Leishmaniasis is classified as a neglected tropical disease (NTD), caused by protozoan parasites of the genus Leishmania, which are transmitted to humans and other animals through the bite of infected female phlebotomine sandflies. There are three forms of the disease: cutaneous leishmaniasis (CL) manifested by ulcers and scars; systemic or visceral leishmaniasis (VL), which can lead to life-threatening complications if left untreated; and mucocutaneous leishmaniasis (MCL), which can destroy the mucous membranes of the nose, mouth and throat. Human leishmaniasis is endemic in many countries across Africa, Asia, Southern Europe, the Middle East, and Central and South America. The interconnection of environmental, animal and human health underlies the spread of the Leishmania parasite. Environmental disruptions, such as climate change, deforestation or urbanisation, but also globalisation and migration, significantly affect the distribution and abundance of sand fly vectors and reservoir hosts. Climate change alters the breeding patterns of sandflies and expands their geographic range; deforestation and misuse of large areas disrupt ecosystems, leading to increased human-vector contact; and urbanisation increases the potential for contact between parties, particularly in densely populated areas. Migration of humans and animals, either through natural migration or, for example, the pet trade and breeding, can facilitate the spread of Leishmania parasites. In addition, socio-economic factors, including poverty and lack of access to healthcare, increase the burden of leishmaniasis in vulnerable populations. Due to this multitude of reasons, the geographic distribution of sandflies has expanded to higher latitudes and altitudes in recent years, with a consequent increase in disease burden. Indeed, despite ongoing challenges in the surveillance systems, data from the last available year have shown an increase in many cases in both humans and dogs. This perspective explores the interconnected factors influencing the spread of leishmaniasis worldwide and the epidemiology of the disease. In addition, it illustrates the importance of integrated strategies in a One Health approach: surveillance, prevention and control of vectors, animals and humans.

Purpose: This study investigates differences and commonalities in data production, sharing and reuse across the widest range of disciplines yet and identifies types of improvements needed to promote data sharing and reuse.Design/methodology/approach: The first authors of randomly selected publications from 2018 to 2019 in 20 Scopus disciplines were surveyed for their beliefs and experiences about data sharing and reuse.Findings: From the 3,257 survey responses, data sharing and reuse are still increasing but not ubiquitous in any subject area and are more common among experienced researchers. Researchers with previous data reuse experience were more likely to share data than others. Types of data produced and systematic online data sharing varied substantially between subject areas. Although the use of institutional and journal-supported repositories for sharing data is increasing, personal websites are still frequently used. Combining multiple existing datasets to answer new research questions was the most common use. Proper documentation, openness and information on the usability of data continue to be important when searching for existing datasets. However, researchers in most disciplines struggled to find datasets to reuse. Researchers' feedback suggested 23 recommendations to promote data sharing and reuse, including improved data access and usability, formal data citations, new search features and cultural and policy-related disciplinary changes to increase awareness and acceptance.Originality/value: This study is the first to explore data sharing and reuse practices across the full range of academic discipline types. It expands and updates previous data sharing surveys and suggests new areas of improvement in terms of policy, guidance and training programs.Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-08-2021-0423.

Primary data collected during a research study is often shared and may be reused for new studies. To assess the extent of data sharing in favourable circumstances and whether data sharing checks can be automated, this article investigates summary statistics from primary human genome-wide association studies (GWAS). This type of data is highly suitable for sharing because it is a standard research output, is straightforward to use in future studies (e.g., for secondary analysis), and may be already stored in a standard format for internal sharing within multi-site research projects. Manual checks of 1799 articles from 2010 and 2017 matching a simple PubMed query for molecular epidemiology GWAS were used to identify 314 primary human GWAS papers. Of these, only 13% reported the location of a complete set of GWAS summary data, increasing from 3% in 2010 to 23% in 2017. Whilst information about whether data was shared was typically located clearly within a data availability statement, the exact nature of the shared data was usually unspecified. Thus, data sharing is the exception even in suitable research fields with relatively strong data sharing norms. Moreover, the lack of clear data descriptions within data sharing statements greatly complicates the task of automatically characterising shared data sets.

Background: Digital health interventions (DHIs) for the prevention and management of cardiometabolic diseases have become increasingly common. However, there is limited evidence for the suitability of these approaches in minority ethnic populations, who are at an increased risk of these diseases. Objective: This study aimed to investigate the use of DHIs for cardiovascular disease and type 2 diabetes among minority ethnic populations in countries with a majority of White, English-speaking populations, focusing on people who identified as South Asian, Black, or African American. Methods: A realist methodology framework was followed. A literature search was conducted to develop context-mechanism-outcome configurations, including the contexts in which DHIs work for the target minority ethnic groups, mechanisms that these contexts trigger, and resulting health outcomes. After systematic searches, a qualitative analysis of the included studies was conducted using deductive and inductive coding. Results: A total of 15 studies on the uptake of DHIs for cardiovascular disease or diabetes were identified, of which 13 (87%) focused on people with an African-American background. The review found evidence supporting the use of DHIs in minority ethnic populations when specific factors are considered in implementation and design, including patients’ beliefs, health needs, education and literacy levels, material circumstances, culture, social networks, and wider community and the supporting health care systems. Conclusions: Our context-mechanism-outcome configurations provide a useful guide for the future development of DHIs targeted at South Asian and Black minority ethnic populations, with specific recommendations for improving cultural competency and promoting accessibility and inclusivity of design.

The global priority of improving neonatal survival could be tackled through the universal implementation of cost-effective maternal and newborn health interventions. Despite 90% of neonatal deaths occurring in low-resource settings, very few evidence-based digital health interventions exist to assist healthcare professionals in clinical decision-making in these settings. To bridge this gap, Neotree was co-developed through an iterative, user-centered design approach in collaboration with healthcare professionals in the UK, Bangladesh, Malawi, and Zimbabwe. It addresses a broad range of neonatal clinical diagnoses and healthcare indicators as opposed to being limited to specific conditions and follows national and international guidelines for newborn care. This digital health intervention includes a mobile application (app) which is designed to be used by healthcare professionals at the bedside. The app enables real-time data capture and provides education in newborn care and clinical decision support via integrated clinical management algorithms. Comprehensive routine patient data are prospectively collected regarding each newborn, as well as maternal data and blood test results, which are used to inform clinical decision making at the bedside. Data dashboards provide healthcare professionals and hospital management a near real-time overview of patient statistics that can be used for healthcare quality improvement purposes. To enable this workflow, the Neotree web editor allows fine-grained customization of the mobile app. The data pipeline manages data flow from the app to secure databases and then to the dashboard. Implemented in three hospitals in two countries so far, Neotree has captured routine data and supported the care of over 21,000 babies and has been used by over 450 healthcare professionals. All code and documentation are open source, allowing adoption and adaptation by clinicians, researchers, and developers.