Data Sources Research Articles

Adipura: Good Environmental Governance Perspective and Fiscal Challenges

The research emphasizes the effectiveness of Adipura as an instrument for monitoring the performance of local governments in waste management from the perspective of good environmental governance. Focus study on public participation and fiscal challenges. This is a qualitative research. The main data source from the National Waste Management Information System (NSWMIS) and in-depth interviews with central and local government, members of the Adipura Advisory Board, and the communities. Also supported by secondary data relevant to the study. The purpose of research are to determine the extent of the effectiveness of Adipura and to analysis the factors that must be improved to increase its effectiveness. The results indicate that the placement of Adipura as an instrument for monitoring the performance of regional waste management is appropriate, but still needs to be improved because the impact of Adipura has not been maximized in encouraging sustainable waste management in the region. It is necessary to recalculate the components and subcomponents of Adipura, by adding Non-Physical Components, increasing the portion of public participation and fiscal support for waste management.

Challenges in Early Years data and their implications on programme evaluations

The Welsh Government funds a wide range of Early Years programmes to both support parents to remain in work and children’s social, emotional and educational development. The interest of Welsh Ministers in this policy area has grown in recent years with expansions in this area carefully considered. The potential for expanding the offer has generated a greater need for evidence to understand the effectiveness of the delivery against the proposed objectives. Data linking is a valuable and efficient way to provide complex evaluations of policy programmes as it maximises the use of existing data sources without increasing the burdens on data providers and provides comprehensive information that would otherwise be limited with a single dataset. However, Welsh Government research on programme evaluation in Early Years is facing challenges and this work aims at providing an overview of these issues, their impact on Early Years analytical work and progress made to address them. In particular, the main challenges are related to data quality and data availability that stem from lack of metadata and contextual information, poor technological infrastructure and, at times, a disconnect between policy / operational delivery and data collection. Progress is being made to address these challenges, such as acquiring new datasets or improving the quality of existing ones but these developments take time. At the same time as helping to tackle these structural challenges, the Early Years analytical team is exploring a range of novel approaches to provide robust evaluation of Early Years interventions despite the data issues.

Mapping UK Pain Datasets to the OMOP Common Data Model: Lessons Learned

ObjectiveChronic Pain is defined as pain lasting for three months or longer and a report by the British Pain Society in 2016 found that 28 million adults live with this condition. Chronic pain research is problematic as it is poorly represented in health data and often in silos. Alleviate is the Advanced Pain Discovery Platform (APDP) Data Hub which is removing barriers to access to data. ApproachThe aim for Alleviate is to transform pain data sets within the UK to be Findable, Accessible, Interoperable and Reusable (FAIR) and be more discoverable for researchers via Health Data Research UK’s Cohort Discovery Tool. The tool enables identification of potential cohorts in real-time from datasets Alleviate provides. The onboarding requires datasets to be transformed to the open and widely used Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). ResultsWe collaboratively developed CaRROT tools and used our mapping expertise to transform datasets to OMOP-CDM. The tools have improved efficient mapping of clinical and research data, covering ~10 million individuals’ records. Subsequently we learned valuable lessons on how to accurately and consistently map across data sets to support federated discovery. We also identified a lack of standard vocabulary representations for pain specific data. ConclusionsMapping data to OMOP is not straightforward. Attention to detail is required to ensure that consistent mapping decisions are made to maximise data reusability and interoperability. ImplicationsIn sharing the lessons learned of mapping datasets to OMOP we aim to help improve interoperability of disparate data sources.

Enhancing population-level research among people who inject drugs: a validation and retrospective cohort study using health administrative data in Ontario, Canada

ObjectiveHealth administrative data can support population-level research among people who inject drugs (PWID), however these data sources remain underutilized due to the difficulty in identifying drug use in routinely collected data. We validated case-ascertainment algorithms to identify PWID and tested their application in a hepatitis C (HCV) cohort in Ontario, Canada. ApproachWe conducted a validation study using reference standard cohorts of PWID recruited via community-based studies and population controls linked to health administrative data in Ontario, Canada (1992-2020). Tested case-ascertainment algorithms included combinations of hospitalizations/emergency department (ED) visits for drug use/poisoning, physician visits for drug use, opioid agonist treatment (OAT), or injecting-related infections. Sensitivity and specificity were estimated for lifetime history and recent injecting (past 1-5 years). We applied a high-performing algorithm among all Ontarians with laboratory-confirmed HCV between 1999-2018, to identify a sub-cohort of PWID with HCV. ResultsAn algorithm including ≥1 hospitalization/ED visit or ≥1 physician visit for drug use or ≥1 OAT record had high accuracy for identifying IDU history (91.6% sensitivity, 94.2% specificity) and recent IDU (using 3 years lookback: 80.4% sensitivity, 99% specificity). When applied to a provincial cohort of 112,947 Ontarians diagnosed with HCV, this algorithm estimated 46% (N=52,248) had a history of IDU, of whom 52% (N=27,246) had an indication of recent IDU (within the past 3 years). ConclusionThe methods developed in this study can enhance the capacity of population-level health research among people who inject drugs and support applied public health interventions towards hepatitis C elimination.

Expanding a cohort of children who had a response to the Household Food Security Survey Module: A novel approach using linked ICES administrative data

ObjectiveThe Canadian Community Health Survey (CCHS) is a national cross-sectional survey, rich with household, health-related measures. Through linkage with administrative health services data, we aimed to investigate the relationship between household food-insecurity in childhood, and incident health conditions (asthma and diabetes). Linking mothers with children and children with siblings, provided a unique opportunity to expand our child cohort. ApproachWe linked five cycles of the CCHS with ICES administrative databases, to conduct longitudinal cohort studies of children <18 years who had a household response to the Household Food Security Survey Module (HFSSM). Three approaches were used to increase our sample of children: we included children who personally completed the HFSSM; the children of mothers who completed the HFSSM (via a unique database linking mothers with biological children); and the siblings of children who completed the HFSSM. We examined the characteristics of children from food secure and insecure households, accounting for the clustering of children living within the same home. In addition, we investigated the incidence of health outcomes using marginal time-to-event regression analysis to adjust for clinical and socio-economic confounders. Results and ConclusionsUsing novel linkages and multiple data sources we were able to study a large cohort of children (20,023 children whose mothers completed the HFSSM, 5,199 siblings of child respondents, 8,820 children who personally responded to the survey). This creative approach built capacity to investigate household-level determinants of health in a large cohort of Canadian children.

Uncharted territory in linking population-based laboratory data for the epidemiology of celiac disease in Canada

IntroductionWe previously investigated the frequency of screening for celiac disease (CD) based on tissue transglutaminase antibody testing (tTG-IgA) and developed the first incident cohort of celiac autoimmunity in Canada. MethodsAdministrative data sources used in this study were population-based and covered the entire province of Alberta (~4.3M residents during study period). Various approaches to querying data were employed within a diverse team of health information managers, data analysts, clinical scientists, and gastroenterologists. This process involved a broad search for CD screening tests, thorough data inspection/cleaning, and numerous discussions to determine potential explanations for the findings. ResultsApproximately ~950,000 records for tTG-IgA were first identified. Records were then excluded due to missing/invalid patient identifiers or test results (0.8%), non-Alberta residency (0.4%), and duplicate records (1.1%). A final dataset included ~920,000 tTG-IgA tests on ~680,000 unique patients, which was also validated through a separate query performed by an analyst external to the study team. A conservative approach to excluding as many potential prevalent cases of CD was applied given a robust algorithm for CD has not yet been established in Canada. The final rate of celiac autoimmunity (34 per 100,000) offered further face validity based on prior estimates of diagnosed CD in Alberta and other countries reporting on celiac autoimmunity. ConclusionWhen developing a novel case definition or investigating unfamiliar outcomes using routinely collected data, collaboration across several disciplines is highly recommended. Certain stages of the project may require additional scrutiny and discussion to ensure findings are valid and reliable.

Navigating Higher Education Challenges: A Review of Strategies among Students with Disabilities in Indonesia

Access to higher education has been widely open for students with disabilities (SwD) as also enforced by the Government of Indonesia (GoI). However, challenges evidently face SwD in their survival of university life including limited accessible services and facilities, as well as negative stereotypes surrounding SwD. This study aimed to explore the diverse strategies adopted by SwD in navigating these challenges, examining the nuances and implications of the deployed strategies. This review explored the experiences of SwD in higher education institutions in Indonesia and adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Review) guidelines. Data were gathered from seven databases, including Scopus, Web of Science, ERIC, ProQuest, Google Scholar, and Portal Garuda. In total, 17 of 1777 articles were selected based on two inclusion criteria viz. peer-reviewed articles derived from empirical studies and utilizing life experiences of SwD in Indonesian HE as the primary data source. The study identified the five key strategies employed by SwD: Adaptation, Technology Assistive Optimization, Requesting Support, Building Relationships, and Passive Action. Detailed elaboration of the findings is presented to shed light on the prominent insight to improve the quality of education services for SwD in Indonesia.

Pengaruh Earning Per Share, Return on Equity dan Price to Book Value Terhadap Harga Saham Periode 2019-2021 (Studi Kasus pada Perusahaan Sub Sektor Otomotif dan Komponenya di BEI)

The phenomenon that occurred during the Covid-19 pandemic that spread in Indonesia in early 2020 has an impact on all aspects of life, including the capital market. The spread of Covid-19 in Indonesia has caused fear for investors so that many of them choose to leave risky assets. This affects the movement of the Composite Stock Price Index (IHSG). The problem in this study aims to determine the effect of EPS (Earning Per Share), ROE (Return on Equity) and PBV (Price to Book Value) on stock prices in automotive companies listed on the IDX for the period 2019 - 2021. The type of research used is quantitative research with secondary data sources. The population in this study is automotive companies listed on the Indonesia Stock Exchange for the period 2019 - 2021 as many as 15 companies. Sampling using purposive sampling technique as many as 10 companies that meet the criteria. Data analysis techniques using multiple linear regression analysis techniques. The results of the study indicate that EPS can influence stock prices, and ROE also influences stock prices, and PBV influences company value. Simultaneously, the variables EPS (Earning Per Share), ROE (Return on Equity) and PBV (Price to Book Value) influence stock prices.

Population health value of being in target: Results from the Canadian Multi-Morbidity Model for Type 2 Diabetes

ObjectiveTo develop a microsimulation model for type 2 diabetes using population-level real-world data. Such a model allows for the synthesis of multiple data sources for comparative effectiveness analysis related to a variety of correlated outcomes. ApproachThe model was built using health state features including sex, age, diabetes duration, laboratory test results, and a history of major acute events. Features update using a cycle length of one month. Events modelled include myocardial infarction, stroke, heart failure, amputation and death. Model outputs include counts of events, lifetime healthcare costs and quality-adjusted life-years (QALYs). We then used the model to calculate the number of events that could be averted if a population with type 2 diabetes achieved treatment targets for HbA1c, blood pressure and LDL-cholesterol for 10 years. ResultsBringing all 60-year-olds with type 2 diabetes into target for 10 years would result in annual event reductions of 87.0 per 100,000 person-years for myocardial infarction, 49.4 for stroke, and 166.8 for heart failure. QALYs would improve by 1,155 per 100,000 patients. For 75-year-olds, annual event reductions would be 178.2, 56.8 and 261.6 per 100,000 person-years, respectively, and QALYs would improve by 2,121 per 100,000 patients. ConclusionsPopulation-level real-world data can be used to develop microsimulation models for type 2 diabetes that estimate long-term event risks, mortality and healthcare costs. This model is capable of comparative effectiveness and cost-effectiveness analysis of novel therapies in diabetes.

Cardiovascular disease surveillance using electronic medical records: a scoping study

ObjectiveCardiovascular diseases (CVD) are the leading cause of mortality and morbidity worldwide. Traditionally, disease surveillance relies on data from surveys, registries, and administrative databases. As medical records undergo global digitization, electronic medical records (EMRs) are emerging as a crucial reservoir of real-world data. However, the extent EMRs are used in CVD surveillance is unknown. We are conducting a scoping review to assess the current state and effectiveness of EMR-based CVD surveillance worldwide. ApproachFollowing the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for Scoping Reviews, we searched MEDLINE and EMBASE bibliographic databases to capture studies on prevalence, incidence, and trend measurements of CVDs using EMRs. Assessed factors include CVD types, modelling methodologies, data linkages, advantages, disadvantages, challenges, and solutions. Due to the qualitative nature of the review, collected data will be narratively synthesized to present overall perspectives. ResultsOur search algorithm yielded 11,979 citations, of which 5,886 abstracts were selected for screening, in progress at the time of this submission. The interim results indicate that most eligible reports came from the USA (49%), followed by the UK (13%), China (6%), Spain (4%) and Canada (3%). The most common diseases were coronary artery diseases (29%), followed by hypertension (26%), stroke (21%), and heart failure (15%). ConclusionsSurveillance of CVD is crucial for prevention and health policy development. While EMRs can be a data source for surveillance, such potential has yet to be fully realized. ImplicationsThis study will inform existing research challenges and future opportunities of EMR-based CVD surveillance.

Towards a "Biography" of Population Health Data about Substance Use Disorders: Qualitative Approaches to Communicating Context

BackgroundUnderstanding contexts of data production is necessary for using data accurately and ethically. “Biographies of data"" have developed as a complement to existing metadata as a way to critically examine and communicate social structures and power dynamics encoded in data. This project is focused on developing a “biography” of diagnostic codes, used to identify substance use disorders (SUD) in population health datasets. People with SUD regularly experience stigma and exclusion, particularly from healthcare settings. MethodsQualitative semi-structured interviews were conducted in 2024 with ""producers"" and “managers” of administrative data, including physicians and health information professionals, in British Columbia, Canada. The interviews explored processes involved with coding and billing, and the meaning of diagnostic codes from the perspective of those involved. This includes considerations of the population, social, and geographic factors, and implications of coding as inputs to decision-making. Reflexive thematic analysis was used to identify broad themes and report findings. ResultsAlthough data collection and analysis is ongoing, emerging findings have revealed structural, institutional, and social-level processes that influence coding decisions, as well as potential implications on research, policy making, and knowledge production about SUD. ConclusionAddressing the complexities of SUD and the overdose crisis will require a variety of data sources and approaches, and linked population health and administrative data will continue to be one important resource. Through better understanding the contexts and processes that inform the production and management of SUD-related population data, we will shed light on the ethical and social implications of their use.

Tracheal resection and anastomosis in postintubation tracheal stenosis: a systematic review.

Surgical resection of the stenotic segment with end-to-end anastomosis is considered the gold standard in postintubation tracheal stenosis. However, outcomes of this concrete aetiology are not well described. With the aim to examine the extent, range and characteristics of the existing evidence, a scoping review was performed. Data sources included MEDLINE, Scopus, Ovid and the Cochrane databases. Inclusion criteria consisted of studies in adult patients with postintubation tracheal stenosis that reported characteristics, surgical management and outcomes. A total of 125 articles were identified, of which 10 were included in the final analysis. All studies were case reports or case series (level 4 evidence) grouping 110 patients, 75 males and 35 females. The age ranged from 15 to 71 years. Cotton-Myer stenosis grade was 1 [1 (0.9%)], 2 [25 (22.7%)], 3 [70 (63.6%)] and 4 [14 (12.7%)]. Stenosis location was in the tracheal upper-third in 108 (98.2%), in the middle-third in 1 (0.9%) and in the lower-third in 1 (0.9%). Stenosis length ranged from 1 to 5.6 cm. Follow-up ranged from 1 to 60 months (2 years for the most). Most frequent complications were transitory dysphagia in 13 (11.3%), granuloma formation in 8 (7.3%), dehiscence or air leak in 5 (4.5%) and wound infection in 4 (3.6%). Restenosis rate ranged from 2% to 25%. There was no perioperative mortality. Tracheal resection and primary anastomosis in postintubation tracheal stenosis appear to be safe and effective in the short and mid-terms; however, the very low level of evidence found prevents definitive conclusions.

Probabilistic Record Linkage for Families (PRLF): A Discussion of the Development and Validation of this Open-Source Linkage Tool

Linking administrative records across programs can yield person-centered information, including client characteristics, public service trajectories, and outcomes and help to answer policy-related questions. Several solutions are available for undertaking record linkage, producing linkage keys for merging data sources for positively matched pairs of records. In this session, we will demonstrate a new application of the Python RecordLinkage package to family-based record linkages with machine learning algorithms for probability scoring, which we call probabilistic record linkage for families (PRLF). First, we will demonstrate the utility of PRLF with a simulation of administrative records and assess linkage accuracy with variations in match rates and data degradation. Second, we will compare generalized linear model estimates across three record linkage solutions (PRLF, ChoiceMaker, and Link Plus). Findings from the simulation study indicate linkage accuracy is largely influenced by degradation (e.g., missing data fields, erroneous or incomplete values) compared to the proportion of simulated matches between datasets. Results from the methods comparison using real world data indicate that all three solutions, when optimized, provide similar results for researchers. We discuss the strengths of our process, such as the use of ensemble methods, to improve match accuracy. We then will identify caveats of record linkage in the context of administrative data. The tool was developed in Python to allow for researchers to work with open-source software and adjust the basic workflow to fit their linkage needs. We will identify several partnerships where this collaboration has worked successfully and empower attendees with access to this useful tool.

An Algorithm for Coding an Additive Manufacturing File from the Pressure Distribution of a Baropodometric Board for 3D Printing Customised Orthopaedic Insoles

Customised orthotic insoles play a critical role in addressing foot pathologies and improving comfort and biomechanical alignment for patients with specific needs. The use of 3D printing technology for the manufacturing of orthotic insoles has received considerable attention in recent years due to its potential for customisation, rapid prototyping, and cost-effectiveness. This paper presents the implementation of an algorithm purposely developed to generate an Additive Manufacturing File (AMF) containing the geometry of a patient-specific insole and the stiffness distribution based on pressure analysis from a baropodometric board. The generated file is used to 3D print via Fused Deposition Modelling an insole with a variable infill percentage depending on the pressure distribution on the patient’s foot. Three inputs are used as source data for the AMF file coding: (i) the 3D model that defines the geometry of the insole designed by the orthopaedist; (ii) the pressure map of the patient’s feet obtained with a baropodometric board; and (iii) the stiffness of the material that will be used to fabricate the insole. The proposed approach allows the fabrication of a patient-specific insole, capable of restoring the correct pressure distribution on the foot by varying the infill percentage. Two types of insoles were successfully fabricated using the implemented algorithm: the first was 3D printed, adding a top layer to be ready-to-use; the second was 3D printed without a top surface to be further customised with different coatings. The method described in this paper is robust for the fabrication of customised insoles and aims at overcoming the limitations of the traditional approach based on milling machining (e.g., time, costs, and path planning) since it can be easily integrated into any orthopaedic workshop.

Study on the applicability of FAI linear fitting model in the extraction of cyanobacterial blooms.

Currently, more and more lakes around the world are experiencing outbreaks of cyanobacterial blooms, and high-precision and rapid monitoring of the spatial distribution of algae in water bodies is an important task. Remote sensing technology is one of the effective means for monitoring algae in water bodies. Studies have shown that the Floating Algae Index (FAI) is superior to methods such as the Standardized Differential Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) in monitoring cyanobacterial blooms. However, compared to the NDVI method, the FAI method has difficulty in determining the threshold, and how to choose the threshold with the highest classification accuracy is challenging. In this study, FAI linear fitting model (FAI-L) is selected to solve the problem that FAI threshold is difficult to determine. Innovatively combine FAI index and NDVI index, and use NDVI index to find the threshold of FAI index. In order to analyze the applicability of FAI-L to extract cyanobacterial blooms, this paper selected multi-temporal Landsat8, HJ-1B, and Sentinel-2 remote sensing images as data sources, and took Chaohu Lake and Taihu Lake in China as research areas to extract cyanobacterial blooms. The results show that (1) the accuracy of extracting cyanobacterial bloom by FAI-L method is generally higher than that by NDVI and FAI. Under different data sources and different research areas, the average accuracy of extracting cyanobacterial blooms by FAI-L method is 95.13%, which is 6.98% and 18.43% higher than that by NDVI and FAI respectively. (2) The average accuracy of FAI-L method for extracting cyanobacterial blooms varies from 84.09 to 99.03%, with a standard deviation of 4.04, which is highly stable and applicable. (3) For simultaneous multi-source image data, the FAI-L method has the highest average accuracy in extracting cyanobacterial blooms, at 95.93%, which is 6.77% and 13.26% higher than NDVI and FAI methods, respectively. In this paper, it is found that FAI-L method shows high accuracy and stability in extracting cyanobacterial blooms, and it can extract the spatial distribution of cyanobacterial blooms well, which can provide a new method for monitoring cyanobacterial blooms.

A systematic review of lean practice in healthcare settings

Purpose: This review mainly aims to identify the lean practice conducted in hospitals, determining what problems lean practice can be helpful to solve in the hospitals. Data sources: Four electronic databases (Scopus, Web of science, Medline, and PubMed) were conducted for searching related literature in this review. Study selection: These studies in the hospitals that related lean healthcare practice and contained outcome variables were included. Data extraction: Related information such as research design, countries, lean tools, outcome variables, results were extracted. Results of data synthesis: 20 eligible articles were identified in this review. There was 20% lean practice being conducted in emergency department of hospitals in this review. Six cases have implemented lean in Brazilian hospitals. There were 12 cases implemented lean practice through Value Stream Mapping. Conclusion: Lean practices were highly valued in Brazilian hospitals, and it was frequently implemented in hospital emergency department. Value Stream Mapping and process mapping were the most commonly used lean tool. Waiting time, lead time and Length of Hospital Stay (LOS) were the primary indicators reflecting improvements in this review.

Multi-scale feature fusion segmentation and lightweight ensemble deep learning approach for urban slum classification using satellite images

ABSTRACT Although there are some drawbacks to satellite imaging, such as the difficulty in identifying slums in highly populated metropolitan areas, it is nonetheless a useful tool for preliminary screening and can support on-the-ground and other data sources. Hence, we proposed the MSFF-Seg (Multi-Scale Feature Fusion-based Segmentation) model for urban slum classification using satellite images. Hence we proposed the MSFF-Seg model for urban slum classification using satellite images. Initially, three types of corrections are performed in preprocessing such as geometric correction which is corrected by Enhanced Scale Invariant Feature Transform (ESIFT), radiometric correction which is corrected by Ground-based Relative Radiometric Calibration (GRRC), and atmospheric correction which is corrected by homomorphic filter. After completion of preprocessing phase, segmentation is initiated by Tweak Mask RCNN which extracts the features in multilevel for segmentation that increases the segmentation accuracy. The segmented images are fed into an ensemble classifier for urban slum identification. Here, three types of deep learning are used for ensemble learning namely Schematic Convolutional Neural Network (SCNN), Shallow MobileNet, and Lite ShuffleNet. They extract multiple features from the segmented region, based on the extracted features. LGBM performed urban slum classification which classifies the images into two classes such as slums and non-slums. Finally, the performance of this research is evaluated based on several performance metrics like accuracy (98%), precision (96%), recall (94%), and F1 score (97%), which proved that the proposed work achieved superior performance in urban slum classification.

Assessing the legal, illegal, and gray ornamental trade of the critically endangered helmeted hornbill.

Monitoring wildlife trade dynamics is an important initial step for conservation action and demand reduction campaigns to reduce illegal wildlife trade. Studies often rely on one data source to assess a species' trade, such as seizures or the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) trade data. Each database provides useful information but is often incomplete. Combining information from multiple sources helps provide a more complete understanding of trade. A recent rapid increase in demand for helmeted hornbill (Rhinoplax vigil) casques (a brightly colored, solid keratinous rostrum) led to its uplisting to critically endangered on the International Union for Conservation of Nature Red List in 2015. However, there is little current information on what factors influence trade trends and what current levels of demand are. We combined data from CITES, seizure records, and previously underused, yet abundant, art and antique auction data to examine the global trade in helmeted hornbill casque products (HHPs). Three decades of auction data revealed that 1027 individual HHPs had been auctioned since 1992; total auction sales were over US$3 million from 1992 to 2021. The number of HHPs auctioned was greatest from 2011 to 2014, just after the global art boom (2009-2011), followed by a decline in volume and price. The auction data also revealed 2 possible markets for HHPs: true antique and speculative, defined by era, price, and trade patterns. Trends in illegal trade matched those of the auction market, but legal trade remained consistently low. Combining data sources from legal, illegal, and gray markets provided an overview of the dynamics of illegal trade in an endangered species. This approach can be applied to other wildlife markets to provide a more complete understanding of trade and demand at the market level to inform future demand reduction campaigns.

The Practice of Changing the Status of Change of Waqf Property in the Islamic Social Economic View

The purpose of this study is to reveal and analyze the practice of changing the exchange status of waqf assets in the view of the Islamic social economy. This type of research is included in qualitative research, which is derived from interviews and data observation. The source of this research data consists of primary data sources, namely researchers directly conduct interviews with the parties concerned in the process of exchanging waqf property at the At-Taqwa mosque, researchers also use secondary data sources, namely books, articles, and other reference materials. The results of the study explained that in the case of exchanging waqf property at Masjid At-Taqwa by Government Regulation Number 28 of 1977 and Law Number 41 of 2004. As for the aspect of Islamic Law, in the opinion of Imam Hanafi, if the waqf property can no longer provide benefits, it can be exchanged or replaced. On the contrary, according to the Shafi'iyah and Maliki Schools, it forbids all forms of exchange and replacement of waqf property under any circumstances. And according to Hanafiyah any form of exchange or replacement of all waqf objects depends on the power of the wakif at the time the pledge is pronounced by the wakif. The originality/novelty of this study offers originality by examining how changes in the status of waqf assets can affect people's welfare, both in terms of economics and society. In Islam, waqf is not only seen as charity but also as an economic instrument that can empower the community. Thus, a change in the status of waqf assets, for example from waqf land to commercial property, can have a significant impact on local economic empowerment.

Developing a nationwide registry of UK veterans seeking help from sector charities—a machine learning approach to stratification

Abstract The assistance to veterans in the UK is provided by the National Health Service and over 1800 military charities. These charities count services using different definitions and reporting systems, so to date a national registry of service usage does not exist. The aim of the Map Of Need Aggregation ResearCH study is to build a standardized registry of service usage data for the military charity sector. Data are completely anonymized by adopting a Secure Hashing Algorithm. A unique anonymous identifier is generated allowing both privacy protection and avoiding double counts. Data are standardized and linked with an automated process to create an aggregated dataset. The dataset describes the population, using both a priori and machine learning approaches. To date a total of 42 509 veterans with 128 423 needs are included. The mean age was 60.1 years, and 90% were male. 65% were receiving other benefits, 5% were homeless and 1% were in prison. 65% of the needs recorded concerned social wellbeing. 40% of veterans received assistance in at least two different years. The k-means clustering approach returned 4 subgroups of use that were identical to those created using a priori knowledge. The dataset is the most comprehensive source of veteran charity usage data in the UK to date. Service usage is generally homogenous among subgroups, but some differences were highlighted indicating that younger, non-officer veterans may be more at risk of presenting with more complex needs. These first useful insights can help allocate resources to build an effective preventive strategy for more complex cases.