A Data-Driven Story on Unveiling the Factors Affecting Life Expectancy Across the Globe

Background

Life expectancy has significantly improved worldwide, but disparities persist. Understanding the factors influencing these variations is crucial for guiding development efforts and improving global health. This study examines the impact of demographics, socioeconomic inequality, and healthcare access, including previously understudied aspects like immunization and the human development index, on life expectancy in both developed and developing countries. Using data from 193 countries spanning 2000 to 2015, this research analyzes the dynamic interplay of these factors across time and geographic regions, shedding light on the complex drivers of life expectancy. By leveraging Power BI, we aim to uncover the key insights on factors affecting life expectancy hidden within this rich data. This comprehensive analysis will empower policymakers to identify areas for targeted interventions, ultimately propelling countries toward a future of improved population health and well-being.

The data used in this exploration originates from the reputable Global Health Observatory (GHO) data repository under the World Health Organization (WHO) and the United Nations website. We believe in the accuracy and integrity of these publicly available sources.

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Objectives of the Study

We aim to uncover valuable insights into the relationship between immunization factors like Hepatitis B, Polio, and Diphtheria, alongside mortality, economic, and social factors and their combined effect on life expectancy across different regions and income levels. The following questions will be answered to draw insights from the data.

1.     Are there significant differences in life expectancy between developed and developing regions?

2.     How do immunization administered contribute to increase life expectancy over the years

3.     Which region has lowest life expectancy and hence has the most significant challenges?

4.     Does change in health factors (e.g., alcohol consumption, BMI, incidents of HIV/AID) influence life expectancy?

5.     How does eating habbits (thiness in children below 19 years ) contribute to infant and under-five mortality rate?

6.     How does Body Mass Index (BMI) correlate with life expectancy across different countries and years and are certain BMI categories more susceptible to low life expectancy?

7.     How do socioeconomic factors (e.g., GDP per capita, schooling) vary across countries with different life expectancies?

Discovering and presenting insights through Data Analysis

To discover meaningful insights, we utilize data visualization techniques in Power BI. These techniques include bar charts, scatter plots, and line charts to illustrate key metrics such as average life expectancy for world and between developed and developing countries, total under five and infants death per 1000 population, top-most UN classified regions with high vaccinationation administering coverage, comparing life expectancy and combined vaccination administered scores for Diptheria, Polio, Measles, and Hepatitis B and influence of early childhood vaccination on schooling. By visually analyzing these aspects, we can easily identify trends, patterns and relationships between the variables of interest .

In this section I will show my visualization along with my analysis of it. I used Microsoft Power BI for my visualizations.

Process for the analysis :

First, you need to install a Power BI desktop on your PC or laptop.

i) Import the data

After installing Power BI, we need to import the downloaded file into the tool.

By clicking the option of get data you can import files into the tool. For this analysis, we will go with the Text/CSV option for uploading the file.

ii) Clean the data

After loading a file into the tool, let’s clean the data which we have imported. Power Query editor is a very useful tool in power bi, it helps to clean the data. To go into the power query editor click on the option of Transform data. You will see the interface like this

 Here’s a look at my dashboard.

The overall average life expectancy estimated was 69 years.  The mortality rate of under 5-year-old, infants and adults (per 1,000 live births) were 123,50, 89,033 and 482,524 between 2000-2015.

 Description: This clustered bar chart compares adult mortality, infant deaths, under-five deaths, and the average life expectancy for the top five countries with the highest average life expectancy and low death coverage. The x-axis represents the country, while the y-axis displays the values for each mortality indicator and life expectancy.

Insights: The chart shows that these countries have generally low mortality rates, reflecting their high average life expectancies. While all countries have low mortality rates, there are variations in specific indicators. For instance, Japan has a significantly higher rate of under-five deaths compared to other countries, while Switzerland has a higher adult mortality rate. Infants deaths are almost non-existent in these countries. The chart demonstrates the importance of having strong public health systems that effectively address various mortality factors to achieve high life expectancies and improve overall health outcomes.

Description: This stacked bar chart compares different health risk factors by SDG region, focusing on children's deaths. The bars are stacked based on the five health risk factors: BMI, thinness in 10-19-year-olds, thinness in 5-9-year-olds, infant deaths, and under-five deaths. The x-axis represents the health risk factors, while the y-axis shows the number of cases.

Insights: Sub-Saharan Africa faces a significantly higher number of cases for all five health risk factors, particularly in terms of infant and under-five deaths. This region requires targeted interventions and support to improve child health outcomes. Latin America and the Caribbean have a high number of under-five deaths. Eastern Asia and Southeast Asia have a relatively high number of thinness cases in children while Central Asia has the highest recorded incidence of infant deaths. Oceania excluding Australia and New Zealand has a very low overall health risk factor count. However, the chart shows that, under-five deaths are a prevalent concern across all regions, highlighting the need for comprehensive public health programs to address this challenge.

Description: This clustered bar chart presents the sum of adult mortality based on life expectancy bins (36-48, 49-60, 61-72, 73-80, and 81-89 years) and categorized by BMI class (normal:18.5-24.9, obesity: >=30, overweight: 25-29.9, and underweight:<18.5). The y-axis shows the sum of adult mortality, while the x-axis displays the life expectancy bin.

Insights: The highest sum of adult mortality consistently occurs within the obesity category across all life expectancy bins. This highlights the significant impact of obesity on mortality rates. The chart emphasizes the need for public health interventions targeting obesity and its associated health risks to reduce adult mortality rates.

Description: This clustered bar chart shows the average adult mortality rate by alcohol consumption, grouped by life expectancy bin and year.

Insights: Generally, the average adult mortality rate tends to decrease over time across all life expectancy bins. This indicates progress in overall health outcomes. The average mortality rate is consistently higher in the lower life expectancy bins (36-48 and 49-60), suggesting that alcohol consumption may contribute to higher mortality rates and lower life expectancy. However, while the chart doesn't explicitly show the specific effects  of and correlation between alcohol consumption on mortality, it provides a starting point for further investigation.

Generally, the average adult mortality rate tends to decrease over time across all life expectancy bins. This indicates progress in overall health outcomes. The average mortality rate is consistently higher in the lower life expectancy bins (36-48 and 49-60), suggesting that alcohol consumption may contribute to higher mortality rates and lower life expectancy. However, while the chart doesn't explicitly show the specific effects  of and correlation between alcohol consumption on mortality, it provides a starting point for further investigation.

 Description: This bar chart compares the total number of all vaccines administered, and their compounding influence on adult mortality, infant deaths, and under-five deaths across different regions. The x-axis shows the region, while the y-axis represents the values for each indicator.

Insights: Africa has the highest number of immunizations administered. Africa also has the highest adult mortality and under-five deaths. Europe has a significantly lower number of immunizations administered compared to other regions, despite having a lower mortality rate. Hence, regions with high mortality rates are receiving more vaccines compared to the developed countries or regions with low mortality rates. This chart therefore reveals that, only administering vaccination drugs can not improve life expectancy, but the other factors must be well targeted.

Description: This line chart displays the trend of social factors, including schooling, alcohol consumption, HIV/AIDS, and average life expectancy over time. The x-axis represents the year, and the y-axis shows the values for each factor.

Insights: : The chart demonstrates the influence of various social factors on average life expectancy. For example, the increase in average life expectancy aligns with the increase in schooling and decrease in HIV/AIDS prevalence. While alcohol consumption generally have negative impacts, their influence might vary over time, however, a direct causative effect  on life expectancy cannot be established. especially.Hence, investments in education, healthcare, and poverty reduction can have a significant impact on public health.

Description: This donnut chart displays the distribution of all immunization vaccines administered by economic status, divided into developing and developed regions. The chart shows the percentage of vaccines administered in each category.

Insights: The chart reveals that a significantly higher percentage of immunization vaccines (75.12%) are administered in developing regions compared to developed regions (24.88%). This suggests that efforts to increase vaccination rates are more successful in developing countries where the burden of preventable diseases is higher.

Description: This line chart shows the change in average life expectancy over time, together with the number of immunizations administered for Hepatitis B, Polio, Measles, and Diphtheria.

Insights: The average life expectancy generally increases over time, with a significant jump between 2010 and 2015. The number of immunizations administered for all four diseases generally increases over time, coinciding with the increase in life expectancy. Hence, underscoring the importance of public health vaccination interventions for improved life expectancy.

Description: This gauger chart shows a comparison of GDP and government expenditure on health.

Insights: Government expenditure on health per the overall GDP is very small, hence .

Recommendations

Here are some recommendations based on the insights from these charts:

1.     Continue to increase vaccination coverage, particularly in regions with lower rates. This can help prevent preventable diseases and improve health outcomes.

2.     Invest in public health programs that address various health risk factors, particularly those identified as major contributors to mortality rates (e.g., obesity, under-five deaths, thinness).

3.     Increase government health spending by prioritizing investments in healthcare to improve access to quality services and reduce health disparities

4.     Focus on social and economic factors that influence health outcomes, such as education, income inequality, and access to healthcare.

5.     There were values in the datasets which are clearly impossible, like India having 1800 per 1000 population which might need to rectified, updated or removed entirely from the data.