Social network characteristics have an important role in understanding HIV transmission among female sex workers. The purpose of this systematic review was to summarize and critically appraise the existing studies on the social network characteristics and HIV risk behaviors among female sex workers.A systematic review was performed using predefined eligibility criteria through searching electronic databases. Two independent reviewers assessed the methodological quality of studies.Nineteen papers met the eligible review criteria. The synthesized evidence suggests that characteristics of social networks, especially functional characteristics such as social support and social capital, are important constructs for understanding the HIV risk behaviors.The findings of the present review enhance our understanding of the role of social network characteristics in HIV risk behaviors among female sex workers. However, the findings also highlighted a dearth of knowledge about the association of structural characteristics of social networks with HIV risk behaviors among female sex workers. Global epidemiological surveillance data indicate that in 2015 about 36.7 million people were living with HIV. Of these, 2.1 million were new HIV infections in 2015 and about half of the newly HIV infected people were adolescent girls and young women . HoweverHIV risk behaviors of injecting drug use and risky sexual behaviors are multidimensional and occur based on biological, individual, and structural factors. While individual attributes  may influence a person’s attitudes, beliefs, and behaviors, various macro-level and social context characteristics can also contribute to engagement in, and continuation of, HIV risk behaviors .A body of literature emphasizes the importance of social networks in HIV transmission and prevention –17. As oSince that time, numerous studies have shown that interpersonal interactions occurring in social networks, as well as network characteristics, are critical to understanding HIV risk behaviors and spread of infectious diseases more generally , 19, 20.A social network is a set of ties among people who have some common interests or interactions . Family,According to the literature, larger networks provide more opportunities for exposure to a variety of risks, health information, and practices affecting health behaviors and outcomes of network members , 28. FurWhile some studies have investigated the association of social network structure and function with HIV risk behaviors in teenagers, HIV-at-risk women, PWID, and MSM , 34–37, Some systematic reviews have assessed networks and health. Perkins et al. focused, in a systematic review in 2013, on how social network structure and influential individuals within a network may reinforce health outcomes and behaviors in low- and middle-income countries . They foDespite these interesting findings, these reviews have produced limited information on the role of social networks for HIV risk behaviors among FSWs. Therefore, a systematic review with a focus on functional and structural characteristics of social networks may be valuable to support future interventions for HIV prevention among FSWs. The purpose of the present review was to review and summarize existing quantitative and qualitative studies about network structure and function of FSWs and their association with HIV risk behaviors.This review was conducted in December 2016 using electronic search in databases including Web of Science, PubMed/Medline, Scopus, Ovid; the publishers Springer and Science Direct; and the key journal of AIDS and Behavior. Google Scholar was also searched. The search period was from 1990 to 2016. Additional articles were identified from manual reference checks of relevant studies. A sensitive search strategy was used to retrieve relevant studies. The Medical Subject Headings (MeSH) controlled vocabulary system was used to define the keywords. The combination of keywords used for PubMed was  AND (AIDS OR HIV OR human immunodeficiency virus) AND  AND , and equivalent specifications were used for the other databases. This review has not registered a protocol. For data management, all retrieved studies were imported into EndNote (version X)., and published prior to December 2016. Only primary studies published in English were included. Studies that did not provide information on the pre-specified PECOT items were excluded, regardless of their methodological quality.Inclusion criteria for the studies, in agreement with the PECOT structure , were observational and qualitative studies of which the studied population consisted of female sex workers (FSWs), assessed the association between structural characteristics  or functional characteristics  of the social network and HIV risk behaviors, were peer reviewedTwo reviewers appraised the papers independently in two steps: title/abstract and full-text review. In the first step, after excluding duplicate papers, if reviewers had disagreements to consider a title or abstract as relevant, discussions were held until consensus was reached. The two independent reviewers examined the abstracts of the remaining articles based on the inclusion criteria. Methodological quality was assessed using forms of the Critical Appraisal Skills Program (CASP) . The CASFigure We retained thirteen quantitative and six qualitative studies. Twelve of the studies were conducted in Asia , 61–70, The HIV risk behaviors outcome was assessed differently across the studies. Although most studies considered only condom use as an HIV risk behavior outcome , 74–76, Measurement of personal networks usually proceeds in two steps: name generator and name interpreter . A name Most of the studies did not consider network structure and only measured functional characteristics such as social support or social capital , 75–77. Structural characteristics were measured in diverse ways. Three articles came from one longitudinal study in the USA of the sociocentric  network, and provided information about the direct and indirect relationships in the network –73. ThisMost studies focused on the role of social support and social capital in condom use with clients or other sexual partners , 65, 77.FSWs with high perceived social support were more likely to use condoms than those who perceived low social support , 70, 75.The role of social capital in FSWs’ condom use was assessed in three studies , 77, 80.n = 6) considered the relationship between social network structural characteristics and HIV risk behaviors. Three quantitative articles with a longitudinal study design, all based on the same study [r = − 0.28, p < 0.01) and stability of the social network  [, on average, infect three of their ties directly in one step, and the entire core of the connected region indirectly in six steps [In the present systematic review study, only 32% of the studies  . Resultsix steps , 72. Theix steps –73. One ix steps . This quix steps . Two stuix steps , 74. Theix steps .In recent decades many countries, especially developing countries, continue to experience a steady increase in the numbers of people living with HIV/AIDS. About 74% of HIV transmission is related to sexual contacts , 7. FemaOnly 19 studies were identified over the period between 1990 and 2016, none of which focused exclusively on the association of social network characteristics and HIV risk behaviors of FSWs. Most of the relevant studies focused only on the role of social support and social capital in condom use among FSWs , 67, 77.Most of the studies did not include a network name generator and an associated network interpreter to obtain relevant information about the content of the ego-alter relationships , 67, 79.Consistent with past research by Qiao , we founThese findings can be used to develop and implement relevant behavior change interventions. Potential intervention strategies include training programs for two types of network members. The first approach would be to train peers to diffuse safe sex information and skills in condom use negotiations with clients and regular partners . A seconIn addition, the review findings show that structural characteristics of FSWs’ social networks are associated with HIV risk behaviors , 72. HowOnly one of the included qualitative studies focused on both the structural and functional characteristics of FSWs’ social networks and their association with HIV risk behaviors . This stThis review highlights the heterogeneity of approaches to measurement used to assess social network characteristics of FSWs. This heterogeneity is related to different study designs, different definitions of the concepts of social support and social capital, and the use of a variety of instruments for measuring social networks and their complex properties. Some limitations of the measures in the related studies were the lack of a theoretical or conceptual framework with respect to potential effects of social network characteristics on HIV risk behaviors of FSWs, insufficient measurement of social network characteristics, measurement of HIV risk behaviors only by self-reports, recall bias, and a lack of information regarding reliability and validity of instruments.The present systematic review hopes to provide insights into understanding the social network characteristics of FSWs, especially the role of structural characteristics of these networks for HIV risk behaviors. This review provides evidence about the positive association of social support with condom use among FSWs. This information may help researchers and public health planners to develop HIV prevention intervention for FSWs. However, due to the heterogeneity of approaches to define and measure social support, we could not combine the results and generalize across all included studies. Despite the findings regarding the role of network structure for HIV transmission and risk behaviors, this evidence, based on just one quantitative and one qualitative study, is not sufficient to provide a reliable conclusion about the role of structural characteristics of FWS’s social network on HIV risk behaviors. Therefore, to address the question regarding which structural characteristics of FWS’s social network may affect HIV risk behaviors, it is necessary to conduct additional research.Limitations of the present systematic review are the following. First, non-English and unpublished studies were not included. Second, the search strategy used was broad, but still some articles may have been missed. Third, collection of demographic variables and social network characteristics was not consistent across the studies; because of this diversity, the findings could not be combined in a meta-analysis. Fourth, most of the studies included were cross-sectional, so that it is impossible to draw any causal inferences between social network characteristics and HIV risk behaviors.Despite these limitations, the findings of the present systematic review have important suggestions for future studies and interventions. First, future studies need to pay attention to methodological and measurement issues. For example, future studies should be guided by theoretical frameworks to examine the mechanisms expressing how social network characteristics may affect HIV-related risk behaviors of FSWs. In addition, we suggest that future social network studies use types of network inventory (name generator and interpreter) that are frequently used in personal network studies , to provSecond, further studies should consider structural characteristics of FSWs’ social network.Social networks with different structural characteristics may have a different effect on HIV risk behaviors and HIV transmission among FSWs. For example, FSWs in a sexual network with high density, where more clients know each other and where the centrality of HIV-positive clients is high, may be more affected by HIV than FSWs who are engaged in a sexual network with a low density in which HIV-positive clients occupy a peripheral or isolated position .Information about structural characteristics of FSWs’ social networks such as density, degree, betweenness, and centrality that can facilitate diffusion of behaviors, information, disease transmission, is necessary to develop an effective HIV prevention intervention among FSWs. Such information will aid in the design of network interventions among FSWs and help policymakers to allocate resources for HIV prevention programs.Third, to provide a more complete picture of FSW’s social networks, future studies should examine both structural and functional characteristics of social networks and their association with HIV risk behaviors among FSWs to provide sufficient information about the structural and psychosocial mechanisms through which the relationships between network members may affect health-related behaviors and outcomes of FSWs. Only one qualitative study in China considered both structural and functional characteristics of social networks and their association with HIV risk behaviors among FSWs.Fourth, further studies should focus on the quantity and quality of ties among peers as well as ties with gatekeepers and sexual partners (clients and regular partners), and the dynamics of these relationships within the social networks of the target population. The HIV risk behaviors of FSWs may be embedded in power dynamics between FSWs, gatekeepers, sexual partners, and peers. For example, FSWs with smaller size peer network or low frequency of contact with peers who can support safe sex behaviors of FSWs may be more dependent on their partners and maintain the emotional intimacy with their partners even through unsafe sex , 86.The present review provides evidence of the complexity of the network of FSWs, composed of different sub-networks or network sources such as family, peers or co-workers, gatekeepers, clients, and regular partners. Different network structures may have different effects on HIV risk behaviors of FSWs. According to the findings of the present review, social support and social capital as functions of social networks are important constructs for understanding FSWs’ HIV risk behaviors, especially condom use. The findings highlighted a lack of knowledge about the association between structural characteristics of the social networks of FSWs and their HIV risk behaviors. The results obtained by the included studies are not sufficient to clarify the mechanisms according to which social network structures of FSWs affect their HIV risk behaviors. Understanding such mechanisms of action, and improved knowledge of social network characteristics of FSWs more generally, may lead to the development and implementation of more effective intervention programs for prevention of HIV transmission. We recommend policymakers and practitioners to design, implement, and evaluate new and more systematic and rigorous network approaches in prevention and harm reduction intervention that target HIV risk behaviors among FSWs.


Cloud computing for microbiome data sets can significantly increase working efficiencies and expedite the translation of research findings into clinical practice. The Amazon Web Services (AWS) cloud provides an invaluable option for microbiome data storage, computation, and analysis.The goals of this study were to develop a microbiome data analysis pipeline by using AWS cloud and to conduct a proof-of-concept test for microbiome data storage, processing, and analysis.A multidisciplinary team was formed to develop and test a reproducible microbiome data analysis pipeline with multiple AWS cloud services that could be used for storage, computation, and data analysis. The microbiome data analysis pipeline developed in AWS was tested by using two data sets: 19 vaginal microbiome samples and 50 gut microbiome samples.Using AWS features, we developed a microbiome data analysis pipeline that included Amazon Simple Storage Service for microbiome sequence storage, Linux Elastic Compute Cloud (EC2) instances  for data computation and analysis, and security keys to create and manage the use of encryption for the pipeline. Bioinformatics and statistical tools  were installed within the Linux EC2 instances to run microbiome statistical analysis. The microbiome data analysis pipeline was performed through command-line interfaces within the Linux operating system or in the Mac operating system. Using this new pipeline, we were able to successfully process and analyze 50 gut microbiome samples within 4 hours at a very low cost (a c4.4xlarge EC2 instance costs $0.80 per hour). Gut microbiome findings regarding diversity, taxonomy, and abundance analyses were easily shared within our research team.Building a microbiome data analysis pipeline with AWS cloud is feasible. This pipeline is highly reliable, computationally powerful, and cost effective. Our AWS-based microbiome analysis pipeline provides an efficient tool to conduct microbiome data analysis. Big data and data-driven analysis has become a primary driver of precision health ,2. The hBeyond data storage, assessing human microbiome data sets requires bioinformatic tools that enable deeper mining, the deciphering of the mechanistic connections among the microbes, and the potential functions of these communities. To examine significant associations between metadata  and DNA sequencing data, special bioinformatic and statistical tools for conducting microbiome analyses are needed . It was For clinical scientists, several challenges need to be overcome before conducting microbiome projects: (1) collective storage space for big microbiome data sets needs to be created so that the data and results can be easily shared within the research team; (2) centralized data computing capabilities need to be established to foster the replicability of results across all current and future projects; and (3) the cost for cloud computing services needs to be determined so that the team can cost-effectively study the human microbiome. The AWS cloud has become a popular platform for big data storage, high performance computing, and analytics -18,23. TThe process of developing and testing the MAP-AWS comprised of four stages, as illustrated in Third, we began pilot testing: we ran a small vaginal microbiome data set (n=19), refined the pipeline and the tutorial, and retested the pipeline with a larger data set from the gut microbiome (n=50). Last, we disseminated the MAP-AWS within our institutional research groups via presentations and workshops and obtained feedback regarding this newly developed MAP-AWS.Through our previous work analyzing microbiome data sets ,24, we fRecruiting information technology (IT) resources early in the preparation phase is critical to ensuring that all IT resources are coordinated by the time they are needed, and support services for troubleshooting activities can be provided in a timely manner. IT staff can help coordinate and organize the diverse data sets that will be used, including the DNA sequencing data, metadata, project-related information, and research protocols. Assignment of policies and permissions for access to all AWS resources is a critical role of the Research Informatics IT group. Assistance with AWS command-line interface (CLI) was provided through the IT support group as well. We set up AWS accounts for each team member intensively involved in developing the MAP-AWS with the support of our IT support group and the online AWS Support Center.AWS cloud provides various options for microbiome data storage, computing, and analysis. For data storage, the Amazon Simple Storage Service (S3) buckets were used. For microbiome data computing, Amazon Elastic Compute Cloud (EC2) instances  were primarily used, and for microbiome data analysis and specific bioinformatics, statistical packages that included QIIME 2  and RStuThe use of QIIME 2 for microbiome data analysis has been tested by our group in a variety of computer systems, such as the Linux operating system, the Mac operating system (OS), and AWS cloud . The QIITo test the feasibility of the MAP-AWS, we undertook three rounds of testing using a case-based formative approach to refine the microbiome analysis pipeline. In round one, we trained two novice students to use the MAP-AWS to determine where major changes were needed to improve the usability of the content and presentation formats of the tutorial and pipeline. Then, we demonstrated the MAP-AWS to a group of cancer scientists to get feedback regarding the content and presentation of the tutorial. In round two, we conducted a pilot test of the workflow in AWS cloud with a small training data set , which we had prepared for the purpose of training research scientists in microbiome data analysis . This stAfter testing and refining the MAP-AWS processes and tutorial, we expanded the use of this pipeline to other microbiome projects within our team, including a gut microbiome and colorectal carcinogenesis study which involved sequence and metadata import, data quality control, results analysis, and model building. We disseminated our pipeline through presentations and workshops.All the microbiome data we used in this study have been deidentified and no Institutional Review Board (IRB) approval is needed.The main components of the MAP-AWS include a multidisciplinary research team, bacterial sequences and corresponding metadata, Amazon S3 buckets for microbiome data storage, Linux EC2 instances  to run microbiome data analysis, and security keys to create and manage the use of encryption . With ouWe primarily used S3 buckets for storage and EC2 instances for analysis. With our MAP-AWS, we used two specific types of storage. The first was Amazon Elastic Block Store (EBS), which is closely integrated with our EC2 instance. EBS is used to store hard drive contents of EC2 instances, as well as snapshots of these instances . The othA Secure Shell (SSH) client  was used to securely connect to our EC2 instances, enabling remote access to a terminal through which Linux commands could be entered to process data. The AWS CLI was installed and used extensively to interact with our AWS resources and infrastructure.Next, we set up encryption to ensure our S3 buckets were secured from unauthorized access. We assigned encryption at the bucket level so that all objects moved into the bucket were automatically encrypted.Two undergraduate nursing students were trained to use the MAP-AWS tutorial we developed and were interviewed after they finished performing analysis of the training microbiome data set . They both successfully completed the training data set analysis under the guidance of the tutorial and our team member (IJ). Both undergraduates were positive about the use of the MAP-AWS and the tutorial, supporting the feasibility of the MAP-AWS.All steps performed using the MAP-AWS in this study were tested with support from the QIIME 2 Development Team and the AWS Support Center. A total of three incidents involving the S3 bucket and EC2 instances needed addressing and were resolved by the AWS Support Center during the MAP-AWS development process. Two trained microbiome team members (JB and IJ) independently analyzed the same vaginal microbiome data set  using the MAP-AWS. Comparisons of the final findings showed identical results in upstream and downstream analyses  24], su, su24], The 50 gut microbiome samples were successfully processed within 4 hours with the MAP-AWS and subsequently processed for microbiome diversity, taxonomy, and abundance analyses using QIIME 2, version 2018.4 . PerformThis paper developed a microbiome data analysis pipeline by using AWS cloud and conducted a proof-of-concept test for microbiome data storage, processing, and analysis. This pipeline is highly reliable, computationally powerful, and cost effective. This study was a proof of concept for building and testing a newly developed pipeline (MAP-AWS) for microbiome data analysis. This pipeline is efficient and highly cost effective. It will provide a convenient environment to share analysis tools and results between collaborators. To accurately assess and utilize this data, we rely on the development of tools, pipelines, and standard operating procedures to handle big data effectively and efficiently via the AWS cloud. Microbiome pipelines using on-demand EC2 instances showed a great capacity for microbiome data analysis at a low cost. This pipeline improved productive and insightful collaboration with clinical scientists across different institutions to help the multidisciplinary research team continue the collaborative use of AWS.With growing interest in evaluating the human microbiome and deciphering its relationship with health and disease, more efficient and cost-effective tools are needed for microbiome big data analysis. The purpose of this study was to develop and evaluate the MAP-AWS platform for use by clinical scientists. We described how researchers can construct their own microbiome data analysis pipeline using AWS. The AWS cloud can significantly expedite the microbiome analysis process and provide a collaborative platform for sharing data and results among research collaborators. The MAP-AWS tool successfully completed all microbiome processing and analysis steps both efficiently and reproducibly. The MAP-AWS not only maintains essential reproducibility of processing steps and analyses but also facilitates the efficiency and cost-effectiveness of microbiome data analysis in contrast with basic, commonly used methods of microbiome data analysis .Compared with standard processing for big data analysis, the AWS cloud brings extensive benefits to current microbiome data analysis, including optimized computational capabilities, flexible EC2 instance configurations, and robust security and policies for all resources. Although common server and desktop environments can provide microbiome processing capabilities, AWS brings a supportive systems environment for storage, computational, and analytical capabilities. For instance, many methods in the microbiome platform benefit from compute-optimized processing since their focus is serving high performance computing targeted for compute-intensive applications. The MAP-AWS includes an integrated tool with a combined tutorial for using AWS tools  and performing raw data processing, advanced QIIME 2 and RStudio analysis, and data sharing and management between researchers. This MAP-AWS platform establishes a common environment for sharing analysis tools and results between project managers and researchers across institutions. Given the appropriate permissions, researchers internal to the University and external collaborators can reliably rerun analyses and share findings. It is easy to deploy the microbiome data platform in multiple regions around the world with just a few clicks.AWS cloud has been widely adopted for whole-genome sequencing (WGS) analysis tasks. For large-scale WGS analyses, AWS was shown to be an efficient and affordable WGS analysis tool . SpecifiSeveral EC2 pricing models exist, including on-demand, reserved, and spot instances. Users can increase or decrease their computing capacity according to the real-time demand of their applications with on-demand instances and by paying by a specified hourly rate. We tested our microbiome pipeline using on-demand instances, showing a great capacity for microbiome data analysis at a low cost.One of the biggest challenges facing researchers is the ability to integrate and correlate the massive amounts of data produced by these protocols and identify biologically relevant information that can be used to formulate testable hypotheses. As a proof-of-concept test for the utilization of AWS in microbiome data analysis, our findings support its value and affordability. Our MAP-AWS efficiently integrated and correlated significant amounts of omics data stored and utilized in a cloud-based environment and provided a streamlined platform for communication between researchers. Microbiome research is on the precipice of producing large data sets of great magnitude. To accurately assess and utilize this data, investigators must rely on the development of tools, pipelines, and standard operating procedures to handle big data effectively and efficiently via the AWS cloud. Together, researchers, clinicians, and computer scientists, with the help of AWS cloud computing services, are poised to revolutionize microbiome research and its applications in human health.Our microbiome data analysis pipeline was undertaken within a cancer nursing research group and tested with data sets of small sample sizes. The technical pipeline should also be applicable to other microbiome data sets, such as oral or skin microbiome data. A dysbiotic human microbiome is associated with a variety of human disease susceptibility ,30, inclThis study was a proof of concept for building and testing a newly developed pipeline (MAP-AWS) for microbiome data analysis. This pipeline is efficient and highly cost effective. It will provide a convenient environment to share analysis tools and results between collaborators. The long-term goal for this platform is to continue the collaborative use of AWS among clinical scientists across different institutions to make our multidisciplinary research team more productive and insightful. A larger-scale testing of the MAP-AWS across different clinical conditions will enhance communications between multidisciplinary researchers and confirm our proposed efficiencies for running a microbiome pipeline in a cloud-based environment.


Rheumatic heart disease (RHD) poses a major disease burden among disadvantaged populations globally. It results from acute rheumatic fever (ARF), a complication of Group A Streptococcal (GAS) infection. These conditions are acknowledged as diseases of poverty, however the role of specific social and environmental factors in GAS infection and progression to ARF/RHD is not well understood. The aim of this systematic review was to determine the association between social determinants of health and GAS infection, ARF and RHD, and the effect of interventions targeting these.We conducted a systematic literature review using PubMed, the Cochrane Library and Embase. Observational and experimental studies that measured: crowding, dwelling characteristics, education, employment, income, nutrition, or socioeconomic status and the relationship with GAS infection, ARF or RHD were included. Findings for each factor were assessed against the Bradford Hill criteria for evidence of causation. Study quality was assessed using a standardised tool.1,164 publications were identified. 90 met inclusion criteria, comprising 91 individual studies. 49 (50.5%) were poor quality in relation to the specific study question. The proportion of studies reporting significant associations between socioeconomic determinants and risk of GAS infection was 57.1%, and with ARF/RHD was 50%. Crowding was the most assessed factor  followed by socioeconomic status (6 and 36 respectively). The majority of studies assessing crowding, dwelling characteristics, education and employment status of parents or cases, and nutrition, reported a positive association with risk of GAS infection, ARF or RHD. Crowding and socioeconomic status satisfactorily met the criteria of a causal association. There was substantial heterogeneity across all key study aspects.The extensive literature examining the role of social determinants in GAS infection, ARF and RHD risk lacks quality. Most were observational, not interventional. Crowding as a cause of GAS infection and ARF/RHD presents a practical target for prevention actions. Rates of rheumatic heart disease (RHD) are high in disadvantaged populations globally. It results from acute rheumatic fever (ARF), a complication of Group A Streptococcal (GAS) infection. These are described as diseases of poverty, but exactly what components of poverty promote them has been unclear. The aim of this review was to find what specific social and environmental factors are associated with GAS infection, ARF and RHD, and if actions targeting these can reduce disease rates. We did a search of published literature and found 90 relevant articles. Many supported an association between GAS infection, ARF or RHD and crowding, dwelling characteristics, low education level and employment status, poor nutrition and low social class. There was enough evidence to show that crowding and socioeconomic disadvantage increase the risk of GAS infection and ARF/RHD. However, most studies were of fair to poor quality in their ability to answer the research question, and there was little interventional research. This may relate to challenges inherent in intervening to change social determinants of health, but may also suggest lesser research attention to health issues affecting disadvantaged populations. The association between crowding and disease risk strongly supports initiatives to reduce crowding. This should become a key target for ARF and RHD prevention. Rheumatic heart disease (RHD) is an important cause of cardiac morbidity and mortality in disadvantaged populations globally –4. It reGlobally, ARF and RHD are almost exclusively seen in developing nations or among disadvantaged populations within developed nations . Among pTherefore primordial prevention should be part of a comprehensive strategy to eliminate RHD as a public health problem–a global goal . But whiTo provide the evidence base for primordial-level preventative interventions to control RHD, we undertook a systematic review with two aims: to determine the association between social determinants of health and GAS infection, ARF and RHD, and to determine the effect on GAS infection, ARF and RHD of interventions targeting these determinants.We conducted a systematic literature review on the association between socioeconomic and environmental factors and GAS infection, ARF or RHD, and on the impact of interventions targeting these factors.The search was conducted between August and October 2016 and eligible articles were identified by searching three databases: PubMed, Cochrane Library and Embase. MeSH terms, key words and Emtree terms (Embase thesaurus headings) searches were conducted. See All titles and available abstracts were screened by one author (PC). Articles were eligible for inclusion in the analysis of interventions if they were in English, reported on an intervention that encompassed: health promotion, education, or behaviour change targeting social determinants of GAS infection (including impetigo), ARF, or RHD; the provision of hygiene hardware, aids or household infrastructure; or household crowding reduction. Interventions could be at an individual, household, health centre, school or community level. Inclusion criteria for observational studies were that they must have reported on at least one socioeconomic or environmental variable and its relationship with GAS infection including impetigo), ARF or RHD incidence or prevalence, measured objectively at an individual, ecologic or population level with a comparison group (either study controls or use of population data e.g. census). Studies were excluded: if they were not in English; assessed pharmacological interventions only; were of very poor quality; or provided only a subjective appraisal of or no description of methods for ascertaining socioeconomic and environmental factors. No restrictions were set with regards to date of publication. Factors specified in the search strategy included: crowding (household or other settings), income, dwelling characteristics, education level, occupation/employment, social class, and nutrition. These were chosen through a scoping scan of selected literature sources informed by the authors’ prior knowledge in this area. There were no limitations on participant age, setting, geographic location or publication date of the studies. References of selected studies were searched for further articles not covered in the primary search strategy.The primary outcome was reduction in GAS infection, ARF, or RHD from an intervention targeting a socioeconomic or environmental factor. The secondary outcome was evidence of a causal relationship or association of GAS infection, ARF or RHD with specific social determinants of health.The full text of the articles were reviewed by the same author (PC) and data were extracted to a template that included information on article details , study type, study methodology, participants and setting, outcomes, and additional notes including salient points from the discussion or authors' conclusions. Study quality and risk of bias pertaining to measurement of determinants and outcomes of interest to this review were assessed for each study using the National Institute of Health Study Quality Assessment Tool, with theDescriptive analysis involved recording the frequency of specific factors measured across the studies and whether a statistically significant association with GAS infection, ARF or RHD was identified, the direction of the association (considered positive if greater degrees of the factor of interest were associated with higher disease rates), mixed, negative, possible or not found. A p value of <0.05 was used to define a statistically significant association. ‘Possible’ designated a reported positive association where a test of significance was not provided, nor means to calculate one. ‘Mixed’ designated a combination of positive, negative or absence of association for different measures of the same variable. Results for GAS infection were presented separately and ARF/RHD to reflect the different epidemiology and natural history of the conditions.The findings were synthesised in the context of study quality and strengths and limitations of findings. Systematic reviews (n = 4) were reviewed for quality and findings separately.The evidence of the link between each of the main factors explored and GAS infection, ARF and RHD was assessed against the Bradford Hill criteria, a set of nine guiding principles for interpreting causal relationships between environmental influences and disease. The criteria are: strength of association, consistency, temporality, biological gradient, plausibility, coherence, experiment, specificity and analogy .The studies were too heterogeneous in exposure and outcome measures for meta-analysis.In some instances where statistical testing was not performed in the original study, but enough data were presented to allow analysis to be undertaken (i.e. a 2 X 2 table with denominators), simple tests of significance  with 95% confidence intervals, or relative risk (RR)) were calculated using the Tables for Epidemiologists function in Stata 13.1 . These additional analyses are indicated in Statistical analysis was conducted to check for correlation of study type, quality of study (fair or greater), outcome of interest  and positive outcomes using chi-squared and Fisher’s exact tests of significance. Correlation between year of publication and study quality was analysed using binomial variables of published in previous 20 years (>1996) and study quality equal to or greater than fair. Analysis was carried out using Stata 13.1 .This review adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist  18]..18].The search strategy identified 1,164 articles in PubMed, 273 articles in Embase and 84 articles in the Cochrane Library . An addiSeveral articles were based on the same study population. Two articles by McDonald et al.2006)  (2008)  shared t006  . Only three studies graded as being of good quality. Studies that were case series were most likely to be of poor quality. Two of the three RCTs, seven of 16 (43.8%) case control, and five of 12 (41.7%) cohort studies were of at least fair quality. Studies published after 1996 (25 of 37) were more likely to be of fair or better quality than those published earlier (6 of 54) . Study quality was not associated with outcomes.12 studies (57.1%) reported a positive relationship between at least one socioeconomic factor and risk of GAS infection, and 35 studies (50.0%) reported a positive relationship between a socioeconomic factor and risk of either ARF or RHD.The likelihood of positive findings was not associated with publication after 1996 or study quality. 100% of the case control studies reported at least one positive association of a socioeconomic factor with disease rates compared to 64.9% of other studies (Fisher’s exact p = 0.002).50 studies assessed the association between crowding and GAS infection 14 studies), ARF (16 studies), RHD (15 studies) or a combination (5 studies)  studies that examined an association between crowding and disease rates did not demonstrate any association between crowding and disease risk.Dwelling characteristics and facilities were assessed across a variety of measures such as general housing condition, construction type, specific characteristics , or facilities  in relationship to GAS infection in seven studies and ARF/RHD in 19 Tables . Four ofThree diverse RCTs assessed interventions aimed to induce or reduce acquisition of GAS or its sequelae through common household fomites , 108 or The education level of mothers, fathers and cases themselves were explored across 17 studies, with 16 studies reporting ARF/RHD as the outcome Tables . Low matdisadvantage rather than advantage in four studies. In these studies, maternal employment was positively association with ARF in two of two studies, [advantage and found maternal unemployment was associated with RHD prevalence [The relationship between employment and ARF/RHD was explored in 15 studies. No studies examined relationship between employment and risk of GAS infection Tables . Maternastudies, , 41 and studies,  but was studies, . In contevalence . There wevalence , 65. Empevalence , 61, 76 evalence .Findings of the three studies that reported GAS infection risk and low income were diverse . For ARFThe method of assessing income  did not affect the likelihood of reporting an association.15 studies (covering 12 separate study populations) assessed the relationship between nutrition and ARF or RHD using either dietary intake or anthropometric measures Tables . Four  or social class was assessed in six studies reporting GAS infection as an outcome, and 36 studies reporting ARF/RHD, with three (50%) and 16 (44.4%) respectively showing a definite association Tables . A furthFour systematic reviews of risk factors for GAS infection, ARF or RHD were identified , 29–31. Each socioeconomic or environmental factor was assessed against the Bradford Hill criteria to establish whether a causal relationship with RHD and its antecedents was supported by the evidence contained in this review . CrowdinThe strengths of the observed relationships of crowding and outcomes were modest; odds ratio or relative risk calculations for binary outcomes  produced around a twofold likelihood or risk of GAS infection, ARF and RHD. In Jaine et al’s ecologic study of ARF cases across New Zealand, after adjusting for average income and number of children aged 5–14 years, the authors noted that a 1% increase in the proportion of households defined as being crowded conferred a 6.5% increase in the expected ARF count at a census area unit level .Regarding consistency, 29 of the 50 studies that assessed crowding reported statistical evidence of an association with GAS infection, ARF or RHD risk. Further studies demonstrated an apparent relationship but did not conduct any significance testing.The necessary criteria of temporality between cause and effect was met.While an individual may experience housing instability or change, the characteristics relevant to crowding, e.g. amount of bedrooms a family can afford, is not likely to change greatly during an individual’s childhood , 98. TheThe biological mechanism by which crowding exerts its effect on ARF and RHD is via its relationship to GAS acquisition. Crowding fosters intimate contact and consequent GAS transmission directly by human-to-human contact and via droplet spread . Higher There were no experimental studies assessing crowding and GAS infection, ARF or RHD risk. The closest such evidence comes from findings of an US Air Base Streptococcal Laboratory . This grCrowding does not exhibit specificity for GAS infection and its sequelae. It is an established health risk for transmissible diseases, especially of those with epidemic potential where outbreaks are more frequent and more severe when the population density is high . An analSocioeconomic status also compared favourably against the Bradford Hill criteria, however the causal relationship is exerted through the influence of intermediary factors , which individually do not carry the same weight of evidence. A gradient of higher disease risk with lower socioeconomic status was demonstrated across ten studies.This systematic review identified 91 studies spanning 80 years that have assessed the relationship of social and environmental factors of crowding, dwelling characteristics and facilities, education, employment, income, nutrition and socioeconomic status with GAS infection, ARF, and RHD. Nearly all studies were observational rather than intervention studies. Crowding was the most frequently assessed factor followed by socioeconomic status. The majority of studies that assessed a measure of crowding and risk of GAS infection, ARF, or RHD reported a positive association with crowding; as did those examining dwelling characteristics, education levels and employment status of parents or cases, and nutrition. However, there was considerable heterogeneity in measures used, study settings and outcome ascertainment.We noted a lack of well-designed research and interventions aimed at unravelling poverty as a mechanism for ARF and RHD, which is at odds with the widespread acceptance of ARF and RHD as diseases of poverty . This paThis issue is well described among the neglected tropical diseases, , 127 a dGiven the study designs and limited quality of papers, we used the Bradford Hill criteria, a set of guiding principles for interpreting links between environmental influences and disease, as a pragmatic framework to consider the findings of this systematic review. These criteria are not definitive rules, rather they provide an analytical framework to consider whether cause and effect is the reasonable inference . The weiOverall socioeconomic status also effectively met the Bradford Hill criteria for causation of the outcomes of interest. Since this overarching category is collinear with (and either determined by or a determinant of) the other measures examined, it is not possible to tease out specifics relating to how each criterion was met. In general, it is well understood that rising socioeconomic status, prior to availability of penicillin, was associated with a steady decline in death rates from ARF in industrialised nations . It is lThe other factors explored in this review had insufficient evidence to suggest causal links. Dwelling characteristics frequently displayed an association with GAS infection, ARF, or RHD risk, but measures were so heterogeneous and context specific that generalizability is impossible. Further, authors consistently omitted a proposed mechanism to explain their findings. The role of fomites in GAS transmission was not supported , 108. SoThis systematic review is the most extensive review to date targeting observational and experimental studies in the area of social determinants of health and GAS infection, ARF and RHD. A limitation was the exclusion of non-English articles; nevertheless, a wide variety of countries were represented. A further limitation was that all stages of article appraisal were undertaken by one reviewer. However, a strict methodological process was followed utilising assessment tools designed for each study type allowing greater specificity in appraisal and uniformity in the reviewing process.The poor quality of this body of evidence is the most critical limitation in guiding preventative actions; however, it would be erroneous to reject all findings. Rather it should be considered how probable or not it is that this diverse collection of studies dispersed in time, person and place could all be flawed and biased in the same way. Confounding is another important consideration in this review. Simple cause-effect relationships within social determinants of health are not readily apparent , 132- raMany studies presented outcomes at a group or ecological level rather than an individual level. However, assessing social determinants of health at the group level is also important because people do not live in isolation; a population level approach ensures that a wide variety of contributing settings and activities are not inadvertently overlooked . Also, tFinally, reporting bias is an important consideration in systematic reviews. The factors extracted for this review were frequently not the primary outcome of studies, and so their positive, neutral or negative findings would be less likely to influence whether a paper was published.Rather than re-describe the problem, the aim of this research was to guide solutions. In ARF and RHD where treatment is logistically intensive and painful,  and vaccBased on these findings, we recommend that ARF and RHD control programs should address household crowding—particularly in high-income countries where funding and resourcing is more feasible. Structural crowding (inadequate living space including number of bedrooms) must be addressed in collaboration with designers and providers of public housing in partnerships that recognise housing needs to support good health.Functional crowding  is more difficult to address, requiring in-depth cultural understanding. For example, in Australian Aboriginal societies, rights and obligations around accommodating extra people in a house must be respected in interventions to reduce household crowding . Where cSite-specific tailoring of interventions are needed: publically-funded interventions in a cold climate high RHD-burden setting for instance include the provision of household insulation and heating to reduce functional bedroom crowding . ConversInterventions to tackle socioeconomic status may be considered beyond the reach of medical research and health service delivery, but this is not so. Research and service delivery initiatives in high RHD-burden settings should ensure that they provide opportunities for community engagement and employment; and funding bodies should ensure that initiatives supporting strengthening of socioeconomic status are valued as being critical in disease prevention.RHD control programs, where they exist, should aim to routinely collect objective metrics on social and environmental factors to further inform advocacy, tailor it to local needs and add to the evidence base. Outcomes of advocacy should be interventions targeting social determinants such as crowding reduction and hygiene hardware improvement; and these should be accompanied by rigorous evaluation and sharing of findings.Findings from this systematic review will be able to inform guidelines and policies regarding primordial-level preventative interventions against GAS infection, ARF and RHD. The wide body of evidence exploring links between certain social and environmental factors and these conditions is limited by poor quality and a lack of interventional studies. Historically, this has hampered the ability of control programs and guidelines to legitimately target these factors. However, when scrutinized against the Bradford Hill criteria assessing the evidence of a causal relationship, the link between overall socioeconomic status and crowding with ARF and RHD risk can be satisfactorily viewed as one of cause and effect. This clear and powerful message should be reflected in ARF and RHD control efforts. A critical role for ARF and RHD control programs and registers is to routinely collect and analyse data on these social determinants alongside clinical markers of case management to inform future advocacy and interventions.S1 Checklist(DOCX)Click here for additional data file.S1 Text(PDF)Click here for additional data file.S1 Fig(TIF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.S8 Table(DOCX)Click here for additional data file.


Antimicrobial stewardship program (ASP) is a distinguished method to improve the prescription and efficacy of antibiotics.The efficacy of ASP and conventional methods was compared to measure the effectiveness of meropenem (MPM) and vancomycin (VMN) antibiotics in pediatric patients.In an interventional quasi-experimental study, 135 children admitted in Children’s Hospital affiliated to University of Medical Sciences in time periods of 2014–2015 and 2015–2016 were assessed.t test, and Fisher’s exact test.The conventional and ASP methods in 2014–2015 and 2015–2016 were respectively utilized to provide the best antimicrobial therapy of MPM and VMN antibiotics in patient children. The data of mortality rate (MR), antibiotic prescription (AP), antibiotic dose (ADe), antibiotic duration (ADn), length of hospital stay (LOHS), and blood cultures (BCs) were compared across the years using the Chi square, independent The levels of MR, AP, ADe, ADn, LOHS, and positive BCs using the ASP method in 2015–2016 were significantly lower those of in 2014–2015 using the conventional one (p < 0.05).The ASP method versus conventional one with a better efficacy can be employed as an antibiotic administration guide for MPM and VMN in the therapy of patients in community-based hospitals. One of the most common therapeutic agents or active pharmaceutical ingredients prescribed in healthcare and medical services is antimicrobial drugs . It was There are several well-known ways to decrease the antibiotic resistance such as hand washing, disinfection of hands and hospital tools, prompted laboratory tests, continuous medical education, vaccination, preparation of antimicrobial resistance files, and use of antimicrobial stewardship program (ASP) method , 8. The The risk of antibiotic resistance versus the beneficial utilization of antibiotics should be balanced . The rouPublished literatures in pediatrics earlier demonstrated the optimistic effect of ASP implementation on the use of antimicrobial agents –31. HoweIn this interventional quasi-experimental study, 135 children  under treatment with MPM and VMN antibiotics in Children’s Hospital affiliated to University of Medical Sciences were eventually evaluated. The initial number of participants was 166. However, infants with a low birth weight or a LOHS lower than 1 week, birth at medical facilities other than NICUs, and having major congenital anomalies were excluded because most out-born infants after the first week of life were transferred and received variable nutritional feedings. In addition, several charts were missing key information, such as birth weight or discharge weight. Other exclusion criteria in this research were children referred to the surgery department and/or received antibiotics for prophylaxis. In general, 31 out of 166 people were excluded from the current study and , infection preventionists (IPs), clinical microbiologists, and two data scientists. From Saturday to Thursday, the ASP team officially inspects the primary instructions and applies daily and weekly usage arrangements of MPM and VMN. These antibiotics were purposefully prescribed according to the antimicrobial susceptibility patterns of clinical isolates or type of antibiotic-resistant infections, misapplication risk, and medical expenses. Besides, the ASP protocol incorporated pharmacist notification by personnel of clinical microbiology laboratory. After generating the real-time data collection, pharmacists present in the ASP team assessed each the documented order by evaluating the patient’s electronic medical record, subsequently issued some of key decisions/consultations after open communication with specialist physicians and sent a feedback to providers in terms of answers like stop, approve, and use of a sole antibiotic with a less/more dose.An electronic and validated checklist including demographic  and disease-related data were filled out for each patient and recorded in the pharmacy database. Clinical data included intensive care unit admission, disease type and intensity, antibiotic consumption history (such as receipt of past antibiotic therapy), ADe, ADn, LOHS, mortality, adverse effects, and results of bacterial cultures in MPM and VMN treatments, recommendations introduced by the ASP team about antibiotic prescription (AP) with a certain ADe, and patient’s agreement rate with consultations. The time difference (in day) between patient’s admission and discharge dates was considered as LOHS. The efficiency of used antibiotics against urinary tract infection (UTI), respiratory tract infection (RTI), and cerebrospinal fluid infection (CSFI) was respectively examined by the urine, tracheal, and cerebrospinal fluid (CSF) standard cultures. Gram staining from positive blood cultures was conducted to diagnose whether bacterial infections induced by Gram-positive cocci or by Gram-negative bacilli. The UTI was recognized from the clinical charts of all pediatric patients with substantial amounts of pathogens isolated in blood cultures. The UTI confirmation was also performed by checking their records to find the same pathogen in the urine and blood cultures, with pyuria by dipstick or sedimentary urine analysis and/or clinical symptoms  . The diat-student test and Chi square (χ2) or Fisher’s exact tests were performed to assess between-group differences for numerical and categorical variables, respectively. The significant level was set at p < 0.05.The data were statistically assessed using the analysis of variance (ANOVA) with SPSS software package, version 13.0 . The independent Figure The overall count of antibiotic prescriptions issued for the studied pediatric patients in time periods of 2014–2015 and 2015–2016 is given in Table Clostridium difficile with a reduced daily consumption dose (25.4%) and medical costs (15.2%). In the current study, the total dose of MPM and VMN was also reduced, but the calculation of costs was impossible.The ASP method is a comprehensive approach to reduce the burden of the antibiotic resistance problem. This method as a practical strategy can notably improve the pattern and amount of antibiotic prescriptions in different hospital wards . In thisSimilar to results of our study, Morril et al.  reportedIn conclusion, because the ASP compared to the conventional method showed a superior effectiveness for MPM and VMN antibiotics, a culture-guided ASP is seriously proposed to community-based local hospitals owing to the assurance of implementation and performance of a proper antimicrobial therapy. A significant reduction in antibiotic consumption also can highly promote patients’ satisfaction and their expectations by decreasing medical costs and long-term disability for patients. Although the present research was limited by its quasi-experimental design, the obtained results in pediatric patients along with the evaluation of care quality can be verified by conducting further studies with larger sample size and multi-center sampling. Furthermore, the efficiency and satisfaction with the available ASP may potentially improve the therapy optimization by incorporating a prospective audit-and-feedback intervention.


We undertook a landscape analysis of vaccinology research and training in sub-Saharan Africa in order to identify key gaps and opportunities for capacity development in the field. We conducted interviews with regional and global immunisation experts, reviewed university and research centre websites, searched the scientific literature and analysed donor databases as part of our mapping exercise. We found that (1) few vaccinology training programmes are available in the region; (2) vaccinology research sites are numerous but unevenly distributed across countries and subregions and of widely varying capacity; (3) donor funding favours HIV, tuberculosis and malaria vaccine development over other high-burden diseases; (4) lack of vaccine design, manufacturing and regulatory capacity slows the progress of new vaccines through the research and development pipeline and (5) vaccine implementation research garners limited support. Regional efforts to strengthen African vaccinology expertise should develop advanced vaccinology training programmes, support clinical trial and implementation research sites in geographic areas with limited capacity and conduct multidisciplinary research to help design, license and roll out new vaccines. A large number of sites conduct vaccinology research in sub-Saharan Africa, but they vary widely in size and capacity and are heterogeneously distributed across the region.Training opportunities for African vaccinologists are limited.Donor funding remains overwhelmingly focused on HIV, tuberculosis and malaria vaccine development, dwarfing investment in high-burden diseases such as respiratory infections and enteric illnesses.The pipeline of new products targeting priority diseases for Africa is extensive, but rapid development is hampered by the lack of vaccine design, manufacturing and regulatory capacity in the region.There is little investment in an implementation science agenda that would increase immunisation programme efficiency and effectiveness in different settings.support the research and development of vaccines and medicines for the communicable and non-communicable diseases that primarily affect developing countries (and) provide access to affordable essential medicines and vaccines’.The UN Sustainable Development Goals (SDGs) set out ambitious targets for the global community over the period 2015–2030. SDG3 focuses on good health and well-being, aiming to ‘Ongoing or completed evaluations of the immunogenicity of reduced dosing schedules for pneumococcal conjugate vaccines,In this context, we undertook a landscape analysis to describe the global and African vaccinology environment and identify key gaps and opportunities for building research and training capacity in the region, with the ultimate aim of accelerating vaccine discovery and clinical evaluation and providing evidence to improve immunisation policy and programmes. (The African Leadership Initiative for Vaccinology Expertise (ALIVE) was established in 2016 as a South African Department of Science and Technology/National Research Foundation Flagship Initiative at the University of Witwatersrand, Johannesburg. This landscape analysis was conducted in preparation for the development of the ALIVE strategy and workplan.)We collected detailed information on 608 universities in 48 countries . We foun10.1136/bmjgh-2018-001363.supp12Supplementary data10.1136/bmjgh-2018-001363.supp1Supplementary dataThis analysis of university data provides a high-level description of training available across the continent and helps to understand the disciplines that vaccinology trainees could have exposure to during their undergraduate or graduate studies. Nevertheless, it has some important limitations. First, there are likely inaccuracies in the information collected from university websites, which may be incomplete, out of date or imprecise. Second, we were unable to access information on the number of students trained annually and on the number of students who have emigrated, limiting our ability to interpret the data in terms of population impact: as an example, Somalia and Sudan have a high density of medical schools but a very low density of physicians,In addition to general information on scientific and medical education in sub-Saharan Africa, we searched specifically for ‘vaccinology’ training programmes. Only 10 ‘vaccinology’ courses are currently offered in the region . These cWe identified 12 854 unique publications on vaccine-preventable diseases over the period of interest , dengue and cholera. There were >7 times more publications on tuberculosis than on hepatitis B in total . The rank order of diseases in terms of research output varied little by geographic area. TB, hepatitis B, HPV and measles were among the top five diseases in all subregions. Polio, dengue, cholera, rotavirus and pneumococcus appeared in certain subregions but not in others .The predominance of TB, hepatitis B and HPV publications likely reflects the association of these diseases with HIV/AIDS, which has drawn the vast majority of the region’s research funding since the 1990s. After HIV/AIDS, malaria, respiratory infections and diarrhoea are the leading causes of years of life lost in sub-Saharan Africa.We compiled a list of medical and public health institutes that conduct research in vaccinology  pipeline. The need to strengthen vaccine manufacturing capacity in low-income and middle-income countries was underlined in the Global Vaccine Action Plan and reiterated in the Addis Ababa Declaration on Immunisation endorsed by African Heads of State in 2016. Local capacity would enable more rapid development of vaccines against diseases that are priorities for Africa but have a limited global market and help secure emergency supply of vaccines for Africans in the event of an outbreak or epidemic. The Institut Pasteur de Dakar  was long the only human vaccine manufacturer in the region, producing yellow fever vaccine for UNICEF supply. In 2003, Biovac was established with support from the South African government to respond to domestic and regional vaccine needs. Biovac runs a ‘fill and finish’ facility that supplies select EPI vaccines to southern African countries and has developed and licensed a Hib conjugate vaccine technology package. It is also working to advance a pandemic influenza vaccine through the regulatory process, with support from the WHO Technology Transfer Initiative, and developing a Group B Streptococcus vaccine. In contrast to the small number of human vaccine manufacturers, approximately 20 countries have small, publicly owned or subsidised veterinary vaccine manufacturers, which rely on the Pan African Veterinary Center of the African Union for quality control and support services.Manufacturing capacity is closely interlinked with vaccine design and regulatory capacity. Several global and local initiatives are working to strengthen these complementary areas. In order to bridge the design gap, the KEMRI-Wellcome Trust Research Programme in Kilifi, Kenya, recently created the first vaccine design laboratory in Africa, aiming to construct and evaluate human and animal vaccines of regional importance. On the regulatory side, the African Vaccine Regulatory Forum (AVAREF) was initiated by WHO AFRO in 2006 to strengthen clinical trial oversight and improve the quality and timeliness of vaccine registration processes at country level. AVAREF developed a standardised protocol format for vaccine clinical trials, provides support to national authorities for review and authorisation of trials and helps strengthen pharmacovigilance systems following vaccine introduction.Global investment in vaccine R&D for ‘neglected diseases’ reached US$1240 million in 2014, according to our analysis of the G-finder donor database (Supplementary methods). This volume is comparable with the period 2010–2013 (excluding the US$73 million of novel funding for Ebola), but is 10% lower than total funding at its 2008–2009 peak . We also10.1136/bmjgh-2018-001363.supp8Supplementary data10.1136/bmjgh-2018-001363.supp9Supplementary data10.1136/bmjgh-2018-001363.supp10Supplementary dataGrant data obtained from the 2015 World Report and The Bill and Melinda Gates Foundation show substantial differences in donor support across countries . Accordi10.1136/bmjgh-2018-001363.supp11Supplementary dataThe 2014–2015 Ebola epidemic galvanised the international community to accelerate the development of vaccines for priority emerging infections and to strengthen clinical trial infrastructure and regulatory preparedness, so that investigational vaccines could be rapidly tested in the event of a large outbreak. At the 2017 World Economic Forum, the newly established Coalition for Epidemic Preparedness and Innovation (CEPI) announced that it had received a US$460 million investment from the governments of Germany, Japan and Norway, the Wellcome Trust and the Bill and Melinda Gates Foundation and would initially focus on the development of vaccines for Nipah virus, MERS-coronovarius and Lassa fever. In parallel, the UK government committed to investing £120 million between 2016 and 2021 on the development of new vaccines for 12 priority emerging infections (three overlapping with CEPI and nine others).Overall, this analysis showed that the public sector consistently provides the majority of funding for vaccinology research, and that HIV, TB and malaria receive over 80% of investments. However, we did not include funding for basic research, as there was no way to identify projects within this category that were relevant to vaccinology from the database we used. Further, vaccine implementation, health economics, behavioural science and vaccine policy research were not reported on in the database. Finally, the data are not specific to Africa; nevertheless, since most of the ‘neglected diseases’ included in the database are prevalent in Africa, vaccine R&D focused on these diseases should be relevant to African populations regardless of individual project location.In this review and analysis of the African vaccinology landscape, we identified a large number of sites conducting vaccinology research but found that these vary widely in size and capacity and are heterogeneously distributed across the region. Training opportunities for African vaccinologists are limited. Donor funding remains overwhelmingly focused on HIV, TB and malaria vaccine development, dwarfing investment in high-burden diseases such as respiratory infections and enteric illnesses. The pipeline of new products targeting priority diseases for Africa is extensive, but rapid development is hampered by the lack of vaccine design, manufacturing and regulatory capacity in the region. There is little investment in an implementation science agenda that would increase immunisation programme efficiency and effectiveness in different settings.These findings clearly point to specific opportunities for future donor investment in the region. In particular, offering advanced training for African vaccinologists, establishing and sustaining clinical trial and implementation research sites in underserved geographic areas, developing a multi-disciplinary approach to accelerate successful development and delivery of new vaccines against high burden pathogens beyond HIV, TB and malaria and strengthening regulatory and manufacturing capacity would contribute to the region reaching vaccine-related sustainable development goals and meeting the objectives outlined in the African Union’s Addis declaration. In order to fully benefit from donor support, sub-Saharan African countries must create the enabling environment for scientists to conduct research and identify solutions to the challenges of vaccine delivery at local level.10.1136/bmjgh-2018-001363.supp2Supplementary data10.1136/bmjgh-2018-001363.supp3Supplementary data10.1136/bmjgh-2018-001363.supp4Supplementary data


As the availability of interoperable electronic health records (iEHRs) or health information exchanges (HIEs) continues to increase, there is greater need and opportunity to assess the current evidence base on what works and what does not regarding the adoption, use, and impact of iEHRs.The purpose of this project is to assess the international evidence base on the adoption, use, and impact of iEHRs.We conducted a systematic review, searching multiple databases—MEDLINE, Embase, and the Cumulative Index to Nursing and Allied Health Literature (CINAHL)—with supplemental searches conducted in Google Scholar and grey literature sources . All searches were conducted in January and February 2017. Articles were eligible for inclusion if they were published in English, were published from 2006 to 2017, and were either an original research study or a literature review. In order to be included, articles needed to focus on iEHRs and HIEs across multiple health care settings, as well as on the impact and effectiveness of iEHR adoption and use.We included 130 articles in the synthesis , with the majority focused on the United States . The primary studies focused on a wide range of health care settings; the three most prevalent settings studied included acute care , primary care , and emergency departments . We identified 29 distinct measurement items in the 113 primary studies that were linked to 522 specific measurement outcomes. Productivity and quality were the two evaluation dimensions that received the most attention, accounting for 14 of 29 (48%) measurement items and 306 of 522 (58.6%) measurement outcomes identified. Overall, the majority of the 522 measurement outcomes were positive . We also identified 17 reviews on iEHR use and impact, 6 (35%) that focused on barriers and facilitators to adoption and implementation and 11 (65%) that focused on benefits and impacts, with the more recent reviews finding little generalizable evidence of benefit and impact.This review captures the status of an evolving and active field focused on the use and impact of iEHRs. While the overall findings suggest many positive impacts, the quality of the primary studies were not evaluated systematically. When broken down by specific measurement item, the results directed attention both to measurement outcomes that were consistently positive and others that were mostly negative or equivocal. Interoperable electronic health records (iEHRs) in Canada increasingly provide individual patients with a secure and private record of their health history and care within their health system . The iEHWhile different in architecture, iEHR solutions are analogous to health information exchange (HIE) initiatives in the United States. Health professionals in Canada, as in most high-income countries, also receive patient information across settings through other digital health solutions, such as hospital information systems and laboratory systems. Other countries have developed systems similar to Canada’s iEHRs. The common element of interest for this project is the provision of information across care settings and health professionals to improve care for patients.As the availability of iEHRs continues to increase , there iWe conducted a systematic review, documenting the key elements of the review, including search strategy, eligibility criteria, article selection process, analysis, and synthesis, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist.We developed and tested multiple search strategies that incorporated subject heading and keyword terms for “interoperable electronic health record” (including “iEHR”), “health information exchange” or “HIE”; “interoperability”; “adoption” or “use”; and “effectiveness,” “impact,” or “value.” We consulted and sought feedback on the search strategies with health informatics experts, including from academic and government agencies each focused on health informatics, and a specialist librarian who performed an abbreviated Peer Review of Electronic Search Strategies (PRESS) assessment of a preliminary MEDLINE search strategy . The heaArticles were eligible for inclusion in the review if they were published in English; published between 2006 and 2017, although, only a small number of articles published in 2017 were available at the time of our review; and were either an original research study, inclusive of both quantitative and qualitative study types, or a literature review. For inclusion in the review, articles needed to focus on iEHRs and HIEs, excluding electronic medical records, across two or more health care settings; they also needed to focus on the impact or effectiveness of iEHR or HIE adoption or use.Two reviewers (JPB and one of three research assistants) independently screened all titles and abstracts or, in the case of Google and Google Scholar search results, titles and excerpts. Discrepancies were resolved through discussion and/or review by a third reviewer (MJD). The same process was used for review of full-text articles.Data from all articles identified for inclusion were extracted independently by two reviewers (JPB and one of three research assistants) using a predeveloped data extraction template that included the year of publication, article type, approach, methodology, jurisdiction, and health care setting. Given the anticipated heterogeneity of study types and the intention to capture the breadth of iEHR evaluation activity, we prioritized evaluation relevance over quality and, therefore, did not use available quality criteria to exclude primary studies -9.For all primary studies, two reviewers (JPB and one of three research assistants) independently extracted distinct measurement items and measurement outcomes verbatim. Measurement items were then coded and recoded inductively over three iterations into thematic categories by one reviewer (JPB) in discussion with the review team. All measurement outcomes were categorized as positive, negative, or mixed or neutral by one reviewer (JPB) and reviewed by the review team. All thematic measurement item categories were classified as one of the eight evaluation dimensions of benefit—system quality, service quality, information quality, user satisfaction, use, productivity, quality, and access—based on the Infoway benefits evaluation framework see . This frWe employed a separate analysis approach for the included literature review articles, conducting a descriptive analysis of each review article that assessed the (1) main focus of the review, (2) main findings, and (3) recommendations for future research.Our search of seven data sources identified 3851 records. After deduplication; title, abstract, and excerpt screening; and full-text review, 130 articles were included in the synthesis; For the 113 primary studies, the majority employed quantitative methodologies exclusively  or in combination with qualitative methods  . The primary studies focused on a wide range of health care settings; the three most prevalent settings studied included acute care , primary care , and emergency departments . Other settings included laboratories , ambulatory care , pharmacies , public health departments , long-term care , payer or purchaser organizations , and home and community care .use or access evaluation dimensions.The primary studies focused on six general dimensions. We identified 29 distinct measurement items, representing evaluation themes, in the 113 primary studies that were linked to 522 specific measurement outcomes see . ProductOverall, the majority of the 522 measurement outcomes were positive , with the remaining measurement outcomes reported as neutral or mixed results  or negative findings . When examining each of the 29 measurement items separately, the majority  had more positive than negative measurement outcomes, with the most frequently studied measurement items having a larger proportion of positive over negative outcomes. The 5 measurement items  with more negative than positive measurement outcomes were (1) stakeholder engagement, (2) performance and reliability, (3) security and privacy, (4) overall quality of information, and (5) ease of use; 2 measurement items  had equal positive and negative measurement outcomes: (1) layout and format and (2) community-based care, public or population health, or preventive services.To provide more details on the findings, we consider each of the six dimensions separately. A total of 2 measurement items  were aligned with the service quality dimension. These included stakeholder engagement, which has mostly negative results, and training and support, which has mostly positive results. For the system quality dimension, 3 of 29 (10%) measurement items applied, including performance and reliability, security and privacy, and assessment and planning. Of these 3, only the latter had positive measurement outcomes. The information quality dimension had 6 of 29 (21%) measurement items, with the 2 most frequently measured items—data accuracy and completeness, as well as information availability—mostly positive, while the 4 less frequently measured items each revealed equivocal results. A total of 4 of 29 measurement items (14%) aligned with the user satisfaction dimension, with 1 showing mostly positive measurement outcomes—perceived usefulness or value and trust or confidence in the system—while the remaining 3 measurement items showed measurement outcomes that were either equivocal or negative. The productivity and quality dimensions each had 7 of 29 (24%) measurement items. All 7 measurement items for productivity had positive measurement outcomes, while 6 of the 7 measurement items for quality also had positive measurement outcomes. As noted above, the productivity and quality dimensions have received the majority of focus for measurement and have yielded mostly positive outcomes.When looking at the results from a setting-specific perspective, where sufficient volumes existed, there were some notable differences from the overall results. Acute care settings were assessed by 59 out of 113 studies (52.2%) and represented 270 out of 522 (51.7%) distinct measurement outcomes. Of these, there was focus on each of the six dimensions, with considerable attention on service quality, system quality, and some aspects of productivity. Primary care settings were assessed by 44 out of 113 (38.9%) studies and represented 183 out of 522 (35.1%) distinct measurement outcomes that covered most of the six dimensions, with attention directed predominantly to productivity measures while service, system, and information quality received less focus. Emergency department settings were assessed by 34 out of 133 (30.1%) studies and represented 112 out of 522 (21.5%) distinct measurement outcomes. There was a lack of measurement outcomes for most of the six dimensions, with the exception of one type of productivity item and one type of quality item.We identified 17 reviews on iEHR use or impact see . Of thesThe more recent reviews  found little generalizable evidence of benefit or impact. The reviews highlight some less methodologically robust research that focused on resource use and perception of outcomes; these reviews found that iEHRs or HIEs increase productivity  and Consideration of the review results against the benefits evaluation framework provides a lens to assess where evaluative work has been targeted and where there may be gaps where future evaluative efforts should focus. A total of 57.1% 298/522) of all measurement outcomes were positive. Quality of care  and productivity  were the dimensions with the highest percentage of positive measurement outcomes. Prominent themes in the quality of care category were around coordination of care and clinical decision support. For the productivity dimension, efficiency in clinical processes, time savings, and costs were the prominent themes. The left side of the benefits evaluation framework see , includi98/522 ofOverall, our findings suggest that positive results tend to attract more evaluation, which may be explained by efforts to use progressively more rigorous methodologies, but also may reflect inefficient allocation of limited evaluation resources that could be applied to less-studied aspects of iEHRs and HIEs. The findings also point to several broader evaluation dimensions and several specific measurement items that require more attention going forward, including use and access, for which we did not identify any measurement items, and the service quality, system quality, user satisfaction, and information quality dimensions, which had fewer measurement items than productivity and quality dimensions. It is important to note that we did not perform quality appraisals of the primary studies; therefore, these review results need to be interpreted cautiously, which is a general theme of the reviews we assessed. While promising work exists, there is a clear need for more rigorous and comprehensive evaluation, with priority to support methodologies that can produce high-quality evidence. Overall, the review findings highlight the need to support more robust and comprehensive evaluative work across Canada on the impact of connected health information, covering more disease domains, health care settings, and populations.This review identified a large number of studies that address the use and impact of connected health information through iEHRs and HIEs. The majority of the studies have been published within the last 5 years, which reflects a developing rather than mature evidence base. Given that the bulk of this evidence base is current, concerns regarding potential temporal biases that might not accurately reflect the quickly evolving developments of iEHRs and HIEs should be limited. However, our analysis did not assess whether systematic temporal differences in dimension-specific evaluations were present . Consistent with the developing nature of the evidence base, it is notable that a sizable proportion of the articles identified in this review  came from grey literature sources, highlighting broader contributions to iEHR and HIE evaluation. Beyond the United States, which is the focus of the vast majority of the primary studies identified, we found few other primary studies globally, with Israel a distant second in terms of evaluative work on iEHRs and HIEs, followed by Canada, Finland, and the United Kingdom. Given fundamental differences in the organization of health systems and services and the dearth of evaluations in non-US settings, there are limits on how generalizable these assessments across jurisdictions will be.In conclusion, this review captures evaluative work from an evolving and active field focused on the use and impact of iEHRs and HIEs. While the overall findings suggest many positive impacts of iEHRs and HIEs, the quality of the primary studies were not evaluated systematically. When broken down by specific measurement items, some measurement outcomes consistently presented positive outcomes, while others were mostly negative or equivocal, highlighting areas for more attention. Setting-specific findings provide further insight on where more evaluative attention is needed.


Physicians and nurses are core staff who are the first points of contact in care provision to patients. We aimed to examine physicians’ and nurses’ perceptions of patient safety in a case hospital by administering the Chinese Safety Attitude Questionnaire (CSAQ), developed from the Taiwan Joint Commission on Hospital Accreditation, in order to provide the patients with a safe environment and excellent medical service.An intra-organizational online survey was conducted. Confirmatory factor analysis (CFA) was first performed on the assessment of the measures for patient safety culture and seven dimensions with 33 items were identified. Pearson correlation analysis was subsequently used to examine the strength and direction of the relationships between seven dimensions of patient safety culture.A total of 800 questionnaires were issued and 405 valid questionnaires were collected, the effective response rate being 50.6%. The findings highlighted that safety climate (SC) was positively and significantly related to teamwork climate (TC) and perception of management (PM), whereas stress recognition (SR) was negatively related to burnout (BUR).Patient safety culture in healthcare organizations has been considered a critical issue for improving the quality of healthcare. This study further focused on the contribution of a better patient safety culture for healthcare organizations in Taiwan. Nowadays, the health care business has been increasingly competitive, and establishing a patient-oriented culture has become important for improving the safety and rights of patients in health care organizations in order to achieve sustainable development. It has proven that patient safety culture is a significant key influencing factor of improving physician-patient relationship and the service quality that patients receive . A positPatient safety culture was presented by the European Network for Patient Safety (EUNetPaS) in 2006 as “An integrated pattern of individual and organizational behavior, based upon shared beliefs and values that continuously seeks to minimize patient harm, which may result from the processes of care delivery” . PatientHospital staff are on the front line of efforts to provide optimal care to patients. Lee et al.  particulIn general, the SAQ contains 30 items, grounded into six dimensions: teamwork climate (TC), safety climate (SC), job satisfaction (JS), stress recognition (SR), perception of management (PM), and working conditions (WC). TC describes relationships and cooperation amongst staff members; SC refers to the perception of organizational commitment to patient safety; JS refers to positive opinions about the work experience; SR is related to the stress factors that might influence work performance; PM refers to management or administration approval; finally, WC is based on the perceived work-environment quality and logistical support regarding staffing and equipment , 21, 22.In 2014, the TJCHA modified the questionnaire and integrated burnout (BUR) and work-life balance (WB), which contain nine and seven question items, respectively. BUR describes a loss of passion for work, emotional exhaustion and feelings of depersonalization  whereas Reviewing patient safety culture on a regular basis enables hospital management to realize and monitor the progress of patient safety within the healthcare processes. Additionally, patient safety culture should be assessed periodically and contextually, because elements affecting patient safety culture might be different in different countries.Consequently, the current research aimed to investigate the perceptions of hospital staff toward patient safety culture by using the CSAQ in order to establish a better patient safety culture for hospitals in Taiwan. Following this introduction, the second section of this paper illustrates details of the methods used in the empirical investigation; the third section presents the research results; implications are discussed in section four; and finally, the findings are concluded.An online-based survey design was conducted to investigate patent safety culture in a case hospital. The hospital contains more than 35 divisions, has 1500 total staff members, and provides clinical education and training to health professionals in Taichung City, Taiwan. An intra-organizational online survey with simple random sampling was conducted at the hospital; a total of 800 questionnaires were issued in 2014, of which 405 valid questionnaires were collected.The surveys were sent to physicians and nurses between November 3 and November 24, 2014 in the case hospital. Approximately two weeks later, a reminder e-mail was sent to the subjects.This study was approved by the Institutional Review Board (IRB) of the general hospital . Hospital staff completed an online-based survey. Consent was implied by the respondents’ participation in the study.As shown in Analysis of the sample profile showed that most respondents were female (86.7%), their age ranging from 21 to 50 years (93.6%), and were educated at the bachelor’s level (92.1%). Over one-half of respondents had more than five years of relevant work experience for the hospital.The Kolmogorov–Smirnov test was first used for the estimation of normality of data. All variables followed the normal distribution at a significance level of .05. Additionally, in order to ensure that no offending estimate affected the loading of the items, the offending estimates were initially calculated before conducting confirmatory factor analysis (CFA). The common forms of offending estimates that appear in measurement were negative error variance, standardized loading of the manifest variables exceeding one, and high standard errors for the estimate coefficients. Seven dimensions with 39 items were estimated to confirm the structure of the data.As shown in CFA was then used to verify CASQ. A model re-specification was applied by confirming measurement items. Items with a weak factor loading (< .70) were removed from the original scale because of an elevated measurement error. In this stage, the six items  shown in All the Cronbach’s alpha coefficients are range between .88 (TC and PM) and .94 (JS). Convergent validity and discriminant validity were respectively assessed to confirm the dimensionality of patient safety culture and that the dimensions are distinct from each other. As presented in Additionally, discriminant validity was demonstrated by comparing the AVE of each measure with the square of correlations between dimensions. As shown in 2), goodness of fit index (GFI), adjusted goodness of fit index (AGFI), root mean square residual (RMR), and root mean squared error of approximation (RMSEA); five incremental fit indices: normal fit index (NFI), Tucker Lewis index (TLI), relative fit index (RFI), comparative fit index (CFI), and incremental fit index (IFI); and two parsimonious fit indices: parsimony normed fit index (PNFI) and parsimony goodness of fit index (PGFI). The CFA results indicate that all fit indices’ values are within their threshold values, indicating a satisfactory goodness of fit for the measurement model to the data. For example, acceptable absolute fit indices , incremental fit indices , and parsimonious fit indices (PNFI = .85 and PGFI = .76) are demonstrated.The CFA results required the removal of six that had the estimated loadings lower than .70. Eventually, seven dimensions with 33 items were assessed to confirm the dimensionality of the scale. Furthermore, overall model fit was assessed using five absolute fit indices: chi-square  have been completely observed by the authors.