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DATA VISUALISATION: WEEK I ASSIGNMENT

TITLE: AN ASSESSMENT OF THE DETERMINANTS OF LIFE EXPECTANCY.

 I have chosen the GapMinder dataset as the dataset of interest.

 After looking through the codebook for the Gapminder study, I have decided to examine life expectancy dependence. I feel that income per person might influence life expectancy dependence, but then other variables of interest are important (e.g. HIVrate, employment rate, alcohol consumption per adult rate, CO2 emission rate, residential electricity consumption rate, and urban population ), so for now I will include all of the relevant variables in my personal codebook.

Life expectancy is one of the key indicators of population health and economic development. Citizens of the poorest countries are likely expected to live for many decades less than those of the wealthiest nations. Interestingly, within the wealthy and advanced countries variations in the average expectation of life are not so great, but there are differences that cannot easily be explained by reference to economic prosperity as adjudged by the Gross Domestic Products of the individual nations.

The following research question would then become of interest:

·         Is income per person associated with life expectancy?

·         Is HIV rate associated with life expectancy?

·         Is employment rate associated with life expectancy?

·         Is alcohol consumption associated with life expectancy?

·         Is CO2 emission associated with life expectancy?

·         Is electricity consumption associated with life expectancy?

·         Is urbanization associated with life expectancy?

 LITERATURE REVIEW

What is life expectancy? Life expectancy is the key metric for assessing population health. Broader than the narrow metric of the infant and child mortality, which focus solely at mortality at a young age, life expectancy captures the mortality along the entire life course. It tells us the average age of death in a population. Life expectancy is a measure of premature death and it shows large differences in health across the world-Max Roser et al, 2019.

Since 1900 the global average life expectancy has more than doubled and is now above 70 years. The inequality of life expectancy is still very large across and within countries. in 2019 the country with the lowest life expectancy is the Central African Republic with 53 years, in Japan life expectancy is 30 years longer.

Similar or contextual terms used in demography whilst discussing life expectancy are, child mortality (death of children under the age of five) and infant mortality (death of those under the age of one).

There have been numerous researches and studies on life expectancy and mortalities and several determinants ranging from social-economic factors such as Gross Domestic Product, Political consideration such as democracy and health factors such as HIV rates. It is generally opined that life expectancy is positively associated with wealth and income.

Some researchers have argued that income inequality has an association with life expectancy. Richard Wilkinson has advanced the view that income inequality is the key determinant of variations in average life expectancy at birth among developed countries. Ken (1995) opined that a careful examination of the two sources of data on income distribution most often used by Wilkinson suggests that if they are analyzed more appropriately, they do not lend support to his claims. In fact, Clough-Gorr (2015) referred to what he called Swiss paradox when he observed that higher income inequality of municipalities is associated with lower mortality in Switzerland.

A study by Chetty et al found that the gaps in life expectancy between the 1% richest and 1% poorest individuals were 14.6 years for men and 10.1 years for women from 2000 to 2014 in the United States. Similar income-mortality gradients have been observed in several high-income nations, which has been attributed to differences in access to health care, behavioral risk factors, inequalities in income, social stress, and neighborhood characteristics.

Table 1 provides a summary of some recent studies on life expectancy and the associations researched by different scholars.

   Table 1: Author’s collation.

 HYPOTHESIS

 Ho1: Income per person is not associated with life expectancy

Ho2: HIV rate is not associated with life expectancy

Ho3: Employment rate associated with life expectancy

Ho4: Alcohol consumption associated with life expectancy

Ho5: CO2 emission associated with life expectancy

Ho6: Electricity consumption associated with life expectancy

Ho7: Urbanization associated with life expectancy

       REFERENCES

 Andoh, S. Y., Umezaki, M., Nakamura, K., Kizuki, M., & Takano, T. (2006). Correlation between national income, HIV/AIDS and political status and mortalities in African countries. Public health, 120(7), 624-633.

 Clough-Gorr KM, Egger M, Spoerri A. Eur J Epidemiol. 2015 Aug; 30(8):627-36. Epub 2015 Jan 20.

 Chetty R, Stepner M, Abraham S, et al. The Association Between Income and Life Expectancy in the United States, 2001-2014. JAMA. 2016;315(16):1750–1766. doi:10.1001/jama.2016.4226

Chuang K, Sung P, Chang C, et al. Political and economic characteristics as moderators of the relationship between health services and infant mortality in less-developed countries. J Epidemiol Community Health 2013;67:1006-1012.

 Kinge JM, Modalsli JH, Øverland S, et al. Association of Household Income With Life Expectancy and Cause-Specific Mortality in Norway, 2005-2015. JAMA. 2019;321(19):1916–1925. doi:10.1001/jama.2019.4329

Max Roser, Hannah Ritchie and Bernadeta Dadonaite (2020) - "Child and Infant Mortality". Published online at OurWorldInData.org. Retrieved from: 'https://ourworldindata.org/child-mortality' [Online Resource]

 Robert E. Hall, Charles I. Jones, The Value of Life and the Rise in Health Spending, The Quarterly Journal of Economics, Volume 122, Issue 1, February 2007, Pages 39–72, https://doi.org/10.1162/qjec.122.1.39

Wilkinson R. G. (1992). Income distribution and life expectancy. BMJ (Clinical research ed.), 304(6820), 165–168. https://doi.org/10.1136/bmj.304.6820.165