Engagement at Work Predicts Changes in Depression and Anxiety Status in the Next Year

Similar documents
Finding Supporters. Political Predictive Analytics Using Logistic Regression. Multivariate Solutions

The Time-to-Degree and Cost Tools. User Guide

Binary Logistic Regression

Multiple logistic regression analysis of cigarette use among high school students

Limiting the Duration of Medication Assisted Treatment for Opioid Addiction: Will New State Policies Help or Hurt?

Experiment on Web based recruitment of Cell Phone Only respondents

Job Insecurity Measures as Predictors of Workers Compensation filing

SPSS Guide: Regression Analysis

If You Think Investing is Gambling, You re Doing it Wrong!

Chapter Seven. Multiple regression An introduction to multiple regression Performing a multiple regression on SPSS

DISEASES OF AGEING IN GHANA

Multinomial and Ordinal Logistic Regression

Examining Professional and Academic Culture in Chilean Journalism and Mass Communication Education

Co-Curricular Activities and Academic Performance -A Study of the Student Leadership Initiative Programs. Office of Institutional Research

The Economics of Wellbeing

The Impact of Familial and Marital Status on the Performance of Life Insurance Agents The Case of Taiwan

Credit Risk Analysis Using Logistic Regression Modeling

Logistic Regression. BUS 735: Business Decision Making and Research

Complementary and alternative medicine use in Chinese women with breast cancer: A Taiwanese survey

Advanced Placement Exam Participation

LOGISTIC REGRESSION ANALYSIS

Disparities in Realized Access: Patterns of Health Services Utilization by Insurance Status among Children with Asthma in Puerto Rico

Healthcare Utilization by Individuals with Criminal Justice Involvement: Results of a National Survey

Using An Ordered Logistic Regression Model with SAS Vartanian: SW 541

Excess Units in Pursuit of the Bachelor s Degree

I L L I N O I S UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN

Trend in Accounting Student Characteristics: Results from Archival Transcript Data

The Effects of the Current Economic Conditions on Sport Participation. Chris Gratton and Themis Kokolakakis

Multiple Regression in SPSS This example shows you how to perform multiple regression. The basic command is regression : linear.

ABSTRACT INTRODUCTION STUDY DESCRIPTION

Hai Fang, PhD Professor China Center for Health Development Studies Peking University

EXPANDING THE EVIDENCE BASE IN OUTCOMES RESEARCH: USING LINKED ELECTRONIC MEDICAL RECORDS (EMR) AND CLAIMS DATA

Lago di Como, February 2006

11. Analysis of Case-control Studies Logistic Regression

Department of Psychiatry, The University of Melbourne

WHAT TYPES OF ON-LINE TEACHING RESOURCES DO STUDENTS PREFER

A Burden or Merely a Load: New Empirical Estimates of Health Insurance Loading Fees by Group Size

ehealth, Inc. National Consumer Survey of Individuals Looking for Private Health Insurance at ehealthinsurance Services

Ordinal Regression. Chapter

Discrepancies in Self-Report Diabetes Survey yquestions using NHANES, NHIS, and CHIS data

IMPACT OF WORKING CAPITAL MANAGEMENT ON PERFORMANCE

Segmentation For Insurance Payments Michael Sherlock, Transcontinental Direct, Warminster, PA

Analysis of 2007 Project Graduate Survey:

Racial and Ethnic Differences in Health Insurance Coverage Among Adult Workers in Florida. Jacky LaGrace Mentor: Dr. Allyson Hall

Ongoing Evaluation of a Self-Exclusion Program

Can Annuity Purchase Intentions Be Influenced?

New York Study of Booster Seat Effects on Injury Reduction Compared to Safety Belts in Children Aged 4-8 in Motor Vehicle Crashes

Logistic (RLOGIST) Example #7

AGE AND EMOTIONAL INTELLIGENCE

BACKGROUND. ADA and the European Association recently issued a consensus algorithm for management of type 2 diabetes

The Burden of Proof: Panel Attrition and Record Usage on the Medicare Current Beneficiary Survey. Ryan Hubbard and Brad Edwards

RECRUITERS PRIORITIES IN PLACING MBA FRESHER: AN EMPIRICAL ANALYSIS

SAMPLE SIZE TABLES FOR LOGISTIC REGRESSION

Caregiving Impact on Depressive Symptoms for Family Caregivers of Terminally Ill Cancer Patients in Taiwan

A PROSPECTIVE EVALUATION OF THE RELATIONSHIP BETWEEN REASONS FOR DRINKING AND DSM-IV ALCOHOL-USE DISORDERS

SUMAN DUVVURU STAT 567 PROJECT REPORT

Running Head: INTERNET USE IN A COLLEGE SAMPLE. TITLE: Internet Use and Associated Risks in a College Sample

HOUSEHOLDS WITH HIGH LEVELS OF NET ASSETS

The Impact of an Economic Downturn on Employment of Nurses: Does Policy Play a Role?

13. Poisson Regression Analysis

The relationship between mental wellbeing and financial management among older people

Calculating the Probability of Returning a Loan with Binary Probability Models

Probability of Selecting Response Mode Between Paper- and Web-Based Options: Logistic Model Based on Institutional Demographics

FACTORS ASSOCIATED WITH HEALTHCARE COSTS AMONG ELDERLY PATIENTS WITH DIABETIC NEUROPATHY

Logistic (RLOGIST) Example #3

What High School Curricular Experience Tells Us About College Success *****

HSRA2011 The Impacts of Health Insurance on Health Care Utilization Among the Elderly in China

Gender Differences in Employed Job Search Lindsey Bowen and Jennifer Doyle, Furman University

Unit 12 Logistic Regression Supplementary Chapter 14 in IPS On CD (Chap 16, 5th ed.)

Data Mining: An Overview of Methods and Technologies for Increasing Profits in Direct Marketing. C. Olivia Rud, VP, Fleet Bank

Total sample Problem gamblers. 1 Includes seven respondents with Bipolar-I disorder

MINING BIG DATA TO SOLVE THE RETENTION

East-West Migration and Gender: Is there a Double Disadvantage vis-à-vis Stayers?

With Depression Without Depression 8.0% 1.8% Alcohol Disorder Drug Disorder Alcohol or Drug Disorder

Correlates of Academic Achievement for Master of Education Students at Open University Malaysia

Issue Brief. A Look at Working-Age Caregivers Roles, Health Concerns, and Need for Support

The Prevalence and Determinants of Undiagnosed and Diagnosed Type 2 Diabetes in Middle-Aged Irish Adults

Modeling Lifetime Value in the Insurance Industry

Online vs. Traditional MBA: An Empirical Study of Students Characteristics, Course Satisfaction, and Overall Success

Austen Riggs Center Patient Demographics

Self-Management and Self-Management Support on Chronic Low Back Pain Patients in Primary Care

How to set the main menu of STATA to default factory settings standards

Who Goes to Graduate School in Taiwan? Evidence from the 2005 College Graduate Survey and Follow- Up Surveys in 2006 and 2008

7TH ANNUAL PARENTS, KIDS & MONEY SURVEY: SUPPLEMENTAL DATA

OUT OF TOUCH: AMERICAN MEN AND THE HEALTH CARE SYSTEM. Commonwealth Fund Men s and Women s Health Survey Findings

Business Statistics: Chapter 2: Data Quiz A

Gallup-USA Funds Minority College Graduates Report

1.1. Simple Regression in Excel (Excel 2010).

Lesson Outline Outline

Generalized Linear Models

Exploring the Association between Working Memory and Parkinson's Disease in a Driving Simulator

HIGH-RISK STOCK TRADING: INVESTMENT OR GAMBLING?

A PUBLICATION OF THE NATIONAL COUNCIL FOR ADOPTION ADOPTION USA: SUMMARY AND HIGHLIGHTS OF A CHARTBOOK ON THE NATIONAL SURVEY OF ADOPTIVE PARENTS

Background & Significance

Oncology Nursing Society Annual Progress Report: 2008 Formula Grant

Journal of College Teaching & Learning July 2008 Volume 5, Number 7

EMPIRICAL INVESTIGATION OF LEADERSHIP STYLE ON ENHANCING TEAM BUILDING SKILLS

End User Satisfaction With a Food Manufacturing ERP

5. Survey Samples, Sample Populations and Response Rates

Correlates of not receiving HIV care among HIV-infected women enrolling in a HRSA SPNS multi-site initiative

Transcription:

Engagement at Work Predicts Changes in Depression and Anxiety Status in the Next Year October 2009 Sangeeta Agrawal, MS and James Harter, Ph.D.

For more information about Gallup Consulting or our solutions for optimizing business performance, please visit consulting.gallup.com or contact Sarah Van Allen at 202.715.3152 or sarah_van_allen@gallup.com. Copyright 2009 Gallup, Inc. All rights reserved. Gallup, Gallup Consulting, Gallup Panel TM, and Q 12 are trademarks of Gallup, Inc. All other trademarks are the property of their respective owners.

Introduction According to the Centers for Disease Control and Prevention, approximately 15.7% of U.S. residents reported that a healthcare provider told them that they had depression at some point in their lifetime, and 11.3% reported that a healthcare provider told them that they had anxiety at some point during their lifetime. Using longitudinal data collected from the Gallup Panel of U.S. households, we studied the impact of employee engagement on health and change in diagnosis of depression/anxiety. The main objective of this research was to examine how employee engagement in 2008 predicted change in depression and anxiety status in 2009 controlling for age, marital status, income level, gender, and education level among those who had no history of depression or anxiety. The findings: Actively disengaged employees were almost twice as likely as engaged employees to report being diagnosed with depression (2.1 times) and anxiety (1.7 times) for the first time in the next year (after controlling for demographic differences). Actively disengaged employees were about twice as likely as engaged employees to report being diagnosed with depression for the first time in the next year 4.6% Engaged 6.0% 8.8% Not Engaged 4.7% Actively Disengaged 6.7% 9.1% Actively disengaged employees were about 1.7 times as likely as engaged employees to report being diagnosed with anxiety for the first time in the next year 6.4% Engaged Methodology Database 7.6% 10.4% Before Adjusting for Demographic Differences Not Engaged 6.6% Actively Disengaged 8.1% 10.7% After Adjusting for Demographic Differences A database was created by merging three independent surveys to form a longitudinal sample of 9,561 employed adults from the Gallup Panel. Among these surveys, two were health surveys (administered February-March 2008 and February-March 2009), and one was a workforce survey (administered July-August 2008). Respondents were employed full-time or part-time in 2008 and 2009. Based on responses to the health survey in 2008, there were 7,993 respondents with no history of depression and 8,232 respondents with no history of anxiety. These respondents were surveyed again in 2009 for their depression/anxiety status. (Question: For each of the medical conditions listed below, please indicate whether you, yourself, have ever been diagnosed by a healthcare professional as suffering from these conditions: Depression/Anxiety). Before Adjusting for Demographic Differences After Adjusting for Demographic Differences The outcome variable of interest was the proportion of respondents who were diagnosed with depression or anxiety in 2009 for the first time. Employee engagement in 2008 was the main predictor variable of interest. The two measures of employee engagement in 2008 were Q 12 GrandMean and engagement status engaged, not engaged, or actively disengaged. Basic demographic variables (age, marital status, income level, gender, and education level) were used as control variables. Among these respondents, the average age was 51.6 (±10.4), 70.4% were married, 77.8% had an annual household income Copyright 2009 Gallup, Inc. All rights reserved. 1

greater than $50,000, 46.1% were male, and 65.1% had at least a college-level education. In terms of engagement status, 30.6% of respondents were engaged, 51.7% were not engaged, and 17.7% were actively disengaged. Statistical Analysis Similar but separate analyses were performed for predicting depression and anxiety status in 2009. Two methods of analysis were used to predict the proportion of newly diagnosed cases. Multiple regression analysis was used to predict the proportion of newly diagnosed depression/ anxiety cases from 2008 engagement (Q 12 GrandMean), controlling for demographic variables. Further, logistic regression analysis was used to predict change in depression/ anxiety status in 2009 from engagement status in 2008 after controlling for demographic differences. Regression coefficients (standardized and unstandardized) and odds ratios were calculated for engagement in 2008 after controlling for age, marital status, income level, gender, and education level. Results Depression Analysis Predicting Proportion of Newly Diagnosed Depression Cases Variables in the Model Unstandardized Coefficients Standardized Coefficients Std. Error Beta t Sig. Employee Engagement (Q 12 GrandMean) -0.018 0.003-0.061-5.305 0.000 Control Variables Age +0.001 0.000-0.034 +2.952 0.003 Marital Status (Married) -0.023 0.006-0.044-3.667 0.000 Income Level (log transformed) -0.005 0.005-0.013-0.994 0.320 Gender (Male) -0.026 0.005-0.054-4.699 0.000 Education Level -0.002 0.003-0.008-0.650 0.516 Constant 0.152 0.53 2.891 0.004 Multiple R = 0.105 2 Copyright 2009 Gallup, Inc. All rights reserved.

Logistic Regression to Predict Change in Depression Status in 2008 95% C.I. for EXP (B) Variables in the Model B S.E. Wald Df Sig. Exp(B) Lower Upper Employee Engagement 28.327 2 0.000 Actively Disengaged +0.751 0.141 28.321 1 0.000 2.118 1.607 2.792 Not Engaged +0.390 0.120 10.482 1 0.000 1.477 1.166 1.870 Control Variables Age +0.013 0.005 8.125 1 0.000 1.014 1.004 1.023 Marital Status (Married) -0.368 0.107 11.703 1 0.000 0.692 0.561 0.855 Income Level (log transformed) -0.070 0.079 0.787 1 0.37 0.933 0.799 1.088 Gender (Male) -0.469 0.101 21.608 1 0.000 0.625 0.513 0.762 Education Level -0.037 0.051 0.549 1 0.46 0.963 0.872 1.064 Constant -2.756 0.920 8.969 1 0.000 +0.092 Anxiety Analysis Predicting Proportion of Newly Diagnosed Anxiety Cases Variables in the Model Employee Engagement (Q 12 GrandMean) Unstandardized Coefficients Standardized Coefficients Std. Error Beta t Sig. -0.016 0.004-0.050-4.400 0.000 Control Variables Age 0.000 0.000 +0.006 +0.490 0.624 Marital Status (Married) -0.017 0.007-0.029-2.446 0.014 Income Level (log transformed) -0.004 0.005-0.009-0.753 0.452 Gender (Male) -0.030 0.006-0.056-4.914 0.000 Education Level -0.001 0.003-0.004-0.344 0.731 Constant +0.188 0.058 +3.230 0.001 Multiple R = 0.084 Copyright 2009 Gallup, Inc. All rights reserved. 3

Logistic Regression to Predict Change in Anxiety Status in 2009 95% C.I. for EXP (B) Variables in the Model B S.E. Wald Df Sig. Exp(B) Lower Upper Employee Engagement 19.539 2 0.000 Actively Disengaged +0.539 0.122 19.413 1 0.000 1.715 1.349 2.179 Not Engaged +0.219 0.101 4.656 1 0.031 1.245 1.020 1.519 Control Variables Age +0.002 0.004 0.197 1 0.657 1.002 0.994 1.010 Marital Status (Married) -0.224 0.095 5.504 1 0.019 0.800 0.663 0.964 Income Level (log transformed) -0.047 0.069 0.470 1 0.493 0.954 0.833 1.092 Gender (Male) -0.426 0.088 23.610 1 0.000 0.653 0.550 0.776 Education Level -0.020 0.045 0.196 1 0.658 0.980 0.898 1.070 Constant -2.040 0.803 6.455 1 0.011 0.163 Summary of Findings A one unit decrease in employee engagement predicts A one unit decrease in employee engagement predicts an increase of 1.8% in the diagnosis of depression by an increase of 1.6% in the diagnosis of anxiety by the the next year (after controlling for demographics). next year (after controlling for demographics). Actively disengaged employees were almost twice as Actively disengaged employees were 1.7 times as likely as engaged employees to report being diagnosed likely as engaged employees to report being diagnosed with depression in the next year (after controlling for with anxiety in the next year (after controlling for demographics). demographics). 4 Copyright 2009 Gallup, Inc. All rights reserved.