A geospatial platform to support visualization, analysis, and prediction of tuberculosis notification in space and time

Abstract

BackgroundTuberculosis has caused significant public health and economic burdens in Vietnam over the years. The Vietnam National Tuberculosis Program is facing considerable challenges in its goal to eliminate tuberculosis by 2030, with the COVID-19 pandemic having negatively impacted routine tuberculosis services at all administrative levels. While the turnaround time of tuberculosis infection may delay disease detection, high transportation frequency could potentially mislead epidemiological studies. This study was conducted to develop an online geospatial platform to support healthcare workers in performing data visualization and promoting the active case surveillance in community as well as predicting the TB incidence in space and time.MethodThis geospatial platform was developed using tuberculosis notification data managed by The Vietnam National Tuberculosis Program. The platform allows case distribution to be visualized by administrative level and time. Users can retrieve epidemiological measurements from the platform, which are calculated and visualized both temporally and spatially. The prediction model was developed to predict the TB incidence in space and time.ResultsAn online geospatial platform was developed, which presented the prediction model providing estimates of case detection. There were 400,370 TB cases with bacterial evidence to be included in the study. We estimated that the prevalence of TB in Vietnam was at 414.67 cases per 100.000 population. Ha Noi, Da Nang, and Ho Chi Minh City were predicted as three likely epidemiological hotspots in the near future.ConclusionOur findings indicate that increased efforts should be undertaken to control tuberculosis transmission in these hotspots.

Spatial Analysis of Drug-Susceptible and Multidrug-Resistant Cases of Tuberculosis, Ho Chi Minh City, Vietnam, 2020–2023

“…Our incidence estimates for DS and MDR TB, derived from TB notifications, are markedly lower than the estimates of WHO for Vietnam (TB incidence 173 cases/100,000 persons, MDR TB 9.1 cases/100,000 persons) ( 14 ), despite evidence that Ho Chi Minh City has some of the highest TB incidences in the country ( 15 , 16 ). The WHO estimates are derived from multiple data sources, including prevalence surveys, case notification data, expert opinion about case detection gaps, and dynamic modeling ( 36 ).…”

“…The World Health Organization (WHO) recognizes Vietnam as a high-burden country for TB and MDR TB; estimated incidence is 173 (95% CI 112–247) cases/100,000 population for TB and 9.1 (95% CI 5.5–13) cases/100,000 population for MDR TB ( 1 , 14 ). The highest incidence is seen in the southern parts of the country, especially in Ho Chi Minh City ( 15 , 16 ). Patients with MDR TB in Ho Chi Minh City can have acquired their disease through selection of drug-resistance mutations while receiving first-line TB drug treatment or directly from others through transmission ( 17 ).…”

Predicting the risk of osteoporosis in older Vietnamese women using machine learning approaches

“…Artificial intelligence (AI) technology has been developed based on mathematical modelling over years. AI software has been applied to different majors, including epidemiological survey 26 , drug discovery 27 , and diagnostic radiology 28 . At this point, the computer-assisted devices have been integrated to clinical routine practice to detect the abnormalities relating to respiratory diseases on chest X-ray images 29 – 31 .…”

Burden of Post-COVID-19 Hair Loss among Older Adults in Vietnam

“…For instance, depression was demonstrated as a factor positively associated with hair loss, and the psychological distress related to the strict lockdown restrictions during the pandemic was considered as factor in the mechanisms that precipitate in hair loss [17]. Before the pandemic, in 2018, the rate of depression in Vietnamese older adults was 66.9% [18]. However, there was only 19.2% of older adults in our study having depression during the pandemic and no significant difference in the rates between the hair-loss and non-hair-loss groups.…”