Metabolic non-communicable disease health report of India: the ICMR-INDIAB national cross-sectional study (ICMR-INDIAB-17).

BACKGROUND: Non-communicable disease (NCD) rates are rapidly increasing in India with wide regional variations. We aimed to quantify the prevalence of metabolic NCDs in India and analyse interstate and inter-regional variations. METHODS: The Indian Council of Medical Research-India Diabetes (ICMR-INDIAB) study, a cross-sectional population-based survey, assessed a representative sample of individuals aged 20 years and older drawn from urban and rural areas of 31 states, union territories, and the National Capital Territory of India. We conducted the survey in multiple phases with a stratified multistage sampling design, using three-level stratification based on geography, population size, and socioeconomic status of each state. Diabetes and prediabetes were diagnosed using the WHO criteria, hypertension using the Eighth Joint National Committee guidelines, obesity (generalised and abdominal) using the WHO Asia Pacific guidelines, and dyslipidaemia using the National Cholesterol Education Program-Adult Treatment Panel III guidelines. FINDINGS: A total of 113 043 individuals (79 506 from rural areas and 33 537 from urban areas) participated in the ICMR-INDIAB study between Oct 18, 2008 and Dec 17, 2020. The overall weighted prevalence of diabetes was 11·4% (95% CI 10·2-12·5; 10 151 of 107 119 individuals), prediabetes 15·3% (13·9-16·6; 15 496 of 107 119 individuals), hypertension 35·5% (33·8-37·3; 35 172 of 111 439 individuals), generalised obesity 28·6% (26·9-30·3; 29 861 of 110 368 individuals), abdominal obesity 39·5% (37·7-41·4; 40 121 of 108 665 individuals), and dyslipidaemia 81·2% (77·9-84·5; 14 895 of 18 492 of 25 647). All metabolic NCDs except prediabetes were more frequent in urban than rural areas. In many states with a lower human development index, the ratio of diabetes to prediabetes was less than 1. INTERPRETATION: The prevalence of diabetes and other metabolic NCDs in India is considerably higher than previously estimated. While the diabetes epidemic is stabilising in the more developed states of the country, it is still increasing in most other states. Thus, there are serious implications for the nation, warranting urgent state-specific policies and interventions to arrest the rapidly rising epidemic of metabolic NCDs in India. FUNDING: Indian Council of Medical Research and Department of Health Research, Ministry of Health and Family Welfare, Government of India.

Achievement of guideline recommended diabetes treatment targets and health habits in people with self-reported diabetes in India (ICMR-INDIAB-13): a national cross-sectional study.

BACKGROUND: There is little information on comprehensive diabetes care comprising glycaemic, lipid, and blood pressure control in India; therefore, we aimed to assess the achievement of treatment targets among adults with self-reported diabetes. METHODS: The Indian Council of Medical Research (ICMR)-India Diabetes (INDIAB) study is a cross-sectional, population-based survey of adults aged 20 years or older in all 30 states and union territories of India. We used a stratified multistage sampling design, sampling states in a phased manner, and selected villages in rural areas and census enumeration blocks in urban areas. We used a three-level stratification method on the basis of geography, population size, and socioeconomic status for each state. For the outcome assessment, good glycaemic control was defined as HbA1c of less than 7·0% (A), blood pressure control was defined as less than 140/90 mm Hg (B), and the LDL cholesterol target was defined as less than 100 mg/dL (C). ABC control was defined as the proportion of individuals meeting glycaemic, blood pressure, and LDL cholesterol targets together. We also performed multiple logistic regression to assess the factors influencing achievement of diabetes treatment targets. FINDINGS: Between Oct 18, 2008, and Dec 17, 2020, 113 043 individuals (33 537 from urban areas and 79 506 from rural areas) participated in the ICMR-INDIAB study. For this analysis, 5789 adults (2633 in urban areas and 3156 in rural areas) with self-reported diabetes were included in the study population. The median age was 56·1 years (IQR 55·7-56·5). Overall, 1748 (weighted proportion 36·3%, 95% CI 34·7-37·9) of 4834 people with diabetes achieved good glycaemic control, 2819 (weighted proportion 48·8%, 47·2-50·3) of 5698 achieved blood pressure control, and 2043 (weighted proportion 41·5%, 39·9-43·1) of 4886 achieved good LDL cholesterol control. Only 419 (weighted proportion 7·7%) of 5297 individuals with self-reported diabetes achieved all three ABC targets, with significant heterogeneity between regions and states. Higher education, male sex, rural residence, and shorter duration of diabetes (<10 years) were associated with better achievement of combined ABC targets. Only 951 (weighted proportion 16·7%) of the study population and 227 (weighted proportion 36·9%) of those on insulin reported using self-monitoring of blood glucose. INTERPRETATION: Achievement of treatment targets and adoption of healthy behaviours remains suboptimal in India. Our results can help governments to adopt policies that prioritise improvement of diabetes care delivery and surveillance in India. FUNDING: Indian Council of Medical Research and Department of Health Research, Ministry of Health and Family Welfare.

Implementation and Analysis of Classification Algorithms for Diabetes.

BACKGROUND: In this era of cutting edge research, though one of the ubiquitous facilities accessible to modern man is state of the art medical care yet diabetes has emerged as one of the major ailments afflicting the present generation. So the prime necessity of this age has transformed into providing cheap and sustainable medical care against such major diseases like diabetes. In layman's terms Diabetes may be defined as a physiological condition wherein the blood glucose level is more than the prescribed level on a regular basis. OBJECTIVES: So the prime objective of this work is to provide a novel classification technique for detection of diabetes in a timely and effective manner. METHODS: The proposed work comprises of four phases: In the first phase a "Localized Diabetes Dataset" has been compiled and collected from Bombay Medical Hall, Mahabir Chowk, Pyada Toli, Upper Bazar, Jharkhand, Ranchi, India. In the second phase various classification techniques namely RBF NN, MLP NN, NBs, and J48graft DT have been applied on the Localized Diabetes Dataset. In the third phase, Genetic algorithm (GA) has been utilized as an attribute selection technique through which six attributes among twelve attributes have been filtered. Lastly in the fourth phase RBF NN, MLP NN, NBs and J48graft DT has been utilized for classification on relevant attributes obtained by GA. RESULTS: In this study, comparative analysis of outcomes obtained by with and without the use of GA for the same set of classification technique has been done w.r.t performance assessment. It has been conclusively inferred that GA is helpful in removing insignificant attributes, reducing the cost and computation time while enhancing ROC and accuracy. CONCLUSION: The utilized strategy may likewise be executed for other medical issues.

Comparative Analysis of Classification Methods with PCA and LDA for Diabetes.

BACKGROUND: The modern society is extremely prone to many life-threatening diseases, which can be easily controlled as well as cured if diagnosed at an early stage. The development and implementation of a disease diagnostic system have gained huge popularity over the years. In the current scenario, there are certain factors such as environment, sedentary lifestyle, genetic (hereditary) are the major factors behind the life threatening diseases such as 'diabetes.' Moreover, diabetes has achieved the status of the modern man's leading chronic disease. So one of the prime needs of this generation is to develop a state-of-the-art expert system which can predict diabetes at a very early stage with a minimum of complexity and in an expedited manner. The primary objective of this work is to develop an indigenous and efficient diagnostic technique for detection of diabetes. Method & Discussion: The proposed methodology comprises of two phases: In the first phase The Pima Indian Diabetes Dataset (PIDD) has been collected from the UCI machine learning repository databases and Localized Diabetes Dataset (LDD) has been gathered from Bombay Medical Hall, Upper Bazar Ranchi, Jharkhand, India. In the second phase, the dataset has been processed through two different approaches. The first approach entails classification through Adaboost, Classification via Regression (CVR), Radial Basis Function Network (RBFN), K-Nearest Neighbor (KNN) on Pima Indian Diabetes Dataset and Localized Diabetes Dataset. In the second approach, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) have been applied as a feature reduction method followed by using the same set of classification methods used in the first approach. Among all of the implemented classification methods, PCA_CVR achieves the maximum performance for both the above mentioned datasets. CONCLUSION: In this article, comparative analysis of outcomes obtained by with and without the use of PCA and LDA for the same set of classification method has been done w.r.t performance assessment. Finally, it has been concluded that PCA & LDA both are useful to remove the insignificant features, decreasing the expense and computation time while improving the ROC and accuracy. The used methodology may similarly be applied to other medical diseases.

Recommendations for in-clinic PoCT for diabetes management in India.

A panel of expert diabetologist clinicians developed consensus standards to address the quality gaps inclinic point of care testing (PoCT) especially pertaining to diabetes care and management in India. The following summarized principles were established- 1. PoCT definition, 2. Advantages and critical aspects of PoCT including guideline recommendations and accreditations, analytical factors (pre &post analytical included) and consensus reached for an ideal PoC analyzer and 3. Key recommendations on in-clinic PoCT implementation by the panel. The experts suggested next steps that included key comparative (PoCT vs NGSP accredited lab) and patient benefit studies on PoCT.