Effects of Particulate Matter Air Pollution on Cardiovascular Disease In San Bernardino County- California - All Nursing AssignmentsAll Nursing Assignments

Abstract

Cardiovascular disease (CVD) has become the greatest influence over human mortality rates. Numerous studies have shown that this may be in strong part to various forms of pollution. Among these, particulate matter (PM2.5) has been seen as the most threatening to the cardiovascular system. This study will examine the possible correlations between CVD and PM2.5 as well as other potential pollutants such as drinking water contamination and economic factors such as poverty in the county of San Bernardino. Areas of the county were shown to have high levels of CVD and pollution according to the Office of Environmental Health Hazard Assessment. The clustering of these variables using spatial auto correlation will reveal any potential patterns and relationships between pollution and human health.

Introduction

Background

According to the Center for Disease Control, one out of every four deaths in the United States is caused by heart disease every year. It remains the leading cause of death for both men and women (CDC 2019). Consequently, over the past several decades, scientists and health officials have researched the affects of pollution on human health in an effort to understand the underlying causes of heart disease.

In July of 2018 The Office of Environmental Health Hazard Assessment (OEHHA) on behalf of the California Environmental Protection Agency updated a catalogue of data called the CalEnviroscreen. This dataset recorded the various levels of pollution among census tracts within the state of California by categorizing the different types of observed pollution, including drinking water, particulate matter, ozone, solid wastes, etc.. In addition, the database also recorded other various levels of socio-economic criteria such as poverty, ethnicity, and health factors. Among the health factors were asthma, low birth weight and cardiovascular disease (CVD). The data showed that the city of Barstow, CA ranked third in CVD and contained above average levels of particulate matter, a pollutant shown to have harmful effects on the human heart. For the purposes of this study, the relationship between these two variables will be examined the most due to links found in previous research.

Particulate matter (PM) is airborne material in the form of solid or liquid particles which are generated from both human caused and natural sources. The composition of PM varies in size and has been classified into three groups: PM10, PM2.5, and UFPs. PM10 are any particles between 2.5 and 10 micrometers and typically originate from road dust, fields, tire wear emissions, construction, demolition and mining. Natural sources include wildfires. PM2.5 and are particles ranging from 0.1 to 2.5 micrometers in size and are primarily derived from traffic and industrial fumes. UFPs are particles less than 0.1 micrometers and generally come from tailpipe emissions. According to previous studies, PM2.5 has been shown to have the biggest impact on human health. It has been shown to cause inflammation of lung pathways as well as impair arteries and microvascular function in the heart, particularly in older age groups. It has also been associated with higher blood pressure. With prolonged exposure, it may also contribute to cardiometabolic disorders (Ming et al. 2015).

In order to further examine the relationship between CVD and PM2.5, the census tracts within the county of San Bernardino will be examined using statistical methods of spatial autocorrelation and regression to establish any links between the two. In addition, other pollutants such as drinking water contamination and ozone will be analyzed similarly as well as the poverty rate. The correlation of these variables will help to establish a backdrop of spatial clustering that can be used to better understand the significance of PM2.5 and its effects on CVD. The results can then be compared to that of the larger population of Los Angeles County in order to examine any differences.

Significance of Research

Cardiovascular disease is the strongest threat to human mortality among all other factors. This research will contribute to a growing list of health studies that will contribute to the overall effort by health officials, civic leaders and the general public to help reduce airborne emissions. Reductions in PM2.5 will have the greatest impact on saving lives by limiting the amount of chemicals emitted from fuel combustion. This could potentially lead to societies seeking cleaner forms of energy and encourage more environmental protection. The results of this study will also be able to inform the OEHHA of the significance of the CalEnviroscreen data and how it can be used to shape California EPA policy in the future.

Thesis Statement

This project will analyze pollution data and its effects, if any, on cardiovascular disease in San Bernardino County, California. The analysis should identify clusters of high pollution and their spatial correlation to populations with a high rate of cardiovascular disease within the census tracts of the county.

Hypothesis

Based on relevant literature and the data provided, it is expected that there will be a strong correlation between PM 2.5 and cardiovascular disease within San Bernardino County at the tract level. Data from the CalEnviroscreen 2018 indicates a high level of cardiovascular disease within these tracts, and particularly in the city of Barstow, which is ranked third in CV rate out of all other cities in California. The data also indicates higher than normal levels of PM 2.5. Previous studies have shown a link between heart disease and this form of air pollution.

Literature Review on the effects of air pollution on cardiovascular disease

and their relation to vulnerable populations

In recent decades, there have been major concerns over the effects of air pollution on the population of industrialized societies. In particular, special attention has been given to the long-term effects of certain pollutants on the human cardiovascular system. This is in large part due to the persistence of heart disease as the number one cause of mortality rates among the world’s population (CDC 2019). Numerous studies have consistently established a link between a specific kind of pollutant known as particulate matter and various health risks. There has also been proven to be a significant disparity between various ethnicities and classes in how they are affected by particulate matter and its negative impacts on health. This is referred to as environmental justice. In order to proceed with new research into how air pollution affects human health, it is important to establish the sources of particulate matter, the health risks associated with it and how those risks are distributed across society.

Previous research has typically demonstrated links between air pollution and cardiovascular disease by examining fixed populations and their exposure to pollutants in their communities over time. Data is usually obtained through hospital records as well as surveys of particular study groups. Geographic Information Science has also allowed for studies of spatial distribution of pollution, health risks, and vulnerable populations. While it is often difficult to account for all variables that can affect a person’s health, enough evidence has been presented to suggest a negative impact from industrialization, particularly among dense populations. In addition, by cross referencing census data with that of polluted areas, certain demographics have been found to be at greater risk of disease. This generally indicates a number of social factors at play, ranging from income, ethnicity, to government policy.

Sources of Air Pollution

Previous research has established the primary sources of air pollution among industrialized nations. Vehicles, power plants, mining of natural resources, agricultural activities and wildfires contribute to the majority of chemicals and particles in the air. Common pollutants include particulate matter, sulfur oxides, nitrogen oxides, carbon monoxide, and ozone (Anderson, et al. 2018, 10,830). Researchers have often focused on a specific cause in order to better guide environmental policy, however a single cause of air pollution can emit several co-pollutants which can have varying effects on human health. For example, smoke from wildfires contains many co-pollutants including nitrogen dioxide, ozone, carbon monoxide, polycyclic aromatic hydrocarbons, aldehydes, and particulate matter 2.5 (Balmes, et al. 2016, 227). Particulate matter (PM) has stood out among the research as the most hazardous pollutant.

Ambient PM is defined as airborne particles, either liquid or solid which is derived from human and natural activities (Chu, et al. 2015, E8). Specifically, PM2.5 are particles smaller than 2.5 micrometers and come mostly from traffic or fuel combustion and industry (Chu, et al. 2015, E9). Many of the most harmful PM2.5 comes from vehicle emissions and tire wear as well as the burning of biomass. These have been found to be closely associated with cardiovascular and respiratory diseases (Berger, et al. 2015, 458).

Effects of PM 2.5 on Human Health

PM2.5 has been considered the main culprit of the adverse cardiovascular effects of air pollution on human health. This is due not only to the multiple sources that emit it, but also to the smaller size of the particles, which are able to penetrate further into vital organs of the body. (Chu, et al. 2015, E9). Even short term exposure to PM’s can affect CVD mortality rates. PMs can cause inflammation and oxidative stress within circulatory systems (Chu, et al. 2015, E13 and E14). But most health officials and researchers are concerned with the long-term effects which have been studied in numerous groups over time. For example, in North Carolina, Satellite-based estimates of long-term PM2.5 were associated with both coronary artery disease and myocardial infarction (Cascio, et al. 2015, 15). Counties in Southern California generally had the biggest years lost in life expectancy as compared to counties in other states when all environmental pollutants were reduced to the regional 25th percentile. It has been suggested that the effect of long-term exposure to PM2.5 may lead to a reduction of life expectancy of more than a year (Chan, et al. 2015, 16). Even though exposure to PM2.5 air pollution has been associated with a number of adverse health effects, it has not always been the underlying cause. One study showed a significant increase in cardiovascular disease and respiratory mortality among the elderly in a study of AARP members aged 50 – 71 (Ahn, et al. 2016, 484). And in Oakland, CA, there was a 12% Increased risk of cardiovascular event among the elderly (65 and older) (Alexeeff, et al. 2018, 10). Although ventricular tachycardia was increased in association with exposure to particulates, secondary organic carbon and ozone. Heart rate variablity was not associated with exposure to particulate matter in the majority of participants (Bartell, et al. 2013, 1140). Impacts between psychosocial stressors and air pollution appear to have similar effects on biological pathways such as immune function, inflammatory response, and sympathetic nervous system. But there is only modest evidence of interactions between air pollution and stress (Hajat, Hazlehurst, and Nurius 2018, 12-14).Perchloroethylene or PCE may be linked to forms of cancer and epilepsy. However, subjects were found to not be at risk of cardiovascular disease or obesity (Aschengrau, et al. 2015, 6). In another study, there was a significant relationship found between PM2.5 from wildfires and respiratory hospitalizations. But largely null results for association with heart disease near wildfire areas (Balmes, et al. 2016, 230 – 231). Low birth weight was significantly associated with interpolated measurements of ozone but not total fine PM or NO. It was associated with ultrafine particles from proximity to roadways and traffic density (Bartell, et al. 2016, 471).

Environmental Justice and Vulnerable Populations

One of the broader concerns among researchers and policy makers is the idea of environmental justice and how environmental hazards can cause disparities among disadvantaged populations. Groups most at risk include the elderly and non-white ethnicities. For example, lower income families had greater exposure to arsenic in a Colorado water supply (Byers, et al., 2015, 132). In another study in the San Joaquin Valley and Greater Los Angeles Area, the odds of living in one of the 10% most affected zip codes were significantly higher for non-whites. Environmental hazards were more regressivly distributed with respect to ethnicity than poverty (Alexeeff, et al. 2015, 2341). Disadvantaged communities are exposed to slightly higher emissions than others according to a California study (Anderson, et al. 2018, 10,829). Exposure to air and water pollution is a strong predictor of respiratory illnesses among children 15 and under. The effects of living in neighborhoods with high diversity increases the relative risk of preventable disease hospitalizations for all except white (Alcala, Capitman, and Lessard 2015, 203).

Socioeconomic status has greater impact on overall burden of disease than enironmental pollutant exposure (Greenfield, Thomas, and Rajan 2017, 13-14). Elder care facilities should be required to be placed no less than 500ft from a major roadway in order to reduce mortality rates due to pollution exposure (Levine and Woodward 2015, 54). The cost of NRAP-attributable heart disease is projected to increase largely by 2035 due to an aging population (Brandt, et al. 2017, 396). Women were generally found to be more at risk of heart disease than males (Balmes, et al. 2016, 231). Within-neighborhood levels of traffic related air pollutants are significant among elderly populations, but not among the general population (Alexeeff, et al. 2018, 15).

Long-term exposure to PM2.5 air pollution was associated with a significant increase in CVD and respiratory mortality among the elderly in a study of AARP members aged 50 – 71 (Ahn, et al. 2016, 484). Significant associations in the elderly between cause-specific mortality and long-term exposure to PM2.5. There have been higher mortality rates in rural areas associated with air pollution, but that may be related to distance and general health knowledge (Garcia, Park, and Weller 2015, 152). Characteristics of different populations can lead to PM-related effects including life stage, preexisting diseases, polymorphisms, and low-socioeconomic status (Brown, et al. 2011, 452).

The literature indicates the severity of particulate matter 2.5 and its adverse effects on health. However it is unclear how much other variables contribute to the these health risks.

Methods

Study Site

The study site will encompass all of San Bernardino County which will be divided by census tracts. This will allow for a larger sample of the population to be studied and for more detailed comparisons between the tested variables.

Data Sources

Secondary data will be downloaded from the Office of Environmental Health Hazard Assessment. The dataset referenced will be the CalEnviroScreen 3.0, last updated in July of 2018. These tables will include multiple categories of pollution hazard scores based on the observations taken from the California Environmental Protection Agency. Also included are various population data including cardiovascular disease rates, and census data indicating median household income and poverty levels (OEHHA 2019).

Measures

Several data points will be used from the CalEnviroScreen dataset. These will include Cardiovascular Disease as the dependent variable, and several other independent variables including PM2.5, drinking water contamination, and poverty. All of these will be used at the tract level. The data will also be sorted to estimate the rate of these factors per city to make additional comparisons.

Analyses

In order to examine a relationship between the data variables and heart disease, the statistical methods of Spatial Autocorrelation and Regression will be used. These will be necessary to provide a visual representation of the dependent and independent variable relationships through clustering as well as quantitative results.

San Bernardino PM2.5 Spatial Autocorrelation Report

Given the z-score of 64.8480586915, there is a less than 1% likelihood that this clustered pattern could be the result of random chance.

Global Moran’s I Summary

Moran’s Index:	0.353908
Expected Index:	-0.002717
Variance:	0.000030
z-score:	64.848059
p-value:	0.000000

San Bernardino Cardiovascular Disease / Particulate Matter 2.5 Correlation

Descriptive Statistics
	Mean	Std. Deviation	N
cvd	11.629	3.0977	369
pm	11.048	2.5953	369

Correlations
		cvd	pm
Pearson Correlation	cvd	1.000	-.330
Pearson Correlation	pm	-.330	1.000
Sig. (1-tailed)	cvd	.	.000
Sig. (1-tailed)	pm	.000	.
N	cvd	369	369
N	pm	369	369

San Bernardino Cardiovascular Disease / Drinking Water Correlation

Descriptive Statistics
	Mean	Std. Deviation	N
cvd	11.629	3.097	369
drink	659.003	200.412	369

Correlations
		cvd	drink
Pearson Correlation	cvd	1.000	-.017
Pearson Correlation	drink	-.017	1.000
Sig. (1-tailed)	cvd	.	.372
Sig. (1-tailed)	drink	.372	.
N	cvd	369	369
N	drink	369	369

Los Angeles Cardiovascular Disease / Particulate Matter 2.5 Correlation

Descriptive Statistics
	Mean	Std. Deviation	N
cvd	7.898	2.324	632
pm	12.448	.421	632

Correlations
		cvd	pm
Pearson Correlation	cvd	1.000	-.114
Pearson Correlation	pm	-.114	1.000
Sig. (1-tailed)	cvd	.	.002
Sig. (1-tailed)	pm	.002	.
N	cvd	632	632
N	pm	632	632

Los Angeles Cardiovascular Disease / Drinking Water Correlation

Descriptive Statistics
	Mean	Std. Deviation	N
cvd	7.898	2.324	632
drink	611.707	126.483	632

Correlations
		cvd	drink
Pearson Correlation	cvd	1.000	-.014
Pearson Correlation	drink	-.014	1.000
Sig. (1-tailed)	cvd	.	.364
Sig. (1-tailed)	drink	.364	.
N	cvd	632	632
N	drink	632	632

Descriptive Statistics
	Mean	Std. Deviation	N
cvd	7.898	2.324	632
drink	611.707	126.483	632

Descriptive Statistics
	Mean	Std. Deviation	N
cvd	7.898	2.324	632
drink	611.707	126.483	632

Correlations
		cvd	drink	pm	pov	edu
Pearson Correlation	cvd	1.000	-.017	-.330	.406	.293
	drink	-.017	1.000	.186	.027	.175
	pm	-.330	.186	1.000	-.112	.230
	pov	.406	.027	-.112	1.000	.739
	edu	.293	.175	.230	.739	1.000
Sig. (1-tailed)	cvd	.	.372	.000	.000	.000
	drink	.372	.	.000	.300	.000
	pm	.000	.000	.	.016	.000
	pov	.000	.300	.016	.	.000
	edu	.000	.000	.000	.000	.
N	cvd	369	369	369	369	369
	drink	369	369	369	369	369
	pm	369	369	369	369	369
	pov	369	369	369	369	369
	edu	369	369	369	369	369

Results

The tables provided indicate the significance of the variables analyzed as well as their correlation with one another. A negative correlation was found between CVD and PM2.5.

Discussion

Gaps and Limitations

The results found in this study are important in directing the aim of future research into adverse health effects from not only pollution, but other environmental and socioeconomic factors. However, it should be noted that this information covers a limited number of variables which affect cardiovascular rates. For example, the CVD rates are taken from emergency room visits only and do not encompass the larger population living daily with heart disease. Particulate matter is also a biproduct of several forms of human activity. Fires, vehicles, agricultural practices, etc., all contribute to PM2.5 in some way. This leaves researches without more clear directions of study in the future.

Despite its limitations, CalEnviroScreen is still a useful tool for analyzing potential health and environmental threats to the state of California. More criteria should be added to the table in the future in order to ascertain more accurate pollution scores.

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www.cdc.gov/heartdisease

, February 19, 2019

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