Abstract:
Background: Autism Spectrum Disorder (ASD) is a complicated neurodevelopmental disorder marked by difficulties with social communication and repetitive activities and becoming more widely acknowledged, but there is still little data from the nations like Nepal. Specifically, there are few reliable epidemiological estimates of perinatal and prenatal risk factors in South Asian populations.
Methods: In western Nepal, 62 ASD cases and 124 controls (1:2 ratio) participated in a case-control research. Clinical record abstraction and structured caregiver interviews were used to gather data. Maternal sociodemographic traits, prenatal exposures, dietary factors, and perinatal outcomes were among the explanatory variables. controls were chosen from local schools and ASD patients were recruited from Autism centres. Data analysed in SPSS using binary logistic regression, chi-square tests and descriptive statistics. The model’s goodness-of-fit was assessed using the Hosmer–Lemeshow test, where a p-value greater than 0.05 indicated an adequate fit. The explanatory power of the model was evaluated using Cox & Snell R² and Nagelkerke R². Multicolinearity among independent variables was checked using the Variance Inflation Factor (VIF),
Results: Male Gender (AOR = 5.997, 95% CI: 2.445 – 14.709, p = 0.00)), Prenatal issues (AOR = 6.027, 95% CI: 2.03-17.895, p = 0.001), postnatal complications (AOR = 6.086, 95% CI: 1.828-20.26, p = 0.00), paternal autism history ( AOR = 5.316, 95% CI: 1.592-17.744, p < 0.05) and living in polluted area during pregnancy(AOR= 3.763, (AOR = 3.763, 95% CI: 1.259-11.245, p = 0.018) and not taking folic acid before conception (AOR = 10.694, 95% CI: 3.244-35.251, p < 0.00) were found to be significant predictors of ASD by binary logistic regression. Cox & Snell R² = 0.362 and Nagelkerke R² = 0.503 - the logistic regression model demonstrated moderate explanatory power, the Hosmer–Lemeshow test - (χ² = 1.077, df = 6, p = 0.983),indicating a strong model fit and Omnibus Tests of Model Coefficients (χ² = 83.626, df = 6, p < 0.001)suggesting that the independent variables included significantly enhanced the outcome prediction in comparison to the null model. ASD risk is largely influenced by prenatal, perinatal, environmental and Genetic variables, underscoring the need of maternal health and preventive measures in Nepal.
Keywords: Autism, ASD, Prenatal risk, Perinatal risk factors, Autism Causes, Biostatistics
Biography:
Prabha Paudel is an assistant professor at Tribhuvan University, Nepal and a biostatistics researcher who specializes in maternal-child health and autism spectrum disorder (ASD). In low-resource settings, she uses logistic regression and epidemiological techniques to detect neonatal and prenatal risk factors. She has expertise in data analysis, research, and public health academic teaching. Her areas of interest are evidence creation for policy and statistical modeling for health research. She focuses on the gaps in neurodevelopmental research in Nepal and other underdeveloped nations.
Copyright 2024 Mathews International LLC All Rights Reserved
