With the rapid development of information and communication technologies, smart homes are being investigated as effective solutions for home health care. The increasing academic attention on smart home health care has primarily been on the development and application of smart home technologies. However, comprehensive studies examining the general landscape of diverse research areas for smart home health care are still lacking.This study aims to determine the intellectual structure of smart home health care in a time series by conducting a coword analysis and topic analysis. Specifically, it investigates (1) the intellectual basis of smart home health care through overall academic status, (2) the intellectual foci through influential keywords and their evolutions, and (3) intellectual trends through primary topics and their evolutions.Analyses were conducted in 5 steps: (1) data retrieval from article databases  and the initial dataset preparation of 6080 abstracts from the year 2000 to the first half of 2019; (2) data preprocessing and refinement extraction of 25,563 words; (3) a descriptive analysis of the overall academic status and period division ; (4) coword analysis based on word co-occurrence networks for the intellectual foci; and (5) topic analysis for the intellectual trends based on latent Dirichlet allocation (LDA) topic modeling, word-topic networks, and researcher workshops.First, regarding the intellectual basis of smart home health care, recent academic interest and predominant journals and research domains were verified. Second, to determine the intellectual foci, primary keywords were identified and classified according to the degree of their centrality values. Third, 5 themes pertaining to the topic evolution emerged: (1) the diversification of smart home health care research topics; (2) the shift from technology-oriented research to technological convergence research; (3) the expansion of application areas and system functionality of smart home health care; (4) the increased focus on system usability, such as service design and experiences; and (5) the recent adaptation of the latest technologies in health care. Based on these findings, the pattern of technology diffusion in smart home health care research was determined as the adaptation of technologies, the proliferation of application areas, and an extension into system design and service experiences.The research findings provide academic and practical value in 3 aspects. First, they promote a comprehensive understanding of the smart home health care domain by identifying its multifaceted intellectual structure in a time series. Second, they can help clinicians discern the development and dispersion level of their respective disciplines. Third, the pattern of technology diffusion in smart home health care could help scholars comprehend current and future research trends and identify research opportunities based on upcoming research waves of newly adapted technologies in smart home health care. The rapid development of information and communication technologies, including the Internet of Things (IoT) and ubiquitous computing, have made home environments highly intelligent and allow smart homes to be realized . In prevVarious terms have been used to describe such technology, such as “smart homes in or for health care” ,8, “healSmart home health care is a health care service in one’s residence incorporated with IoT technology and ubiquitous computing, which has the characteristics of home automation and home intelligence, communication networks, and remote access and control by authorized health care personnel. It offers informal health care services such as real-time or long-term health monitoring, unobtrusive activity support without interference with daily lives, and disease prevention through anomaly detection. It can reduce care costs, allow satisfactory service experience in a comfortable and private home environment, and ensure the independence of residents.The academic focus of previous smart home research has mainly been on the development and application of smart home technologies in the fields of computer science and engineering -4,14. LiTherefore, it is necessary to comprehend the current landscape of smart home health care research and seek future research opportunities. The specific research questions we used to investigate the intellectual structure of smart home health care are the following: (RQ1) What is the overall academic status of the current research on smart home health care, and which research fields have mainly been focused on? (RQ2) What are the representative keywords in the research on smart home health care, and how have they evolved over time? (RQ3) What are the intellectual trends in smart home health care research, and what are the main research orientations?Thus, the objective of this study is to determine representative research topics on smart home health care and their evolutionary trends, and determine the intellectual structure of smart home health care research by conducting bibliometric network analysis. The resulting findings could lead to a comprehensive understanding of the current literature on smart home health care and enable scholars to extend their academic interests in future research.Bibliometric network analysis refers to a computer-assisted scientific review methodology that provides quantitative and statistical analysis by summarizing a large number of research publications through various descriptors and indicators . HavemanTo complement the coword analysis with word co-occurrence networks, we adopted topic modeling to identify more detailed topic groups and their evolutionary trends. In machine learning and natural language processing, topic modeling is a prominent technique for data mining, latent data detection, and finding associations among data and text documents to discover hidden semantic structures ,30. LateWe collected articles relevant to this research from the journal databases of Web of Science, Scopus, and PubMed. Web of Science and Scopus are two of the most authoritative scholarship databases, with peer-reviewed papers covering a wide range of subjects in various disciplines . PubMed Query = ((“intelligent” OR “smart”) AND “home”) AND  OR “ehealth” OR “mhealth” OR “uhealth”))Article types were limited to journal articles and proceeding papers, article language was constrained to English, and the time period was limited to the year 2000 onwards, considering the recency of smart home technologies. The data retrieval was conducted on July 1, 2019, and 6579 articles were initially collected . From the raw dataset, we excluded data that were duplicated among databases or that had no information on the year of publication, author, or abstract. The initial dataset of 6080 articles was prepared using the following bibliometric data: title, year of publication, author, journal, abstract, and keywords.Noun data needed to be extracted from the abstract dataset in order to constitute a text corpus. For this, we conducted data preprocessing according to the stages of dictionary development: exception, definition, and synonyms. First, the “exception” dictionary was established to remove any general terminologies which are routinely utilized in abstracts to explain research structures, processes, and methodologies . Second, the “definition” dictionary was developed to define compound nouns as 1 word . To expand the definition dictionary, academic compound nouns were added to reflect the author keywords data. Third, the “synonym” dictionary was produced to detect duplicate meanings and replace them with 1 representative word . The synonym dictionary also combined the singular and plural forms of the same terms into singular words. After this process, 25,563 words from 6080 abstracts were prepared as the text corpus for the following steps.After defining the text corpus, data refinement was conducted in order to filter terms of high frequency but general usage. The dataset was arranged as a word-document matrix by occurrence, and was transformed into a co-occurrence matrix using the symmetric Ochiai-Salton algorithm. To filter normally used terminology, this study adopted the term frequency–inverse document frequency (TF-IDF) text-mining method. For data preprocessing and refinement, we utilized Python  and NetFirst, we conducted a descriptive analysis based on the frequency of publication. From this, the research areas and principal journals relevant to smart home health care research could be identified. The results from this analysis could answer the first research question regarding overall academic status as an intellectual basis for smart home health care. After the descriptive analysis, time periods were divided by a certain scale in order to conduct further time-series analyses and explore various evolutions. To decide the time scale, we examined the evolution of technology and health research, or how health care research has evolved in response to recent emerging technologies. For this, we searched for several keywords directly pertinent to the study and emerging technology keywords   were positioned according to their degree centrality values and compared within the time series to understand their evolution.Third, we conducted a topic analysis combining topic modeling and SNA to establish word-topic networks. This allowed us to investigate the primary topics in smart home health care research and their evolutions within the time series, answering the third research question about intellectual trends. For topic modeling, we adopted an LDA with a Markov Chain Monte Carlo learning method to identify latent topics and classify words and documents into topics. Word-topic networks were then constructed and visualized according to allocation probability values. Three authors of this study held 3 consecutive researcher workshops to interpret the topic analysis results qualitatively to (1) name the individual topics from the results of LDA separately, (2) compare the name results of the 3 researchers and establish 1 set of topic names by reaching a consensus on any discrepancies, and (3) classify them into superordinate topic groups regarding the visualized word-topic networks. Lastly, the document frequencies allocated to the topic groups were counted and compared among different time periods to comprehend the topic evolution.R2=0.979).Using the initial 6080 articles, an overview of smart home health care research was compiled. Journal of Medical Internet Research holds the highest rank with 504 publications, followed by JMIR mHealth and uHealth, JMIR Research Protocols, International Journal of Medical Informatics, BMC Medical Informatics and Decision Making, BMC Public Health, and JMIR Mental Health. The top 3 journals from JMIR Publications constituted 17.62% (1071 of 6080 articles) of the total articles on smart home health care research.Computer science information system (23.77%) and Electrical or electronic engineering (22.48%). However, studies from medical and health care areas, such as Medical informatics (17.83%) and Health care sciences and services (17.31%), also constitute considerable portions of smart home health care research. Various medical fields, such as Geriatrics or gerontology, Neurosciences, Rehabilitation, and Psychiatry, emerged as small portions of the total research since advanced smart home health care technologies have already been heavily investigated in engineering domains.According to the academic area classification of Web of Science, the subject or type of journals that are pertinent to smart home health care research, derived with the same search query, is shown in As explained in the Methods section, the evolution of technology in association with health research was examined in order to select a time scale for further time-series analyses. The advent of new technologies can influence health care research and be adopted in health care and medical fields in diverse ways. Hence, several keywords used in the query of this study and emerging technology keywords  coupled We specifically focused on the keywords “mHealth” , “AI and health,” “IoT and health,” and “blockchain and health,” which are technologies with significant influence on data collection and processing, service delivery, and interactions. The moments of advent were identified as the moments where publication frequency doubled compared to the previous year, exceeded 10 for the first time, and continued to gradually increase; this occurred for AI and health in 2008, mHealth in 2011, IoT and health in 2014, and blockchain and health in 2017. As AI had been greatly researched prior to 2000, the adaptation of AI in the health care field also occurred early, particularly in AI-assisted medical diagnosis research. Given this background, the advent year of the AI and health topic could be decided earlier than those of other topics. In accordance with the distribution of frequency, a time scale of 4 stages of 3-year blocks  was chosen. Despite the existence of telehealth research in the years between 2000 and 2007, this initial period was not included in the time-series analysis because the number of articles published at the time was insufficient to conduct a bibliometric network analysis, as shown in In accordance with the time scale division, Both the number of links created from word-document networks and those calculated by TF-IDF (which can represent how significant a word is in a certain document by eliminating ordinary words with high frequency but low importance) constituted more than half of the total networks. The evolution of documents and keywords could also imply that smart home health care research has been rapidly developing quantitatively and qualitatively.Among the word-document networks, the links whose TF-IDF values were above 0.5 and the top 10% of words in terms of document frequency were extracted. Then, word co-occurrence networks were also constructed in order to determine the primary keywords based on precise figures, namely degree centrality. Since the links from the original networks were too massive . According to the allocation probability of a word per topic, word-topic networks were constructed and visualized, extracting the top 100 to 150 words in probability values for effective plotting. During the 3 researcher workshop sessions, we established the name of each topic, embracing the top 5 keywords of allocation probability individually in the first session and together in the second, in order to arrive at a consensus regarding any discrepancies. In the last session, we classified the topics into groups, considering their meanings and network dispositions from the visualized word-topic networks. Through the analysis of topic modeling and word-topic networks, the dynamics of topic evolution were used to identify the intellectual trends regarding smart home health care. These trends can be summarized in 5 aspects: (1) the diversification of smart home health care research topics; (2) the shift from technology-oriented research to technological convergence research; (3) the expansion of application areas and system functionality of smart home health care; (4) the increased focus on system usability, such as service design and experiences; and (5) the recent adaptation of the latest technologies in health care.The research topics on smart home health care were diverse throughout the 4 time periods. Based on the preliminary topics from LDA modeling, 10 topic groups were identified: (A) smart home technologies and infrastructure, (B) health monitoring services (application areas), (C) web-based health care , (D) telemedical services and policies by medical institutions, (E) health literacy, (F) mobile-based health care , (G) mobile health care system design , (H) health worker training, (I) assessment, and (J) new technologies in health careIt was found that the number of topic groups present increased in every period. As shown in Second, results showed that the majority of the research focus gradually shifted from technology-oriented research to technological convergence research. In Third, the application areas and functionality of smart home health care expanded as topic group B  attainedFourth, the focus on system usability, such as service design experiences, increased from period 3 , recent research interest has been focused on personal health records (PHR) due to the adaptation of enabling mobile technologies  ,48. MoreThird, the pattern of technology innovation diffusion ,50 in smLastly, this pattern of the technology diffusion process in smart home health care could have particular aspects caused by the medical or health care field's unique characteristics. Ethical and legal issues pertaining to personal health data are frequent concerns when technologies are integrated into health care services. Policies, regulations, and legislation systems relating to privacy and security issues in smart home health care services require greater attention than other commercial services ,52. MoreDespite the findings on the intellectual structure of smart home health care research, this study has several limitations due to its methodological approach. First, as coword analysis and topic analysis are quantitative and statistical analyses that summarize a large number of publications, qualitative research would be required in order to investigate specific topics in-depth among the various topic groups exhibited in Coword analysis is suggested as a prior stage to a systematic review to guide and accelerate the review process . TherefoThis research aimed to determine the representative research keywords and topics in smart home health care research and their evolutionary trends to demonstrate the intellectual structure of smart home health care by conducting bibliometric network analysis. First, we identified the academic status of the intellectual basis for smart home health care. With the recent increase in academic interest, smart home health care has been primarily investigated in research domains such as computer science information systems, electrical engineering, medical informatics, and health care science and services. Second, keywords and centrality analysis were conducted based on word co-occurrence networks to determine the intellectual foci. The evolution of primary keywords was demonstrated throughout 4 periods, and the top 3 keywords in each period were identified: “monitoring, communication, smart home,” “smart home, monitoring, sensor,” “mobile phone, SMS, monitoring,” and “mobile app, barrier, experience,” respectively. Third, we conducted a topic analysis based on topic modeling and word-topic networks in order to determine the intellectual trends. Accordingly, 5 aspects of topic evolution emerged: (1) the diversification of smart home health care research topics, (2) the shift from technology-oriented research to technological convergence research, (3) the expansion of application areas and system functionality of smart home health care, (4) the increased focus on system usability, such as service design and experiences, and (5) the recent adaptation of the latest technologies in health care.Through these findings, this study discussed the role of mobile and smart technologies in relation to the expansion of home care to smart health care services, and the extension of hospital-led services to personalized services. Moreover, the pattern of technology diffusion in smart home health care research was verified as (1) the adaptation of technologies, (2) the proliferation of application areas, and then (3) the extension into system design and service experiences. This pattern may take more time to evolve due to the ethical and legal concerns surrounding personal health data; however, the cycle of technology diffusion in smart home health care is expected to shorten gradually.The value and potential contributions of these findings can be summarized in 3 aspects. First, they promote a comprehensive understanding of the smart home health care domain by determining its intellectual structure in a time series. The results disclosed and structuralized the massive knowledge body of smart home health care, which has been investigated from diverse research domains but still lacked in comprehending the general landscape. Second, these findings can help clinicians recognize the development and dispersion level of their disciplines and specialty topics. Moreover, practitioners can recognize other related topics to their major disciplines and seek any cooperation opportunities associated with smart home health care services. Third, the pattern of technology diffusion in smart home health care can enable scholars to understand current and future research trends. Particularly, this study is valuable because it confirmed that the pattern of technology diffusion could be applicable in smart home health care; thus, researchers can anticipate the upcoming research waves of newly adapted technologies in smart home health care and explore further research opportunities. Moreover, as a practical implication, the technological trends and their diffusion patterns in smart home health care can be utilized when professionals investigate market status, analyze their competitive advantages, and establish business strategies in their respective practice areas.


Evidence to date indicates that patients from ethnic minority backgrounds may experience disparity in the quality and safety of health care they receive due to a range of socio-cultural factors. Although heightened risk of patient safety events is of key concern, there is a dearth of evidence regarding the nature and rate of patient safety events occurring amongst ethnic minority consumers, which is critical for the development of relevant intervention approaches to enhance the safety of their care.To establish how ethnic minority populations are conceptualised in the international literature, and the implications of this in shaping of our findings; the evidence of patient safety events arising among ethnic minority healthcare consumers internationally; and the individual, service and system factors that contribute to unsafe care.A systematic review of five databases  were undertaken using subject headings (MeSH) and keywords to identify studies relevant to our objectives. Inclusion criteria were applied independently by two researchers. A narrative synthesis was undertaken due to heterogeneity of the study designs of included studies followed by a study appraisal process.Forty-five studies were included in this review. Findings indicate that: (1) those from ethnic minority backgrounds were conceptualised variably; (2) people from ethnic minority backgrounds had higher rates of hospital acquired infections, complications, adverse drug events and dosing errors when compared to the wider population; and (3) factors including language proficiency, beliefs about illness and treatment, formal and informal interpreter use, consumer engagement, and interactions with health professionals contributed to increased risk of safety events amongst these populations.Ethnic minority consumers may experience inequity in the safety of care and be at higher risk of patient safety events. Health services and systems must consider the individual, inter- and intra-ethnic variations in the nature of safety events to understand the where and how to invest resource to enhance equity in the safety of care.This systematic review is registered with Research Registry: reviewregistry761. A multitude of factors contribute to health inequity amongst ethnic minority populations including limited social support, lower health literacy, lower socio-economic status, greater incidence of ill health and a sense of disempowerment –4. AccesWhilst health inequities amongst ethnic minorities internationally are well-established, patients’ ethnicity, language and culture are increasingly recognised as significant predictors of the quality of health care delivery in addition to health outcomes –16. An eInternationally, there has been significant investment in enhancing patient safety mechanisms across health systems  through Despite international efforts to enhance patient safety and care quality, there has been limited focus on improving safety for ethnic minority populations, which remains an under-researched area , 24. PatIn the context of established disparities in the rate of safety events experienced by ethnic minority and majority populations, the nature and rate of patient safety events and the underpinning factors associated with safe care involving ethnic minorities are not well understood . SystemaThe Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) statement was used to guide the reporting of this systematic review .We included available publications in English that reported original, primary empirical, conceptual or theoretical work published from January 2000 to October 2019. Conceptual, theoretical, quantitative or qualitative studies of any research design were eligible including systematic reviews. Studies had to include a sub- or full sample of ethnic minority patients/consumers or data related to ethnic minority patients/consumers. Ethnic minority patients/consumers were defined broadly to include any group who did not speak one of the national languages of the study country, were born in another country, or those who belonged to an ethnic minority group. Outcomes relating to any patient safety events were included except for the specific exclusion applied below. We defined patient safety event as an event that could have or did result in unnecessary harm to the patient.Publications were excluded if they were not from countries within the Organisation for Economic Co-operation and Development (OECD). Case reports, letters, editorials, and comments were excluded. Studies discussing the following were also excluded: 1) disparity in clinical outcomes or health outcomes in ethnic minorities; 2) diagnostic disparities in ethnic minorities in mental health settings; 3) disparities in access to health care, health prevention and health promotion activities; 4) disparity in quality of healthcare delivery; and 5) safety issues or incidents relating to multicultural healthcare staff. Though these studies are important in understanding the health disparities between ethnic minorities and the mainstream population, they present their own line of inquiry and were considered too broad for this systematic review.An initial range of text words, synonyms and subject headings for the two concepts of this study, patient safety events and ethnic minority, were compiled using several documents as a guide , 28, 29.The database search was uploaded to Covidence systematic review software  with articles available online for initial title and abstract review. Based on the information available in the title and abstract, one reviewer (AC) completed the initial title and abstract review. The inclusion criteria were then independently applied for full text review by two reviewers . Disagreements were resolved by consultation. The following data were extracted from included studies: author, publication year, location & setting, research methodology & sample, population studied, objectives and key findings.Due to heterogeneity of the study types included, we used a revised version of the Quality Assessment Tool for Studies with Diverse Designs (QATSDD) with 13 items . The revWe used a narrative synthesis to analyse our findings due to the heterogeneity of the study types, as a pure quantitative or qualitative approach for synthesis was not suitable. In addition, the outcomes discussed in each study were not directly comparable. The data extraction provided us with key findings from each study. A textual approach was used to summarise and synthesise the findings from the included studies against the stated objectives.After removing duplicates, 1578 articles were identified. Initial title and abstract screening led to identification of 225 studies. Based on the inclusion and exclusion criteria, two reviewers then reviewed the full text which resulted in 40 studies for inclusion. The reference list searching of the included articles led of inclusion of additional 5 studies. See PRISMA diagram for full search strategy  (25), The Netherlands (5), The United Kingdom (UK) (2), Australia (5), Canada 1), Denmark (1), Switzerland (1), Sweden (1), and Israel (1). Three studies did not specify a geographic setting. A large number of studies were based in hospital settings (26) with others based in the community such as a pharmacy (7), and primary health setting (5). Two studies did not specify a setting. A majority of the included studies used large administrative datasets or retrospective chart review (14) followed by qualitative studies conducting explorative analysis of patient safety events (8) and systematic reviews (8). Summary of the study findings are presented in Table , DenmarkOverall, the included studies varied against the quality assessment criteria. Most studies achieved the total possible score for criteria relating to: statement of aims, objectives or goals; study design being appropriate to address research aims; format of data collection tool to address research aims; method of data analysis; and discussing strengths and limitations. Most studies achieved a nil score against the quality criteria for the involvement of consumers or stakeholders in the research design and conduct. Only two studies described any involvement of consumers or stakeholders in the process of study design and conduct , 51. OurThree studies defined or included a definition of ethnic minority , 33, 69.The nature and severity of adverse events and disparity in the occurrence of these events was examined in thirteen studies, primary emerging from the US. Differences between ethnic minority patients and others in safety events were predominantly examined using large administrative datasets, retrospective record reviews and systematic reviews focused on rates of (i) hospital acquired infections (HAIs), (ii) complications in care and (iii) adverse drug events (ADEs) , 69, 71.Five retrospective studies using large administrative datasets, three using the Patient Safety Indicators (PSIs) as outcome measures , 62, 66 The evidence relating to ADEs among ethnic minorities was mixed and was observed to depend on contextual features such as geographical, genetic and cultural features , 57, 59.Five systematic reviews concerning adverse events in surgical patients and ADEs were also included , 58, 59.Medicine-related problems were further discussed in eleven included studies , 61, 63.Language proficiencySeveral factors contributed to the risk of patient safety events in health care for ethnic minority patients, although their independent and collective contribution was not clear from work to date. Our narrative thematic synthesis identified following factors: 1) language proficiency; 2) errors associated with formal and informal interpretation; 3) misinterpretation of instructions; 4) income and insurance; 5) beliefs about illness and treatment; 6) family and friends for safety vigilance; and 7) patient-professional interactions. We found that often these factors were integrated and not readily distinguished as uniquely individual or service or system level factors. For example, issues around language proficiency were an individual issue in terms of one’s own language proficiency, but it was also about readiness or feasibility of accessing a health system without strong proficiency in a national or native language.b)Errors associated with formal and informal interpretationIn their prospective incident record review study, Divi et al. identified some degree of detectable physical harm occurred in almost 50% of the LEP identified patients as compared to 30% harm among English speaking patients . They den = 57) revealed that the use of ad hoc or no interpreters was related to double the errors of potential clinical consequence when compared to using trained medical interpreters [c)Misinterpretation of instructionsThe review revealed that use of professional interpreting services was free in most developed countries. However, their use by healthcare professionals was limited with many choosing to use ad hoc interpreters, such as carers, family members or people other than the qualified interpreters, or no interpreters , 68, 70.rpreters . They norpreters .c)Misinn = 48,968) found that LEP increased the odds of difficulty in understanding the prescription instructions three times for Central Americans, Mexicans and Koreans and four times for Chinese. They also found that low income (OR1.7), low education (OR1.6) and recent migration (OR1.5) increased the chances of difficulty in understanding prescription [d)Income and InsurancePatient safety events resulting from the misinterpretation of medication prescription labels and misunderstanding of discharge instructions were more commonly reported among ethnic minority patients in nine studies , 63, 65.cription . While hcription . The autcription . Two stucription , 73.d)Ie)Beliefs about illness and treatmentSix included studies discussed impact of income or insurance on patient safety with five studies emerging from the US , 69, 71.n = 68) receiving education about medication prescriptions by Korean speaking pharmacy students demonstrated the adults significantly increased their understanding of medication directions and had improved trust with pharmacists [n = 8) to demonstrate generalisability. Neither of these interventions demonstrated quantitative impact on patient safety events.f)Family and friends for safety vigilanceSuurmond et al. and Masland et al. noted that consumers from ethnic minorities have different beliefs about illness and treatment to that of the health professionals depending on their cultural and religious beliefs , 55. Sixrmacists . Anotherrmacists . Howeverg)Patient-professional interactionsFamilies and friends were identified in having an important role for safety vigilance of their loved ones in two studies. Van Rosse et al. observed that patients from ethnic minorities were more often accompanied by families or friends compared to Dutch born patients during hospitalisation and voiced concerns to the health professionals which enhanced their safety . Anothern = 18), authors noted that the lack of communication between various departments within a hospital as well as with community practitioners such as general practitioners can impact on care such as ceasing a patient’s medicines prescribed by a GP during hospitalisation [Ethnic minority patients’ interactions with health professionals and systems were discussed in seven studies with focus on bias and lack of communication resulting in inappropriate care with potential for a patient safety event , 63, 64.lisation . This frlisation .Our review findings provide substantial evidence to suggest that people from ethnic minorities are vulnerable to a higher rate of patient safety events in the hospital and community setting compared to the mainstream population. Specifically, included studies showed that ethnic minorities experienced higher incidences of healthcare associated infections, dosing errors, ADEs and complications resulting from their care , 57, 66.Factors contributing to the increased threat to ethnic minority populations include socioeconomic factors such as income, insurance and education; language proficiency; health literacy; length of stay in the host country; feeling of alienation and distrust for the health professional, services and systems; and, engagement , 64, 66.Health systems change towards providing culturally competent care by hiring more interpreters or using bilingual health professionals were commonly cited in the reviewed articles , 55, 64.Mobile technologies that can translate for interpretation are recognised as a potential solution to overcome language barriers; research indicates ethnic minority consumers use this technology . Google The importance of the active participation and engagement of healthcare consumers both in care and study design is recognised through this review as contributing to safety outcomes and research quality. The relationship between consumer involvement in healthcare and enhanced health outcomes including patient safety is well-established and reinforced through this review . The AusPolicy organisations focused to improve health care quality internationally identify ethnic minorities as a priority group but specific guidance about reducing patient safety events is limited , 84. KeyWe also found deficiencies in routinely collected healthcare data. Our synthesis of the included studies showed insufficient data regarding factors, that collectively create an individual’s ethnic profile, with most data sets relying on information about country of birth, language spoken and in some instances race to classify ethnic minority individuals. Classifications such as ‘Asian’ or ‘Caucasian’ can be deemed to be homogenous and fails to capture the range of different ethnic minorities included under either of the terms . Given tSeveral authors commented on the superficial identification of ethnic minority patients and recommended systems be strengthened to allow complete identification of ethnic minorities , 70. TheThis review has some limitations. The diversity of the definitions used to determine the ethnic minority population shaped the findings. For example, in United States, ethnic minorities were primarily defined based on the race or the first language, with many studies focused to differentials between African Americans, Hispanic and Caucasian populations , 66. ThiOur findings indicate substantial disparity in safety events occurring between ethnic minority and mainstream populations. The nature of patient safety events to which ethnic minority populations are exposed to is dependent on the setting and minority population. Patient safety appears to be linked with the availability and use of interpreters, degree of engagement with family and carers, and the availability of systemic processes to overcome language and socio-economic barriers. Enhanced socio-cultural data across health systems is critical. Knowledge of the factors that contribute and compound to create safety for ethnic minority groups, and of the individual differences between various ethnic minority groups in terms of their healthcare safety is limited. Engaging ethnic minority consumers in the design of interventions to enhance safety is key.Additional file 1: Supplementary file 1 for search strategy.Additional file 2: Supplementary file 2 for quality assessment criteria.


This study examined the process that the Department of Mental Health and Substance Abuse of WHO used to develop a global toolkit for dementia friendly initiatives (DFI). Data were collected through a mix-method approach consisting of individual interviews of 20 DFI leaders, four focus group interviews of persons living with dementia (PWD), three group interviews of professionals, and an online survey of 129 participants from 46 countries. Data from multiple sources were examined. The meaning of DFIs centered on the needs of PWD, multi-sector collaboration, and physical and social environmental changes. Over 70% participants in the survey reported their DFIs targeted PWD and included PWD as important partners. The EASTT model can be used to summarize DFI strategies including Education, Advocacy, Support, Training and Transforming environment. Countries advanced in DFI tended to focus on enhancing professional capacity and environmental adaptation, while countries launching DFI appeared to prioritize dementia awareness campaigns.


Gödecke, Stein and Qaim (2018) (GSQ) recently analysed the 1990–2010 trend and determinants of the global burden of chronic and hidden hunger. We reanalyse and extend GSQ's study and demonstrate that after 2010, significant reductions in the burden of hunger were achieved. In contrast with GSQ, we find that (i) hidden hunger is more prevalent than chronic hunger; (ii) cereal availability and the supply of vegetables and fruits matter; and (iii) gender equality only affects hidden hunger. We further provide evidence on the importance of spatial spillovers in GDP affecting the burden of hunger. Policy makers should therefore prioritize (i) enhancement of micronutrient density of cereals; (ii) diversification of production systems and consumer diets; and (iii) development of nutrition-sensitive food value chains. •We reanalyse and extend the study of Gödecke et al. (2018).•Hidden hunger is more prevalent than chronic hunger.•Supply of cereals, vegetables and fruits significantly alleviates the burden of hunger.•Gender equality only affects hidden hunger and not chronic hunger.•Neighbours' GDP has a major positive effect on the alleviation of the burden of hunger. T2The global state of food security is largely a reflection of the global state of economic development . Indeed,Second, cultural factors such as ethnolinguistic fragmentation can affect development, for example through imposing a coordination cost on countries, potentially leading to a negative impact on the quality of government and economic growth , p. 104.Third, institutions and policies can play an important role in fostering development . As indiApart from food security, other outcomes of the development process include demography (such as population (growth) and age distribution ), agglom3Based on The burden of hunger was measured using Disability-Adjusted Life Years (DALYs) . DALYs cGSQ included seven determinants as fundamental causes for the global burden of hunger, which can be grouped into four categories: economic performance, demography, institutions and climate . GSQ alsWe propose to expand GSQ's set of four categories with a fifth category: economic geography . To captA second drawback of using (group) fixed effects is that it makes slow-changing variables over time  to appear either statistically or economically insigniﬁcant . UnfortuAnother potential problem is the fact that many high-income countries show DALY estimates that are near-zero and therefore are difficult to compare with middle- and low-income countries in terms of the burden of hunger. The burden of hunger in high-income countries is—albeit not zero—marginal compared to that in low-income countries. In addition, by being often located in the Western world, high-income countries differ markedly from middle- and low-income countries. We can formally test this using a binomial regression on a dummy indicating whether a country suffers substantially from hunger . Both hiGiven these concerns, we suggest (pooled) OLS estimates with clustered (or panel correct) standard errors . We allo44.1Between 1990 and 2017, the total burden of chronic hunger reduced by 72 per cent, while the total burden of hidden hunger declined by 41 per cent . ExpressComparing across regions, we find that Sub-Saharan Africa is the region suffering the most from the burden of chronic hunger, while South Asia features the highest burden of hidden hunger. Expressed per capita, Sub-Saharan Africa clearly suffers more than any other region from both burdens. A similar picture emerges from The largest contributions to the reduction in the global burden of chronic hunger between 1990 and 2017 can be attributed to India (33 per cent), Bangladesh (10 per cent), Ethiopia (6 per cent), China (6 per cent) and Nigeria (5 per cent), while in the case of hidden hunger, the largest contributors to its global alleviation are India (29 per cent), China (14 per cent), Bangladesh (7 per cent), Pakistan (6 per cent) and Indonesia (5 per cent). Although Sub-Saharan Africa and South Asia still suffered the most from the relative burden of hunger in 2017, most of these countries experienced significant reductions in the relative burden of chronic (>75 per cent) and hidden hunger (>50 per cent). However, not every country reported a reduction in the burden of hunger as some high-income countries experienced an increase in the relative burden of chronic hunger. This additionally indicates that care should be taken when assessing the determinants of the burden of hunger for the entire sample of countries worldwide.Before looking at the regression results, we focus on two factors that affect the burden of hunger and are at the forefront of development policy: per capita GDP and per capita cereal availability (sum of imports and production). The burden of chronic and hidden hunger displays a strongly downward-sloping trend for both factors . Sub-Sah4.2Further, our results show that urbanisation and good governance have a negative but statistically insignificant effect on the burden of hunger once per capita GDP is controlled for, which is in contrast with the significantly negative effect found by GSQ. A possible explanation for this might be that although urbanisation and governance positively affect food security, they are only indirect effects . The reaIn terms of environmental factors, higher levels of rainfall have a statistically significantly negative effect on the burden of hidden hunger while higher temperature levels have a statistically significantly positive effect on both the burden of chronic and hidden hunger. As pointed out by GSQ, this is a troubling finding given that many middle- and low-income countries already face high temperatures and water stress, and climate change is likely to negatively affect many regions in Africa  and AsiaTo capture economic geography, we included a country's physical centrality to markets  and openness to trade. Trade openness only appears a statistically significant driver for the burden of hidden hunger for the full sample of countries and when controlling for GDP (column 3a). However, the positive effect is likely a spurious finding  as tradeTo isolate the effect of the data, we replicated the exact model used by GSQ but with the newer data , on a global scale, they are indirect effects.Contrasting GSQ, we find that after controlling for per capita GDP, the import and production of cereals and the supply of vegetables (including pulses) and fruits matter in the alleviation of the burden of chronic and hidden hunger; we find no statistically significant impact of root and tubers. These updated conclusions are important as previous findings were not in line with nutritional compositions of the respective food supplies and had far-reaching implications for development policy, especially for cereals. In terms of public health, our results largely follow GSQ, although ours indicate that gender equality only affects hidden hunger and not chronic hunger. Given the ongoing Covid-19 pandemic, improving the understanding of the role of public health and gender equality on hunger is a priority towards assessing the impact of the pandemic on middle- and low-income countries.Most determinants have larger eﬀects on the burden of chronic hunger than on the burden of hidden hunger, which corroborates the steeper decline in the burden of chronic hunger. As a result, hidden hunger has surpassed chronic hunger over the last three decades. This means that policy makers should prioritize (i) enhancement of micronutrient density of cereals through biofortification and industrial fortification; (ii) diversification of production systems and consumer diets; and (iii) development of nutrition-sensitive food value chains, in which the food environment, women's empowerment and gender equality play crucial mediating roles.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Despite the growing body of literature examining social media in health contexts, including public health communication, promotion, and surveillance, limited insight has been provided into how the utility of social media may vary depending on the particular public health objectives governing an intervention. For example, the extent to which social media platforms contribute to enhancing public health awareness and prevention during epidemic disease transmission is currently unknown. Doubtlessly, coronavirus disease (COVID-19) represents a great challenge at the global level, aggressively affecting large cities and public gatherings and thereby having substantial impacts on many health care systems worldwide as a result of its rapid spread. Each country has its capacity and reacts according to its perception of threat, economy, health care policy, and the health care system structure. Furthermore, we noted a lack of research focusing on the role of social media campaigns in public health awareness and public protection against the COVID-19 pandemic in Jordan as a developing country.The purpose of this study was to examine the influence of social media platforms on public health protection against the COVID-19 pandemic via public health awareness and public health behavioral changes as mediating factors in Jordan.A quantitative approach and several social media platforms were used to collect data via web questionnaires in Jordan, and a total of 2555 social media users were sampled. This study used structural equation modeling to analyze and verify the study variables.The main findings revealed that the use of social media platforms had a significant positive influence on public health protection against COVID-19 as a pandemic. Public health awareness and public health behavioral changes significantly acted as partial mediators in this relationship. Therefore, a better understanding of the effects of the use of social media interventions on public health protection against COVID-19 while taking public health awareness and behavioral changes into account as mediators should be helpful when developing any health promotion strategy plan.Our findings suggest that the use of social media platforms can positively influence awareness of public health behavioral changes and public protection against COVID-19. Public health authorities may use social media platforms as an effective tool to increase public health awareness through dissemination of brief messages to targeted populations. However, more research is needed to validate how social media channels can be used to improve health knowledge and adoption of healthy behaviors in a cross-cultural context. Infection with coronavirus disease COVID-19) has become a severe public health issue worldwide. COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel coronavirus that recently emerged from China. In March 2020, the World Health Organization (WHO) declared that COVID-19 can be characterized as a pandemic. Therefore, it is of utmost importance to prevent further spread of the pandemic in public and health care settings 9 has bec. GeneralJordan has been under nearly total lockdown since March 14, 2020. After only a few COVID-19 cases appeared around the country, the Jordanian government took extraordinary measures, including implementing strict emergency laws. Travel restrictions were imposed on passengers in Jordan; all unauthorized travel into and out of the country and between cities was halted. Nonpharmaceutical physical distancing interventions, such as extended university and school closures and workplace distancing, were introduced to reduce the impact of COVID-19. Public awareness and prevention of COVID-19 infection play important roles in disease control; a lack of reasonable knowledge of infectious diseases leads to low detection rates. Therefore, to stop the spread of COVID-19 infection in Jordan, the Jordanian Ministry of Health launched specific national disease control measures, using several media campaigns , postersResearchers have indicated that most developing countries encounter serious difficulties in preventing the spread of infectious diseases due to inadequate medical facilities and lack of resources -10. In lDespite the growing body of literature examining social media in health contexts, including public health communication, promotion, and surveillance ,2, limitEvidence about social media’s impact on health knowledge, behavior and outcomes shows that these tools can be effective in meeting individual and population health needs. Most research addresses specific interventions and approaches, which vary widely in focus, target population, theoretical foundations, mode of delivery, functionality, and usability. This wide variation makes it difficult to find out what works and how, and complicates efforts to compare approaches.Korda et al noted a lack of research focused on the role of social media campaigns on public health awareness of pandemic diseases such as COVID-19, particularly in Jordan. Therefore, in this study, we attempt to answer the following questions:Does the use of social media platforms raise public health awareness of COVID-19 as a pandemic disease?Does the use of social media platforms increase public behavioral changes toward COVID-19 as a pandemic disease?Does the use of social media platforms increase public protection against COVID-19 as a pandemic disease?Do public health awareness and behavioral changes play important roles in enhancing the relationship between the use of social media platforms (interventions) and public health protection against COVID-19 as a pandemic disease?The findings of this study are expected to be helpful and important for public health authorities and governments to understand who receives the intervention (message), the impact of social media platform campaigns, and the extent to which changes in public health behavior and health outcomes can be attributed to the intervention, in addition to determining how the disseminated information is perceived.Social media platforms are widely deployed by the WHO, health care professionals, and regulatory authorities worldwide to address key issues relating to public health . They caSocial media platforms include a wide variety of networking sites , information-disseminating platforms , and microblogging services . These platforms and many others can be used to create and publish knowledge and information about potential health and disease risks and interventions as well as healthy lifestyles and effective health policies and strategies. In contrast to the campaigns occasionally launched by traditional media, campaigns launched through social media platforms often successfully convert knowledge and information on different health topics into daily fruitful web-based discussions and conversations . AnotherSocial media platforms constitute a powerful means of communication that can be used to elevate public awareness of infectious diseases, particularly new ones, in terms of outbreak dates and spreading developments . MembersSocial media platforms have attracted the interest and attention of researchers and practitioners in the health domain, who use them for different purposes. These include professional training and development of clinicians; formation of health networks and support groups; provision of funding for health institutions; facilitation of cooperation and coordination among health professionals; monitoring of infectious diseases .Even though social media platforms provide professionals in the public health domain with numerous valuable opportunities and benefits, usage of social media platforms by professionals is associated with several challenges, the most important of which are detecting infectious disease outbreaks, monitoring emergencies, predicting disease trends, and measuring the public’s awareness and responses. However, many studies have reviewed and explored the potential applications of social media platforms for public health communication. For example, Huebsch et al  suggesteIn a study conducted by Bennett and Glasgow , they exStudies on social media campaigns and healthy behavior have reported that social media campaigns can elicit positive behavior changes and even prevent negative behavior changes in individuals. Social media platforms can be used to reduce the spread of pandemics, thereby lowering the levels of fear and anxiety among the general public. Researchers have argued that social media communication can transfer useful information about infectious diseases based on identifying and tracking users’ behavioral patterns . A modelFurther, it was assumed that social media health campaigns grow as the mortality rate of the disease increases. Researchers investigated the role of social media platforms in eliminating infectious diseases in the presence of treatment ; they foPrevious research has found that disease prevalence is controlled by social media coverage. Behavioral changes related to infectious diseases through campaigns launched by print media, social media, and the internet are limited to a smaller population of educated people. In comparison, television advertisements tend to be capable of affecting the behavior toward infectious diseases of a larger population that generally consists of less educated people; therefore, television advertisements are more effective than other types of media. It has been found that social media health campaigns are associated with the number of infected individuals, where launching more campaigns on social media platforms reduces the number of infected people. The effectiveness of interventions made by social media networking sites in modifying the health behaviors of individuals was the subject of an investigation conducted by Laranjo et al , who uncElevating public health awareness was found to require the incorporation of some theories of behavioral change into social media health interventions . AccordiBehavior changes include but are not limited to hand washing, wearing masks, social distancing, avoiding public gatherings, sanitation, and isolation. Interventions aimed at promoting public health can improve the quality of health in society and support the policies and programs run by official health authorities in fighting the outbreak and spread of infectious diseases. If people have trust in these policies and programs, they are likely to respond positively to public health interventions and participate in the launched health promotion programs in large numbers. Social media health campaigns can induce positive behavioral changes and even eliminate negative ones in individuals. According to Laranjo et al , the advTherefore, an integrated conceptual model was developed to guide the objectives of this study. It was assumed that social media interventions as tools of health promotion programs would increase public protection and prevention against COVID-19 via the interaction between public awareness and behavior changes as mediating factors. These primary constructs are mainly derived from a theoretical background, relevant previous studies, and behavior change theory . In this study, the variables of primary interest (the independent variables) were social media platforms. The influence of the independent variables on the variance in the dependent variable (public protection against COVID-19) via the mediating factors  was studied. The expected relationships among these constructs are illustrated in Based upon the above model, the following hypotheses were formulated concerning the role of social media campaigns in increasing public awareness of COVID-19 as a pandemic disease in Jordan:1): The use of social media platforms is significantly increasing public health awareness.Hypothesis 1 (H2): Public health awareness is significantly contributing to public health behavioral change.Hypothesis 2 (H3): Public health behavioral change is significantly increasing public health protection.Hypothesis 3 (H4): The use of social media platforms is significantly increasing public health protection.Hypothesis 4 (H5): Public health awareness is significantly mediating the relationship between social media platforms and public health awareness.Hypothesis 5 (H6): Public health behavioral change is significantly mediating the relationship between the use of social media platforms and public health awareness.Hypothesis 6  was performed to confirm the properties of the research items. Scholars have reported that the measurement model shows how latent variables or hypothetical variables are evaluated under the conditions of observed variables, representing how the validity and reliability of the observed variables answers for the latent variables ,43. Figu1544=4281.7 (P<.001), which implies that the measurement model fitted the data. Also, χ²/df (4281.742/1544) = 2.773; this is an absolute fit index with a threshold of <3 for a serious viewpoint or <5 for adequate criteria. The incremental fit index of 0.89, the Tucker-Lewis index of 0.86, the comparative fit index of 0.87, and the root mean square error of approximation of 0.052 all meet the threshold of <1 for sufficient criteria [The initial CFA model provided an acceptable fit without eliminating any items to achieve an enhanced fitting measurement model. The goodness of fit indices of the evaluation of the initial research model indicated that the findings of the initial model could be deemed as the final model. CFA showed that for the model, χ²criteria . Based oTo determine the reliability and validity of the research model, factor loadings, Cronbach alpha, composite reliability, and average variance extracted (AVE) for the variables were calculated. The entire research indicator  surpassed 0.50 ,44; this1, H2, H3, and H4 were supported.As shown in 5 and H6) and indirectly , resulting in total effect sizes of α=.720 and α=.728 for H5 and H6, respectively; as a result, the data support partial mediation.To examine the mediating effects of public awareness, public behavioral change, and social media platforms on public protection, we considered both direct and indirect effects. It was found that public awareness and public behavioral change significantly affected public protection both directly  were developed to determine whether there were mediating effects of public health awareness and public health behavioral changes on the relationship between the use of social media platforms and health protection against COVID-19. The results clearly showed that public health awareness and public health behavioral changes mediated the effects of social media platform use on health protection; however, the mediating effect was partial. Additionally, the results indicated a significant and positive indirect effect of social media platform use on health protection against COVID-19 through public health awareness and public health behavioral changes, with standardized indirect effects of 0.571 and 0.579 and P values <.001. The results showed a significant and statistical effect of social platforms use on health protection against COVID-19 without the mediating effects of public health awareness and public health behavioral changes. The total standardized effect was 0.149, which was significant (P<.001) but weak.The fifth and sixth hypotheses  for public health awareness and of behavioral health changes to protect their communities and nations from the spread of pandemic diseases such as COVID-19. Second, the role of social media (innervations) to enhance public health awareness and behavioral health changes should be adequately considered in any choice of strategic health promotion plan. Social media campaigns should be considered as critical components of comprehensive approaches to improving public health behaviors.This study is not without limitations. The application of the conclusions of the study may be limited by time and geographical location. This study is a cross-sectional survey; the underlying identified associations may contrast across divisions and countries or may even lose their meaning over time. Most importantly, these data are self-reported by self-selected participants; also, the lockdown period was a constraint to gathering more representative data.Our findings suggest that the use of social media platforms can positively influence awareness of public health behavioral changes and public protection against COVID-19. Public health authorities may use social media platforms as useful tools to increase public health awareness through the dissemination of brief messages to targeted populations. More research is needed to validate how social media channels can be used to improve health knowledge and adopt healthy behaviors in a cross-cultural context.


E. coli as an indicator of fecal contamination. Bayesian analyses are used to estimate the percentage of the population exposed and the mean dose of fecal exposure from microbiological and behavioral data. Fecal exposure from nine pathways  can be compared through a common metric–estimated ingestion of E. coli units (MPN or CFU) per month. The Tool generates data visualizations and recommendations for interventions designed for both scientific and lay audiences. When piloted in Accra, Ghana, the results of the Tool were comparable with that of an in-depth study conducted in the same neighborhoods and highlighted consumption of raw produce as a dominant exposure pathway. The Tool has been deployed in nine cities to date, and the results are being used by local authorities to design and prioritize programming and policy. The SaniPath Tool is a novel approach to support public-health evidence-based decision-making for urban sanitation policies and investments.Inadequate sanitation can lead to exposure to fecal contamination through multiple environmental pathways and can result in adverse health outcomes. By understanding the relative importance of multiple exposure pathways, sanitation interventions can be tailored to those pathways with greatest potential public health impact. The SaniPath Exposure Assessment Tool allows users to identify and quantify human exposure to fecal contamination in low-resource urban settings through a systematic yet customizable process. The Tool includes: a project management platform; mobile data collection and a data repository; protocols for primary data collection; and automated exposure assessment analysis. The data collection protocols detail the process of conducting behavioral surveys with households, school children, and community groups to quantify contact with fecal exposure pathways and of collecting and analyzing environmental samples for  Sanitation services and infrastructure often fail to keep up with rapid urbanization, and as a result, the urban poor are disproportionately exposed to fecal contamination in the environment ,2. Open Improved water, sanitation, and hygiene (WASH) is expected to reduce exposure to fecal contamination, which may in turn reduce associated acute enteric illness or other adverse health effects. Several studies have associated poor WASH with a variety of health outcomes including: diarrheal disease, soil-transmitted helminth infections, vector-borne diseases, and environmental enteric dysfunction that could lead to undernutrition, stunting, and poor cognitive development . HoweverExposure to WASH-related pathogens can occur along multiple exposure pathways, whereby a pathway of exposure starts at the environmental reservoir of fecal contamination (e.g. contaminated water or food) and ends at the point of oral ingestion . The majInformation identifying dominant pathways of fecal exposure can guide intervention decisions so that programs are more effectively targeted to reduce total fecal exposure and in turn, adverse health outcomes. Despite this, the use of public health evidence to inform sanitation planning is inconsistent. Furthermore, increasing decentralization of urban sanitation initiatives and the subsequent diversity of actors in the urban sanitation sector result in highly varied sanitation program planning and policy . While tThe SaniPath Exposure Assessment Tool (hereafter referred to as the “SaniPath Tool” or “the Tool”), was developed by adapting the protocols  from the formative study to identify and compare risk of exposure to fecal contamination across multiple exposure pathways associated with inadequate sanitation and fecal sludge management. This approach follows the framework for quantitative microbial risk assessment, with an emphasis on hazard identification, exposure assessment, risk characterization, and risk management . The TooE. coli) and quantified as the amount of E. coli units ingested per month for each pathway. The primary users of the Tool are local municipal governments, water and sanitation utilities, development banks, non-profits, or other organizations working in sanitation at a local level who have access to basic laboratory facilities with necessary supplies for detecting E. coli in environmental samples, experience conducting surveys, and the ability to disseminate results to the local community and stakeholders. Users should also have identified priority communities where public health data could inform decision making and consider holding a meeting of WASH and urban development stakeholders to better align with sector priorities and leverage ongoing efforts. Typical cost of a deployment of the SaniPath Tool is $2000–4000 USD per neighborhood, depending on the local cost of personnel and supplies.The primary goal of the Tool is to allow users to quantify and compare exposure to fecal contamination through multiple environmental pathways for adults and children residing in an urban neighborhood. Exposure is calculated as the estimated ingestion of fecal contamination  the public domain is more likely to be affected by public sanitation policies and action; 2) contamination in the public domain is likely to affect private domain contamination; 3) in crowded, low-resource urban areas, the majority of behavior that leads to contact with the environment likely occurs in communal spaces ,20; and Within the public domain, users may collect data along nine environmental fecal exposure pathways: drinking water, bathing water, surface waters, ocean water, flood water, open drains, raw produce, street foods, and public or shared toilets (described further in E. coli) for each fecal exposure pathway. www.sanipath.org [The SaniPath Tool’s protocols guide users in collecting data on the frequency of exposure-related behaviors for adults and children and the contamination level of fecal indicator bacteria  provide information on which pathways are relevant in the setting, 2) select appropriate environmental sampling sites, and 3) provide context for the data collection. Key informants are interviewed about local WASH infrastructure and access, fecal sludge management practices, common WASH behaviors, diet, sanitation policies, and environmental health concerns. Transect walks in target neighborhoods reveal sanitation-related risk factors and identify appropriate environmental sampling locations where local residents are most likely to interact with exposure pathways (e.g. a water pump that services a large cluster of households or a popular street food vendor). Here, a neighborhood is defined as a community with either formally- or informally-recognized boundaries. The information from these interviews and walks is used to customize the SaniPath behavioral surveys and environmental data collection forms.Three types of survey approaches are used in the SaniPath Tool: household surveys, school surveys, and community surveys; all three types collect identical information on exposure behaviors. The three survey types can be used together or separately based on available resources and the population of interest. Household surveys are administered to the adult who manages WASH  and the households are selected through a systematic random sampling method. School and community surveys are administered through an anonymous voting method to groups of children and adults, respectively, who are recruited via convenience sample. In the school and community surveys, males and females are interviewed in separate groups, where culturally appropriate. We recommend conducting 100 household surveys per neighborhood, and four school and four community surveys with 15–20 participants each per neighborhood. Details on the survey protocols can be found on the SaniPath website .Each survey contains questions regarding the frequency of behaviors that may lead to exposure to fecal contamination. For example, the surveys ask about the frequency of eating raw produce, drinking from water sources, having contact with open drains and flood waters, and using public or shared toilets. In household and community surveys, adults are asked about their behavior as well as that of one of their children aged 5–12. Children interviewed in the school surveys are asked about their own behavior as well as that of the adults in their household. The questions and answer choices have been tested and refined through multiple survey pilots to better quantify the frequency of behaviors in relevant time scales (per month vs. per week) and improve comprehension among respondents.The environmental sampling protocols for the SaniPath Tool were designed for a variety of urban contexts. In addition to samples collected from the nine pathways included in the exposure assessment , the sample collection protocol includes the collection of soil from public gathering spaces (e.g. playgrounds) to assess “background” levels of fecal contamination in a neighborhood. We recommend collecting and analyzing a minimum of 10 samples per environmental pathway. This sample size is small enough to be feasible, yet adequate to run the Bayesian analysis and allow the user to compare the magnitude of exposure across pathways within a neighborhood and identify the dominant pathway(s), as well as characterize the variability of individual pathways across neighborhoods.E. coli was chosen as the fecal indicator bacteria because of the simple laboratory methods used for identification and quantification and their widespread use as a measure of fecal contamination [E. coli in environmental samples can be measured using membrane filtration and m-ColiBlue24 ® broth media  or Chromocolt ® Coliform Agar , or IDEXX-Colilert-24® and the Quanti-Tray/2000  [E. coli using protocols developed for the formative study [E. coli can be substituted, but that may affect the sensitivity and reliability of the data. Two to three dilutions are analyzed for each sample in order to get a reliable estimate of concentration. To calculate the concentration of E. coli in a given sample, a selection step is used to choose dilutions for calculation (averaging) and identify any samples that present conflicting results between dilutions. The dilution protocol and process for calculating concentrations of E. coli are described in mination –24. The ook, ME) –27. Publve study . Liquid The SaniPath Tool uses both behavioral and microbiological data to estimate exposure at a neighborhood level . Our expE. coli units (CFU or MPN) ingested per month per pathway, and the population ingesting fecal contamination, percent of the population exposed. This allows for comparison of pathways where exposure is through direct ingestion of contamination  with pathways where exposure occurs through indirect ingestion via hand-to-mouth transfer after hand contact with contaminated surfaces (public toilets) or waters . Once the ingested dose and the percentage of population exposed are estimated for each pathway, the dominant pathway(s) can be identified using a systematic method described in The exposure assessment uses common metrics for dose, average E. coli concentrations for each type of environmental sample; 2) exposure assessment profiles; and 3) an automated report with key findings and recommendations for interventions. Descriptive statistics for behavioral frequencies and environmental fecal contamination concentrations, shown as pie charts and histograms, respectively, allow users to understand the driving forces behind exposure (E. coli ingested for those exposed via a specific pathway (The SaniPath Tool assessment results are presented in three types of outputs: 1) descriptive statistics of exposure behavior frequencies for each pathway and distribution of exposure . The exp pathway . The Peohttps://tool.sanipath.org) comprised of sequential modules was developed [To guide users through the deployment process , a web-based platform  comparing Tool results from simultaneous data collection by two different teams of enumerators in one neighborhood.Behavioral surveys, environmental sampling, and laboratory analyses methods were evaluated through analysis of data from pilot deployments in Accra, Ghana, as well as analysis of formative study data. The validity and reproducibility of the Tool were assessed using several approaches: 1) comparing results from the Tool to that of the formative study; 2) examining the sampling error in estimated In 2013, we conducted a pilot of the SaniPath Tool in the same four neighborhoods of Accra, Ghana where the formative study was conducted . The goaE. coli concentrations for different sample types by simulating and resampling 10 samples from the formative study environmental data 10,000 times using R version 3.4.3 (R Core Team 2013). The purpose of this exercise was to examine the variation in the estimates of E. coli concentration from small samples for different types of environmental samples.We also estimated the sampling error of 10 dose of E. coli ingested per month per pathway were compared between these two parallel deployments.Lastly, in 2016, we examined the reproducibility of the SaniPath Tool results in two simultaneous deployments in Chorkor, Accra, Ghana, a neighborhood that was not previously included in the formative study. Two different groups of trained enumerators collected SaniPath Tool data in Chorkor independently and simultaneously. The dominant pathway(s), percent of population exposed to fecal contamination, and average logThe study protocol was approved by the Institutional Review Board at Emory University, GA, USA (Protocol number: IRB00051584) and the University of Ghana Noguchi Memorial Institute for Medical Research Institutional Review Board (Protocol number: IRB00001276). Human subject data were collected between July 2013 and October 2013, and March 2016 and July 2016 in Accra, Ghana.10 differences between exposure estimates for different environmental pathways and that results are replicable within a study site. The multi-pathway exposure assessment approach of the SaniPath Tool may reveal overlooked hazards, such as fecal contamination of raw produce or street food. In Ghana, the SaniPath Tool results have influenced planning for the inclusion of food safety in the National Urban Sanitation Strategy. In conjunction with other urban sanitation planning and decision-support tools (e.g. Sanitation Safety Plan), the SaniPath Tool can augment our understanding of urban sanitation challenges and potentially improve the impact of WASH programs by providing additional evidence on pathways and geographic areas that should be targeted for intervention.The SaniPath Tool quantifies exposure to fecal contamination for people living in urban environments and identifies environmental pathways that may pose the greatest risks to human health. The Tool can generate exposure estimates to inform sanitation policies, interventions, and investments and provides rapid, useful information for advocacy. Validation studies have demonstrated that the Tool protocols can distinguish 1 log10 CFU E.coli/month; 54% exposed, average dose 5.4 log10 CFU E.coli/month, respectively), which is consistent with findings from the formative study detailed in Wang et al. [The SaniPath pilot in Accra, Ghana provided critical feedback for refining the survey questions and sampling protocols as well as information on the accuracy of results generated from the SaniPath Tool. Results from Shiabu demonstrated the ability of the Tool to distinguish pathways based on their exposure assessment profiles within a neighborhood and identify dominant pathways of exposure. The SaniPath Tool identified ingestion of raw produce from markets as a dominant pathway of exposure to fecal contamination for both adults and children  from resampling is small (<0.67)  .Although larger sample sizes are preferable for analysis, practical considerations and resource limitations often make large sample sizes unrealistic. Sample sizes for environmental samples are limited by funding, time needed for laboratory analyses, physical size and geography of a particular neighborhood, and the prevalence of a pathway in a neighborhood. The desire for relatively rapid results demands a balance between time allocated for sample collection and sample size. The Tool’s recommended minimum number of samples ensures that there is sufficient data to inform the exposure model and provide valid outputs. Users should collect more environmental samples where financially and logistically feasible, especially for those samples where variation in contamination may be large .10 difference in estimated dose for each of the pathways, and there was a less than about 10% difference for the estimated percent of the population exposed for each pathway between the two deployments.Results from simultaneous deployments in the Chorkor neighborhood of Accra, Ghana in 2016 demonstrated that the relative ranking of the exposure pathways and characterization of dose and frequency of exposure were the same in both deployments . Ingesti2). While the accuracy of the Tool may not allow us to detect small differences between different pathways or neighborhoods (less than 1 log10 average dose), the methods can be used to quantify and compare the relative contribution of these environmental pathways to the total exposure to fecal contamination.The data from Chorkor, Accra demonstrates that results from the SaniPath Tool are reproducible in neighborhoods with a geographic area similar to or less than that of Chorkor ; Vellore, India (2014); Maputo, Mozambique ; Siem Reap, Cambodia (2016); Dhaka, Bangladesh (2017); Atlanta, USA (2017); Lusaka, Zambia ; Kumasi, Ghana (2018); Kampala, Uganda (2018); and Dakar, Senegal (2019). These deployments have explored the ability of the Tool to evaluate sanitation programs and interventions, characterize fecal exposure in varied cultural and geographical contexts, and explore differences between low- and high- income communities. Future studies will explore how to select a range of representative neighborhoods to characterize fecal exposure for an entire city and compare exposure to fecal contamination across multiple cities to understand broader trends in exposure.Salmonella Typhi and Salmonella Paratyphi A, and Vibrio cholera.Additionally, the SaniPath Tool methodology can be adapted for other, more specific, applications. For example, the Tool can be adapted to assess exposure to human-specific fecal contamination  through multiple environmental pathways. The Tool can also be adapted to focus on specific pathogens of interest where resources and capacity are available for more complex laboratory analyses. Currently, the SaniPath Tool is being adapted to examine risks of environmental exposure to Regional SaniPath training hubs are being established at selected institutions that receive training to build capacity in the SaniPath Tool methods. The goal of these training hubs is to make deploying the SaniPath Tool, and microbiological and survey methods in general, more accessible to municipalities and other organizations that would benefit from the evidence and the capacity to collect such data. The first training hub was established in 2018 in West Africa, led by the TREND group in Accra, Ghana with laboratory support from the Water Research Institute (WRI). TREND has led or supported trainings of municipal health and environmental workers in Kumasi, Ghana, Kampala, Uganda, Lusaka, Zambia, and Dakar, Senegal. The training hubs will also play a crucial role in research translation and dissemination to ensure that public health evidence is used to inform sanitation programming and policy.The Tool is freely and openly accessible to not only those who may want to use it, but also to those who may want to further improve or build upon it. The protocols and source code for the Tool are available online . As it eThe relationships between sanitation, environment, behavior, and health are complex. To effectively address the multiple factors that can influence population health and well-being in low-income urban settings, an understanding of fecal exposure is crucial. The SaniPath Tool provides valuable and reliable data through novel and standardized protocols grounded in rigorous scientific principles. Furthermore, the Tool results can be integrated with and complement other urban sanitation planning tools to further support evidence-based decision making about urban sanitation policies and investments. The Tool can facilitate a paradigm shift in valuing public health evidence as a critical part of sanitation planning and use exposure data to maximize health impact and increase accountability. Additionally, the Tool can decrease the barriers to evidence-based decision-making where such practices are not well established. With the recent focus on citywide inclusive sanitation, urban sanitation planning will require a greater understanding of the environmental, social, cultural, and political factors that affect sanitation quality and access. The SaniPath Tool provides support to the WASH and urban development sectors to make informed decisions and customize urban sanitation solutions to the local contexts to maximize positive impact.S1 Appendix(DOCX)Click here for additional data file.S2 Appendix(DOCX)Click here for additional data file.S3 Appendix(DOCX)Click here for additional data file.S1 TableThe table depicts results of sampling error analysis using data from the formative study in Accra, Ghana.(DOCX)Click here for additional data file.S1 Dataset(ZIP)Click here for additional data file.


This paper describes a rapid response project from the Chartered Institute of Ergonomics & Human Factors (CIEHF) to support the design, development, usability testing and operation of new ventilators as part of the UK response during the COVID-19 pandemic.A five-step approach was taken to (1) assess the COVID-19 situation and decide to formulate a response; (2) mobilise and coordinate Human Factors/Ergonomics (HFE) specialists; (3) ideate, with HFE specialists collaborating to identify, analyse the issues and opportunities, and develop strategies, plans and processes; (4) generate outputs and solutions; and (5) respond to the COVID-19 situation via targeted support and guidance.The response for the rapidly manufactured ventilator systems (RMVS) has been used to influence both strategy and practice to address concerns about changing safety standards and the detailed design procedure with RMVS manufacturers.https://covid19.ergonomics.org.uk/).The documents are part of a wider collection of HFE advice which is available on the CIEHF COVID-19 website ( The COVID-19 pandemic has led to a massive demand for Intensive Care Unit (ICU) facilities, with healthcare providers working to increase the surge capacity of hospitals. To respond to the anticipated demand, the UK Government called for UK manufacturers to increase the number of available ventilators through a process of rapid manufacturing [In the UK, National Health Service design has been accepted as an important component in patient safety since the 2000s  and was https://covid19.ergonomics.org.uk/).This paper describes the response process by the CIEHF to develop rapid advisory guidance documents, which was circulated by the UK Government to all RMVS manufacturers. Figure ASSESS: assess the COVID-19 situation and decide to formulate a response.MOBILISE AND COORDINATE: mobilise and coordinate HFE specialists.IDEATE: HFE specialists collaborate to identify, analyse issues and opportunities and develop strategies, plans and processes.GENERATE OUTPUTS AND SOLUTIONS: outputs and solutions were produced.RESPOND: response includes targeted support and guidance.The first rapid project provided guidance on basic HFE principles Figure . The aimIt was recommended that, where possible, the new ventilator designs should be aligned to existing designs to support existing operational mental models, allow rapid learning and reduce use errors.The user interface should be intuitive with buttons/controls spaced to minimise accidental operation. There should be informative feedback to users, which is informed by a risk analysis to identify any required warnings or alarms for critical steps and/or unsafe situations. Alarm design should consider the environment(s) of use and be audible in a noisy critical care environment, the potential for alarm fatigue due to multiple alarm systems, as well as lighting at different times of day/night . GeneralThe physical design recommendations included ensuring that physical connectors were easily recognisable and worked across settings. To design for relocating the ventilator, the weight should be considered, with easy repositioning/adjustments to avoid musculoskeletal health risks to staff (including the adjustment of screens and displays). Retractable cables could reduce trip hazards in the bed space and for storage.To reflect the different use during the COVID-19 pandemic, it was recommended that manufacturers design interfaces for users wearing personal protective equipment (PPE). This includes eye cover , face cover , body cover including surgical gowns (with and without sleeves), plastic aprons, one-piece disposable protection suit (and possibly a full gas-tight protection suit) and hand cover with two layers of gloves sticky taped onto the sleeves of gowns in between the layers.A range of operational tasks were considered, including frequently occurring and safety critical tasks, exceptional or emergency responses, tasks where novice users may make mistakes or where errors are known to be common, and maintenance/inspection and moving tasks. The task  requiresErrors were identified from previous research  to use bFailure to set up correctly: including ability to use, despite failure to pass self-test; ability of novice to set up ventilator circuit according to on-screen instructions; inter-changeability of circuit with other types of ventilator circuitry that look similar.Failure to find a setting site or display site: difficulty with indirect adjustment of a requested setting; difficulty manipulating multiple controls of different types; difficulty making basic adjustments; confusion and error for the new or occasional user when adjusting for advanced parameters.Setting site identified correctly but inappropriate setting: illogical default settings, not necessarily immediately obvious to user; errors in adjusting the inspiratory trigger; unclear indication on the controls of the trigger sensitivity where changing one parameter leads to change in other parameters which is not immediately recognised.Failure to confirm settings: poor tactile and visual interface design/feedback.Errors of interpretation: difficulty in reading/interpreting display linked to information design and mode presentation .Errors of cleaning: risks associated with poor cleaning or failure to replace contaminated parts, missing parts during reassembly.Errors of maintenance: lack of knowledge  of technical support staff; lack of awareness of common failures and failure modes.As this was a rapid manufacturing project, the Government specification only allowed for one day of formative usability testing . To suppThe task scenario and patient profiles used previously published templates  and werePatient profiles were developed to reflect common issues and patient presentations. Each profile included details about the patient, the task and the equipment to be used, for example:Patient: 62-year-old male, COVID positive, assessed by ICU consultant as deteriorating and tiring. Decision has been made to transfer to ICU for intubation and ventilation and ICU care. Standard operating procedure (SOP) requires transfer in full PPE and intubation and stabilisation in a dedicated area on ICU before transfer to bed space.Task: Set up ventilator, intubate patient and re-programme ventilator based on feedback once patient ventilated  according to values on ventilator, ETCO2 trace, oxygen saturations and arterial blood gases).Equipment to be used: patient bed, transfer monitor, ventilator under test and tubing, arterial and central venous pressure (CVP) transducer sets, intubation equipment including face mask, airway adjuncts, video laryngoscope, bougie, range of endotracheal tube (ETT) sizes, ETCO2 monitoring, tube ties, heat and moisture exchanger (HME) filter, waters’ circuit, airway rescue trolley, Naosgastric (NG) tube, drip stand, full PPE for aerosol generating procedures, intubation drugs.A user evaluation questionnaire was developed based on previous research , 12–14 tFinally, an issue reporting template was designed to support the systematic collection and recording of issues, including:Issue IDWhat was being tested (task)Task step or system functionIssue description Issue severity (used for prioritisation) should be agreed with the multidisciplinary design team before testing. A fatality, for example, would be classed as high severityRecommendation (proposed solution)Action or closure status (open/closed/rejected)This was a global crisis; everyone was trying to help and to adapt. As new players entered the field , it was important that efforts to respond to the crisis were based on established practice and structured approach . ClinicaThe CIEHF community responded by providing structured guidance to help manufacturers with the novel requirements and challenges. The CIEHF guidance was issued to RMVS manufacturers to support the design and testing of new machines and to encourage standard designs and protocols to prevent avoidable harm to patients. The usability testing protocol supported realistic testing (work-as-done), including operability whilst wearing a range of PPE. The guidance and usability evaluation protocol are simple tools with the potential to make a significant contribution and could be adapted to other medical devices or equipment. This opens debate for national policymakers and others about the role and contribution of HFE in healthcare, which should be sustained beyond the immediate COVID-19 pandemic.