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

Osteoporosis contributes significantly to health and economic burdens worldwide. However, the development of osteoporosis-related prediction tools has been limited for lower-middle-income countries, especially Vietnam. This study aims to develop prediction models for the Vietnamese population as well as evaluate the existing tools to forecast the risk of osteoporosis and evaluate the contribution of covariates that previous studies have determined to be risk factors for osteoporosis. The prediction models were developed to predict the risk of osteoporosis using machine learning algorithms. The performance of the included prediction models was evaluated based on two scenarios; in the first one, the original test parameters were directly modeled, and in the second the original test parameters were transformed into binary covariates. The area under the receiver operating characteristic curve, the Brier score, precision, recall and F1-score were calculated to evaluate the models' performance in both scenarios. The contribution of the covariates was estimated using the Permutation Feature Importance estimation. Four models, namely, Logistic Regression, Support Vector Machine, Random Forest and Neural Network, were developed through two scenarios. During the validation phase, these four models performed competitively against the reference models, with the areas under the curve above 0.81. Age, height and weight contributed the most to the risk of osteoporosis, while the correlation of the other covariates with the outcome was minor. Machine learning algorithms have a proven advantage in predicting the risk of osteoporosis among Vietnamese women over 50 years old. Additional research is required to more deeply evaluate the performance of the models on other high-risk populations.

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

Background: Tuberculosis 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. Method: This 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. Results: An 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. Conclusion: Our findings indicate that increased efforts should be undertaken to control tuberculosis transmission in these hotspots.

Movement restrictions, vaccine coverage, and reduction of the COVID-19 incidence rate in the fourth wave of the pandemic: Analysis results from 63 provinces in Vietnam.

On April 27, 2021, the fourth wave of the coronavirus disease 2019 (COVID-19) pandemic originating from the Delta variant of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) began in Vietnam. The adoption of travel restrictions, coupled with rapid vaccination and mask-wearing, is a global strategy to prevent the spread of COVID-19. Although trade-off between health and economic development are unavoidable in this situation, little evidence that is specific to Vietnam in terms of movement restrictions, vaccine coverage, and real-time COVID-19 cases is available. Our research question is whether travel restrictions and vaccine coverage are related to changes in the incidence of COVID-19 in each province in Vietnam. We used Google's Global Mobility Data Source, which reports different mobility types, along with reports of vaccine coverage and COVID-19 cases retrieved from publicly and freely available datasets, for this research. Starting from the 50th case per province and incorporating a 14-day period to account for exposure and illness, we examined the association between changes in mobility (from day 27 to 04-03/11/2021) and the ratio of the number of new confirmed cases on a given day to the total number of cases in the past 14 days of indexing (the potentially contagious group in the population) per million population by making use of LOESS regression and logit regression. In two-thirds of the surveyed provinces, a reduction of up to 40% in commuting movement (to the workplace, transit stations, grocery stores, and entertainment venues) was related to a reduction in the number of cases, especially in the early stages of the pandemic. Once both movement and disease prevalence had been mitigated, further restrictions offered little additional benefit. These results indicate the importance of early and decisive actions during the pandemic.