Predicting U.S. Army Enlisted Attrition After Initial Entry Training (IET) Using Survival Analysis - Sophisticated Research Modeling Using Medical Information, Dental and Hearing Readiness Important PDF Download

Author: U S Military
Publisher:
ISBN: 9781688024540
Category :
Languages : en
Pages : 108

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Book Description
For the first time since 2005, the U.S. Army fell short of its recruiting goal in 2018 by about 6,500 recruits. A strong economy and an increasing pool of recruit candidates who require a waiver to enlist add to the Army's recruitment troubles. Mental health issues, obesity, and other medical issues have become barriers that disqualify recruits from enlisting. For those who are eligible, they complete a training period called Initial Entry Training (IET). After finishing IET, many soldiers do not finish their first-term service obligation. This research continues the research conducted by Speten (2018) on post-IET attrition, with the added benefit of having medical data available in the Person-event Data Environment (PDE), a secure, virtual environment with a database that provides information on manpower, service, personnel, and medical data. Currently, no research has been conducted that uses detailed medical information to predict post-IET attrition. To estimate the expected number of soldiers who attrite at a specific time post-IET and prior to the end of their first-term obligation, we construct survival tree models using time-varying and time-constant covariates. We find several medical covariates that are important in forecasting attrition including dental readiness and hearing readiness. The effectiveness of the models is assessed on independent test sets. They perform well in predicting expected number of attrition, but not in predicting individual soldier attrition.This compilation includes a reproduction of the 2019 Worldwide Threat Assessment of the U.S. Intelligence Community.For the first time in 13 years, the Army did not meet its recruiting goal (Dickstein 2018). This failure to recruit qualified personnel is especially dire in a time when threats from Russia and China continue to grow. One issue that continues to threaten the ability to recruit soldiers is the lack of a qualified pool of candidates. Criminal convictions, mental health issues, obesity, and other medical issues have become roadblocks that disqualify young recruits from enlisting. In the past, the Army has relaxed certain standards and has given waivers to enlistees for conduct, aptitude, or minor medical issues. However, in 2019, Secretary of the Army, Mark Esper, mandated that fewer less-qualified recruits that require waivers be accepted into the ranks (Myers 2018). This research identifies the demographic and medical factors that contribute to first term service obligation attrition of enlisted U.S. Army soldiers who complete Initial Entry Training (IET). We develop a predictive survival model using survival analysis to forecast the probability that a soldier will either leave the service through attrition within the first t years into their first term or will continue to serve in the Army past their initial first term obligation. The data we use is stored and analyzed in the Person-Event Data Environment (PDE). The PDE is a remote cloud computer environment where data is stored centrally and accessed safely from verified users. The remote access feature of the database ensures there are no privacy or security breaches involving personal information. The PDE contains millions of records on Department of Defense employees, military personnel, and their family members. All personally identifiable information in the database is absent and each individual is assigned a unique Person Identifier (PID).