INSIGHT AND SIGNIFICANCE OF WORLD HEALTH INDICATOR USING EXPLORATORY DATA ANALYSIS (EDA)

  

Introduction

 

Health indicators play a significant role in shaping  understanding of  global health trends and its importance cannot be overstated. Health indicators provide essential data that informs policymakers, researchers, and other professionals in the health industry about the state of health in populations. This article explores the insights and significance of world health indicators through Exploratory Data Analysis (EDA), emphasizing the processes of data cleaning, transformation, visualization, and training using Python, Scikit-learn, and Pandas as pivotal tools in analyzing the trends in the global health sector. 

WHAT ARE HEALTH INDICATORS? 

Health indicators are statistical measures that reflect the health of a population or the performance of its health system.  Researchers typically gather data about a population sample, and then generalize the information collected to the entire population, before using statistical analysis to make a statement about the health status of the population.

Health indicators can encompass a wide range of data points, including mortality rates, disease prevalence, level of access to  healthcare, and environmental factors affecting health. The World Health Organization (WHO) provides a comprehensive framework for these indicators through its Global Health Observatory (GHO).

Health indicators are not just identified but used to make large-scale community health-related decisions. By describing the current health status of a population, the areas that need improvement become evident, and health professionals as well as policy makers can make informed decisions that will fill these gaps. Once interventions are put in place to try to improve the health of a population, This means that health indicators  inspire interventions and can also be used to evaluate the success of an intervention by collecting data regularly for comparison.

Additionally, health indicators can highlight differences in health in a population. This disparity might be detected among genders, races, ethnic groups, socioeconomic classes, and other sub- groups in a particular society.  These indicators can be used to guide policies that will bring health equity in the future. 

Health indicators are used by many international health institutions, like the United Nations and World Health Organization (WHO).They are also used by local hospitals, community health organizations, and other public health organizations such as the  Global Alliance for Africa, National Institute of Health (NIH), Center for Disease Control (CDC), and The African Comprehensive HIV/AIDS Partnerships (ACHAP).  

Examples of Health Indicators

Some of the global health indicators include the following

1. Health status

This includes data points like life expectancy at birth, maternal mortality rate, infant mortality rate, activities of daily living, rate of cancer, diabetes, and depression occurrence, rate of mortality from cardiovascular disease among many others.  

2. Risk factors

These include points like level of alcohol consumption, rate of obesity, rate of asthma, physical fitness habits, level of air pollution, rate of stunting in children, rate of wasting in children, 

3. Health systems

Data points under this category includes number of hospitals in an environment, number of beds in each hospital, number of doctors and nurses per capita, birth registration rates, death registration rates, rate of awareness of terminal diseases like cancer and AIDS, revenue and expenditure of hospitals, as well as level of access to insurance options.

Exploratory Data Analysis (EDA)

Overview of EDA

Exploratory Data Analysis is a critical step in data analysis that involves summarizing the main characteristics of a dataset with the aim of uncovering patterns, detecting anomalies, and testing hypotheses without making prior assumptions about the data.  Unlike Initial Data Analysis which focuses on confirming certain assumptions, EDA explores, investigates and learns from a data set. EDA relies on graphics and visualizations to explore and analyze a data set.  

• Steps in EDA

1. Data Collection: this includes gathering relevant health indicator data from reliable sources such as WHO databases, national health surveys, or from primary sources such as hospitals and various health centers. 

2. Data Cleaning: this is the process of identifying and correcting errors or inconsistencies in the dataset. This includes correcting typos, handling missing values,fixing duplicate entries,  and ensuring uniformity in data formats.

3. Data Transformation: This involves normalizing numerical values or encoding categorical variables and other methods of modifying data to make it suitable for analysis.

4. Data Wrangling: This is organizing data into a format that is easier to analyze. This includes merging datasets, filtering rows based on specific criteria, and reshaping data structures.

5. Visualization: This is the use of graphical representations to identify trends and relationships within the data. Common visualizations include histograms for distribution analysis and scatter plots for correlation assessment. You can use libraries like Matplotlib and Seaborn in Python to create informative visualizations. 

6. Statistical Summary: This involves generating descriptive statistics (mean, median, mode) to summarize the  key characteristics of the dataset.

Python as a tool for Exploratory Data Analysis (EDA)

Python is widely recognized for its versatility in data analysis tasks. Libraries such as Pandas  and Scikit-learn can facilitate data manipulation and cleaning processes efficiently. 

Scikit-learn is a powerful machine learning library, with tools for both supervised and unsupervised learning, that can be used for model training after EDA has been performed. While Pandas is essential for handling structured data effectively. It allows the analyst to perform complex manipulations with simple commands.

To carry out an exploratory analysis of your data using python, you should follow the steps highlighted below: 

Step 1: Import Python Libraries

Step 2: Reading Dataset

Step 3: Data Reduction

Step 4: Feature Engineering

Step 5: Creating Features

Step 6: Data Cleaning/Wrangling

Step 7: EDA Exploratory Data Analysis

Step 8: Statistics Summary

Step 9: EDA Univariate Analysis

Step 10: Data Transformation

Step 12: EDA Bivariate Analysis

Step 13: EDA Multivariate Analysis

Step 14: Impute Missing values

Insights from EDA on Health Indicators

Insights gained from Exploratory Data Analysis (EDA) on health indicators are essential for enhancing understanding and improvement of public health. EDA allows researchers and healthcare professionals to uncover hidden patterns, trends, and relationships within complex health datasets, and facilitates informed decision-making and targeted interventions. IT is evident that this approach allows for: 

• Identifying Trends

Through EDA, health analysts can identify trends over time in various health indicators. For instance, visualizing life expectancy across different countries can reveal differences between high-income and low-income nations.

•  Correlation Analysis

EDA allows for examining correlations between different health indicators. For example, a strong correlation may exist between life expectancy and healthcare spending per capita.

• Outlier Detection

Detecting outliers is crucial in health datasets as they might indicate errors or cases to be further investigated. For instance, an unusually high infant mortality rate in a specific region may prompt public health interventions.

• Data Quality Assessment

Assessing the quality of health indicator datasets through EDA helps ensure that analyses are based on reliable information. Identifying missing values or inconsistencies early on prevents misleading conclusions.

Conclusion

The insights derived from world health indicators through exploratory data analysis are invaluable for research and formulating policies in the public health sector. By leveraging tools like Python with Scikit-learn and Pandas, analysts can clean, transform, visualize, and interpret complex datasets effectively. As global health challenges continue to evolve, it is essential to combat them with robust analytical frameworks that will guide policy responses and improve population health outcomes.

In summary, health indicators offer critical insights into population health and EDA serves as a foundational step in analyzing these indicators. Continuous exploration of  comprehensive health datasets is vital for informed decision-making in public health strategies. This approach ensures that stakeholders are equipped with accurate information to address pressing global health issues effectively.