# data[].attributes.body
2010-2020
United States
Higher Education
English

# data[].attributes.body
pedagogy
"voice initiative"
"higher ed*"

# data[].attributes.body
Karen's research process

# data[].attributes.body
What are the effects of incorporating student voice initiatives into the college classroom

# data[].attributes.body
Can you think of different key words I can use?

# data[].attributes.body
How does recycling affect and cause problems to company businesses?

# data[].attributes.body
Why should someone be payed more or less due to a non controllable position such as your gender?

# data[].attributes.body
1970-2010
united states
gender pay

# data[].attributes.body
equality
equal pay
gender pay gap

# data[].attributes.body
How do you know what key words are the right ones? What must they include?

# data[].attributes.body
Should the use of plastic bags be banned?

# data[].attributes.body
"plastic bags"
"public health"
"plastic pollution"

# data[].attributes.body
2000-2020
United States
English
Plastic Bags

# data[].attributes.body
What filters can help you refine your search better?

# data[].attributes.body
How education impacts our economical sustainability and what change must happen in relation to it?

# data[].attributes.body
“Economy"

“Child education”

# data[].attributes.body
Scholarly journaly
2010-2016

# data[].attributes.body
Is there any specific information I should be focused on when looking into a source in relation to my question? or any key words that can help with my search?

# data[].attributes.body
In what ways in racial discrimination present in healthcare?

# data[].attributes.body
"Patient discrimination"
"Doctor discrimination"
"Discrimination in healthcare"

# data[].attributes.body
2000-2020
United States
English
Physician-Patient Relations

# data[].attributes.body
Does anyone have any ideas of different key words I could use?

# data[].attributes.body
What is the impact of air pollution on people in California?

# data[].attributes.body
"Zero Trash"
"Decrease Deaths"
"Better Health"

# data[].attributes.body
“Why is promotion of great health so important for society, especially today?”

# data[].attributes.body
Should employees be allowed mental health days?

# data[].attributes.body
"employees"
"mental health"

# data[].attributes.body
Scholarly (peer reviewed) Journals

# data[].attributes.body
What kind of filters should I apply? Should I be more specific with my key words?

# data[].attributes.body
How does Spain manage their universal healthcare system?

# data[].attributes.body
“European Healthcare System”
“Benefits“
“Oppositions”
“Spains Healthcare Budget“

# data[].attributes.body
"celebrity endorsements"
"health"
"awareness"
"youth"

# data[].attributes.body
2000-2020,
"Children & Youth", "Humans", and "Celebrities".
Articles.

# data[].attributes.body
Does the direction that I am trying to go in match the article? What other keywords should I be using?

# data[].attributes.body
https://csustan-primo.hosted.exlibrisgroup.com/permalink/f/17e32m8/TN_cdi_crossref_primary_10_1093_eurpub_ckz168

# data[].attributes.body
I’m not too sure how broad or narrow to base my research on?

# data[].attributes.body
Peer reviewed
Full text
2010-2020

# data[].attributes.body
Why are black children between 5-13 are viewed as less innocent than white children in the United States?

# data[].attributes.body
"Black Children"
"Less innocent"
"'In recent years"
"Ages 5-13"
"racism"

# data[].attributes.body
2015-2020
Peer-reviewed
Full text

# data[].attributes.body
What impact has rising sea waters had the ecosystems in islands in the South Pacific?

# data[].attributes.attachment_link.readable_text
WASHINGTON - Black boys as young as 10 may not be viewed in the same light of childhood innocence as their white peers, but are instead more likely to be mistaken as older, be perceived as guilty and face police violence if accused of a crime, according to new research published by the American Psychological Association.

"Children in most societies are considered to be in a distinct group with characteristics such as innocence and the need for protection. Our research found that black boys can be seen as responsible for their actions at an age when white boys still benefit from the assumption that children are essentially innocent," said author Phillip Atiba Goff, PhD, of the University of California, Los Angeles. The study was published online in APA's Journal of Personality and Social Psychology®.

Researchers tested 176 police officers, mostly white males, average age 37, in large urban areas, to determine their levels of two distinct types of bias - prejudice and unconscious dehumanization of black people by comparing them to apes. To test for prejudice, researchers had officers complete a widely used psychological questionnaire with statements such as "It is likely that blacks will bring violence to neighborhoods when they move in." To determine officers' dehumanization of blacks, the researchers gave them a psychological task in which they paired blacks and whites with large cats, such as lions, or with apes. Researchers reviewed police officers' personnel records to determine use of force while on duty and found that those who dehumanized blacks were more likely to have used force against a black child in custody than officers who did not dehumanize blacks. The study described use of force as takedown or wrist lock; kicking or punching; striking with a blunt object; using a police dog, restraints or hobbling; or using tear gas, electric shock or killing. Only dehumanization and not police officers' prejudice against blacks - conscious or not - was linked to violent encounters with black children in custody, according to the study.

The authors noted that police officers' unconscious dehumanization of blacks could have been the result of negative interactions with black children, rather than the cause of using force with black children. "We found evidence that overestimating age and culpability based on racial differences was linked to dehumanizing stereotypes, but future research should try to clarify the relationship between dehumanization and racial disparities in police use of force," Goff said.

The study also involved 264 mostly white, female undergraduate students from large public U.S. universities. In one experiment, students rated the innocence of people ranging from infants to 25-year-olds who were black, white or an unidentified race. The students judged children up to 9 years old as equally innocent regardless of race, but considered black children significantly less innocent than other children in every age group beginning at age 10, the researchers found.

The students were also shown photographs alongside descriptions of various crimes and asked to assess the age and innocence of white, black or Latino boys ages 10 to 17. The students overestimated the age of blacks by an average of 4.5 years and found them more culpable than whites or Latinos, particularly when the boys were matched with serious crimes, the study found. Researchers used questionnaires to assess the participants' prejudice and dehumanization of blacks. They found that participants who implicitly associated blacks with apes thought the black children were older and less innocent.

In another experiment, students first viewed either a photo of an ape or a large cat and then rated black and white youngsters in terms of perceived innocence and need for protection as children. Those who looked at the ape photo gave black children lower ratings and estimated that black children were significantly older than their actual ages, particularly if the child had been accused of a felony rather than a misdemeanor.

"The evidence shows that perceptions of the essential nature of children can be affected by race, and for black children, this can mean they lose the protection afforded by assumed childhood innocence well before they become adults," said co-author Matthew Jackson, PhD, also of UCLA. "With the average age overestimation for black boys exceeding four-and-a-half years, in some cases, black children may be viewed as adults when they are just 13 years old."

Article: "The Essence of Innocence: Consequences of Dehumanizing Black Children," Journal of Personality and Social Psychology, published online Feb. 24, 2014; Phillip Atiba Goff, PhD, and Matthew Christian Jackson, PhD; University of California, Los Angeles; Brooke Allison, PhD, and Lewis Di Leone, PhD, National Center for Post-Traumatic Stress Disorder, Boston; Carmen Marie Culotta, PhD, Pennsylvania State University; and Natalie Ann DiTomasso, JD, University of Pennsylvania.

Phillip Atiba Goff, PhD, can be contacted by phone at (310) 206-8614 (preferred) or by email. If Goff is unavailable, contact Matthew Christian Jackson, PhD, by phone at (814) 574-9781 or by email.

The American Psychological Association, in Washington, D.C., is the largest scientific and professional organization representing psychology in the United States. APA's membership includes more than 134,000 researchers, educators, clinicians, consultants and students. Through its divisions in 54 subfields of psychology and affiliations with 60 state, territorial and Canadian provincial associations, APA works to advance the creation, communication and application of psychological knowledge to benefit society and improve people's lives.

# data[].attributes.attachment_link.title
Black boys viewed as older, less innocent than Whites, research finds

# data[].attributes.attachment_link.description
Black boys as young as 10 may not be viewed in the same light of childhood innocence as their white peers, but are instead more likely to be mistaken as older, be perceived as guilty and face police violence if accused of a crime.

# data[].attributes.body
When it came to me being able to find a source it was difficult, I started off with a very specific search of what I wanted but nothing would come up so I had to be less specific which is why I just used two main key words.

# data[].attributes.body
2000-2020
English
Articles
Full text

# data[].attributes.body
What filters should I focus on? What research should I include in my essay?

# data[].attributes.body
"Climate change"
"Sea water rising"
"South Pacific"
"impacts"

# data[].attributes.body
Is it too late to stop the melting of the glaciers?

# data[].attributes.body
" Climate change"
" Melting"
" Glaciers"

# data[].attributes.body
Does anyone have any suggestions on different key words and filters that I could use?

# data[].attributes.body
2000-2020
English
Articles
Full Text

# data[].attributes.body
Should the minimum wage be raised?

# data[].attributes.body
"Minimum Wage"
"Economy"
"Poverty"
"Cost of Living"

# data[].attributes.body
2000-2020
Article

labor market
minimum wage
wages & salaries
economic growth

# data[].attributes.body
Is my article from a period in time where it doesn't help my topic or since the article is talking about when to change the wage is it fine to use?

# data[].attributes.body
What drives people who believe they want to save the environment into destroying it if they are supposedly environmentally-friendly?

# data[].attributes.body
How can renewable energy involving solar energy help change  the environment?

# data[].attributes.body
"solar energy"
"environment"
"carbon"
"climate"

# data[].attributes.body
1998-2018
U.S
Full text
peer reviewed

# data[].attributes.body
"Renewable Energy"
"Environmental Friendly"
"Environment"
"Sustainability"

# data[].attributes.attachment_link.title
climate change associated with solar energy

# data[].attributes.body
Peer review article
English
2000-2020
Articles
Full Text

# data[].attributes.body
Are there any other key words I should use?

# data[].attributes.attachment_link.title
Article- Shifting Paradigm in Corporate Environmentalism- From Poachers to GameKeepers

# data[].attributes.body
Should I use more keywords when doing research? If so, which ones would suit my topic?

# data[].attributes.body
Is there any other keywords that you guys would recommend that I use when doing my research?

# data[].attributes.body
How does poverty affect access to proper healthcare for African Americans in the United States?

# data[].attributes.attachment_link.readable_text
The San Joaquin Valley of California has poor air quality and high rates of asthma. Surveys were collected from 744 residents of the San Joaquin Valley from November 2014 to January 2015 to examine the public's views about air quality. The results of this study suggest that participants exposed to high PM 2.5 (particulate matter less than 2.5 microns in size) concentrations perceived air pollution to be of the worst quality. Air quality in the San Joaquin Valley was primarily perceived as either moderate or unhealthy for sensitive groups. Females perceived air pollution to be of worse quality compared to males. Participants perceived unemployment, crime, and obesity to be the top three most serious community problems in the San Joaquin Valley. Participants viewed cars and trucks, windblown dust, and factories as the principle contributors to air pollution in the area. There is a need to continue studying public perceptions of air quality in the San Joaquin Valley with a more robust survey with more participants over several years and seasons.

1. Introduction

Air quality is an important component to everyday life. The San Joaquin Valley (SJV) has some of the most polluted air in the nation [ 1]. This economically disadvantaged and ethnically diverse region [ 2- 4] currently fails to comply with the federal standard for particulate matter 2.5 microns and smaller (PM 2.5). This air pollutant is regulated under the National Ambient Air Quality Standards (NAAQS) to protect public health.

Air quality in the SJV contributes to the high number of emergency room visits and hospitalizations for a variety of respiratory and cardiovascular diseases including asthma, myocardial infarction, acute bronchitis, and pneumonia [ 5, 6]. The adverse impacts of the poor air quality in the SJV are not distributed equally. Recent research has shown that vulnerable populations bear a disproportionately large part of the health burden [ 2, 7]. Many efforts have been conducted by local air pollution control districts to educate the public about ways to reduce pollution in this area, including the provision of real time access to PM 2.5 data that residents can use to plan activities and avoid being outdoors during the worst air pollution times [ 8]. Other efforts include the air quality flag program that provides public organizations such as schools with flags that show the air quality of the day [ 9, 10]. Avoiding exposure to air pollutants is integral to health and requires that the individual be aware when air quality is unhealthy and manage their personal exposure [ 11, 12]. However, little is known about the effectiveness of current communication efforts. Understanding the public's perception of air quality is an important aspect to aid the effort of risk communication and to develop tools to assist the public in avoiding exposure to air pollutants [ 13- 15]. Studies regarding the public's perceptions of air quality in the SJV are lacking.

A survey was conducted with SJV residents to understand their sources of air quality information, perceptions of air quality, and behaviors related to air quality. The survey was developed and information collected through a partnership between a community advisory group and the University of California Merced's Health Sciences Research Institute. Other studies have been conducted from this data that focus on different questions [ 16]. However, the aim of this study was to assess public views and understanding of air quality and air pollution sources in the San Joaquin Valley.

2. Methods

2.1. Sample

The study area lies in the SJV of the California Central Valley and is shown in Figure 1. Residents of the SJV () were surveyed in person (via community organizations and public locations) and from an online panel. The participants surveyed at community and public locations resided in Modesto and Merced, two cities in the north part of the SJV. An online survey (conducted through an online survey company) was available for participants from all locations in the SJV. The survey was conducted from November 2014 to January 2015. Further details of the survey methodology are available elsewhere [ 16]. Data were collected for 744 individuals. A total of 24 survey questions were asked, including demographic information: gender, age, education level, and zip code.

Ethical Institutional Review Board approval was obtained from the University of California, Merced.

2.2. Survey Method

Out of the 24 questions, three questions of the survey were used in this study (the other items are analyzed elsewhere [(i) "In the past month, what was the air quality like in other areas of the San Joaquin Valley?" (1 =(ii) "How serious of a problem is each of the following (car accidents, unemployment, crime, air pollution, infectious diseases (e.g., flu), forest fires, and obesity) in Modesto?" (1 =(iii) "How much do each of the following (cars and trucks in the SJV, pollution from the bay area, farms and agriculture, factories, forest fires, windblown dust, construction, blowers and lawn mowers) contribute to air pollution in the San Joaquin Valley?" (1 = 16]): Very unhealthy, 2 = Not at all serious, 2 = Not at all, 2 = A little bit, 3 = Somewhat, 4 = A lot, 5 = Don't know). A little serious, 3 = Somewhat serious, 4 = Serious, 5 = Very serious). Unhealthy, 3 = Unhealthy for sensitive groups, 4 = Moderately healthy, and 5 = Good air quality).

2.3. Air Quality Data

The air quality data was downloaded from the California Air Resources Board website. PM 2.5 air data was used to assess the participants' exposure. After comparing to the one-, two-, and three-month PM 2.5 averages, the two-month PM 2.5 mean was used as the air quality exposure metric. The air quality exposure data were calculated based on the county of residence of the participant. The timeframe is the months before the date for each participant survey. The two-month PM 2.5 mean concentrations were further grouped into three categories based on the United States NAAQS and the European Air Quality Standards: low, medium, and high. Low or good PM 2.5 concentrations ranged from 0 to 12  μg/m ³ [ 17], medium or moderate ranged between 12 and 25  μg/m ³ [ 17, 18], and high or unhealthy concentrations were greater than 25  μg/m ³ [ 18].

2.4. Statistical Analysis

Descriptive statistics were used to describe the participants' demographics and responses to the questionnaire. Multivariable linear regression was used to determine the factors associated with participants' awareness of air quality in the SJV. Also, linear regression was used to examine if participant demographics could account for air pollution exposure level. Statistical significance was considered at the level. Statistical analyses were performed using SPSS 20.

3. Results

3.1. Participant Characteristics and Air Pollution Exposure Levels

The participant demographics as well as outdoor air pollution exposure levels (PM 2.5 level: high, medium, or low) are presented in Table 1. The majority of the participants (63%) were female. Close to half of the participants (51%) were over 40 years old. The general overall pattern is that PM 2.5 concentrations, during the implementation of the survey, decrease as one moves south in the San Joaquin Valley (Figure 2). Only small percentage (7%) of our sample experienced low PM 2.5 concentrations. The majority of our participants were exposed to medium levels (75%) and high levels (19%). The participants surveyed in Merced and Modesto experienced medium and low air pollution levels only. Very few (2%) of participants who responded via Internet experienced low air pollution levels and the majority (59%) experienced high air pollution exposure.

Results from a multivariate linear regression analysis of factors related to air quality exposure levels are presented in Table 2. Not surprisingly (see Table 1), location was found to be the only predictor (, ) of air quality exposure levels.

3.2. Perceptions of Air Quality

Figure 3 shows that 6% of the respondents perceived air quality in the SJV to be good; 34% responded that air quality was moderate; 32% answered that air quality was unhealthy for sensitive groups. Twenty-two percent of the respondents believed that the air quality was unhealthy. Only 6% responded that air quality was very unhealthy.

3.3. Factors Associated with Perceptions of Air Quality

Results from a multivariate linear regression analysis of factors associated with air quality perceptions are presented in Table 3. Air pollution exposure levels were found to be negatively associated (, ) with perception of air quality in the SJV. Participants who were exposed to higher PM 2.5 levels ranked air quality worse when compared to participants who experienced lower PM 2.5 levels. Females perceived the air quality to be worse compared to males (, ). Modality (web versus in person) and the location of the survey were associated (, ) with perception of air quality.

3.4. Relative Seriousness of Air Pollution

Table 4 presents information about the degree of concern about air quality framed in relation to other community problems (unemployment, crime, obesity, car accidents, infectious diseases, and forest fires). Table 4 shows that, for participants overall, the top three-ranking problems rated by the participants were unemployment, crime, and obesity with air pollution rated as the fourth most serious problem. The table also presents information about the perceived seriousness of air pollution when compared with other community problems categorized by air quality exposure level. For participants who were exposed to low and medium PM 2.5 levels, air pollution ranked as the 4th most serious problem when compared to the other community problems. However, for participants who experienced high air pollution exposure levels, air pollution is ranked as the most serious problem, followed by unemployment, crime, and obesity.

3.5. Participants' Beliefs about Sources of Air Pollution

Table 5 presents the mean ratings of perceived contributions of sources to air pollution, along with the rankings of these means, and data on the actual air pollution sources (accounting for all reportable sources) gathered from the California Air Resources Board emissions data. Participants ranked farming and agriculture as the 5th most significant source of air pollution, which is discrepant with emissions data showing that farming and agriculture ranked 1st in actual emissions (Table 5). Most of the participants responded that the main contributor to air pollution in the SJV was cars and trucks which are the number 2 source of emissions (Table 5). Participants ranked windblown dust as the number two source (5th ranked actual emissions) and also ranked factories (6th ranked actual emissions) as number three.

4. Discussion

4.1. Air Pollution Exposure Levels and Perception and Comparison to Other Studies

This manuscript investigated factors that were associated with air pollution perception. Air pollution exposure level was found to be the most important factor. The higher the pollution exposure level, the worse the respondents' ranking of air pollution. This is surprising as many studies have found perceptions about air quality to be inaccurate [ 13, 19, 20]. However, our findings are similar to Claeson et al. [ 21] who found that air pollution exposure did not directly influence symptoms, but it did influence perceived pollution. The study by Claeson et al. [ 21] also suggests that perceived air pollution influences health risk perception which influences symptoms. It has also been suggested by Forsberg et al. [ 22] that people can detect air quality conditions below air quality standards. In that study, only nitrogen dioxide was correlated with annoyance related to air pollution and traffic exhaust fumes. It is worth noting that the participants in the Forsberg et al. [ 22] study have different sources and types of air pollutant exposures than people in the SJV. It is difficult to say that people can detect all types of air pollution, as they all cause different symptoms.

It is unclear how air pollution exposure levels influence the public's response. It is beyond the scope of this study to determine if direct experience with sensory cues (e.g., seeing or smelling dirty air) and impact (symptoms) of air quality influenced their response, or if it was due to outreach and continuing communication by the local air district that led to the participants' perceptions. A potential explanation for these results might come from the mass media. People rely heavily on information from mass media when it comes to environmental risks [ 23]. The high air pollution levels in the southern (Fresno and South to Bakersfield) part of the SJV receive a lot of media attention due to yearly reports [ 1].

In this study, females viewed air quality conditions to be worse compared to males. This result is similar to that of Lai and Tao [ 24], who found that women were more concerned about environmental hazards than men. This is different compared to the study by Howel et al. [ 25] who found no association with gender regarding perception of air pollution and health.

When investigating factors that were associated with air pollution exposure level, location was found to be the only predictor. These results are not surprising since PM 2.5 exposure levels decrease north to south in the SJV.

4.2. Air Pollution versus Other Problems

When the participants were asked to rate air pollution in comparison to other problems (unemployment, crime, obesity, car accidents, infectious diseases, and forest fires), they rated air pollution as the number four problem out of the seven listed. The participants responded that unemployment, crime, and obesity are the top three problems. This creates a challenge for health education and communication. When the responses were categorized by air quality exposure level, those who experienced high exposure levels rated air pollution as the number one problem. This implies that perception of the intensity of poor air quality may relate to its prioritization.

4.3. Participant Knowledge of Air Pollution Sources

The participants were asked about their knowledge of the sources of air pollution in the SJV. The responses confirm that the participants' perceptions did not match the actual contribution source. It is apparent that participants underestimated farm and agriculture emissions which are the number one source contributor to air pollution in this area. Participants rank farms and agriculture as the number five contributor out of seven options. The participants' perceptions were closer in their ranking of cars' and trucks' contribution to air quality (which they ranked as number one), since the actual contribution of cars and trucks is ranked at number two.

4.4. Forest Fires Importance as Source of Air Pollution

Proximity to the Sierra Nevada forest ecosystem is another source of pollution that can impact people with asthma in this area at unpredictable times [ 26]. Increased drought and past land management fire suppression practices have removed fire from this fire-prone ecosystem. These practices have created conditions that make this area a current and future source of wildland fire smoke emissions that might further impact the SJV particularly via large high intensity wildfires not typical of Sierra Nevada forests [ 27, 28].

There has been much interest among policy makers, managers, and regulators from state and government agencies dealing with forest fire smoke impacts to communities of the SJV [ 27, 28]. Based on the participants' responses, it is obvious that forest fires are not a serious participant concern in the SJV. They rank forest fires as the least of the problems faced by SJV residents. When asked about their knowledge of contribution, they responded that forest fires were the number six contributor out of seven while they ranked 4th in actual emissions.

5. Conclusion

Air quality is of concern to residents of the San Joaquin Valley. There is a need to continue to monitor and study public perceptions of air quality in the SJV with a more robust survey that will have more participants and encompass several years. This is important if objectives of air quality and environmental management are to be achieved.

The following are our main conclusions:(1) Sixty-four percent of the participants responded that air quality was moderate and unhealthy for sensitive people. (2) Air quality exposure level was found to be the most important factor associated with perception of air pollution. Participants who were exposed to high PM(3) When asked about the most serious problems in the SJV, the top three problems were unemployment, crime, and obesity. However, when categorized by air quality exposure level, those with high PM 2.5 levels perceived air pollution to be of worst quality. 2.5 exposure responded that air pollution was the top problem in the SJV. (4) The top three air pollution contributors viewed by the participants were cars and trucks, windblown dust, and factories. The actual rank contribution is different compared to the participants' view, with farms and agriculture, cars and trucks, and pollution from the bay area being the actual top three contributors to air pollution. (5) Forest fires are the least of the participants' concern in the SJV.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

References

1. P. G. Billings, J. E. Nolen, T. A. Tran et al., State of the Air 2016, American Lung Association, Chicago, Ill, USA, 2016.

2. Place Matters, Place Matters for Health in the San Joaquin Valley: Ensuring Opportunities for Good Health for All, 2012, https://www.fresnostate.edu/chhs/cvhpi/documents/cvhpi-jointcenter-sanjoaquin.pdf.

3. J. A. Capitman and R. T. Tyner, "The Impacts of Short-term Changes in Air Quality on Emergency Room and Hospital Use in California's San Joaquin Valley," 2011, View at: https://www.fresnostate.edu/chhs/cvhpi/documents/aqr-web.pdf. Google Scholar

4. San Joaquin Valley Air Pollution Control District's Real-Time Air Advisory Network, 2016, http://www.valleyair.org/Programs/RAAN/raan_monitoring_system.htm.

5. D. G. Shendell, M.-M. Rawling, C. Foster et al., "The outdoor air quality flag program in central California: a school-based educational intervention to potentially help reduce children's exposure to environmental asthma triggers," View at: Journal of Environmental Health, vol. 70, no. 3, pp. 28-31, 2007. Google Scholar

6. Air Now Quality Flag Program, 2016, https://www.airnow.gov/index.cfm?action=flag_program.index.

7. Air Quality Index (AQI), "A Guide to Air Quality and Your Health," 2014, View at: http://airnow.gov/index.cfm?action=aqibasics.aqi. Google Scholar

8. Air Quality in Europe, European Environment Agency, Copenhagen, Denmark, 2015.

9. D. Schweizer and R. Cisneros, "Wildland fire management and air quality in the southern Sierra Nevada: using the Lion Fire as a case study with a multi-year perspective on PM2.5 impacts and fire policy," Journal of Environmental Management, vol. 144, pp. 265-278, 2014.

# data[].attributes.attachment_link.title
Understanding Public Views about Air Quality and Air Pollution Sources in the San Joaquin Valley, California

# data[].attributes.attachment_link.description
The San Joaquin Valley of California has poor air quality and high rates of asthma. Surveys were collected from 744 residents of the San Joaquin Valley from November 2014 to January 2015 to examine the public’s views about air quality.

# data[].attributes.body
healthcare
poverty
African Americans

# data[].attributes.attachment_link.readable_text
Jasmine L. Travers,

Catherine C. Cohen,

Andrew W. Dick,

Patricia W. Stone

Subject Areas

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Abstract

Objective

During the Great Recession in America, African-Americans opted to forgo healthcare more than other racial/ethnic groups. It is not understood whether disparities in forgone care returned to pre-recession levels. Understanding healthcare utilization patterns is important for informing subsequent efforts to decrease healthcare disparities. Therefore, we examined changes in racial disparities in forgone care before, during, and after the Great Recession.

Design

Data were pooled from the 2006-2013 National Health Interview Survey. Forgone medical, mental, and prescription care due to affordability were assessed among African-Americans and Whites. Time periods were classified as: pre-recession (May 2006-November 2007), early recession (December 2007-November 2008), late recession (December 2008-May 2010) and post-recession (June 2010-December 2013). Multivariable logistic regressions of race, interacted with time periods, were used to identify disparities in forgone care controlling for other demographics, health insurance coverage, and having a usual place for medical care across time periods. Adjusted Wald tests were performed to identify significant changes in disparities across time periods.

Results

The sample consisted of 110,746 adults. African-Americans were more likely to forgo medical care during the post- recession compared to Whites (OR = 1.16, CI = 1.06, 1.26); changes in foregone medical care disparities were significant in that they increased in the post-recession period compared to the pre-recession (OR = 1.17, CI = 1.08, 1.28 and OR = 0.89, CI = 0.77, 1.04, respectively, adjusted Wald Test p-value < 0.01). No changes in disparities were seen in prescription and mental forgone care.

Conclusion

A persistent increase in forgone medical care disparities existed among African-Americans compared to Whites post-Great Recession and may be a result of outstanding issues related to healthcare access, cost, and quality. While health insurance is an important component of access to care, it alone should not be expected to remove these disparities due to other financial constraints. Additional strategies are necessary to close remaining gaps in care widened by the Great Recession.

Citation: Travers JL, Cohen CC, Dick AW, Stone PW (2017) The Great American Recession and forgone healthcare: Do widened disparities between African-Americans and Whites remain? PLoS ONE 12(12): e0189676. https://doi.org/10.1371/journal.pone.0189676

Editor: Mark G. Shrime, Harvard Medical School, UNITED STATES

Received: February 12, 2017; Accepted: November 30, 2017; Published: December 27, 2017

Copyright: © 2017 Travers et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: At the time this study took place J.T. was supported by an award from the Jonas Center for Nursing and Veterans Healthcare and National Institute of Nursing Research under Grant [R01NR013687] and is currently supported by an award from the National Institute of Nursing Research under Grant [5 T32 NR009356-07]. C.C. was supported by an award from the National Institute of Nursing Research under Grants [F31 NR015176 and T32NR014205]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Jonas Center for Nursing and Veterans Healthcare or the National Institutes of Health.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Eliminating health disparities has been a national priority and an ongoing pursuit [ 1]. The U.S. Department of Health and Human Services defines health disparities as "differences in health outcomes that are closely linked with social economic and environmental disadvantage" [ 2]. A critical resource to addressing health disparities has been through the acquisition of affordable health insurance coverage. Because of the positive association between wealth and utilization of healthcare services, however, it is possible that economic factors unrelated to health insurance coverage may prevent specific racial groups (i.e., African-Americans) from utilizing needed services [ 3].

Background

The economic recession from December 2007 to June 2009, now known as "The Great Recession" [ 4], disrupted financial stability in the U.S. and imposed obstacles to achieving and maintaining adequate health insurance coverage for many Americans. Furthermore during this time, financial burdens, loss of health insurance, and other hardships such as home foreclosure and food insecurity, resulted in many Americans forgoing healthcare services (not obtain healthcare that they thought they needed because of costs) at a higher rate than before the recession [ 5]. Forgone care was not just seen in the unemployed. Many who remained employed experienced loss of health insurance coverage during this period and/or had an associated decrease in healthcare services utilization [ 6]. While the Great Recession impacted the healthcare utilization patterns of Americans from all backgrounds, prior studies indicate that this crisis took a greater toll on African-Americans than on Whites [ 7, 8]. Mortensen and colleagues found office-based physician visits to be lower for African-Americans compared to Whites during the Great Recession while use of prescription drugs were highest among Whites [ 8]. Burgard and colleagues found African-Americans more likely to forgo medical care because of costs compared to Whites during the Great Recession [ 7]. Reduced care seeking behaviors among racial minorities during the Great Recession may contribute to existing health disparities. Disparities in the type of care used can also lead to increased emergency department visits, increased costs, and facility overcrowding, placing burden on the entire healthcare system [ 9, 10].

As economic recovery from the Great Recession was characterized by persistently high unemployment [ 11], the forgone healthcare patterns thought to be a result of unemployment and strained finances that were previously documented may have continued long after the economy formally recovered. Findings presented in a 2015 Commonwealth Report further suggests newly implemented mandates for insurance coverage may not be sufficient to close racial gaps in forgone care [ 12]. Understanding healthcare utilization patterns throughout the Great Recession and prior to the full implementation of 2014 Healthcare Reform can inform modifications and efforts to decrease healthcare disparities. The purpose of this study is to examine changes in disparities between non-Hispanic African-Americans and Whites in forgone healthcare before, during and after the Great Recession.

Methods

Data set

We used eight years of National Health Interview Survey (NHIS) data, which is an annual cross-sectional survey of U.S. households conducted by the National Center for Health Statistics [ 13]. Through in-person interviews, NHIS records detailed health, demographic and socioeconomic information of non-institutionalized civilians. Interviews take place with 35,000 households containing approximately 87,500 individuals annually. ¹⁰ NHIS' cluster sampling and weighting methodology allows for generation of data that are nationally representative of the population [ 14]. Because the data were publicly available, in aggregate, and deidentified, the methods of this study did not warrant Institutional Review Board approval.

Analysis

Pooled NHIS data collected in years 2006-2013 were analyzed [ 15]. Observations missing any of the variables of interest were dropped and the sample was limited to adults, aged 25-64 years old interviewed within the time period of interest (May 2006-December 2013). This age group was chosen because these individuals are the ages of working adults and have been found to be most likely to experience forgone care because of costs as opposed to those who have reached retirement age or still maintain insurance under their parents' plan. The independent variable of interest, race, was categorized as White and African-American. Individuals also identifying as Hispanic were removed from the sample. Demographic variables included as controls were gender, age, and highest educational attainment. Age was categorized as 25-34, 35-44, 45-54 and 55-64 years of age and highest educational attainment categories were less than high school completion, high school graduate or General Educational Development test, some college or associate's degree, and bachelor's degree or more; both categorizations have been used in previous research [ 7]. Whether individuals were covered with health insurance and/or had a usual place for medical care was also included as a set of control variables in the analysis. Time periods were categorized to align with previous research, based on the National Bureau of Economic Research (NBER) classification of the Great Recession: pre-recession (May 2006-November 2007), early recession (December 2007-November 2008), late recession (December 2008-May 2010) and post-recession (June 2010-December 2013) [ 7, 16].

The three dependent variables of interest were forgone medical care, mental care, and prescription care. These variables were operationalized using the NHIS self-reported answers to questions: "during the past 12 months, was there any time when you needed any of the following, but didn't get it because you couldn't afford it?" This question was asked of respondents once for each type of care.

Using Pearson χ ², we described the sample by examining bivariate associations between race and the following variables: gender, age, educational attainment, health insurance coverage, usual place for medical care, and forgone care. We then developed a multivariable logistic regression model for each dependent variable of interest. In these models, the time period indicators were fully interacted allowing us to test whether the model estimates describing disparities in forgone care changed across time periods among African-Americans compared to Whites (reference group). The models controlled for gender, age, education attainment, health insurance coverage, and usual place for medical care. To understand if these disparities among African-Americans changed significantly across time periods, we performed adjusted Wald tests. All analyses were conducted using Stata 13.1 statistical software with p < 0.05 as the level of significance [ 17]. Regression results are reported using odds ratios and 95% confidence intervals. Appropriate use of NHIS adult sample weights was confirmed with a representative of the National Center for Health Statistics' Division of Health Interview Statistics.

Results

The sample size over the four time periods consisted of 110,746 NHIS adult participants. Whites accounted for 78% (n = 86,752) of the sampled population while African-Americans, accounted for 22% (n = 23,994). In the descriptive analyses, there was variation in the gender, age, and education composition of the sample by race (see Table 1). Whites were more likely to be older (> 44 years of age, p < 0.001), have insurance coverage, and maintain a usual place for medical care (p < 0.001).

Table 2 shows the results of our multivariable logistic regression analysis with fully interacted time-period indicators. In these analyses, African-Americans, were more likely to forgo medical and prescription care in the post-recession time period when compared to Whites (OR = 1.17, CI = 1.08,1.28 and OR = 1.19, CI = 1.10,1.30, respectively). On the other hand, African-Americans were less likely to forgo mental care compared to Whites in the post-recession period (OR = 0.68, CI = 0.59,0.79). The c-statistic which measures the predictive accuracy of a logistic regression model was greater than 0.70 for each of the three models indicating that the final models have good predictive ability in predicting our forgone care outcomes [ 18]. We present odds ratios for the other covariates in the full model in supplementary material ( S1 Table).

Table 3 provides p-values for the adjusted Wald tests that examines if these racial disparities in the likelihood of forgone care changed significantly across time periods. Between African-Americans and Whites, the disparity in forgone medical care increased from pre-recession to post-recession (p < 0.01). When performing a joint test of disparities across the four time periods for each forgone care among African-Americans relative to White, we rejected the null hypothesis that disparities were unchanged in forgone medical care (p = 0.03). No significant changes in disparities were seen in prescription and mental forgone care.

Discussion

This study is the first to determine whether changes in the racial disparities in forgone healthcare during the Great Recession persisted in the years following. Furthermore, unlike prior work, we additionally controlled for health insurance coverage allowing us to identify disparities unexplained by insurance status alone [ 7].

We found that African-Americans were disproportionately affected after the Great Recession. They experienced increased financial constraints compared to Whites in obtaining needed medical care after the recession ended compared to the pre-recession. The steadfast increase in forgone care disparities among African-Americans compared to Whites found in this study may be a result of outstanding issues related to healthcare access, cost, and quality.

Decreased access to care for African-Americans compared to Whites is a common finding in the literature [ 19- 21]. Addressing access solely through health insurance coverage, which is primarily the case with the 2014 Healthcare Reform, however, may ignore other major components that play a role in one's ability to prioritize and utilize healthcare services when affordability is in question. A recent review by Levesque and colleagues conceptualized important dimensions to accessing perceived needed care as the ability to seek, reach, pay, and engage with care services [ 22]. Andersen's behavioral model of health services use additionally describes several characteristics that determine access including: predisposing characteristics (e.g., demographics, social, and beliefs), enabling characteristics (e.g., health policy, financing, and organization), and need characteristics (e.g., environmental need, population health indices, perceived need, and evaluated need) specific to the environment and/or individual [ 23]. These researchers describe how such factors, better known as social determinants of health, influence whether or not healthcare need remains unmet. For example, decreased access to care related to affordability may come as a result of one not being able to afford to take time off from work to attend a healthcare appointment.

Healthcare cost relative to ability to pay, is also a concern. Increased disparities in forgone medical care between African-Americans and Whites during the post-recession period compared to the pre-recession found in this study may be explained by a ballooned wealth gap [ 22]. In recent reports median wealth of Whites increased by 2.4% between the years 2010 and 2013 (post-recession period), but decreased for African-Americans by 33.7% during this same period [ 24]. Despite access to insurance coverage, such disparities in wealth may effect one's ability to finance co-pays, co-insurance, and deductibles associated with care visits [ 23]. Unfortunately, required contributions vary across healthcare plans including the current exchanges and may not be affordable for all especially those who remain hard-hit during the Great Recession; using different 2012-2013 data, authors of a recent Commonwealth Fund Report found that African-Americans were more likely to not visit a doctor due to cost when compared to Whites [ 12]. Further restructuring of contribution requirements according to one's ability to pay as well as decreasing the gap in wealth disparities is critical to decreasing barriers to care associated with cost.

Poor quality healthcare services may also affect African-Americans' choice to forgo needed medical care when one must prioritize what they can afford. This racial group generally experiences increased dissatisfaction with care compared to Whites [ 19, 20]. When perceiving healthcare quality as less than optimal, it is likely that racial minorities and those who are socioeconomically disadvantaged may place competing priorities ahead of healthcare when needing to balance multiple costs. Making accommodations for patients of racial minority backgrounds (e.g., linguistic and cultural) and implementing quality improvement programs have been found to improve care quality, decrease health disparities, and increase one's likelihood to obtain needed services [ 25].

Consistent with other reports, no changes in disparities were identified in mental care among African-Americans compared to Whites [ 26- 28]. In fact, researchers have found that some racial minorities including African-Americans use mental healthcare services less than Whites [ 29]. Further, experts posit that racial minorities may not perceive mental healthcare as a necessity [ 26- 28]. Understanding the rationale for forgone mental care in these minority populations is particularly important to our society as a whole as life experiences generally requiring mental healthcare support (e.g., homelessness, poverty, incarceration, unemployment, violence, and racism) affect racial minority groups (i.e., African-Americans) more than Whites [ 29- 37]. A better understanding of the perceptions of mental health and care among high-risk groups may facilitate access to needed mental care not previously recognized [ 38].

Limitations

This study has limitations. First, the data used are self-reported and subject to the recollection of the individuals interviewed. Despite this limitation, a study was conducted that compared data from the NHIS to data from the Behavioral Risk Factor Surveillance System and found the national estimates to be comparable [ 39]. Second, because this is cross-sectional data, associations could only be examined without temporality. We did however, examine multiple time periods and employed an interaction analysis which allowed us to identify significant changes in disparities over time. Third, these data do not contain information on reasons why cost influenced the decision to forgo care (e.g., lack of funds for a co-payment and/or transportation to obtain services or inability to afford time off from work to seek care). This is a limitation of the national data which warrants independent study of these questions. For example, a recent study conducted by Sommers et al. found that lack of available appointments were associated with cost-related delays in care [ 40].

Conclusion

Disparities in the likelihood of forgone care have increased from pre-recession levels between African-Americans and Whites after the Great Recession. As higher rates of morbidity, disability, and mortality are pervasive among this racial group compared to all others, these findings have important policy implications [ 19]. Direct attention to the needs of African-Americans related to access, cost, and quality is necessary to combat ongoing disparities in medical care obtainment and ultimately health disparities. While health insurance is an important component of access to care, it alone should not be expected to remove increased disparities in forgone care due to financial constraints [ 12].

Policy initiatives that may address healthcare inequity issues beyond insurance coverage include: increasing workforce diversity; ensuring cultural and linguistic competence among healthcare workers; providing community outreach; promoting community participation; and implementing cost sharing insurance plans [ 41]. Future research should assess the specific financial burdens (i.e., transportation, co-payments, and opportunity cost of lost wages during care visits) along with predisposing, enabling, and need characteristics that augment increased disparities in forgone care among African-Americans.

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The Great American Recession and forgone healthcare: Do widened disparities between African-Americans and Whites remain?

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Objective During the Great Recession in America, African-Americans opted to forgo healthcare more than other racial/ethnic groups. It is not understood whether disparities in forgone care returned to pre-recession levels. Understanding healthcare utilization patterns is important for informing subsequent efforts to decrease healthcare disparities.

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How does universal healthcare affect the lives of minorities?

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"healthcare"
"minorities"

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Full Text
Scholarly Peer Reviewed Journals
United States
1998-2020

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What's the impact on Americans voting on economic/environmental factors?

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