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:
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:
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.
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 🙂
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)
• 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?
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.
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.
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.
• 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?
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