorporated data from sources that are reliable and relevant to the country of interest. We aimed to review the quality of the data sources used in all published economic evaluations for cardiovascular disease and diabetes in SSA. Methods We systematically searched selected databases for all published economic evaluations for CVD and diabetes in SSA. We modified a hierarchy of data sources and used a reference case to measure the adherence to reporting and methodological characteristics, and descriptively analysed author statements. Results From 7,297 articles retrieved from the search, we selected 35 for study inclusion. Most were modelled evaluations and almost all focused on pharmacological interventions. The studies adhered to the reporting standards but were less adherent to the methodological standards. The quality of data sources varied. The quality level of evidence in the data domains of resource use and costs were generally considered of high quality, with studies often sourcing information from reliable databases within the same jurisdiction. The authors of most studies referred to data sources in the discussion section of the publications highlighting the challenges of obtaining good quality and locally relevant data. Conclusions The data sources in some domains are considered high quality but there remains a need to make substantial improvements in the methodological adherence and overall quality of data sources to provide evidence that is sufficiently robust to support decision making in SSA within the context of UHC and health benefits plans. Many SSA governments will need to strengthen and build their capacity to conduct economic evaluations of interventions and health technology assessment for improved priority setting. This capacity building includes enhancing local infrastructures for routine data production and management. If many of the policy makers are using economic evaluations to guide resource allocation, it is imperative that the evidence used is of the feasibly highest quality.

Modular construction is becoming more popular because of its efficiency, cost-saving, and environmental benefits, but its successful implementation necessitates detailed planning, scheduling, and coordination. Building information modeling (BIM) and fourdimensional (4D) simulation techniques have emerged as invaluable tools for visualizing and analyzing the construction process in order to meet these requirements. However, integrating distinctive data sources and developing comprehensive 4D BIM simulations tailored to modular construction projects present significant challenges. Case studies are used in this paper to define precise data needs and to design a robust data integration framework for improving 4D BIM simulations in modular construction. The validation of the framework in a real-world project demonstrates its efficacy in integrating data, promoting cooperation, detecting risks, and supporting informed decision-making, ultimately enhancing modular building results through more realistic simulations. By solving data integration difficulties, this research provides useful insights for industry practitioners and researchers, enabling informed decision-making and optimization of modular building projects. [ABSTRACT FROM AUTHOR]

The expansion in the scope, scale, and sources of data on the wider social determinants of health (SDH) in the last decades could bridge gaps in information available for decision-making. However, challenges remain in making data widely available, accessible, and useful towards improving population health. While traditional, government-supported data sources and comparable data are most often used to characterize social determinants, there are still capacity and management constraints on data availability and use. Conversely, privately held data may not be shared. This study reviews and discusses the nature, sources, and uses of data on SDH, with illustrations from two middle-income countries: Kenya and the Philippines. The review highlights opportunities presented by new data sources, including the use of big data technologies, to capture data on social determinants that can be useful to inform population health. We conducted a search between October 2010 and September 2020 for grey and scientific publications on social determinants using a search strategy in PubMed and a manual snowball search. We assessed data sources and the data environment in both Kenya and the Philippines. We found limited evidence of the use of new sources of data to study the wider SDH, as most of the studies available used traditional sources. There was also no evidence of qualitative big data being used. Kenya has more publications using new data sources, except on the labor determinant, than the Philippines. The Philippines has a more consistent distribution of the use of new data sources across the HEALTHY determinants than Kenya, where there is greater variation of the number of publications across determinants. The results suggest that both countries use limited SDH data from new data sources. This limited use could be due to a number of factors including the absence of standardized indicators of SDH, inadequate trust and acceptability of data collection methods, and limited infrastructure to pool, analyze, and translate data.

Depression accounts for a large share of the global disease burden, with an estimated 264 million people globally suffering from depression. Despite being one of the most common kinds of mental health (MH) disorders, much about depression remains unknown. There are limited data about depression, in terms of its occurrence, distribution, and wider social determinants. This work examined the use of novel data sources for assessing the scope and social determinants of depression, with a view to informing the reduction of the global burden of depression.This study focused on new and traditional sources of data on depression and its social determinants in two middle-income countries (LMICs), namely, Brazil and India. We identified data sources using a combination of a targeted PubMed search, Google search, expert consultations, and snowball sampling of the relevant literature published between October 2010 and September 2020. Our search focused on data sources on the following HEALTHY subset of determinants: healthcare (H), education (E), access to healthy choices (A), labor/employment (L), transportation (T), housing (H), and income (Y).Despite the emergence of a variety of data sources, their use in the study of depression and its HEALTHY determinants in India and Brazil are still limited. Survey-based data are still the most widely used source. In instances where new data sources are used, the most commonly used data sources include social media (twitter data in particular), geographic information systems/global positioning systems (GIS/GPS), mobile phone, and satellite imagery. Often, the new data sources are used in conjunction with traditional sources of data. In Brazil, the limited use of new data sources to study depression and its HEALTHY determinants may be linked to (a) the government's outsized role in coordinating healthcare delivery and controlling the data system, thus limiting innovation that may be expected from the private sector; (b) the government routinely collecting data on depression and other MH disorders (and therefore, does not see the need for other data sources); and (c) insufficient prioritization of MH as a whole. In India, the limited use of new data sources to study depression and its HEALTHY determinants could be a function of (a) the lack of appropriate regulation and incentives to encourage data sharing by and within the private sector, (b) absence of purposeful data collection at subnational levels, and (c) inadequate prioritization of MH. There is a continuing gap in the collection and analysis of data on depression, possibly reflecting the limited priority accorded to mental health as a whole. The relatively limited use of data to inform our understanding of the HEALTHY determinants of depression suggests a substantial need for support of independent research using new data sources. Finally, there is a need to revisit the universal health coverage (UHC) frameworks, as these frameworks currently do not include depression and other mental health-related indicators so as to enable tracking of progress (or lack thereof) on such indicators.

Background: Policymakers in sub-Saharan Africa (SSA) face challenging decisions regarding the allocation of health resources. Economic evaluations can help decision makers to determine which health interventions should be funded and or included in their benefits package. A major problem is whether the evaluations incorporated data from sources that are reliable and relevant to the country of interest. We aimed to review the quality of the data sources used in all published economic evaluations for cardiovascular disease and diabetes in SSA. Methods: We systematically searched selected databases for all published economic evaluations for CVD and diabetes in SSA. We modified a hierarchy of data sources and used a reference case to measure the adherence to reporting and methodological characteristics, and descriptively analysed author statements. Results: From 7,297 articles retrieved from the search, we selected 35 for study inclusion. Most were modelled evaluations and almost all focused on pharmacological interventions. The studies adhered to the reporting standards but were less adherent to the methodological standards. The quality of data sources varied. The quality level of evidence in the data domains of resource use and costs were generally considered of high quality, with studies often sourcing information from reliable databases within the same jurisdiction. The authors of most studies referred to data sources in the discussion section of the publications highlighting the challenges of obtaining good quality and locally relevant data. Conclusions: The data sources in some domains are considered high quality but there remains a need to make substantial improvements in the methodological adherence and overall quality of data sources to provide evidence that is sufficiently robust to support decision making in SSA within the context of UHC and health benefits plans. Many SSA governments will need to strengthen and build their capacity to conduct economic evaluations of interventions and health technology assessment for improved priority setting. This capacity building includes enhancing local infrastructures for routine data production and management. If many of the policy makers are using economic evaluations to guide resource allocation, it is imperative that the evidence used is of the feasibly highest quality. [ABSTRACT FROM AUTHOR]

Although rivers are inherently dynamic systems that are susceptible to change, human impact on rivers in the last century is considered to have been so significant that it has caused an unprecedented intensity of geomorphological change in river channels and floodplains. As these changes often lead to deterioration of ecological conditions as well as increased flood risks, the approach to river management has changed over the past twenty years. There is an increasing emphasis on a holistic approach based on the understanding of river system processes, for which studies of geomorphological change in rivers represent a very important source of information. The aim of this review is to present the basic methods used in studies of geomorphological change in rivers, including the spatio-hierarchical delineation of the river system, data sources, and the most commonly analysed geomorphological characteristics and factors of change. The results of previous research are presented for the period of the last 150 years. The most important geomorphological changes include channel narrowing, incision, and reduction in the complexity of fluvial landforms and processes due to channelization and the construction of numerous barriers that disrupt the connectivity in water flow and sediment transport. Explaining the cumulative impacts and predicting future changes are the major research challenges. These challenges are related to the complexity of the river system, i.e. a large number of causal factors, connections, and interactions in the river system, and to the nonlinearity of the evolutionary trajectory of changes in rivers. [ABSTRACT FROM AUTHOR]

This study examines poverty through the lens of a set of approaches mostly used by economists that identifies poverty in terms of a monetary indicator and which derives an 'objective' poverty line. All indicators of poverty, regardless of whether they are based on material deprivation or human capital, or whether they are multidimensional or unidimensional, try to gauge the welfare of the individual. The most popular method is financial poverty, which takes into account control over marketable products and services. Indicators that are simple to grasp allow for comparisons across time and geographies and are connected to a household's current situation. This analysis for Albania is based on INSTAT data using surveys such as the Household Budget Survey and Statistics on Income and Living Conditions (EU-SILC). The latter provides two types of data: a. cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions; and b. longitudinal data pertaining to individual-level changes over time, observed periodically over a four-year period. [ABSTRACT FROM AUTHOR]

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