Background Self-diagnosis is the process of diagnosing or identifying a medical condition in oneself. Artificially intelligent digital platforms for self-diagnosis are becoming widely available and are used by the general public; however, little is known about the body of knowledge surrounding this technology. Objective The objectives of this scoping review were to (1) systematically map the extent and nature of the literature and topic areas pertaining to digital platforms that use computerized algorithms to provide users with a list of potential diagnoses and (2) identify key knowledge gaps. Methods The following databases were searched: PubMed (Medline), Scopus, Association for Computing Machinery Digital Library, Institute of Electrical and Electronics Engineers, Google Scholar, Open Grey, and ProQuest Dissertations and Theses. The search strategy was developed and refined with the assistance of a librarian and consisted of 3 main concepts: (1) self-diagnosis; (2) digital platforms; and (3) public or patients. The search generated 2536 articles from which 217 were duplicates. Following the Tricco et al 2018 checklist, 2 researchers screened the titles and abstracts (n=2316) and full texts (n=104), independently. A total of 19 articles were included for review, and data were retrieved following a data-charting form that was pretested by the research team. Results The included articles were mainly conducted in the United States (n=10) or the United Kingdom (n=4). Among the articles, topic areas included accuracy or correspondence with a doctor’s diagnosis (n=6), commentaries (n=2), regulation (n=3), sociological (n=2), user experience (n=2), theoretical (n=1), privacy and security (n=1), ethical (n=1), and design (n=1). Individuals who do not have access to health care and perceive to have a stigmatizing condition are more likely to use this technology. The accuracy of this technology varied substantially based on the disease examined and platform used. Women and those with higher education were more likely to choose the right diagnosis out of the potential list of diagnoses. Regulation of this technology is lacking in most parts of the world; however, they are currently under development. Conclusions There are prominent research gaps in the literature surrounding the use of artificially intelligent self-diagnosing digital platforms. Given the variety of digital platforms and the wide array of diseases they cover, measuring accuracy is cumbersome. More research is needed to understand the user experience and inform regulations.
Background: So far few studies were done to determine predictors of neonatal mortality in Ethiopia. This study was aimed to provide information on the incidence and risk factors of neonatal survival in Eastern Ethiopia from September, 2007 to August, 2012. Methods: The study uses data extracted from Kersa Health and Demographic Surveillance (Kersa HDSS) System database, which is located in the Oromiya Regional State, Eastern Ethiopia. The surveillance system is an open cohort, which was established in 2007. Data extraction includes all live births recorded in the system. The main outcome variable was the occurrence of death within 28 days after birth (neonatal death). The survival time was calculated in days using the time interval between the date of birth and date of event (death). Kaplan-Meier model and Cox-proportional hazard techniques were used to identify predictors of neonatal death. Results: The overall, Early and Late Neonatal Mortality Rate was 28.37, 19.55 and 8.82 per 1000 live births, respectively. A Neonatal Mortality Incidence Rate was 1 per 1000 (95% CI, 0.87-1.15) person days. Risk factors of neonatal survival include birth type (HR=5.40; 95% CI, 3.64-8.02), preterm birth (HR=11.17; 95% CI, 7.17-17.40), and previous infant sibling born (died HR=2.15; 95% CI, 1.39-3.33: no previous birth HR=1.78; 95% CI, 1.17-2.72). Conclusion: A significantly high level of neonatal mortality incidence rate was observed, which majority of the neonatal deaths were found to occur at early neonatal period. Therefore, efforts needs to be exerted in addressing the risk factors identified as predictors of neonatal mortality.
The coronavirus disease (COVID-19) pandemic resulted in major disruptions to the food service industry and regulatory food inspections. The objective of this study was to conduct an interrupted time series analysis to investigate the impact of the COVID-19 pandemic on food safety inspection trends in Toronto, Canada. Inspection data for restaurants and take-out establishments were obtained from 2017 to 2022 and ordered as a weekly time series. Bayesian segmented regression was conducted to evaluate the impact of the pandemic on weekly infraction and inspection pass rates. On average, a 0.31-point lower weekly infraction rate (95% credible interval [CI]: 0.23, 0.40) and a 2.0% higher probability of passing inspections (95% CI: 1.1%, 3.0%) were predicted in the pandemic period compared to pre-pandemic. Models predicted lower infraction rates and higher pass rates immediately following the pandemic that were regressing back toward pre-pandemic levels in 2022. Seasonal effects were also identified, with infraction rates highest in April and pass rates lowest in August. The COVID-19 pandemic resulted in an initial positive effect on food safety outcomes in restaurants and take-out food establishments in Toronto, but this effect appears to be temporary. Additional research is needed on seasonal and long-term inspection trends post-pandemic.
Background Collaborative research is being increasingly implemented in Africa to study health-related issues, for example, the lack of evidence on disease burden, in particular for the presumptive high load of foodborne diseases. The FOCAL (Foodborne disease epidemiology, surveillance, and control in African LMIC) Project is a multi-partner study that includes a population survey to estimate the foodborne disease burden in four African low- and middle-income countries (LMICs). Our multi-partner study team had members from seven countries, all of whom contributed to the project from the grant application stage, and who play(ed) specific roles in designing and implementing the population survey. Main text In this paper, we applied Larkan et al.’s framework for successful research partnerships in global health to self-evaluate our project’s collaboration, management, and implementation process. Our partnership formation considered the interplay and balance between operations and relations. Using Larkan et al.’s seven core concepts (i.e., focus, values, equity, benefit, communication, leadership, and resolution), we reviewed the process stated above in an African context. Conclusion Through our current partnership and research implementing a population survey to study disease burden in four African LMICs, we observed that successful partnerships need to consider these core concepts explicitly, apply the essential leadership attributes, perform assessment of external contexts before designing the research, and expect differences in work culture. While some of these experiences are common to research projects in general, the other best practices and challenges we discussed can help inform future foodborne disease burden work in Africa.
Low and middle-income countries, in particular from Africa, bear the highest burden of foodborne disease (FBD). However, because research and disease surveillance data from Africa are limited, previous burden estimates are subject to uncertainty. The main challenge to estimating burden of FBD in Africa is lack of data, where factors ranging from lack of capacity to lack of political commitment, and a focus on priority diseases, limit existing surveillance systems. To address this, we are working with Ethiopia, Mozambique, Nigeria, and Tanzania, to estimate the burden of, and strengthen surveillance systems for, FBD in Africa. We are conducting a population survey (to estimate incidence and distribution of diarrhea in the community), a systematic literature review (to estimate proportions of diarrheal disease caused by different agents), and an active review of available FBD reports (to estimate the extent of under-reporting in existing surveillance). Together, these findings will provide more accurate estimates of the burden of FBD for African countries. Lessons from this large-scale project can be extrapolated to other countries and regions where the burden is high but data are scarce. We highlight applying leadership attributes, including delegation of duties, setting milestones, regular meetings, transparency, and risk mitigation plans. The leading role of experts in this project helps to reduce hurdles. We have also adapted existing data collection tools for use across our diverse African study populations. We are engaging stakeholders who will use our research outputs, by involving them at all stages of the project. This integrated Knowledge Translation approach is translatable to other settings. These studies are part of FOCAL (Foodborne Disease Epidemiology, Surveillance, and Control in African LMIC), a multi-partner, multi-study project co-funded by the Bill and Melinda Gates Foundation and the United Kingdom's Department for International Development.
BACKGROUND Self-diagnosis is the process of diagnosing or identifying a medical condition in oneself. Artificially intelligent digital platforms for self-diagnosis are becoming widely available and are used by the general public; however, little is known about the body of knowledge surrounding this technology. OBJECTIVE The objectives of this scoping review were to (1) systematically map the extent and nature of the literature and topic areas pertaining to digital platforms that use computerized algorithms to provide users with a list of potential diagnoses and (2) identify key knowledge gaps. METHODS The following databases were searched: PubMed (Medline), Scopus, Association for Computing Machinery Digital Library, Institute of Electrical and Electronics Engineers, Google Scholar, Open Grey, and ProQuest Dissertations and Theses. The search strategy was developed and refined with the assistance of a librarian and consisted of 3 main concepts: (1) self-diagnosis; (2) digital platforms; and (3) public or patients. The search generated 2536 articles from which 217 were duplicates. Following the Tricco et al 2018 checklist, 2 researchers screened the titles and abstracts (n=2316) and full texts (n=104), independently. A total of 19 articles were included for review, and data were retrieved following a data-charting form that was pretested by the research team. RESULTS The included articles were mainly conducted in the United States (n=10) or the United Kingdom (n=4). Among the articles, topic areas included accuracy or correspondence with a doctor’s diagnosis (n=6), commentaries (n=2), regulation (n=3), sociological (n=2), user experience (n=2), theoretical (n=1), privacy and security (n=1), ethical (n=1), and design (n=1). Individuals who do not have access to health care and perceive to have a stigmatizing condition are more likely to use this technology. The accuracy of this technology varied substantially based on the disease examined and platform used. Women and those with higher education were more likely to choose the right diagnosis out of the potential list of diagnoses. Regulation of this technology is lacking in most parts of the world; however, they are currently under development. CONCLUSIONS There are prominent research gaps in the literature surrounding the use of artificially intelligent self-diagnosing digital platforms. Given the variety of digital platforms and the wide array of diseases they cover, measuring accuracy is cumbersome. More research is needed to understand the user experience and inform regulations.
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