Abstract

Introduction: Although covid-19 has numerous adverse effects on children, few beneficial effects have been observed such as adapting to learn in a new digital environment, coming more closer to family, creating a bond of love, affection among the family members, and awareness of a novel disease. The aim of our study to examine the causal impact of covid-19 measures as factual probability and the counterfactual probability for average number of pediatric admission due to Covid-19 measures, using the Google Causal Impact, BSTS model and to explore the use of this model in healthcare research

Method: Study Design: A retrospective observational study. Two-time series data collected from Dec-2016 to Oct-2018 as control, and Dec-2018 to Oct-2020 as the experimental group. Our hypothetical assumption, if all the 0bserved or unobserved covariate which influence the hospitalization of children due to respiratory illness are essentially static. Our assumption during the control period the observed mean admission and experimental period predicted mean admission should not differ in the absence of Covid-19 measures. Impact of covid-19 pandemic measures on pediatric admission during the post-intervention time could be drawn by subtracting the factual probability of admission from the counterfactual probability of mean monthly admission.

Setting: Imam Abdulrahman bin Faisal Hospital, under National Guard health affairs, located in Dammam, KSA. The Hospital is approximately 100-bed capacity, Pediatric ward bed capacity twenty with average annual admission 1200, winter season exceeded the bed capacity, monthly exceeding 140-150 admission.

Data collection: Monthly total number of admission data collected retrospectively from the Pediatric Ward admission log book for the define time frame.

Results: During the control period, the observed and predicted mean admission was statistical, not significant (P- 0.171), in addition, the observed average admission during the control and predicted during the intervention, was the same. As a result of Covid-19 measures, the monthly admissions average value of 29. By contrast, in the absence of covid-19 pandemic measures, we would have expected an average admission of 87(9.4). counterfactual prediction CI [68-106]. Causal effects -58, CI (-78, -39), p values 0.001.

Conclusion: Admission was appreciably diminished as a positive impact of covid-19 measures indeed, it was the opposite direction of an adult, nevertheless, it was beyond expectation for admission for pediatric age group during a pandemic. On the other hand, the Google causal Impact algorithm well fitted to explore the casual Inference, and healthcare researchers could use it for causal effect estimation for any interventional time series setup.

Keywords: Positive Impact of Covid-19; Covid-19 in Children; Covid-19 Infection; Corona in Children; Structural Bayesian Time -series Model; Forecasting; Google Causal Impact

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Citation

Citation: Mohammed Shahab Uddin and Khouloud Abdulrahman Al-Sofyani. “Impact of Covid-19 Measures on Children Infection Related Hospitalization. Estimation of Causal Inference from observational Data, Using the Google Causal Impact, A Structural Bayesian Time-series Model". Acta Scientific Microbiology 4.1 (2021): 118-123.

Copyright

Copyright: © 2021 Mohammed Shahab Uddin and Khouloud Abdulrahman Al-Sofyani. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.