Diabetes: A review of awareness, comorbidities, and quality of life in India

Bansode, Balasaheb; Nagarajan, R.

doi:10.1055/s-0038-1676248

Cited by 14 publications

(7 citation statements)

References 23 publications

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“…[3] It still finds itself loitering behind other countries on health care outcomes as the response from the government has not been resilient. [4] This is probably due to the large size of the population which generate difficulties for the effective availability of health services to all. Diabetes is among the most common chronic NCD affecting both developed and developing countries, including India.…”

Section: Introductionmentioning

confidence: 99%

Diabetes-free life in India: Application of survival function analysis

Singh

Pedgaonkar

Puri

et al. 2018

Journal of Social Health and Diabetes

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Background:India being a diverse country has an assortment of various socio-economic and demographic structures. These socio-economic and demographic factors often intervene with the diabetes-free survival in any geographical region. The present study explores diabetes-free survival patterns of adult respondents with respect to various socio-economic and demographic factors in eighteen states of India with better demographic indicators. Material and Methods:The study utilizes data from the fourth round of the District Level Household and Facility Survey (DLHS) undertaken during 2012–2013. The study computes the survival functions by five year age groups followed by Peto and Pike's test for assessing the significant differences in the survival pattern between the various sub-groups in 18 states of India. Results:The study points out that the diabetes-free survival of a respondent is affected by factors such as the place of residence, sex, social group, the standard of living (wealth) index, and region of residence. Conclusions:Timely detection, screening, and treatment of non-communicable diseases, as well as palliative care, are recommended to the community for diabetes prevention. A healthy lifestyle including healthy diet, physical activity on regular basis, and avoiding tobacco needs to be promoted.

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Section: Introductionmentioning

confidence: 99%

Diabetes-free life in India: Application of survival function analysis

Singh

Pedgaonkar

Puri

et al. 2018

Journal of Social Health and Diabetes

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“…India faces the largest burden of noncommunicable diseases in the world with an estimated 73 million reported with diabetes and over 400 million people reported with hypertension [ 2 , 3 ]. Moreover, an estimated 58% of the patients with diabetes and 60%-75% of the patients with hypertension in India are undiagnosed, thereby creating a population health crisis [ 4 ]. Early detection via screening and subsequent treatment initiation can significantly reduce the burden of both diabetes and hypertension [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Risk Stratification for Early Detection of Diabetes and Hypertension in Resource-Limited Settings: Machine Learning Analysis

Boutilier¹,

Chan²,

Ranjan³

et al. 2021

J Med Internet Res

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Background The impending scale up of noncommunicable disease screening programs in low- and middle-income countries coupled with limited health resources require that such programs be as accurate as possible at identifying patients at high risk. Objective The aim of this study was to develop machine learning–based risk stratification algorithms for diabetes and hypertension that are tailored for the at-risk population served by community-based screening programs in low-resource settings. Methods We trained and tested our models by using data from 2278 patients collected by community health workers through door-to-door and camp-based screenings in the urban slums of Hyderabad, India between July 14, 2015 and April 21, 2018. We determined the best models for predicting short-term (2-month) risk of diabetes and hypertension (a model for diabetes and a model for hypertension) and compared these models to previously developed risk scores from the United States and the United Kingdom by using prediction accuracy as characterized by the area under the receiver operating characteristic curve (AUC) and the number of false negatives. Results We found that models based on random forest had the highest prediction accuracy for both diseases and were able to outperform the US and UK risk scores in terms of AUC by 35.5% for diabetes (improvement of 0.239 from 0.671 to 0.910) and 13.5% for hypertension (improvement of 0.094 from 0.698 to 0.792). For a fixed screening specificity of 0.9, the random forest model was able to reduce the expected number of false negatives by 620 patients per 1000 screenings for diabetes and 220 patients per 1000 screenings for hypertension. This improvement reduces the cost of incorrect risk stratification by US $1.99 (or 35%) per screening for diabetes and US $1.60 (or 21%) per screening for hypertension. Conclusions In the next decade, health systems in many countries are planning to spend significant resources on noncommunicable disease screening programs and our study demonstrates that machine learning models can be leveraged by these programs to effectively utilize limited resources by improving risk stratification.

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“…Unfortunately, patient awareness of chronic diseases is reportedly low (Cavanaugh et al., 2008). Moreover, even if most patients are aware of their primary medical diagnosis, a large number may not be aware that they have comorbidities (Bansode & Nagarajan, 2017). Those who are affected by dementia (Fox et al., 2014; Scrutton & Brancati, 2016) or are less educated (Hoffmann et al., 2018) are compromised in their ability to provide timely reports to their formal caregivers about their comorbid health problems.…”

Section: Introductionmentioning

confidence: 99%

The use of functional performance tests and simple anthropomorphic measures to screen for comorbidity in primary care

Nevill

Duncan

Cheung

et al. 2020

Int J Older People Nursing

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Background: Many older adults are unaware that they have comorbid diseases. Increased adiposity and reduced muscle mass are identified as key contributors to many chronic diseases in older adults. Understanding the role they play in the development of comorbidities in older populations is of prime importance. Objectives: To identify the optimal body shape associated with three common functional performance tests and to determine which anthropometric and functional performance test best explains comorbidity in a sample of older adults in Hong Kong. Methods: A total of 432 older adults participated in this cross-sectional study. Researchers assessed their body height, body mass index, waist circumference, waist-to-hip ratio, handgrip strength (kg), functional reach (cm) and results in the timed-up-and-go (TUG) test (seconds). The Charlson Comorbidity Index was used to assess comorbidity. Results: Allometric modelling indicated that the optimal body shape associated with all functional performance tests would have required the participants to be taller and leaner. The only variable that predicted comorbidity was the TUG test. The inclusion of body size/shape variables did not improve the prediction model. Conclusion: Performance in the TUG test alone was found to be capable of identifying participants at risk of developing comorbidities. The TUG test has potential as a screening tool for the early detection of chronic diseases in older adults. Implications for Practice: Many older people are unaware of their own co-existing illnesses when they consult physicians for a medical condition. TUG can be a quick and useful screening measure to alert nurses in primary care to the need to proceed with more detailed assessments. It is an especially useful screening measure in settings with high patient volumes and fiscal constraints. TUG is low cost and easy to learn and is therefore also relevant for nurses and health workers in low-resource, low-income countries.

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Diabetes: A review of awareness, comorbidities, and quality of life in India

Cited by 14 publications

References 23 publications

Diabetes-free life in India: Application of survival function analysis

Diabetes-free life in India: Application of survival function analysis

Risk Stratification for Early Detection of Diabetes and Hypertension in Resource-Limited Settings: Machine Learning Analysis

The use of functional performance tests and simple anthropomorphic measures to screen for comorbidity in primary care

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