Population based screening for diabetes: experience in Mumbai slums, Maharashtra, India

Dyavarishetty, Padmavathi V.; Kowli, Shobha S.

doi:10.18203/2320-6012.ijrms20161947

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…Out of the total 214 individuals, the proportion of males and females was 78% and 22% in the current study. Similar results were found in a study conducted by Dyavarishetty 6 et al in Mumbai where males comprised 79% of the sample and females 21%. Although in a study 7 conducted for screening at a workplace in India, the representation of males in the sample was 39% and that of females was as high as 61%, this can be explained due to wide difference in the study setting.…”

Section: Discussionsupporting

confidence: 90%

Prevalence of Type 2 Diabetes and Hypertension: Opportunistic Screening in Delhi

Khobragade

Bharti

Jaikaran

et al. 2020

JoARM

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Section: Discussionsupporting

confidence: 90%

Prevalence of Type 2 Diabetes and Hypertension: Opportunistic Screening in Delhi

Khobragade

Bharti

Jaikaran

et al. 2020

JoARM

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“…The higher percentage is because the screening for diabetes and confirmation were carried out in the hospital setting. Another study that employed a population screening approach reported that <1% underwent confirmation [10]. The number screened and at high risk was substantially higher in our study than the three mentioned above.…”

Section: Discussioncontrasting

confidence: 51%

Are people at high risk for diabetes visiting health facility for confirmation of diagnosis? A population-based study from rural India

Venkateshmurthy

Kathirvel

Gummidi

et al. 2018

Global Health Action

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Background: India is witnessing a rising burden of type 2 diabetes mellitus. India’s National Programme for Prevention and Control of Diabetes, Cancer, Cardiovascular diseases and Stroke recommends population-based screening and referral to primary health centre for diagnosis confirmation and treatment initiation. However, little is known about uptake of confirmatory tests among screen positives. Objective: To estimate the uptake of confirmatory tests and identify the reasons for not undergoing confirmation by those at high risk for developing diabetes. Methods: We analysed data collected under project UDAY, a comprehensive diabetes and hypertension prevention and management programme, being implemented in rural Andhra Pradesh, India. Under UDAY, population-based screening for diabetes was carried out by project health workers using a diabetes risk score and capillary blood glucose test. Participants at high risk for diabetes were asked to undergo confirmatory tests. On follow-up visit, health workers assessed if the participant had undergone confirmation and ask for reasons if not so. Results: Of the 35,475 eligible adults screened between April 2015 and August 2016, 10,960 (31%) were determined to be at high risk. Among those at high risk, 9670 (88%) were followed up, and of those, only 616 (6%) underwent confirmation. Of those who underwent confirmation, ‘lack of symptoms of diabetes warranting visit to health facility’ (52%) and ‘being at high risk was not necessary enough to visit’ (41%) were the most commonly reported reasons for non-confirmation. Inconvenient facility time (4.4%), no nearby facility (3.2%), un-affordability (2.2%) and long waiting time (1.6%) were the common health system-related factors that affected the uptake of the confirmatory test. Conclusion: Confirmation of diabetes was abysmally low in the study population. Low uptake of the confirmatory test might be due to low ‘risk perception’. The uptake can be increased by improving the population risk perception through individual and/or community-focused risk communication interventions.

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“…Targeted screening strategies developed for LMICs must be implemented in settings where primary care health services are often under-resourced and/or are built within the framework of community outreach rather than in-clinic consultations. Community health-worker facilitated ‘all comers’ screening programmes for diabetes and hypertension have been variably instituted in parts of India, Brazil and Uganda,9–11 but few CKD screening strategies exist to facilitate appropriate referrals for confirmatory testing 12 13…”

Section: Introductionmentioning

confidence: 99%

Early detection of chronic kidney disease in low-income and middle-income countries: development and validation of a point-of-care screening strategy for India

et al. 2019

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IntroductionAlthough deaths due to chronic kidney disease (CKD) have doubled over the past two decades, few data exist to inform screening strategies for early detection of CKD in low-income and middle-income countries.MethodsUsing data from three population-based surveys in India, we developed a prediction model to identify a target population that could benefit from further CKD testing, after an initial screening implemented during home health visits. Using data from one urban survey (n=8698), we applied stepwise logistic regression to test three models: one comprised of demographics, self-reported medical history, anthropometry and point-of-care (urine dipstick or capillary glucose) tests; one with demographics and self-reported medical history and one with anthropometry and point-of-care tests. The ‘gold-standard’ definition of CKD was an estimated glomerular filtration rate <60 mL/min/1.73 m2 or urine albumin-to-creatinine ratio ≥30 mg/g. Models were internally validated via bootstrap. The most parsimonious model with comparable performance was externally validated on distinct urban (n=5365) and rural (n=6173) Indian cohorts.ResultsA model with age, sex, waist circumference, body mass index and urine dipstick had a c-statistic of 0.76 (95% CI 0.75 to 0.78) for predicting need for further CKD testing, with external validation c-statistics of 0.74 and 0.70 in the urban and rural cohorts, respectively. At a probability cut-point of 0.09, sensitivity was 71% (95% CI 68% to 74%) and specificity was 70% (95% CI 69% to 71%). The model captured 71% of persons with CKD and 90% of persons at highest risk of complications from untreated CKD (ie, CKD stage 3A2 and above).ConclusionA point-of-care CKD screening strategy using three simple measures can accurately identify high-risk persons who require confirmatory kidney function testing.

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Population based screening for diabetes: experience in Mumbai slums, Maharashtra, India

Cited by 6 publications

References 6 publications

Prevalence of Type 2 Diabetes and Hypertension: Opportunistic Screening in Delhi

Prevalence of Type 2 Diabetes and Hypertension: Opportunistic Screening in Delhi

Are people at high risk for diabetes visiting health facility for confirmation of diagnosis? A population-based study from rural India

Early detection of chronic kidney disease in low-income and middle-income countries: development and validation of a point-of-care screening strategy for India

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