The Social Impact of Zip Codes on Human Health

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In the modern world, social differences between groups of people exist on various bases and often cause inequalities. While some of those inequalities are related to characteristics such as race, gender, or age, others depend on factors such as the place of residence. Areas in which people reside affect their lives, whether positively or negatively. Those areas can be classified as to their zip codes that need to be examined to identify possible reasons for inequality due to infrastructural development. As zip codes differ on factors such as socioeconomic or ecological, people have various health issues, and the level of development of certain areas can affect people’s access to the means to improve their health. Different levels of development of certain places raise the question of whether residents of zip codes have equal access to such basics as health services.

There are several ways to justify assessing the connection between zip codes and human health. First, with the spread of COVID-19, research shows that zip code analysis can help identify risk groups and locations (Sen-Crowe et al., 2021). Moreover, zip codes can determine whether those groups are at more risk due to racial discrimination inequities in areas with a lack of necessary services (Long and Albert, 2021). However, there is not enough information nationwide regarding COVID-19 on the zip code level to possibly prevent a further range of the virus (Sen-Crowe et al., 2021). Second, although the zip code approach to collect data needs to be done for over a year, it is expected to be cost-efficient and easy to implement (Bi et al. 2020). Third, the results of data collected on the zip code level can be used to indicate zip code trends and determine areas that need changes (Bi et al., 2020). Overall, these days the social impact of zip codes on human health needs to be examined to determine areas that need to be developed better to provide people with better health care services.

A literature review relevant to this research proposal suggests that the connection between zip codes and human health has been examined before. However, as mentioned above, such information is not enough nationwide, meaning that more research needs to be done to determine areas that need improvement. For example, a study conducted in New Mexico examined the connection between socioeconomic status and cases of COVID-19 using the zip code level (Huyser et al., 2021). New Mexico was chosen to focus on areas populated by Indigenous people who account for a significant part of the COVID-19 cases in the state (Huyser et al., 2021). The researchers used the American Community Survey and the New Mexico Department of Health to create a zip code level dataset, with the number of COVID-19 cases as the dependent variable (Huyser et al., 2021). The research showed that more cases were detected in zip codes with higher concentrated disadvantages characterized by access to resources, economic stability, and resources (Huyser et al., 2021). However, as the study focused on Indigenous people, there is a need for further investigation of the rest of the population.

Following that, this research will focus on people who live in zip codes with various levels of education. A similar study examined whether the prices of real estate markets depend on the presence of colleges and hospitals in zip codes (Rivas et al., 2019). The researchers used data from Zillow Home Value Index for 2017, the United States Census Bureau for 2010, and Wikipedia to collect details about universities (Rivas et al. 2019). The study has found that zip codes with colleges tend to have higher rental rates and home prices (Rivas et al., 2019). Therefore, one can assume that areas with universities may be better developed and may offer better health services, thus having higher rental rates. However, the study used some outdated information showing the need to research current data. Moreover, there is a need to examine zip codes by looking at the number of people with higher education levels to determine if they live in areas with more or bigger hospitals.

Furthermore, while the described above study examines the cases of COVID-19 emphasizing physical health, there is a need to analyze mental health as an integral part of human health in zip codes. A related study has examined the geographic availability of mental health resources for low-income populations (Cummings et al., 2017). The researchers used data from the 2013 American Community Survey and 2010 data from the US Census Bureau (Cummings et al., 2017). The study has found that mental health professionals were typically located in higher-income areas (Cummings et al., 2017). While the research shows the impact of underdeveloped zip codes on the resident’s ability to access mental help to improve their well-being, it is becoming less relevant to the present times. As COVID-19 has widely spread across the nation, it significantly affects people from low socioeconomic status (Sen-Crowe et al., 2021). As people struggle more physically and financially, it is expected that they will struggle emotionally as well. With that being said, there is a need to determine the connection between the development of zip codes and people’s mental health affected by the pandemic.

As mentioned above, there is a need to analyze the impact of zip codes on human health in more areas focusing on the resident’s ability to access health care with varying levels of education. As it may be difficult to examine each or even most of the zip codes at once, it may be better to start with a smaller area to identify which method would work better. With regard to the consequences of COVID-19, zip codes in Florida may be more appropriate for this research. According to the latest statistics, Florida ranks among the top three states with higher numbers of cases, with Miami-Dade County leading in the state (USA Facts 2021). Being one of the areas with high rates of COVID-19 cases, zip codes in Florida may be useful to determine the availability of health care services and the necessity for further infrastructural development of areas.

Following that, assessing the social impact of zip codes on human health will be focused on the level of education and accessibility of health services but requires justification from literature to proposed hypotheses. Research suggests an increase in mental health symptoms among those with and without prior history of mental health conditions due to the consequences of COVID-19 (Holingue et al., 2020). Therefore, there is a need to assess the availability of mental health help, especially in rural areas with low income (Cummings et al., 2017). Hypothesis 1: Individuals living in rural areas will have higher rates of mental unwellness due to less access to mental health facilities than those living in urban areas. Another study proposes that education can affect one’s health behavior and suggests a thought of whether more educated people choose to reside in areas with better health services (Schüz et al. 2020). Hypothesis 2: Individuals living in zip codes with more highly educated populations will have better health outcomes than those with less-educated populations. The two hypotheses need to be proven to determine the impact of the development of zip codes.

As one of the hypotheses is related to the impact of COVID-19, the first variable for the research will be the physical health indicator. The indicator will be measured by the number of confirmed cases in zip codes with data gathered from sources such as USA Facts, which provides weekly information on the virus, or from the Florida Department of Health if the state is finally chosen for the research (USA Facts 2021). The number of cases of COVID-19 is expected to show the necessity of health care services for a significant part of the population.

The second variable will be access to health care facilities. Hospital accessibility refers to the ease with which residents can receive care and can be measured by the density of facilities in zip codes and the number of beds (Wang et al., 2020). The data is also expected to be gathered from health departments of each area and is meant to show if people with confirmed cases of COVID-19 can get the necessary help.

The next variable will be the accessibility of mental health help. The geographic availability of mental health facilities in zip codes will be measured using data from Behavioral Health Treatment Services Locator, which provides information on mental health clinics across the US (Cummings et al., 2017). The measurements are expected to show whether people can receive mental health services in areas with various rates of COVID-19.

The fourth variable will be the level of education among the residents. The level of education will be measured based on data from sources such as Town Charts that provide various information about cities in the US, with an ability to select zip codes (Town Charts n.d.). This information is expected to show the levels of education of residents of zip codes to determine whether there are more educated people in areas with better access to health care services.

The last variable will be the rural and urban zip codes. The variable will be measured using Town Charts by assessing data on primal jobs in areas to decide whether they are more inherent to urban or rural places (Town Charts n.d.). The measurements are expected to determine the development of zip codes and examine whether they have more or less access to health care services for the residents.

Next, the measures need to be tested for reliability and validity to evaluate their quality. To assess reliability, one should look at its main criteria: stability, consistency, and equivalence (Souza, Alexandre, and Guirardello 2017). Stability can be tested by the intraclass correlation coefficient to estimate continuous variables’ stability and determine the changes in the variables (Souza, Alexandre, and Guirardello 2017). As the research will use statistical data, it may be better to focus on Cronbach’s alpha coefficient for consistency (Souza, Alexandre, and Guirardello 2017). Finally, equivalence can be assessed by the interobserver reliability involving two independent raters to evaluate the concordance of the measures (Souza, Alexandre, and Guirardello 2017). As there are different types of validity, it can be assessed based on hypothesis testing to see differences between groups of individuals or correlation tests to see correlations between measures (Souza, Alexandre, and Guirardello 2017). Reliability and validity must be tested based on their criteria and type.

Furthermore, a crucial part of research is a sampling design. While the sampling frame for this study could consider every person in the US who gets mail, as mentioned before, it may be difficult to assess most of the zip codes at once. With that being said, Florida may be one of the appropriate areas for the topic due to the high rates of COVID-19. The sampling design will be based on cluster probability sampling to choose zip codes and residents within the state. Cluster probability sampling is more appropriate for this study as it deals with relatively large populations of zip codes (Berndt 2020). Moreover, cluster sampling refers to selecting naturally occurring groups that are zip codes in this study and is economical and feasible compared to other methods (Berndt 2020). The proposed sampling design considers zip codes and is based on cluster probability sampling to work with a large number of residents.

To summarize, the proposed research aims to determine whether the development of zip codes impacts the residents’ access to health services. First, the study suggests focusing on the level of education to see if more educated people live in areas with more means to support health. Second, the study suggests determining whether people in rural and urban areas have equal access to mental health care. Due to the worldwide spread of COVID-19, the research considers the virus and its consequences on people. The research is expected to assess the connection between zip codes and human health and identify areas that need more development to decrease inequality in receiving means to improve one’s health.

References

Berndt, Andrea. 2020. “Sampling Methods.” Journal of Human Lactation, 36 (2: 224-226. Web.

Bi, Qifang, Fangtao He, Kevin Konty, Hannah Gould, Stephen Immerwahr, and Amber Seligson. 2020. “ZIP Code-Level Estimates from a Local Health Survey: Added Value and Limitations.” Journal of Urban Health, 97 (4): 561-567. Web.

Cummings, Janet, Lindsay Allen, Julie Clennon, Xu Ji, and Benjamin Druss. 2017. “Geographic Access to Specialty Mental Health Care Across High-and Low-Income US Communities.” JAMA Psychiatry, 74 (5): 476-484.

Holingue, Calliope, Elena Badillo-Goicoecheaa, Kira E. Riehma, Cindy B. Veldhuis, Johannes Thrula, Renee M. Johnson, Daniele Fallina, Frauke Kreuterd, Elizabeth Stuart, and Luther G. Kalb. 2020. “Mental Distress During the COVID-19 Pandemic among US Adults without a Pre-Existing Mental Health Condition: Findings from American Trend Panel Survey.” Preventive Medicine, 139: 1-8. Web.

Huyser, Kimberly, Tse-Chuan Yang, and Aggie J. Yellow Horse. 2021. “Indigenous Peoples, Concentrated Disadvantage, and Income Inequality in New Mexico: A ZIP Code-Level Investigation of Spatially Varying Associations between Socioeconomic Disadvantages and Confirmed COVID-19 Cases.” J Epidemiol Community Health, 0: 1-6.

Long, Kevin, and Steven M. Albert. 2021. “Use of Zip Code Based Aggregate Indicators to Assess Race Disparities in COVID-19.” Ethnicity & Disease, 31 (3): 399-406.

Rivas, Ryan, Dinesh Patil, Vagelis Hristidis, Joseph R. Barr, and Narayanan Srinivasan. 2019. “The Impact of Colleges and Hospitals to Local Real Estate Markets.” Journal of Big Data, 6 (1): 1-24. Web.

Schüz, Benjamin, Cameron Brick, Sarah Wilding, and Mark Conner. 2020. “Socioeconomic Status Moderates the Effects of Health Cognitions on Health Behaviors within Participants: Two Multibehavior Studies.” Annals of Behavioral Medicine, 54 (1): 36-48.

Sen-Crowe, Brendon, I-Chun Lin, Robert Alfaro, Mark McKenney, and Adel Elkbuli. 2021. “COVID-19 Fatalities by Zip Codes and Socioeconomic Indicators Across Various US Regions.” Annals of Medicine and Surgery 67: 1-10. Web.

Souza, Ana Cláudia, Neusa Maria Costa Alexandre, and Edinêis de Brito Guirardello. 2017. “Psychometric Properties in Instruments Evaluation of Reliability and Validity.” Epidemiologia e Serviços de Saúde, 26: 649-659.

Town Charts. n.d. “United States Education Data.” Town Charts (website).

USA Facts. 2021. USA Facts (website).

Wang, Jiaoe, Fangye Dua, Jie Huanga, and Yu Liu. 2020. “Access to Hospitals: Potential vs. Observed.” Cities, 100: 1-12. Web.

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The Social Impact of Zip Codes on Human Health

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