How is the Prevalence of High Blood Pressure Spatially Clustered in Portland, OR

Introduction

In this report, we will examine how high blood pressure prevalence is spatially clustered in Portland, OR. An important association in demography is how spatial relationships between places are formed and how places are influenced by other nearby geographies and populations. These associations impact neighborhood level public health, and demonstration how different environmental characteristics have an effect on our well-being. These two concepts are related in the sense that nearby, bordering neighborhoods or Census tracts likely deal with many of the same health concerns. Using spatial analysis techniques, we can study where and why certain health outcomes are clustered in certain areas of a city.

Neighborhoods don’t exist in a vacuum, they are part of a larger region in which environmental factors can either promote or discourage good health practices. As a result, nearby neighborhoods may also suffer or benefit from these attributes due to havening demographically similar residents, or due to where the neighborhoods are located within a city. Portland, OR is a city that has experienced a lot of change in the last decade. Increasing gentrification has caused longtime residents to be displaced from their neighborhoods as the racial and economic diversity of the city has shifted. Measuring a single health outcome such as high blood pressure at the Census tract level is one way to look at how neighborhoods differ and how certain areas of the city may suffer from underinvestment or environmental hazards.

The numerous characteristics and attributes that make up a neighborhood can be determinants in specific health outcomes. Hypertension is a unique health outcome to study because it is highly impacted by both an individual’s physical and social environment. We know that diet and exercise play a critical role in blood pressure levels, as well as individually experienced stress levels. We also know that the built environment and social health are associated with one another, and we can determine why certain geographic clusters of this outcome may exist. There may be environmental reasons why a certain area experiences greater prevalence of high blood pressure, such has having limited access to outdoor recreation or low food access. In neighborhoods with less community organizations, individual level social health can suffer. All of these factors are important in ensuring positive public health outcomes.

 

Literature Review

There is a significant amount of existing literature that discusses where and why different measures of health are clustered across cities. There are a range of categories that researchers can study with regard to public health including health outcomes, preventative care, and unhealthy behaviors.  This report will ultimately focus on high blood pressure prevalence in Portland, OR, but reviewing the studies done on other classifications of health may help us better understand the motivating factors in outcomes as we determine independent variables to include in a model. Since there is literature on prevention and health behaviors and how they are spatially distributed, it will be useful to see what methodologies were used in the research process. We’ll also discuss studies done specifically on neighborhood level determinants of health outcomes in Portland, OR.

It is a common research topic throughout public health and demography journals that the built environment has an effect on our health. The neighborhood environment influences many of our individual behaviors. Differences in the physical environment impact what types of transportation are available, the prevalence of grocery stores, and our social relationships with neighbors. All of these factors can vary by neighborhood and be clustered in different parts of a metro area. In “Neighborhood built environment and income: Examining multiple health outcomes”, Fallis et al. study outcomes including physical activity, body mass index, and quality of life. This article is particularly useful to this review because the study was conducted on 32 neighborhoods in the Seattle, WA and Baltimore, MD regions (Fallis et al., 2009). Seattle is similar to Portland in the socioeconomic characteristics of the population while Baltimore and Portland are similarly sized in their overall population.

This research cites the neighborhood quality of life study (NQLS) as method of identifying neighborhoods as having low or high walkability ratings. Their findings showed that

living in neighborhoods with a high walkability rating was associated with higher physical activity and lower levels of obesity, while these patterns were sustained across income levels. However, individuals in high-income areas did experience lower body mass index (Fallis et al., 2009). Therefore, they conclude that better walkability leads to healthier behaviors, but our research wants to answer how behavior led outcomes are clustered. Living in a pedestrian friendly neighborhood with a high density of available resources could be a factor that leads to clustering of adults with lower BMI, and therefore potentially lowers levels of high blood pressure or diabetes.

Air pollution is another commonly researched environmental risk that is associated with a wide range of health outcomes. A study conducted in Barcelona found that asthma cases related to air pollution were worse among tracts that were lower on the MEDEA social deprivation index that was used in the study. This index includes parameters such as share of manual workers, education levels and unemployment rates. The research states that the wealthiest groups in Barcelona are more likely to live in the city center where traffic density is high leading to increased air pollution (Pierangeli, 2020). They found that this correlation differs among other countries within Europe, but the reverse trend is found throughout cities in North America.

We can also look at research that has been done on changes to the neighborhood environment, and how those changes affect the health of its residents. An article in Health & Place studied how diabetes incidence is associated with neighborhood level social and economic changes in Madrid. This study is valuable because they used a large sample size of 199,621 adults over six years of socio-economic change. They state that their research was unique from  studies that had been done on the health effects of refugees relocating to wealthier areas, with a change of residential mobility (Bilal et al., 2019). Here, their focus was on the individuals who do not move which is a much higher share of the population.

To do our analysis in this report, we will use data from the CDC 500 Cities Project. While a longitudinal study like the one done by Bilal et al. could be informative, it appears that the 500 Cities Project only has data for 2017. Therefore, with a health outcome of high blood pressure as the dependent variable in the model, we won’t be able to see how tract levels have changed over a certain amount of time. While this study will be different from the study done in Madrid, it will be helpful as a framework as they focused on shifts in socioeconomic status at the tract level. Its also noted that there are few studies on neighborhood dynamics and diabetes incidence at the this large of a scale.

Another issue that is widely research in neighborhood level health is food access and food insecurity, the latter with specific focus typically on children and their development. Access to fresh produce and healthy foods is a major barrier to positive health outcomes for many Americans. In a study on food accessibility in the Detroit metro area, proximity to the nearest grocery store is similar across Census tracts that experience very low levels of poverty, irrespective of the racial / ethnic characteristics of the neighborhood. In the most impoverished neighborhoods, there was a racial disparity as African Americans lived on average 1.1 miles further from a grocery store than White neighborhoods (Zenk et al., 2005). If this research was done specifically on children, it could provide information into health disparities over time and the health risks that are involved with low food accessibility.

Research by Breyer and Voss-Andreae look at the issue of food access in Portland through the lens of geographic and economic barriers. The geographic component to this study is critical because it addresses the reality that food prices differ at different grocery stores. Due to increased levels of gentrification, some low-income individuals live in a neighborhood with access to a high-end grocery stores, but not a low-cost store where they want to shop because it is what is affordable (Breyer & Voss-Andreae, 2013). With this realization the researchers concluded that while food deserts are less common in Portland than in other American cities, what they identified as “food mirages” are much more prevalent as “81% of low income households in Portland reside in food mirages” (Breyer & Voss-Andreae, 2013, p. 134).

Due to the findings of Breyer & Voss-Andreae, the use of data involving the USDA declaration of a food desert will be omitted as a predictive variable of health outcomes in this study. If low-income Portland residents are traveling an additional 1.9 miles to reach a low-cost grocery store, they are functionally in the same position as those who live in food deserts. The Mapping Food Deserts in the United States page on the USDA site mentions the definition of a food desert to be “lacking access to healthy and affordable food” but doesn’t detail the process of how they determine affordability. At least for low-income Portland residents, this is an important distinction.

While we won’t be using food insecurity as a variable in this research, it still has a significant effect on neighborhood level health outcomes. Food access is associated with other socio-economic determinants of health, and neighborhoods with low food access may still be where we find clusters of highly concentrated negative health outcomes. Leonard et al. discuss this idea in their research on overlapping geographic clusters of poor health and food insecurity. There are two keys results from their research; the first states that the incidence of overlapping geographies of unfavorable outcomes was more likely than those of favorable outcomes. This is to say that a county that suffers from low food access is more likely to also face negative health outcomes, while positive determinants of health don’t overlap as often. Also, geographies with higher black populations and higher levels of poverty were more likely to see an increase in overlapping negative outcomes (Leonard et al. 2018).

From this research we can see that counties with high shares of black residents, poverty and food insecurity are most likely to be at risk of experiencing unfavorable health outcomes. While Portland overall has a low share of black residents, there are historically black neighborhoods throughout north and northeast Portland (Bodenner, 2016). This specific area has also seen some of the highest levels of gentrification in the city over the last ten years and may be prone to the “food mirage” issue of food accessibility. In looking at the spatial clustering of health outcomes, from the existing literature we would expect north and northeast Portland to experience different outcomes than other parts of the city.

Most of the research that has been discussed thus far is focused on neighborhood environments or specific health behaviors in outcomes. In this review it is also critical to study the methods of how these topics have been spatially analyzed in previously published literature.

The paper “A spatial analysis of health status in Britain, 1991–2011” examines disparities across the country. The research utilized spatial analysis techniques to conclude that health outcomes are spatially clustered in Britain, and it is has become more segregated by long-term illness (Dearden et al. 2019).  They also used what was called Supergroup Area Classification to identify regions of Britain including identifiers such as “Scottish Countryside”, “Prosperous England”, and “Business & Education Centres”. A similar classification could be used for the city of Portland that is commonly divided into six different sections. They are known as the four ordinal directions (NE, SE, NW and SW) plus North and East Portland. Tract boundaries follow lines that these four sections can be divided by including the Willamette River, Burnside St and N Williams Ave.

Another report uses the CDC’s variation of a social vulnerability measure that was discussed previously in the research that was done in Barcelona. Here, researchers are studying the spatial relationship between heat-related illness throughout Georgia and social vulnerability. The CDC’s SVI is found by using “15 U.S. census variables at tract level to help local officials identify communities that may need support in preparing for hazards; or recovering from disaster” (Center for Disease Control and Prevention, 2016). While the study in Georgia uses the SVI at the county level, the measure is also available down to the Census tract level which could be a useful variable in determining how health outcomes are clustered. The rest of the study mostly discusses the issues faced by large rural areas of poverty throughout the state (Lehnert et el. 2020). This isn’t applicable to our research, but the methods and analysis used for spatial exploration are useful to review.

By reviewing the existing literature on spatial clustering of health outcomes, we’ve seen some methods that will be used in our analysis as well as some gaps in areas that haven’t been studied. It should be noted that this isn’t an exhaustive and comprehensive review of all of the published research on this topic. However, there doesn’t seem to be extensive research on specific outcomes at the Census tract level in Portland. Most of the research we’ve seen in the review is done at the county level of observation for a state or a larger city. Another takeaway is that we see unfavorable health outcomes in much higher numbers in areas with both more black and more low-income residents. In Portland specifically, low income groups face a unique challenge to food access that isn’t captured in traditional data on food deserts. Due to these factors and Portland’s low racial / ethnic diversity, we may see at risk groups more highly concentrated than we would in other cities.

 

Data and Methods

To examine the prevalence of high blood pressure in Portland, we will perform exploratory spatial data analysis (ESDA) to better understand how this health outcome is spatially clustered. We also use a regression model to look at the relationship between high pressure, and two independent variables of educational attainment and median household income. This provides a view of how socioeconomic indictors relate to health outcomes in Portland. The data used for high blood pressure prevalence is from the CDC 500 Cities Project. This dataset is a collection of information on health outcomes, prevention, and unhealthy behaviors. The data is available at the tract level as well as aggregated data for each state. For our socio-economic variables, we used American Community Survey data and analyzed the information in R.

First, we’ll look at maps of our variables. This allows us to see how the tracts are dispersed throughout Portland and provides some insight on how each variable is clustered with neighboring tracts. The map below shows high blood pressure prevalence by tract.

 

 

 

 

 

 

 

 

 

 

This is the map we want to explain in our analysis. We can see that there are some tracts clustered in the center of the map, on the inner east side of Portland where high blood pressure prevalence is below 20%. The western area of the map shows fairly low prevalence values, while the highest area is in outer east Portland. Next, we’ll look at maps of median family income by tract and share of the population twenty-five years and older who have a bachelor’s degree.

 

 

 

 

 

 

 

 

 

 

 

Median Family Income by Tract in Portland, OR

 

 

 

 

 

 

 

 

 

 

 

Percent of the Population over 25 with a Bachelor’s Degree by Tract in Portland, OR

 

 

 

 

 

 

 

 

 

 

 

 

In these maps, we can see some similarities in the independent variables as well as education and median income’s association with high blood pressure. We could hypothesize that education and income would have a negative relationship with high blood pressure from what we see here. Outer east Portland has the lowest levels in educational attainment, while median income is more scattered by tract throughout the city. In both maps we see dark blue tracts on the west side of the city in the West Hills neighborhoods that have an older, higher income population than the rest of Portland.

Now we’ll look at some of the ESDA methods that we’ve done to examine these relationships. First, the Moran’s I statistic is a very common measure of spatial autocorrelation. This statistic provides a correlation coefficient between -1 and +1 that is a measure of the association of tracts that are nearer to one another. The Moran’s I statistic for high blood pressure prevalence is 0.527, which indicates that tract clustering is present since the value is above 0. Perfect clustering would be a value of +1, while -1 would imply perfect dispersion of the variable. We can also see this affirmation of spatial autocorrelation in the Moran’s scatterplot that indicates the relationship between observed values and spatially lagged values.

 

 

Scatterplot of Blood Pressure Prevalence by Tract

 

 

 

 

 

 

 

 

 

The other component of ESDA to examine is the LISA clusters map. The LISA map shows where different clusters of tracts with high values of the variable border other high tracts, as well as the same for bordering low value tracts. It will also provide observations of bordering high/low and low/high tracts which would indicate more randomness among tracts and less correlation. In our map below, we have 15 instances of tracts with low high blood pressure prevalence that border each other. This includes the area of inner east Portland that we’ve pointed out previously, as well as a cluster in northwest Portland. The low/low tract cluster in the map are highlighted in red.

In addition to the ESDA components of this study, we’ll also discuss a regression model that compares the relationship of education and income to high blood pressure. For this model, we’ve merged the CDC 500 Cities data set with American Community Survey data from the Census in R. The regression results from the linear model are shown below. Educational attainment for the population 25 years and over did have a highly significant negative relationship with high blood pressure at the 0.001 level. Even though it is significant, it has a low correlation coefficient of -2.668e-01 which indicates it isn’t highly associated. The adjusted R squared denotes that 24.61% of the variation in high blood pressure among tracts is explained by the two variables in the model.

Regression Results Table
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.968e+01 3.208e+00 15.489 <2e-16
Education (pop >25 years) -2.668e-01 3.983e-02 -6.700 1.7e-10
Med. Household Income -4.008e-06 1.241e-05 -0.323 0.747

Summary of Results

The findings from this report signify that there is spatial autocorrelation present among high blood pressure in Portland, but there are still other variables that could better explain the relationship than the two we’ve examined here. We had a fairly high Moran’s I statistic that tells us that tracts are clustered, and this was supported in the LISA map where 15 tracts of low observed prevalence were found to border each other. In future steps for this research, it would be ideal to test more independent variables for their association with high blood pressure. If several variables with high significance are found, we could then discuss whether or not the independent variables are endogenous. If the independent variables are correlated with one another we could then research if there are built environment effects on the variables or other reasons why certain characteristics predict high blood pressure prevalence. Overall, this project has identified spatial clustering and discussed the relationship between education, income and high blood pressure prevalence.

 

 

 

 

 

 

 

 

 

 

References

Bilal, U., Glass, T. A., Cura-Gonzalez, I. D., Sanchez-Perruca, L., Celentano, D. D., & Franco, M. (2019). Neighborhood social and economic change and diabetes incidence: The HeartHealthyHoods study. Health & Place, 58, 102149. doi: 10.1016/j.healthplace.2019.102149

Bodenner, C. (2016, August 23). Gentrification in Portland: Residents and Readers Debate. Retrieved April 11, 2020, from https://www.theatlantic.com/notes/2016/08/albina/493793/

Breyer, B., & Voss-Andreae, A. (2013). Food mirages: Geographic and economic barriers to healthful food access in Portland, Oregon. Health & Place, 24, 131–139. doi: 10.1016/j.healthplace.2013.07.008

CDC 500 Cities Project (2017). Portland, OR High Blood Pressure Prevalence. [Data File]. Retrieved from https://chronicdata.cdc.gov/browse?category=500+Cities

CDC Social Vulnerability Index (SVI). (n.d.). Retrieved April 11, 2020, from https://svi.cdc.gov/

Dearden, E. K., Lloyd, C. D., & Catney, G. (2019). A spatial analysis of health status in Britain, 1991–2011. Social Science & Medicine, 220, 340–352. doi: 10.1016/j.socscimed.2018.11.014

Lehnert, E. A., Wilt, G., Flanagan, B., & Hallisey, E. (2020). Spatial exploration of the CDCs Social Vulnerability Index and heat-related health outcomes in Georgia. International Journal of Disaster Risk Reduction, 46, 101517. doi: 10.1016/j.ijdrr.2020.101517

Leonard, T., Hughes, A. E., Donegan, C., Santillan, A., & Pruitt, S. L. (2018). Overlapping geographic clusters of food security and health: Where do social determinants and health outcomes converge in the U.S? SSM – Population Health, 5, 160–170. doi: 10.1016/j.ssmph.2018.06.006

Mapping Food Deserts in the United States. (n.d.). Retrieved April 11, 2020, from https://www.ers.usda.gov/amber-waves/2011/december/data-feature-mapping-food-deserts-in-the-us/

Pierangeli, I., Nieuwenhuijsen, M., Cirach, M., & Rojas-Rueda, D. (2020). Health equity and burden of childhood asthma – related to air pollution in Barcelona. Environmental Research, 109067. doi: 10.1016/j.envres.2019.109067

Sallis, J. F., Saelens, B. E., Frank, L. D., Conway, T. L., Slymen, D. J., Cain, K. L., … Kerr, J. (2009). Neighborhood built environment and income: Examining multiple health outcomes. Social Science & Medicine, 68(7), 1285–1293. doi: 10.1016/j.socscimed.2009.01.017

Zenk, S. N., Schulz, A. J., Israel, B. A., James, S. A., Bao, S. L., & Wilson, M. undefined. (2005). Neighborhood Racial Composition, Neighborhood Poverty, and the Spatial Accessibility of Supermarkets in Metropolitan Detroit. American Journal of Public Health, 95(4), 660–667. doi: 10.2105/ajph.2004.042150

 

 

 

 

 

 

 

Reflection #2

Throughout the research for this project, I wasn’t sure exactly how I was going to perform the ESDA and which program I was going to use. I’m more comfortable using GeoDa as I’ve used it for two other classes before, but I was having trouble getting the shapefile to load in that program. I’m glad that I did end up doing everything in R because it allowed me to reflect on the course and see how much I’ve learned about the program. I was able to successfully create the maps I wanted and was able to merge the 500 Cities Data with the ACS data to perform a linear regression model.

The research itself does inform us about the spatial distribution of high blood pressure in Portland, but there are many ways it could be expanded in a future project. I thought about looking at a regression model that had other health outcomes as independent variables rather than socioeconomic characteristics. It could be an interesting way to examine where the highest and lowest risk tracts are with regard to health, and why outcomes are clustered in certain neighborhoods. A more in-depth project would then look at neighborhood characteristics and attempt to determine if there are built and social environment factors that highly influence certain behaviors that lead to bad health. Looking at our map of Portland, we can generally see that higher income, more educated neighborhoods have lower prevalence of high blood pressure, even if income isn’t significantly associated. What is also important to discuss is not just the socioeconomic predictors, but how the environment of the neighborhood effects our health.

 

SOC573 Final SRBE

Final Report Proposal

            The proposal assignment did help in the final report because it was beneficial to have an area of focus so early in the semester. Even though work on the paper didn’t begin until later once the course went on and we learned more techniques, I was working on how to frame migration’s impact on the economy of Atlantic City.

Rough Draft and Peer Review

            This process was also particularly helpful with my paper. Each time I’ve created a population pyramid, I’ve struggled with the formatting. In the peer review I received from Jim after the rough draft, he made some helpful comments on how I could improve the graphs. Also, after the peer review was submitted, Dr. Sacco and I were able to speak over the phone about the progress of my report.

Final Report Paper

            Surprisingly, my topic didn’t change significantly from what was initially proposed. I had said that I was going to focus on the differences in domestic and international migration and the paper did shift away from that focus, but the main proposal largely stayed the same. In hindsight, I would have had more of the research completed for the rough draft. That way, I could have received feedback on a larger portion of the paper. Otherwise, I was confident in the research process and wouldn’t make any major changes in a second attempt.

Project Challenges

            The main challenge of the final report, was reviewing some of the demographic techniques that we learned earlier in the semester. I had to go back and look at some of the course materials on growth models as well as dependency ratios. Another unexpected issue I encountered was locating the data for vital events in Atlantic City. Since birth and deaths aren’t published for the city itself, I had the choice of using data for Atlantic County or the Atlantic City metro area. I used the metro area data for this section but was disappointed because I wanted to focus on the specific population of the city itself.

Final Report – Population Change and Migration Trends in Atlantic City, NJ

Introduction

Population change has a significant role in shaping our local economies. Current and future populations determine who is in the labor force, what types of firms and industries are clustered in certain areas, and who the employees are for those firms. Atlantic City has long been a center of tourism and casino gambling along the New Jersey shore. With this unique concentration of economic activity, it has seen many periods of growth and decline in the population throughout the city’s history. Currently, Atlantic City is experiencing population decline. From 2010 to 2018, the population has decreased by 4.4% to just 37,804 residents (U.S. Census). In comparison, New Jersey as a whole has grown by 1.3% over the same time span (U.S. Census). This variation in growth rates infers that there is something to be explained about the population change in Atlantic City that is unique from the rest of the state.

This report will discuss how the population has changed in Atlantic City in comparison to New Jersey as a whole. There are two components of population change, the natural increase (births minus deaths) and net migration. This report will primarily focus on population change through migration, both domestic and international. There will also be a detailed review of the age and sex structure of each geography. Since population change determines the future demographic structure of any area, we will also perform different methods of population projections including arithmetic, geometric, and exponential methods. Each method involves a different technique of population projection. To conclude, we will review how the projections and migration trends can inform both the private and public sectors on how to make more informed policy and business decisions.

 

 

Age and Sex Structure

First, we need to examine the components of the population in 2017 versus our base year of 2010. Constructing a population pyramid for these years will give us a simple graph that shows the population distribution by age group and sex for both Atlantic City and the state of New Jersey. In an initial review of these graphs, we see that the population by age in the state is more normally distributed than it is in Atlantic City. We’ll also notice that the change in how ages are distributed is more consistent in New Jersey than in Atlantic City between 2010 and 2017. This is mainly due to the fact that we are comparing one city with the whole state, and since the state is a much larger sample size, and is less subject to drastic changes in their population than a singular city.

Figure 1.1, ACS 2010

Figure 1.2, ACS 2010

 

Figure 1.3, ACS 2017

 

Figure 1.4, ACS 2017

 

Within these population pyramids, there are several differences that we can observe between the two years. In Atlantic City, the largest female age group in 2010 was children under 5 years of age. In 2017, The majority of those individuals aged into the 5 to 9 year old category making that the largest female age group, with a slightly higher share of the population than 20 to 24 and 30 to 34 year olds. Among males, we can see a similar pattern where children under 5 accounted for the greatest percentage of the population in 2010, but in 2017 20 to 24 year olds were the largest group, followed by children under 5 years. There is also some variation by sex within the same year. In 2017, there were over 300 more females in Atlantic City than there were males aged 30 to 34 years old.

With regard to New Jersey as a whole, we see a more normal distribution of ages than we do in Atlantic City. In 2010, 45 to 49 year olds are the largest group for both males and females. In 2017 as that group aged, the 50 to 54 age group is the largest age group for both sexes. This is likely more representative of the U.S. as a whole since the sample size of New Jersey is much larger than Atlantic City. In a city of just 40,000 people, we’re going to see more variation in the data and population trends that are more specific to that area.

In figure 1.5, we have the sex ratios by five-year age group for New Jersey and Atlantic City in 2017. This measure shows us the ratio of males to females, and allows us again to see how the population in Atlantic City is unique compared to New Jersey as a whole. Overall, Atlantic doesn’t seem to follow any particular pattern as the ratio varies from majority male to majority female in many of the age groups. Most prominently, the ration of males to females in Atlantic City is 135 for those aged 60 to 64. New Jersey is an example of a more common trend that demographers observe in sex ratios, where the ration hovers around 100 during childhood and early adulthood, then slowly decrease with age as females outnumber males. This occurs due to women typically having a longer life expectancy than men.

Figure 1.5, ACS 2017

The table of different dependency ratios for New Jersey and Atlantic City is shown below. These ratios have significant implications on future demand ranging from the healthcare industry to housing. Each ratio expresses the number of a certain age group population per 100 of another age group. Therefore, for the dependency ratio, we can state that New Jersey has 50 persons in dependency (those ages 0-14 and 65 and over) for every 100 individuals age 15 to 64 years old. The significance of these age ranges is defining those who are typically not in the labor force (children and retired persons), and those who are in their working years.

There are several reasons this ratio is important. People in these two age groups are likely to require more services such as education, elderly care, and healthcare in general. A population with a high ratio of dependents will need to be cognizant of the needs for these individuals. On the other hand, a greater dependency ratio means there are less people in their working years. If this leads to a smaller labor force, a shortage of workers could potentially become an issue for the local economy. Referencing Table 1.1, we see that the dependency ratio in Atlantic City is three years greater than in New Jersey, likely not a significant enough difference to infer how it would affect each of the local areas.

 

Dependency Ratios
  New Jersey Atlantic City, NJ
Dependency Ratio 50.328 53.416
Aged dependency Ratio 22.71651844 20.25127601
Ageing Index 82.27265933 61.06309933
Child Dependency Ratio 27.61126069 33.16450726

                   Table 1.1

The aged dependency and child dependency ratios look at each of the two groups in the dependency ratio individually, children and elderly people. If we look at the table, we can see how this changes for New Jersey and Atlantic City. While the aged dependency ratio is approximately 2.5 years higher in New Jersey, Atlantic City has 5.5 more child dependents. Since the population of children makes up a larger share when compared to those in non-dependent ages in Atlantic City, there should be a higher concentration of both services for children and resources for parents in this area. Also, an area with a higher child dependency ratio will require more assets for the public education system. In Atlantic City, this becomes particularly important. Since the city is experiencing population decline in recent years, but has a relatively high share of children compared to rest of the state, policy makers understand that these children will age into the future workforce in Atlantic City. High rates of domestic out-migration (see Table 3.1) are causing the city to struggle economically. Focus on educating the future population is critical if Atlantic City wants to end its population loss.

Atlantic City has a much lower ageing index of 61 aged person who are 65 and over compared to children 14 and under. This is similar to our other ratios but excludes that proportion of caretakers for these dependents. An ageing index 20 years less than the state further evidences that Atlantic City has a younger population, but it also brings into question the health of ageing person in the city. A large population of children can certainly contribute to a low ageing index, but another component would be a high mortality rate. This would amplify the ageing index if elderly residents of Atlantic City are not living as long as their counterparts in the rest of the state. This pattern would then introduce a discussion of prominent causes of death and the healthcare that is available to this population as demographers want to explain the significance of this ratio. A thorough review of mortality rates and cause of death is beyond the scope of this report, but a higher death rate than the rest of New Jersey (see Table 3.1) is also contributing to the population loss in Atlantic City.

 

Migration

            Now that we have reviewed the basic age and sex structure for our areas of interest, we will examine the migration trends both internationally and domestically. In the tables below, we can see the share of migrants by age and geography of residence before migration for Atlantic City. If we consider the median age of movers for each migration category, there is a noteworthy change in the median age for movers from a different state in 2010 compared to 2017. In 2010, the median age for this group of migrants was 35.4, more than five years younger than it is in 2017. The median age of movers in the other three categories didn’t move more than 1.5 years in either direction.

Figure 2.1, ACS 2010

 

Figure 2.2, ACS 2017

 

Migration to Atlantic City, NJ has also changed by where migrants are moving from over 2010 to 2017. The overall trend observed here is that there is a much higher share of individuals moving within the county in 2017. The population of Atlantic County in 2017 was 269,918 while the population of Atlantic City was 38,429 (American Community Survey, 2017). Since Atlantic City makes up approximately 15% of the total county population, we don’t know if these movers are coming into Atlantic City from other places in the county. What we can determine from this data is that there are fewer people moving into Atlantic City while there is a large increase in moving local among some age groups.  In 2010, a full 4% of 18 to 24 year olds were new international immigrants. Seven years later, that number fell to just 0.90%.  If we focus on the growth in local movers in 2017, we’ll see that the largest groups are children and elderly adults.

There are several factors that are possibly influencing these shifts in local migrants. Children aged 1 to 4 years old that moved locally more than doubled in 2017, while children aged 5 to 17 saw nearly a 40% from 2010 in the same category of movers. We can see similar increases among individuals who are likely to be their parents, 25 to 44 years olds. A hypothesis for this increase in local my migration is that the Great Recession caused financial pressure for families, possibly making renting a more viable option for housing rather than owning. Since renters are more exposed to volatile housing costs than homeowners, they may be more likely to move from year to year. This trend could explain the increase that we observe among local movers.

Population Growth

Now that we’ve taken a thorough review of the age and sex structure in our two geographies of interest, we’re going to examine population change as a whole. This discussion will prelude the population growth models that will come later in the report. In table 3.1 shown below, the different components of population change are shown as a percent of the total population in 2010. This allows for a comparison of New Jersey and the Atlantic City metro area[1] based on percent change rather than absolute values. A negative 7.16% net change in domestic migration explain much of the Atlantic City metro population loss during this time. While New Jersey and the Atlantic City metro both saw positive international migration from 2010-2017, their overall net migrations were -0.21% and -3.13%, respectively.

Population Change, as Percentages
April 1, 2010 to July 1, 2017
  Vital Events Net Migration
  Natural Increase Births Deaths Total International Domestic
New Jersey 2.66 8.62 5.96 -0.21 4.32 -4.53
Atlantic City, NJ

Metro Area

 1.40 8.35 6.95 -3.13 4.03 -7.16

Table 3.1: US Census, Components of Resident Population Change 2017

Determining the proper growth model to use for population projections is crucial in forecasting future population. The growth rate from the model is what will be used to extrapolate the size of a population, and organizations in both the private and public sector. Industries such as healthcare, government and education all depend on reliable and accurate forecast in order to plan for the demand of different types of goods and services. First, we will define the three different types of growth models in the arithmetic, geometric and exponential models. The arithmetic model assumed that the population will grow at a fixed amount into a future interval of time. For example, if a city grew by 5,000 individuals over the last ten years, in the next decade, the arithmetic approach forecasts that the population would grow again by 5,000.

The geometric method predicts population growth using percentage growth of the base population rather than a fixed interval. This model has become more popular than the arithmetic model, but still assumes that the future period will grow in a similar pattern than the previous base period. In order for these models to accurately predict the population, observed trends in the natural increase and net migration will have to continue. The exponential method is unique in that it uses a natural log function of growth rate between two previous base periods. This method assumes that if both natural increase and net migration are growing, they will become more and more rapid in the future.

Total Population

ACS 2010, 2015-2017
Year New Jersey Atlantic City, NJ

Metro Area
2010 8,721,577 273,162
2015 8,904,413 275,376
2016 8,915,456 274,026
2017 8,960,161 272,926

The tables below show each of these methods for both New Jersey and the Atlantic City metro area in 2017. The total population provides that necessary figure we to calculate the growth rate for each model. We want to define the methods the best explains growth in these two places, in order to forecast future populations. Therefore, the row titled ‘Percentage Difference’ is what we are interested in for this report. This tells us the percent error of the population that was projected using each method from the estimated value observed by the Census in 2017. A lower percent error indicates a more accurate projection.

Arithmetic Population Growth
  New Jersey Atlantic City, NJ Metro Area
  2017 (arithmetic growth) 2017 2017 (arithmetic growth) 2017
Population                     8,950,297 8,960,161                           273,992 272,926
Growth Rate 0.391%   -0.012%  
Absolute Difference                          (9,864)                                1,066  
Percent Difference -0.1102%   0.3891%  

Table 3.2, ACS 2010-2017

Geometric Population Growth
  New Jersey Atlantic City, NJ Metro Area
  2017 (geometric growth) 2017 2017 (geometric growth) 2017
Population                 8,920,982.63 8,960,161                           273,353 272,926
Growth Rate 0.062%   -0.245%  
Absolute Difference                          39,178                                  (427)  
Percent Difference 0.4392%   -0.1564%  

Table 3.3, ACS 2010-2017

Exponential Population Growth
  New Jersey Atlantic City, NJ Metro Area
  2017 (exponential growth) 2017 2017 (exponential growth) 2017
Population                     8,926,506 8,960,161                           272,679 272,926
Growth Rate 0.124%   -0.491%  
Absolute Difference                         (33,655)                                   247  
Percent Difference -0.3770%   0.0905%  

Table 3.4, ACS 2010-2017

The results from these tables provide the best methods for population projection for each geography. For New Jersey, the arithmetic growth model has just a 0.1102% difference from the observed population in 2017. For the Atlantic City metro area, we see that the geometric growth model produces the lowest percent error of 0.1564%. The fact the different models are the best fit for each geography further evidences that New Jersey and Atlantic city are experiencing difference patterns of growth. Each of these trends implies a different future for their respective populations.

Economic Implications

            This report has reviewed several demographic ratios and growth rates to explain the population structure in Atlantic City in comparison with the state of New Jersey as a whole. As a resort and casino gambling destination, the city has long struggled with the ability to diversify its economy and attract year round residents. There is existing literature and reports detailing how gambling is attempted to be used as an instrument for economic development. A chapter of the book “Legalized Casino Gaming in the United States: The Economic and Social Impact” by Cathy Hc Hsu is devoted to Atlantic City adoption of casinos. When casinos were first introduced to Atlantic City, they estimated nearly 30,000 new jobs by 1985. By 1996 gambling was responsible for 11,000 jobs in Atlantic City, but unemployment was still at 14.3% (Hsu, 1999).

A more recent report titled “Atlantic City: Past as Prologue” further details the challenges that Atlantic City has faced over the last 50 years. Here, an idea called the Labor Force Paradox is discussed. This refers to the fact that there is an abundance of jobs, but a high unemployment rate remains due to the fact that many in the labor force don’t have a high school diploma, nor the skills necessary for the available jobs. The high school graduation rate in Atlantic city is 16 points below the state average in New Jersey (Newburger, Sand, Wacks 2009). This paradox has another component in that casino employment typically higher for night and weekend shift. As we saw in our review of the child dependency ratio, there is a proportion of children in Atlantic City compared to the number of working age adults. The lack of affordable childcare can become a barrier to employment for many single parents and young families.

Conclusion

            Atlantic City has tried many strategies in its history to build a sustainable tourism-based economy. In order to stave off population decline, the city’s long term, future goal should be to diversify their economy and focus on the education and retention of their youth. Certainly, it is easy to make these plans, the challenge is often having the power to get these plans funded to allow for continued development. However, the tax revenue that Atlantic City generates should allow for more efforts to support the local population and workforce.

As the city moves forward, if they are going to diversify into other industries, there is no indication that they shouldn’t have a continued focus on increasing tourism. Atlantic City is much different than other declining cities of 40,000 people because of its attraction as a destination. Since it already holds this reputation, it should continue to invest in finings ways that tourism spending can best be funneled into supporting and educating their population.

 

 

 

 

 

 

 

 

References

Hsu, C. H. C. (2016). Legalized Casino Gaming in the United States: The Economic and Social Impact.

Lafrombois, M. E. H., Park, Y., & Yurcaba, D. (2019). How U.S. Shrinking Cities Plan for Change: Comparing Population Projections and Planning Strategies in Depopulating U.S. Cities. Journal of Planning Education and Research. doi: 10.1177/0739456×19854121

Newburger, H., Sands, A., & Wackes, J. (n.d.). Atlantic City: Past as a Prologue. Atlantic City: Past as a Prologue. Federal Reserve Bank of Philadelphia, Community Affairs Department.

Population Forecasting. (1950). American Society of Planning Officials, 17. Retrieved from https://www.planning.org/pas/reports/report17.htm

[1] The Atlantic City metro area has been used rather than Atlantic City proper for the Population Growth section of the report, due to the available data.

Demographic Report for Arizona and California

Demographic Comparison of Arizona and California

Age and Sex Composition, Population Growth

 

By: William Murray

This report compares the age and sex composition, as well as the population growth between Arizona and California in 2017. While these two states share a border, they have significant differences in their population structure and size. California is the largest state in the U.S. with a total 2017 population of 38,982,847, while Arizona hosted a 2017 population of 6,809,946. Through the use of population pyramids, sex ratios and dependency ratios we will examine how the states differ in age and sex composition. Then, we will go on to discuss the manner in which each population has grown over recent years using arithmetic, geometric and exponential growth models. The contrast in population between Arizona and California is important information for the governments, businesses, and residents who live and work in each state. The report will also include a review of why these statistics are important and hypothesize why Arizona and California are similar is some population structures, while differing in others.

Age and Sex Composition

In the chart below, we see the population pyramid by five-year age groups for Arizona in 2017. In the initial review we can observe that among both men and women, 20-24 year olds make up the largest share of individuals in the state. We can also see a normal trend of a decreasing population share beginning from the 60-64 year age group to the 85 and over group as mortality rates rise as individuals continue to age.

 

 

U.S. Census, S0101: AGE AND SEX, 2013-2017 ACS 5-year Estimates

 

In comparison, we can look at the population pyramid for California. Here, we can see 25-29 year olds are the most populous age group, as 25-29 year old males are accounting for a full 8% of all males in the state. Without any statistical analysis, we can observe that California has lower percentages of elderly individuals than Arizona. Using different demographic methods, we will be able to attain a more nuanced look at how the populations are unique to one another across age and sex.

U.S. Census, S0101: AGE AND SEX, 2013-2017 ACS 5-year Estimates

 

In order to gain further understanding of the age structure of Arizona and California, we need to examine the different sex and dependency ratios of these two states. In the table of sex ratios shown below, we can see a similar pattern among both states. There isn’t a significant difference in the share of men and women, but we can see the common trend of men outnumbering women until mid-life, where the women outnumber men as women tend to have longer life expectancy.

Sex Ratios for Arizona and California, 2017
 Age Arizona California
  Under 5 years 104.483 104.678
  5 to 9 years 103.882 104.419
  10 to 14 years 103.459 104.135
  15 to 19 years 104.766 104.677
  20 to 24 years 108.443 107.315
  25 to 29 years 108.045 106.818
  30 to 34 years 105.498 104.650
  35 to 39 years 102.037 101.520
  40 to 44 years 101.940 100.084
  45 to 49 years 99.880 100.045
  50 to 54 years 96.909 98.359
  55 to 59 years 92.215 95.385
  60 to 64 years 88.555 92.225
  65 to 69 years 89.093 88.383
  70 to 74 years 88.694 85.100
  75 to 79 years 89.923 80.936
  80 to 84 years 85.736 73.265
  85 years and over 63.294 55.803
Ratios Arizona California
Dependency Ratio 56.3474467 48.3862217
Child Dependency Ratio 28.0006489 28.7889166
Age Dependency Ratio 25.4006234 19.5973051
Ageing Index 82.0782901 68.0723953

 

 

Dependency ratios can provide policy makers, the private sector and the healthcare industry with a particular understanding of the demands of the population they serve based on their age composition. The dependency ratio in Arizona is 56.35, compared with 48.39 in California. This means that Arizona has approximately six more people “in dependency” than California. The qualifying ages for “in dependency” are ages 0-14 and ages 65 and over. This means that there are more people in Arizona depending on the working age population than in California, but their needs as dependents is determined by whether they are children or whether they are elderly. These two different groups require different services from government, family and the community in their pre or post working years. The child dependency ratio along with the age dependency ratio provide the detail necessary to determine the difference between these two cohorts.

Child dependency shown in the figure above is similar in both states in our report. This ratio tells us that there are approximately 28 children for every 100 working age adults in both Arizona and California. Since the difference is less than one individual between the two states, they don’t have considerably different priorities when allocating the total share of state resources for public school education and other child services.

The aged dependency ratio is calculated by finding the proportion of the 65 years and over population to the working population ages 15-64 years old. In Arizona, this ratio is 25.40 while in California it is 19.60. This means that there are nearly a full six additional aged individuals over 65 years old in Arizona for every 100 working aged persons than there is in California. The aged dependency ratio accounts for most of the difference in the overall dependency ratio between these two states. This pattern is due to Arizona’s prominence as a destination for retirees. In A report from Arizona State University titled Migration to and from Arizona, Tom Rex details how the state experiences disproportionally high migration among retirement aged individuals. Arizona is such a popular destination for retiree’s that different towns attract retirement aged people at different stages such as Gila, AZ, a popular destination for young retirees (Rex, 2016).

Having a population with a larger share of older, nonworking dependent has implications for how the state’s economy will function. Older populations require a different set a government resources, healthcare facilities, and strategic planning priorities than a state with either a younger population, or a less age dependent population. The ageing index furthers supports how much older the population of Arizona is when compared to California. This equation omits the working age population and finds the proportion of the population over 65 relative to the total number of children aged 0-14. In Arizona this ratio is 82 aged persons per 100 children, while in Arizona it is just 68 per 100. While there are still more children than aged persons in Arizona, the ratio is much higher than we see in California.

Since there is a greater number of children living in California in proportion aged persons compared to Arizona, it will represent a different set of challenges for the state. It is important to plan how the child population will grow in the future and determine the amount of resources those individuals will require as they age into adulthood. One of the main issues facing California’s economy currently is the housing supply and how rapidly rising real estate and rental prices may affect the future of the labor market. In the article Do land use regulations stifle residential development? Evidence from California cities, Jackson explores how this constraint limited the housing supply. While the observation of this study focuses on 1970-1995, the principles surrounding the need to expand housing options in a growing population remain the same. The study concludes that zoning and general controls are the main determinants that deter new housing starts. A housing market that isn’t meeting the demands for the residents of its own state will face economic consequences. As we will see in the ‘Estimates of Population Change, expressed as Percentages’ table on the following page, California has seen a decrease in net domestic migration from 2010 to 2017. Further research would be needed to determine if a portion of this relationship could be explained by California’s rising housing prices and workers migrating to other states in search of a lower cost of living (Jackson 2015).

Population Growth

Now we will review how each of these states have experienced population growth and determine which model of growth best explains each state’s increase in population. The table below shows us how the total population has grown in Arizona and California from 2010 to 2017. Arizona has seen just over 9% growth over this seven-year span, while California has seen just 6.40% growth.

Total Population by Year
Year Arizona California
2010 6,246,816 36,637,290
2015 6,641,928 38,421,464
2016                  6,728,577 38,654,206
2017 6,809,946 38,982,847
Growth Rate, 2010 -2017
  Arizona California
Growth Rate 9.01% 6.40%

 

To gain a more detailed view of population growth, we can look at how the population has changed through natural increase (births minus deaths) along with net international and domestic migration. The U.S. Census provides the raw data for each state and below it is shown as a percent of the 2010 population for Arizona and California. In this table, we can easily compare the growth rates between the states for the different demographic measures. Since the first two tables contain data from the ACS 5-year estimates and the below table sources data from the Estimates of the Components of Resident Population Change, the figures from 2010 to 2017 will be slightly incongruous as they are observed in different months of those two years.

 

Estimates of Population Change, expressed as Percentages
April 1, 2010 to July 1, 2017
    Vital Events Net Migration
  Natural Increase Births Deaths Total International Domestic
Arizona 3.93% 9.96% 6.03% 6.03% 1.57% 4.45%
California 4.86% 9.84% 4.98% 1.41% 2.93% -1.52%

 

 

 

 

By looking at this table, we have information that further supports the research findings of positive migration among retirees. While Arizona grew at a faster rate than California, California had nearly 1% greater growth in natural increase. This means that Arizona outpaced California in net migration. We can notice a stark difference specifically in net domestic migration as Arizona grew by 4.45% in this component, while California saw a net loss of 1.52% among domestic migrants.

In the tables below, we can see the real population estimates for 2017, compared with estimates using the arithmetic, geometric, and exponential growth models. Each of these models uses a different equation to determine a rate of growth, then we can calculate the absolute different and percent difference between the ACS 2017 population estimate, and the estimate calculated using each respective method. To determine the manner in which the population is growing in each state, we determine which method has the lowest percent difference. For both Arizona and California, we find that the arithmetic growth method is the most accurate projection when compared to the ACS estimate.

Arithmetic Population Growth
 

 

 Arizona California
2017 (arithmetic growth) 2017 2017 (arithmetic growth) 2017
Population                        6,815,228 6,809,946                     39,007,732 38,982,847
Growth Rate 1.288% 0.915%
Absolute Difference                               5,282                            24,885
Percent Difference 0.0775% 0.0638%

 

 

 

 

 

 

 

 

 

Geometric Population Growth
 

 

 Arizona California
2017 (geometric growth) 2017 2017 (geometric growth) 2017
Population                   6,772,324 6,809,946                     38,771,105 38,982,847
Growth Rate 0.650% 0.302%
Absolute Difference                             37,622                          211,742
Percent Difference 0.5555% 0.5461%

 

Exponential Population Growth
 

 

 Arizona California
2017 (exponential growth) 2017 2017 (exponential growth) 2017
Population                        6,815,789 6,809,946                     38,887,652 38,982,847
Growth Rate 1.296% 0.604%
Absolute Difference                               5,843                            95,195
Percent Difference 0.0857% 0.2448%

 

We have found that there are some similarities in the population structure and growth rates in Arizona and California. They are each home to a similar age and sex composition, until we reach ages 65 and over. The retirement migration greatly shifts the population composition of the state of Arizona and makes significant impacts on its overall economy and healthcare industry. Since California is in comparison a younger state, they must plan for the needs of a growing population of adults who will be entering the workforce in the next 10-15 years. We have also determined that over the last seven years of population growth in each state, there is variation in the data when it comes to vital statistics and net migration.

 

 

 

 

 

References

Jackson, K. (2016). Do land use regulations stifle residential development? Evidence from California cities. Journal of Urban Economics, 91, 45–56. doi: 10.1016/j.jue.2015.11.004

Migration to and From Arizona. (2016). Center for Competitiveness and Prosperity Research, 1–49.

U.S. Census Bureau. AGE AND SEX 2013-2017 American Community Survey 5-year Estimates. Washington, DC: U.S. Government Printing Office.

U.S. Census Bureau.  Estimates of the Components of Resident Population Change: April 1, 2010 to July 1, 2017. Washington, DC: U.S. Government Printing Office.

U.S. Census Bureau. TOTAL POPULATION, Universe: Total Population, 2013-2017 American Community Survey 5-year Estimates. Washington, DC: U.S. Government Printing Office.

 

 

Final Report Proposal

Population and Housing Unit Growth in Multnomah County, Oregon

 

            In this report, the focus will be to examine the relationship between population growth and housing in Multnomah County, Oregon. First, we will look at how the population has grown through migration and natural increase from 2010 and 2018 and determine if housing units have grown at a similar rate to meet demand. Then we will complete a population projection to estimate the future population and discuss the implications for the future of the housing market. Multnomah County and the Portland metro-area has seen significant growth over this time horizon and the demand for housing is shown in the increase in real estate prices. The results of this report will be able to assist policy makers in planning for future population growth throughout the county.

The majority of the data used for this report will derive from the Census. The Census site has the necessary detailed statistics on births and deaths, as well as international and domestic migration. In order to perform the projections, we will have to make basic assumption on how the population will change going forward. The report will include a low, medium and high projection for fertility, mortality and migration in order to forecast a range of how the population could grow. The Census also offers housing data from the American Community Survey, including basic counts by year and housing structure characteristics.

Through Proquest, I was able to find several reports on both the housing market and population in Portland and Multnomah County. Since this report will be focused on the last eight years as well as population projection and its implications, it is useful that these reports are all from the relevant time horizon. The Comprehensive Housing Market Analysis and the Portland, OR-WA Area Economic Summary provide an overview view of the current economic conditions in the metro area. While this report focuses on Multnomah County, it is helpful to review statistics for the whole metro area. Another report from 2011, Portland: Two Decades of Growth looks back at Multnomah County’s 26% growth rate from 1990 to 2017.

 

References

Local Highlights: Portland Population-Two Decades of Growth. (2011). Oregon Employment Department. Retrieved from https://statistical-proquest-com.ezaccess.libraries.psu.edu/statisticalinsight/result/pqpresultpage.previewtitle?docType=PQSI&titleUri=/content/2011/S6592-1.507.1.xml

Portland, Oreg.-Wash., Area Economic Summary: May 2019. Selected Economic Indicators. (2019). Bureau of Labor Statistics. Retrieved from https://statistical-proquest-com.ezaccess.libraries.psu.edu/statisticalinsight/result/pqpresultpage.previewtitle?docType=PQSI&titleUri=/content/2019/6996-3.16381.xml

Portland-Vancouver-Hillsboro, Oregon-Washington Housing Market: Comprehensive Market Analysis Report as of Sept. 1, 2018. (2019). Department of Housing and Urban Development. Retrieved from https://statistical-proquest-com.ezaccess.libraries.psu.edu/statisticalinsight/result/pqpresultpage.previewtitle?docType=PQSI&titleUri=/content/2019/5186-18.11063.xml

Author Profile SRBE

My name is Wil Murray, I currently live in Portland, OR but grew up in Cape May County, NJ. I studied Finance and Economics at York College of Pennsylvania where I received by bachelor’s degree in 2015. While at was at York, I had two different internships with local non-profits who were working on the revitalization of downtown York. This experience spurred my interest in local economic development and studying how local economies change over time.

I currently work at Bank of America on the Global Trade Operations team as a Trade Finance specialist. This role has exposed me to the scope of international economics and the trade industry as a whole. As I was researching graduate programs, the demography program here at Penn State was the first time I had heard of the discipline. I was drawn to the program because I thought it had an interesting combination of geography, economics and statistics. I also felt that training in demography would be useful in economic and labor research later in my career. My near term goals are to focus and my Capstone project throughout this semester, then graduate in Spring of 2020. After graduation, I want to focus on steering my career back to to economic development at the local and regional level. In my free time I enjoy cycling, running, playing guitar and cooking!

 

Group Project 2

Storyboard

The health services industry faces several challenges as it looks to plan for population change in rural America. The issue involves many different aspects of our culture including employment, social norms, and density that define the landscape of rural health. While 75% or the land area in the U.S. is classified as rural, the population accounts for approximately 15% of the total number of U.S. residents. Dispersed populations over such a wide geography as we see here in America face unique challenges and require their own initiatives to address the health and well-being of its populace. In this report, we will detail the structure of some of these challenges, as well as two policy recommendations aimed at addressing both health care coverage and access to care. While coverage and access may seem to be synonymous, they are not mutually exclusive in a rural geography.

Labor is a defining aspect of every local economy, but it has a disproportionally significant effect on rural communities. In a small town or geographically dispersed area, options for employment can be few and far between.  In 2016, the rural employment to population ratio was nearly five points lower than in urban areas, according to the USDA Economic Research Service. Since the financial crisis of 2008, the 2015 American Community Survey reports that metropolitan area job growth has outpaced rural areas by nearly ten percent. A struggling labor market plants the roots of many of the other problems that rural populations are facing. In the realm of healthcare, many prime age working families depend on their employer for healthcare coverage before they age into Medicare eligibility. Healthcare coverage for this cohort of the population is vital, as they are the individuals contributing to the economic viability of the region while also raising the younger generation. If this massive share of the population is facing high levels of unemployment, healthcare coverage will also decline.

In order to address this issue, policy makers should introduce legislation that severs the tie between employment and healthcare coverage. All too often, young working families are faced with a temporary bout of unemployment and lose their healthcare coverage in the process. These short-term lapses in coverage can lead to missed preventative screenings and gaps in access to prescriptions. Also, individuals who are facing long term unemployment never have the opportunity to gain healthcare coverage which can lead to even greater problems in population health. This is a national trend that needs to be addressed at the federal level. The Affordable Care Act took significant strides in distancing the relationship between healthcare coverage and employment, but its inefficiencies and nuisances have been barriers to wide spread adoption of the policy. Presenting and passing legislation that answers the call in the lack of rural healthcare coverage with have a substantial impact on this population.

Rural health issues can’t be discussed without mentioning the mental health challenges of this population. Nationally, awareness of mental health has increased as it becomes part of the mainstream conversation in overall well-being. Rural areas are suffering from both a culture in which social isolation is leading to high levels of mental disorder along with social norms that shun much of the dialogue surrounding understanding and solutions to the subject. A deeper assessment of rural sociology behind a culture that promotes personal responsibility is beyond the scope of this report, but it is important to mention because of how it impacts patients and their relationship to healthcare. Due to this social stigma and a shortage of mental health facilities, we have seen the opioid epidemic declared as a national emergency in 2018. While drug abuse and overdose are symptoms of the problems an individual is personally confronting, it speaks to a larger system of how rural populations view their association with mental health.

The very identify of rural geography leads to challenges in providing access to care among dispersed populations. In dense urban environments, healthcare services and physicians are concentrated so the residents of those areas have the ability to be treated by professionals across a wide range of disciplines. A report from the North Carolina Rural Health Research Program states that per 10,000 residents, there are 55.1 primary care physicians in rural areas compared to 79.3 in urban areas. The distribution of specialists is even more disproportionate as these figures are 30 and 263 per 100,000 for rural and urban areas, respectively. The presence of public transportation also provides mobility to and from appointments for individuals and families who don’t own a vehicle. There are still many issues in the urban healthcare landscape, but rural America is facing a much larger probem in terms of access. This can’t be solved simply by opening more physicians and doctors’ offices in small towns as there is a systemic reason that these populations are underserved. Individuals below the poverty line are most at risk to limited access, as there are extremely limited options for transportation to appointments for non-car owners in rural areas.

Since this issue is integrated into the geography of rural America, the structure of the health services industry needs to adjust. A policy solution to this problem is to incentive universities to include home visits to patients as part of the coursework in nursing and medicine programs. This approach will accomplish two main goals in rural healthcare access. First, it will satisfy some of the demand for care by providing in-home visits by practicing medical students and their professors. More importantly, by the exposing these future nurses and physicians to patients in rural areas, it will raise awareness of the healthcare needs of the rural community. In order to meet demand by region, this policy may be most effective if adopted by large universities who have several satellite campuses across their respective states. Many of these satellite campuses are often in smaller towns or more remote areas than the main university campus. By integrating this service into the university health network, it could have a key impact on the future of the rural healthcare system and how we address access to care.

 

Poster

Created using Social Explorer

This image shows the unemployment rate by county for 2016. We can see that unemployment is dispersed throughout the country across rural areas in each region. We see high clustered levels of unemployment throughout the Southeast, a region that also faces population health issues.

 

 

Created using Social Explorer

 

Here we see two visualizations that help us in understanding the lack of healthcare professionals in rural areas. On the left, we see population density per square mile by county. We can then see the large areas in gray as rural. On the right, we see a density map of primary care physicians. In this map, we can not just see the absence of physicians in the West and Midwest, but we also see gaps in density throughout the Southeast.

 

 

Variable Rural Urban
Physicians per 10,000 55.1 79,3
Specialists per 100,000 30 263
Avg per capita income $45,482 $53,657
Percent covered by Medicaid 16% 13%

from National Rural Health Association

 

This table provides a simple snapshot of statistics between urban and rural areas. While the disparity in providers is discussed in our storyboard, it is important to note the difference in income and Medicaid recipients between urban and rural counterparts.

SRBE Group Project 2

For the Group Project 2 assignment, I found communicating with Maha easy and efficient. With only two members in the group, it was simple to assign each of us different parts of the project to do on our own. In a larger group it might be more difficult to communicate in an online learning setting. In the past, I’ve found some of the most productive time spent on a group project occurs when all team members are present and collectively contributing to the project. Obviously, this is a barrier in online courses but with just two people, we used the Canvas messaging platform to assign sections of the assignment. From there we completed the research individually. After completing the first group project on our own, we agreed to do this project together in order to divide the workload.

Each of us have different professional and academic experience that contributed to the research for this assignment. For the poster, I used the data visualization platform Social Explorer that we have used in the Data and GIS course to create maps that explained some of the rural health issues in the U.S. Maha used the contents of our research to form the structure of our storyboard and poster into a final presentation. Due to our varied experience, we were able to provide different components that benefited the project overall. If we were to go through this process again, there isn’t any major aspect of the assignment that I would change. After realizing the ease of working together on the second group project, I think we could have done the first project together as well.

Throughout this course and this assignment in particular, it has showed me how population health can be a determining variable to many other aspects of culture and industry. Studying population change and the future of health service in rural America is important not only to those specific regions, but to the country as a whole. The components of our economy are highly integrated and a shift in the population health of rural areas wil affect individuals across the nation. For rural residents, this report is important because it discusses policy recommendations on how to address some of the negative health outcomes rural areas are experiencing. As far as developing my own personal skills, this assignment was the first time I had to review population projections and analyze how that would change the needs of a certain industry. Identifying major health concerns are fairly straight forward, but determining which issues to address and how to address them is more challenging.

APDEM806 Comparative Health Policy Essay

The two health policies detailed in this report were introduced in the Pennsylvania General Assembly in 2016. The first policy adds an amendment to the Professional Nursing Law from 1951 that establishes a peer assistance program for nurses who are struggling with mental health issue or drug and alcohol problems. The program will present nurses who a facing these issues with rehabilitation if they are unable to care for patients “with reasonable skill and attention to the safety of patients” due to their circumstances. The other policy we will review is also an amendment to a former act and its goal is to introduce education modules and guidelines for school employees on diabetes care and treatment. The instruction will include a review of different types of diabetes, monitoring blood glucose, treatment of glucose levels outside of target range and training on administering insulin.

The intent of the Nurses Health Program policy is to ensure that our healthcare professionals are performing at the best of their ability. While acknowledging the stress of the occupation of nursing, the policy is offering outlets for medical professionals to address their issues. Ultimately the goal of this policy is to provide patients in Pennsylvania with nurses who are able to deliver the proper care they require. The Education of School Employees in Diabetes Care and Management is a preventative care measure that is meant to prepare school employees for treatment of a student in the absence of a school nurse. The school nurse and the chief school administrator are responsible for designating an employee to complete the annual instructional modules. Since this individual is not a licensed health care practitioner, schools are adding a trained employee who will be able to assist the school nurse in the event that diabetes care is needed.

Each of these policies will have a unique impact on population health. They are both in the category of promoting preventative care and can have a direct effect on patients. Ensuring that nurses are practicing under a healthy mental state can affect all of the population and their health. Maybe the more important question to ask is what would happen in the absence of this policy and nurses weren’t providing patients with sufficient care? The consequence of that problem could leave a lasting effect on the condition of patients and the reputation of the health care industry. The implications of a diabetes education program would have a more acute effect for students diagnosed with diabetes. The program would certainly raise awareness of diabetes throughout schools in Pennsylvania and provide a second option to students if they are in need of diabetes treatment.

Though the two policies are beneficial to overall population health, the amendment to the Professional Nursing Law would have the largest impact on improving population health. While both of these health policies address seemingly minor aspects of the state’s health system, nurses are fully integrated into the care structure. We depend on nurses and their expertise for health screenings, observing a patient’s conditions, and communicating with doctors. All of these functions are vital in the healthcare systems. If nurses are facing mental health issues or practicing under this influence of drugs or alcohol and it goes unnoticed, it could have dire effects for patients, families and the nurses themselves. While diabetes awareness and education is important and children need the best services to treat the disease while they are in school, it effects only a portion of the state’s population. Nearly all individuals that receive health services are in contact with a nurse, and if those nurses are suffering from certain issues this policy will allow them to address those issues in an effective manner.

 

 

 

Health Policies

https://www.livehealthypa.com/docs/default-source/substance-use/substanceuse_senate_1404_2226_2015—2016-regular-session.pdf?sfvrsn=0

 

https://www.livehealthypa.com/docs/default-source/diabetes/diabetes_senate_1385_2134_2015—2016-regular-session.pdf?sfvrsn=0

APDEM 806 – SRBE Group Assignment 1

For this assignment, we ended up working individually on the project rather than in a group. I have yet to work in a group environment in this online program, but I do think it has value if collaborative projects can be done effectively. In our class it is easy to get to know each other because there are only two students. However, one of the positives of the discussion forums in a larger class is to see what other students have thought about the course content and readings. In a larger online class, group projects still have their difficulties as it is hard to truly work together, rather than assigning group members different aspects of the project.

Leading up to our project, I thought the format of submitting research questions then ranking your preference in each question was a great way to form research groups. Even with our small class, I enjoyed reading the questions that Maha had submitted. The only issue I had with the assignment was that it was unclear which research question we would be working on and I wasn’t sure what the final research question was until a week before the assignment was due. Overall, I think much of this was due to our unique class situation.

I’m not sure exactly how to improve the group dynamic in an online learning setting. It seems like from the courses I have taken so far, a lot of students like to interact on the discussion forum. It is a great way to find a community of students that are interested in the same discipline, something that is one of the main draw-backs of exclusively online programs. With students having different schedules and living in different time zones it is difficult to find a time to meet, but video conferencing would be a great way to collaborate with other students. I think we will do the second group project individually as well because of our small class size.