Data Collection System Research Articles

Information System for Data Collection on Building Tools and Materials Using the Rational Unified Process (RUP) Method

UD. FLORA records the re-inventory of building equipment that has been sold by reading the transactions one by one in the ledger. A proper way is needed to overcome the problems that occur. This research aims to create an information system software. In making information system software, a method is needed that can carry out the stages from initial design to implementation of the software. This research uses the Rational Unified Process (RUP) method, namely a software development approach that is carried out repeatedly (iterative), focuses on architecture (architecture-centered), is more directed based on use cases (use case driven). This research produces a tool data collection application at UD. FLORA using the RUP method. The existence of an information system for data collection on building equipment using the Rational Unified Process method means that UD. FLORA can be helped in managing building equipment data collection. The system enables real-time transaction recording, providing better insight into inventory status, and supporting more accurate decision-making.

Transnational Intersectionality at Sea: Gender, Appearance, Ethnicity, Age and Marine Knowledge Production

<p>Knowledge production is inherently social, as humans interpret their environment. Scientific knowledge production differs from non-scientific ones in their systematic data collection for validation, yet both involve a social element shaping our understanding of the world. The article investigates social contestation processes as part of knowledge producing processes on a German research vessel and in German and Brazilian marine science institutes with a particular focus on the social identity markers of gender, ethnicity, and age and how they affect team-based sense-making processes. Methodologically, our research draws on participant observation of following marine scientists and associated non-scientists in their daily working routines, as well as semi-structured interviews on a research vessel in 2021 and in marine science institutes from 2022 to 2024. Conceptually, the research follows approaches of the Sociology of Knowledge (Keller et al., 2018) and intersectional approaches (Grabe & Else-Quest, 2012; Patil, 2013) that integrate transnational experiences across national borders and other (physical) boundaries. Based on the empirical research, we assess transnational intersectional sense-making practices at sea. Our findings show that 1) (sexual) harassment in marine knowledge production processes occurs independent of localities, 2) intersectional discrimination at sea leads either to emancipation processes or to withdrawal, 3) tensions especially arise between scientists and non-scientists, which broadens the gap between these social groups and knowledge systems. We conclude by providing recommendations for increased intersectional and female workforce at sea and in marine sciences, so that the doing of research and work at sea can be more inclusive, equal, and safe.</p>

Wearable Sensors for Healthcare of Industrial Workers: A Scoping Review

Background and Objectives: This scoping review evaluates the use of wearable sensor technologies for workplace safety and health monitoring in industrial settings. The aim is to synthesize evidence on the impact of these sensors and their application in high-risk environments. Materials and Methods: Following the PRISMA guidelines, a systematic search across four international electronic databases yielded 59 studies, of which 17 were included in the final review. The selection criteria involved studies that specifically utilized wearable sensors to monitor various health and environmental parameters relevant to industrial workers. Results: The analysis categorizes wearable technologies into five distinct groups based on their function: gas monitoring technologies, heart rate and physiological data collection, fatigue and activity monitoring, comprehensive environmental and physiological monitoring, and advanced sensing and data collection systems. These devices demonstrated substantial benefits in terms of early detection of health risks and enhancement of safety protocols. Conclusions: The review concludes that wearable sensor technologies significantly contribute to workplace safety by providing real-time, data-driven insights into environmental hazards and workers’ physiological status, thus supporting proactive health management practices in industrial settings. Further research is recommended to address the challenges of data privacy, sensor reliability, and cost-effective integration to maximize their potential in occupational health safety.

Pest Control and Safety Management Monitoring in Outdoor Plantation Using Unmanned Aerial Vehicle Captured Images

The National Institute of Ecology in Seocheon, Chungcheongnam-do, exhibits and studies Korea’s diverse ecosystems to communicate the importance of biodiversity conservation to the general public. The site faces the challenge of preserving the natural environment while ensuring visitor convenience. This requires systematic data collection to quickly detect and respond to threats from pests and natural disasters. In this study, high-resolution orthoimages were acquired to monitor ecological changes using unmanned aerial vehicles. Images were captured through flights monthly from March to July 2024. Digital elevation models and orthoimages were generated to identify dead trees and assess areas of damage caused by heavy rainfall. The study covered an area of 998,655 m2, and the image resolution was 3.6 cm/pixel. The orthophotos were useful in identifying plant pest damage. They also helped evaluate damage caused by rain, showing that the total area of damage was 29,384 m2, mainly due to soil erosion. Furthermore, it is expected that the accumulation of such unmanned remote sensing image data can also be applied to the safety management of various natural conservation areas and public facilities.

A survey of speech-language pathology service delivery in Australian schools

Purpose Speech-language pathologists (SLPs) work in Australian schools yet, little is documented about their service delivery practices. This study therefore explores Australian speech-language pathology service delivery in schools, inclusive of work activities, caseloads, and assessment and intervention practices. Method An online survey comprising multiple choice, constant sum, Likert scale, and open text questions was completed by 105 SLPs. Quantitative data was analysed using descriptive statistics while open-text responses were analysed using content analysis. Result Participants were predominantly government education department employees working in multiple schools, and caseloads ranged from 10 to 240 students for full-time SLPs. While responses varied considerably, some general trends emerged. Most time was spent on intervention, followed by non-contact tasks, and assessments, with differences across employment sectors. Language was the most common student need, followed by literacy and speech. Frequently reported factors contributing to client complexity and intervention frequency were co-occurring presentations, severity of student needs, caseload size, and impact on learning. Caseload manageability was a concern for participants who reported larger caseloads, spent more time in assessment or less time in therapy than others, and worked in education departments. Conclusion Findings have implications for policy makers, professional bodies and employers, and provide direction for future systematic data collection.

Twenty years of volcano data at GeoNet—collection, custodianship, and evolution of open data on New Zealand’s volcanoes

The GeoNet programme at GNS Science has monitored and managed data for volcanoes, earthquakes, landslides, and tsunami in Aotearoa New Zealand since 2001. Volcano monitoring data are collected from seismometers, acoustic sensors, GNSS receivers, webcams, remote gas monitoring sensors, and a range of environmental sensors, as well as manually during visits to volcanoes. The primary user of volcano data is the internal cross-specialised Volcano Monitoring Group (VMG), which fulfils the role of the national volcano observatory. GeoNet concentrates on automatic data collection and analysis, while supporting members of the VMG with manual data collection and interpretation. The application of open-data principles to both data and metadata has always been a core aspect of GeoNet; responses have been overwhelmingly positive, despite concerns regarding some high value, manually collected datasets. The website www.geonet.org.nz represents the primary data access portal. Data analysis and delivery applications are organised by data type rather than hazard, with no volcano-specific data applications. Most datasets have web-based and API delivery application options; both provide standard data formats from a cloud-based archive. One of the challenges for volcano data collection and management has been shifting from a reliance on manually collected data to automatic collection. Additionally, awareness of important questions related to Indigenous Māori data governance is increasing, although the associated impact is not yet understood. Overall, the current centralised, cooperative volcano monitoring and data collection and management system, which benefits from improved efficiency, interoperability, and data quality, has proved effective in Aotearoa New Zealand. Ongoing work aims to ensure optimal data collection and management for volcano monitoring and downstream activities.

A VMD-LSTNet-Attention model for concentration prediction of mixed gases

Gases typically exist as mixed states, which normally contain more information and exhibit more intricate features than single gas. Hence, the task of predicting mixed gas concentrations poses a challenging endeavor in the field of gas detection. In this study, a Variational Mode Decomposition method was combined with Long and Short-term Time-series Network-Attention to build a VMD-LSTNet-Attention model, based on which the response signals of electronic nose (E-nose) is processed, achieving a high-precision concentrations prediction of carbon monoxide (CO) and ethylene mixed gases. Firstly, a dataset containing the raw response signals of CO and ethylene was collected using an gas data collection system. Then, VMD algorithm was used to extract and decompose the essential features from the E-nose signals into multiple components. Further, in pursuit of optimal decomposition results, Particle Swarm Optimization (PSO) algorithm was employed to optimize the penalty factor α and determine the most effective decomposition level K. Subsequently, the LSTNet-Attention model was employed to analyze the concentrations of mixed gases, and the predictive R-square values for CO and ethylene reach 0.993 and 0.975, respectively. Finally, the comparison with the traditional deep learning models and the popular models demonstrates the effectiveness of the proposed VMD-LSTNet-Attention model for concentration prediction of mixed gases.

EuMAR stakeholder engagement: an analysis of medically assisted reproduction (MAR) data collection practices in EU countries†.

What are the current national medically assisted reproduction (MAR) data collection systems across EU Member States, and how can these countries contribute to a unique, cycle-by-cycle registry for the European Monitoring of Medically Assisted Reproduction (EuMAR) project? The study identified significant variation in MAR data collection practices across Member States, with differences in data types, collection methods, and reporting requirements; the EuMAR project emerges as an opportunity to enhance data standardization and improve MAR data collection in the EU. There is a need for new approaches in MAR data collection that include long-term and cross border follow-up. The EuMAR project intends to establish a unified, cycle-by-cycle registry of data on MAR treatments in EU countries, from which accurate cumulative outcomes can be calculated. This cross-sectional study involved a survey and interviews with stakeholders from 26 EU Member States conducted in 2023 over a period of seven months. Representatives from national competent authorities and professional associations involved in MAR data collection in EU countries were invited to complete the survey and interviewed to assess current data flows, information requirements, and their interest in the EuMAR project. Half of the participating countries reported having a national MAR registry with cycle-by-cycle data (n = 13), while 31% reported having a national registry with aggregated data (n = 8) and 19% reported having no national registry (n = 5). Of the countries with a national cycle-by-cycle registry, eight countries collect identifiable data, five countries collect pseudonymized data, and one country collects fully anonymized data. Informed consent is required in 10 countries. The main advantages that participants expected from a European registry like EuMAR were the possibility of obtaining national statistics in the absence of a national registry and improving the calculation of cumulative outcomes. The results of the study are based on self-reported data, which may be subject to bias, however, the validity of the collected information was verified with different means, including follow-up calls for clarifications and sharing final transcript reports. The feasibility of the proposed data flow models will be tested in a pilot study. Despite the heterogeneity of data collection practices across EU countries, the results show that stakeholders have high expectations of the benefits that the EuMAR registry can bring, namely the improvement of data consistency, cross-border comparability, and cumulative live birth rates, leading to better information for patients, health care providers and policy makers. The EuMAR project was co-founded by ESHRE and the European Commission (101079865-EuMAR-EU4H-2021-PJ2). No competing interests were declared. N/A.

Multi-omics-based Machine Learning for the Subtype Classification of Breast Cancer

AbstractCancer is a complicated disease that produces deregulatory changes in cellular activities (such as proteins). Data from these levels must be integrated into multi-omics analyses to better understand cancer and its progression. Deep learning approaches have recently helped with multi-omics analysis of cancer data. Breast cancer is a prevalent form of cancer among women, resulting from a multitude of clinical, lifestyle, social, and economic factors. The goal of this study was to predict breast cancer using several machine learning methods. We applied the architecture for mono-omics data analysis of the Cancer Genome Atlas Breast Cancer datasets in our analytical investigation. The following classifiers were used: random forest, partial least squares, Naive Bayes, decision trees, neural networks, and Lasso regularization. They were used and evaluated using the area under the curve metric. The random forest classifier and the Lasso regularization classifier achieved the highest area under the curve values of 0.99 each. These areas under the curve values were obtained using the mono-omics data employed in this investigation. The random forest and Lasso regularization classifiers achieved the maximum prediction accuracy, showing that they are appropriate for this problem. For all mono-omics classification models used in this paper, random forest and Lasso regression offer the best results for all metrics (precision, recall, and F1 score). The integration of various risk factors in breast cancer prediction modeling can aid in early diagnosis and treatment, utilizing data collection, storage, and intelligent systems for disease management. The integration of diverse risk factors in breast cancer prediction modeling holds promise for early diagnosis and treatment. Leveraging data collection, storage, and intelligent systems can further enhance disease management strategies, ultimately contributing to improved patient outcomes.

Drowning Among Children 1-4Years of Age in California, 2017-2021.

Drowning, the leading cause of unintentional injury death among California children less than five years of age, averaged 49 annual fatalities for the years 2010-2021. The California Pool Safety Act aims to reduce fatalities by requiring safety measures around residential pools. This study was designed to analyze annual fatality rates and drowning incidents in California among children 1-4years of age from 2017-2021. We identified fatalities, injury hospitalizations, and emergency department (ED) visits from California state vital statistics death data and state hospital and ED discharge data using the EpiCenter California Injury Data Online website. Over the five-year study period, 4,166 drowning incidents were identified: 234 were fatalities, 846 were hospitalizations, and 3,086 were ED visits. The observed difference in fatality rates from 2017 to 2021 failed to achieve statistical significance (P = 0.88). Location-based analysis of the 234 fatal drowning incidents revealed that pools were the most common injury site, accounting for 65% of the cases. Drowning remains the leading cause of unintentional, injury-related death among California children 1-4years of age, as the annual rate of fatality over the five-year study period did not decline. While the EpiCenter California Injury Data Online website is excellent for analyzing annual rates of drowning incidents among California residents over time, it is limited in providing insight into modifiable risk factors and event circumstances that can further inform prevention. The development of robust integrated fatal and non-fatal local, state, and national systematic data collection systems could aid in moving the needle in decreasing pool fatalities among young children.

Contested quantification for planetary health – A sociotechnical analysis of Bangladesh's water salinity monitoring infrastructure

In climate change and planetary health, the datafication of natural processes and their effects on human health is gaining importance, not least due to data's role in international funding mechanisms. In Bangladesh, water salinity has become the focus of much research and could emerge as an asset to access climate funding. Taking Bangladesh's water salinity monitoring infrastructure as a case study, this paper problematizes the “neutrality” of water salinity data. Adopting the methodological approach of an “infrastructural inversion”, we foreground the relational nature of data production. The study draws on ethnographic fieldwork in Bangladesh along the data production chain. We highlight how the involvement of a large variety of actors gave rise to vastly different data infrastructures and explicate the influence of actors' “problematization”. We further illustrate the importance of pre-existing materiality for the implementation of data collection systems on the ground and their power to influence whose reality is represented and whose is left out. Discussing these findings in the context of the international development sector reveals the complex interplay between the dual function of data as a mediator of knowledge and proof of legitimacy. Attention is paid to the tendencies of perpetuating patterns of exclusion and inclusion through data in a setting of project-based funding. We thereby provide global health researchers, policy makers and development practitioners with a detailed case study whilst stimulating reflection on the hegemony of datafication.

Exploring the ecosystem structure of Zanzibar archipelago, trophic flows and fisheries interaction by using a mass balance modelling approach

The marine ecosystem in the Zanzibar archipelago has great economic and ecological importance due to high fisheries productivity. Yet, limited information is available on its status and possible impacts of fishing on it. Ecological models have been recognized as powerful tools for assessing the structure and function of ecosystems and forecasting the impacts of human activities on marine ecosystems. However, the use of ecological models in understanding ecosystems in the Western Indian Ocean region is inadequate. This study constructed a model to examine the ecosystem functioning, trophic flows, and fisheries interactions of the marine region affected by fishing gears in the Zanzibar archipelago using Ecopath with Ecosim software. The model includes 34 functional groups and 10 most common fishing gears, the input parameters were gathered from published reports. The ecosystem indicators showed that the Zanzibar ecosystem is in the early developmental stage contributed by strong fishing pressure that reduces the stability and maturity of the ecosystem. The total catch estimated at 5.538 t km−2year−1 showed a sign of the high exploitation rate of fisheries resources. The catch is dominated by higher trophic level fish as reflected in the mean trophic level of the fishery (3.2). The keystoneness analysis identified pelagic cephalopods, sharks and rays, turtles, tuna and tuna like species and toothed whales for their importance in the Zanzibar marine ecosystem. Fishing pressure on the ecosystem of Zanzibar is high, hence for better management, the focus should be for coral reef and pelagic species that are highly targeted by number of gears while protecting keystone species as important groups of the ecosystem. Fisheries harvest data collection system should be improved to collect information at species level and focus on all target species, including invertebrates.

Temporal trends in lower extremity amputation in Middle East and North Africa (MENA) region: analysis of the GBD dataset 1990–2019

BackgroundLower extremity amputations (LEAs) significantly contribute to mortality and morbidity, often resulting from peripheral artery disease and diabetes mellitus (DM). Traumatic injuries also account for many LEAs. Despite the global burden, the epidemiology of LEAs, particularly in the Middle East and North Africa (MENA) region, remains underexplored. This study utilizes the Global Burden of Disease (GBD) dataset to analyze temporal trends in LEAs in the MENA region from 1990 to 2019.MethodsThe study utilized the 2019 GBD dataset, which includes estimates for incidence, prevalence, and disability-adjusted life-years (DALYs) across 369 diseases. Age-standardized incidence rates (ASIRs) for LEAs were extracted for 21 MENA countries. Trends were analyzed using percentage change calculations and Joinpoint regression to identify significant shifts in LEA rates over time.ResultsFrom 1990 to 2019, male LEA rates generally decreased, while female rates increased. Significant increases in LEA rates were observed in Syria, Yemen, and Afghanistan, correlating with periods of conflict and instability. Conversely, countries like Iraq, Palestine, Sudan, Lebanon, Iran, and Kuwait saw marked decreases. The study highlighted a complex interplay of socio-political factors, natural disasters, and chronic diseases like DM in shaping LEA trends across the region.ConclusionThe study reveals variable LEA trends in the MENA region, influenced by conflicts, natural disasters, and chronic diseases. These findings underscore the need for targeted public health interventions, improved healthcare access, and robust data collection systems to reduce the burden of LEAs and improve patient outcomes in the MENA region.

Establishing Quality Indicators and Implementation Priorities for Post-Stroke Aphasia Services Through End-User Involvement.

Currently, there are no agreed quality standards for post-stroke aphasia services. Therefore, it is unknown if care reflects best practices or meets the expectations of people living with aphasia. We aimed to (1) shortlist, (2) operationaliseand (3) prioritise best practice recommendations for post-stroke aphasia care. Three phases of research were conducted. In Phase 1, recommendations with strong evidence and/or known to be important to people with lived experience of aphasia were identified. People with lived experience and health professionals rated the importance of each recommendation througha two-round e-Delphi exercise. Recommendations were then ranked for importance and feasibility and analysed using a graph theory-based voting system. In Phase 2,shortlisted recommendations from Phase 1 were converted into quality indicators for appraisal and voting in consensus meetings. In Phase 3,priorities for implementation were established by people with lived experience and health professionals following discussion and anonymous voting. In Phase 1, 23 best practice recommendations were identified and rated by people with lived experience (n = 26) and health professionals (n = 81). Ten recommendations were shortlisted. In Phase 2,people with lived experience (n = 4) and health professionals (n = 17) reached a consensus on 11 quality indicators, relating toassessment (n = 2), information provision (n = 3),communication partner training (n = 3),goal setting (n = 1),person and family-centred care (n = 1)and provision of treatment (n = 1). In Phase 3,people with lived experience (n = 5) and health professionals (n = 7) identified three implementation priorities: assessment of aphasia,provision of aphasia-friendly informationand provision of therapy. Our 11 quality indicators and 3 implementation priorities are the first step to enabling systematic, efficientand person-centred measurement and quality improvement in post-stroke aphasia services. Quality indicators will be embedded in routine data collection systems, and strategies will be developed to address implementation priorities. Protocol development was informed by our previous research, which exploredthe perspectives of 23 people living with aphasia about best practice aphasia services. Individuals with lived experience of aphasia participated as expert panel members in our three consensus meetings. Wereceived support from consumer advisory networks associated with the Centre for Research Excellence in Aphasia Rehabilitation and Recovery and the Queensland Aphasia Research Centre.

TATA KELOLA ASET DESA DI DESA MANDIRI JAYA KECAMATAN KELAM PERMAI KABUPATEN SINTANG

The problem in the research is the importance of managing village assets in the village government area as capital in village development. The data collection process requires an approach, coordination and supervision so that the data obtained can become valid data for the village government. The research results show that the data collection system implemented requires a work team that has been allocated data collection time as well as various village assets that must be included in the data collection. The village assets that must be recorded are village treasury land and existing village inventory items. Data collection is intended to obtain an overview of various village potentials which can be used as development planning based on community interests. Coordination during data collection is carried out in stages externally and internally to avoid conflicts with the community and also with villages bordering Mandiri Jaya village.

Modeling of specific fuel consumption for compression ignition engines fueled with polymer-based fuel: a response surface methodology approach

Background. Diesel engines play a crucial role in ensuring human comfort and well-being across residential, commercial, transportation, and emergency response sectors because of their reliability and versatility. However, identifying alternative fuels remains a significant challenge. Objective. This study aims to develop a comprehensive mathematical model using Response Surface Methodology (RSM) to optimize the performance of Compression Ignition (CI) engines utilizing different types of plastic pyrolysis oil. Methods. Through systematic data collection and analysis, this study examines the importance of design parameters, specifically injection pressure, compression ratio, engine load, and type of plastic pyrolysis oil, which are important for specific fuel consumption. A prediction model was developed to identify the complex correlations between these factors and the fuel use. Results. The developed model serves as an effective tool for optimizing the CI engine performance under diverse operational conditions. Experimental validation involved testing diesel engines with conventional diesel fuel and various plastic pyrolysis oils, followed by optimization using RSM to achieve optimal engine performance. The engine load was identified as the most significant parameter affecting the specific fuel consumption, followed by the fuel type, injection pressure, and compression ratio. The high R-squared (99.35%) and adjusted R-squared (98.02%) values indicate that the proposed model effectively fits the experimental data. Conclusion. The RSM-based model effectively optimizes CI engine performance under varied operational conditions. It significantly reduces the time and effort required to optimize engine design variables, thus enhancing engine performance and sustainability.

Visual field loss and falls requiring hospitalisation: results from the eFOVID study.

Visual fields are important for postural stability and ability to manoeuvre around objects. Examine the association between visual field loss and falls requiring hospitalisation in adults aged 50 +. Older adults aged 50+ with and without visual field loss were identified using a fields database obtained from a cross-section of ophthalmologists' practices in Western Australia (WA). Data were linked to the Hospital Morbidity Data Collection and WA Hospital Mortality System to identify participants who experienced falls-related hospitalisations between 1990 and 2019. A generalised linear negative binomial regression model examined the association between falls requiring hospitalisation for those with and without field loss, based on the better eye mean deviation (mild: -2 to -6dB, moderate: -6.01dB to -12dB, severe < -12.01dB) in the most contemporaneous visual field test (3years prior or if not available, 2years after the fall), after adjusting for potential confounders. A total of 31 021 unique individuals of whom 6054 (19.5%) experienced 11 818 falls requiring hospitalisation during a median observation time of 14.1years. Only mean deviation index of <-12.01dB (severe) was significantly associated with an increased rate of falls requiring hospitalisations by 14% (adjusted IRR 1.14, 95% CI 1.0-1.25) compared with no field loss, after adjusting for potential confounders. Other factors included age, with those aged 80+ having an increased rate (IRR 29.16, 95% CI 21.39-39.84), other comorbid conditions (IRR 1.49, 95% CI 1.38-1.60) and diabetes (IRR 1.25, 95% CI 1.14-1.37). Previous cataract surgery was associated with a decreased rate of falls that required hospitalisations by 13% (IRR 0.87, 95% CI 0.81-0.95) compared with those who did not have cataract surgery. The findings highlight the importance of continuous clinical monitoring of visual field loss and injury prevention strategies for older adults with visual field loss.

The Role of Financial Data Processing and Application in Strengthening National Financial Supervision

This study focuses on the role of financial data processing and application in strengthening national financial supervision. It begins by discussing the background and significance, purpose and methods of studying this topic in the digital age. The theoretical foundations of financial data processing and application are introduced in detail, including the concept and characteristics of financial data, related theories of data processing and application, and the objectives and principles of financial supervision. The current status and challenges of financial data utilization in national financial supervision are analyzed. The multifaceted role of financial data processing and application in strengthening financial supervision is explored in depth, such as improving the transparency of fiscal fund use, accurately identifying problems and risks, optimizing budget management, and improving the performance evaluation system. Strategies for using financial data analysis to strengthen budget management and performance evaluation are proposed, including establishing and improving financial data collection and management systems, using data analysis tools for budget management, and building data analysis models for performance evaluation. The importance of financial data in maintaining national financial security and stability is emphasized, including preventing financial risks and ensuring sustainable fiscal development. Experiences, lessons, and insights are summarized and enlightened through the analysis of successful and problematic cases. Finally, the study is concluded with an outlook on the future application of financial data in financial supervision, including aspects such as technological development, system construction, talent cultivation, and data sharing.

P043 PREACH IMPLEMENTATION PROJECT A SOCIO-ECOLOGICAL FRAMEWORK FOR HYPERTENSION IN PAKISTAN

Background: Worldwide hypertension prevalence is projected to reach 1.56 billion by 2025, affecting 29% of the global population according to the WHO1. It contributes to 7.5 million deaths, 3.7% of total disability-adjusted life years (DALYs) and is responsible for causing stroke in 54% of hypertensives2,3. Pakistan has a prevalence of 43% hypertension with merely 12.5% of controlled hypertensives4,5. There is a pressing need for a national level intervention through uniform clinical approaches for optimum hypertension prevention, treatment and control6, 7. Through PREACH project, we prioritize primary care physicians as first-line responders and are training them on standardized hypertension guidelines to alleviate the burden from tertiary care hospitals. To detect new onset hypertension the project also incorporates early surveillance and community level screenings. Objectives: • To build capacity of practitioners on standardized guidelines endorsed by health ministry, PCS (Pakistan Cardiac Society) &amp; PHL (Pakistan Hypertension League) • To raise community awareness, to screen and develop self-referral pathways to specialty care • To develop a robust national hypertension registry • To include hypertension in essential primary healthcare packages Methodology: Hypertension guidelines are developed. Accredited national certified workforce of more than 14,000 general practitioners (GPs) will be built through guidelines trainings via train the trainer model. Impact assessment studies will be done to assess training effectiveness and implementation at clinics. Community mobilization and awareness campaigns will be run to identify and direct self-referral pathway to an accredited GP. Development of national hypertension registry as a centralized system for data collection to report actual disease burden. Ministry of health will be advocated to prioritize hypertension management via multi-sectoral collaboration i.e. medical societies, academic, non-profit and private-sector for incorporations into annual development programs. Results \Conclusion: We aim to create an impact in the lives of 4.3 million beneficiaries (anti-hypertensive naïve hypertensive patients of Pakistan) by increasing self-referral of hypertensive cases to specialists through PREACH implementation project at all healthcare service delivery levels.

Road traffic crash data in Southwestern Nigeria: a qualitative analysis of sources, contents and management methods

Objective This study qualitatively examined road traffic crash (RTC) data collection and management in southwest Nigeria, with the goal of addressing the tenets of the UN Decade of Action Plan on Road Safety’s call for an effective data system. Methods Data on RTCs data collection in the study area was obtained through key informant interviews with five hospital accident emergency unit directors, three Federal Road Safety Commission (FRSC) Sector commanders, and three Nigeria Police Force (NPF) commissioners. Datasets were transcribed, categorized, and interpreted using content analysis, descriptive statistics, and WHO recommended minimal crash data element requirements. Results The study found disparate systems, a lack of synergy, and discrepancy in the various data sources in the country when compared with WHO recommendations on road traffic data collection systems, with 55% of agencies using non-standardized paper forms. The study also reveals that the minimum crash data elements recommended by WHO are not consistently captured in the NPF, FRSC, and hospital traffic crash data sources. Conclusions The study suggested the use of an upgraded National Road Traffic Crash Data Management System (NRTCDMS) Data Template to unify data gathering and linkage issues, but recommends a digital version of the template.