An Appraisal of Statistical Methods in Analyzing Child Mortality Rates in Plateau State
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Background of the Study
Child mortality remains a significant public health issue in Nigeria, with the country ranking among the highest globally in under-five mortality rates (UNICEF, 2024). According to the National Population Commission (NPC, 2024), Nigeria’s child mortality rate stands at approximately 100 deaths per 1,000 live births, with northern states such as Plateau exhibiting particularly high figures.
Accurate and reliable statistical methods are crucial in assessing child mortality trends and identifying key determinants. Traditional methods such as direct and indirect estimation, life table analysis, logistic regression, and survival analysis have been widely used in child mortality studies (Olatunji & Eze, 2023). However, inconsistencies in data collection, underreporting of child deaths, and limited statistical capacity in health institutions hinder effective mortality analysis in Nigeria (Adepoju et al., 2024).
Plateau State, located in north-central Nigeria, experiences a high child mortality rate due to malnutrition, poor healthcare access, infectious diseases, and socio-economic disparities (Bello et al., 2023). Despite numerous health interventions, mortality rates remain high, emphasizing the need for robust statistical methodologies to analyze and interpret mortality data accurately. This study seeks to appraise the statistical techniques used in analyzing child mortality rates in Plateau State, evaluating their effectiveness in identifying risk factors and informing policy decisions.
Statement of the Problem
The persistent high child mortality rate in Plateau State highlights a gap in effective data-driven interventions. While various statistical techniques have been applied in child mortality studies, challenges such as incomplete data, reporting bias, and lack of methodological standardization affect the accuracy and reliability of mortality estimates (Olatunji & Eze, 2023).
There is limited research on the comparative effectiveness of different statistical methods in analyzing child mortality trends in Plateau State. Existing approaches often rely on outdated census data, household surveys with recall bias, or hospital records that do not account for deaths occurring outside healthcare facilities (Adepoju et al., 2024).
This study aims to evaluate the statistical methods applied in analyzing child mortality in Plateau State, identifying their strengths and limitations to enhance data accuracy and policy formulation.
Objectives of the Study
1. To assess the statistical methods commonly used in analyzing child mortality rates in Plateau State.
2. To compare the accuracy and reliability of different statistical techniques in estimating child mortality.
3. To recommend improved statistical approaches for analyzing child mortality trends in Plateau State.
Research Questions
1. What statistical methods are commonly used to analyze child mortality in Plateau State?
2. How do different statistical techniques compare in terms of accuracy and reliability in child mortality estimation?
3. What improvements can be made to enhance statistical analysis of child mortality trends?
Research Hypotheses
1. Traditional mortality estimation methods are less accurate than modern statistical techniques in analyzing child mortality in Plateau State.
2. The use of machine learning and predictive modeling improves the reliability of child mortality analysis.
3. Inadequate data quality significantly affects the effectiveness of statistical methods in child mortality studies.
Scope and Limitations of the Study
This study will focus on statistical methods applied in child mortality research in Plateau State, analyzing government records, survey data, and hospital reports. It will evaluate different analytical techniques, including regression models, life table analysis, and survival analysis.
Limitations may include incomplete mortality data, inconsistencies in reporting systems, and potential difficulties in accessing reliable datasets.
Definitions of Terms
• Child Mortality Rate: The probability of a child dying before the age of five per 1,000 live births.
• Life Table Analysis: A statistical method used to estimate mortality rates by examining survival probabilities over different age intervals.
• Regression Models: Statistical techniques used to analyze relationships between dependent and independent variables in child mortality studies.
• Survival Analysis: A statistical approach used to examine time-to-event data, such as the duration until child mortality occurs.
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