Analyzing Early Discharges in the Coast Guard

Behavioral health conditions can have a big impact on whether service members can do their jobs and stay in the Coast Guard. By knowing what factors might make it more likely for members to leave before their service ends, the Coast Guard can create better policies and programs to keep them.

This analysis looked at various demographic factors and behavioral health Diagnoses among active-duty Coast Guard members to see what might lead to early discharge. This area has not been studied much within the military.

#Study Overview

The study used survival analysis, a method that looks at how long it takes for an event to occur. In this case, the event was service members getting discharged early. Researchers used machine learning methods, which are computer algorithms that can find patterns in large amounts of data, to help identify trends.

The data came from two main sources: one provided information on health services used by service members, and the other had personnel records. Combined, these data sets included information about visits to Mental Health specialists from January 1, 2016, to December 31, 2019. Only members who had behavioral health visits were included, excluding those who saw other types of medical professionals.

#Data Collection

The final data set contained several factors, such as:

• Rank group (the level of seniority)
• The most common behavioral health diagnosis
• Race and gender
• The number of therapy visits
• Time served in active duty

These variables were analyzed to see if they could predict early discharge. The main outcome measured was whether a member was discharged before completing their service.

#Handling Missing Data

When some data points were missing, researchers filled in those gaps using averages based on rank or other relevant categories.

#Machine Learning in the Study

To analyze the data, researchers split it into training and testing groups. They used a technique called SMOTE to ensure that both groups included an equal number of service members who left early and those who completed their service.

Several machine learning algorithms were applied, including decision trees, random forests, and gradient boosting. The team used software to run these algorithms and measured their performance based on precision, recall, and F1 score. Precision was especially important because it showed how accurately the models predicted early discharges.

#Key Findings

The analysis found that around 26 out of every 1,000 members who sought behavioral health care did not complete their service. While females used more behavioral health services than males, the rates of early discharge were similar for both genders. Race and gender did not show significant differences in discharge status.

The most common diagnoses among those who left early were mood disorders, anxiety disorders, and adjustment disorders. The analysis indicated that being White or Asian/Pacific Islander and holding a senior enlisted rank were risk factors for early discharge.

Notably, the logistic regression model identified male gender and alcohol-related disorders as important predictors of early termination, which was different from the leading diagnosis groups identified earlier.

#Implications of the Findings

The findings highlight the importance of recognizing the factors that contribute to early discharges. Understanding why some members leave the service can help the Coast Guard design better support and intervention programs.

While not all members who seek behavioral health care leave the service, the study found a small but significant number of early discharges. This suggests that there are opportunities to improve retention through targeted efforts, especially among males and those with specific disorders.

Additionally, the results indicate that senior enlisted members may face unique challenges that could affect their performance and well-being. The study suggests that the Coast Guard should consider these factors in its policies and programs.

#Limitations and Strengths

There are some limitations to the study. Members who received behavioral health care outside of the main database were not included, which might limit the findings. Additionally, the number of members who discharged early was relatively small, which could impact the analysis.

On the positive side, the study had a four-year follow-up period and used multiple machine learning models to provide robust results. The findings can serve as a foundation for future research aimed at improving service member retention.

#Future Research Opportunities

Future studies could further examine the reasons behind why members seek behavioral health services and how those services relate to their decisions to stay in the Coast Guard. There is also potential to look at geographic trends, long-term impacts of behavioral health conditions, and how specific career fields might be affected.

The findings from this analysis can help the Coast Guard and other military branches better understand the relationship between behavioral health care and service member retention. This understanding can lead to improved strategies and interventions to support service members throughout their careers.

By learning more about these issues, the military can take steps not only to retain its members but also to promote their overall health and well-being.