Acta Scientific Microbiology (ISSN: 2581-3226)

Research Article Volume 4 Issue 1

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

Mohammed Shahab Uddin1* and Khouloud Abdulrahman Al-Sofyani2

1National Guard Health Affairs, Ministry of National Guard, Imam Abdurahman Bin Faisal Hospital, Dammam, Kingdom of Saudi Arabia

2Department of Pediatric, Pediatric Intensive Critical Care Unit, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia

*Corresponding Author: Mohammed Shahab Uddin, National Guard Health Affairs, Ministry of National Guard, Imam Abdurahman Bin Faisal Hospital, Dammam, Kingdom of Saudi Arabia.

Received: October 24, 2020; Published: December 30, 2020

×

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

×

References

  1. Gupta S and Jawanda M K. “The impacts of COVID-19 on children”. Acta Paediatrica (2020): 1-3.
  2. Social distancing impacts other infectious diseases | MDedge Pediatrics.
  3. Kadambari S., et al. “Decrease in Infection-Related Hospital Admissions During COVID-19”. Pediatric Infectious Disease Journal 11 (2020): e385-e386.
  4. Kurz C F., et al. “The effect of bariatric surgery on health care costs: A synthetic control approach using Bayesian structural time series”. Health Economics (2019).
  5. Anderson R M., et al. “How will country-based mitigation measures influence the course of the COVID-19 epidemic?” Lancet 10228 (2020): 931-934.
  6. Iacobucci G. “Covid lockdown: England sees fewer cases of colds, flu, and bronchitis”. BMJ 370 (2020): m3182.
  7. Yeoh DK., et al. “The impact of COVID-19 public health measures on detections of influenza and respiratory syncytial virus in children during the 2020 Australian winter”. Clinical Infection Disease (2020).
  8. Krivec U., et al. “COVID-19 lockdown dropped the rate of paediatric asthma admissions”. Archives of Disease in Childhood 105 (2020): 809-810.
  9. Gary Connett S. U. H. N. T. S. S. 6YD, U. gary. connett@uhs. nhs. u. Evaluation of the impact ofshielding to avoid COVID-19infection on respiratorysymptoms in children withsevere asthma.
  10. Brendish N J., et al. “Clinical impact of molecular point-of-care testing for suspected COVID-19 in hospital (COV-19POC): a prospective, interventional, non-randomised, controlled study”. Lancet Respiratory Medicine 8 (2020): 1192-1200.
  11. Greer A L. “Can informal social distancing interventions minimize demand for antiviral treatment during a severe pandemic?” BMC Public Health 13 (2013).
  12. Inferring Causal Impact Using Bayesian Structural Time-Series Models | the morning paper.
  13. Pinilla J., et al. “Using a Bayesian Structural Time-Series Model to Infer the Causal Impact on Cigarette Sales of Partial and Total Bans on Public Smoking”. Jahrbücher für Nationalökonomie und Statistik 238 (2018): 423-439.
  14. McQuire C., et al. “Forecasting the 2021 local burden of population alcohol-related harms using Bayesian structural time-series”. Addiction 114 (2019): 994-1003.
  15. Liu J., et al. “Bayesian Structural Time Series for Mobile Health and Sensor Data: A Flexible Modeling Framework for Evaluating Interventions”. bioRxiv (2020).
  16. Kurz, C. F., et al. “The effect of bariatric surgery on health care costs: A synthetic control approach using Bayesian structural time series.”. Heal. Econ. (United Kingdom) 28, 1293–1307 (2019).
×

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.




Metrics

Acceptance rate33%
Acceptance to publication20-30 days

Indexed In



News and Events


  • Certification for Review
    Acta Scientific certifies the Editors/reviewers for their review done towards the assigned articles of the respective journals.
  • Submission Timeline for Upcoming Issue
    The last date for submission of articles for regular Issues is December 25, 2024.
  • Publication Certificate
    Authors will be issued a "Publication Certificate" as a mark of appreciation for publishing their work.
  • Best Article of the Issue
    The Editors will elect one Best Article after each issue release. The authors of this article will be provided with a certificate of "Best Article of the Issue"

Contact US