Case Study: Incidence of Lifestyle Diseases In IT Industry

There has been an increasing trend in non-communicable diseases worldwide. Lifestyle
factors contribute to this rising prevalence. IT industry workers face many health challenges
due to shift duties, odd working hours, erratic eating habits, sedentary lifestyle (desk job)
& constant stress levels; which can hamper their performance. This study throws light on
current health scenario of IT industry employees.

Aim was to establish prevalence of cardio-metabolic risk factors in employees of IT
industry.& to observe clustering of cardio-metabolic risk factors within body mass index
(BMI) & age groups.

Healthcare Expert team who conducted this study : Dr R. L. Kulkarni, Dr C.S. Yajnik , Ms
Tejas . Y. Limaye, Dr Manisha R .Deokar & Dr Rasika M.Phutane

1. Case study :Case study :
Incidence of Lifestyle Diseases in Incidence of Lifestyle Diseases in
IT Industry IT Industry
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3. Investigator: Dr. Ravindra L KulkarniInvestigator: Dr. Ravindra L Kulkarni
Consultant & Interventional
Cardiologist
MD, FSCAI specialize in clinical
Research and interventional
cardiology.
Practicing in Leading multi specialty
hospitals in Pune.
Involved in health talks, health
check ups and Corporate wellness.
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4. Background Background
Increasing trend in NCDs worldwide
(WHO, 2008)
Contribution of lifestyle factors
IT industry workers are at risk
◦Odd working hours
◦Erratic eating habits
◦Sedentary work style
◦Constant stress levels
•Effect on work performance &
productivity
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5. ObjectivesObjectives
1.To establish
prevalence of cardio-metabolic risk factors
in employees of IT industry.
2. To observe clustering of cardio-metabolic
risk factors (CMRF) within
body mass index (BMI), height, weight
& age groups.
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6. MethodsMethods
Observational study
Data obtained from annual medical health records of
employees (from 2 leading BPO industries in Pune)
CMRF clustering i.e. ≥ 2 risk factors (IDF 2005)
◦TG ≥ 150 mg/dl
◦HDL < 40 mg/dl in males OR <50 mg/dl in females
◦BP systolic ≥ 130 OR diastolic ≥ 85mmHg
◦FPG ≥ 100 mg/dl
ADA 2011 criteria & JNC-7 guidelines used for T2DM,
HTN
Analyzed across
◦Height, Weight, BMI Categories
◦Age Categories
◦Gender
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7. Observations Criteria Total (%)
(n = 1350)
Males (%)
(n = 1063)
Females (%)
(n = 287)
IFG FPG ≥ 100 mg/dl 10.0 10.2 9.2
HypertensionSBP ≥ 140 mmHg
DBP ≥ 90 mmHg
19.3 20.1 16.7
Obesity ≥ 30 kg/m
2
9.4 8.8 11.5
≥ 27.5 kg/m
2
22.5 21.6 25.6
High T. CholeT.Chole ≥200
mg/dl
19.2 20.3 14.9
High TG ≥ 150 mg/dl 23.8 26.6 13.1
Low HDL <40 mg/dl (M)
< 50 mg/dl (F)
67.3 60.4 93.1
High LDL ≥ 130 mg/dl 19.5 19.7 18.5
ResultsResults
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8. Overall PrevalenceOverall Prevalence
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Results

9. Prevalence of CMRF clustering Prevalence of CMRF clustering
across age groupsacross age groups
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P
r
e
v
a
le
n
c
e
Age Groups
< 30 Y < 40 Y <50 Y ≥ 50 Y

10. Prevalence of CMRF clustering acrossPrevalence of CMRF clustering across
BMI groups BMI groups
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%
P=0.000
Public health
achievable
targets for Asians
WHO Criteria

11. Gender
Prevalence of CMRF clustering
across Genders
P
r
e
v
a
le
n
c
e
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12. Determinants of CMRF Determinants of CMRF
(Logistic Regression)(Logistic Regression)
Independent
Variables
Groups Sig Odd’s
Ratio
95% CI
Lower Upper
AGE < 31 Y 1.00
≥ 31 < 33 Y 0.002 2.75 1.42 5.29
≥ 33 < 35 Y 0.003 2.71 1.4 5.25
≥ 35 Y 0.012 2.27 1.19 4.32
HEIGHT ≥ 174 cm 1.00
≥ 168 < 174 cm0.662 1.10 0.71 1.7
≥ 162 < 168 cm0.061 1.56 0.98 2.51
< 162 cm 0.030 1.90 1.06 3.39
WEIGHT < 63 Kg 1.00
≥ 63 < 71 Kg 0.062 1.58 0.97 2.57
≥ 71 < 79 Kg 0.008 1.98 1.19 3.30
≥ 79 Kg 0.000 3.55 2.09 6.01
GENDER Females 1.00
Males 0.317 0.79 0.5 1.24
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13. ConclusionConclusion
1. There is a high burden of cardio-metabolic risk factors in
young employees working in IT industry.
2.The prevalence of CMRF clustering increases with increasing
BMI, body weight & age.
3.The prevalence of CMRF clustering decreases with
increasing height. (i.e. short height = high risk)
4. Need to spread awareness among IT employees about far-
reaching effects
5. Need to initiate Workplace Health Promotion programs
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14. LimitationsLimitations
Opportunistic analysis
No data on-
◦SES
◦Family history of DM/
HTN
◦Abdominal obesity
(WC)
◦Tobacco & alcohol
consumption
◦Duration of exposure to
work style
No OGTT was performed
Future Plans
•To initiate diabetes
prevention program in IT
industry.
•Impact of a lifestyle
modification program on
CMRFs.
•Use of email & SMS
technologies
•Benefits both for the
employees and the
employers
•Better industrial outputs
and growth
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