|Year : 2017 | Volume
| Issue : 5 | Page : 417-423
Quantification of vulnerability to Type 2 diabetes: A study among shopkeepers in Kolkata
Aparajita Dasgupta, Bijit Biswas, Bobby Paul, Lina Bandyopadhyay, Ayon Ghosh, Sembagamuthu Sembiah
Department of PSM, AIIH&PH, Kolkata, West Bengal, India
|Date of Submission||23-Mar-2017|
|Date of Acceptance||25-May-2017|
|Date of Web Publication||14-Nov-2017|
Sarat Sarani More (G. T. Road), Post Bandel, Hooghly, Kolkata - 712 123, West Bengal
Source of Support: None, Conflict of Interest: None
Context: Urban people are more prone to develop diabetes due to their sedentary lifestyle and high-calorie food intake. Shopkeepers with their long sitting hours, unhealthy food habits, and lack of regular physical activity are one of the high-risk groups for developing diabetes. Hence, it is necessary to detect this large pool of undiagnosed people with diabetes to offer them early treatment. Aims: The present study aimed at assessing people with a high risk of developing type 2 diabetes and its correlation with some known risk factors. Materials and Methods: The study was a cross-sectional community-based study conducted from May to July among 152 shopkeepers in a permanent market with a structured schedule. Statistical Analysis Used: Data were analyzed using appropriate statistical methods by SPSS software (version 16). Results: Thirty-five (23.02%) out of 152 shopkeepers were found having high blood sugar (>140 mg/dl). In univariate analysis, the study participants with higher age, perceived stress score, Indian Diabetic Risk Score (IDRS), body weight, centrally obese, and less physically active had shown significantly greater odds of having high blood sugar level. In multivariable model, PSS (adjusted odds ratio [AOR]-1.90), waist circumference (AOR-3.34), and physical activity (AOR-3.09) remained significantly adjusted with other significant variables in univariate analysis excepting IDRS. Sensitivity and specificity of IDRS were 68.6% and 65.8%, respectively, with diagnostic accuracy of 72.3% calculated using receiver operating characteristic curve. Conclusions: The study revealed that shopkeepers are indeed at high risk of developing diabetes. There is an urgent need for increasing awareness regarding diabetes among them.
Keywords: Indian Diabetic Risk Score, perceived stress, risk factors, shopkeepers
|How to cite this article:|
Dasgupta A, Biswas B, Paul B, Bandyopadhyay L, Ghosh A, Sembiah S. Quantification of vulnerability to Type 2 diabetes: A study among shopkeepers in Kolkata. Med J DY Patil Univ 2017;10:417-23
|How to cite this URL:|
Dasgupta A, Biswas B, Paul B, Bandyopadhyay L, Ghosh A, Sembiah S. Quantification of vulnerability to Type 2 diabetes: A study among shopkeepers in Kolkata. Med J DY Patil Univ [serial online] 2017 [cited 2018 May 27];10:417-23. Available from: http://www.mjdrdypu.org/text.asp?2017/10/5/417/218199
| Introduction|| |
Of the 57 million deaths that had occurred globally in 2008, 36 million were caused due to noncommunicable diseases (NCDs) consisting of mainly cardiovascular diseases, diabetes, cancers, and chronic lung diseases. Once NCDs were considered as diseases of economically wealthier countries, but they are now quite common in low- and middle-income countries. Diabetes mellitus (DM) is now a leading cause of death and disability worldwide. Its prevalence was about 8% in 2011 and it is predicted to rise by 10% by the end of 2030 and it will be the 7th leading cause of death by then as projected by the WHO.,,
According to the WHO Global Report 2016 in India, the prevalence of diabetes was 7.9% in males and 7.5% in the year 2014, while the prevalence of diabetes-related risk factors such as overweight, obesity, and physical inactivity was 19%, 3.1%, and 9.2% in males and 23.9%, 6.5%, and 15.1% in females, respectively. According to the International Diabetes Federation Estimation, India will have a substantial rise in people living with diabetes up to 87.0 million by 2030 from 50.8 million in 2010, making it the diabetes capital of the world overtaking China.,,
The risk of Type 2 DM is determined by certain metabolic and genetic factors such as older age, physical inactivity leading to overweight and central obesity, unhealthy infrequent diet, ethnicity, and family history of diabetes. Emotional stress and smoking add on to the risk.,
In DM, the primary prevention strategies are more effective in controlling its burden. Population education and information may play a vital role in this regard.
Urban people are more prone to develop diabetes due to their sedentary lifestyle and high-calorie food intake. A shopkeeper is an individual who owns or runs a shop. Shopkeepers' work profile include answering to customers' inquirers, giving advice about products to customers, catering to customers' needs and requests, handle cash, and stock keeping. Hence, they lead a sedentary lifestyle as most of the day they sit/stand in a shop to fulfill their job responsibilities. They also have infrequent, unhealthy food habits as most of them take street food during business hours. Long sitting hours, unhealthy food habits, and lack of regular physical activity make them vulnerable to develop obesity which in turn acts as a risk factor for the development of diabetes, and the increased stress level adds on to the risk. Lack of knowledge regarding diabetes and its prevention, importance of regular blood and urine examination, and importance of regular physical activity as per the WHO norms also play a vital role in this regard. Hence, to detect this large pool of undiagnosed people with diabetes and for offering them early treatment, the present study was conducted among shopkeepers at a marketplace in Chetla, Kolkata, to assess their risk toward the development of diabetes and validate the Indian Diabetic Risk Score (IDRS) as a screening tool.
| Materials and Methods|| |
It was a community-based observational study and cross-sectional in design. The study was conducted in a permanent marketplace in Chetla between May and July, 2016, after getting necessary permission from the chairperson of Kolkata Improvement Trust (KIT). Persons aged 20 years and above and either owner or worker of a shop registered with the market trade union and working for at least 1 year as a shopkeeper were included in the study. Although diabetes may occur at any age, prevalence increases steeply with age and other studies of this kind also used 20 years as lower cutoff of age., Hence, in the present study, 20 years was taken as a lower cutoff age. Census method of sampling was followed. Known people with diabetes (18 shopkeepers) and those who were unwilling (12 shopkeepers) were excluded from the study. Therefore, out of the total 182 shopkeepers registered with KIT market trade union, only 152 shopkeepers were included in the study. Structured schedule (based on demographic, behavioral, PSS4 scale items, International Physical Activity Questionnaire (IPAQ) short form, morbidity, and IDRS information), stethoscope, sphygmomanometer, weighing machine, measuring tape, and glucometer were used for data collection. Pretesting of the questionnaire was done among thirty shopkeepers in an adjacent market place among shopkeepers and the necessary modifications were included in the final questionnaire. At first, line listing of the shopkeepers was done based on shop number allotted to them by the KIT market trade union. 2 days in a week for 3–4 hrs a day was available for data collection. The study was conducted in the stipulated time. Then, the shopkeepers were asked to come to the club room of KIT market on an appointment basis. In a day, on an average of 8–10 people were interviewed and clinically examined. Operational definitions used are listed next.
Family history of DM was considered as positive if either or both parents of individuals were diagnosed to have DM.
In case of tobacco consumption, individuals who were currently smoking or taking tobacco products by any other means were reported as tobacco consumers.
Blood pressure was measured in sitting position on the right arm using aneroid sphygmomanometer while the patient's leg rests on a flat surface. Three readings were taken 5 min apart, and the mean of last two readings was taken as the final blood pressure (BP) reading. Hypertension was defined as systolic BP of ≥140 mmHg or diastolic BP of ≥90 mmHg.
Height was measured while patient standing upright on a flat surface by stadiometer taking the highest point of parietal tuberosity as the upper limit of the body. Patients were asked to stand with feet together, heels against the back board, and knees straight after removing any footwear and headgear while measuring the height.
Body weight was measured while patient wearing light clothes by aspiring a loaded calibrated weighing scale. Participants were asked to step onto the scale with one foot on each side of the scale, stand still, face forward, place arms on the side, and wait until asked to step off after removing any footwear and headgear while measuring the height.
Socioeconomic status was assessed by modified B. G. Prasad scale 2016.
Waist circumference (WC) was measured by keeping the tape (nonstretchable) at the maximum measurement at the belly at the end of expiration (midpoint between lowest rib and iliac crest nearest to 0.1 cm). WC values >90 and >80 cm for men and women, respectively, were considered high. Participants were asked to stand with their feet together with weight evenly distributed across both feet; hold the arms in a relaxed position at the sides; breathe normally for a few breaths, and then make a normal expiration while measuring WC.,
Blood glucose measurement
Estimation of random capillary blood glucose was done using a standardized digital glucometer. Study participants having blood sugar level >140 mg/dl were considered as having high blood sugar.
Indian Diabetes Risk Score
The Indian Diabetes Risk Score was developed by Mohan et al. for identifying undiagnosed diabetic patients using four simple parameters - age, WC, family history of diabetes, and physical activity.
Physical activity was assessed by IPAQ , and each individual was classified as mild/moderate/heavy based on physical activity status.
Stress was assessed using PSS4 scale. It is a 4-item scale where the maximum attainable score is 12 and the minimum attainable score is 4. Interpretation of increased score indicated increased perceived stress level.
Data were analyzed using appropriate statistical methods by IBM Statistical Package for Social Sciences version 16 (SPSS 16). Chi-square test was used to find the association between sociodemographic, behavioral, and other variables with the blood sugar level of the study participants. Univariate logistic regression model was employed to assess the predictive values of the potential risk factors. The strength of the associations was assessed by odds ratios (OR) at 95% confidence interval (CI). Multivariable logistic regression model was done for the variables which were found significant in univariate analysis. The strength of associations was assessed by adjusted OR (AOR) at 95% CI. Statistical significance for all analyses was set at P < 0.05.
| Results|| |
Among the study participants, 78.9% (n = 120) were males and 21.1% (n = 32) were females. The age range of the study participants was from 25 to 85 with mean ± standard deviation of 48.2 ± 13.2 years. Age seemed to be significantly correlated with their blood sugar level. Majority of the study participants were Hindus (96.7%), married (87.5%), and belonged to nuclear family (63.1%). About 53.5% were educated above secondary level while 25% of them had educational qualification of graduation and above. In the addiction profile, 12.5% of them were addicted to alcohol while 46.1% of them were addicted to tobacco by means of smoking or chewing or by both means [Table 1]. In the IDRS particulars, 30.9% of the participants had a family history of diabetes. Nearly 65.1% of the study participants had central obesity which was significantly associated with their blood sugar level. Most of them were involved in mild physical activity (57.9%) which also had shown a significant association with their blood sugar level. The study revealed that 32.2% of the study participants had hypertension (examined/self-reported) while 42.1% of the study participants were overweight/obese [Table 1].
|Table 1: Baseline characteristics of the study participants with their random blood sugar level (n=152)|
Click here to view
[Table 2] shows an association between IDRS and random blood sugar (RBS). A total of 35 (23.0%) study participants were found to have high blood sugar. IDRS of the study participants is as follows: low risk (7%), medium risk (57%), and high risk (36%) which had a significant association with their blood sugar level.
In univariate logistic regression analysis, persons aged ≥48 years (median age of the study population) had higher odds of having higher blood sugar compared to persons aged <48 years. With an increased PSS score, the odds of having high blood sugar also increase. Centrally obese shopkeepers had 2.59 times more odds of having high blood sugar compared to others. Shopkeepers with mild physical activity had higher odds of having higher blood sugar compared to shopkeepers with moderate physical activity [Table 3]. In multivariate logistic regression model, age, PSS score, WC, and physical activity were entered by forced entry method which were found to be significant in univariate analysis. IDRS was not taken in multivariate model as other significant variables such as age, WC, and physical activity are constituent of IDRS scores [Table 3]. In multivariate model, PSS score, WC, and physical activity remained significant while age lost its significance adjusted with other variables in the model. The model explains 43.7% variability of the outcome variable [Table 3].
|Table 3: Univariate and Multivariable Logistic regression analysis showing association of high blood sugar with some modifiable & non modifiable risk factors (n=152)|
Click here to view
On account of sensitivity and specificity of IDRS in predicting high blood sugar when compared with RBS, it was observed that sensitivity and specificity was optimum at IDRS ≥60 with sensitivity of 68.6% and specificity of 65.8% with a Kappa value of.266, which indicates a fair agreement between IDRS and high RBS. Receiver operating characteristic (ROC) curve of IDRS predicting high blood sugar level (RBS >140 mg/dl) had a diagnostic accuracy of 72.3% [Figure 1] while ROC of IDRS in predicting DM (RBS >200 mg/dl), the diagnostic accuracy increases to 74.5% [Figure 2].
|Figure 1: Receiver operating characteristic curve showing performance of Indian Diabetes Risk Score in predicting individuals with high blood sugar (random blood sugar >140 mg/dl) (n = 152). Area under the curve 0.723|
Click here to view
|Figure 2: Receiver operating characteristic curve showing performance of Indian Diabetes Risk Score in predicting individuals with diabetes mellitus (random blood sugar >200 mg/dl) (n = 152). Area under the curve 0.745|
Click here to view
| Discussion|| |
The present study has reported that 36% of the study participants are in IDRS high-risk group which is similar to the finding of Patil and Gothankar  (36.6%), while the study conducted by Anjana and Dattatreya  has reported more (45%) and the study conducted by Arun et al. has reported less (14.9%) individuals at high risk. This variability of findings may be due to geographical plausibility.
In the present study, 23% of the study participants had blood sugar level >140 mg/dl while 11.2% had blood sugar level >200 mg/dl which is much above the recently reported National Family Health Survey-4 data which showed that 12.9% of urban population had blood sugar >140 mg/dl. The findings of the study by Dyavarishetty and Kowli  (Mumbai slums) and Patil and Gothankar  (Pune slums) were also quite less compared to our study. All these studies were conducted in general urban population not in a high-risk population (shopkeepers) like ours.
Age is an important nonmodifiable risk factor of diabetes. Our study revealed that persons with higher age are at higher risk of development of diabetes. This finding is similar to that of studies conducted in general population (Arun et al.) and specific occupation group like that of Llone et al., Kumar et al., Tharkar et al., etc.
Family history of diabetes is an important risk factor for diabetes as per findings of the studies conducted by PatilandGothankar, Anjana and Dattatreya, Arun et al., Kumar et al., and Tharkar et al., but our study did not had such findings which indicates that it is an important risk factor but not an essential one for diabetes.
Stress is a subjective thing. Our study identified perceived stress as an important risk factor for diabetes. The study conducted by Pouwer et al. revealed similar findings.
Lack of physical activity is an important modifiable risk factor for diabetes. Physically inactive persons are prone to develop obesity and stress which further increases the risk. Our study identified the lack of physical activity as an important risk factor for diabetes. Other studies conducted by Patil and Gothankar, AnjanaandDattatreya, and Arun et al. had also reported similar findings.
WC is an important indicator of one's body fat proportion. Increase in fat deposition decreases insulin sensitivity. Our study revealed central obesity as a significant predictor of diabetes. This finding was in concordance to studies conducted by Patil and Gothankar, Arun et al., Prajapati and Kedia, Kumar et al., and Tharkar et al.
The present study shows that sensitivity and specificity is optimum at IDRS ≥60 with a sensitivity of 68.6% and specificity of 65.8% with a Kappa value of.266. The study conducted by Arun et al. shows 81.4% of sensitivity and 72% of specificity with diagnostic accuracy of 73.3%. In contrast, the present study found a sensitivity of 68.6% and specificity of 65.8% with similar diagnostic accuracy of 72.3%. The study conducted by Mohan and Anbalagan  shows that IDRS value ≥60 had the optimum sensitivity (72.5%) and specificity (60.1%) for determining undiagnosed diabetes in the community. The study conducted by Appajigol et al. at IDRS value ≥60 had a sensitivity of 37.5% and specificity of 89.4% with similar diagnostic accuracy of 75.5%.
Strengths of the study
It was a community-based study which tried to explore all the known risk factors of DM except the nutritional factors.
Limitations of the study
The study population was very small. RBS was used for screening instead of fasting blood sugar/postprandial blood glucose (FBS/PPBS) for feasibility issues.
A written informed consent was obtained from all the shopkeepers before participation. Their confidentiality was maintained during data collection.
| Conclusions|| |
The study reveals that shopkeepers are indeed at a high risk of developing diabetes. There is an urgent need for increasing awareness regarding diabetes among them. Definitive testing by FBS/PPBS is recommended to detect the status of diabetes in participants with random capillary blood glucose level above 140 mg/dl. Regular awareness campaigns regarding the importance of regular physical activity as per the WHO norms and blood glucose monitoring at all levels of health-care delivery system is recommended. Development of suitable primary and secondary preventive strategies, including lifestyle and dietary modifications, is recommended for these high-risk participants.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Mathers CD, Loncar D. Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med 2006;3:e442.
Boutayeb A, Boutayeb S. The burden of non communicable diseases in developing countries. Int J Equity Health 2005;4:2.
Pouwer F, Kupper N, Adriaanse MC. Does emotional stress cause type 2 diabetes mellitus? A review from the European Depression in Diabetes (EDID) Research Consortium. Discov Med 2010;9:112-8.
Green LW, Brancati FL, Albright A; Primary Prevention of Diabetes Working Group. Primary prevention of type 2 diabetes: Integrative public health and primary care opportunities, challenges and strategies. Fam Pract 2012;29 Suppl 1:i13-23.
Patil RS, Gothankar JS. Assessment of risk of type 2 diabetes using the Indian Diabetes Risk Score in an urban slum of Pune, Maharashtra, India: A cross-sectional study. WHO South East Asia J Public Health 2016;5:53-61.
Anjana P, Dattatreya D. A community based cross-sectional study to identify individuals at high risk for diabetes in urban slums of Hubli. Int J Community Med Public Health 2016;3:212-7.
WHO. Global Strategy on Diet Physical Activity and Health. Geneva: World Health Organization; 2004.
Vasudevan J, Mishra AK, Singh Z. An update on B. G. Prasad's socioeconomic scale: May 2016. Int J Res Med Sci 2016;4:4183-6.
World Health Organization (WHO). WHO STEPS Surveillance. Part 3: Training and Practical Guides. Section 3: Guide to Physical Measurements (Step 2) Last Updated: 12 December 2008. [Internet]. Avaialable from: http://www.who.int/chp/ steps/Part3_Section3.pdf
. [Last cited on 2017 Mar 04].
World Health Organization (WHO): Waist circumference and waist-hip ratio: report of a WHO expert consultation. Geneva, 8-11 December 2008. Geneva, Switzerland: WHO; 2011.
Mohan V, Deepa R, Deepa M, Somannavar S, Datta M. A simplified Indian Diabetes Risk Score for screening for undiagnosed diabetic subjects. J Assoc Physicians India 2005;53:759-63.
Oyeyemi AL, Oyeyemi AY, Adegoke BO, Oyetoke FO, Aliyu HN, Aliyu SU, et al.
The Short International Physical Activity Questionnaire: Cross-cultural adaptation, validation and reliability of the Hausa language version in Nigeria. BMC Med Res Methodol 2011;11:156.
Guidelines for data processing and analysis of the International Physical Activity Questionnaire (IPAQ): short and long forms. 2005. Avaialable from: http://www.ipaq.ki.se/scoring.pdf
. [Last cited on 2017 Mar 04].
Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav 1983;24:385-96.
Arun A, Srivastava JP, Gupta P, Sachan B, Prakash D, Zaidi ZH. Indian diabetes risk score (IDRS), a strong predictor of diabetes mellitus: A cross sectional study among urban and rural population of Lucknow. Int J Appl Res 2015;1:135-8.
Dyavarishetty P, Kowli S. Population based screening for diabetes: Experience in Mumbai slums, Maharashtra, India. Int J Res Med Sci 2016;4:2766-9.
Llone D, Thakre S, Thakre S, Borkar A, Deshmukh N. Prevalence of diabetes mellitus among school teachers in urban area of Nagpur city, Central India. Indian J Appl Res 2014;4:376-8.
Kumar P, Mallik D, Mukhopadhyay DK, Sinhababu A, Mahapatra BS, Chakrabarti P. Prevalence of diabetes mellitus, impaired fasting glucose, impaired glucose tolerance, and its correlates among police personnel in Bankura district of West Bengal. Indian J Public Health 2013;57:24-8. [Full text]
Tharkar S, Kumpatla S, Muthukumaran P, Viswanathan V. High prevalence of metabolic syndrome and cardiovascular risk among police personnel compared to general population in India. J Assoc Physicians India 2008;56:845-9.
Prajapati D, Kedia G et al.
Prevalence of diabetes and its risk factors: A cross-sectional study in Ahmedabad, Gujarat. Int J Med Sci Public Health 2016;5:425-9.
Mohan V, Anbalagan VP. Expanding role of the Madras Diabetes Research Foundation – Indian Diabetes Risk Score in clinical practice. Indian J Endocrinol Metab 2013;17:31-6.
Appajigol JS, Somannavar MS, Araganji RR. Performance of diabetes risk scores with or without point of care blood glucose estimation. J Sci Soc 2015;42:24-6. [Full text]
[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3]