An appraisal of statistical methods in measuring the impact of COVID-19 on hospital admissions in Kogi State
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Background of the Study
The COVID-19 pandemic has had a profound impact on global health systems, with hospitals worldwide experiencing an unprecedented surge in patient numbers. In Nigeria, the pandemic placed significant strain on the healthcare infrastructure, and Kogi State was no exception (Ogunleye et al., 2024). Hospitals in Kogi State faced challenges in managing both COVID-19 patients and patients with non-COVID-19 related conditions due to increased admissions, limited resources, and high demand for medical services. To assess the extent of these challenges, various statistical methods have been used to analyze the impact of the pandemic on hospital admissions.
Statistical methods, such as time series analysis, regression models, and hypothesis testing, have been pivotal in measuring changes in hospital admission rates, identifying trends, and predicting future healthcare needs (Umar et al., 2023). However, challenges arise in applying these methods, particularly in low-resource settings where data availability and quality are often limited. Additionally, the need for real-time data and the rapid evolution of the pandemic require the use of robust, flexible statistical techniques that can accurately reflect the dynamics of COVID-19 and its impact on healthcare services.
This study aims to evaluate the statistical methods used to measure the impact of COVID-19 on hospital admissions in Kogi State, focusing on the effectiveness and limitations of these methods in providing accurate, actionable insights.
Statement of the Problem
The COVID-19 pandemic has significantly affected healthcare systems worldwide, and Kogi State has been no exception. However, there has been limited research on the specific impact of the pandemic on hospital admissions in Kogi State, particularly with regard to the statistical methods used to measure this impact. Existing studies have employed a range of statistical techniques, but the limitations of these methods, such as data quality issues and model assumptions, need to be thoroughly examined. An evaluation of these statistical methods will provide a clearer understanding of how COVID-19 affected hospital admissions in Kogi State and help improve data-driven healthcare policies for future health crises.
Objectives of the Study
1. To evaluate the statistical methods used to measure the impact of COVID-19 on hospital admissions in Kogi State.
2. To identify the key trends in hospital admissions during the COVID-19 pandemic in Kogi State.
3. To assess the effectiveness and limitations of these statistical methods in informing public health strategies.
Research Questions
1. What statistical methods have been used to measure the impact of COVID-19 on hospital admissions in Kogi State?
2. What are the trends in hospital admissions during the COVID-19 pandemic in Kogi State?
3. How effective are the statistical methods used in providing insights for public health strategies in Kogi State?
Research Hypotheses
1. The application of time series analysis provides accurate predictions of hospital admission trends during the COVID-19 pandemic in Kogi State.
2. Regression models reveal a significant increase in hospital admissions due to COVID-19 in Kogi State.
3. The limitations of statistical methods, such as data quality issues and assumptions in model fitting, significantly affect the accuracy of hospital admission estimates during the COVID-19 pandemic in Kogi State.
Scope and Limitations of the Study
This study will focus on hospital admission data from public and private hospitals in Kogi State during the COVID-19 pandemic. It will evaluate statistical methods such as time series analysis, regression analysis, and other relevant techniques. Limitations include potential gaps in data availability, particularly for non-COVID-19 related admissions, and the challenge of applying complex statistical models in a low-resource setting with limited data.
Definitions of Terms
• Statistical Methods: Techniques used to collect, analyze, interpret, and present data in order to uncover patterns, trends, and relationships.
• Hospital Admissions: The number of patients who are admitted to a hospital for treatment or observation during a specific period.
• COVID-19 Pandemic: A global outbreak of the novel coronavirus (SARS-CoV-2) that began in late 2019 and led to widespread illness and disruption worldwide.
• Time Series Analysis: A statistical method used to analyze data points collected or recorded at specific time intervals to identify trends and patterns over time.
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