Impact of the health education and preventive equipment package (HEPEP) on prevention of Strongyloides stercoralis infection among rural communities in Northeast Thailand: a cluster randomized controlled trial.

BACKGROUND: Strongyloidiasis is prevalent in northeast Thailand. This study aimed to evaluate the impact of the Health Education and Preventive Equipment Package (HEPEP), a package we developed to improve awareness and aid in the prevention of Strongyloides stercoralis infection among rural communities in northeast Thailand. METHODS: This was an intervention trial conducted in 12 villages (six interventions and six controls) in rural areas of northeast Thailand from March 2016 to September 2017. Single stool sample was collected from each participant and examined using agar plate culture (APC) technique. Each participant was interviewed using a pre-tested questionnaire, treated with single dose of ivermectin (200 µg/Kg), and allocated to either the intervention or control group. Members of the intervention group were given "Practices to Prevent Strongyloidiasis" poster and vinyl boards containing information aimed at raising awareness of S. stercoralis and strongyloidiasis. In addition, they were given a poster lecture regarding the lifecycle of S. stercoralis before being treated with ivermectin. Aside from that, they were also given a protective equipment package. Monthly refresher courses were provided by village health volunteers (VHVs) regarding the health information they had received and proper equipment usage. The control group, on the other hand, was only provided with a five-minute lecture regarding strongyloidiasis. Assessment of new infection was conducted 3 months later in 327 and 318 participants in the intervention group and control group, respectively. RESULTS: The HEPEP had 41% greater efficacy in preventing S. stercoralis infection in the intervention group than the measures taken in the control group (adjusted Odds Ratio (aOR) = 0.59; 95%CI: 0.41 to 0.85, P-value = 0.005). The intervention group also scored significantly higher on all aspects of a test of S. stercoralis knowledge compared with the control group (mean difference (mean dif.) = 2.89, P-value = < 0.05). CONCLUSIONS: The HEPEP was the first model that has been found to be effective in controlling of S. stercoralis in rural communities in the northeast Thailand. The results should encourage policy makers and public health personnel to improve control programs, as well as health promotion, with regard to parasites. TRIAL REGISTRATION: Thai Clinical Trials Registry (TCTR), Medical Research Foundation of Thailand, Medical Research Network of the Consortium of Thai Medical Schools: MedResNet (Thailand) (identification number: TCTR20180404002 ) Registered 4 April 2018 (retrospectively registered).

Current high prevalences of Strongyloides stercoralis and Opisthorchis viverrini infections in rural communities in northeast Thailand and associated risk factors.

BACKGROUND: Two important helminths, Strongyloides stercoralis (an intestinal roundworm) and Opisthorchis viverrini (a liver fluke), are endemic in northeast Thailand. There have been national campaigns in place aimed at the control and eradication of soil-transmitted helminthiasis and opisthorchiasis in Thailand for several decades. However, these helminths still exist and raise concerns regarding public health. This study aimed to evaluate the current prevalence of S. stercoralis and O. viverrini infections in rural communities in northeast Thailand. The data from this study will be useful to improve strategies for future helminth prevention and control. METHODS: A cross-sectional study was conducted from December 2016 to June 2017 in Mueang Khon Kaen district in Khon Kaen, Thailand. The participants were selected using a simple random sampling method. Demographic data were collected using a questionnaire. Stool samples were collected and processed using agar plate culture to determine the presence of S. stercoralis infection and an in-house formalin-ethyl acetate concentration technique to determine the presence of O. viverrini and other intestinal parasite infections (IPIs). RESULTS: In total, 602 persons were enrolled. However, only 526 were analyzed for S. stercoralis and 387 for O. viverrini risk factors. The overall prevalence of S. stercoralis infection was 23.0% (95% confidence interval [95%CI]: 19.4 to 26.6). The prevalence of O. viverrini infection and IPIs other than S. stercoralis was 20.4% (95%CI: 16.5 to 24.8). The prevalence of O. viverrini infection was 19.4% (95%CI: 15.6 to 23.7). Male sex was significantly associated with S. stercoralis infection [Adjusted Odds Ratio (aOR) 4.0; 95%CI: 2.5 to 6.2; P-value < 0.001]. Males were significantly more likely to be infected with O. viverrini and other IPIs (aOR 4.1; 95%CI: 2.3 to 7.2, P-value < 0.001). CONCLUSIONS: This study demonstrated that the updated prevalence of intestinal parasite infections is still high in rural communities in northeast Thailand, especially that of strongyloidiasis and opisthorchiasis.

An eleven-year retrospective hospital-based study of epidemiological data regarding human strongyloidiasis in northeast Thailand.

BACKGROUND: Human strongyloidiasis is a chronic and persistent gastrointestinal disease caused by infection with soil-transmitted helminths of the genus Strongyloides. The aim of this research was to obtain diagnostic prevalence regarding strongyloidiasis in northeast Thailand through a hospital-based study. METHODS: Patients' demographic data and the results of stool examinations conducted using the formalin ethyl acetate concentration technique were collected from the parasitology laboratory records at Srinagarind Hospital in Khon Kaen, Thailand. The relevant information from years 2004 to 2014 was collected and descriptively analyzed. RESULTS: Of a total of 22,338 patients, 3889 (17.4%) had stool samples that tested positive for Strongyloides larvae. The highest prevalence was 22.8% (95% CI = 19.6-26.2%) in the year 2004. This percentage progressively decreased, reaching 11.2% (95% CI = 10.2-12.4%) in 2013 and remaining stable at 12.9% (95% CI = 11.8-14.1%) in 2014. Males (2741 cases) had double the positivity rate of females (1148 cases). The prevalence of infection was highest (25.9%; 95% CI = 24.5-27.3%) among patients that were 51-60 years of age. CONCLUSIONS: Areas endemic for strongyloidiasis should be emphasized under the national helminth control program and health education campaigns. Nationwide assessments should also be performed regarding Strongyloides infection, including risk factors, treatment, and prevention. The diagnostic laboratory data presented here identify the geographical focus of disease to be the northeastern region of the country. Further targeted surveillance using more sensitive methods will almost certainly reveal a higher individual disease burden than found in this report.

Smart Glasses: A New Tool for Assessing the Number of Patients in Mass-Casualty Incidents.

INTRODUCTION: Mass-casualty incidents (MCIs) are events in which many people are injured during the same period of time. This has major implications in regards to practical concerns and planning for both personnel and medical equipment. Smart glasses are modern tools that could help Emergency Medical Services (EMS) in the estimation of the number of potential patients in an MCI. However, currently there is no study regarding the advantage of employing the use of smart glasses in MCIs in Thailand. STUDY OBJECTIVE: This study aims to compare the overall accuracy and amount of time used with smart glasses and comparing it to manual counting to assess the number of casualties from the scene. METHODS: This study was a randomized controlled trial, field exercise experimental study in the EMS unit of Srinagarind Hospital, Thailand. The participants were divided into two groups (those with smart glasses and those doing manual counting). On the days of the simulation (February 25 and 26, 2022), the participants in the smart glasses group received a 30-minute training session on the use of the smart glasses. After that, both groups of participants counted the number of casualties on the simulation field independently. RESULTS: Sixty-eight participants were examined, and in the smart glasses group, a total of 58.8% (N = 20) of the participants were male. The mean age in this group was 39.4 years old. The most experienced in the EMS smart glasses group had worked in this position for four-to-six years (44.1%). The participants in the smart glasses group had the highest scores in accurately assessing the number of casualties being between 21-30 (98.0%) compared with the manual counting group (89.2%). Additionally, the time used for assessing the number of casualties in the smart glasses group was shorter than the manual counting group in tallying the number of casualties between 11-20 (6.3 versus 11.2 seconds; P = .04) and between 21-30 (22.1 versus 44.5 seconds; P = .02). CONCLUSION: The use of smart glasses to assess the number of casualties in MCIs when the number of patients is between 11 and 30 is useful in terms of greater accuracy and less time being spent than with manual counting.

Deep Learning Approach for Ascaris lumbricoides Parasite Egg Classification.

A. lumbricoides infection affects up to 1/3 of the world population (approximately 1.4 billion people worldwide). It has been estimated that 1.5 billion cases of infection globally and 65,000 deaths occur due to A. lumbricoides. Generally, allied health classifies parasite egg type by using on microscopy-based methods that are laborious, are limited by low sensitivity, and require high expertise. However, misclassification may occur due to their heterogeneous experience. For their reason, computer technology is considered to aid humans. With the benefit of speed and ability of computer technology, image recognition is adopted to recognize images much more quickly and precisely than human beings. This research proposes deep learning for A. lumbricoides's egg image recognition to be used as a prototype tool for parasite egg detection in medical diagnosis. The challenge is to recognize 3 types of eggs of A. lumbricoides with the optimal architecture of deep learning. The results showed that the classification accuracy of the parasite eggs is up to 93.33%. This great effectiveness of the proposed model could help reduce the time-consuming image classification of parasite egg.