Case study :Case study :Incidence of Lifestyle Incidence of Lifestyle
Diseases in IT Industry Diseases in IT Industry
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Investigator: Dr. Ravindra L Investigator: Dr. Ravindra L KulkarniKulkarni
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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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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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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MethodsMethodsObservational studyData 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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Observations Criteria Total (%)(n = 1350)
Males (%)(n = 1063)
Females (%)(n = 287)
IFG FPG ≥ 100 mg/dl 10.0 10.2 9.2
Hypertension SBP ≥ 140 mmHgDBP ≥ 90 mmHg
19.3 20.1 16.7
Obesity ≥ 30 kg/m2 9.4 8.8 11.5
≥ 27.5 kg/m2 22.5 21.6 25.6
High T. Chole T.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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Overall Overall PrevalencePrevalence
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Results
Prevalence of CMRF clustering Prevalence of CMRF clustering across age groupsacross age groups
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Pre
vale
nce
Age Groups
< 30 Y < 40 Y <50 Y ≥ 50 Y
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
Gender
Prevalence of CMRF clustering across Genders
Pre
vale
nce
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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 cm 0.662 1.10 0.71 1.7
≥ 162 < 168 cm 0.061 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 1.24www.justforhearts.org
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 programswww.justforhearts.org
LimitationsLimitationsOpportunistic analysisNo data on-
◦ SES◦ Family history of DM/
HTN◦ Abdominal obesity
(WC)◦ Tobacco & alcohol
consumption◦ Duration of exposure to
work styleNo 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 growthwww.justforhearts.org
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