As per the results of a large scale survey conducted across India, over ** percent of the respondents who had diabetes in 2021 were above 60 years of age. Notably, about *** percent of respondents in the 20 to 29 year old age bracket also reported to have diabetes that year. This was a worrying trend and was linked with an unhealthy lifestyle.

According to a survey conducted between 2019 and 2021 in India, about **** percent of women respondents aged 35 to 49 years old reported having diabetes. At the same time, *** percent of women in the 15 to 19 years old age group had diabetes.

According to a survey conducted in 2025 in India, the prevalence of diabetes increased sharply with age for both women and men. Indian men aged 55 years and above are slightly more prone to diabetes than women of the same age group.

This dataset, "Diabetes in Young Adults in India", contains 100,000 records of synthetic but realistic data reflecting the prevalence of diabetes and associated factors among young adults (ages 15-25) in India. The data is designed to capture genetic predispositions, lifestyle habits, and key health metrics that influence the onset of diabetes in this demographic.

The dataset includes columns for demographic details, genetic risk factors, lifestyle habits, health metrics, and diabetes outcomes. It offers opportunities for exploring trends, patterns, and correlations that contribute to diabetes onset in young populations.

Insights You Can Derive from This Dataset:

1) Risk Factors Analysis:

Evaluate how genetic predisposition (e.g., family history, parental diabetes) contributes to diabetes onset.

Analyze the impact of lifestyle factors (e.g., BMI, physical activity, dietary habits) on diabetes risk.

2) 3)3Diabetes Prediction:

Build predictive models for identifying individuals at high risk of prediabetes or Type 2 diabetes.

Health Metrics Correlation:

Examine correlations between fasting blood sugar, HbA1c levels, and cholesterol with diabetes types.

Investigate how stress levels, sleep hours, and screen time influence health outcomes.

3) Demographic Trends:

Identify regional variations in diabetes prevalence.

Study differences in diabetes onset between genders or income groups.

4) Behavioral Insights:

Analyze how fast food intake, smoking, and alcohol consumption relate to diabetes outcomes.

Determine the combined effects of physical activity and dietary habits on health metrics.

This statistic represents the prevalence of diabetes among males across India in 2016, by age group. Men between 75 and 79 years old had diabetes prevalence of **** percent, the highest share among other age groups during the measured time period.

ObjectiveIndia has the second largest diabetic population in the world. The chronic nature of the disease and high prevalence of co-existing chronic medical conditions or “co morbidities” makes diabetes management complex for the patient and for health care providers. Hence a strong need was felt to explore the problem of co morbidity among diabetics and its dimensions in primary health care practices.MethodThis cross sectional survey was carried out on 912 type 2 diabetes patients attending different urban primary health care facilities at Bhubaneswar. Data regarding existence of co morbidity and demographical details were elicited by a predesigned, pretested questionnaire“Diabetes Co morbidity Evaluation Tool in Primary Care (DCET- PC)”. Statistical analyses were done using STATA.ResultsOverall 84% had one ormore than one comorbid condition. The most frequent co morbid conditions were hypertension [62%], acid peptic disease [28%], chronic back ache [22%] and osteoarthritis [21%]. The median number of co morbid conditions among both males and females is 2[IQR = 2]. The range of the number of co morbid conditions was wider among males [0–14] than females [0–6]. The number of co morbidities was highest in the age group > = 60 across both sexes. Most of the male patients below 40 years of age had either single [53%] or three co morbidities [11%] whereas among female patients of the same age group single [40%] or two co morbidities [22%] were more predominantly present. Age was found to be a strong independent predictor for diabetes co morbidity. The odds of having co morbidity among people above poverty line and schedule caste were found to be[OR = 3.50; 95%CI 1.85–6.62]and [OR = 2.46; CI 95%1.16–5.25] respectively. Odds were increased for retired status [OR = 1.21; 95% CI 1.01–3.91] and obesity [OR = 3.96; 95%CI 1.01–15.76].ConclusionThe results show a high prevalence of co morbidities in patients with type 2 diabetes attending urban primary health care facilities. Hypertension, acid peptic disease, chronic back ache and arthritis being the most common, strategies need to be designed taking into account the multiple demands of co morbidities.

This dataset is designed to support researchers, data scientists, and healthcare professionals in predicting and analyzing diabetes prevalence and risk factors among the Indian population. It incorporates a diverse range of demographic, lifestyle, and clinical attributes to ensure a holistic representation of potential diabetes determinants. The dataset's features include:

Demographics: Age, gender, urban/rural residence, and pregnancies (specific to women). Lifestyle Factors: Physical activity, diet type, smoking status, alcohol intake, and stress levels. Medical History: Family history of diabetes, hypertension, thyroid conditions, and regular checkups. Clinical Metrics: BMI, cholesterol levels, fasting and postprandial blood sugar, HBA1C, vitamin D levels, and more. Target Variable: Binary diabetes status (Yes/No).

A cross-sectional study was conducted in Dharwad district, India including 1600 students between 11-17 years age group, across both private and government schools located in both rural and urban areas; information on socio-demographic variables, physical activity, dietary habits, substance abuse, and family history of hypertension, diabetes mellitus and level of stress among participants was collected. Anthropometric measurements were taken, blood pressure was measured, general physical examination was done to look for signs of insulin resistance. Urine was examined for the presence of glucose using Urine Glucose Strips in overweight children with ≥2 risk factors (ADA criteria for children).

The India Oral Anti-Diabetic Drug Market is Segmented by Drug Class (Biguanides, Sulfonylureas, Meglitinides, and More), Age Group (Adults, Pediatric, and Geriatric), Diabetes Type (Type 1 Diabetes and Type 2 Diabetes), Distribution Channel (Hospital Pharmacies, Retail Pharmacies, and Online Pharmacies). The Market and Forecasts are Provided in Terms of Value (USD).

In 2021, India ranked second in the world with about **** million diabetic adults in the 20 to 79 years old age group. The number of diabetic individuals was projected to increase to over *** million in 2045.

Study participant details having both HTN and DM as per NFHS 4 and NFHS 5.

BackgroundThe prevalence of diabetes is increasing rapidly in low- and middle-income countries (LMICs), urgently requiring detailed evidence to guide the response of health systems to this epidemic. In an effort to understand at what step in the diabetes care continuum individuals are lost to care, and how this varies between countries and population groups, this study examined health system performance for diabetes among adults in 28 LMICs using a cascade of care approach.Methods and findingsWe pooled individual participant data from nationally representative surveys done between 2008 and 2016 in 28 LMICs. Diabetes was defined as fasting plasma glucose ≥ 7.0 mmol/l (126 mg/dl), random plasma glucose ≥ 11.1 mmol/l (200 mg/dl), HbA1c ≥ 6.5%, or reporting to be taking medication for diabetes. Stages of the care cascade were as follows: tested, diagnosed, lifestyle advice and/or medication given (“treated”), and controlled (HbA1c < 8.0% or equivalent). We stratified cascades of care by country, geographic region, World Bank income group, and individual-level characteristics (age, sex, educational attainment, household wealth quintile, and body mass index [BMI]). We then used logistic regression models with country-level fixed effects to evaluate predictors of (1) testing, (2) treatment, and (3) control. The final sample included 847,413 adults in 28 LMICs (8 low income, 9 lower-middle income, 11 upper-middle income). Survey sample size ranged from 824 in Guyana to 750,451 in India. The prevalence of diabetes was 8.8% (95% CI: 8.2%–9.5%), and the prevalence of undiagnosed diabetes was 4.8% (95% CI: 4.5%–5.2%). Health system performance for management of diabetes showed large losses to care at the stage of being tested, and low rates of diabetes control. Total unmet need for diabetes care (defined as the sum of those not tested, tested but undiagnosed, diagnosed but untreated, and treated but with diabetes not controlled) was 77.0% (95% CI: 74.9%–78.9%). Performance along the care cascade was significantly better in upper-middle income countries, but across all World Bank income groups, only half of participants with diabetes who were tested achieved diabetes control. Greater age, educational attainment, and BMI were associated with higher odds of being tested, being treated, and achieving control. The limitations of this study included the use of a single glucose measurement to assess diabetes, differences in the approach to wealth measurement across surveys, and variation in the date of the surveys.ConclusionsThe study uncovered poor management of diabetes along the care cascade, indicating large unmet need for diabetes care across 28 LMICs. Performance across the care cascade varied by World Bank income group and individual-level characteristics, particularly age, educational attainment, and BMI. This policy-relevant analysis can inform country-specific interventions and offers a baseline by which future progress can be measured.

With diabetes, your body either doesn't make enough insulin or can't use it as well as it should. Diabetes is a chronic (long-lasting) health condition that affects how your body turns food into energy. Most of the food you eat is broken down into sugar (also called glucose) and released into your bloodstream.

Women age 15 years and above with high (141-160 mg/dl) Blood sugar level23 (%)

Women age 15 years and above wih very high (>160 mg/dl) Blood sugar level23 (%)

Women age 15 years and above wih high or very high (>140 mg/dl) Blood sugar level or taking

medicine to control blood sugar level23 (%)

Men age 15 years and above wih high (141-160 mg/dl) Blood sugar level23 (%)

Men (age 15 years and above wih very high (>160 mg/dl) Blood sugar level23 (%)

Men age 15 years and above wih high or very high (>140 mg/dl) Blood sugar level or taking medicine to control

blood sugar level23 (%)

Multivariate logistic regression analysis for various variables with adjusted OR like age, BMI and marital status with 95 CI for having HTN, diabetes and both of NFHS-4 and NFHS -5.

Abstract

One of the goals of the Study on Global Ageing and Adult Health (SAGE) is to examine and measure the relationship between health and well-being. SAGE is the first study to measure and distinguish between two concepts of well-being: experiential and evaluative, in low and middle income countries. The short version WHO Quality of Life (WHOQOL) instrument was used to measure evaluative well-being. An adapted version of the Day Reconstruction Method (DRM) was used to measure emotional or experiential well-being. However further substantiation for measuring emotional well-being and adjusting for systematic biases in self-report is required.

Objectives: 1. To compare short versus fullday version of the DRM questionnaire 2. Reproducibility of the questionnaire: test-retest 3. To compare self-report to biological markers of stress and negative emotions

Methods: The short and fullday versions of the DRM questionnaires were implemented in Jodhpur, Rajasthan, India. The target sample size was 1560 respondents, 200 respondents on Monday through Saturday and 360 respondents on Sunday. The version of the short questionnaire was randomly assigned to each selected respondent: short "morning" (Set A) short "afternoon" (Set B) short "evening" (Set C) short "full day" (Set D).
Each set was assigned to 390 respondents.
The interviewer returned to the same household and respondent one or two weeks later on the same weekday or weekend day for a second interview. On this occasion the full day DRM questionnaire was administered. Interviewers were asked about the previous day (i.e. day prior to the interview) in each interview. Duration of the short version module of the DRM was at most 15 minutes of interview time in total.

Blood samples: Blood samples were collected after the final interview by dry blood spot(DBS) and venepuncture (i.e. 2 blood samples per respondent - one DBS and one test tube). Protocols for the DBS and venepuncture followed procedures previously used for the SAGE pilot and full survey. The blood samples will be examined for fibrinogen, while also assessing the characteristics of DBS versus venepuncture.

Review of hospital records: Each respondent was asked if the survey team would be allowed to review their hospital records. Two groups of subjects will be selected and their records reviewed: 1. positives: individuals with a previous diagnosis of any of the 8 conditions included in the fullday questionnaire: arthritis; angina; diabetes; chronic lung disease; asthma; depression; hypertension; cataracts. 2. negatives: matched individuals free of all 8 conditions. This would allow us to determine the accuracy, reliability and predictive values of self-reported health conditions as part of this survey.

Content: - Short questionnaire Section 0100: Contact Record Section 0500: Housing Section 0700: Assets and Household Income Section 1000: Socio-Demographic Characteristics Section 2000: Health State Descriptions Section 7000: Subjective Well-Being and Quality of Life (Set A, B, C or D)

• Full Day questionnaire Section 0100: Contact Record Section 4000: Chronic Conditions and Health Services Coverage Section 7000: Subjective Well-Being and Quality of Life

Geographic coverage

Jodhpur, Rajastan, India

Analysis unit

individuals

Universe

The survey covered all de jure household members (usual residents), aged 18 years and above.

Kind of data

Sample survey data [ssd]

Sampling procedure

Target sample was 1560 randomly selected respondents:1150 aged 50 years and older and 410 18-49 years. The sample was stratified by urbanicity, age-groups and sex. There was oversampling of persons aged 70 years and older.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the DRM Validation Study were structured questionnaires based on the Study on Ageing and Adult Health (SAGE) instruments with some modifications and additions. Questionnaires were developed in English and translated into Hindi.

In 2021, it was estimated that China had about 141 million diabetics aged from 20 to 79 years, which was the highest number of any country. The figure would very likely climb to 174 million by 2045. Diabetes is one of the leading death causes across the globe.

An overview of diabetes

Diabetes mellitus, commonly known as diabetes, is an incurable chronic health condition in which dangerously high levels of glucose flood the body due to the lack of insulin production (type 1 diabetes) or the body’s inability to use insulin to regulate blood sugar levels (type 2 and gestational diabetes). Globally, the number of people suffering from this chronic disease amounted to 537 million in 2021. The largest number of diabetics were from China, followed by India and Pakistan in that year. In terms of diabetes prevalence, French Polynesia, Mauritius, and Kuwait had the highest rates. With regard to diabetes-related health expenditure, China alone spent over half of the amount spent by the entire Western Pacific region.

Key figures of diabetes in China

Back in the 1980s, less than one percent of the Chinese population was said to have diabetes. In the recent decade, the prevalence rate has jumped to an alarming level, and about one in five of all adult diabetes sufferers worldwide were in China. Records from 2021 show that most of such patients in the country fell within the age group of 20 to 79 years - mainly type 2 diabetes. Some experts point out the nation’s economic growth coupled with unhealthy diets and reduced physical activity as major risk factors which cause type 2 diabetes. It is worth noting that the awareness and control rates of diabetes were relatively low in China compared with the situations in other strong economies.

Context

This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage.

Content

The datasets consist of several medical predictor variables and one target variable, Outcome. Predictor variables includes the number of pregnancies the patient has had, their BMI, insulin level, age, and so on.

Acknowledgements

I thank the researchers who gave their precious time to make this dataset

Inspiration

You are on world's largest data science community platform. Just practice using this dat aset to level up your game.

Context This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage.

Content The datasets consists of several medical predictor variables and one target variable, Outcome. Predictor variables includes the number of pregnancies the patient has had, their BMI, insulin level, age, and so on.

Acknowledgements Smith, J.W., Everhart, J.E., Dickson, W.C., Knowler, W.C., & Johannes, R.S. (1988). Using the ADAP learning algorithm to forecast the onset of diabetes mellitus. In Proceedings of the Symposium on Computer Applications and Medical Care (pp. 261--265). IEEE Computer Society Press.

Inspiration Can you build a machine learning model to accurately predict whether or not the patients in the dataset have diabetes or not?

According to a survey conducted in 2021 in India, lifestyle diseases such as diabetes, cholesterol, and blood pressure were higher among older adults and seniors. Among seniors aged 60 years and above, ** percent were suffering from high blood pressure.

India IN: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data was reported at 3.700 % in 2024. This records an increase from the previous number of 3.400 % for 2023. India IN: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data is updated yearly, averaging 2.300 % from Dec 2000 (Median) to 2024, with 25 observations. The data reached an all-time high of 3.700 % in 2024 and a record low of 2.100 % in 2013. India IN: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WDI: Social: Health Statistics. Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME).;Weighted average;Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues. Estimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates.