Online journal of public health informatics最新文献

Background: Reducing maternal and infant mortality rates remains challenging. Illiteracy, lack of reliable information, long distances to health centers continue to limit access to quality maternal healthcare in Uganda. Mobile health technologies could be promising affordable strategies for enhancing access to maternal health services. However, there is lack of studies assessing the experiences of illiterate rural pregnant women regarding these technologies. Objective: To explore how illiterate pregnant women perceive a maternal health mobile application composed of tailored video and audio messages, appointment reminders and calling function. Methods: We purposively sampled illiterate pregnant women initiating antenatal care at Mbarara Regional Referral Hospital. We carried out three focus group discussions with 14 women to elicit information on perceptions of the proposed mobile phone based multimedia application. We used STATA 13 to describe study participants and their preferences. Results: Pregnant women anticipated that intervention would enhance maternal health by reminding them to attend antenatal appointments, enabling transport cost and time saving, providing tailored information that is easy to understand, and recall. However, financial constraints and phone sharing would limit the functionality. Conclusion: Mhealth application may provide acceptable and affordable alternative approaches to providing maternal health services, especially in settings where face-to-face approaches are challenging.

Objective: To support the End TB strategy with an informatics system that integrates genomic data and the geographic information system (GIS) of Mycobacterium tuberculosis (MTB) clinical isolates. We aim to develop a system prototype for implementing genomic data to support multiple drug-resistant tuberculosis (MDR-TB) control.

Methods: A 12-step data value chain was applied to describe the information flow within the system. A prototyping-oriented system development method was utilized to test the feasibility of certain technical aspects of a system, and as specification tools to determine user requirements. A simulated dataset was entered as input for initial system testing.

Results: System prototype, namely Integrated MOL Outbreak detection and Joint investigation (iMoji), was established. The data entry modules consisted of (1) patient registration, (2) sample registration, (3) laboratory data entry and data analysis, and (4) verification and approval of the analyzed data. The initial system test demonstrated connectivity among modules without error. The system was able to report integrated genomic data and GIS information of MDR-TB for clustering analysis.

Conclusion: iMoji provides an interactive model for determining molecular epidemiological links of MDR-TB and corresponding spatial information to guide public health interventions for tuberculosis control.

This commentary explores the potential and challenges of developing syndromic surveillance systems with the ability to more rapidly detect epidemics of addiction, poverty, housing instability, food insecurity, social isolation and other social determinants of health (SDoH). Epidemiologists tracking SDoH heavily rely on expensive government surveys released annually, delaying for months if not years the timely detection of social epidemics, defined as sudden, rapid or unexpected changes in social determinants of population health. Conversely, infectious disease syndromic surveillance is an effective early warning tool for epidemic diseases using various types of non-traditional epidemiological data from emergency room chief complaints to search query data. Based on such experience, novel social syndromic surveillance systems for early detection of social epidemics with health implications are not only possible but necessary. Challenges to their widespread implementation include incorporating disparate proprietary data sources and database integration. Significantly more resources are critically needed to address these barriers to allow for accessing, integrating and rapidly analyzing appropriate data streams to make syndromic surveillance for social determinants of health widely available to public health professionals.

Subjective well-being (SWB) is an individual's judgment about their overall well-being. Research has shown that activities that elevate people's sense of SWB have a significant effect on their overall health. There are two dimensions of SWB: Affective and Cognitive dimensions. However, studies on SWB usually focus more on one dimension, ignoring the other dimension. Also, most existing studies on SWB focused on individuals from Western cultures. Research has shown that the influence of personality on subjective well-being is moderated by culture. Thus, to advance research in personalizing persuasive health interventions, this study focuses on Africans (n=732). Specifically, we investigate the relationship between the Big-Five personality traits and both dimensions of SWB using the constructs: Happiness, Satisfaction with Life, Social, Psychological and Emotional well-being. Our results reveal that health informatics designers who design persuasive technologies to promote SWB would need to tailor designs along personality traits and SWB constructs. Accordingly, for users high in Agreeableness, the design should be focus on promoting their feelings of Happiness and Social Well-being. For users who exhibit Neuroticism, designers should focus on designing to promote Psychological well-being and Emotional well-being. Based on our findings, we offer guidelines for tailoring persuasive health interventions to promote individuals' SWB based on their personality. We thus highlight areas personal health informatics design can benefit.

Ccs concepts: • Human-centered computing → Personalization → HCI design and evaluation methods → User models.

Social media allows for the exploration of online discussions of health issues outside of traditional health spaces. Twitter is one of the largest social media platforms that allows users to post short comments (i.e., tweets). The unrestricted access to opinions and a large user base makes Twitter a major source for collection and quick dissemination of some health information. Health organizations, individuals, news organizations, businesses, and a host of other entities discuss health issues on Twitter. However, the enormous number of tweets presents challenges to those who seek to improve their knowledge of health issues. For instance, it is difficult to understand the overall sentiment on a health issue or the central message of the discourse. For Twitter to be an effective tool for health promotion, stakeholders need to be able to understand, analyze, and appraise health information and discussions on this platform. The purpose of this paper is to examine how a visual analytic study can provide insight into a variety of health issues on Twitter. Visual analytics enhances the understanding of data by combining computational models with interactive visualizations. Our study demonstrates how machine learning techniques and visualizations can be used to analyze and understand discussions of health issues on Twitter. In this paper, we report on the process of data collection, analysis of data, and representation of results. We present our findings and discuss the implications of this work to support the use of Twitter for health promotion.

Background During the 2009 H1N1 influenza pandemic (pH1N1), the proportion of outpatient visits to emergency departments, clinics and hospitals became elevated especially during the early months of the pandemic due to surges in sick, 'worried well' or returning patients seeking care. We determined the prevalence of return visits to a multispecialty clinic during the 2009 H1N1 influenza pandemic and identify subgroups at risk for return visits using model-based recursive partitioning technique. Methods This study was a retrospective analysis of ILI-related medical care visits to multispecialty clinic in Houston, Texas obtained as part of the Houston Health Department Influenza Sentinel Surveillance Project (ISSP) during the 2009 H1N1 pandemic influenza (April 2009 - March 2010). The data comprised of 2680 individuals who made a total of 2960 clinic visits. Return visit was defined as any visit following the index visit after the wash-out phase prior to the study period. We applied nominal logistic regression and recursive partitioning models to determine the independent predictors and the response probabilities of return visits. The sensitivity and specificity of the outcomes probabilities were determined using receiver operating characteristic (ROC) curve. Results Overall, 4.56% (Prob. 0.0%-17.5%) of the cohort had return visits with significant variations observed attributed to age group (76.0%), type of vaccine received by patients (18.4%) and Influenza A (pH1N1) test result (5.6%). Patients in age group 0-4 years were 9 times (aOR: 8.77, 95%CI: 3.39-29.95, p<0.0001) more likely than those who were 50⁺ years to have return visits. Similarly, patients who received either seasonal flu (aOR: 1.59, 95% CI 1.01-2.50, p=0.047) or pH1N1 (aOR: 1.74, 95%CI: 1.09-2.75, p=0.022) vaccines were about twice more likely to have return visits compared to those with no vaccination history. Model-based recursive partitioning yielded 19 splits with patients in subgroup I (patients of age group 0-4 years, who tested positive for pH1N1, and received both seasonal flu and pH1N1 vaccines) having the highest risk of return visits (Prob.=17.5%). The area under the curve (AUC) for both return and non-return visits was 72.9%, indicating a fairly accurate classification of the two groups. Conclusions Return visits in our cohort were more prevalent among children and young adults, and those that received either seasonal flu or pH1N1 or both vaccines. Understanding the dynamics in care-seeking behavior during pandemic would assist policymakers with appropriate resource allocation, and in the design of initiatives aimed at mitigating surges and recurrent utilization of the healthcare system.

This concept article introduces a transformative vision to reduce the population burden of chronic disease by focusing on data integration, analytics, implementation and community engagement. Known as PHOENIX (The Population Health OutcomEs aNd Information EXchange), the approach leverages a state level health information exchange and multiple other resources to facilitate the integration of clinical and social determinants of health data with a goal of achieving true population health monitoring and management. After reviewing historical context, we describe how multilevel and multimodal data can be used to facilitate core public health services, before discussing the controversies and challenges that lie ahead.

Introduction The transmission of test results by laboratories and their receipt by health facilities are common tasks in the processing of medical information. Managing the flow of information generated by these tasks remains a challenge for these centers. We describe a new system that will allow for electronic management of the transmission of results. Materials and methods The information system implemented is a client-server system consisting three main components: the server installed in the laboratory, the client distributed in the Anti-Tuberculosis Center and the communication channel represented by a Virtual Network. The exchange protocol is based on the HL7 standard that used messages of type ORU_R01. Results During the two months of implementation of this electronic result transmission system between the National Tuberculosis Reference Center in Abidjan and the Anti-Tuberculosis Center in Adzopé, which is about 110 kilometers away, twenty laboratory results were transmitted as soon as they left the laboratory, an improvement from the previous long turn-around-time of about 1 month. The minimalist interface and ease of use of the system have allowed it to be adopted by users. Discussion The use of the HL7 protocol for electronic notifications has proven its effectiveness in making transmissions of results instantaneous. Our system specifically addresses the problems related to efficient transmission of results; reduction of transmission time, information loss attributed to the use of paper, and transport costs incurred when transmitting results from remote sites. This system representing the 1rst version use a local codification that limits it to an interoperability with other environment that use a different code system. The use of a code system such as LOINC would allow full interoperability between different information systems.

Objectives Information and communication technology (ICT) tools are increasingly important for clinical care and international research. Many technologies would be particularly useful for healthcare workers in resource-limited settings; however, these individuals are the least likely to utilize ICT tools due tolack of knowledge and skills necessary to use them. Our program aimed to train researchers in low-resource settings on using ICT tools and to understand how different didactic modalities build knowledge and skills in this area. Methods We conducted a tiered, blended learning program for researchers in Kenya on three areas of ICT: geographic information systems, data management, and communication tools. Each course included three tiers: online courses, skills workshops, and mentored projects. Concurrently, a training of trainers course was taught to ensure sustainable ongoing training. A mixed qualitative and quantitative survey was conducted at the end of each training to assess knowledge and skill acquisition. Results Course elements that incorporated local examples and hands-on skill building activities were most valuable. Discussion boards were sometimes distracting, depending on multiple factors. Mentored projects were most useful when there were clear expectations, pre-existing projects, and clear timelines. Discussion Training in the use of ICT tools is highly valued among researchers in low-income settings, particularly when it includes hands-on skill-building and local examples. Our students demonstrated acquisition of new skills and felt these skills to be valuable in their workplaces. Conclusions Further training in ICT skills should be considered in other low-resource settings using our program as a foundational model.

This qualitative review explores how established citizen science models can inform and support meaningful engagement of public in health research in Australia. In particular, with the growth in participatory health research approaches and increasing consumer participation in contributing to this research through digital technologies, there are gaps in our understanding of best practice in health and biomedical citizen science research to address these paradigm shifts. Notable gaps are how we might more clearly define the parameters of such research and which citizen science models might best support digitally-enabled participation falling within these. Further work in this area is expected to lead to how established citizen science methods may help improve the quality of and the translation of public engagement in health research.