Background The reduction and control over neonatal, infant, and maternal mortality is a collective mission of the World Health Organization under United Nations. Methods This article summarizes the automation of verbal autopsy reporting for neonatal, infant, and maternal mortality with primary focus on user-centered design for technologically illiterate workforce with minimum available resources. The diminution in neonatal, infant, and maternal deaths is not possible until grassroot level quality data are available for mortality. The estimated data are less effective for developing countries like Pakistan because it has heterogeneous demographic pockets with respect to mortality causes. The Neonatal, Infant, and Maternal Death E-surveillance System is a project in which a real-time reporting system is innovated that is useful in detecting the causes of mortality and effective in adopting appropriate countermeasure policies. In a pilot study, the system was implemented initially in nine districts of Punjab, Pakistan. The initial system was refined after getting detailed feedback from district management staff including Lady Health Workers and Lady Health Supervisors. The refined surveillance system was finally implemented in all 36 districts of Punjab, Pakistan. Results The results exhibited 31% improvement in infant data collection and 6% improvement in maternal data collection regarding mortality. Conclusion This research will be helpful in achieving the milestone of gathering real-time mortality data from grassroot level using user-centered design methodology.
Objective Verbal autopsy is a technique used to collect information about a decedent from his/her family members using questionnaires, conducting interviews, making observations, and sampling. In substantial parts of the world, particularly in Africa and Asia, many deaths are unrecorded. In 2017, globally pregnant women were dying daily around 810 and 295,000 in a year because of pregnancy-related problems, pointed out by World Health Organization. Identifying the cause of a death is a complex process which requires in-depth medical knowledge and practical experience. Generally, medical practitioners possess different knowledge levels, set of abilities, and problem-solving skills. Additionally, the medical negligence plays a significant part in further worsening the situation. Accurate identification of the cause of death can help a government to take strategic measures to focus on, particularly increasing the death rate in a specific region. Methods This research provides a solution by introducing a semantic-based verbal autopsy framework for maternal death (SVAF-MD) to identify the cause of death. The proposed framework consists of four main components as follows: (1) clinical practice guidelines, (2) knowledge collection, (3) knowledge modeling, and (4) knowledge codification. Maternal ontology for the framework is developed using Protégé knowledge editor. Resource description framework application programming interface (API) for PHP (RAP) is used as a Semantic Web toolkit along with Simple Protocol and RDF Query Language (SPARQL) is used for querying with ontology to retrieve data. Results The results show that 92% of maternal causes of deaths assigned using SVAF-MD correctly matched manual reports already prepared by gynecologists. Conclusion SVAF-MD, a semantic-based framework for the verbal autopsy of maternal deaths, assigns the cause of death with minimum involvement of medical practitioners. This research helps the government to ease down the verbal autopsy process, overcome the delays in reporting, and facilitate in terms of accurate results to devise the policies to reduce the maternal mortality.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.