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Characteristics of undiagnosed diabetes in men and women under the age of 50 years in the Indian subcontinent: The National Family Health Survey (NFHS-4)/Demographic Health Survey 2015-2016

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Objective Prior studies examining diabetes prevalence in India have found that nearly 50% of the diabetes population remains undiagnosed; however, the specific populations at risk are unclear. Research design and methods First, we estimated the prevalence of undiagnosed diabetes in India for 750 924 persons between the ages of 15 years and 50 years who participated in the National Family Health Survey (NFHS-4)/Demographic Health Survey (2015–2016), a cross-sectional survey of all 29 states and 7 union territories of India. We defined ‘undiagnosed diabetes’ as individuals who did not know about their diabetes status but had high random (≥200 mg/dL) or fasting (≥126 mg/dL) blood glucose levels. Second, using Poisson regression, we associated 10 different factors, including the role of healthcare access, and undiagnosed diabetes. Third, we examined the association of undiagnosed diabetes with other potential comorbid conditions. Results The crude prevalence of diabetes for women and men aged 15–50 years was 2.9%, 95% CI 2.9% to 3.1%, with self-reported diabetes prevalence at 1.7%, 95% CI 1.6 to 1.8. The overall prevalence of undiagnosed diabetes for 15–50 year olds was at 1.2%, 95% CI 1.2% to 1.3%. Forty-two per cent, 95% CI 40.7% to 43.4% of the individuals with high glucose levels were unaware of their diabetes status. Approximately 45%, 95% CI 42.9% to 46.4% of undiagnosed diabetes population had access to healthcare. Men, younger individuals, and those with lower levels of education were most at risk of being undiagnosed. Geographically, the Southern states in India had a significantly higher prevalence of undiagnosed diabetes despite having nearly universal access to healthcare. Risk factors combined with random glucose could predict undiagnosed diabetes (area under the curve of 97.8%, 95% CI 97.7% to 97.8%), Nagelkerke R ² of 66%). Conclusion Close to half (42%) of the people with diabetes in India are not aware of their disease status, and a large subset of these people are at risk of poor detection, despite having health insurance and/or having access to healthcare. Younger age groups and men are the most vulnerable.
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BMJ Open Diab Res Care 2020;8:e000965. doi:10.1136/bmjdrc-2019-000965 1
Open access
Characteristics of undiagnosed diabetes
in men and women under the age of 50
years in the Indian subcontinent: the
National Family Health Survey (NFHS-
4)/Demographic Health Survey 2015–2016
Kajal T Claypool,1,2 Ming- Kei Chung,1 Andrew Deonarine,1 Edward W Gregg,3
Chirag J Patel 1
1Department of Biomedical
Informatics, Harvard Medical
School, Boston, Massachusetts,
USA
2Human Health and
Performance Systems, MIT
Lincoln Laboratory, Lexington,
Massachusetts, USA
3Department of Epidemiology
and Biostatistics, Imperial
College London, London, UK
Correspondence to
Dr Chirag J Patel;
chirag_ patel@ hms. harvard. edu
To cite: ClaypoolKT,
ChungM- K, DeonarineA, etal.
Characteristics of undiagnosed
diabetes in men and women
under the age of 50 years
in the Indian subcontinent:
the National Family Health
Survey (NFHS-4)/Demographic
Health Survey 2015–2016.
BMJ Open Diab Res Care
2020;8:e000965. doi:10.1136/
bmjdrc-2019-000965
Additional material is
published online only. To view
please visit the journal online
(http:// dx. doi. org/ 10. 1136/
bmjdrc- 2019- 000965).
Received 10 October 2019
Revised 4 December 2019
Accepted 4 January 2020
Original research
Cardiovascular and Metabolic Risk
© Author(s) (or their
employer(s)) 2020. Re- use
permitted under CC BY.
Published by BMJ.
Significance of this study
What is already known about this subject?
Evidence from prior studies indicates that diabetes
in India is associated with higher socioeconomic
status populations, predominantly in men and in the
oldest individuals with high conversion rates from
prediabetes to diabetes and from healthy to predi-
abetes (totaling nearly 45.1%).
Evidence on the prevalence and risk factors for un-
diagnosed diabetes in India is limited to specic re-
gions of the country, with the largest study covering
15 out of the 29 states of India. Given the geograph-
ic, ethnic and sociocultural diversity in India, it is dif-
cult to draw any nationwide conclusions.
What are the new ndings?
Based on the largest, nationally representative
survey, the fourth National Family Health Survey/
Demographic Health Survey (NFHS-4/DHS) conduct-
ed in 2015–2016, of women aged 15–49 years and
men aged 15–54 years and covering all 29 states
and 7 union territories in India, our analysis provides
risk factors associated with undiagnosed diabetes in
India and, further, highlights the geographic discrep-
ancies across the states of India. Our ndings further
draw attention to three aspects.
First, 42% of the individuals in India with dia-
betes are unaware of their diabetes status (are
‘undiagnosed’).
Second, there is poor detection of diabetes in
India. Nearly 45% of undiagnosed diabetes indi-
viduals have access to healthcare.
Third, region of the country is a signicant fac-
tor for undiagnosed diabetes more so than urban
versus rural dwelling populations disproportional-
ly effecting men and younger individuals.
ABSTRACT
Objective Prior studies examining diabetes prevalence
in India have found that nearly 50% of the diabetes
population remains undiagnosed; however, the specic
populations at risk are unclear.
Research design and methods First, we estimated the
prevalence of undiagnosed diabetes in India for 750 924
persons between the ages of 15 years and 50 years who
participated in the National Family Health Survey (NFHS-4)/
Demographic Health Survey (2015–2016), a cross- sectional
survey of all 29 states and 7 union territories of India. We
dened ‘undiagnosed diabetes’ as individuals who did not
know about their diabetes status but had high random
(≥200 mg/dL) or fasting (≥126 mg/dL) blood glucose levels.
Second, using Poisson regression, we associated 10
different factors, including the role of healthcare access, and
undiagnosed diabetes. Third, we examined the association of
undiagnosed diabetes with other potential comorbid conditions.
Results The crude prevalence of diabetes for women and
men aged 15–50 years was 2.9%, 95% CI 2.9% to 3.1%, with
self- reported diabetes prevalence at 1.7%, 95% CI 1.6 to 1.8.
The overall prevalence of undiagnosed diabetes for 15–50 year
olds was at 1.2%, 95% CI 1.2% to 1.3%. Forty- two per cent,
95% CI 40.7% to 43.4% of the individuals with high glucose
levels were unaware of their diabetes status. Approximately
45%, 95% CI 42.9% to 46.4% of undiagnosed diabetes
population had access to healthcare. Men, younger individuals,
and those with lower levels of education were most at risk
of being undiagnosed. Geographically, the Southern states in
India had a signicantly higher prevalence of undiagnosed
diabetes despite having nearly universal access to healthcare.
Risk factors combined with random glucose could predict
undiagnosed diabetes (area under the curve of 97.8%, 95% CI
97.7% to 97.8%), Nagelkerke R2 of 66%).
Conclusion Close to half (42%) of the people with diabetes
in India are not aware of their disease status, and a large
subset of these people are at risk of poor detection, despite
having health insurance and/or having access to healthcare.
Younger age groups and men are the most vulnerable.
INTRODUCTION
Diabetes is the ninth leading cause of death
in India.1 2 The International Diabetes Feder-
ation estimates the diabetes cases in India (in
2017) at nearly 73 million persons between
the ages of 20 years and 79 years, a prevalence
of nearly 10.4%.3 4 Half of this population
might be unaware of their diabetes status,3
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Cardiovascular and Metabolic Risk
Figure 1 (A) Overall methodology; (B) cohort selection from the National Family Health Survey/Demographic Health Surveys
(NFHS/DHS) conducted in India in 2015–2016.
Significance of the study
How might these results change the focus of research or
clinical practice?
Our ndings suggest that access to healthcare should potentially be
coupled with routine and rapid low- cost, serendipitous screening
of individuals for high glucose levels. Further renement of these
results to the district level can aid in decision support for individu-
al healthcare providers and tertiary healthcare centers throughout
India to determine how and when to screen for diabetes.
presenting a quandary for policy makers.5 The enormous
size of the population of undiagnosed diabetes with
higher proportions in the under 50 years of age means
that non- identification of such cases before 50 years of
age has the potential to seriously stress the healthcare
system.
In this study, we identify characteristics of individuals
with undiagnosed diabetes and analyze the dichotomy
between poor awareness (undiagnosed diabetes) and
poor detection of diabetes (those that remain undiag-
nosed despite access to healthcare) in participants of a
cross- sectional survey called the fourth Indian National
Family Health Survey (NFHS-4)/Demographic Health
Surveys (DHS). The Indian Ministry of Health and
Family Welfare conducted NFHS-4 between 2015 and
2016 on women of reproductive age (15–49 years) and
their partners (15–54 years). The survey provides essen-
tial information on health and family welfare together
with biometric measurements including height, weight,
blood pressure, and blood glucose levels. We estimated
the prevalences and identified the risk factors associated
with poor awareness and poor detection of diabetes.
Lastly, we examined the burden of undiagnosed diabetes
on other comorbid conditions.
METHODS
The NFHS and DHS 2015–2016
The NFHS-4/DHS conducted in 2015–2016 was designed
to be nationally representative of the household popula-
tion of women aged 15–49 years and men aged 15 –54
years covering all 29 states and 7 union territories in
India.6 Participants were surveyed from 20 January 2015
to 4 December 2016. The survey used the 2011 Census
of India as the sampling frame, with a two- stage sample
stratification. The primary sampling units were villages
in rural areas and the census enumeration blocks in
urban areas and were selected with a probability propor-
tional to the size within each stratum. All women aged
15–49 years who resided or spent the previous night in
selected households were eligible for participation in the
women’s survey. In a random subsample of about 15%
of households, all men aged 15–54 years who resided or
spent the night in these households were eligible for the
men’s survey. In addition to survey questions, the survey
included measurements of height, weight, blood pres-
sure, and random blood glucose levels on participants.
The survey response rate was nearly 98% at the house-
hold level and was 97% and 92% among eligible women
and men, respectively. A total of 793 194 people (women:
684 845, men: 108 349) participated in the survey.
Analytic sample
We analyzed data on 750 924 participants from the 2015–
2016 NFHS/DHS6 of India (see figure 1A for overall
methodology). We analyzed both men and non- pregnant
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Cardiovascular and Metabolic Risk
women under the age of 50 years separately and in combi-
nation (figure 1B).
Diabetes denitions
Participants of the NFHS-4/DHS 2015–2016 had random
blood glucose measured from a finger- stick blood spec-
imen using the FreeStyle Optium H glucometer with
glucose test strips. A referral form to a health facility
for additional medical evaluation was provided for any
respondent with random blood glucose 200 mg/dL.
While individuals were not instructed to fast, the survey
asked when participants last ate or drank.
The survey elicited information from all interviewed
men and women on: (1) their health status including
whether they currently have diabetes, asthma, heart
disease, thyroid disorder or cancer; and (2) their access
to healthcare to ascertain if the participant or a member
of their household has health insurance, has seen a
doctor in the past 12 months, and/or has visited a health-
care facility in the past 3 months.
We defined ‘self- report diabetes’ as all non- pregnant
individuals who answered ‘yes’ to the question do you
currently have diabetes. We defined ‘undiagnosed
diabetes’ as participants who answered ‘no’ to the ques-
tion do you currently have diabetes7 8 and following a
laboratory assessment either had an opportunistic fasting
glucose level 126 mg/dL (referred to as ‘fasting’) or
had a random glucose level 200 mg/dL (referred to as
‘random’). We define ‘opportunistic fasting’ as individ-
uals who self- reported that they had not eaten or had
any calorie intake for 8 or more hours. This conforms to
the US Preventive Services Task Force9 and the Research
Society for the Study of Diabetes in India (RSSDI)
guidelines (2017)10 for diabetes screening, where three
tests can be used to screen for the presence of diabetes
(including hemoglobin A1C (HbA1c), a fasting plasma
glucose level 126 mg/dL, or oral glucose tolerance test
glucose level of 200 mg/dL).
Comorbid conditions
The NFHS-4/DHS survey also collected participants’
blood pressure measured using an Omron Blood Pres-
sure Monitor to determine the prevalence of hyperten-
sion. Blood pressure measurements for each respondent
were taken three times with an interval of 5 min between
readings. Respondents whose average systolic blood pres-
sure (SBP) was >140 mm Hg or average diastolic blood
pressure (DBP) was >90 mm Hg were considered to have
elevated blood pressure readings. We defined self- report
hypertension as participants who answered ‘yes’ when
asked if they were told by a doctor that they have high
blood pressure or if they were currently taking any blood
pressure medications.
All interviewed women and men in the NFHS-4/DHS
survey were also asked whether they have asthma, thyroid
disorder, heart disease or cancer. We defined self- reported
heart disease and self- reported thyroid disorder as partic-
ipants who answered ‘yes’ to these specific questions.
Healthcare access
The NFHS-4/DHS survey participants were asked a series
of questions to determine health insurance coverage,
the sources of healthcare, and frequency of contact with
healthcare workers/healthcare professionals. We defined
healthcare access (yes/no) as either having health insur-
ance, seeing a healthcare provider in the past 12 months,
or visiting a healthcare facility in the past 3 months.
Sociodemographic characteristics
We identified a set of potential sociodemographic risk
factors of diabetes in India11–14 including sex, age, age
groups (in 10- year bins), wealth index (poorest, poor,
middle, rich, and richest), level of education (none,
primary, secondary, and higher), body mass index (BMI)
(kg/m2), smoking (in packs per day), drinking (in drinks
per day), place of residence (urban vs rural) and state of
residence (reference state: Gujarat). We grouped Indian
states and union territories into the six administrative
regions,15 including North, North East, Central, South,
East, and West, to ensure adequate sample size within
each region (reference region: Central).
The NFHS-4/DHS survey included a ‘wealth score’
based on the number and type of consumer goods in a
household, such as television, bicycle or car, and housing
characteristics such as source of drinking water, toilet
facilities, and flooring materials6 and derived using prin-
cipal component analysis. The survey compiled national
wealth quintiles by assigning the household score to each
usual (de jure) household member, ranking each person
in the household population by their score, and then
dividing the distribution into five equal categories, each
with 20% of the population.6
Statistical analysis
We computed prevalence and proportion estimates for
diabetes, self- report and undiagnosed diabetes using
sampling weights and survey- weighted proportions to
account for the survey design. We expressed estimates
as means with 95% CI. We derived prevalence ratios to
examine the association of the sociodemographic expo-
sures and outcomes (diabetes, self- report, undiagnosed)
using a log- bionomial model implemented using survey-
adjusted Poisson regression16 in R. We used this model to
compute the prevalence ratio for each independent expo-
sure using both univariate and fully adjusted models. We
corrected all p values for multiple comparisons using the
Bonferroni method and deemed all Bonferroni- adjusted
p values<0.05 to be significant.
We assessed the accuracy with which individuals with
undiagnosed diabetes can be predicted with sociodemo-
graphic variables (ie, sex, age, place of residence, region
of residence, BMI, and lifestyle behaviors), in addition to
comparable models for self reported diabetes. We trained
two logistic regression models on a random sample of the
three quarters of the entire cohort of undiagnosed and
non- diabetic individuals. The first model included all
sociodemographic variables described above, while the
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Cardiovascular and Metabolic Risk
Figure 2 (A) Prevalence of healthcare access in India by state. (B) Prevalence of undiagnosed diabetes in India by state.
second model included (in addition) random glucose
values. We report test accuracy and the area under the
curve (AUC) for predicting undiagnosed diabetes in the
left- out test cohort (the one quarter left- out sample).
Furthermore, we hypothesized that undiagnosed
diabetes is associated with prevalence of self- reported
comorbid conditions, namely hypertension, heart disease
and thyroid disorders. To test this hypothesis, we used
multivariate regression models to compute the difference
in prevalences of comorbid conditions between persons
with undiagnosed diabetes and those that self- reported.
RESULTS
The NFHS4/India DHS in 2015–2016 surveyed a total
of 753 038 non- pregnant individuals between the ages of
15 years and 50 years (online supplementary table S1).
We removed 2114 individuals that were missing fasting
or healthcare access status. This yielded a sample size
of 750 924 (see figure 1B). Eighty- seven per cent of the
surveyed individuals were women. Sixty- four per cent of
the surveyed individuals were between 15 years and 35
years of age. Nearly two- thirds of the cohort population
lived in rural areas with 63% belonging to the middle-
class or higher. Thirty- eight per cent (45% for men and
37% for women) of this cohort had access to healthcare
(online supplementary table S1). Figure 2A shows the
prevalence of healthcare access for the different states.
Prevalence of undiagnosed diabetes
Table 1 provides the survey- adjusted prevalences for the
self- report and undiagnosed diabetes groups. online
supplementary table S2 provides the distribution of the
diabetes population (self- report vs undiagnosed) strati-
fied by sex. The crude prevalence of diabetes for men
and women aged 15–50 years was estimated at 2.9%,
95% CI 2.9% to 3.1% with self- reported diabetes prev-
alence at 1.7%, (95% CI 1.6% to 1.8%) similar to the
overall prevalence reported in reference.6 The overall
prevalence of undiagnosed diabetes among 15–50 year
olds was 1.2%, (95% CI 1.2% to 1.3%) (table 1). Forty- two
per cent (95% CI 40.7% to 43.4%) of the individuals
with diabetes were unaware (undiagnosed diabetes) of
their diabetes status, and 27.6%, 95% CI 26.5% to 28.6%
of the individuals with diabetes were undiagnosed with
random glucose 200 mg/dL (table 1). A percentage of
50.5,(95% CI 47.2% to 53.7%) of men had undiagnosed
diabetes versus 40.5%(95% CI 39.1% to 42.0%) women.
Among those that were undiagnosed, 44.6%(95% CI
42.9% to 46.4%) had access to healthcare (men: 53%,
95% CI 48.9% to 57.1%, women: 42.7%, 95% CI 40.8% to
44.6%, online supplementary table S2).
Characteristics of undiagnosed diabetes population
Table 1 provides the survey- adjusted means for the self-
report and undiagnosed diabetes group, highlighting
the demographic, biological and lifestyle differences
between the two groups. Compared with persons who
self- reported diabetes, persons with undiagnosed
diabetes were younger (39.1 years vs 37.8 years). Notably
for persons aged 15–24 years, the prevalence of undi-
agnosed diabetes was 50% higher than the prevalence
of self- reported diabetes for the same age group (self-
reported: 7%, 95% CI 6.1% to 7.9%; undiagnosed: 10%,
95% CI 9.1% to 10.9%). A lower proportion of persons
over the age of 45 years had undiagnosed diabetes (29%,
95% CI 27.5% to 30.5% compared with 35%, 95% CI
33.4% to 36.6% self- reported). Rural areas had a higher
percentage of people with undiagnosed diabetes (rural:
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Cardiovascular and Metabolic Risk
Table 1 Survey- adjusted means and prevalence for self- report and undiagnosed diabetes groups
Self reported diabetes
Undiagnosed diabetes
All
Healthcare
Yes No
Unweighted sample size
(diabetes n=18 878)
10 686 8192 3417 (8192) 4775 (8192)
Unadjusted prevalence (95% CI),
%
(overall: 2.9 (2.9 to 3.1))
1.7 (1.7 to 1.9) 1.2 (1.2 to 1.3) N/A N/A
Diabetes proportions (95% CI),
%
58 (56.6 to 59.3) 42 (40.7 to 43.4) 44.6 (42.9 to 46.4) 55.4 (53.6 to 57.1)
Sex (95% CI), %
Female 87 (85.8 to 88.2) 82 (80.6 to 83.4) 78 (75.8 to 80.2) 84 (82.3 to 85.7)
Male 13 (11.8 to 14.2) 18 (16.6 to 19.4) 22 (19.8 to 24.2) 16 (14.3 to 17.7)
Mean age (95% CI), years 39.1 (38.7,39.4) 37.8 (37.5 to 38.1) 38.26 (37.9 to 38.6) 37.41 (37.0 to 37.8)
Age categories (95% CI), %
15–24 7 (6.1 to 7.9) 10 (9.1 to 10.9) 7 (5.8 to 8.2) 12 (10.7 to 13.3)
25–34 18 (16.8 to 19.3) 20 (18.7 to 21.3) 21 (19.0 to 23.0) 20 (18.3 to 21.7)
35–44 40 (38.6 to 41.5) 41 (39.4 to 42.6) 43 (40.6 to 45.4) 40 (37.8 to 42.2)
45–49 35 (33.4 to 36.6) 29 (27.5 to 30.5) 29 (26.8 to 31.2) 29 (27.0 to 31.0)
Wealth index (95% CI), %
Poorest 8 (7 to 9) 10 (9.1 to 10.9) 9 (7.8 to 10.2) 11 (9.8 to 12.2)
Poor 11 (10.1 to 11.9) 15 (13.9 to 16.1) 14 (12.4 to 15.6) 16 (14.5 to 17.5)
Middle 16 (14.9 to 17.1) 21 (19.6 to 22.4) 22 (19.9 to 24.1) 21 (19.2 to 22.8)
Rich 28 (26.5 to 29.5) 27 (25.5 to 28.5) 29 (26.7 to 31.3) 25 (23.0 to 27.0)
Richest 36 (34.2 to 37.8) 27 (25.3 to 28.7) 26 (23.5 to 28.5) 27 (24.8 to 29.2)
Education (95% CI), %
No education 24 (22.7 to 25.3) 31 (29.4 to 32.6) 31 (28.7 to 33.3) 31 (28.9 to 33.1)
Primary 15 (13.8 to 16.2) 15 (13.8 to 16.2) 16 (14.2 to 17.8) 14 (12.5 to 15.6)
Secondary 48 (46.5 to 49.5) 43 (41.4 to 44.7) 42 (39.6 to 44.5) 44 (41.8 to 46.2)
Higher 13 (11.8 to 14.2) 11 (9.8 to 12.2) 11 (9.2 to 12.8) 11 (9.5 to 12.5)
Place of residence (95% CI), %
Urban 52 (49.7 to 54.4) 44 (41.9 to 46.1) 43 (40.1 to 45.9) 44 (41.4 to 46.6)
Rural 48 (45.7 to 50.4) 56 (53.9 to 58.1) 57 (54.1 to 59.9) 56 (53.4 to 58.6)
Region of country (95% CI), %
Central 15 (13.9 to 16.1) 19 (17.8 to 20.2) 17 (15.4 to 18.6) 21 (19.5 to 22.6)
East 20 (18.2 to 21.8) 19 (17.3 to 20.7) 16 (13.9 to 18.1) 22 (19.7 to 24.3)
North 9 (8.2 to 9.8) 9 (8.2 to 9.8) 9 (7.9 to 10.1) 9 (8.0 to 10.0)
Northeast 2 (1.7 to 2.3) 2 (1.7 to 2.3) 2 (1.7 to 2.3) 3 (2.6 to 3.4)
South 42 (39.5 to 44.6) 34 (32.0 to 36.0) 47 (44.1 to 49.9) 24 (21.8 to 26.2)
West 12 (10.6 to 13.41) 16 (14.2 to 17.8) 10 (8.1 to 11.9) 21 (18.4 to 23.6)
Lifestyle
Smokes (95% CI), % 13 (12.0 to 14.0) 15 (13.8 to 16.2) 19 (17.0 to 21.0) 12 (10.6 to 13.5)
Drinks (95% CI), % 7 (6.0 to 8.0) 8 (7.1 to 8.9) 11 (9.3 to 12.7) 5 (4.1 to 5.9)
Mean body mass index 25.4 (25.2 to 25.6) 25.3 (25.1 to 25.4) 25.6 (25.3 to 25.9) 25.0 (24.7 to 25.2)
Mean glucose (95% CI), mg/dL 172.4 (168.7 to 176.1) 234.0 (230.7 to 237.4) 231.5 (226.7 to 236.2) 236.1 (231.6 to 240.5)
Mean systolic BP (95% CI), mm
Hg
123.5 (123.0 to 124.1) 125.4 (124.8 to 125.9) 125.5 (124.5 to 126.4) 125.3 (124.6 to 126.0)
Mean diastolic BP (95% CI), mm
Hg
83.58 (83.1 to 84.1) 84.9 (84.3 to 85.5) 85.1 (84.3 to 85.9) 84.7 (83.8 to 85.6)
Continued
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Self reported diabetes
Undiagnosed diabetes
All
Healthcare
Yes No
Access to healthcare (95% CI),
%
Yes 50 (48.1 to 51.9) 45 (43.3 to 46.7) 100 (0) 0 (0)
No 50 (48.1 to 51.9) 55 (53.26 to 56.74) 0 (0) 100 (0)
BP, blood pressure.
Table 1 Continued
56%, 95% CI 53.9% to 58.1% undiagnosed). In contrast,
the percentage of people who reported as having
diabetes in urban areas was higher (urban: 52%, 95% CI
49.7% to 54.4% self- report diabetes). Geographically,
while the Southern and Eastern states had the highest
proportions of diabetes, the Central and Western states
of India had higher proportions of undiagnosed diabetes
when compared with the self- report proportions. Higher
proportions of people belonging to poorest, poor or
middle classes (46% undiagnosed compared with 35%
self- report) were identified as having undiagnosed
diabetes with lower prevalence in undiagnosed diabetes
for the upper two classes (table 1).
As expected, individuals with undiagnosed diabetes
had higher blood glucose levels (undiagnosed: 234.0 mg/
dL, 95% CI 230.7 to 237.4 mg/dL; self- report: 172.4 mg/
dL, 95% CI 168.7 to 176.1mg/dL). The undiagnosed
diabetes group also had higher blood pressure levels
(undiagnosed systolic: 125.4 mm Hg, 95% CI 124.8 to
125.9 mm Hg; self- report systolic: 123.5 mm Hg, 95% CI
123.0 to 124.1 mm Hg; undiagnosed diastolic: 84.9 mm
Hg, 95% CI 84.3 to 85.5 mm Hg; self- report: 83.6 mm Hg,
95% CI 83.1 to 84.1 mm Hg). This difference in blood
pressure was not clinically significant (both groups were
in the elevated blood pressure category as per the Amer-
ican College of Cardiology/American Heart Association
(ACC/AHA) guidelines17).
Differences in the undiagnosed diabetes population
Online supplementary table 3 highlights the differences
within the undiagnosed diabetes populations, that is,
between those that were undiagnosed with a non- fasting
random glucose level 200 mg/dL and those that had
a fasting random glucose level 126 mg/dL. A high
percentage, 65.5 of undiagnosed diabetes population,
had random glucose levels 200 mg/dL compared with
the fasting group (random glucose level 126 mg/dL
and time since they last ate 8 hours). Participants with
random undiagnosed diabetes were older (39.37 years
vs 34.79 years) and had on average higher BMI (26.2 kg/
m2 vs 23.5 kg/m2) compared with persons with fasting
undiagnosed diabetes. A higher percentage of persons
with random undiagnosed diabetes lived in urban areas
(46% compared with 40%) with some key regional/
state level differences. We observed the biggest differ-
ences in the Western region where 13%, 95% CI 11.3%
to 14.7% had random undiagnosed diabetes compared
with 22% (95% CI 18.7% to 25.3%) with fasting undi-
agnosed diabetes (see online supplementary table S3).
There was no significant difference in access to health-
care between the random and the fasting undiagnosed
groups.
Sociodemographic associations with undiagnosed diabetes
versus self-reported diabetes
Table 2 shows the mean differences in the prevalence of
undiagnosed diabetes when compared with self- report
diabetes for the sociodemographic, biological (including
comorbid conditions), and lifestyle factors using a fully
adjusted model. online supplementary table S4 summa-
rizes the mean differences obtained from univariate
models. The undiagnosed population was different than
those who reported diabetes. Undiagnosed diabetes (vs
self- report diabetes) was associated with sex, age, educa-
tion, state of residence, BMI, and lifestyle behaviors (such
as smoking and drinking) (table 2, see online supple-
mentary table S4 for univariate models). Women had
a 28% lower prevalence of undiagnosed diabetes than
men (Prevalence Ratio (PR)=0.72, 95% CI 0.67 to 0.79,
p<0.0001). Older age groups (45–49 year olds) had a
26% lower prevalence of undiagnosed diabetes (PR=0.74,
95% CI 0.66 to 0.82, p<0.0001) than 15–24 year olds. Indi-
viduals aged 24–34 years who had access to healthcare
had a 28% higher prevalence of undiagnosed diabetes
(PR=1.28, 95% CI 1.11 to 1.49, p<0.05) than 15–24 year
olds. Persons with higher education had a 17%–20%
lower prevalence of being undiagnosed compared with
those with no education. Overall, higher BMI was asso-
ciated with increased prevalence of both diabetes and
undiagnosed diabetes. A 1- unit increase in BMI was
associated with a 1% increase in prevalence of undiag-
nosed diabetes (PR=1.01, 95% CI 1.01 to 1.02, p<0.001).
Persons in the Eastern, Northern and Southern regions
had lower prevalence of undiagnosed diabetes compared
with the Central region. However, in the Southern states,
persons with access to healthcare had a nearly 54% higher
prevalence of undiagnosed diabetes (PR=1.54, 95% CI
1.41 to 1.67, p<0.0001). Last, persons who smoked and
had access to healthcare had a 37% higher prevalence
of undiagnosed diabetes (PR=1.37, 95% CI 1.24 to 1.52,
p<0.0001).
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Cardiovascular and Metabolic Risk
Table 2 Comparing adjusted prevalence of undiagnosed diabetes with self- report diabetes and the mean difference for
prevalence of comorbid conditions
Diabetes versus healthy
Prevalence ratio (95% CI)
Undiagnosed versus selfreport
Prevalence ratio (95% CI)
Undiagnosed with healthcare versus
undiagnosed without healthcare
Prevalence ratio (95% CI)
Sex (ref: male)
Female 0.95 (0.87 to 1.04) 0.72 (0.67 to 0.79)*** 0.95 (0.84 to 1.06)
Age category (ref: 15–24 years)
25–34 2.09 (1.91 to 2.29)*** 0.9 (0.83 to 0.99) 1.28 (1.11 to 1.49).
35–44 4.89 (4.46 to 5.37)*** 0.83 (0.76 to 0.91)* 1.26 (1.09 to 1.45).
45–49 8.5 (7.66 to 9.42)*** 0.74 (0.66 to 0.82)*** 1.19 (1.02 to 1.38)
Wealth (ref: poorest)
Poor 1.12 (1.03 to 1.23) 1.1 (1 to 1.21) 1.03 (0.9 to 1.18)
Middle 1.31 (1.2 to 1.44)*** 1.14 (1.03 to 1.25) 1.13 (0.98 to 1.3)
Rich 1.67 (1.52 to 1.84)*** 0.99 (0.89 to 1.09) 1.15 (1 to 1.32)
Richest 1.89 (1.7 to 2.09)*** 0.85 (0.76 to 0.96) 1.16 (0.99 to 1.36)
Education level (ref: no education)
Primary 1.17 (1.1 to 1.26)** 0.84 (0.78 to 0.91)** 1.01 (0.91 to 1.12)
Secondary 1.06 (1 to 1.13) 0.8 (0.75 to 0.85)*** 0.95 (0.87 to 1.05)
Higher 0.92 (0.83 to 1.02) 0.83 (0.74 to 0.92). 1.02 (0.88 to 1.19)
Body mass index (kg/m2)1.06 (1.05 to 1.06)*** 1.01 (1.01 to 1.02)** 1 (1 to 1.01)
Residence (ref: urban)
Rural 0.84 (0.8 to 0.9)*** 1.08 (1.01 to 1.15) 1.09 (1 to 1.18)
Region (ref: central)
East 1.43 (1.32 to 1.54)*** 0.83 (0.76 to 0.9)** 0.92 (0.81 to 1.04)
North 0.83 (0.77 to 0.89)*** 0.89 (0.82 to 0.96). 1.08 (0.96 to 1.2)
Northeast 0.98 (0.9 to 1.07) 0.87 (0.79 to 0.96) 0.69 (0.59 to 0.81)**
South 1.66 (1.55 to 1.79)*** 0.8 (0.74 to 0.87)*** 1.54 (1.41 to 1.67)***
West 1.01 (0.93 to 1.1) 1.07 (0.98 to 1.16) 0.7 (0.59 to 0.84)*
Smokes (ref: no smoking) 1.04 (0.96 to 1.12) 0.94 (0.87 to 1.02) 1.37 (1.24 to 1.52)***
Drinks (ref: no drinking) 1.15 (1.02 to 1.29) 0.94 (0.84 to 1.05) 1.07 (0.94 to 1.2)
Healthcare 1.13 (1.08 to 1.19)*** 0.92 (0.87 to 0.98) NA
Mean diff (95% CI) Mean diff (95% CI) Mean diff (95% CI)
Glucose (95% CI), mg/dL 91.5 (88.7 to 94.3)*** 66.2 (6.5 to 70.8)*** −11.4 (−17.6 to −5.2)
Systolic BP (95% CI), mm Hg 4.2 (3.9 to 4.6)*** 2.3 (1.6 to 3.1)*** −0.5 (−1.6 to 0.6)
Diastolic BP (95% CI), mm Hg 2.6 (2.2 to 3.0)*** 1.4 (0.6 to 2.2) −0.37 (−1.4 to 0.7)
Self- report:
Hypertension (95% CI), % 7.5 (6.5 to 8.4)*** −4.1 (−5.9 to −2.3)* 1.1 (1.3)
Heart disease (95% CI), % 4.5 (3.8 to 5.2)*** −9.0 (−10.3 to −7.7)*** 1.4 (0.5 to 2.3)
Thyroid disorder (95% CI), % 4.3 (3.5 to 5.1)*** −7.6 (−9.1 to −6.1)*** 3.1 (1.5 to 4.6)
Univariate prevalence ratios and adjusted prevalence ratios for men and women are given in online supplementary tables 4-6. Bonferroni- corrected
p values are denoted as follows: corrected p value <0.0001 (***), corrected p value <0.001 (**), corrected p value <0.01 (*), corrected p value <0.05
(.). Factors that are associated with increased prevalence of undiagnosed (diabetes, undiagnosed with healthcare access) are shown in red. Factors
associated with decreased prevalence of undiagnosed (diabetes, undiagnosed with healthcare access) are shown in blue. Factors that are not
signicant are given in black.
BP, blood pressure.
Variance explained of undiagnosed diabetes in the overall
population
Overall, our analyzed factors explained nearly 9% of
the variance (R2=0.09) for undiagnosed diabetes versus
individuals without diabetes (excluding the self- report
diabetes group) and nearly 66% (R2=0.66) of the
variance when combined with random glucose measures.
These factors had an AUC of 74.8% (AUC=74.8%, 95%
CI 74.7% to 74.9%) and an accuracy of 98.9% (95% CI
98.8% to 98.9%) when discriminating individuals with
undiagnosed diabetes from individuals with no diabetes.
When combining these factors with random glucose test
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8BMJ Open Diab Res Care 2020;8:e000965. doi:10.1136/bmjdrc-2019-000965
Cardiovascular and Metabolic Risk
values, these factors had an AUC of 97.8% (95% CI 97.7%
to 97.8%) and an accuracy of 99.5% (95% CI 99.53% to
99.57%). When discriminating the undiagnosed diabetes
population from the self- report diabetes group, these
factors explained 6% of the variance (R2=0.06) and nearly
19% when combined with random glucose measures.
The model had an area under the curve (AUC) of 60.4%
(95% CI 59.4% to 61.3%) and an accuracy of 58.3% (95%
CI 57.6% to 58.9) when classifying undiagnosed diabetes.
With random glucose values, the AUC for discriminating
undiagnosed diabetes from self- report individuals is
73.2% (95% CI 72.3% to 74%) with an accuracy of 66.3%
(95% CI 65.6% to 67.0%). Online supplementary table
S2 gives the comparative AUCs for these models.
Undiagnosed diabetes associated with lower prevalence of
heart disease
Table 2 highlights the differences in the glucose, SBP
and DBP levels, and the prevalence of comorbid condi-
tions between the undiagnosed and self- report diabetes
groups. In online supplementary table S6, we highlight
the differences of the same between women and men
and examine the differences with respect to healthcare
access. Glucose levels between the undiagnosed, and the
self- report groups was significantly different (66.2 mg/
dL, 95% CI 61.5 to 70.8 mg/dL, p<0.0001), after adjusting
for sociodemographic, geographic and lifestyle factors.
Individuals with undiagnosed diabetes also on average
had slightly higher systolic (2.3 mm Hg, 95% CI 1.6 to 3.1
mm Hg, p<0.00001) and diastolic (1.4 mm Hg, 95% CI
0.6 to 2.2 mm Hg, p<0.1) blood pressure when compared
with the self- report diabetes group (table 2).
As shown in table 2, heart disease prevalence in the
undiagnosed group was 9.0% (95% CI −10.3% to –7.7%,
p<0.00001) lower than the prevalence in the self report
group. This difference was significantly pronounced in
men with a difference of 13.7% (95% CI −18.1% to 9.2%,
p<0.00001) (online supplementary table S6). Hyperten-
sion and thyroid disorder prevalence were both signifi-
cantly lower in the undiagnosed group (hypertension:
−4.1%, 95% CI −5.9%, to 2.3%, p<0.005, thyroid disorder:
−7.6%, 95% CI −9.1% to 6.1%, p<0.00001, see table 2).
Among persons with undiagnosed diabetes, prevalence
of comorbid conditions were not associated with access
to healthcare (see online supplementary table S6).
DISCUSSION
A significant portion of the diabetes population in India,
at least 42%, remains unaware of their diabetes status,
and an overwhelming subset of this population (approx-
imately 45%) is at risk of poor detection: undiagnosed
diabetes despite having access to healthcare. This finding
highlighting the poor awareness (undiagnosed) and
poor detection of diabetes (undiagnosed with access to
healthcare) in India is troubling from several aspects.
In addition to the high proportion of undiagnosed
cases, our study had four key findings. First, men are
more likely to be unaware of their diabetes status and
more vulnerable to poor detection of diabetes compared
with women. Our findings on poor diabetes detection in
men conforms to the overall trends in diabetes—lower
crude prevalence of diabetes in women (7.3%, 95% CI
7.1% to 7.4%) compared with 7.8%, 95% CI 7.6% to
8.0% in men11) and a significantly higher prevalence
of diabetes (10% higher prevalence of diabetes relative
to women; online supplementary table S2)—reported
here and in prior studies.11 14 18 Furthermore, men had
a nearly 10% higher prevalence of being undiagnosed
despite having healthcare access compared with women
in the same category.
Second, younger age groups are more likely to be
unaware of their diabetes status and susceptible to poor
diabetes detection. The proportion of 15–39 year olds with
undiagnosed diabetes (both with and without access to
healthcare) was nearly double the proportion of individuals
that reported having diabetes for the same age categories.
Overall, a 10- year increase in age lowered the prevalence
of poor awareness by 10%. These findings are of particular
concern given the additional burden that this population
is likely to place on an already strained healthcare system.
These findings also highlight the need to perhaps revisit
the recommended age for routine screening of diabetes:
the American Diabetes Association recommends routine
diabetes screening for overweight and obese individuals of
age 40 years and for others at age 45 years.19
Third, perhaps not surprisingly, individuals with higher
education levels are more likely to be aware of their
diabetes status. Individuals with higher levels of education
(from primary to higher secondary) had a nearly 20%
lower prevalence of undiagnosed diabetes when compared
with individuals with no education. Thus, while it is reas-
suring to see that higher education reduces the prevalence
of poor awareness of diabetes, it highlights the disparity in
health outcomes associated with unequal access to educa-
tion in India. Socioeconomic status and education did not
significantly alter the prevalence of poor detection.
Lastly, the Eastern, North- Eastern and Southern regions
of India all showed higher levels of diabetes awareness
when compared with the Central states. Fewer individ-
uals in these states were undiagnosed compared with the
Central states. Despite having the highest access to health-
care (55.9%) and health insurance (45.4%), the Southern
region in India had a significantly higher prevalence of
poor detection compared to the Central region. Individ-
uals in the Southern region had a nearly 54% higher prev-
alence of poor detection (PR=1.5, 95% CI 1.4% to 1.7%,
p<0.0001) when compared with the Central region.
We also found that poor awareness of diabetes is asso-
ciated with lower prevalence of comorbid conditions in
India (vs self- report diabetes). We claim that this could
be attributed to the younger age of the cohort or poten-
tial under- reporting of comorbid conditions. Given
that nearly 45% of these undiagnosed individuals have
healthcare access, we posit that providing healthcare
access alone to individuals may not be sufficient and/or
should be coupled with screening using random glucose
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Cardiovascular and Metabolic Risk
tests.20–25 This is also in accordance with the RSSDI guide-
lines that specify that screening should be implemented
‘based on the prevalence of undiagnosed diabetes and
available support from healthcare’.10
Our study has several strengths. While several studies
have reported on the undiagnosed and the prediabetes
population in India,14 18 this population has not been
examined in the context of access to healthcare. We are, to
the best of our knowledge, the first to examine the undiag-
nosed diabetes population in India with and without access
to care. We emphasize that our analysis is representative of
all 29 states and 7 union territories in India and includes
both urban and rural regions, in contrast to studies that
have focused on individual states and cities5 26–28 or subsets
of states and union territories.11 14
However, our study also has several limitations. First,
our estimates of undiagnosed diabetes are based on
random glucose (capillary blood glucose (CBG))
measurements and opportunistic fasting information.
While we used self- report information on an individual’s
calorie intake to attain opportunistic fasting informa-
tion, it does not meet the standards of diabetes diag-
nosis that call for using fasting venous plasma glucose,
repeat measurements, or HbA1c.29 Since our random
glucose definition is conservative (specific, but less sensi-
tive), our estimates of undiagnosed diabetes are likely
an underestimate, and the degree of underestimation is
likely to be greater in younger people because they were
over- represented among those having random measure-
ments.22 30–33 Second, our sample of persons with undiag-
nosed diabetes was skewed towards persons with random
glucose measurements 200 mg/dL (nearly 65% of the
undiagnosed population), potentially biasing the assess-
ment of the prevalences. Third, our analysis and findings
are limited to 15–49 year old non- pregnant women and
men. Our results do not include children 14 years of age
or individuals 50 years. Furthermore, given that we only
had access to random blood glucose readings, we are
unable to make any distinction between type 1 and type
2 diabetes. Finally, the dataset was heavily skewed towards
females, which could result in greater misrepresentation
of the problem in men compared with women.
In conclusion, while prior studies have reported undi-
agnosed diabetes as high as 47% of the overall diabetes
population14 for a subset of the Indian states, we extend
these findings to provide a representation of the undi-
agnosed population across India and for a younger age
demographic (15–49 years in women and men). We are,
to the best of our knowledge, also the first to highlight
that for certain age demographics (the younger age
groups) and regions of the country (eg, in the Southern
states of India) a high proportion of the diabetes popu-
lation remains undiagnosed despite access to healthcare.
These findings are especially of great importance as India
works to put national attention on non- communicable
diseases through its National Programme for Prevention
and Control of Cancer, Diabetes, Cardiovascular Diseases
and Stroke established in 2010 and its focus on bringing
healthcare access to the poorest in the nation through
the recent establishment of the Ayushman Bharat, the
National Protection Mission.
Contributors CJP conceived and supervised the project. KTC performed the bulk
of the data analysis. CJP and KTC wrote, read and reviewed the manuscripts. EWG
provided guidance on the effort and reviewed the manuscript. MKC and AD read
and reviewed the manuscript. All authors revised the report and approved the nal
submitted version. CJP and KTC take full responsibility for the overall content of
this work.
Funding This effort was funded by NIH R00 ES023504-05 and NIH R01 AI127250-
03. The corresponding author had full access to all of the data in the study and had
responsibility for submission for publication.
Disclaimer The funding sources had no role in writing of the manuscript.
Map disclaimer The depiction of boundaries on the maps in this article do not
imply the expression of any opinion whatsoever on the part of BMJ (or any member
of its group) concerning the legal status of any country, territory, jurisdiction or area
or of its authorities. The maps are provided without any warranty of any kind, either
express or implied.
Competing interests None declared.
Patient consent for publication Not required.
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement Data are available on reasonable request. Our data
are from the Demographic and Health Surveys, freely available data here on
application of analyst: https:// dhsprogram. com/ Data/.
Open access This is an open access article distributed in accordance with the
Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits
others to copy, redistribute, remix, transform and build upon this work for any
purpose, provided the original work is properly cited, a link to the licence is given,
and indication of whether changes were made. See:https:// creativecommons. org/
licenses/ by/ 4. 0/.
ORCID iD
Chirag JPatel http:// orcid. org/ 0000- 0002- 8756- 8525
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... The gap created by the weakening and non-existence of public health services is being occupied by the private health sector, leading to a rise in the number of private hospitals from 14% to 68% 1974 to 1995 (10,18). The percentage of child births occurring in private facilities ranges from 39% in 2005-06 to 79% in 2015-16 (19). Examining maternity patient perceptions of service quality in small and medium-sized private hospitals in India has not been documented in the literature. ...
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Background: This study used the SERVPERF measurement approach to analyze the perception of service quality in maternity centers in the small and medium-sized private hospitals in India. Methods: A cross-sectional study was performed on 463 new mothers' perception of the service quality of maternity centers in three states of India using convenience sampling. Results: The results of the study confirm a positive relationship between reliability and service quality; indeed, reliability plays a significant role in determining the outcome of maternity service quality. Conclusion:The results of this study provide a platform for private healthcare strategies and policies to enhance and improve service quality in hospitals.
... In Korea, 35% of patients with diabetes aged ≥30 years are unaware of their diabetes [23], which was similar to the 30% detected in the current study. Interestingly, the current study also found that there was low awareness of the presence of diabetes among those High diabetes unawareness in the young population has also been previously reported [24], which might be due to the low accessibility of health care services among younger members of the population [25]. Late detection of diabetes in the young population may also be linked to severe hyperglycemia at the period of diagnosis of diabetes. ...
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Background: We investigated the prevalence of diabetic retinopathy (DR) in patients with undiagnosed diabetes through a nationwide survey, compared to those with known diabetes. Methods: Among the participants of the Korean National Health and Nutrition Examination Surveys (KNHANES) from 2017 to 2018, individuals aged ≥40 years with diabetes and fundus exam results were enrolled. Sampling weights were applied to represent the entire Korean population. Newly detected diabetes patients through KNHANES were classified under "undiagnosed diabetes." Results: Among a total of 9,108 participants aged ≥40 years, 951 were selected for analysis. Of them, 31.3% (standard error, ±2.0%) were classified under "undiagnosed diabetes." The prevalence of DR in patients with known and undiagnosed diabetes was 24.5%±2.0% and 10.7%±2.2%, respectively (P<0.001). The DR prevalence increased with rising glycosylated hemoglobin (HbA1c) levels in patients with known and undiagnosed diabetes (P for trend=0.001 in both). Among those with undiagnosed diabetes, the prevalence of DR was 6.9%±2.1%, 8.0%±3.4%, 5.6%±5.7%, 16.7%±9.4%, and 42.6%±14.8% for HbA1c levels of <7.0%, 7.0%-7.9%, 8.0%-8.9%, 9.0%-9.9%, and ≥10.0% respectively. There was no difference in the prevalence of hypertension, dyslipidemia, hypertriglyceridemia, or obesity according to the presence or absence of DR. Conclusion: About one-third of patients with diabetes were unaware of their diabetes, and 10% of them have already developed DR. Considering increasing the prevalence of DR according to HbA1c level was found in patients with undiagnosed diabetes like those with known diabetes, screening and early detection of diabetes and DR are important.
... Our estimate of the prevalence of undiagnosed diabetes is broadly comparable to prevalence estimates from countries around the world, although there is considerable global variability. For example, in India, among 15-50 years old, the prevalence of undiagnosed diabetes was estimated to be 1.2%, [20], in Kenya 14% [21], Malaysia 8.9%, [22], and Iran 5% [23]. It is lower than in some high-income countries (e.g., Italy, 10%) [24] but higher than in the US (2.8%) based on 2016 National Health and Nutrition Examination Survey data [25]. ...
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Aim: To estimate the prevalence of undiagnosed diabetes, and to identify the relative importance of risk factors for undiagnosed diabetes among Bangladeshi adults. Method: Data from 11, 421 Bangladeshi adults aged 18 years and older available from the most recent nationally representative Bangladesh Demographic and Health Survey 2017-18 were used. Anthropometric measurements and fasting blood glucose samples were taken as part of the survey. Prevalence estimates of undiagnosed diabetes was age-standardised with direct standarisation, and risk factors were identified using multilevel mix-effects Poisson regression models with robust variance. Results: The overall age-standardised prevalence of undiagnosed diabetes was 6.0% (95%CI, 5.5-6.4%) (men: 6.1%, women: 5.9%). Risk factors associated with undiagnosed diabetes were older age, elevated body mass index (BMI), highest wealth quintile, hypertension, and being male. The top two modifiable risk factors contributing over 50% to undiagnosed diabetes were BMI and wealth quintiles. Conclusion: Undiagnosed diabetes affects a substantial proportion of Bangladeshi adults. Since elevated BMI and the highest wealth quintile are strong modifiable risk factors, these offer an opportunity for early detection and screening to reduce undiagnosed diabetes in Bangladesh. In addition, wide-reaching awareness campaigns among the general public, clinicians, and policymakers are needed.
... challenges than men. These prevents them from adopting healthy behaviors [31,32], thus rendering them more vulnerable to long term diseases like diabetes. Given the proportion of women in reproductive years by region, the majority of the diabetes burden appears in Southern, followed by the Central and Eastern regions. ...
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The diabetes burden is rapidly accelerating in India, particularly since the 2000s. We explore the burden and contribution of modifiable risk factors in diabetes among reproductive women across geographic regions of India. The study uses data from the National Family Health Survey in India 2015-2016, Census of India 2011, and World Population Prospects 2015. We computed Population Attributable Fractions and the number of total and estimated avoidable diabetic cases across regions. The prevalence of diabetic cases in India were 24.4 per 1000 women, varying across geographic regions. Diabetes affected around 8.2 million women (15-49 years) in India. Overweight (PAF = 19.5%) and obesity (PAF = 18.3%) contributed to the diabetes burden; if mitigated optimally, these can reduce diabetic cases by 2.8 million in India. Controlling diabetes should be region specific for maximum impact. Extending chronic disease screening during maternal and child health consultations might help decelerate the growing menace of diabetes in the country. Keywords BMI · Diabetes · India · Modifiable risk factors · Women Key message 1. The burden of diabetes is uneven across geographic regions and highest for the southern region. 2. Overweight, obesity, and hypertension emerged as crucial contributing factors for diabetes. 3. The study shows that diabetes affected 8.2 million women in the reproductive age group in India.
... Indeed, in 2014 a meta-analysis 16 estimated that in India, ~33% of individuals living in urban settings, and 25% of individuals living in rural settings, are hypertensive. Both diabetes and hypertension are under-diagnosed and under-treated, particularly in rural India [17][18][19] . Furthermore, cardiovascular diseases have been ranked among the leading contributors to death and disability in India, and it is predicted that the burden of these diseases will continue to increase in the coming decades 20 . ...
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In India, increasing lifespan and decreasing fertility rates have resulted in a growing number of older persons. By 2050, people over 60 years of age are predicted to constitute 19.1% of the total population. This ageing of the population is expected to be accompanied by a dramatic increase in the prevalence of dementia. The aetiopathogenesis of dementia has been the subject of a number of prospective longitudinal studies in North America and Europe; however, the findings from these studies cannot simply be translated to the Indian population. The population of India is extremely diverse in terms of socio-economic, cultural, linguistic, geographical, lifestyle-related and genetic factors. Indeed, preliminary data from recently initiated longitudinal studies in India indicate that the prevalence of vascular and metabolic risk factors, as well as white matter hyperintensities, differs between urban and rural cohorts. More information on the complex role of vascular risk factors, gender and genetic influences on dementia prevalence and progression in Indian populations is urgently needed. Low-cost, culturally appropriate and scalable interventions need to be developed expeditiously and implemented through public health measures to reduce the growing burden of dementia. Here, we review the literature concerning dementia epidemiology and risk factors in the Indian population and discuss the future work that needs to be performed to put in place public health interventions to mitigate the burden of dementia.
... It is known that obesity increases insulin resistance and thus induces hyperglycemia 22 , which may explain the fact that individuals with hyperglycemia had an average BMI of 29.8 ± 5.7, which represents advanced overweight/obesity. Several studies showed the association of BMI with hyperglycemia 13,14,23 . Furthermore, Holm et al. 15 analyzed a population with and without periodontitis and found that individuals with periodontitis and overweight have a higher risk factors of developing pre-diabetes and DM. ...
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Introduction: Individuals with pre-diabetes have altered glycemic levels, are generally asymptomatic, and are at increased risk for developing type 2 diabetes mellitus. Objective: Identify the prevalence of periodontal individuals with undiagnosed hyperglycemia and associated impact factors. Material and method: Fifty-six patients with periodontitis and without diabetes self-report, users of dental clinic services at Federal University of Juiz de Fora were included in this research, during one year and a half of experimental evaluation. Socioeconomic and demographic data, anthropometric patterns, fasting capillary blood glucose, and complete periodontal examination (six sites per tooth) were evaluated. Result: The sample consisted of 58.9% female, mean age 53 years old, 58.9% obese/overweight and 45.3% had a low level of education. A total of 28.6% (n=16) participants had undiagnosed hyperglycemia (between 100 to 160 mm / dL), of which 81.3% were obese/overweight, 25% were smokers, 56.3% reported having a history of diabetes in the family, 93.8% had a family income up to 2 brazilian´s minimum wages. BMI values were higher in the group of patients with hyperglycemia (29.8 ± 5.7, p = 0.03) compared to the group without hyperglycemia (26.6 ± 5.6). Patients with hyperglycemia had a greater number of sites with clinical attachment loss (CAL) between 4 and 6 mm (p = 0.04) when compared with the normoglycemic group. Conclusion: Undiagnosed CAL attachment loss between 4 and 6 mm due to periodontitis than normoglycemic individuals.
... Therefore, our estimates are likely to be underestimated since our random glucose definition is conservative (more specific but less sensitive). 51 Finally, the OWOB-diabetes relationship is likely to be mediated by more than one mediator variable, such as physical activity and diet. Future studies could explore the mediation role of these factors in the OWOB-diabetes relationship. ...
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Aims Overweight and obesity (OWOB) is a modifiable risk factor for both hypertension and diabetes. However, the association between OWOB and diabetes among Bangladeshi adults and how hypertension may mediate this relationship are not well explored. This study aimed to examine (1) whether OWOB is independently associated with diabetes among Bangladeshi adults, (2) whether this association is mediated by hypertension, and (3) the effect modification by wealth status and place of residence in the relationships. Research design and methods We used data of 9305 adults aged ≥18 years from the most recent nationally representative cross-sectional study of Bangladesh Demographic and Health Survey 2017-2018. Design-based logistic regression was used to assess the association between OWOB and diabetes, and counterfactual framework-based weighting approach was used to evaluate the mediation effect of hypertension in the OWOB-diabetes relationship. We used stratified analyses for the effect modifications. Results The prevalence of OWOB, diabetes and hypertension was 48.5%, 11.7% and 30.3%, respectively. We observed a significant association between OWOB and diabetes and a mediating role of hypertension in the OWOB-diabetes association. The odds of diabetes was 51% higher among adults with OWOB than those without OWOB (adjusted OR: 1.51, 95% CI 1.29 to 1.77). We observed that 18.64% (95% CI 9.84% to 34.07%) of the total effect of OWOB on the higher odds of diabetes was mediated through hypertension, and the mediation effect was higher among adults from non-poor households and from both rural and urban areas. Conclusions Adult OWOB status is independently associated with diabetes in Bangladesh, and hypertension mediates this association. Therefore, prevention policies should target adults with both OWOB and hypertension, particularly those from non-poor households and from both rural and urban areas, to reduce the growing burden of diabetes and its associated risk.
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Background To determine the prevalence, awareness, treatment and control of diabetes mellitus (DM) and associated factors amongst adults (18–69 years) in India from the National Noncommunicable Disease Monitoring Survey (NNMS). Methods NNMS was a comprehensive, cross-sectional survey conducted in 2017–18 on a national sample of 12,000 households in 600 primary sampling units. In every household, one eligible adult aged 18–69 years were selected. Information on NCD risk factors and their health-seeking behaviors were collected. Anthropometric measurements, blood pressure and fasting capillary blood glucose were measured. DM was defined as fasting blood glucose (FBG) ≥126 mg/dl including those on medication. Awareness, treatment, and control of DM were defined as adults previously diagnosed with DM by a doctor, on prescribed medication for DM, and FBG <126 mg/dl, respectively. The weighted data are presented as mean and proportions with 95% CI. We applied the Student t -test for continuous variables, Pearson's chi-square test for categorical variables and multivariate regression to determine the odds ratio. For statistical significance, a p -value < 0.05 was considered. Results Prevalence of DM and impaired fasting blood glucose (IFG) in India was 9.3% and 24.5% respectively. Among those with DM, 45.8% were aware, 36.1% were on treatment and 15.7% had it under control. More than three-fourths of adults approached the allopathic practitioners for consultation (84.0%) and treatment (78.8%) for diabetes. Older adults were associated with an increased risk for DM [OR 8.89 (95% CI 6.66–11.87) and were 16 times more aware of DM. Better awareness, treatment and control levels were seen among adults with raised blood pressure and raised cholesterol. Conclusions The prevalence of DM and IFG is high among adults, while the levels of awareness, treatment and control are still low in India, and this varied notably between the age groups. Multifaceted approaches that include improved awareness, adherence to treatment, better preventive and counseling services are crucial to halt diabetes in India. Also, expanding traditional systems of medicine (Ayurveda, Yoga, Naturopathy, Unani, Siddha, and Homeopathy [AYUSH]) into diabetes prevention and control practices open solutions to manage this crisis.
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Importance: Cardiovascular disease and risk factors represent a major and increasing burden of death and disability in India, although socioeconomic aspects have been debated in recent years. Objective: To conduct a comprehensive equity analysis of the socioeconomic gradients and distribution of diabetes, hypertension, and obesity in India using the latest national data set. Design, setting, and participants: Cross-sectional study of data originating from the fourth Indian National Family Health Survey collected from January 20, 2015, to December 4, 2016. The study population was based on a nationally representative cross-sectional sample of women aged 15 to 49 years and men aged 15 to 54 years in India, with a response rate of 97% and 92% among eligible women and men, respectively. Biomarker sampling of survey respondents captured height, weight, blood pressure, and random blood glucose levels. Markers of socioeconomic status (SES) were household wealth, education, and social caste. Descriptive analyses and logistic regression models that account for multistage survey design and sampling weights were estimated. Main outcomes and measures: Diabetes, hypertension, and obesity assessed by predetermined thresholds based on biomarker sampling or current medication were the primary outcomes. Results: The survey covered 757 958 individuals (weighted prevalence of 51.2% female). The overall prevalence of diabetes, hypertension, and obesity in the sample was 2.9%, 14.4% and 9.7%, respectively. Positive socioeconomic gradients were observed by household wealth, education, and social caste, and in a majority of states. The magnitude of the SES gradient was strongest for obesity (adjusted odds ratio for highest SES quintile vs lowest, 8.76; 95% CI, 7.70-9.95), followed by diabetes (adjusted odds ratio, 2.31; 95% CI, 1.88-2.85) and hypertension (adjusted odds ratio, 1.58; 95% CI, 1.45-1.72) (P < .001 for all associations). Analyses of the socioeconomic distribution indicated that between 70% and 90% of the population burden of diabetes, hypertension, and obesity was among the higher SES groups, and this figure was similar across states. Conclusions and relevance: Cardiovascular risk factors have an uneven distribution in India. Prevention and treatment strategies should reflect the distribution of the risk factor burden.
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Importance Understanding how diabetes and hypertension prevalence varies within a country as large as India is essential for targeting of prevention, screening, and treatment services. However, to our knowledge there has been no prior nationally representative study of these conditions to guide the design of effective policies. Objective To determine the prevalence of diabetes and hypertension in India, and its variation by state, rural vs urban location, and individual-level sociodemographic characteristics. Design, Setting, and Participants This was a cross-sectional, nationally representative, population-based study carried out between 2012 and 2014. A total of 1 320 555 adults 18 years or older with plasma glucose (PG) and blood pressure (BP) measurements were included in the analysis. Exposures State, rural vs urban location, age, sex, household wealth quintile, education, and marital status. Main Outcomes and Measures Diabetes (PG level ≥126 mg/dL if the participant had fasted or ≥200 mg/dL if the participant had not fasted) and hypertension (systolic BP≥140 mm Hg or diastolic BP≥90 mm Hg). Results Of the 1 320 555 adults, 701 408 (53.1%) were women. The crude prevalence of diabetes and hypertension was 7.5% (95% CI, 7.3%-7.7%) and 25.3% (95% CI, 25.0%-25.6%), respectively. Notably, hypertension was common even among younger age groups (eg, 18-25 years: 12.1%; 95% CI, 11.8%-12.5%). Being in the richest household wealth quintile compared with being in the poorest quintile was associated with only a modestly higher probability of diabetes (rural: 2.81 percentage points; 95% CI, 2.53-3.08 and urban: 3.47 percentage points; 95% CI, 3.03-3.91) and hypertension (rural: 4.15 percentage points; 95% CI, 3.68-4.61 and urban: 3.01 percentage points; 95% CI, 2.38-3.65). The differences in the probability of both conditions by educational category were generally small (≤2 percentage points). Among states, the crude prevalence of diabetes and hypertension varied from 3.2% (95% CI, 2.7%-3.7%) to 19.9% (95% CI, 17.6%-22.3%), and 18.0% (95% CI, 16.6%-19.5%) to 41.6% (95% CI, 37.8%-45.5%), respectively. Conclusions and Relevance Diabetes and hypertension prevalence is high in middle and old age across all geographical areas and sociodemographic groups in India, and hypertension prevalence among young adults is higher than previously thought. Evidence on the variations in prevalence by state, age group, and rural vs urban location is critical to effectively target diabetes and hypertension prevention, screening, and treatment programs to those most in need.
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Background Previous studies have not adequately captured the heterogeneous nature of the diabetes epidemic in India. The aim of the ongoing national Indian Council of Medical Research–INdia DIABetes study is to estimate the national prevalence of diabetes and prediabetes in India by estimating the prevalence by state. Methods We used a stratified multistage design to obtain a community-based sample of 57 117 individuals aged 20 years or older. The sample population represented 14 of India's 28 states (eight from the mainland and six from the northeast of the country) and one union territory. States were sampled in a phased manner: phase I included Tamil Nadu, Chandigarh, Jharkhand, and Maharashtra, sampled between Nov 17, 2008, and April 16, 2010; phase II included Andhra Pradesh, Bihar, Gujarat, Karnataka, and Punjab, sampled between Sept 24, 2012, and July 26, 2013; and the northeastern phase included Assam, Mizoram, Arunachal Pradesh, Tripura, Manipur, and Meghalaya, with sampling done between Jan 5, 2012, and July 3, 2015. Capillary oral glucose tolerance tests were used to diagnose diabetes and prediabetes in accordance with WHO criteria. Our methods did not allow us to differentiate between type 1 and type 2 diabetes. The prevalence of diabetes in different states was assessed in relation to socioeconomic status (SES) of individuals and the per-capita gross domestic product (GDP) of each state. We used multiple logistic regression analysis to examine the association of various factors with the prevalence of diabetes and prediabetes. Findings The overall prevalence of diabetes in all 15 states of India was 7·3% (95% CI 7·0–7·5). The prevalence of diabetes varied from 4·3% in Bihar (95% CI 3·7–5·0) to 10·0% (8·7–11·2) in Punjab and was higher in urban areas (11·2%, 10·6–11·8) than in rural areas (5·2%, 4·9–5·4; p<0·0001) and higher in mainland states (8·3%, 7·9–8·7) than in the northeast (5·9%, 5·5–6·2; p<0·0001). Overall, 1862 (47·3%) of 3938 individuals identified as having diabetes had not been diagnosed previously. States with higher per-capita GDP seemed to have a higher prevalence of diabetes (eg, Chandigarh, which had the highest GDP of US$ 3433, had the highest prevalence of 13·6%, 12.8–15·2). In rural areas of all states, diabetes was more prevalent in individuals of higher SES. However, in urban areas of some of the more affluent states (Chandigarh, Gujarat, and Tamil Nadu), diabetes prevalence was higher in people with lower SES. The overall prevalence of prediabetes in all 15 states was 10·3% (10·0–10·6). The prevalence of prediabetes varied from 6·0% (5·1–6·8) in Mizoram to 14·7% (13·6–15·9) in Tripura, and the prevalence of impaired fasting glucose was generally higher than the prevalence of impaired glucose tolerance. Age, male sex, obesity, hypertension, and family history of diabetes were independent risk factors for diabetes in both urban and rural areas. Interpretation There are large differences in diabetes prevalence between states in India. Our results show evidence of an epidemiological transition, with a higher prevalence of diabetes in low SES groups in the urban areas of the more economically developed states. The spread of diabetes to economically disadvantaged sections of society is a matter of great concern, warranting urgent preventive measures. Funding Indian Council of Medical Research and Department of Health Research, Ministry of Health and Family Welfare, Government of India.
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AimsIndia is the diabetes capital with home to 69.1 million people with DM, the second highest number of cases after China. Recent epidemiological evidence indicates a rising DM epidemic across all classes, both affluent and the poor in India. This article reports on the prevalence of diabetes and pre-diabetes in the North Indian state of Punjab as part of a large household NCD Risk Factor Survey. MethodsA household NCD STEPS survey was done in the state of Punjab, India in a multistage stratified sample of 5127 individuals. All the subjects were administered the WHO STEPS questionnaire, anthropometric and blood pressure measurements. Every alternate respondent in the sample (n = 2499) was assayed for blood parameters. ResultsOverall prevalence of DM among the study participants was found out to be 8.3% (95% CI 7.3–9.4%) whereas prevalence of prediabetes was 6.3% (5.4–7.3%). Age group (45–69 years), marital status, hypertension, obesity and family history of DM were found to be the risk factors significantly associated with DM. Out of all persons with DM, only 18% were known case of DM or on treatment, among whom only about one-third had controlled blood glucose status. Conclusions The study reported high prevalence of diabetes, especially of undiagnosed cases amongst the adult population, most of whom have uncontrolled blood sugar levels. This indicates the need for systematic screening and awareness program to identify the undiagnosed cases in the community and offer early treatment and regular follow up.
Article
Introduction: Since the year 2000, IDF has been measuring the prevalence of diabetes nationally, regionally and globally. Aim: To produce estimates of the global burden of diabetes and its impact for 2017 and projections for 2045. Methods: A systematic literature review was conducted to identify published studies on the prevalence of diabetes, impaired glucose tolerance and hyperglycaemia in pregnancy in the period from 1990 to 2016. The highest quality studies on diabetes prevalence were selected for each country. A logistic regression model was used to generate age-specific prevalence estimates or each country. Estimates for countries without data were extrapolated from similar countries. Results: It was estimated that in 2017 there are 451 million (age 18-99 years) people with diabetes worldwide. These figures were expected to increase to 693 million) by 2045. It was estimated that almost half of all people (49.7%) living with diabetes are undiagnosed. Moreover, there was an estimated 374 million people with impaired glucose tolerance (IGT) and it was projected that almost 21.3 million live births to women were affected by some form of hyperglycaemia in pregnancy. In 2017, approximately 5 million deaths worldwide were attributable to diabetes in the 20-99 years age range. The global healthcare expenditure on people with diabetes was estimated to be USD 850 billion in 2017. Conclusion: The new estimates of diabetes prevalence, deaths attributable to diabetes and healthcare expenditure due to diabetes present a large social, financial and health system burden across the world.
Article
Introduction: Random glucose <200 mg/dL is associated with undiagnosed diabetes but not included in screening guidelines. This study describes a case-finding approach using non-diagnostic random glucose values to identify individuals in need of diabetes testing and compares its performance to current screening guidelines. Methods: In 2015, cross-sectional data from non-fasting adults without diagnosed diabetes or prediabetes (N=7,161) in the 2007-2012 National Health and Nutrition Examination Surveys were analyzed. Random glucose and survey data were used to assemble the random glucose, American Diabetes Association (ADA), and U.S. Preventive Services Task Force (USPSTF) screening strategies and predict diabetes using hemoglobin A1c criteria. Results: Using random glucose ≥100 mg/dL to select individuals for diabetes testing was 81.6% (95% CI=74.9%, 88.4%) sensitive, 78% (95% CI=76.6%, 79.5%) specific and had an area under the receiver operating curve (AROC) of 0.80 (95% CI=0.78, 0.83) to detect undiagnosed diabetes. Overall performance of ADA (AROC=0.59, 95% CI=0.58, 0.60), 2008 USPSTF (AROC=0.62, 95% CI=0.59, 0.65), and 2015 USPSTF (AROC=0.64, 95% CI=0.61, 0.67) guidelines was similar. The random glucose strategy correctly identified one case of undiagnosed diabetes for every 14 people screened, which was more efficient than ADA (number needed to screen, 35), 2008 USPSTF (44), and 2015 USPSTF (32) guidelines. Conclusions: Using random glucose ≥100 mg/dL to identify individuals in need of diabetes screening is highly sensitive and specific, performing better than current screening guidelines. Case-finding strategies informed by random glucose data may improve diabetes detection. Further evaluation of this strategy's effectiveness in real-world clinical practice is needed.
Article
Importance: Diabetes is a known risk factor for cardiovascular disease (CVD). Substantial uncertainty remains, however, about the relevance to CVD risk for blood glucose levels below the diabetes threshold. Objective: To examine the association of random plasma glucose (RPG) levels with the risk for major CVD in Chinese adults without known diabetes. Design, setting, and participants: This prospective cohort study included 467 508 men and women aged 30 to 79 years with no history of diabetes, ischemic heart disease (IHD), stroke, or transient ischemic attack. Participants were recruited from 5 urban and 5 rural diverse locations across China from June 25, 2004, to July 15, 2008, and followed up to January 1, 2014. Exposures: Baseline and usual (longer-term average) RPG level. Main outcomes and measures: Cardiovascular deaths, major coronary events (MCE) (including fatal IHD and nonfatal myocardial infarction), ischemic stroke (IS), major occlusive vascular disease (MOVD) (including MCE or IS), and intracerebral hemorrhage. Preliminary validation of stroke and IHD events demonstrated positive predictive values of approximately 90% and 85%, respectively. Cox regression yielded adjusted hazard ratios (aHRs) for CVD associated with RPG levels. Results: Among the 467 508 participants (41.0% men; 59.0% women; mean [SD] age, 51 [11] years), a significant positive association of baseline RPG levels with CVD risks continued to 4.0 mmol/L (72 mg/dL). After adjusting for regression dilution bias, each 1-mmol/L (18-mg/dL) higher usual RPG level above 5.9 mmol/L (106 mg/dL) was associated with an 11% higher risk for cardiovascular death (6645 deaths; aHR, 1.11; 95% CI, 1.10-1.13). Similarly strong positive associations were seen for MCE (3270 events; aHR, 1.10; 95% CI, 1.08-1.13), IS (19 153 events; aHR, 1.08; 95% CI, 1.07-1.09), and MOVD (22 023 events; aHR, 1.08; 95% CI, 1.07-1.09). For intracerebral hemorrhage, the association was weaker, but also significant (4326 events; aHR, 1.05; 95% CI, 1.02-1.07). These associations persisted after excluding participants who developed diabetes during follow-up. Conclusions and relevance: Among adult Chinese without diabetes, lower RPG levels are associated with lower risks for major CVDs, even within a normal range of blood glucose levels.