Exposing the Gaps—The Key Determinants of Health


Health indicators are important factors necessary for improving individuals, communities and nations’ health. The association between determinants of health are inextricably linked to health outcomes. Determinants of health are intrinsic and extrinsic factors associated with health outcomes (Cohen, Chavez, & Chehimi, 2012). The intrinsic factors of determinants of health includes the biological and genetic characteristic profiles, lifestyle, culture, ethnicity, etc. The extrinsic factors involve socio-economic status, physical environment, access to health care services, and other external factors (Cohen, Chavez, & Chehimi, 2012; Herbes-Sommers, & Smith, 2008; Laureate Education, 2011; The Office of Disease Prevention and Health Promotion, 2010; Wilkinson, & Pickett, 2010; World Health Organization (WHO), 2008). Both the internal and external health determinants that promotes health inequalities and inequities are partly the underlying primary precursors of health disparity associated with poor health outcomes within a community (Cohen, Chavez, & Chehimi, 2012; Herbes-Sommers, & Smith, 2008; Laureate Education, 2011; The Office of Disease Prevention and Health Promotion, 2010; Wilkinson, & Pickett, 2010; World Health Organization (WHO), 2008). Perhaps, the exploration of life expectancy, child mortality rate, and income distribution gaps between some countries (developed and developing nations) will illustrate the temerity and impacts of some of the primary health determinants within a population.

Exposing the Gaps—The Key Determinants of Health

Health Indicators

Determinants of health factors within a society are multifactorial variables and inter-connected (Cohen, Chavez, & Chehimi, 2012). Life expectancy, child mortality rate, and income distribution gaps are not commonly viewed as proximate health determinant precursors. These determinants are crucial and inseparable factors of health outcomes (Cohen, Chavez, & Chehimi, 2012; Herbes-Sommers, & Smith, 2008; Laureate Education, 2011; The Office of Disease Prevention and Health Promotion, 2010; Wilkinson, & Pickett, 2010; World Health Organization (WHO), 2008). Life expectancy is the statistical expected number of years of life remaining for a given age and time (Sullivan, & Steven, 2012). Mathematically, life expectancy is represented as the mean value of subsequent years of life for an individual aged “x”, a factor dependent on a particular mortality exposure (Sullivan, & Steven, 2012). As a result, life expectancy is an average value, and many individuals within a specified society may be deceased many years prior or post the “expected” average survival span.

Life expectancy is a very good indicator of a population-based health determinants. To illustrate the impacts of life expectancy on health within a given social construct, the life expectancy comparison between Cuba, Finland, US, Japan, Sweden, and Norway are necessary for exploring health outcomes. Using the Gapminder (2013) database, evaluation of the specified nations’ life expectancy showed interesting patterns over time. Although, the inequality index (income gap) of Cuba was not reported in the Gapminder (2013); Finland, Japan, Sweden and Norway had similar income inequality (Gini index) clustering around income inequality index of 25 and 27 (Gapminder, 2013). In contrast, the Gini income inequality index for the US was 41, which is almost twice the Gini inequality index observed in the other three countries (Gapminder, 2013).

The relationship between income gap and life expectancy was apparent. Based on the Gapminder Gini index, Japan and Sweden had the lowest Gini index income gap of 25 and life expectancy of 80 years. Norway and Finland had Gini index income gap of 26 and 27, and the life expectancy were represented as 79 and 78 years respectively (Gapminder, 2013). In contrast, the US Gini index income gap was 41 with a life expectancy of 77 years (Gapminder, 2013). Cuba, on the other hand, a developing country, had a life expectancy of 79 years (Wilkinson, & Pickett, 2010).

Another interesting health determinant is the association between child mortality and inequality. According to the Gapminder (2013), the inequality index of the US, Finland, Norway, Sweden, and Japan are 41, 27, 26, 25, and 25 respectively. Using the inequality income index values as inferential factors, the number of child deaths (0-5 years old) relative to the inequality index of the US (41), Finland (27), Norway (26), Sweden (25), and Japan (25) were standardized to 8.4, 4.3, 4.9, 4.1, and 6.0 per 1,000 births respectively (Gapminder, 2013). Clearly, the US has the highest child mortality rate among these nations and in some cases twice as much.

Nations’ Indicators Compared to Other Nations

Although the associations between life expectancy may not be directly linked to causality. However, the association was interestingly unique and undeniably established interesting theory on the correlation between income gaps and life expectancy. The correlation implication suggested that with an increase in income gaps or income inequalities, life expectancy decreases, and that a decrease in income gaps is associated with an increase in life expectancy on a population-based approach (Gapminder, 2013; Wilkinson, & Pickett, 2010). Based on the relationship described above, the association between the two factors is a negative correlation. Perhaps, it could be inferred that the social disparity associations with income inequalities are factors attributable to health inequalities such as life expectancy differences observed between the specified inter-regional countries (Wilkinson, & Pickett, 2010). Considering these facts, the US ranked very low in terms of income equality and high life expectancy compared to most developed countries, and, perhaps in some developing countries such as Cuba and Costa Rica. Also, among the 23 developed country’s income gaps inequality depicted in Wilkinson and Pickett (2010), the US ranked 22nd.

Rankings of Nations’ Quality of Health.                                                                

In 1950s, the US had the best health outcomes (Laureate Education, 2011). Unfortunately, the US currently ranked 37th in the world on childbirth mortality among women (Laureate Education, 2011). The correlation analysis is more appropriate in explaining inferential association in the absence of experimental studies. In a different study, Rothstein & Uslaner (2005) demonstrated a causality model that inequality affects trust, and that there is no direct effect of trust on inequality but rather, the causal starts with inequality. In addition, studies show that social/economic positioning determines health status. In other words, the higher the social and economic status the higher the quality of life and life expectancy on individual levels or intra-income gap levels or income per capita levels (Laureate Education, 2011).

In the US for instance, the intra-income gaps are substantially different than it is in many developed and some developing countries (Wilkinson, & Pickett, 2010). Based on the disproportionate predisposition of unequal socio-economic constructs, wealthy individuals have more resources, options, and power. As a result, the upper class could easily afford luxuries and essential necessities of life (Laureate Education, 2011; Wilkinson, & Pickett, 2010). While, individuals at the low socio-economic status, who do not have access to and could not afford quality and essential necessities of life have increased risks of health outcomes. Interestingly, the economic gaps between the rich and the poor (predictor variables) in the US, is linked to mortality rate (outcome), hence depicting a negative correlation between the predictor and outcome variables (Laureate Education, 2011; Wilkinson, & Pickett, 2010). In other words, rich individuals experience less negative health outcomes while economically deprived (poor) individuals experience more negative health outcomes on a population-based analysis. The association is not necessarily due to poverty, but because of lack of access to necessary health infrastructures/infostructures or lack of provision of health infrastructures/infostructures within a target population.

Using the US as a model to illustrate the impacts of income inequality on the basis of a state-wise analysis, income inequality is relatively low in the state of Iowa, Vermont, Maine, and Minnesota. Arkansas, Utah and New Hampshire are the best states with the lowest income inequality (Wilkinson, & Pickett, 2010). These states ranked best with the lowest health-social problems (Infant deaths per 1,000 births, homicides, life expectancy) (Wilkinson, & Pickett, 2010). New York, Connecticut, Mississippi, Alabama, and California had the highest income inequality. Also, Mississippi, Alabama, LA-California had substantially worse health and social problems in the US (Laureate Education, 2011; Wilkinson, & Pickett, 2010). More so, within the state of Mississippi and Alabama that already had high-income inequality compared to other states in the US, Mississippi and Alabama’s national income per person are substantially lower compared to other states, specifically, their income person are lower than Minnesota and New Hampshire (Wilkinson, & Pickett, 2010). In addition, Mississippi and Alabama are the strong bases with remnants of racial discrimination in the US. The historical accounts of racial discrimination in the US also presented a different issue on social inequalities, mistrust, and lack of access to health care services among minorities, poor individuals and families (Laureate Education, 2011; Wilkinson, & Pickett, 2010).


Some of the intrinsic and extrinsic factors associated with social determinants of health are connected to many other social variables. Nonetheless, some factors are more attributable risks of health outcomes than others. More so, inextricably linked health risks could facilitate antagonistic (inhibitory) or synergistic (enhancer or promoter) effects (Aschengrau, & Seage, 2014). In an ideal situation, the elimination of extraneous factors or confounders would substantially help in specifying the attributable factors of health outcomes to its primary risk factors.

Therefore, establishing a valid inferential analysis and assessing temporality cues are crucial factors in predicting the link between health outcomes and predictor variables. For instance, Uslaner (2005) stated, “it is inequality that affects trust, not the other way around.” (Wilkinson, & Pickett, 2010, p.55). In furtherance of Uslaner’s (2013) ideation on the inequality-trust phenomenon, and for the interest of expanding the thinking process of temporality phenomenon, I could propose a different theoretical model that the “decline of trust leads to inequality and not that primarily, inequality leads to the decline of trust”. Both assumptions are relevant depending on the condition and temporality sequence taken; but the basis of the proposed new phenomenon is that under normal experimental condition where people adhere together by a common denominator of trust, they tend to exclude and discriminate others they did not trust. Therefore, high level social/career/political opportunities are made available only to the trusted and not to others where perceived-trust is lacking. Hence, to illustrate the validity of the imposed statements or phenomena, investigators must first establish a condition that shows absence of social inequality or mistrust prior to the decline of trust or inequality, respectively. As a result of the social complications within these phenomena, it is fair to conclude that the implications of causality in social science is a difficult and problematic assumption.

In the US, for example, even when a state such as Arkansas had the lowest income inequality, the state had substantial higher obese/overweight issues and homicides compared to New York. And based on the data reported by Wilkinson and Pickett (2010), New York had the worst income inequality in the US. Also, even when the national income per person in North Dakota was very low compared to Connecticut, North Dakota had substantially better health and less social problems than Connecticut (Wilkinson, & Pickett, 2010). With these observations, it is crucial to address public health and health promotion issues with a systematic and dynamic approach in solving population health problems. Therefore, health practitioners should be open to sound reasons and the consideration of multi-variant implications of health outcomes.


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