Air Pollution in Lower Income Families People

ENVIRONMENTAL HEALTH

Environmental Health on Air Pollution on Lower Income Families/People

Background

Low income families are disproportionately affected by pollution, particularly air pollution. Essentially, environment is a major determinant of resident health. Research indicates that air pollution is a predictive of different health problems that low income families face. For instance, Kriegler and Higgins (2002) indicate that low-income families live in poor housing conditions that are prone to air pollution. As a result, low income families are largely affected by various health conditions which are inclusive of, but they are not limited to; chronic diseases, asthma, poor nutrition especially among children, and infectious diseases (Kriegler and Higgins, 2002). I will be investigating how mortality is related to air pollution in the urban areas and income levels in the neighborhoods.

Rationale and Purpose

The association between health and air pollution in low income families have been explored in various studies. In some quarters, this has informed public agency efforts aimed at improving housing conditions for low-income families. However, there are some gaps that exist in as far as current knowledge on the association between air pollution and overall life expectancy is concerned. Therefore, additional research on the effects of air pollution on life expectancy among lower income families is needed so as to add on to current knowledge. This will also come in handy in efforts to establish whether the diseases caused by air pollution are the reasons for early death in low-income families. The study will attempt to provide an understanding of how diseases caused by air pollution lead to decreased life expectancy among lower income families. It would also be prudent to note that the study will provide a more detailed overview of the health effects of air pollution on lower income families and assess their life expectancy in terms of death rates. It is anticipated that the data collected from this assessment will help come up with better interventions meant to reduce or eliminating air pollution in low-income households and neighborhoods. In addition, it will make recommendations on how evidence-based research can be carried out in public health.

Research Question, Objectives, and Hypothesis

The research question that will be addressed in this research will be: How does air pollution affect overall life expectancy in lower income families? This question has been given minimal consideration in preliminary literature and is an important questions owing to the reasons that had been outlined earlier. This research will dwell on life expectancy and air pollution in low-income families. Effort will be made to determine whether life expectancy is associated with air pollution in low-income families. The main objective of the current study is to examine the effects of air pollution so as to determine whether this phenomenon is responsible for early death in low-incomes families. The study will provide a comprehensive literature review of resources related to the topic. The study will also outline the theoretical framework for estimating mortality. It is hypothesized that there is a direct relationship between air pollution and overall life expectancy.

Literature Review

There are a few studies that have in the past sought to establish if there is a link between air pollution and overall life expectancy. Others have sought to determine whether the health effects of air pollution are associated with high death rates among low-income families. For instance, in a study conducted by Hill, Jorgenson, Ore, Balistreri, and Clark (2019) to investigate whether air pollution causes harmful health effects on populations with inequitable income distribution in US, two datasets observations were conducted. The first was conducted from 2000-2010, while the second included the years of 2000, 2005, and 2010. Observational results from the regression model showed a close relationship between life expectancy, PM2.5 levels, and levels of income. Essentially, high levels of PM2.5 was associated with lower life expectancy among lower income families (Hill et al., 2019). In another study conducted by Bennet, Tamura-Wicks, Parks, Burnet, Pope, Bechle, Marshall, Danaei, and Ezzati (2019), the said researchers intended to estimate the impact of particulate matter on longevity and health. The said study was conducted from 1999-2015. Results from the study showed that particulate matter is related to various diseases associated with air pollution such as cardiovascular diseases (Bennet et al, 2019). The said cardiovascular diseases were linked to high death rates in the United States. However, a significant death rate was observed in adults who were middle-aged compared to the older adults. Further, it would be prudent to note that the said death rates were high in states that had low income (Bennet et al., 2019).

In yet another study conducted by Finkelstein, Jerret, DeLuca, Finkelstein, Verma, Chapman and Sears (2003), the authors investigated the relationship between mortality, air pollution, and income levels in neighborhoods. In this study, 5228 people from Hamilton-Burlington in Southern Ontaria participated. The participants used were individuals who had been named for testing of the pulmonary function from 1985-1999. Household income and air pollution levels were estimated through postal codes (Finkelstein et al., 2003). Observational results from the said study showed Hamilton state had high pollution owing to heavy industrial zones. In addition, it was observed that the rate of mortality was high in low-income neighborhoods which had air pollution exposure levels above the median level (Finkelstein et al., 2003).

From the studies highlighted, it would be prudent to note that the authors agree on the fact that air pollution is related to low life expectancy among low-income families. Hill et al. (2019) and Bennet et al. (2019) conducted observational studies. Since observational studies cannot be guaranteed to be causal, the methodologies that appear to be more reliable and valid happens to be those extracted from the study conducted by Finkelstein et al. (2003). In addition, mortality rates in the two studies was based on particulate matter, disregarding other likely determinants of mortality.

Methods

Theory/Model Used for Research Design

The model that I will use to answer my research question will be based on cox proportional hazards regression model that was used in a previous study by Finkelstein et al. (2003). The Cox proportional regression model places the focus on measuring the hazard rate by investigating the relation between survival time and predictor variables. Essentially, the Cox proportional regression model measures several exposures or risk factors which are considered up to a certain time (LaMorte, 2016). The risk factor is also referred to as the probability of the event taking place and its range must be between 0 and 1. In my research, I will be exploring the probability of death occurring in low-income families and the relationship it has on air pollution. In this case, I will be comparing low income families with wealthy families, with respect to air pollution. It is important to note that to a large extent, the Cox proportional regression model directs me to choose/select one or more risk factors that I can explore in depth. Towards this end, the most appropriate means of understanding my research question from the hazards approach would be to carry out a cohort study and observe the participants so as to determine the mortality rate.

Research Design

The research design that I would employ would be qualitative research. The group I will be investigation would comprise of lower income families living in urban areas. There are various reasons as to why I chose the qualitative research design. First, my research question is focused on studying a subset of the entire population. I am interested in studying lower income families. Second, what is of interest in my research question is the overall life expectancy of the said families. Therefore, the main focus would be to observe the mortality rate among low income families. In addition, my research question aims to determine whether persons from lower income families die at an early age when exposed to air pollution. I will use random sampling for my research whereby the participants will be selected randomly. The said participants would be a representation of the low income families.

Data Collection Procedures

To collect data for my research, I will rely on primary data whereby I will observe the participants through participant observation data collection method. According to Kaluwich (2005), participant observation is a method that enables researchers to learn about a specific activity of the persons being studied. Further, the author suggest that the researcher observes or participates in those activities. Participant observation happens to be a valid method of data collection owing to the fact that it helps researchers have a clear understanding about a phenomenon or context of study (Kaluwich, 2005). In this case, I will survey the relevant health records so as to understand life expectancy after a specified period of time.

Data Analysis Procedures

Given that my research would be based on qualitative design, data analysis would involve the process of arranging and systematically searching non-textual materials, observational notes, and interview transcripts accumulated to understand a phenomenon. In my research, I will search and arrange the observational notes that I had gathered so that I can understand whether air pollution affects the overall life expectancy of lower income families. According to Wong (2008), data analysis process involves categorizing and coding the relevant data. Coding happens to be a crucial step and it involves the process of dividing the raw data into various categories. Essentially, codes are labels or tags that are used to allocate topics or themes that were retrieved from the study (Wong, 2008). One of the ways through which I will categorize data would comprise of the use software whereby I will utilize computer software packages to retrieve data. One computer software package that could be useful in analyzing data for my research is the NVivo. According to Wong (2008) NVivo is a type of computer assisted data analysis software (CADAS) that enhances the quality of research. The said software will help me retrieve and sort data even after coding the said data. To use the said software in my research, I would first design and explore nodes and documents after coding the data. This would help me refer age factor as one of the characteristics of the required data. The NVivo software will also help me identify the relevant themes. As Won (2008) points out, the said software allows researchers to identify themes and remove any manual tasks that may be tedious.

Study Design Validation and Ethics

Maintaining validity happens to be a major challenge in qualitative analysis. This is more so the case given that a qualitative research involves human errors and studies may not be replicated. To ensure that I increase validity in my research study, I would ensure that I spend adequate time with the studied participants, i.e. what is referred as prolonged involvement. I could also partake in peer debriefing. According to Waheed, Kiazai, and Bahadur (2020) ethics and validity happen to be core issues in qualitative research. On this front, qualitative research involves certain risks which are inclusive of, but they are not limited to; stigmatization, inconvenience, discomfort, and embarrassment. Such risks may harms the justice, self-esteem, social utility, non-maleficence, privacy, respect for person, and confidentiality (Waheed, Kiazai, and Bahadur, 2020). Towards this end, I will ensure that the participants only provide information which they are willing to provide. Any participant who is not willing to share any information will not be forced to do so. Participation in the study will also be voluntary. In addition, I will ensure that personally identifiable data of participants is concealed.