RESUMEN
Autism spectrum disorder (ASD) is the assorted uneven conditions of the human brain that lead to developmental disabilities. This cross-sectional study aimed to identify the substantial risk factors of ASD among children in Bangladesh. The data were collected using convenience sampling through a questionnaire filled up by the trained interviewers. Mann-Whitney U and Kruskal-Wallis H tests were applied as bivariate analysis, and generalized beta regression was performed to determine the significant risk factors of autism spectrum disorder. The odds ratio (OR) along with 95% confidence interval (CI) were the measuring parameters of the risk factors of ASD. The result revealed that later birth order children have more risk of ASD (OR = 1.13, CI: 1.014-1.264, p = 0.027) compared to the children whose birth order is first. Premature birth of the child (OR: 0.87, CI: 0.76-1.00, p = 0.05) and father's age (OR: 0.86, CI: 0.76-0.97, p = 0.020) substantially affects ASD. The maternal history of specific illness (diabetes, thyroiditis, and hypertension) during pregnancy also significantly affect ASD (OR: 1.34, CI: 1.14-1.61, p = 0.002). The results of this study would assist policymakers in taking necessary steps to reduce the incidence of this disorder by targeting the potential risk factors.
Asunto(s)
Trastorno del Espectro Autista , Femenino , Embarazo , Humanos , Niño , Adolescente , Trastorno del Espectro Autista/epidemiología , Trastorno del Espectro Autista/etiología , Estudios Transversales , Bangladesh/epidemiología , Factores de Riesgo , Orden de Nacimiento , Factores SocioeconómicosRESUMEN
The COVID-19 omicron variant is exceptionally complicated and uncertain due to its rapid transmission and volume of infections. This study examines the impact of climatic factors on daily confirmed cases of COVID-19 omicron variant in Bangladesh. The secondary data of daily confirmed cases from 1 January 2022, to 31 March 2022, of eight distinct geographic divisions have been used for the current study. The multivariate generalized linear negative binomial regression model was applied to determine the effects of climatic factors on omicron transmission. The model revealed that the maximum temperature (Odds: 0.67, p < 0.05), sky clearness (Odds: 0.05, p < 0.05), wind speed (Odds: 0.76, p < 0.05), relative humidity (Odds: 1.02, p < 0.05), and air pressure (Odds: 0.27, p < 0.05) significantly impacted COVID-19 omicron transmission in Bangladesh. The study's findings can assist the concerned authorities and decision-makers take necessary measures to control the spread of omicron cases in Bangladesh.