The predictors of voluntary participation in pulmonary tuberculosis screening program: a study in a suburban community of southern Thailand.

Background: The health belief model (HBM), baseline health condition, and sociocultural factors impact the decision to participate in a tuberculosis screening program. Methods: This cross-sectional and descriptive study was carried out among the "Kao Taew" community dwellers aged 18 years and above, who voluntarily underwent the provided pulmonary tuberculosis (PTB) screening by chest radiographs (CXRs). The level of individual HBM domain perception, attitudes toward PTB prevention, and regularity of PTB prevention practices by the participants were evaluated. The significantly associated or correlated factors such as demographic characteristics, individual HBM domain perception, and attitudes toward PTB prevention with the regularity of PTB prevention practices from the univariate analysis were further analyzed by multiple linear regression (p < 0.05) to determine the independent significant predictors of PTB prevention practices. Results: Among 311 participants comprising 65% women, 57.9% aged ≥ 65 years and 67.2% had an underlying disease. The study participants had a high level of perception of HBM domains but a low level of perception of the barrier. In addition, a high level of attitudes toward PTB prevention and a high regularity of PTB prevention practices were found. A multiple linear regression analysis revealed that the perceived benefits of PTB screening [Beta = 0.20 (0.04, 0.36) p = 0.016] and acquiring underlying diseases [Beta = 1.06 (0.38, 1.73), p = 0.002] were significant predictors for PTB prevention practices, while belief in Islam was a reverse predictor [Beta = -0.84 (-1.47, -0.21), p = 0.010]. Conclusions: The level of perception of the individual domain of HBM, health status, and religious belief significantly predicted voluntary participation in PTB screening programs. Careful consideration by integration of the relevant health psychology, physical, and sociocultural factors is crucial for planning a health screening program.

Teamwork Management and Benefit of Telemedicine in COVID-19 Outbreak Control on an Offshore Vessel in the Gulf of Thailand, Songkhla Province, Thailand: A Case Report.

In May 2021, there was a COVID-19 outbreak on board a construction support ship traveling from India to Thailand. Controlling the outbreak on this offshore vessel from 11 May to 2 June 2021 was applied. This case report describes the teamwork management of COVID-19 control on the vessel in the Gulf of Thailand. We summarized the COVID-19 outbreak control process on board, including active COVID-19-infected cases (CoIC) and close contacts (CoCC) identification, isolation, quarantine, treatment, and clinical monitoring using telemedicine to report their health measurements twice daily, including emergency conditions if they occurred. Active COVID-19 cases were identified by two rounds of reverse transcription polymerase chain reaction (RT-PCR) tests in all crew members, in which 7 of 29 (24.1%) showed positive results. Both the CoIC and CoCC were strictly and absolutely isolated and quarantined on the vessel. No serious medical conditions were reported during the monitoring. The third-round RT-PCR tests were conducted, and all tested negative one week later. Teamwork management in proactive COVID-19 case identification, isolation, comprehensive treatment, and close monitoring of health conditions using telemedicine devices is beneficial for controlling the COVID-19 outbreak on board.

Prediction of Acid-Base and Potassium Imbalances in Intensive Care Patients Using Machine Learning Techniques.

Acid-base disorders occur when the body's normal pH is out of balance. They can be caused by problems with kidney or respiratory function or by an excess of acids or bases that the body cannot properly eliminate. Acid-base and potassium imbalances are mechanistically linked because acid-base imbalances can alter the transport of potassium. Both acid-base and potassium imbalances are common in critically ill patients. This study investigated machine learning models for predicting the occurrence of acid-base and potassium imbalances in intensive care patients. We used an institutional dataset of 1089 patients with 87 variables, including vital signs, general appearance, and laboratory results. Gradient boosting (GB) was able to predict nine clinical conditions related to acid-base and potassium imbalances: mortality (AUROC = 0.9822), hypocapnia (AUROC = 0.7524), hypercapnia (AUROC = 0.8228), hypokalemia (AUROC = 0.9191), hyperkalemia (AUROC = 0.9565), respiratory acidosis (AUROC = 0.8125), respiratory alkalosis (AUROC = 0.7685), metabolic acidosis (AUROC = 0.8682), and metabolic alkalosis (AUROC = 0.8284). Some predictions remained relatively robust even when the prediction window was increased. Additionally, the decision-making process was made more interpretable and transparent through the use of SHAP analysis. Overall, the results suggest that machine learning could be a useful tool to gain insight into the condition of intensive care patients and assist in the management of acid-base and potassium imbalances.