Student’s Name
Institution Affiliation
Evaluation Plan for Opioid Use among Veterans
Systematic Evaluation Plan

Standard: Mission
The mission of the research on opioid administration among veterans is to identify ways of protecting veterans from becoming addicted to opioid medications. This research will focus on the causes of addiction to opioid medication and overdoses among veterans. It will also aim at identifying ways of having better health outcomes using alternative therapies for the treatment and management of various conditions that are currently managed using opioid medications.
Model Activity Goals and Objectives Type of Data Needed Measurement, monitoring, evaluation
Logistic regression model.
This model will show the likelihood of each category of veterans, based on their demographic features such as pain score, pain condition, mental health, and opioid characteristic, to become addicted to opioid medication.
The Charlson Comorbidity Index will control for other physical health diagnosis (Charlson, Szatrowski, Peterson, & Gold, 1994).
The first activity in the evaluation of opioid use will be conducting an empirical research. This process will entail the use of both quantitative and qualitative approaches. With regards to quantitative approach, I will carry a research to establish whether increased use of opioid medication leads to opioid addiction (Hudson et al., 2017). In the qualitative approach, I will interview nurses and other health officials to identify whether they have observed an increase in addiction to opioid medication among veterans. The main goal of this activity will be to establish whether exposure to opioid medication can lead to addiction among veterans.
The objective of this research will be to establish the appropriate quantities in mg (example 26 mg) of opioid medical dosages that can be effectively administered without resulting in addictions (Price et al., 2015).
Another objective of this research will be to identify if there is a maximum length of time that should not be exceeded during opioid medical therapies, to prevent addictions (Lucas, 2017).
The study will help in the identification of proper alternative medical therapies for various forms of ailments that are treated using opioid medication.
Quantitative and qualitative data will be used in this research.
This evaluation process will take two weeks.
I will extract ICD-9 (International Classification of Diseases, Ninth Revision, Clinical Modification) codes to identify the clinical indicators for veterans receiving opioids. Later, I will establish the 5 categories of pain that are common among veterans: neck, back, joint, migraine, neuropathic.
I will also identify veterans with diagnosis associated with opioid use by using the VA Northeast Program Evaluation Center
I will use a scale of 0-10 to rank the pain score for each patient. (0) minimal pain, (1-3) mild pain, (4-6) moderate pain, (7-10) severe pain. The veterans will be included in the regression model using their ‘pain-rank.’
Opioid days will be used to define the number of days in a year that a veteran has taken a dosage of opioid medication.
Greater than 90 dosages per year will be classified as chronic, while less than that will be non-chronic.
For standardization purposes, opioid prescriptions will be converted to a MED, by multiplying their strength and dispensed quantity. Accordingly, I will obtain a total MED/patient/year for the examined veterans.
Veterans receiving more than 100mg and 120mg daily doses will be categorized as chronic users (Hudson et al., 2017). I will calculate their proportion relative to that of all the examined veterans.
Multiple opioid measures will also be created to identify veterans who are taking more than I opioid medication concurrently. For medications to fall into the category of multiple medications, they must be consumed at least 30 days in any 45-day period.
Using the above information, I will develop descriptive tables of opioid use among the veterans.
Regression models will also show the likelihood of veterans becoming chronic users of opioid medication.
Structural Equation Modelling
This modeling will be appropriate since mediation analysis will be one of the goals of this study (Rigg & DeCamp, 2014).
This activity will largely be a survey aimed at understanding the main causes of opioid medicated addiction among veterans.
A sample of veterans from all states in the United States will be examined to establish what makes some individuals to be prone to opioid abuse.
The variables in this study will be insurance and income. The reason for this selection is because a person’s level of income and access to healthcare, which is facilitated by a healthcare insurance, increases a person’s ability to purchase medicines, including opioid medications (Rigg & DeCamp, 2014).
Other variables that will be considered are a person’s substance abuse, mental health status, post-traumatic stress disorder (PTSD), drinking problems, suicide attempts, prescription opioid misuse (POM).
The goal of this activity will be to identify whether veterans are more prone to abuse opioids than the rest of the citizens.
The objectives will be to identify whether increased access to health care and medication, including opioid medication, is a cause of addiction among veterans.
Another objective will be establishing if veterans’ incomes can be a predictor of opioid abuse.
This research will also establish whether veterans with ailments such as PTSD, mental health issues, depression, and stress are prone to abusing opioid medication.
Both quantitative and qualitative data will be used in this study.
This process will take two weeks.
The weighted least squares with a mean-and-variance-adjusted chi-square test (WLSMV) will be used to address the bounded and categorical nature of the variables (Rigg & DeCamp, 2014).
The fully endogenous dependent variables in the model are whether a patient uses a non-prescribed drug to treat an ailment or for the feeling it causes (Hinrichs, et al., 2016).
The model will use several mediating variables as endogenous variables or predictors.
The results of the model will be estimated using the planned conceptual model. The comparative fit index (CFI) and the root mean square error of approximation (RMSEA) will be examined to check if they lie within the acceptable margins.

Gantt Chart for the Implementation of Activities

Stage Task  
(Measure to indicate the task is completed)
Resource Activity by Weeks From the Start of the Project
Month May – 2018 June July August
Date 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Start Out Feasibility and Gaining the Consent of Various Authorities Approval by Executive Board Project Team
Project Progress Phase I Interviews
(clinical practitioners)
Interview Reports Project Team
Phase II Surveys
Survey Reports Project Team
Phase II Interviews
Interview Reports Project Team
Close Thematic Analysis and Final Interpretation Final Report Project Team
Evaluation Evaluation Planning of abuse of opioid medication Evaluation Plan Project Team

Charlson M, Szatrowski TP, Peterson J, & Gold, J. (1994). Validation of a combined comorbidity index. J Clin Epidemiol, 47, 1245–51.
Hinrichs, K. L., Sharma, S., Thurston, J., Sivashanker, K., & Chang, G. H. (2016). Management of opioid use disorders among veterans in subacute rehab: Use of an interdisciplinary task force to address an emerging concern. Substance abuse37(1), 4-6.
Hudson, T. J., Painter, J. T., Martin, B. C., Austen, M. A., Williams, J. S., Fortney, J. C., … & Edlund, M. J. (2017). Pharmacoepidemiologic analyses of opioid use among OEF/OIF/OND veterans. Pain158(6), 1039. Retrieved from:
Lucas, P. (2017). Rationale for cannabis-based interventions in the opioid overdose crisis. Harm reduction journal14(1), 58.
Price, R. K., Shroff, M., van den Berk-Clark, C., Widner, G., Balan, S., & Nelson, E. (2015). Nonmedical use of oxycodone and other opiate analgesics in the US, 2004–2011: Are military veterans at increased risk? Drug & Alcohol Dependence146, e153.
Rigg, K. K., & DeCamp, W. (2014). Explaining prescription opioid misuse among veterans: A theory-based analysis using structural equation modeling. Military Behavioral Health2(2), 210-216.