Neighborhoods, Obesity, and Diabetes, Research Paper Example

Introduction

An observational study conducted by Ludwig, Sanbonmatsu, Gennetian, Adam, Duncan, Katz, Kessler, Kling, Lindau, Whitaker, & McDade (2011) addresses the relationship between obesity, diabetes, and the neighborhoods in which individuals live. The research study was designed to determine if neighborhoods and their specific conditions have any impact on whether or not diabetes and obesity are prevalent within those areas, thereby addressing a challenging area of research that has been difficult to decipher to date (Ludwig et.al, 2011). Most importantly, it is necessary to determine from an epidemiological perspective whether or not individuals are influenced by their surroundings and the behaviors and activities taking place in these communities (Ludwig et.al, 2011). Therefore, it is important to consider whether or not environment is a legitimate influence for individuals on obesity and diabetes. The following discussion will address the scope and purpose of the article in greater detail and will also emphasize the importance of the findings that were identified, along with a discussion of those results and whether or not they supported the study’s purpose.

Analysis

A study was originally conducted between 1994 and 1998 by the US Department of Housing and Urban Development (HUD) and classified 4,498 women living in public housing with their children into three categories: 1) those receiving housing vouchers and receiving moving guidance; 2) those with vouchers not receiving moving support; and 3) those with no vouchers (Ludwig et.al, 2011). The study authors then examined the group by examining different types of health data, including but not limited to weight, height, and glycated hemoglobin (HbA1c) (Ludwig et.al, 2011). This study is relevant because it provides further support for the belief that there are significant challenges for many people who live at or near the poverty level and are required to live in public housing in racially segregated areas, including a higher risk of obesity and/or diabetes in many cases (Ludwig et.al, 2011). Many people within these communities do not have the knowledge or the access to resources that include healthier foods and other resources that will have a positive impact on their health and wellbeing; therefore, they face a higher risk of health issues that may impact them throughout the rest of their lives (Ludwig et.al, 2011).

The study utilized data from a prior study known as Moving to Opportunity (MTO), sponsored by HUD, as a means of evaluating neighborhood conditions and whether or not they influence the risk of obesity and diabetes in these areas; however, the original intent was to examine factors that included “employment, income, education, and well-being” (Ludwig et.al, 2011, p. 1510). The study population was comprised of females who had children under the age of 18 years living in the home in select cities such as Baltimore, New York, Los Angeles, Boston, and Chicago, including those in census tracts where poverty rates were above 40 percent (Ludwig et.al, 2011). The original study provided vouchers to selected participants via randomization in a lottery format, using sample sizes for power to address matters such as income, employment, and education (Ludwig et.al, 2011). The study utilized data collection techniques that included a survey to participants to consider their backgrounds, from housing, work experience, neighborhood, and those in the home environment, along with a health-based consideration of Supplemental Security Income for those with some type of disability (Ludwig et.al, 2011). HUD sought the author’s expertise in order to address health and other factors that were critical and were examined between June 2008 – April 2010 (Ludwig et.al, 2011). Those who participated had height and weight assessments, along with blood samples, using two-phase sampling to reach as many original participants as possible (Ludwig et.al, 2011). An evaluation of obesity by determining height and weight and a diabetes evaluation using blood sampling were conducted, and outcome measures were determined based upon Body Mass Index 30 or more, 35 or more, or 40 or more, along with glycated hemoglobin of 6.5% or higher (Ludwig et.al, 2011). The statistical analyses were conducted using an omnibus F-test, using intention-to-treat, in order to compare the control group to the voucher groups (Ludwig et.al, 2011). Linear regression was used to determine the impact on dependent variables and logistic regression was used to determine the impact on dichotomous variables (Ludwig et.al, 2011). The study used a two-sided P value that was less than 0.05 as a means of statistical significance, and the analysis portion was conducted using Stata version 11.0 (Ludwig et.al, 2011).

The study results indicate that a total of 4,498 families were participants in one of the three randomized groups, and in the 2008-2010 data collection phase, 84.7% provided data regarding BMI and 70.1% provided data regarding glycated hemoglobin in the low-poverty group; 82.8% and 73.7 percent in the traditional voucher group; and 84.4% and 71.3% in the control group (Ludwig et.al, 2011). It was determined that poverty rate had a significant relationship to the group to which participants were assigned, and for participants who received low-poverty vouchers, the poverty rate was 17.1 percent lower than for those in the control group (Ludwig et.al, 2012). It was also evident that at the 10-15 year follow-up window, the low-poverty voucher participant group had a lower risk of diabetes and obesity, including lower BMI scores and lower glycated hemoglobin levels, in comparison to those assigned to the control group (Ludwig et.al, 2012). Therefore, the study results primarily indicated that those in the low-poverty areas were more inclined to have reduced diabetes and obesity rates; however, other factors were less significant in the data analysis (Ludwig et.al, 2011).

Based upon the study results, it is believed that transferring to a low-poverty neighborhood and gaining exposure to new people and surroundings has an important impact on the ability of individuals to achieve lower diabetes and/or obesity rates (Ludwig et.al, 2011). The study determined that the glycated hemoglobin level was significantly lower in the low-poverty group than that which was received in the traditional group, thereby demonstrating that the neighborhood has a real impact on health to some degree (Ludwig et.al, 2011).Therefore, it is important to consider the neighborhood in how individuals maintain their health and wellbeing and if any changes to the neighborhood setting have a greater impact (Ludwig et.al, 2011).

The study possesses a number of strengths in that it provides information regarding the need to consider social-based studies and how they might have an impact on epidemiological research (Ludwig et.al, 2011). In this context, the study originally posed some issues regarding bias; however, they were resolved because it demonstrated the ability of individuals to live in a variety of neighborhoods and to examine how this impacts health in different ways (Ludwig et.al, 2011). Furthermore, the study evaluated the conditions under which neighborhood research is valuable to epidemiological studies, particularly when a wider range of areas are used in the study, as this supports the development of wider-range studies for consideration that may capture data and information from a larger group of people (Ludwig et.al, 2011). This information is important because it reflects the challenges of epidemiological research that includes socially-driven objectives, which are often difficult to capture on a larger scale (Ludwig et.al, 2011).

The study is limited in that some of the participants in the original study who did not partake in the long-term study, and this could have contributed to different outcomes than what were observed (Ludwig et.al, 2011). In addition, the 6.5% level for glycated hemoglobin is not indicative of individuals who may possess a diabetic condition that is controlled and managed by medication or other options (Ludwig et.al, 2011). Also, the original study sponsored by HUD did not focus on health, which poses a problem for the long-term data that was collected and its overall efficacy, thereby creating a challenging issue regarding the matters associated with movement form one neighborhood to the next (Ludwig et.al, 2011). Finally, since participants were not specifically recruited and volunteered, they were predominantly Black or Hispanic, which does not necessarily translate into the public housing scenario in other communities, where there is a larger number of Whites than supported by this study (Ludwig et.al, 2011). Nonetheless, the study demonstrated that it is important to address the challenges associated with neighborhood living and how it impacts health issues such as diabetes and obesity on different levels (Ludwig et.al, 2011).

Conclusion

This study examined the importance of neighborhoods and how they impact two primary health issues, diabetes and obesity. In this context, it is observed that there are significant factors to consider that have an impact on health and wellbeing in many communities. This study combined social aspects and also addressed the importance of evaluating how daily living and the conditions in which individuals live may impact their health and wellbeing in different ways throughout the life span. The study is important because it contributes to other data and information that support the development of programs that might be able to target individuals and families who face a high risk of obesity and diabetes. This is an important study because it supports these objectives and encourages the development of questions and hypotheses that support the integration of social issues and concerns that are directly related to health and how it impacts the family unit.

References

Ludwig, J., Sanbonmatsu, L., Gennetian, L., Adam, E., Duncan, G. J., Katz, L. F., … & McDade, W. (2011). Neighborhoods, obesity, and diabetes—a randomized social experiment. New England Journal of Medicine, 365(16), 1509-1519.