OBJECTIVE: This study aims to examine the association between the spatio-temporal distribution of leptospirosis hotspot areas from 2011 to 2019 with the hydroclimatic factors in Selangor using the geographical information system and remote sensing techniques to develop a leptospirosis hotspot predictive model.
METHODS: This will be an ecological cross-sectional study with geographical information system and remote sensing mapping and analysis concerning leptospirosis using secondary data. Leptospirosis cases in Selangor from January 2011 to December 2019 shall be obtained from the Selangor State Health Department. Laboratory-confirmed cases with data on the possible source of infection would be identified and georeferenced according to their longitude and latitudes. Topographic data consisting of subdistrict boundaries and the distribution of rivers in Selangor will be obtained from the Department of Survey and Mapping. The ArcGIS Pro software will be used to evaluate the clustering of the cases and mapped using the Getis-Ord Gi* tool. The satellite images for rainfall and land surface temperature will be acquired from the Giovanni National Aeronautics and Space Administration EarthData website and processed to obtain the average monthly values in millimeters and degrees Celsius. Meanwhile, the average monthly river hydrometric levels will be obtained from the Department of Drainage and Irrigation. Data are then inputted as thematic layers and in the ArcGIS software for further analysis. The artificial neural network analysis in artificial intelligence Phyton software will then be used to obtain the leptospirosis hotspot predictive model.
RESULTS: This research was funded as of November 2022. Data collection, processing, and analysis commenced in December 2022, and the results of the study are expected to be published by the end of 2024. The leptospirosis distribution and clusters may be significantly associated with the hydroclimatic factors of rainfall, land surface temperature, and the river hydrometric level.
CONCLUSIONS: This study will explore the associations of leptospirosis hotspot areas with the hydroclimatic factors in Selangor and subsequently the development of a leptospirosis predictive model. The constructed predictive model could potentially be used to design and enhance public health initiatives for disease prevention.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/43712.
METHODS: This ecological cross-sectional study utilised a geographic information system (GIS) and remote sensing techniques to analyse the spatiotemporal distribution of leptospirosis in Selangor from 2011 to 2019. Laboratory-confirmed leptospirosis cases (n = 1,045) were obtained from the Selangor State Health Department. Using ArcGIS Pro, spatial autocorrelation analysis (Moran's I) and Getis-Ord Gi* (hotspot analysis) was conducted to identify hotspots based on the monthly aggregated cases for each subdistrict. Satellite-derived rainfall and land surface temperature (LST) data were acquired from NASA's Giovanni EarthData website and processed into monthly averages. These data were integrated into ArcGIS Pro as thematic layers. Machine learning algorithms, including support vector machine (SVM), Random Forest (RF), and light gradient boosting machine (LGBM) were employed to develop predictive models for leptospirosis hotspot areas. Model performance was then evaluated using cross-validation and metrics such as accuracy, precision, sensitivity, and F1-score.
RESULTS: Moran's I analysis revealed a primarily random distribution of cases across Selangor, with only 20 out of 103 observed having a clustered distribution. Meanwhile, hotspot areas were mainly scattered in subdistricts throughout Selangor with clustering in the central region. Machine learning analysis revealed that the LGBM algorithm had the best performance scores compared to having a cross-validation score of 0.61, a precision score of 0.16, and an F1-score of 0.23. The feature importance score indicated river water level and rainfall contributes most to the model.
CONCLUSIONS: This GIS-based study identified a primarily sporadic occurrence of leptospirosis in Selangor with minimal spatial clustering. The LGBM algorithm effectively predicted leptospirosis hotspots based on the analysed hydroclimatic factors. The integration of GIS and machine learning offers a promising framework for disease surveillance, facilitating targeted public health interventions in areas at high risk for leptospirosis.
DESIGN: Cross-sectional.
SETTING: Central and eastern regions of Peninsular Malaysia.
PARTICIPANTS: A stratified random sampling was employed to select 917 secondary school-going adolescents (aged 15-17 years).
RESULTS: The prevalence of under-reporters was 17·4 %, while no over-reporters were identified. Under-reporters had higher body composition and lower dietary intakes (except for vitamin C, Cr and Fl) compared with plausible reporters (P < 0·05). Adolescents with overweight and obesity had a higher odds of under-reporting compared with under-/normal weight adolescents (P < 0·001). In model 3, the highest regression coefficient (R2 = 0·404, P < 0·001) was obtained after adjusting for reporting status.
CONCLUSIONS: Overweight and obese adolescents were more likely to under-report their food intake and consequently affect nutrient intakes estimates. Future analyses that include nutrient intake data should adjust for reporting status so that the impact of misreporting on study outcomes can be conceded and consequently improve the accuracy of dietary-related results.
METHODS: A computerized literature search using Medline (Ovid) and Scopus were conducted to identify relevant observational studies on the influence of different level of PA on bone acquisition among the healthy adolescent population. All articles included, were limited to original articles and English language.
RESULTS: Nine studies met the inclusion criteria. Reported bone outcomes were of bone mass, bone structure and bone strength. Eight studies showed positive association between adolescents' PA and high bone variables. The influence of PA may differ according to sex, skeletal sites and bone outcomes.
CONCLUSION: This study supported the importance of increase adolescents' regular PA in optimizing PBM thus preventing osteoporosis at later life.
INTRODUCTION: Osteoporosis management poses a substantial healthcare challenge, necessitating effective strategies and Clinical Practice Guidelines (CPG) adherence.
METHODS: The study employed a self-administered online questionnaire via Google Forms. Orthopedic clinicians from all study sites were invited to participate via messaging platforms. A total of 135 participants completed the questionnaire and the data was proceeded to statistical analyses.
RESULTS: The study identified significant barriers, including inadequate knowledge of current osteoporosis guidelines and medications (p = 0.014), limited choice of anti-osteoporosis medication (p