Exploring the Association Between Military Base Neighborhood Characteristics and Soldiers' and Airmen's Outcomes by Sarah O. Meadows

Author:Sarah O. Meadows [Meadows, Sarah O.]
Language: eng
Format: epub
ISBN: 978-0-8330-7880-3
Publisher: RAND Corporation
Published: 2013-01-15T00:00:00+00:00

Summary

The social indicator analysis of 36 active-duty Army bases revealed that one cannot infer neighborhood quality from an installation’s population size, geographic location, or primary mission.27 The six domains measured overlapping but distinct characteristics, and the housing domain was particularly highly correlated with the overall MNRI. The domain rankings may be especially useful in identifying some of the community challenges beyond the Army’s control that Soldiers and their families face and in tailoring programs to target the potential impact of those challenges or provide resources to compensate for the lack of local support.

The multilevel modeling analysis of the data from the Army revealed few statistically significant neighborhood effects on attrition, separation, or BMI as recorded in the personnel database. For enlisted Soldiers, we found a significant association between the MNRI and separation, such that military neighborhoods with higher MNRI scores were associated with higher likelihood of separation and higher BMI scores. Once we controlled for other covariates, these associations were no longer significant. When we looked at individual domains, one statistically significant relationship emerged. We found that higher scores on the housing domain in military neighborhoods were associated with a higher BMI among the enlisted, a finding that was upheld when we adjusted for covariates, such as marital status and age.

Among officers, none of the associations between the MNRI and the available outcome data was significant. In examining the individual domains, we found that higher scores in the income and poverty domain were associated with higher probability of separation for officers but that these results were not upheld when we adjusted for covariates.

In sum, we found little evidence that military neighborhoods had a significant impact on the BMI or retention of Soldiers. However, a few limitations of our analysis are worth nothing. First, the personnel database that we used contained only one health outcome, and we cannot be sure that it was, in fact, a recent measure of BMI. Second, we did not include in our models controls for neighborhood exposure. That is, we were unable to account for how long an individual had lived on or near a particular base. Third, we were unable to assess why an individual left active-duty service. It is possible that, if we had information that would allow us to distinguish choice from medical discharges or termination of service due to disciplinary reasons, more significant associations may have emerged. Finally, we had very broadly defined neighborhoods using ZIP Codes. Although this allowed us to use Census data to describe and rank military neighborhoods, we did not assess where individual Soldiers actually live or how they might define their own installation neighborhood. Nor were we able to assess some neighborhood characteristics that might also be relevant to the outcomes in the analysis (e.g., crime rates, school quality, availability of hospitals or physicians). We discuss these data limitations, as well as what the “ideal” type of data for this type of analysis might be, in Chapter Six. First, though, we present our analysis of AFB neighborhoods, for which we had some more-extensive data available for the multilevel modeling.

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