|Year : 2013 | Volume
| Issue : 3 | Page : 149-155
Diabetes in rural Pondicherry, India: a population-based studyof the incidence and risk factors
Arun Gangadhar Ghorpade1, Sumanth M Majgi2, Sonali Sarkar3, Sitanshu Sekhar Kar3, Gautam Roy3, PH Ananthanarayanan3, AK Das3
1 Sri Manakula Vinayagar Medical College and Hospital, Pondicherry, India
2 Mysore Medical College and Research Institute, Mysore, Karnataka, India
3 Jawaharlal Institute of Postgraduate Medical Education and Research, Pondicherry, India
|Date of Web Publication||23-May-2017|
Arun Gangadhar Ghorpade
Assistant Professor, Department of Community Medicine, Sri Manakula Vinayagar Medical College and Hospital, 605107, Pondicherry
Background: For India, the ‘diabetes capital’ of the world, it is essential to know the incidence of type 2 diabetes mellitus (T2DM) and its key determinants. As two thirds of Indians live in rural areas, a study was undertaken to assess the incidence and risk factors of T2DM in rural Pondicherry, India.
Methods: In a population-based cohort study initiated in 2007, a sample of 1223 adults > 25 years ofage from two villages of Pondicherry were selected using cluster random sampling. Data on risk factor exposure were collected using a structured questionnaire, anthropometric tests and fasting blood glucose assessment. During house visits, 1223 of 1403 invited subjects participated. Of these, 71 (5.8%) were found to have diabetes. In 2010–2011, 85% of the non-diabetics (979/1152) were followed up using the same protocol. We calculated the risk of T2DM per annum standardized by age and sex. Population estimates of the risk factors associated with T2DM were analysed using the Generalized Estimating Equation model and the Population Attributable Risk (PAR) for T2DM calculated.
Results: During 2937 person-years (PY) of follow-up, 63 new cases of T2DM occurred, giving an incidence rate of 21.5/1000 PY. Almost one third (31.7%) of cases occurred in people aged below 40 years. The incidence was double among males (28.7/1000 PY; 95% confidence interval (CI): 21.0–38.7) compared with females (14.6/1000 PY; 95% CI: 9.4–21.7). Applying these rates to rural populations, it is estimated that each year 8.7 million people develop T2DM in rural India. Nearly half of the T2DM incidence was attributed to overweight/obesity and alcohol usage.
Conclusion: T2DM incidence was 2% per year in adults in rural Pondicherry, India, with the rate increasing twice as fast in men. Increasing age, obesity, alcohol use and a family history of T2DM independently predicted the development of diabetes. As half of T2DM incidence was attributed to overweight/obesity and alcohol use, health promotion interventions focusing on maintaining an optimal weight and decreasing alcohol consumption may be effective in reducing the rise in T2DM cases.
Keywords: type 2 diabetes, incidence studies, risk factors, rural population, India
|How to cite this article:|
Ghorpade AG, Majgi SM, Sarkar S, Kar SS, Roy G, Ananthanarayanan P H, Das A K. Diabetes in rural Pondicherry, India: a population-based studyof the incidence and risk factors. WHO South-East Asia J Public Health 2013;2:149-55
|How to cite this URL:|
Ghorpade AG, Majgi SM, Sarkar S, Kar SS, Roy G, Ananthanarayanan P H, Das A K. Diabetes in rural Pondicherry, India: a population-based studyof the incidence and risk factors. WHO South-East Asia J Public Health [serial online] 2013 [cited 2019 Jul 21];2:149-55. Available from: http://www.who-seajph.org/text.asp?2013/2/3/149/206761
| Introduction|| |
According to the International Diabetes Federation (IDF), the number of people globally with type 2 diabetes mellitus (T2DM) will increase to 552 million by 2030, over twice the number in 2000. Nearly 21% of these new cases will be from India, which has the highest number of cases in any country. India currently has 61.3 million diabetics, a figure that is projected to increase to 103 million by 2030.
Several studies from different regions of India have shown that prevalence of T2DM is increasing – from 8.2% in 1992 to 18.6% in 2008 for urban areas, and from 2.4% in 1992 to 9.2% in 2008 in rural areas.,, Accurate knowledge on incidence of T2DM and its determinants is key to controlling a potential T2DM pandemic in India. A T2DM incidence of 20–22/1000 person-years (PY) has been reported by two studies on the urban population of Chennai, south India.,
Though 72% of Indians reside in rural areas, T2DM incidence and its determinants among rural residents has not been studied to date. We followed a population-based sample of adults aged > 25 years for three years with the aim to describe the incidence and risk factors for T2DM in rural Pondicherry, India.
| Materials and methods|| |
The research protocol for this study was approved by the Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER) Institute Ethics Committee. All participants gave verbal informed consent.
Pondicherry is a Union Territory situated on the Coromandal Coast, 170 km south of Chennai. The total population as per the 2011 census was 1.25 million with a sex ratio of 1038 females to 1000 males, and literacy levels of 92% in men and 81% in women.
This population-based cohort study on T2DM started in 2007 in a rural population of Pondicherry. Sample size was calculated using a freely available open source software, Open Epi Version 2.3. With a 95% confidence interval (CI) and 80% power for a ratio of normal to prediabetics of 1:11, and incidence of diabetes within each group at 13% and 41%, respectively, a minimum sample of 171 was to be followed for eight years, i.e. 1368 PY. Taking into account a design effect of 2 and a non-response rate of 35% (including refusals and individuals who could not be reached), the cohort was to be followed for 4209 PY that is, 1403 individuals were to be followed over three years (1368*2)/([1–0.35]*3)=1403.
The study was carried out in two of the four villages under the Rural Health Centre, namely Ramanathapuram and Pillaiyarkuppam, with a population of 2165 and 2412 respectively. The sampling frame comprised individuals aged above 25 years (n=2608). Single stage cluster random sampling was carried out. Using streetsas the primary sampling unit, four streets in Ramanathapuram and six streets in Pillaiyarkuppam were chosenby lot method. From the houses of selected streets, all participants (n=1403) aged more than 25 years (n=1403) were invited to take part in the baseline study in 2007–2008 ([Figure 1]). Unavailability of subjects during home visits on three separate days and pregnant women were excluded. Of the 1223 subjects (87%) who participated in the baseline survey, 71 subjects (5.8%) were diagnosed with T2DM. In 2010–201l a follow-up survey was carried out using the same protocol. All participants except those with T2DM at baseline (n=1152) were called for assessment. A total of 979 individuals (85%) completed the follow-up.
|Figure 1: Population flow chart showing participant recruitment and follow-up|
Click here to view
The methodology was the same in both the baseline and follow-up surveys. It consisted of questionnaire-based assessments of lifestyle behaviours, a physical examination and blood collection. Two interviewers were trained and collected the data during house visits. After obtaining informed consent and ensuring adequate privacy, individuals were interviewed face-to-faceusing the pretested questionnaires. Details regarding sociodemographic factors, predisposing biological factors like family history of T2DM, and behavioural components (physical activity, smoking and alcohol consumption) were collected. Anthropometric measurements (height, weight and waist circumference) were taken.
The Accu-CheK® Advantage glucometer was used to measure fasting blood glucose (FBG). The instrument was standardized with the Olympus AU400 automatic analyzer in the Department of Biochemistry, JIPMER. Sixty blood samples from patients of JIPMER’s outpatient department were assessed for blood glucose values. The correlation coefficient was 0.84 (P < 0.001). Indian Council of Medical Research Guidelines for Management of Type 2 Diabetes, 2005 were used for diagnosis and classification of diabetes. On the basis of FBG, subjects were classified as having normal glucose tolerance (NGT): < 110 mg/dL; prediabetes: 110–125 mg/dL; and suspected T2DM: > 125 mg/dL. For those with suspected T2DM, an oral glucose tolerance test (OGTT) was carried out on a separate day. Subjects with FBG of > 125 mg/dL and/ or postprandial blood glucose of > 200 mg/dL were labelled as diabetic. An incident T2DM was defined as an individual with no T2DM at the baseline survey but with T2DM during the follow-up survey. In both surveys, diabetics were informed about their condition, given health education, and were referred to the health facility for further management.
Education was classified using International Standard Classification of Education as (a) no formal schooling and (b) attended school. Work status was categorized as unemployed and employed (economically productive occupations in both formal and informal sectors) as per the Government of India Census 2001. Socioeconomic status (SES) was measured as per capita income in Indian rupees (Rs) and graded as per Prasad’s classification; modifying for Consumer Price Index for 2006–2007, the social classification value was determined as 486. Per capita income per month was grouped as Rs > 2400 (Class 1) and Rs < 2400 (other classes).Family history of T2DM in first- or second-degree relatives was obtained. Physical activity was measured using the International Physical Activity Questionnaire (short version). Total metabolic equivaients/week (MET/wk) were calculated and individuals grouped as physically inactive (< 600 MET/wk) and physically active (> 600MET/wk). Smoking was defined as the current use of any tobacco product (cigarettes, bidis, chewing tobacco or snuff on a regular basis for at least the previous six months. Individuals who said they had not smoked during the survey period were classified as non-smokers. Alcohol use was defined as the consumption of any type of alcohol in the past 12 months.Body mass index (BMI) was calculated and classified as per BMI classification for Indians (< 23 kg/m2 as normal and ≥ 23 kg/m2 as overweight and obese).
| Statistical analysis|| |
Data were analysed using the SPSS statistical package version 19.0 for Windows (SPSS Inc., Chicago, United States of America). The significance level was set at a two-sided P < 0.05. BMI was not calculated for five individuals as height could not be measured due to kyphosis morbidity. Variables with missing data were excluded from the analysis. Using the Chi-squared test, proportions for categorical variables compared characteristics of the participants and non-participants in the follow-up survey. Incidence was reported as rates per 1000 PY (95% CI) for the overall population and for individuals with NGT and prediabetes.
Age-specific incidence rates were calculated separately for men and women. Incidence rates and crude odds ratios (OR) (with 95% CI) were calculated across the sociodemographic and health-related variables, with each explanatory variable expressed using binary categories. The generalized estimation equation (GEE), model was used to account for changes in noncommunicable disease (NCD) risk factors of the study cohort during the follow-up period. GEE with an independent link function was used to calculate the adjusted OR of developing T2DM for the independent variables.
Population attributable risks (PAR) for the risk factors were estimated using the formula PAR=P*(AOR-1)/[1+P*(AOR-1)] where P is the prevalence and AOR is the adjusted odds ratio of the modifiable risk factors of obesity, alcohol usage and physical inactivity. Age-standardized rates were calculated for the rural Pondicherry and rural Indian populations using population characteristics from the Census of India, 2011.
| Results|| |
In the baseline survey, the prevalence of T2DM was found to be 5.8%. After three years, a follow-up survey was conducted on the original non-diabetic cohort, 85% (979/1152) of whom were re-examined. Respondents and non-respondents differed in terms of age, gender, physical activity, family history of T2DM, smoking and alcohol consumption, with non-respondents being more likely to be male, physically inactive, smokers and alcohol users and less likely to have a family history of T2DM (P < 0.05) ([Table 1]). Among the responders, differences were found across the sexes, with a higher proportion of men having attended school, being employed, physically inactive and overweight. None of the participating women reported smoking or consuming alcohol.
|Table 1: Characteristics of the population cohort during baseline survey in 2007–2008|
Click here to view
Incidence rate of T2DM
There were 63 incident T2DM cases in the follow-up of 2937 PY with a crude incidence rate of 21.5 (16.6–27.3) per 1000 PY. Among the follow-up sample (n=979), 44 (5.5%) of the 801 participants with NGT at baseline were diagnosed with T2DM, while 19 (10.7%) of the 178 prediabetics developed T2DM. The incidence rates of T2DM in NGT and prediabetics were 18.3 (13.5–24.4) and 33.3 (22.1–54.5) per 1000 PY respectively. Almost two thirds of the incident cases (44=?) occurred in subjects < 50 years of age. The incidence rate was twice as high in males (28.7/1000 PY; 95% CI: 21.0–38.7) compared with females (14.6/1000 PY; 95% CI: 9.4–21.7), the highest incidence rate being in the 35–50 year age group (38.0/1000 PY). In women, the rate rose sharply with increasing age, with the greatest T2DM incidence of 21.7/1000 PY in those above 50 years old.
Risk factors for T2DM
[Table 2] shows the incidence rates and OR of developing T2DM by each of the selected sociodemographic and health-related risk factors. Univariate analysis showed that the risk of T2DM differed significantly across sex, age group, educational status, per capita income, family history of T2DM, overweight and obesity, and alcohol use. In multivariate analysis, no interaction between sex and other variables; hence no separate analysis was performed for men and women. Gender, educational status and per capita income were no longer significant after adjustment for other risk factors (P > 0.05). The risk of T2DM was higher for individuals aged 35–50 years (OR: 2.9; 95% CI: 1.4–5.9) and those aged > 50 years (OR: 3.5; 95% CI: 1.6–7.6) compared with the risk in the younger age group. In addition, positive family history of T2DM and overweight or obesity more than doubled the chance of developing diabetes. One third of men consumed alcohol in the study cohort. Drinkers had 2.3 times higher risk of T2DM than those who did not ([Table 4]). One third of incident cases of T2DM in our population were attributed to overweight/obesity (PAR=34.3%)' In addition, PAR for alcohol intake and physical inactivity were 19.5% and 1.4% respectively.
|Table 2: Incidence rates and the risk of T2DM as stratified by risk factors|
Click here to view
| Discussion|| |
This was the first report of its kind on rural population-based incidence of T2DM in a country of the South-East Asian Region. Overweight/obesity and alcohol use were important modifiable factors that were attributed to more than half of the T2DM incidence. Among non-modifiable factors, older age and positive family history of T2DM predicted the development of diabetes.
Comparison with other studies
The 5.8% prevalence of T2DM in the baseline study population was comparable to the prevalence of 6.4% reported for rural south Indians. Two studies in India, both from Chennai, described the incidence of T2DM. One of these, a study among staff of the Indian Institute of Technology and their relatives in 1992, showed that in an urban population, the incidence of T2DM was 22/1000 PY over one year. The second study in India in 2005 reported an incidence of 20.2/1000 PY over eight years in subjects > 20 years of age.
Asian ethnicity is a known risk factor for T2DM and within Asia , the incidence in the Indian population is the highest. Examples of studies from neighbouring countries and regions reporting T2DM incidence are from Taiwan (3.6/1000 PY), Japan (8.8/1000 PY), China (5.1-9.5/1000 PY),, Islamic Republic of Iran (10.6/1000 PY) and Thailand (11.4-13.6/1000 PY),. In the literature, a few of the highest incidences have been found in small subgroups of populations like Micronesian Nauruans (22.5/1000 PY), rural Papua New Guineans of Australia (24.0/1000 PY) and the Pima Indians fromUSA (26.5/1000 PY). The ‘Indian phenotype’ for T2DM may have contributed to its high incidence in India, and to the country’s status as the diabetes capital of the world.
Incidence rates for India’s rural populations were similar to its urban counterparts. It is emphasized that a third of men consumed alcohol and one in three adults in the community were overweight or obese. The effects of socio-technological transition – a shift from traditional diet, improved transport facilities etc. – has increased susceptibility of the rural population to develop T2DM. It is pertinent to note that the incidence rates in rural Pondicherry were similar to those of the metro city population. Diminishing differences in the NCD risk factor profile of rural and urban groups have been reported in India., The present study also points to a similar trend for the incidence of diabetes.
Age and sex standardized incidence rates of T2DM for adults of rural Pondicherry and the rural Indian population were 22.1/1000 PY and 21.8/1000 PY respectively ([Table 3]). The incidence rate for men are higher than that for women. Extrapolating standardized annuaľ incidence rates to rural adults of Pondicherry, it is estimated that each year around 3500 individuals develop T2DM. At the national level for rural India, it is estimated that 8.7 million individuals develop T2DM each year, of whom two thirds are men.
|Table 3: Age and sex standardized incidence rates and estimates of T2DM for rural Pondicherry and rural India|
Click here to view
In the present study, older age, positive family history of T2DM, overweight/obesity and alcohol use were associated with T2DM incidence. Two thirds of the new cases of diabetes were aged 50 years or less. This supports the view of IDF that the age of onset of diabetes has dropped. Similar to results from other regions,,, our study found that positive family history strongly predicted the risk of developing T2DM. Among alcohol drinkers, the odds of T2DM incidence were 2.3 times greater when compared with non-drinkers. The associations were stronger than the finding from the systematic review where as compared with no alcohol use, heavy consumption (>3 drinks/d) was associated with up to 1.4 times higher incidence of T2DM. Obesity was confirmed as a risk factor for the development of T2DM. This concords with the meta-analysis of cohort studies from the Asia-Pacific region which concluded that 2 kg/m2 lower BMI was associated with 25% lower risk of T2DM.
In the present study, the prevalence of alcohol use in men and the prevalence of overweight/ obesity in both sexes increased by 12% (from 37.4% to 49.6%) and 8% (33.5% to 41.3%) respectively. The stronger effects of these two modifiable risk factors on T2DM incidence and their rising prevalence may have contributed to higher incidence in our study. Similar conclusions from PAR showed that overweight/obesity and alcohol consumption accounted for 53.8% of the T2DM cases. This implies that half the burden of incident T2DM could be addressed by supporting populations to maintain a healthy weight and minimize alcohol consumption. Another study from the Asian region attributed more than half of the T2DM incidence to overweight and obesity in Iranians .
Evidence shows the feasibility of implementing lifestyle interventions for weight reduction in different settings., As highlighted by public health practitioners in 2010 in a call for action to tackle the diabetes epidemic, there is a need to focus on social and lifestyle factors that can reverse the epidemic trend.
Strengths and weakness
The response rate in the follow-up population-based sample was strong at 85%. The GEE model was used to account for changes in the risk factor profile over the study duration. However, our study had some limitations. The comparison of risk profiles of non-respondents and respondents at baseline showed that non-respondents comprised a higher proportion of men, physically inactive individuals, smokers and alcohol users, and who were less likely to have a positive family history of T2DM. Second, OGTT was done to confirm diabetes status only in subjects with above normal fasting glucose. Both limitations suggest that our incidence rates may be underestimated. Finally, other important risk factors like diet, central obesity, stress, lipid profile, and blood pressure were not considered for logistic reasons.
| Conclusion and recommendations|| |
This population-based, longitudinal study indicates that T2DM incidence was 2% per year in adults of rural Pondicherry, India. Increasing age, overweight or obesity, alcohol use and positive family history of T2DM independently predicted the future development of diabetes. Identifying individuals with these risk factors may be the initial approach to address the problem. As half of the T2DM incidence was attributed to obesity and alcohol usage, interventions focusing on promoting a healthy weight and low alcohol consumption in populations may be an effective strategy to control the diabetes epidemic in rural Indians.
| Acknowledgements|| |
We thank the Indian Council of Medical Research, New Delhi for financial assistance and the study participants for their important contributions
Source ofSupport: Indian Council of Medical Research, New Delhi, India.
Conflict of Interest: None (the sponsors of the study had no role in study design, data collection, analysis, interpretation, or writing of the report). Contributorship: GAG, SMM, ss, SSK, PHA, AKD designed the study. GAG, SMM collected the data. GAG, SMM, ss analysed the data. GAG, ss, SSK drafter the paper. SMM, ss, SSK, GR, PHA, AKD reviewed and modified the data. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.
| References|| |
Ramachandran A, Snehalatha C, Dharmaraj D, Viswanathan M. Prevalence of glucose intolerance in Asian Indians: urban-rural difference and significance of upper body adiposity. Dia Care. 1992 Oct l;15(10):1348–55.
Ramachandran A, Snehalatha C, Baskar ADS, Mary S, Sathish Kumar CK, Selvam S, et al. Temporal changes in prevalence of diabetes and impaired glucose tolerance associated with lifestyle transition occurring in the rural population in India. Diabetologia. 2004 May l;47(5):860–5.
Ramachandran A, Mary S, Yamuna A, Murugesan N, Snehalatha c. High prevalence of diabetes and cardiovascular risk factors associated with urbanization in India. Diabetes Care. 2008 May;31(5):893–8.
Shashank R Joshi. Incidence data on diabetes from India. J Assoc Physicians India. 2008 Mar;56:152–7.
Bai PV, Krishnaswami cv, Chellamariappan M. Prevalence and incidence of type-2 diabetes and impaired glucose tolerance in a selected Indian urban population. J Assoc Physicians India. 1999 Nov;47(ll):1060–1.
Mohan V Deepa M, Anjana RM, Lanthorn H, Deepa R. Incidence of diabetes and pre-diabetes in a selected urban south Indian population (CUPS-19). J Assoc Physicians India. 2008 Mar;56:152–7.
Indian Council of Medical Research. Guidelines for management of type 2 diabetes. New Delhi: Indian Council of Medical Research, 2005.
Agarwal AK. Social classification: the need to update in the present scenario. Indian J Community Med. 2008;33:50–1.
World Health Organization. STEPwise approach to surveillance (STEPS) field manual appendices. 1–31. Geneva: WHO. http://www. who.int/chp/steps/en/
- accessed 14 February 2014.
Tseng C-H, Chong C-K, Heng L-T, Tseng C-P, Tai T-Y. The incidence of type 2 diabetes mellitus in Taiwan. Diabetes Research and Clinical Practice. 2000 Oct;50:S61-S64.
Goto A, Goto M, Noda M, Tsugane S. Incidence of Type 2 Diabetes in Japan: A Systematic Review and Meta-Analysis. PLoS ONE. 2013 Sep 6;8(9):e74699.
Zeng P, Zhu X, Zhang Y Wang S, Zhang T. Metabolic syndrome and the development of type 2 diabetes among professionals living in Beijing, China. Diabetes Res Clin Pract. 2011 Nov;94(2):299–304.
Shang X, Li J, Tao Q, Li J, Li X, Zhang L, et al. educational level, obesity and incidence of diabetes among Chinese adult men and women aged 18–59 years old: an 11-year follow-up study. PLoS ONE. 2013;8(6):e66479.
Hosseinpanah F, Rambod M, Azizi F. Population attributable risk for diabetes associated with excess weight in Tehranian adults : a population-based cohort study. BMC Public Health. 2007;7:328.
Jiamjarasrangsi W, Aekplakorn w. Incidence and predictors of type 2 diabetes among professional and office workers in Bangkok, Thailand. J MedAssoc Thai. 2005 Dec;88(12):1896–904.
Jiamjarasrangsi W, Lohsoonthorn V Lertmaharit S, Sangwatanaroj S. Incidence and predictors of abnormal fasting plasma glucose among the university hospital employees in Thailand. Diabetes Res Clin Pract. 2008 Feb;79(2):343–9.
Dowse GK. Incidence of NIDDM and the natural history of IGT in Pacific and Indian Ocean populations. Diabetes Res Clin Pract. 1996 Oct;34 Suppl:S45-50.
Knowler WC, Bennett PH, Hamman RF, Miller M. Diabetes incidence and prevalence in Pima Indians: a 19-fold greater incidence than in Rochester, Minnesota. Am J Epidemiol. 1978 Dec; 108(6):497–505.
Kar ss, Thakur JS, Virdi NK, Jain S, Kumar R. Risk factors for cardiovascular diseases: is the social gradient reversing in northern India? Natl Med J India. 2010 Aug;23(4)206–9.
Unwin N, Whiting D, Roglic G. Social determinants of diabetes and challenges of prevention. Lancet. 2010 Jun 26;375(9733):2204–5.
Magliano DJ, Barr ELM, Zimmet PZ, Cameron AJ, Dunstan DW, Colagiuri S, et al. Glucose indices, health behaviors, and incidence of diabetes in Australia the Australian diabetes, obesity and lifestyle study. DiaCare. 2008 Feb l;31(2):267–72.
Joseph J, Svartberg J, Njølstad I, Schirmer H. Incidence of and risk factors for type-2 diabetes in a general population: the Tromsø Study. Scand J Public Health. 2010 NO,38(7):768–75.
Harati H, Hadaegh F, Saadat N, Azizi F. Population-based incidence of Type 2 diabetes and its associated risk factors: results from a six-year cohort study in Iran. BMC Public Health. 2009 Jun 16;9(1):186.
Howard AA, Arnsten JH, Gourevitch MN. Effect of alcohol consumption on diabetes mellitus: a systematic review. Ann. Intern. Med. 2004 Feb 3;140(3):211–9.
Ni Mhurchu C, Parag V Nakamura M, Patel A, Rodgers A, Lam TH. Body mass index and risk of diabetes mellitus in the Asia-Pacific region. Asia Pac J Clin Nutr. 2006;15(2):127–33.
Cardona-Morrell M, Rychetnik L, Morrell SL, Espinel PT, Bauman A. Reduction of diabetes risk in routine clinical practice: are physical activity and nutrition interventions feasible and are the outcomes from reference trials replicable? A systematic review and meta-analysis. BMC Public Health. 2010;10:653.
Hopper I, Billah B, Skiba M, Krum H. Prevention of diabetes and reduction in major cardiovascular events in studies of subjects with prediabetes: meta-analysis of randomised controlled clinical trials. Eur J Cardiovasc Prev Rehabil. 2011 Dec;18(6):813–23.
Editorial. Type 2 diabetes--time to change our approach. Lancet. 2010 Jun 26; 375(9733):2193.24.
[Table 1], [Table 2], [Table 3]
|This article has been cited by|
||Incidence of type 2 diabetes mellitus and prediabetes in Kerala, India: results from a 10-year prospective cohort
| ||Gadadharan Vijayakumar,Sreeja Manghat,Revathi Vijayakumar,Leena Simon,Liss Maria Scaria,Aswathi Vijayakumar,Ganapathy K. Sreehari,V. Raman Kutty,Arun Rachana,Abdul Jaleel |
| ||BMC Public Health. 2019; 19(1) |
|[Pubmed] | [DOI]|
||Association of obesity with hypertension and type 2 diabetes mellitus in India: A meta-analysis of observational studies
| ||Giridhara R Babu,GVS Murthy,Yamuna Ana,Prital Patel,Deepa R,Sara E Benjamin Neelon,Sanjay Kinra,K Srinath Reddy |
| ||World Journal of Diabetes. 2018; 9(1): 40 |
|[Pubmed] | [DOI]|
||Measuring adolescent friendly health services in India: A scoping review of evaluations
| ||Andrea J. Hoopes,Paras Agarwal,Sheana Bull,Venkatraman Chandra-Mouli |
| ||Reproductive Health. 2016; 13(1) |
|[Pubmed] | [DOI]|
||Dengue Contingency Planning: From Research to Policy and Practice
| ||Silvia Runge-Ranzinger,Axel Kroeger,Piero Olliaro,Philip J. McCall,Gustavo Sánchez Tejeda,Linda S. Lloyd,Lokman Hakim,Leigh R. Bowman,Olaf Horstick,Giovanini Coelho,Duane J. Gubler |
| ||PLOS Neglected Tropical Diseases. 2016; 10(9): e0004916 |
|[Pubmed] | [DOI]|
||Cross-sectional Serosurvey of Crimean-Congo Hemorrhagic Fever Virus IgG in Livestock, India, 2013–2014
| ||Devendra T. Mourya,Pragya D. Yadav,Anita M. Shete,Padmakar S. Sathe,Prasad C. Sarkale,Bramhadev Pattnaik,Gaurav Sharma,Kamlesh J. Upadhyay,Surekha Gosavi,Deepak Y. Patil,Gouri Y. Chaubal,Triparna D. Majumdar,Vishwa M. Katoch |
| ||Emerging Infectious Diseases. 2015; 21(10): 1837 |
|[Pubmed] | [DOI]|