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

6

1911

100

614

Demographic distribution of IMPD sample

25-34 years

35-44 years

45+ 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

52.8

37.4

26.7

23.8

21.4

21.2

19.6

15.4

11.7

11.2

7.3

7.0

2.0

Percentage prevalence of health risk factors in IMPD at baseline

%

2008

Factor(s) for IMPD officers

Mean in 2008

Age 43 years

Body Mass Index 28.9

Total 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

91

64 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 I...

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

THANK YOU

For questions email: kulinkmehta@gmail.com

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