Air Pollution Impact Low Income Families Life Expectancy

Research question: How does air pollution affect overall life expectancy on lower income families?

Research objective: To determine whether air pollution affects the life expectancy of low income families.

Hypothesis: In low income families, there is a direct relationship between air pollution and overall life expectancy.

Null Hypothesis: Air pollution does not have an effect on life expectancy among low income families.

Low-income families are largely affected by air pollution, i.e. in terms of overall life expectancy. Essentially, this means that persons from low-income families exposed to air pollution tend to have high mortality rates. The said high mortality rates are caused by diseases brought about by air pollution. These are inclusive of, but they are not limited to; cardiovascular diseases, malignant neoplasms, cerebrovascular disease, heart disease, and cardiorespiratory disease.

Various authors have examined the effects of air pollution on low income families to determine whether it is responsible for the increased death risks in the said families. For instance, Hill et al. (2019) investigated the effects of air pollution on populations with inequitable income distributions in the US. Results from the regression models used revealed a close relationship between life expectancy, PM2.5, and income levels. The authors observed that lower life expectancy was associated with high levels of PM2.5 levels. The life expectancy of a population was lower in a population whose income share was at the bottom 10%. Therefore, it would be prudent to note that lower life expectancy is associated with low income and high PM2.5 levels.

Bennet et al. (2019) examined the impact or particulate matter on longevity and health. From their study, the authors found that, from the different pollutants, form air pollution, PM2.5 was related to various diseases that are caused by air pollution. Essentially, excess PM2.5 pollution was associated with cardiovascular diseases which led to high death rates. The said death rates were more in adults who were middle aged while they declined in older ages (Bennet et al., 2019). In addition, loss of life expectancy owing to PM2.5 was high in countries that had low income compared to counties that were wealthy. The said relation was constant both at the national and at the county level and other variables such as having a lower proportion of graduates from school, being black or African American race, and being poor were included (Bennet et al., 2019). Therefore, according to Bennet et al. (2019), particulate matter causes diseases which lead to deaths and thus loss of longevity. However, since the said study was observational in nature, the results of the study were not causal and cannot guarantee the fact that PM2.5 causes diseases which thereafter leads to higher mortality.

Finkelstein, M., Jerret, DeLuca, Finkelstein, N.,Verma, Chapman, and Sears (2003) conducted a study to investigate the relationship between mortality, air pollution, and income levels in neighborhoods. The authors used proportional hazards regression models to determine the risk to mortality in relation to diagnoses of chronic disease, body mass index, pulmonary functions, pollutant levels, and income. In their study, the authors used 5228 people from Hamilton-Burlington in Southern Ontaria. The said people had been named for testing of the pulmonary function from 1985-1999. Ontario Health Insurance Plan was included from 1992-1999. Death rate of the said individuals was then monitored at the start of January 1992. During follow-up, 64 people who did not have the Ontario Insurance plan were included to ensure that individuals roped into the study were alive. The OHP records were used to group the status of respiratory disease, chronic ischemic heart disease, and diabetes (Finkelstein et al., 2003). Postal codes were then used to estimate air pollution levels and household income. Data for three years was used to determine pollution. It was observed that the north-eastern and north-central of Hamilton had heavy industrial zones which led to pollution. The Cox regression model was used to determine the hazards ratio owing to cardiopulmonary disease and non-accidental causes. According to the authors, the number of people who died during follow-up were 626 whereby 604 of them were from non-accidental causes (Finkelstein et al, 2003). From the 2035 subjects derived from OHIP records, 431 died of respiratory disease. The risk of death owing to non-accidental causes was then computed by the category of income. It was observed that the rate of mortality was high in neighborhoods with income below median levels and those above the median level of air pollution exposure (Finkelstein et al., 2003). High mortality rates may therefore be due to health issues caused by high levels of particulate matter (PM2.5) in the air.

Air pollution affects the overall life expectancy among low-income families. Results conducted from one study show a relationship between health, air pollution, and inequitable income distribution. Another study observed that health and longevity were linked to the impacts of particulate matter. From the said studies, overall life expectancy could be related to income levels, pollution, and health. However, care must be taken while referring to the results of the said studies. This is more so the case given that the study conducted by Bennet et al. (2019) was only observation - essentially meaning that it cannot be guaranteed as being causal. Studies conducted by Hill et al. (2019) included only the urban areas. In addition, while the studies conducted by Finkelstein et al. (2019) involved measuring and monitoring data, data used on this front was only limited to ten years. Therefore, future studies that include rural areas and include data for several years should be considered.