Sankofa pediatric HIV disclosure intervention cyber data management: building capacity in a resource-limited setting and ensuring data quality.

Prevalence of pediatric HIV disclosure is low in resource-limited settings. Innovative, culturally sensitive, and patient-centered disclosure approaches are needed. Conducting such studies in resource-limited settings is not trivial considering the challenges of capturing, cleaning, and storing clinical research data. To overcome some of these challenges, the Sankofa pediatric disclosure intervention adopted an interactive cyber infrastructure for data capture and analysis. The Sankofa Project database system is built on the HUBzero cyber infrastructure ( https://hubzero.org ), an open source software platform. The hub database components support: (1) data management - the "databases" component creates, configures, and manages database access, backup, repositories, applications, and access control; (2) data collection - the "forms" component is used to build customized web case report forms that incorporate common data elements and include tailored form submit processing to handle error checking, data validation, and data linkage as the data are stored to the database; and (3) data exploration - the "dataviewer" component provides powerful methods for users to view, search, sort, navigate, explore, map, graph, visualize, aggregate, drill-down, compute, and export data from the database. The Sankofa cyber data management tool supports a user-friendly, secure, and systematic collection of all data. We have screened more than 400 child-caregiver dyads and enrolled nearly 300 dyads, with tens of thousands of data elements. The dataviews have successfully supported all data exploration and analysis needs of the Sankofa Project. Moreover, the ability of the sites to query and view data summaries has proven to be an incentive for collecting complete and accurate data. The data system has all the desirable attributes of an electronic data capture tool. It also provides an added advantage of building data management capacity in resource-limited settings due to its innovative data query and summary views and availability of real-time support by the data management team.

Health Information Crisis during COVID-19 in the South Asian Region: Challenges, Experiences, Resilience, and Collaborative Efforts.

The fight against the COVID-19 pandemic is as much an information war as it is a medical war. Members from South Asia and the Asia Pacific countries share their experiences and challenges faced with collaborative responses for the ASIS&T Special Chapter funded project on "Dealing with COVID-19 and saving people's lives in South Asia (SA) areas & beyond-A Health Informatics Promotion Project" awarded to the South Asia Chapter in 2021. The panel discusses the challenges faced within the context of geopolitical, socio-economic, religious, and cultural conditions prevalent within their countries. In the first 40 minutes, panel members narrate their own experiences by sharing their personal stories about this collaborative project and share the challenges of content creation and promotion from within the context of their respective countries. The next 30 minutes will be facilitated by the panel chair inviting a discussion between panel members and the audience to engage and come up with innovative ideas, discuss challenges in creating multilingual content and suggestions for improving the project outcome as well as shed light on initiating future health informatics project in similar regions. The last 20 minutes will culminate with the summarization of these collaborative experiences.

Implementation of an individual patient prospective database of hospital births in Sri Lanka and its use for improving quality of care.

OBJECTIVES: This study was aimed at piloting a prospective individual patient database on hospital deliveries in Colombo, Sri Lanka, and at exploring its use for developing recommendations for improving quality of care (QoC). DESIGN: Observational study. SETTING: De Soysa Maternity Hospital, the largest referral hospital for maternity care in Sri Lanka. DATA COLLECTION AND ANALYSIS: From July 2015 to June 2017, 150 variables were collected for each delivery using a standardised form and entered into a database. Data were analysed every 8 months, and the results made available to local staff. Outcomes of the study included: technical problems; data completeness; data accuracy; key database findings; and use of data. RESULTS: 7504 deliveries were recorded. No technical problem was reported. Data completeness exceeded that of other existing hospital recording systems. Less than 1% data were missing for maternal variables and less than 3% for newborn variables. Mistakes in data collection and entry occurred in 0.01% and 0.09% of maternal and newborn data, respectively. Key QoC indicators identified in comparison with international standards were: relatively low maternal mortality (0.053%); relatively high maternal near-miss cases (3.4%); high rate of induction of labour (24.6%), caesarean section (30.0%) and episiotomy (56.1%); relatively high rate of preterm births (9.4%); low birthweight rate (16.5%); stillbirth (0.97%); and of total deaths in newborn (1.98%). Based on key indicators identified, a list of recommendations was developed, including the use checklists to standardise case management, training, clinical audits and more information for patients. A list of lessons learnt with the implementation of the data collection system was also drawn. CONCLUSIONS: The study shows that the implemented system of data collection can produce a large quantity of reliable information. Most importantly, this experience provides an example on how database findings can be used for discussing hospital practices, identifying gaps and to agree on recommendations for improving QoC.

Motor neuron morphology estimation for its classification in the Drosophila brain.

Type-specific dendritic arborization patterns dictate synaptic connectivity and are fundamental determinants of neuronal function. We exploit the morphological stereotypy and relative simplicity of the Drosophila nervous system to model the diverse dendritic morphologies of individual motor neurons (MNs) to understand underlying principles of synaptic connectivity in a motor circuit. The genetic tractability of Drosophila allows us to label single MNs with green fluorescent protein (GFP) and serially reconstruct identifiable MNs in 3D with confocal microscopy. Our computational approach aims at the robust segmentation of the MN volumes and the simultaneous partitioning into their compartments, namely the soma, axon and dendrites. We use the idea of co-segmentation, where every image along the z-axis (depth) is clustered using information from 'neighboring' depths. As appearance we use a 3D extension of Haar features and for the shape we define an implicit representation of the segmentation domain.