Category Archives: Demography

When Social Policy Isn’t A Turn On


Last night I had a date(why yes it was with someone I met online). My date had asked if I wanted to meet for drinks or dinner and I suggested we meet up for drinks and then take it from there(less pressure). I was excited, we were both looking forward to meeting.

So when I finally meet up(after my Uber driver did not know where to go and I had to walk to the place), I find my date at a table. The restaurant is really nice. I sit down and check out the menu. We exchange pleasantries and then my date spends the rest of our time together talking. Not talking about just anything but talking about social policy.

Now don’t get me wrong, I do love a good social policy discussion but only when it involves how to improve or change existing social policy. My date had some rather interesting(but not surprising) views about social policy. But he didn’t really frame the conversation in a way that would have provided for back and forth banter it was more like insight into just how little people actually think about social policy(even though they are often the ones to implement it).

For instance, my date said that one reason for the ills of the Black community was due to their love of fried chicken(narrator: indeed it is not). So I flipped it back on him and asked him how he would like it if we told his community that we would take away their cultural food ways (narrator: indeed he did not). So let’s just say I drank a lot of water and listened. Until I could not listen any more.

Needless to say, online dating is hard. Dating is hard. But it is what one has to do in order to make an effort to find love.

The date also gave me insight into why a lot of social policy indeed doesn’t work, because those who have the ability to change and improve existing policy seek to change the cultures of the communities that are in need to help instead of actually working to eliminate the real issues that negatively impact these communities aka systemic and institutional racism.

My date also gave me insight into how men set up their online dating profiles. It turns out the reason why many of them choose not to put effort into their profiles is due to the belief that people only care about their pics and that no one indeed reads profiles.

I asked my date if he was indeed looking to date and he mentioned that he was but he also brought up how he approaches it as a kind of networking of sorts. I don;t know what to think about that because on the one hand, it is important to meet different people to find out who you would be a good match with but on the other hand, I don’t want to approach a date with an agenda either. I don’t know, maybe I am a tad basic in that regard.

What is funny about that is most of my interactions with men on online dating sites involves them asking me if I have actually read their profiles(because in weird passive aggressive language written in odd font, they actually tell me what their kinks are and shame on me for not reading the multiple paragraph intros that they curate). This is then followed by me wishing them well. Listen, I have no problem with kinks(they are your business) but if I am not on an explicit kink site, I believe that should be a discussion had in person and/or behind closed doors). Don’t get me wrong, I am a fan of stimulating conversation but stimulating just might be in the eye(or ear) of the beholder.

So here I am attempting to see how long I will be able to swim in the online dating waters this go round. I want to be able to say that I gave it a shot but each attempt seems to end up with the round about same result.

We shall see what is to become of these online dating adventures.






Academic site


Hey blog readers,

Just wanted to update you on my academic and professional updates. I now have a professional page that you can check out at:


Thank you!

2016 Gratitude


    Hey y’all! I just wanted to post some good stuff. With all that is going on in the world, I don’t think positive vibes hurt anything.
In line with the year of yes, I accepted a super cool job. My family is proud of me. My friends want me around. I am doing well in my classes.
     I know that doesn’t sound much but it means a lot to me.     No I haven’t met the great love of my life yet but I still manage to smile at the world. I’m trying to be the change that I want to see in the world and on most days, I think I’m on the right track.
    It seems like everyday I have an epiphany about something and I’m humbled. I know that my journey is only through the help and strength of a ton of angels on Earth.
    I got to hear my sponsor’s experience, strength, and hope tonight and I know that God blessed me with her awesome example of living life on life’s terms. I have had the opportunity to hear so much wisdom from my professors and I’m blessed to learn from them.
    So I’m just a bundle of gratitude because when we talk about the promises, I know that I didn’t envision anything that even remotely looks like my life today.
   I’m grateful for you taking the time to read my roller coaster of a blog.
Thank you!

2016 The Year Of Being Open To Life


Hey blog readers and viewers. I hope you are having a great new year so far. I know I am. I told God and the universe that I wanted to be open and a lot of good things have come my way.
     I had two interesting job interviews this week. I hope to hear a good word soon. Classes started back in my doctoral program. I don’t think I have been more excited about a semester. Anxiety aside, things are looking up. People are showing interest in my research. I’m getting amazing feedback and that’s great fuel to keep writing.
     I renewed my gym membership so now I have to go. I’m going swimming tomorrow. I have been getting in some good workouts.
     I met someone. I know it’s early but it is so interesting. We are getting to know each other. We spend time talking. He seems to be into me. Which is surprising. I already laid down the law. He knows where I stand. So even though things feel great, I’m still waiting for the other shoe to drop. I think he is intrigued because I’m not looking to hookup. I’m also very honest.
     We are using SAS this semester and I had to get another laptop and amazingly found one for just my price and all I need. I’m calling it an early birthday/dissertation gift.
     I feel like I’m in a really good place at the moment and that’s great. One funny thing is that now that I found someone to be interested in, all of these other people that I was interested in before have all made a point to reach out to me. One day I was thinking about how many guys may still have my number in their phones. I know that’s an odd thought.
     But whatever happens, it’s nice to have someone to think fondly of and know that someone is thinking about me too.
Well I better try and get some sleep.
May your tomorrow bring even more joy than today 🙂

Exploring the inequality-mortality relationship in the US with Bayesian spatial modeling (Yang and Jensen 2015)


Research questions: 1) Is the relationship between socioeconomic inequality and place due to different levels of deprivation and social capital? 2) Does income inequality threaten population health?
Purpose: To test the relationship between socioeconomic inequality and place.
Hypotheses: 1) Without any other independent covariates, inequality is positively related to mortality 2) Including control variables into the analysis will not fully explain the inequality-mortality relationship 3) After controlling for deprivation, social capital, and other variables in the analysis, inequality is not associated with mortality.
Data: United States County level data from U.S. Census Bureau and the 2003-2007 Compressed Mortality Files (CMF) by the National Center for Health Statistics (NCHS)
Main findings:
• There was a positive and significant association between income inequality and mortality that was partially explained by racial composition, SES scores, and metropolitan status
• The inequality-mortality association increased (by over 20%) when controlling for social capital and deprivation which impacted mortality in the expected direction
• The Gini coefficient remained a significant correlate of mortality.
• Deprivation and social capital partly but do not completely account for why inequality is positively associated with mortality
• Spatial modeling generates more accurate predictions than does the traditional approach.
1. What are some mechanisms that can be utilized to reduce the relationship between income inequality and mortality?
2. How would you devise a system that better informs how we view place and its role in explaining population?
3. What other frameworks would you suggest to better explain place and mortality?

Reflection-Space and Place Effects on Mortality


There continues to be high rates of infant mortality in poor and minority communities (Sparks et al. 2013; Sparks et al. 2009; Cossman et al, 2010). This is particularly true for rural areas with high concentration of minority households that also tend to be poor (Xu et al. 2014; Sparks and Sparks 2010). The purpose of this response is to discuss the contributions to this area of mortality specifically through spatial models. The continued increase in differentials of infant mortality in rural areas is a cause of concern and continued research.
When discussing infant mortality rates specifically when discussing minority infant mortality rates, much of the research has not focused on the role of space and place as a contributor to infant mortality (Xu et al. 2014; McLaughlin et al. 2007). Spatial statistical modeling serves to fill in the gap of not only how we define and discuss space and place in relation to individuals but also society as a whole (Xu et al. 2014; Sparks et al. 2010). With a better understanding of space and place comes new areas of discussion for not only improvement but also functionality that best serves those who interact in spaces and places that is unlike what may have previously occurred. Spatial modeling provides more in depth information as to how space and place function to inform a number of outcomes including infant mortality (Sparks et al. 2013; Xu et al. 2014; Yang et al. 2015). Spatial modeling gives greater detail into patterns of health and mortality that serve to benefit a variety of industry and agency which can translate into improved health outcomes for those who are at risk for higher rates of infant mortality as a result of living in a high-risk area.

Research studies show that there are higher rates of infant mortality in nonmetropolitan areas as a result of lack of access to needed health resources that occur less in metropolitan areas (McLaughlin et al. 2007; Xu et al. 2014; Sparks et al. 2009). When nonmetropolitan areas continue to have smaller pathways to access compared to metropolitan areas, there is a higher likelihood of higher mortality which would include infant mortality. With a lack of access to the nearest hospital for treatment and preventative measures throughout pregnancy in the case of expectant mothers, there is a higher rate of infant mortality due to complications and concerns that would be preventative in nature as a result of having quality access to health care services. McLaughlin and colleagues (2007) make the point that when we discuss inequality related to health, inequality by definition becomes a multilayered source of mortality (McLaughlin et al. 2007). As a result, when inequality is discussed, the framework of the discussion has to be multi-dimensional in order to capture all of the varied aspects that are impacted by inequality.
Previous statistically analysis in the area of mortality and health care research often contributed to the short sited nature of framing the issue of inequality (Xu et al. 2014; McLaughlin et al. 2007; Sparks and Sparks 2010). As a result, in order to get a more complete picture of inequality, the use of spatial statistical modeling is imperative. Spatial statistically modeling provides the insight into the highest areas of need and also the proximity to needed resources in order to better inform the allocation of resources for preventative measures at a larger scale.

Historically cities had higher mortality rates (Xu et al. 2014) and as a result of using spatial statistical modeling, there was greater insight into the historical health of a neighborhood and also insight into future health research. In the case of excess nonmetropolitan death, spatial statistical models provided the data needed to make the case for more research being needed in nonmetropolitan areas (McLaughlin et al. 2007; Xu et al. 2014; Sparks et al. 2013). This research shows that there is a need for reinvestment into nonmetropolitan areas in order to prevent further excess mortality and devastation of nonmetropolitan land. This is critical for areas that have historically experienced wide scale inequality specifically in the rural South and also Native American reservations (Sparks et al. 2013; Xu et al. 2014).
Using the idea that McLaughlin and colleagues (2007) present of spatial inequality equates to mortality inequality, which Sparks and Sparks (2010) also contend, makes the argument for the need of not only resource reinvestment but also ecological reinvestment through various means such as the addition of green spaces and the elimination of known spaces that contribute to poor health outcomes such as revitalizing unused spaces, cleaning rivers, reimaging recycling/landfill structures.
These are some of the ways that spatial statistical analysis can inform not only health practices but help to combat rising infant mortality rates in areas of high inequality that are often nonmetropolitan in nature.

Reflection: Distal Causes of Mortality


There is continued discussion related to numerous environmental effects on mortality rates in the United States (Browning et al. 2011; Browning et al. 2006; Meijer et al. 2012). There is a focus on urban mortality rates in much of the research (Browning et al. 2006; Meijer et al. 2012; Nandi et al. 2012; Nandi &Kawachi 2011; Wight et al. 2010). There is a focus on urban areas along with attempts to explain the contributions to increasing mortality rates among minority populations (Bond Huie et al. 2002; Browning et al. 2011; Meijer et al. 2012). The purpose of this response is to discuss the impact of individual and contextual level effects on mortality. The continued differentials in mortality rates indicates further research is needed on contextual factors particularly related to neglected or underserved populations. Current research continues to highlight the increased effect of segregation on mortality rates (Bond Huie et al. 2002; Geronimus et al. 2014; Browning et al. 2006). Those who live in low-income, high-minority communities are found to be at increased risk for mortality (Meijer et al.; Browning et al. 2006; Nandi and Kawachi; Wight et al; Geronimus et al; Bond Huie et al). With the consistent findings that negative health effects continue to be positive for low-income, poor neighborhoods indicates that more needs to be done regarding transforming the health and environments of neighborhoods. This would include investing in long-term structural improvements in both the environment (housing conditions, air pollution) and also in preventative health measures including nutritional education that would help to prevent continued worsening health outcomes.
The research focuses on individual versus neighborhood effects on mortality. Bond Huie and colleagues found that the effects were the same for minority populations regardless of individual or neighborhood effects (Bond Huie et al. 2002). This leads to a pessimistic outlook for minority populations. It also emphasizes the importance of the role of environment on overall health and health outcomes. (Browning et al. 2011; Browning et al. 2006; Meijer et al. 2012; Nandi et al. 2011; Wight et al. 2010). If it is known that low-income, poor communities are bad for the health of minority populations, more policy and resources are needed to help buffer these communities from poor health effects that are the result of direct contact with these environmental hazards. There are a number of different ideas related to the reason for individuals to continue to live in poor health inducing environments. We are often left with more questions than answers in relation to what should be done in order to see change in these areas (Bond Huie et al. 2002; Geronimus et al. 2014). This is one reason why there is a continued discussion regarding how neighbhorhoods are measured and how we discuss environment (Wight et al. 2010; Browning et al. 2011). The research also indicates that the impact of neighborhoods on individual health is a complex system and a multi-measure approach is preferred over a single measure approach when determining the impact on health.
The research discusses the impact of social isolation on mortality. Usually social isolation refers to social isolation at the individual level (Browning et al. 2011Browning et al. 2006; Nandi & Kawachi 2011). I would take this idea a step further and suggest that low income communities as a whole are socially isolated and as a result, this negatively impacts individual level mortality rates exponentially. Social isolation in a poor built environment would seem to have a negative impact on health compared to social isolation in a healthy built environment. Poor built environments are also associated with increased mortality rates. Higher income suburban areas have higher rates of new built environments while low income areas may have a slight increase in built environment improvements that are often inaccessible to those who live in the area.
There is limited research on health effects and mortality in the Hispanic and Latino populations (Bond-Huie et al. 2002; Wight et al. 2010; Browning et al. 2006). With the continued growth of the Hispanic and Latino populations in the United States it would be indicative to include datasets that include these populations along with research to include longitudinal studies to give a more complete picture of the health impacts on these populations. Although mortality data was not always available for these populations, the data that is available can offer insight into new areas to pursue.
Bond-Huie and colleagues along with Browning and colleagues link low income communities to the concept of disorder (Bond Huie et al. 2002; Browning et al. 2011). Disorder is seen as a constant tax on the mental, emotional, and physical health of residents in low-income neighborhoods. Usually this would be likened to the hustle and bustle of city life but due to the numerous negative exposure points that groups of people will face each day, it can wear down health and well-being. This constant barrage of toxic elements on health contribute to higher mortality rates (Bond Huie et al. 2002; Wight et al. 2010).
An interesting finding was that individual and contextual effects did not have much of an impact on the elderly population (Geronimus et al. 2014; Browning et al. 2006). This could be due to their bodies adapting to various outside effects over the course of the lifespan. More research is needed regarding the health of older people in the population. This can give much needed information regarding how to better serve this population and also how to approach health and wellness of younger populations.
The readings give further proof that individual and contextual effects need to continue to be studied in order to have more insight into mortality rates and also mortality prevention. Individual effects on health cannot be studied independent of contextual effects or there is a risk of having incomplete findings. Having a multi-level approach to measurement and also how neighborhoods are viewed is vital in current day.

Neighborhood Social Processes, Physical Conditions, and Disaster-Related Mortality: The case of the 1995 Chicago Heat Wave Browning et al. 2006


Research questions: 1. What structural characteristics of neighborhoods (affluence, residential stability, population density, and age structure) were associated with mortality during the heat wave? 2. What social processes and commercial characteristics of communities (collective efficacy, social network interaction and exchange, commercial density, and commercial decline) were associated with heat wave mortality (and to what extent did variation in these conditions account for structural associations)?3. Did differences in social or commercial conditions result in some communities largely escaping the lethal effects of the heat wave?
Purpose: To generate insight into neighborhood-level characteristics that buffer local populations from the impact of heat waves and potentially from other types of disasters
Hypotheses: Focus on the components of Kleinberg’s work that includes hypothesized links among neighborhood social structure, commercial infrastructure, and conditions that support or impede the engagement of elderly residents with their communities 1) estimate the mortality rate for older adults age 60 and older during the July 1995 heat wave 2) investigate the relationship between structural characteristics of neighborhoods(affluence, residential stability, population density, and age structure) and variation in heat related mortality 3) examine whether neighborhood level collective efficacy, social networks, and commercial conditions are associated with heat wave mortality and account for neighborhood structural effects on this outcome 4) consider the extent to which some neighborhoods were effectively protected from heat related mortality
Data: Four data sources are used including 1) the 1990 Decennial Census 2) the 1994-1995 Project on Human Development in Chicago Neighborhoods Community Survey (CPHDCN-CS) 3)the 1995 Project on Human Development in Chicago Neighborhoods Systematic Social Observation(PHDCN-SSO) 4) the 1990-1996 Illinois Department of Public Health Vital Statistics data on mortality in Chicago. The sample focuses on those aged 60 and older.
Main findings:
• Substantially higher magnitude of the intercept for the heat wave week (-8.23) compared to the intercept for non-heat wave weeks(-9.29)
• The mortality rate for the 1995 was 2.9 times the average rate for the entire period (e1.06)
• Age, African American race, and male sex increase the log mortality rate substantially
• As age increases, the heightened vulnerability of African Americans declines compared to Whites
• The relative advantage of women over men declines with age
• Latino women did not experience a decreased risk of death compared to men
• Kleinberg’s 2002 data from the City of Chicago coroner’s office did not indicate if the relative vulnerability of men and African Americans differed from their average levels
• There was no evidence of variation in the effects of social composition during the heat wave
• There was no evidence of a Latino advantage in mortality rates during the heat wave compared to Whites
• Kleinberg’s expectations regarding the effects of population density and social isolation held under average conditions
1. What are strategies to address vulnerabilities in order to reduce mortality?
2. How would you seek to make the case for commercial increase throughout a city?



When discussing mortality differences in the United States, one factor that is mentioned is the effect of educational attainment (Brown et al. 2012; Denney et al. 2010; Hummer et al. 2011; Masters et al. 2012; Miech et al. 2011; Montez et al. 2012; Rogers et al. 2010). Educational attainment is often linked to a variety of aspects of the life course experience (Denney et al. 2010, Brown et al. 2012). There continues to be discussion regarding how much influence educational attainment has on mortality and at what point educational attainment has a stronger role in mortality versus other factors in a person’s life (Denney et al. 2010; Montez et al. 2012). The purpose of this response paper is to discuss the impact of educational attainment on mortality as an explanation for disparities. This is important in order to get a clearer picture of the relationship between educational attainment and mortality (Brown et al. 2012, Miech et al. 2011). Educational attainment is important to measure because it highlights increasing educational inequality. Educational attainment provides a picture of overall social standing when measuring socioeconomic determinants (Brown et al. 2012; Hummer et al. 2011; Montez et al. 2012). The impact of educational inequality on mortality continues to be critical in the discussion of health in the United States particularly when we talk about underserved populations. Educational attainment is often looked upon as one of the great equalizers in modern society. As a result, educational attainment will continue to be an area of research in the study of mortality differentials.
Although preventative programs and initiatives are promoted and implemented that seek to reduce inequality and mortality in low socioeconomic groups, research showed that educational attainment continued to persist (Miech et al. 2011; Everett et al. 2013). As a result, to help explain the causal mechanisms, Link and Phelan’s theory of fundamental causes was implemented in order to provide a more comprehensive explanation for these inequalities. Research also showed that educational attainment affects critical aspects of life such as health outcomes, potential earnings, and even social standing (Everett et al 2013; Masters et al. 2012). Much of the research on educational attainment and adult mortality in the U.S. has focused on the Non-Hispanic White and Non-Hispanic Black populations (Montez et al. 2012; Masters et al. 2012). When looking at mortality, the research provides insight into the reason for viewing mortality from a comprehensive framework. This is also one reason why Link and Phelan’s fundamental cause theory can be applied to any group regardless of race (Miech et al. 2011; Everett et al. 2013).
Many of the studies on educational attainment and mortality in the U.S. focus on adult mortality (Denney et al. 2010; Masters et al. 2012; Montez et al. 2012). There could also be benefit to looking at other age cohorts in order to have a better picture of young adults and early stage adulthood. This would provide insight into the health risks for younger ages and could also help to inform health and education policy that would be beneficial for younger ages.
Although much of the research on educational attainment and U.S. adult mortality highlights worse health outcomes for those with less education(less than a HS diploma), this research does not indicate the life experiences that these cohorts lived through which impacted their ability to obtain more education (Montez et al. 2012; Rogers et al. 2010; Everett et al. 2013). By looking solely at education attainment, the research indicates that policy is needed in order to reduce these dispairities. When discussing educational inequality, many of the studies mention that disparities often lead to new health outcomes (Denney et al. 2010; Everett et al. 2013). Looking at earlier cohorts would indicate new issues that have not been seen by previous cohorts (increase in war, increase in poverty, and increase in lack of health insurance, increase in poor living environments). When looking at the impact of these other factors on educational attainment or lack of educational attainment, this will include a better understanding of the significance of certain health outcomes over others.
Previous research indicating the impact of educational attainment on mortality is not surprising, the widening inequalities along the line of educational attainment continue to be (Rogers et al 2010, Masters et al. 2012; Miech et al. 2011). Addressing the issues associated with educational inequality requires addressing economic inequality along with opportunity and access (Denney et al. 2010). Much of the research alludes to these factors influencing health but an argument can be made that these inequalities are presented before birth for younger cohorts and as a result, when addressed effectively can bring about a lessening of the educational inequality that so many populations who lack access can benefit from. When pathways to inequality are filled with resources to remove the stark deficit of need for at risk populations, there will be a greater propensity for improved health outcomes. This may also reduce the variety of poor health outcomes.



There is continued discussion regarding socioeconomic status and the role it plays in mortality differentials in the United States (Bond Huie et al. 2003; Elo 2009; Geruso 2012; Link et al. 1995, 2002; Phelan et al. 2010). The role of socioeconomic status on mortality differentials between White and Non-Hispanic Black populations in the United States continues to be a cause of concern for researchers (Bond Huie et al. 2003; Geruso 2012). The purpose of this response paper is to highlight the continued impact of socioeconomic status on mortality. By acknowledging the increasing role that socioeconomic status contributes to mortality, new insight can help to inform future research and aid in reducing mortality effects for low-socioeconomic status groups.
Socioeconomic status influences all aspects of life including mortality (Elo 2009; Link et al. 2002; Phelan et al. 2010). Low socioeconomic status is attributed to higher likelihood of illness and higher mortality rates (Bond Huie et al. 2003; Elo 2009; Geruso 2012). Low socioeconomic status has less of a protective factor for individuals who may already be at risk for certain diseases. While McKeown’s theory asserts that the improvements in health throughout populations were due to changes in economic and social conditions rather than other known causes was discredited (Bruce et al.2002; Geruso 2012), the causes have been proven to be a number of processes which include social conditions (Bruce et al. 2002; Link and Phelan 1995). The inequalities in socioeconomic conditions are exacerbated by widening inequality in public health.
As a result of continued inequality in public health, socioeconomic disparities have been shown to be an issue for numerous populations as early as the nineteenth century (Antonovsky 1967; Bruce et al. 2002; Bond Huie et al. 2003). Looking at the continued widening gaps in socioeconomic conditions today is an indication that more needs to be done in order to create and promote solutions that have the potential to diminish these conditions and would result in better health outcomes across populations. These outcomes have persisted while the conditions have continued to decline throughout the years.
Researchers have also sought to find the underlying causes of socioeconomic disparities (Link and Phelan 1995; Phelan et al. 2010). Elo (2009) mentions that there are still unclear ideas regarding the determination of these underlying causes and the point in which they begin to affect a person’s health outcomes. Looking at the historical research that identifies various factors in early life gives an indication that a person’s socioeconomic status and related health outcomes are influenced by their family and all of the various parts of life that the role of family impacts(where you live, wealth, education, what you eat). These discrepancies continue to be larger for those who fall into the lower socioeconomic status. The continued gap would indicate that those with higher socioeconomic status form a foundation for future generations that will enable them to continue to have higher socioeconomic status (Elo 2009). This also indicates that more intervention will be needed at the early stages of life for those in low socioeconomic status in order to help improve their health outcomes throughout their lifespan. In order for policies and interventions related to health outcomes for low socioeconomic groups to be effective, the stance taken by policymakers has to be from a contextual standpoint instead of the status quo individual lens (Link and Phelan 1995). This is one reason why such disparities in relation to socioeconomic status continue to persist because if the collective view is of issues being a concern for the individual, there will continue to be less preventative measures taken collectively as a result of having a collective idea that it is up to the individual to solve an individual problem.
The areas in which socioeconomic status influences health policy often correlates to those who have higher socioeconomic status. Those with poor socioeconomic status are not necessarily lacking in education about their own health, they are often lacking the resources, like fluid income, to afford the services needed to not only prevent poor health outcomes but also to have needs met for health emergencies that those with higher socioeconomic status often do not have to handle as a result of already having the resources to prevent these issues (Bond-Huie et al. 2003; Krueger et al. 2003). This is particularly true in the case of those populations that are at risk for premature mortality outcomes. This is especially the case for Non-Hispanic Blacks, who have higher rates of premature mortality in both males and females. This is evident when we consider wealth disparities and race when discussing premature mortality outcomes. Non-Hispanic Blacks are found to have higher gaps in wealth compared to Non-Hispanic Whites. These findings are similar to those found by Geruso (2012). Even when noting limitations in their findings, Bond-Huie and colleagues (2003) mention that their findings likely reflect a true relationship to the population.
Even if we hold to the idea that socioeconomic status disparities will always be a factor in the population, there are policies and programs that can be created in order to help eliminate the drastic outcomes that are a result of these discrepancies (Link and Phelan 1995; Phelan et al. 2010; Bond Huie et al. 2003). These policies can be in the areas of education, occupation, nutrition, etc. It may take time in order to see significant changes but the attempt would be an improvement for the population as a whole. These changes would need to apply not only to individuals but to structures as well in order to serve as a buffer to the extreme outcomes that are currently seen. The motivation for health promotion and preventative measures will be required to flow from a collective problem solving framework versus a pathological framework that is often individualistic in nature. Until then we must continue to do the research needed to inform these changes that includes diverse samples and new ways of looking at previous research in order to reduce limitations and get a greater picture of what is required in order to inhibit growth.