IMPACT OF THE PUBLIC SAFETY MEDICAL SERVICES WELLNESS PROGRAM ON THE HEALTH RISK FACTORS OF THE INDIANAPOLIS METROPOLITAN POLICE DEPARTMENT2008-2010
Kulin Mehta Dr. Terrell W. Zollinger Indiana University School of Medicine,
Department of Public health
Public Safety Medical Services
Inc.
STUDY PROTOCOL#1207009097
Introduction Policing is recognized as dangerous,
demanding and stressful occupation Police officers face conventional
cardiovascular risk factors as well as occupation-specific risks
Conventional cardiovascular risk factors: Obesity Hypertension Diabetes mellitus/Metabolic syndrome Dyslipidemia Smoking
(Zimmerman, 2012)
Introduction Occupation-specific cardiovascular risk factors:
Lack of regular exercise (especially in older/retired officers)
Poor nutrition (attributed to limited opportunities of healthy food choices while on-duty)
Shift work (sleep disruption or deprivation) Noise exposure Imbalance between job demands and personal health
care discipline Intense physical and mental stress Life-threatening encounters leading to fatal bodily
injuries preceded by stressful bursts of unpredictable emergencies
(Kales et al, 2009)
Purpose of the study
This study examines the impact of a wellness program “Public Safety Medical Services(PSMS)” on the health of a cohort of police officers from the Indianapolis Metropolitan Police department (IMPD)
Public Safety Medical Services Premier provider of disease prevention and health
promotion services for public safety departments, employer groups, and individuals.
Public Safety Medical Services Comprehensive medical screening and fitness testing
program for police officers Expert medical & fitness personnel onboard Technologically advanced medical equipment Laboratory testing Clinical consultation and referral, as and when required Annual follow up and feedback to the IMPD
Materials & Methods Retrospective cohort study design Sample population includes the IMPD officers
enrolled with PSMS Inclusion criteria:
All police officers who were clinically evaluated by PSMS in 2008 and who have follow-up reports from 2009 and 2010
Exclusion criteria: Any police officer who skipped a year of follow up
was excluded from the analysis Any police officer whose baseline evaluation
occurred in 2009 or 2010 was excluded from the analysis
Materials & Methods Cohort size: 382 police officers from IMPD Variables used for data analysis:
Height, Weight, birth year, age, race, gender Family history Systolic & Diastolic blood pressure Total cholesterol, HDL, LDL and triglycerides Fasting blood glucose Smoking status
IMPD screened at baseline in 2008 Database available for this study from 2008-
2010
Statistical analysis
All statistical analysis performed using SAS v9.2 [PC-SAS by SAS Institute Inc., Cary, NC)
Statistical Significance denoted at P< 0.05 McNemar’s test used to assess
change(proportions) in the health risk factors of IMPD from 2008-2010
Proportional differences for each of the health risk factors were tested for statistical significance using the Z test
Study sample - demographics
0
20
40
60
80
100
120
140
6
33
0812
107
619
11
100
614
Demographic distribution of IMPD sample
25-34 years35-44 years45+ years
Distribution by Race & Age-group
Sam
ple
of IM
PD o
ffice
rs
Males [N=269]
Females [N=53]
Average age: 43 years M:F : 5:1
Caucasian: 87%African American:
13%25-34 years: 14%
35-44 years : 45%45+ years: 41%
Borderline Risk HDL
Obesity
Borderline risk cholesterol
High risk HDL
Smoking
Borderline Risk LDL
Borderline risk glucose
Borderline Risk triglycerides
Hypertension
High risk triglycerides
High risk LDL
High risk cholesterol
High risk glucose
0 10 20 30 40 50 60 70 80 90 100
52.837.4
26.723.8
21.421.2
19.615.4
11.711.2
7.37.0
2.0
Percentage prevalence of health risk factors in IMPD at baseline
%
2008
Factor(s) for IMPD officers
Mean in 2008
Age 43 yearsBody Mass Index 28.9Total Cholesterol
188.0 mg/dl
HDL 49.2 mg/dl
LDL 113.5 mg/dl
Triglycerides 126.0 mg/dl
Blood glucose 93.1 mg/dl
Obesity (BMI>29.9)
11% improvement
2008 2009 20100
10
20
30
40
50
60
70
80
90
100
37.4% 34.2% 33.2%
Obesity in IMPD 2008-2010
Obesity in IMPD(%)
Obesity in IMPD[n=382]
%
143 131 127
p= 0.2262
Hypertension [Systolic/Diastolic blood pressure >= 140/90]
69% improvementp<0.05
2008 2009 20100
5
10
15
20
25
30
35
40
45
50
11.8%
4.2% 3.6%
Hypertension in IMPD 2008-2010
Hypertension in IMPD (%)
Hypertension in IMPD[n=382]
%
4516 14
Borderline risk total cholesterol [200-239 mg/dl]
2008 2009 20100
5
10
15
20
25
30
35
40
45
50
26.7%
13.6% 13.3%
Borderline risk cholesterol in IMPD 2008-2010
Borderline risk cholesterol in IMPD (%)
Borderline risk cholesterol in IMPD [n=382]
%
50% improvementp<0.05
102
52 51
High risk total cholesterol [>240 mg/dl]
2008 2009 20100
5
10
15
20
25
30
35
40
45
50
7.0%1.8% 2.6%
High risk cholesterol in IMPD 2008-2010
High risk choles-terol in IMPD (%)
High risk cholesterol in IMPD [n=382]
%
27
37% improvementp=0.0041
7 10
Borderline Risk HDL (41-59 mg/dl)
2008 2009 20100
10
20
30
40
50
60
70
80
90
100
52.8%42.3% 40.3%
Borderline risk HDL in IMPD 2008-2010
Borderline risk HDL in IMPD (%)
Borderline Risk HDL in IMPD[n=382]
%
202 162 154
23% improvement
p= 0.0005
High risk HDL [<40 mg/dl]
2008 2009 20100
5
10
15
20
25
30
35
40
45
50
23.8%
16.7% 14.9%
High risk HDL in IMPD 2008-2010
High risk HDL in IMPD(%)
High risk HDL in IMPD[n=382]
%
30% improvement
p= 0.0018
9164 57
Borderline Risk LDL (130-159 mg/dl)
2008 2009 20100
5
10
15
20
25
30
35
40
45
50
21.2%
11.7%8.9%
Borderline risk LDL in IMPD 2008-2010
Borderline risk LDL in IMPD (%)
Borderline Risk LDL in IMPD [n=382]
%
81
4534
58% improvement
p<0.05
High risk LDL [>160 mg/dl]
2008 2009 20100
5
10
15
20
25
30
35
40
45
50
7.3%1.5% 2.6%
High risk LDL in IMPD 2008-2010
High risk LDL in IMPD(%)
High risk LDL in IMPD[n=382]
%
64% improvementp=0.0027
28 6 10
Borderline Risk Triglycerides (150-199 mg/dl)
2008 2009 20100
5
10
15
20
25
30
35
40
45
50
15.4%
3.4% 4.9%
Borderline risk triglycerides in IMPD 2008-2010
Borderline risk triglycerides in IMPD (%)
Borderline Risk Triglycerides in IMPD[n=382]
%
59 13 19
68% improvementp< 0.05
High risk triglycerides [>200 mg/dl]
2008 2009 20100
5
10
15
20
25
30
35
40
45
50
11.3%4.7% 5.5%
High risk triglyceride levels in IMPD 2008-2010
High risk triglyceride levels in IMPD(%)
High risk triglyceride levels in IMPD [n=382]
%
43 18 21
51% improvementp= 0.0041
Borderline risk blood glucose [100-125 mg/dl]
2008 2009 20100
5
10
15
20
25
30
35
40
45
50
19.6%
8.9% 8.3%
Borderline risk blood glucose levels in IMPD 2008-2010
Borderline risk blood glucose levels in IMPD(%)
Borderline risk blood glucose levels in IMPD[n=382]
%
75 34 32
57% improvementp< 0.05
High Risk Blood Glucose(>126 mg/dl)
2008 2009 20100
5
10
15
20
25
2.0% 1.0% 1.0%
High risk blood glucose levels in IMPD 2008-2010
High risk blood glucose levels in IMP...
High Risk Blood Glucose in IMPD[n=382]
%
8
50% improvement p=0.2460
4 4
Smoking status
2008 2009 20100
5
10
15
20
25
30
35
40
45
50
21.4%
13.6% 13.6%
Smoking status in IMPD 2008-2010
Smoking status in IMPD(%)
%
38% improvementp=0.0127
55 35 33
Impact of the PSMS program Statistically significant improvement of IMPD
for all health risk factors over 2 years of PSMS program intervention except Obesity High risk glucose levels
With comparable baseline values of IMPD sample with previous studies, PSMS wellness program has been successful in alleviating major cardiovascular risk factors including Hypertension, dyslipidemia, blood glucose
levels(borderline risk) and smoking
Limitations
Small sample size Selection bias Missing values Data available is for a shorter length of
the cohort study Bias due to self reporting at baseline (eg.
smoking data) Inaccuracy of data entered by officers
into the Motivation survey database Non-compliance and lost to follow up
Recommendations Data quality check at regular intervals Missing entries in the database to be cross
verified for accuracy Data verification system should be installed
in the program to check for human error that could occur on the part of IMPD officers while entering data into their system
Case-specific modification of the wellness protocol from annual to bi-annual(or more) follow up of high risk public safety personnel
Feedback loop following physician referrals
Conclusion Without a wellness program, baseline data
for IMPD officers is suggestive of increased cardiovascular risk which is of concern given the added stress from the occupation
The PSMS intervention has brought about statistically significant improvement in the clinical profiles of the IMPD officers
Prospectively improving trends in the health status of the police officers following PSMS intervention- long term gain both in health, quality of life and work efficiency
Conclusion Besides clinical intervention, PSMS program
has led to a behavioral impact on the IMPD officers as noted by the statistical significant drop in their smoking status over 2 years
This study supports the idea of a wellness program like PSMS to be accepted on a wider scale by most public safety departments that have job-specific risk factors in addition to the traditional cardiovascular risks faced by the general population
Dr. Steven Moffatt, Medical Director, Public Safety Medical Services Inc.
Nelson Hale, Director of IT, Public Safety Medical Services Inc.
Alex Lopes, Manager of Market Analysis and Strategic Planning, Public Safety Medical Services Inc.
Dr. Robert Saywell, Professor Emeritus and Senior Investigator, Bowen Research Center, Indiana University School of Medicine
Dr. Terrell Zollinger, Professor, Department of Public Health, Indiana University School of Medicine
Acknowledgement
References Franke W, Ramey S and Shelley M (2002). Relationship between
cardiovascular disease morbidity, risk factors, and stress in law enforcement cohort. Journal Of Occupational and Environmental Medicine 2002; 44: 1182-1189
Kales Stefanos, Tsismenakis Antonios, Zhang Chunbai and Soteriades Elpidoforos (2009). Blood pressure in firefighters, Police officers, and other emergency responders. American Journal of Hypertension 2009 Jan; 22(1): 11-20
Perrin M.A, DiGrande L, Wheeler K, Thorpe L, Farfel M, Brackbill R (2007). Differences in PTSD Prevalence and Associated Risk Factors Among World Trade Center Disaster Rescue and Recovery Workers. American Journal of Psychiatry 2007;164:1385- 1394. doi: 10.1176/appi.ajp.2007.06101645
Public Safety Medical Services Inc. (2012). Retrieved from www.publicsafetymed.com on June 15, 2012
Ramey Sandra, Downing Nancy and Franke Warren (2009). Milwaukee Police Department Retirees – Cardiovascular Disease Risk and Morbidity Among Aging Law Enforcement Officers. American Association of Occupational Health Nurses. 2009 v57 n11 (20091101): 448-453
U.S Bureau of Labor Statistics (May 2010). Retrieved from http://www.bls.gov on June 15, 2012
Zimmerman Franklin (2012). Cardiovascular Disease and Risk Factors in Law Enforcement Personnel: A Comprehensive Review. Cardiology in Review 2012;20: 159–166