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Dynamics of chronic diseases in metro and non-metro regions of India: evidence from India Human Development Survey I and II

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p class="abstract"> Background: The growth of metropolitan cities had significantly contributed to the process of urbanization in India. About two-fifth of the urban population, out of total India’s urban population, live in 35 metropolitan cities. It is important to look into the disease dynamics in the population of metro and non-metro regions of India. The study aims to find the differences in the distribution of chronic diseases in metro and non-metro regions of India and depicts the contributions of background factors causing a change in the prevalence of chronic diseases in metro and non-metro regions of India. Methods: Data from India Human Development Survey (IHDS) I and II conducted in 2004 and 2012 respectively have been used. Bivariate analysis has been performed to find the association between independent variables and chronic diseases, and logistic regression has been used to find the effect of predictor variables on chronic diseases by metro and non-metro regions. Fairlie decomposition technique has been used to find the contribution of each predictor variable accounting for differences in chronic diseases between metro and non-metro regions. Results: Age, sex, socio-economic status (education and wealth), alcohol consumption, tobacco consumption, and body mass index status are significantly associated with chronic conditions in metro regions of India. Age, wealth, and developed regions contributed most to the differences in chronic diseases between metro and non-metro areas. Conclusions: Metro regions in India suffers from a massive burden of chronic conditions. Metro regions should be given a special focus to tackle the menace of chronic diseases. </p
International Journal of Scientific Reports | August 2020 | Vol 6 | Issue 8 Page 322
International Journal of Scientific Reports
Srivastava S et al. Int J Sci Rep. 2020 Aug;6(8):322-331
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pISSN 2454-2156 | eISSN 2454-2164
Original Research Article
Dynamics of chronic diseases in metro and non-metro regions of India:
evidence from India Human Development Survey I and II
Shobhit Srivastava1, Tarique Anwar2, Ratna Patel3*, Shekhar Chauhan4
INTRODUCTION
Urbanization is the prime phenomenon currently visible
in the Indian scenario. The rapid growth of industries and
the phenomenon of globalization acted as a fuel to
urbanization in India. Percent Urban has increased from
11 percent in 1901 to 31 percent in 2011.1 The process of
globalization in the 1990s has played a significant role in
catalysing the speed of urbanization in India.2,3 The
growth of metropolitan cities had significantly
contributed to the process of urbanization in India. About
two-fifth of the urban population, out of total India’s
urban population, live in only 35 metropolitan cities.2
1Department of Mathematical Demography and Statistics, International Institute for Population Sciences, Mumbai,
Maharashtra, India
2International Institute for Population Sciences, Mumbai, Maharashtra, India
3Department of Public Health and Mortality Studies, International Institute for Population Sciences, Mumbai,
Maharashtra, India
4Department of Population Policies and Programmes, International Institute for Population Sciences, Mumbai,
Maharashtra, India
Received: 07 April 2020
Revised: 07 May 2020
Accepted: 08 May 2020
*Correspondence:
Ratna Patel,
E-mail: ratnapatelbhu@gmail.com
Copyright: © the author(s), publisher and licensee Medip Academy. This is an open-access article distributed under
the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial
use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
Background: The growth of metropolitan cities had significantly contributed to the process of urbanization in India.
About two-fifth of the urban population, out of total India’s urban population, live in 35 metropolitan cities. It is
important to look into the disease dynamics in the population of metro and non-metro regions of India. The study
aims to find the differences in the distribution of chronic diseases in metro and non-metro regions of India and depicts
the contributions of background factors causing a change in the prevalence of chronic diseases in metro and non-
metro regions of India.
Methods: Data from India Human Development Survey (IHDS) I and II conducted in 2004 and 2012 respectively
have been used. Bivariate analysis has been performed to find the association between independent variables and
chronic diseases, and logistic regression has been used to find the effect of predictor variables on chronic diseases by
metro and non-metro regions. Fairlie decomposition technique has been used to find the contribution of each predictor
variable accounting for differences in chronic diseases between metro and non-metro regions.
Results: Age, sex, socio-economic status (education and wealth), alcohol consumption, tobacco consumption, and
body mass index status are significantly associated with chronic conditions in metro regions of India. Age, wealth,
and developed regions contributed most to the differences in chronic diseases between metro and non-metro areas.
Conclusions: Metro regions in India suffers from a massive burden of chronic conditions. Metro regions should be
given a special focus to tackle the menace of chronic diseases.
Keywords: Decomposition, Chronic condition, India, Metro regions
DOI: http://dx.doi.org/10.18203/issn.2454-2156.IntJSciRep20203116
Srivastava S et al. Int J Sci Rep. 2020 Aug;6(8):322-331
International Journal of Scientific Reports | August 2020 | Vol 6 | Issue 8 Page 323
The effect of urbanization on the health is two-edged. On
the one hand, there are the benefits of ready access to
healthcare, sanitation, and secure nutrition, while on the
other, there are the evils of overcrowding, pollution,
social deprivation, crime, and stress-related illness.4 The
major drawback of rapid urbanization is that it paves the
way to the burden of chronic diseases too. Lifestyle and
dietary factors, which are by-product of the urbanization,
pose a great challenge and contribute most to the burden
of chronic diseases.5,6 Patterns of urban growth in the
present and future, combined with advances in the
treatment technology, will cause a shift of the burden of
diseases from communicable to non-communicable
diseases.7
Heavy congestion in metro cities is a significant obstacle
in access to health care services. Moreover, an increase in
motor vehicles and inadequate infrastructure may
increase the level of air pollution and road accidents,
respectively. It is also observed that obesity is already
emerging as a significant risk.7 One of the previous
studies reported that in Bengaluru, the prevalence of
chronic conditions was 12 percent, with hypertension and
diabetes being the most common conditions.8 The study
further found that older people, women, and people from
below poverty line were more likely to suffer from
chronic diseases.8 Earlier research shows that there are
significant disparities in health, provision for health care,
and housing conditions between the poorest quartile and
the rest of the population in urban areas in India.9
Similarly, a study states that urban characteristics like
dilapidated housing and inadequate access to health care,
in turn, are associated with concentrated poverty in cities.
10 Many cities experience sharp disparities in wealth
between relatively proximate neighbourhood, which are
related to inequalities in availability and quality of health
care utilization.10 Socio-economic status (SES) assessed
by income, education and occupation is associated with a
wide range of health problems, including cardiovascular
diseases, hypertension, and diabetes.11 Lower SES is
associated with high mortality and morbidity.11 One of
the previous studies argued that earlier infectious diseases
were widespread in developed and developing
countries.12 With the rapidly growing populations, air
pollution and accidents, sedentary lifestyles, the rise in
obesity and diabetes, ultimately resulted in the growing
menace of life-threatening diseases in the urban arena and
the condition is worse than non-urban areas.12
There are many life-styles and dietary risk behaviours
which are the entailments of urbanism that are associated
with chronic conditions. One of the studies carves out the
fact that unhealthy life-style involving tobacco use, lack
regular physical activity, consumption of diets rich in
highly saturated fats, sugars, and salt, typified by fast
foods are highly associated with chronic diseases.13
Obesity caused by an unhealthy diet is one of the prime
factors for the occurrence of chronic disease in a
population in general and the urban population in
particularly.14,15
There is the paucity of studies focusing on the dynamics
of chronic diseases in metro and non-metro regions of
India. Therefore, the present study tries to investigate the
factors contributing to the residential gap of chronic
diseases in the metro and non-metro regions of India.
METHODS
Sample selection
We have not filtered our data that is we did our analysis
on whole sample. We bifurcated the data into two parts
that is in metro and non-metro regions of the country.
The total sample size of the IHDS-I and IHDS-II are
215754 and 204568, respectively. The sample was thus
distributed accordingly comprising of 196,497 and
186,574 respondents in non-metro regions and 19,257
and 17,995 respondents in metro region of India in 2004-
05 and 2011-12 respectively. Moreover, we did our
analysis for chronic diseases as an outcome variable.
Therefore, again the data was bifurcated for respondents
having chronic diseases or not for non-metro and metro
regions respectively.
Type of study
India Human Development Survey is a longitudinal data
but we have used it in a cross-sectional manner to fulfil
our aims and objectives.
Data source
Data from Indian Human Development Survey I and II
(IHDS I and II) carried out in 2004 and 2012,
respectively have been used for the analysis. The India
Human Development Survey (IHDS) is a nationally
representative, the multi-topic survey of 41,554
households in 1503 villages and 971 urban
neighbourhoods across India in 2004-05. The first round
of interviews was completed in 2004-05, and the second
round of IHDS re-interviewed most of these households
in 2011-12 (N=42,152). Six cities namely Mumbai,
Delhi, Kolkata, Chennai, Bangalore and Hyderabad are
clubbed as metro cities in both the rounds of IHDS.
Metropolitan areas were defined as any district included
in the census definition of “urban conglomerates” for
each of these six areas.16
The IHDS administered two sets of questionnaires: a
household economic questionnaire and a health and
education questionnaire. The household economic
questionnaire was administered to the individual having
good piece of knowledge and information of household
income and expenditures, typically, the male head of the
household. Living arrangement variable is constructed
from the household roster. Health information, including
questions on short-term illnesses of any family members
Srivastava S et al. Int J Sci Rep. 2020 Aug;6(8):322-331
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in the last 30 days, were collected in the education and
health questionnaire, typically administered to the wife of
the household’s head. Some differences in reporting of
illness were observed between older women and their
daughters or daughters-in-law; to adjust for this potential
difference, we include the identity of the respondent as a
control variable.
Measurements
Variable description.
Dependent variable
Diabetes, high blood pressure, and heart diseases, which
were self-reported in IHDS data in both rounds of survey
i.e., 2004 and 2012, are clubbed into variable named
chronic diseases coded as 0 and 1.17 Six cities, which
include Mumbai, Delhi, Kolkata, Chennai, Bangalore,
and Hyderabad, are clubbed as metro cities in both
rounds of IHDS.
Independent variables
Background and behavioural factors are taken into
consideration to assess their effects on chronic conditions
among people from the metro and non-metro regions of
India. The categorization of independent variables are as
age (<60 and ≥60 years), sex (male and female), caste
(deprived - scheduled caste (SC) and scheduled tribes
(ST) and non-deprived- other than SC and ST), religion
(Hindu, Muslim, Christian, and others), educational status
(illiterate, primary completed, secondary completed,
higher secondary completed and graduate and above),
wealth quartile (Q1, Q2, Q3, Q4, and Q5) Q1 as poorest,
Q2 as poorer, Q3 as middle, Q4 as richer and Q5 as
richest. The regions of India are classified as less
developed and more developed; eighteen less developed
states include eight empowered action group states
(Bihar, Jharkhand, Madhya Pradesh, Chhattisgarh, Uttar
Pradesh, Uttaranchal, Odisha, and Rajasthan), eight
north-eastern states (Assam, Arunachal Pradesh,
Manipur, Mizoram, Meghalaya, Nagaland, Sikkim,
Tripura), Himachal Pradesh and Jammu and Kashmir
(government of India, 2010). Tobacco consumption (yes
or no), alcohol consumption (yes or no), BMI
(underweight - <18.5, normal - 18.5 to 24.9, overweight -
25 to 29.9 and obesity - 30 and above).
Statistical analysis
Bivariate analysis has been performed to find the
association between independent variables and chronic
diseases by metro and non-metro regions of India.
Logistic regression has been used to find the effect of
predictor variables on chronic diseases by metro and non-
metro regions. To assess the results from simple logistic
regression, outcome variables was recoded in binary form
i.e., coded in 0 and 1. Now to find the contribution of
each predictor variable, which accounts for differences in
chronic diseases between metro and non-metro regions,
the Fairlie decomposition technique has been used.
Before the invent of Fairlie decomposition, the Blinder-
Oaxaca decomposition technique was used for identifying
and quantifying the separate contributions of group
differences in measurable characteristics, such as
education, experience, marital status, and geographical
location, to racial and gender gaps in outcomes. The
technique is easy to apply and only requires coefficient
estimates from linear regressions for the outcome of
interest and sample means of the independent variables
used in the regressions. A problem arises, however, if the
outcome is binary i.e., coded in 0 and 1, such as
employment, college attendance, or teenage pregnancy,
and the coefficients are from a logit or probit model.
These coefficient estimates cannot be used directly in the
standard Blinder-Oaxaca decomposition equations.18 A
relatively simple method of performing a decomposition
that uses estimates from a logit or probit model was first
described in Fairlies decomposition analysis of the
causes of the black/white gap in self-employment rates.
The non-linear decomposition technique may be useful
for identifying the causes of racial, gender, geographical,
or other categorical differences in a binary outcome.19
RESULTS
Figure 1: Population increase in six metro cities of
India from 1971-2011.
Figure 2: Percentage distribution of selected chronic
conditions among people in metro and non-metro
regions of India, IHDS-I and IHDS-II.
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Figure 1 shows the graph for trends of the population in
six metropolitan cities of India from 1971-2011. The
population in all the six metropolitan cities (Mumbai,
Delhi, Kolkata, Chennai, Bangalore, and Hyderabad) has
been increasing at a high pace from 1971 to 2011.
Figure 2 shows the graph depicting chronic disease
conditions in metro and non-metro cities. The graphs
depict that all the diseases (chronic diseases, diabetes,
high blood pressure, and heart diseases) have a
significant preponderance in the metro cities in both the
datasets. A drastic increase in chronic and high blood
pressure can be noticed in metro cities from IHDS-I to
IHDS-II.
Table 1 presents the profile of the population in metro
and non-metro regions in India. In both IHDS-I and II,
the majority of the respondents were ≤60.
Table 1: Percentage distribution of background characteristic by metro and non-metro regions in IDHS I and II,
India.
IHDS I (n=215, 754)
IHDS II (n=204, 568)
Non-metro
Metro
Non-metro
Metro
Age (years)
91.7
92.4
89.1
90.1
8.4
7.6
10.9
10.0
50.6
51.7
49.6
50.5
49.4
48.4
50.4
49.5
29.5
26.7
30.1
27.4
70.5
73.3
69.9
72.6
Religion
81.1
82.6
81.4
82.2
12.9
13.1
13.2
13.9
2.4
1.6
2.1
1.7
3.7
2.7
3.3
2.2
Educational status
40.7
28.2
35.3
24.3
23.8
21.7
21.8
18.0
26.1
32.3
29.4
33.8
5.8
8.7
8.6
13.3
3.6
9.1
4.9
10.6
Wealth quantile
23.7
6.4
24.3
14.2
21.9
16.8
22.5
13.6
20.3
17.6
19.9
16.7
18.3
26.4
17.9
23.5
15.8
32.8
15.3
32.0
Regions
55.3
4.2
56.5
4.5
44.7
95.8
43.5
95.5
Tobacco consumption
84.3
89.3
83.6
87.0
15.7
10.7
16.4
13.0
Alcohol consumption
95.0
95.6
94.0
94.5
5.0
4.4
6.0
5.5
BMI
22.3
15.1
31.4
19.2
11.5
12.9
28.5
31.3
2.0
4.3
6.5
10.1
1.1
1.8
2.7
3.8
63.1
65.9
30.9
35.7
⁺Missing values are system missing values, taken into consideration so that logistic regression can run on full sample.
Srivastava S et al. Int J Sci Rep. 2020 Aug;6(8):322-331
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Table 2: Rate of chronic diseases per 1000 population by background characteristics in metro and non-metro
regions of India, IHDS 2004 and 2012.
Background
characteristics
IHDS I (n=215, 754)
IHDS II (n=204, 568)
Non-metro
(n=194, 653)
Metro
(n=21, 101)
P value
<0.05
Non-metro
(n=184, 000)
Metro
(n=20, 568)
P value
<0.05
Age (years)
>60
14
26
*
26
44
*
≤60
91
203
*
153
260
*
Sex
Male
18
41
*
34
58
*
Female
24
37
*
45
74
*
Caste
Deprived class
12
38
*
23
54
*
Non-deprived class
25
39
*
47
70
*
Religion
Hindu
20
42
*
38
68
*
Muslim
20
19
45
56
Christian
55
64
79
103
*
Others
19
32
*
54
37
*
Educational status
Illiterate
19
41
*
39
78
*
Primary completed
18
35
*
38
51
*
Secondary completed
23
35
*
41
69
*
Higher secondary
completed
22
29
*
32
43
*
Graduate
37
70
*
58
82
*
Wealth quantile
Poorest
6
10
*
31
67
*
Poorer
13
28
30
47
*
Middle
17
17
34
65
*
Richer
26
41
48
67
*
Richest
52
61
*
67
72
Regions
Less developed states
13
21
29
60
*
More developed stated
31
40
*
54
66
*
Tobacco consumption
No
19
39
*
37
60
*
Yes
29
39
*
54
104
*
Alcohol consumption
No
21
39
*
39
63
*
Yes
24
36
49
123
*
BMI
Underweight
3
23
10
13
Normal
18
21
48
63
*
Overweight
52
55
118
150
*
Obese
39
54
*
124
179
*
Total
21
39
*
40
66
*
*If p<0.05.
Around one-fourth of the population in metro cities
belonged to deprived caste in both the rounds of IHDS,
with a small increment in such population from IHDS-I
to IHDS-II. The majority of the population belonged to
Hindu religion in metro as well as non-metro region in
IHDS 1 and in IHDS 2, while illiterates were higher in
the non-metro areas (40.7 percent and 35.3 percent in
both surveys round respectively). The respondents
belonging to poorest wealth quintiles were more (23.7
percent and 24.3 percent in both surveys round,
respectively) in non-metro regions, whereas, respondents
Srivastava S et al. Int J Sci Rep. 2020 Aug;6(8):322-331
International Journal of Scientific Reports | August 2020 | Vol 6 | Issue 8 Page 327
belonging to richest wealth quintiles were more in metro
regions.
Table 2 presents the bivariate association between
chronic diseases and background as well as behavioural
characteristics by metro and non-metro regions of India.
Chronic diseases showed a significant increase from
IHDS-I to IHDS-II in metro as well as non-metro regions.
In both IHDS I and II, the majority of the population
having chronic diseases belong to the 60+ age group.
Table 3: Relationship between chronic diseases and background and behavioural characteristics by metro and non-
metro regions of India, IHDS 2004 and 2012.
Background
characteristics
IHDS I
IHDS II
Non-Metro
Metro
Non-Metro
Metro
Age (years)
<60®
1.00
1.00
1.00
1.00
≥60
5.73* (5.3, 6.2)
8.85* (7.32, 10.69)
6.66* (6.32, 7.03)
7.59* (6.58, 8.77)
Sex
Male®
1.00
1.00
1.00
1.00
Female
1.59* (1.48, 1.71)
1.34* (1.11, 1.61)
1.47* (1.39, 1.55)
1.37* (1.19, 1.58)
Caste
Deprived class®
1.00
1.00
1.00
1.00
Non-deprived class
1.74* (1.59, 1.9)
0.92 (0.75, 1.13)
1.62* (1.52, 1.72)
1.21* (1.03, 1.42)
Religion
Hindu®
1.00
1.00
1.00
1.00
Muslim
1.34* (1.22, 1.48)
0.74* (0.55, 0.99)
1.22* (1.14, 1.31)
0.95 (0.78, 1.16)
Christian
1.81* (1.58, 2.07)
1.27 (0.81, 1.97)
1.38* (1.22, 1.56)
1.59* (1.08, 2.34)
Others
1.05 (0.91, 1.21)
0.82 (0.51, 1.31)
1.68* (1.52, 1.85)
0.49* (0.27, 0.87)
Educational status
Illiterate®
1.00
1.00
1.00
1.00
Primary completed
1.3* (1.19, 1.42)
1.07 (0.81, 1.42)
1.29* (1.2, 1.38)
1.01 (0.82, 1.25)
Secondary completed
1.26* (1.16, 1.38)
1.19 (0.94, 1.5)
1.28* (1.2, 1.37)
1.15 (0.97, 1.37)
Higher secondary
completed
1.01 (0.88, 1.17)
0.86 (0.62, 1.21)
0.92 (0.83, 1.01)
0.86 (0.67, 1.1)
Graduate
1.46* (1.27, 1.68)
1.82* (1.38, 2.4)
1.38* (1.24, 1.53)
1.34* (1.07, 1.69)
Wealth quantile
Poorest®
1.00
1.00
1.00
1.00
Poorer
1.59* (1.38, 1.85)
1.02 (0.52, 1.99)
1.08 (1, 1.18)
0.95 (0.71, 1.27)
Middle
2.08* (1.8, 2.39)
1.61 (0.87, 2.96)
1.17* (1.08, 1.27)
1.17 (0.91, 1.51)
Richer
2.92* (2.55, 3.35)
2.59* (1.44, 4.65)
1.37* (1.27, 1.48)
1.24 (0.98, 1.59)
Richest
4.43* (3.88, 5.06)
4.5* (2.53, 8.02)
1.74* (1.62, 1.88)
1.21 (0.95, 1.54)
Regions
Less developed
states®
1.00
1.00
1.00
1.00
More developed
states
1.51* (1.41, 1.61)
1.21 (0.75, 1.95)
1.22* (1.16, 1.28)
0.85 (0.62, 1.16)
Tobacco consumption
No®
1.00
1.00
1.00
1.00
Yes
1.2* (1.09, 1.32)
1.2 (0.91, 1.59)
1.32* (1.23, 1.41)
1.44* (1.18, 1.77)
Alcohol consumption
No®
1.00
1.00
1.00
1.00
Yes
1.13 (0.97, 1.31)
0.87 (0.55, 1.38)
1.15* (1.04, 1.28)
1.63* (1.24, 2.14)
BMI
Underweight®
1.00
1.00
1.00
1.00
Normal
4.32* (3.48, 5.35)
8.19* (3.25, 20.65)
3.7* (3.37, 4.06)
4.94* (3.48, 7)
Overweight
10.16* (8.04, 12.85)
22.26* (8.75, 56.62)
8.54* (7.71, 9.46)
11.70* (8.17, 16.76)
Obese
9.15* (6.91, 12.11)
26.34* (9.81, 70.73)
10.95* (9.7, 12.35)
17.51* (11.89, 25.8)
*If p<0.05; ®reference category.
Srivastava S et al. Int J Sci Rep. 2020 Aug;6(8):322-331
International Journal of Scientific Reports | August 2020 | Vol 6 | Issue 8 Page 328
Table 4: Fairlie decomposition analysis depicting contribution of background and behavioural characteristics in the
difference of chronic diseases by metro and non-metro regions of India, IHDS 2004 and 2012.
Background
IHDS I
IHDS II
Coefficient
SE
Percent
contribution
Coefficient
SE
Percent
contribution
Age (years)
0.0037
0.00023
20.26
0.0062
0.0002
23.78
Sexual status
0.0000
0.00007
0.02
0.0004
0.0001
1.35
Religion
-0.0003
0.00006
-1.60
-0.0005
0.0001
-1.73
Caste
0.0000
0.00003
0.23
0.0002
0.0000
0.61
Education
0.0003
0.00012
1.88
0.0000
0.0001
0.19
Wealth
0.0040
0.00025
21.93
0.0032
0.0002
12.13
Regions of India
0.0059
0.00056
32.05
0.0071
0.0005
27.37
Tobacco consumption
-0.0005
0.00028
-2.97
-0.0009
0.0002
-3.48
Alcohol consumption
0.0000
0.00005
0.04
-0.0001
0.0000
-0.46
BMI
0.0003
0.00009
1.68
0.0005
0.0002
1.80
Total
73.6
61.6
Number of observations
2,15,754
2,03,881
N of OBS G=0
196497
185915
N of OBS G=0
19257
17966
Predictive mean for chronic
diseases in non-metro
region
0.021
0.040
Predictive mean for chronic
diseases in metro region
0.039
0.066
Difference
0.018
0.026
Total explained
0.013
0.016
The proportion of chronic diseases among females in
metro regions was lesser than males in metro regions in
2004, however, a higher proportion of females than males
were found from suffering chronic diseases in metro
regions in 2012. Concerning states, chronic diseases were
more prevalent in the metro as well as non-metro regions
in the developed states as comparison to less developed
states. Also, the overall prevalence of chronic diseases
showed a significant increase from IHDS-I to II in both
the regions. The prevalence of chronic diseases among
population consuming tobacco and alcohol is higher in
metro regions than in non-metro regions for both the
datasets.
Table 3 presents the relationship between chronic
diseases and background as well as behavioural
characteristics by the metro and non-metro regions of
India. Results show that for IHDS-I dataset, the
population in the age group 60 and above showed a
higher likelihood of suffering from chronic diseases in
both non-metro (OR=5.73, CI 5.3-6.2) and metro regions
(OR=8.85, CI 7.32, 10.69) in comparison to their
counterparts. Among metro and non-metro regions, the
population in metro regions had significantly more odds
to suffer from chronic diseases. Though the likelihood of
chronic diseases increased in IHDS-II in both metro and
non-metro regions, the pattern remained the same. In both
the data sets, females were more likely to suffer from
chronic diseases. A higher likelihood of females suffering
from chronic disease was found in the non-metro regions
in both IHDS-I (OR=1.59, CI 1.48-1.71) and II
(OR=1.47, CI 1.39-1.55). Graduates were more likely to
suffer from chronic diseases in metro regions as per
IHDS-I (OR=1.82, CI 1.38-2.4), while it was non-metro
regions according to IHDS-II dataset (OR=1.38, CI 1.24-
1.53). Concerning household wealth index in both the
datasets, the richest showed the highest likelihood of
having chronic diseases in the metro as well as non-metro
regions in comparison to the poorest ones. According to
both IHDS-I and II, the highest risk of choric diseases
was found among the in-metro regions of more developed
states. The population consuming tobacco showed a
higher risk of suffering from chronic diseases in metro
regions as per IHDS-II (OR=1.44, CI 1.18-1.77), and
similar was the case with alcohol consumption (OR=1.63,
CI 1.24-2.14). In IHDS-I, the obese population showed
the highest risk of suffering from chronic diseases in
metro regions (OR=26.34, CI 9.81, 70.73) in 2004.
Table 4 shows the results of Fairlie’s decomposition
analysis depicting the contribution of background and
behavioural characteristics in the difference of chronic
diseases by metro and non-metro regions of India. The
decomposition analysis suggests in both the surveys i.e.,
in IHDS-I and IHDS-II, the predictive probability of
suffering from chronic diseases was more among
residents of the metro region (0.039 in IHDS-I and 0.066
in IHDS-II). The model explained the 73.62 percent and
61.39 percent of variation for chronic disease between
metro and non-metro regions in IHDS-I and IHDS-II,
Srivastava S et al. Int J Sci Rep. 2020 Aug;6(8):322-331
International Journal of Scientific Reports | August 2020 | Vol 6 | Issue 8 Page 329
respectively. The positive values of the coefficient show
that variables are contributing to widening the gap of
chronic illnesses among residents from metro and non-
metro-regions. In contrast, a negative value indicates that
those variables are contributing to narrowing the gap of
chronic diseases among residents from metro and non-
metro-regions. In both the surveys, it is evident that age
(20.3 and 23.8 percent), wealth status (21.9 and 12.1
percent) and regions of India (32 and 27.4 percent) were
contributing positively i.e., widening the gap for chronic
diseases among people residing in metro and non-metro
regions in India in IHDS-I and IHDS-II respectively.
DISCUSSION
The result found in our study that metro cities are having
high rates of chronic diseases in 2004 and 2011. The
prevalence of chronic disease was 21 and 39 per 1000 in
non-metro and metro regions in 2004, where the
prevalence increased to 40 and 66 per 1000 in non-metro
and metro in 2012, respectively.
Chronic conditions are highly associated with the elderly
population, as found in the present study, which is
justified by other studies.20 Previous studies state that
diabetes increases with age, and the absolute increase in
incidence is observed among adults aged 65 years and
above. Also, individuals who have diabetes are at higher
risk of acquiring cardiovascular diseases. Therefore age
strongly predicts cardiovascular complications.21 The
reason why odds of chronic diseases among elderly are
higher in metro regions than non-metro regions is
probably because of drastic changes in lifestyle behaviour
i.e., change in dietary habits, low physical activity, and
nuclear family setup causing loneliness.22 Gender
inequality i.e., treating girls and women as socially
inferior in many countries, predicts the higher prevalence
of chronic conations among them. Gender inequalities in
the allocation of resources, such as income, education,
health care, nutrition, and the political voice, are strongly
associated with poor health and reduced well-being.23
Earlier studies found that incidence, morbidity, and
mortality from cardiovascular diseases are related to the
socio-economic conditions of the individual. It has been
found that high blood pressure is the by-product of high
educational level, whereas interestingly, diabetes is
independent of age, education, and income level. In the
case of diabetes, other study reports different results that
people from highest wealth quintile were significantly
more likely to have diabetes or co-existence of diabetes
and hypertension.24-26 People from highly developed
states are very much prone to chronic diseases like
Ischaemic heart disease, COPD and strokes, etc. The
probable reasons for this are highly developed
infrastructure causing low physical activity, dependence
on processed food, increase in the proportion of obese
people, increase in aging population and environmental
factor such as air pollution.27-29
The present study also pointed out that people from more
developed states are more prone to chronic conditions.
However, there was an insignificant lower likelihood of
suffering from the chronic condition in metro cities in
2012, the reason was unexplained, and the result is
ambiguous as many previous studies confirm that
smoking is one of the main contributing factors for heart
diseases and high blood pressure.30-32 High alcohol intake
significantly raised systolic and diastolic blood pressure
in both men and women.33 The association of alcohol
intake with diabetes and heart disease are interesting as it
was found in the literature that low level of alcohol
consumption reduces the risk of heart diseases and
diabetes whereas high intake results in the opposite
direction i.e., it causes a high risk of diabetes and heart
diseases.34-37 It was visible from the results that in 2011-
12, in both non-metro and metro regions, people who
consume alcohol were having a significantly higher
likelihood of suffering from chronic conditions. The risk
was much higher in metro regions; alcohol intake is
higher among youth in metro regions of India. Body mass
index has a strong relationship with diabetes and insulin
resistance.38 As found in the present study that how
obesity is significantly associated with chronic
conditions, other literature verified the fact that increased
BMI is highly related to high blood pressure and heart
diseases.39 Policy interventions call out that urban
pollution should adopt a much healthier lifestyle, which
comprised of less consumption of junk foods, sugary
products, low alcohol consumption, improved smoking
practices as well as a regular exercise routine like running
yoga or walking should be include.
Limitations
The limitation of the study is that its diabetes, high blood
pressure, and heart diseases are self-reported that can
cause some validity issues.
CONCLUSION
There is growing evidence that redesigning urban areas
and investing in ‘active’ transport to promote physical
activity has both health and environmental co-benefits.
The fundamental principle is to incorporate physical
activity into the daily routine of the urban-dweller; the
healthy, active choice must become the easy choice. It is
possible with proper urban planning, which can create an
efficient public transport system, including provision for
pedestrians and cyclists, both physical activity levels and
urban air quality will improve. Some parts of the
developing world are leapfrogging developed countries.
The city of Ahmedabad is the winner of the 2010
sustainable transport award for the successful
implementation of “Janmarg,” India’s first full bus rapid
transit (BRT) system. City residents have embraced their
new BRT system; 18, 000 daily passengers use
“Janmarg” to commute to work, to school and elsewhere.
In just a few months of operation, it has transformed the
transport landscape in the city. “Janmarg” uses innovative
Srivastava S et al. Int J Sci Rep. 2020 Aug;6(8):322-331
International Journal of Scientific Reports | August 2020 | Vol 6 | Issue 8 Page 330
central median stations pulled away from the junctions.
Ahmedabad has also initiated car-free days.40 The holistic
approach to city planning is increasingly adopted, and the
WHO’s healthy cities project in the 1990s was an
important step in the right direction. Addressing the
challenges of chronic diseases will require a paradigm
shift in urban planning that takes account of the differing
patterns of urbanization across the world and the need to
reconnect it to public health.
Funding: No funding sources
Conflict of interest: None declared
Ethical approval: The study was approved by the
institutional ethics committee
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Cite this article as: Srivastava S, Anwar T, Patel R,
Chauhan S. Dynamics of chronic diseases in metro
and non-metro regions of India: evidence from India
Human Development Survey I and II. Int J Sci Rep
2020;6(8):322-31.
... Therefore, it is considered that a person's higher level of education serves as a safety net for them and lowers their risk of contracting communicable diseases. Since educated people are more likely to be wealthy and have sedentary lifestyles, which increase their risk of non-communicable illness, they have higher probabilities of developing NCD [27][28][29]. ...
... Sanitation is an important aspect in the occurrence of communicable [30], and it is evident in rural areas sanitation facilities are poorer than in urban areas [31] which can be linked to higher CD among older adults in rural areas than in urban areas. Older adults in urban areas follow a sedentary lifestyle which is why they have higher odds of NCD than their rural counterparts [27,28]. Results noticed slightly lower odds for NCDs among those who consume tobacco than their counterparts. ...
... Education was identified as one of the key contributors to the CD and NCD disparity between urban and rural areas among older persons. In India, the importance of education is widely studied in relation to healthcare among older adults [27,28,39,40]. A person's education influences their awareness and helps them accept a diagnosis and make the necessary behavioural changes [41]. ...
Article
Full-text available
Background A rising proportion of elderly in India has infused notable challenges to the healthcare system, which is already underdeveloped. On one side, NCDs are increasing among the elderly in India; however, on the other side, CDs are also a cause of concern among the elderly in India. While controlling the outbreak of communicable diseases (CDs) remained a priority, non-communicable diseases (NCDs) are placing an unavoidable burden on the health and social security system. India, a developing nation in South Asia, has seen an unprecedented economic growth in the past few years; however, it struggled to fight the burden of communicable and non-communicable diseases. Therefore, this study aimed at examining the burden of CDs and NCDs among elderly in India. Methods Data from Longitudinal Ageing Study in India (LASI Wave-I, 2017–18) were drawn to conduct this study. The LASI is a large-scale nationwide scientific study of the health, economics, and social determinants and implications of India's aged population. The LASI is a nationally representative survey of 72,250 aged 45 and over from all Indian states and union territories. Response variables were the occurrence of CDs and NCDs. The bi-variate and binary logistic regression were used to predict the association between communicable and non-communicable diseases by various socio-demographic and health parameters. Furthermore, to understand the inequalities of communicable and non-communicable diseases in urban and rural areas, the Fairlie decomposition technique was used to predict the contribution toward rural–urban inequalities in CDs and NCDs. Results Prevalence of communicable diseases was higher among uneducated elderly than those with higher education (31.9% vs. 17.3%); however, the prevalence of non-communicable diseases was higher among those with higher education (67.4% vs. 47.1%) than uneducated elderly. The odds of NCDs were higher among female elderly (OR = 1.13; C.I. = 1–1.27) than their male counterparts. Similarly, the odds of CDs were lower among urban elderly (OR = 0.70; C.I. = 0.62–0.81) than rural elderly, and odds of NCDs were higher among urban elderly (OR = 1.85; C.I. = 1.62–2.10) than their rural counterparts. Results found that education (50%) contributes nearly half of the rural–urban inequality in the prevalence of CDs among the elderly. Education status and current working status were the two significant predictors of widening rural–urban inequality in the prevalence of NCDs among the elderly. Conclusion The burden of both CD and NCD among the elderly population requires immediate intervention. The needs of men and women and urban and rural elderly must be addressed through appropriate efforts. In a developing country like India, preventive measures, rather than curative measures of communicable diseases, will be cost-effective and helpful. Further, focusing on educational interventions among older adults might bring some required changes.
... 6 Moreover, old age is associated with several chronic conditions. 7 It is already noted that the share of the older ...
... 15 A few recent studies in different community settings in India reported an increasing prevalence of multimorbidity and suggested that older people are more prone to multimorbidity. 7 20 21 Developing countries are undergoing an epidemiological transition, resulting from a decline in infectious diseases and a constant increase in non-communicable diseases or chronic diseases. 22 In recent years, increased longevity in life expectancy in India has increased the prevalence of chronic conditions among older adults. ...
... 22 In recent years, increased longevity in life expectancy in India has increased the prevalence of chronic conditions among older adults. 7 Despite a rising concern of chronic diseases in India, the issue of multimorbidity has yet to be explored extensively in India. 7 A growing body of research substantiates the effects of multimorbidity on health outcomes beyond risk attributable to individual disease 23 and pinned down specific factors of multimorbidities. ...
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Objective This study examines the prevalence, patterns and factors of chronic disease-related multimorbidity. Also, this study examines the inequality in the prevalence of multimorbidity among older adults in India. Design Cross-sectional study; large nationally representative survey data. Setting and participants We have used the first wave of a Longitudinal Ageing Study in India conducted in 2017–2018 across all the 35 states (excluded Sikkim) and union territories in India. This study used information from 31 373 older people aged 60+years in India. Primary and secondary outcome measures The outcome variable for this study is multimorbidity. The study used multinomial logistic regression to examine the risk factors for multimorbidity among older adults. To measure the inequality in multimorbidity, the slope of index inequality and relative index of inequality have been used to understand the ranked-based inequality. Results Almost one-fourth (24.1%) reported multimorbidity. The relative risk ratio (RRR) of multimorbidity (RRR=2.12; 95% CI=1.49 to 3.04) was higher among higher educated older adults than uneducated older adults. Furthermore, the RRR of multimorbidity (RRR=2.35; 95% CI=2.02 to 2.74) was higher among urban older adults than their rural counterparts. Older adults in the richest wealth quintile were more likely to report multimorbidities (RRR=2.86; 95% CI=2.29 to 3.55) than the poorest older adults. Good self-rated health and no activities of daily living disability were associated with a lower risk of multimorbidities. Conclusions This study contributes to the comprehensive knowledge of the prevalence, factors and inequality of the chronic disease-related multimorbidity among older adults in India. Considering India’s ageing population and high prevalence of multimorbidity, the older adults must be preferred in disease prevention and health programmes, however, without compromising other subpopulations in the country. There is a need to develop geriatric healthcare services in India. Additionally, there is a need to disseminate awareness and management of multimorbidity among urban and highly educated older adults.
... Urbanization also contributes to the increase in the prevalence of NCD risk factors [53][54][55]. Moreover, living in urban areas provides easy access to healthcare facilities, leading to higher health-seeking behaviour , leading to prompt diagnosis of NCD, raising the prevalence of multimorbidity in urban areas than in rural areas [56]. Furthermore, increasing nuclear family trends set up in urban areas could also be attributed to a higher risk of multimorbidity among older adults in urban [53]. ...
... In agreement with previous studies [26,82,83], the prevalence of multimorbidity was higher among nonworking groups than in a working group. Working older adults might be involved in some work-related physical activity and may not follow a sedentary lifestyle, which can explain low risk of multimorbidity [56]. The prevalence of multimorbidity was highest among older adults in the Southern region in the country and regions of India highly explained urban-rural inequality in the prevalence of multimorbidity. ...
Article
Full-text available
Background Multimorbidity is defined as the co-occurrence of two or more than two diseases in the same person. With rising longevity, multimorbidity has become a prominent concern among the older population. Evidence from both developed and developing countries shows that older people are at much higher risk of multimorbidity; however, urban-rural differential remained scarce. Therefore, this study examines urban-rural differential in multimorbidity among older adults by decomposing the risk factors of multimorbidity and identifying the covariates that contributed to the change in multimorbidity. Methods The study utilized information from 31,464 older adults (rural-20,725 and urban-10,739) aged 60 years and above from the recent release cross-sectional data of the Longitudinal Ageing Study in India (LASI). Descriptive, bivariate, and multivariate decomposition analysis techniques were used. Results Overall, significant urban-rural differences were found in the prevalence of multimorbidity among older adults (difference: 16.3; p < 0.001). The multivariate decomposition analysis revealed that about 51% of the overall differences (urban-rural) in the prevalence of multimorbidity among older adults was due to compositional characteristics (endowments). In contrast, the remaining 49% was due to the difference in the effect of characteristics (Coefficient). Moreover, obese/overweight and high-risk waist circumference were found to narrow the difference in the prevalence of multimorbidity among older adults between urban and rural areas by 8% and 9.1%, respectively. Work status and education were found to reduce the urban-rural gap in the prevalence of multimorbidity among older adults by 8% and 6%, respectively. Conclusions There is a need to substantially increase the public sector investment in healthcare to address the multimorbidity among older adults, more so in urban areas, without compromising the needs of older adults in rural areas.
... Furthermore, women in India are more likely to ignore their health and are less likely to seek appropriate healthcare [37], which may further aggravate their risk of ADL and IADL [37,38]. Also, gender inequalities in allocating resources like education, income, political voice, nutrition, and healthcare, are very strongly associated with poor health and reduced well-being [39,40]. A study noted that men were more likely to report needing help with cooking meals, doing laundry, and taking medicines. ...
... To corroborate with the findings of Hung et al. (2011) and Lin et al. (2012), it is imperative to be apprised of and address modifiable factors amalgamated with ADL and IADL [8]. A positive relationship between age and chronic disease suggests that chronic diseases among the elderly increase with their age [40]. Further, literature has established an association between chronic disease and ADL and IADL disability among the elderly [48,49]. ...
Article
Full-text available
Background The increase in life expectancy has proliferated the number of elderly and subsequently increased the prevalence of disability among the elderly. This study assesses the prevalence of Activity of Daily Living (ADL) and Instrumental Activity of Daily Living (IADL) and analyzes determinants of ADL and IADL among elderly aged 60 and over living in India. Methods The study utilized the Longitudinal Ageing Study in India (LASI, 2017–18) data, and information was sought from 31,464 elderly aged 60 years and above. An index of ADL and IADL was created on a scale of three levels, exhibiting no, moderate, or severe levels of ADL/IADL disability. Multinomial logistic regression was used to determine the effect of socio-demographic parameters on ADL and IADL disability among the elderly. Results Around 3% of the elderly reported severe ADL disability, and 6% elderly reported severe IADL disability. Elderly who were not involved in any physical activity than their counterparts were more likely to report severe ADL (RRR = 2.68, C.I. = 1.66–4.32) and severe IADL (RRR = 2.70, C.I. = 1.98–3.67) than no ADL and no IADL, respectively. Conclusion Amidst the study finding, the study emphasizes the importance of setting-up of geriatric care centers in rural and urban areas. It would be feasible to provide geriatric care under the umbrella of already functioning government health facilities in different parts of the country. Community interventions earmarking the elderly with a focus on physical activity, specifically based in group physical exercise and implemented through existing networks, are rewarding for the elderly.
... Also, it has been noted that females tend to suffer from chronic debilitating conditions but not fatal ones, and this explains the paradox of high morbidity and less mortality among them compared to men [29]. In line with previous studies [26,30], the study noted a higher odds of CDs among rural elderly, whereas the risk of NCDs was higher among urban elderly than their respective counterparts. A sedentary lifestyle and physical inactivity could expose the urban population to a high risk of NCDs [31,32]. ...
... Furthermore, nuclear family setup causing loneliness lack of care could be another reason of high NCDs among the urban population [33]. The ndings of higher odds of NCDs among highly educated and richest elderly agree with previous literature [30]. Educated and richest elderly are more likely to follow sedentary lifestyles, which could be a plausible reason for higher NCDs. ...
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Background: While controlling the outbreak of communicable diseases (CDs) remained a priority, non-communicable diseases (NCDs) are placing an unavoidable burden on the health and social security system. India, a developing nation in South Asia, has seen an unprecedented economic growth in the past few years; however, it struggled to fight the burden of communicable and non-communicable diseases. Therefore, this study aimed at examining the burden of CDs and NCDs among elderly in India. Methods: Data from Longitudinal Ageing Study in India (LASI Wave-I, 2017-18) were drawn to conduct this study. Response variables were the occurrence of CDs and NCDs. The bi-variate and binary logistic regression were used to predict the association between communicable and non-communicable diseases by various socio-demographic and health parameters. Furthermore, to understand the inequalities of communicable and non-communicable diseases in urban and rural areas, the Fairlie decomposition technique was used to predict the contribution toward rural-urban inequalities in CDs and NCDs. Results: Prevalence of communicable diseases was higher among uneducated elderly than those with higher education (31.9% vs. 17.3%); however, the prevalence of non-communicable diseases was higher among those with higher education (67.4% vs. 47.1%) than uneducated elderly. The odds of NCDs were higher among female elderly (OR=1.13; C.I. = 1-1.27) than their male counterparts. Similarly, the odds of CDs were lower among urban elderly (OR=0.70; C.I. = 0.62-0.81) than rural elderly, and odds of NCDs were higher among urban elderly (OR=1.85; C.I. = 1.62-2.10) than their rural counterparts. Results found that education (50%) contributes nearly half of the rural-urban inequality in the prevalence of CDs among the elderly. Education status and current working status were the two significant predictors of widening rural-urban inequality in the prevalence of NCDs among the elderly. Conclusion: The burden of both CD and NCD among the elderly population requires immediate intervention. The needs of men and women and urban and rural elderly must be addressed through appropriate effort. In a developing country like India, preventive measures, rather than curative measures of communicable diseases, will be cost-effective and helpful.
... To add more, the differences in the prevalence of hypertension between rural and urban areas could also be attributed to the differences in socioeconomic conditions, risk factors, and quality of healthcare services available in rural and urban areas [33]. Older adults having diabetes are at higher risk of cardiovascular disease, and higher diabetes among urban older adults could well be associated with higher chronic heart diseases among urban older adults [34]. ...
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Background This study examined the prevalence and related factors of four major chronic diseases among older adults in India with a focus on living arrangement and gender. Methods Longitudinal Ageing Study in India survey data (LASI- Wave I) conducted in 2017-18 was used. This study is based on 31,464 older people aged 60+ years in India. The outcome variables for this study are four chronic diseases namely; Hypertension, Diabetes, Chronic lung diseases, and Chronic heart diseases. Binary logistic regression was used to evaluate the association of diagnosed chronic diseases with socio-demographic and health characteristics. Results Prevalence rates of chronic heart diseases (5.8% vs. 4.6%), chronic lung diseases (9% vs. 8%), and diabetes (14.6% vs. 13.9%) were higher among the male older adults than in the female older adults. In contrast, the prevalence of hypertension was higher among the female older adults (37.1% vs. 28%) than in the male older adults. The odds of diabetes were lower among the older adults living with spouse and/or others [Odds Ratio (OR)=0.54, 95% Confidence Interval (CI)=0.32-0.91] and living with spouse and children (OR=0.48, 95% CI=0.29-0.81) than living alone. The odds of chronic lung diseases (OR=0.62, 95% CI=0.50-0.78) and chronic heart diseases (OR=0.72, 95% CI=0.54-0.96) were lower among females than in males. Conclusions Given the higher prevalence of chronic diseases among older adults, there is a need to set-up the geriatric clinics to cater to the needs of the older adult population. Furthermore, special attention should be given to the older adults living alone.
... The results explicitly found that the prevalence of hypertension and diabetes was much higher among the elderly in urban areas than in rural areas. Rapid urbanization is directing a change in lifestyle accompanied by inadequate physical activity and over-dependence on junk food among elderly in urban areas, which is the prime cause of higher diabetes among elderly in urban areas [13][14][15][16]. Previous studies are in concordance with this study in nding a higher prevalence of hypertension in urban areas than in rural areas [6,17]. ...
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Objectives: The study estimates the prevalence of hypertension and diabetes among older adults and bring forth the urban-rural differentials in the said morbidities. The treatment-seeking approach of older adults is also looked at with regard to hypertension and diabetes. Methods: The data for this study come from the Longitudinal Ageing Study in India (LASI) conducted in 2017-18. Bivariate analyses were used to understand the rural-urban gap in hypertension and diabetes with socioeconomic and demographic parameters. Further, logistic regression was used to check the likelihood of hypertension and diabetes with socioeconomic and demographic variables. Finally, a non-linear decomposition technique, Fairlie’s decomposition technique was applied to check the difference in the probability of hypertension and diabetes between rural and urban by estimating contributions of a group (rural-urban) differences. Results: Study noted a higher prevalence of hypertension and diabetes among elderly residing in urban areas than their counterparts. Prevalence of hypertension and diabetes was higher among those aged 70+, elderly females, less educated, and non-poor. Education status alone accounts for more than four-fifths (88.62%) and more than half (52.02%) of the inequality in the prevalence of urban-rural hypertension and diabetes, respectively. Elderly with higher education were 2.88 times (OR=2.88; C.I.= 1.40-5.90) more likely to sought treatment for hypertension than uneducated older people in urban areas Conclusion: Since treatment-seeking is relatively low among elderly in poor households, practices must be identified for a poverty-stricken elderly population to overcome the financial barriers that may prevent the elderly from seeking and complying with treatment.
... Furthermore, a multi-country study has confirmed that over the three decades from 1985 to 2017, a large share of the increase in obesity worldwide is attributed to the rise in obesity in rural areas [68]. People in rural areas fast adopt the lifestyle followed in urban areas and are more vulnerable and susceptible to chronic illnesses, including obesity [69]. The high prevalence of obesity may be due to eating more carbohydrates and fats-rich diets like bread and rice [70], which could be attributed to a higher risk of obesity among rural women. ...
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Background Nutrition has been a low-priority area in Pakistan, with low visibility from the political leadership. Despite various efforts, Pakistan has been reported to have one of the highest prevalences of child and women malnutrition compared to other developing counties. Therefore, this study intends to examine the prevalence and determinants of nutritional status of women and children in Pakistan. Methods The present study uses the Demographic Health Survey (DHS) data from Pakistan 2012–13 (PDHS-3). The nutritional status of women was examined through Body-Mass Index (Underweight, normal, overweight, & obese), and that of children was examined through stunting (severe and moderate), wasting (severe, moderate, overweight), and underweight (severe, moderate, overweight). Descriptive statistics and bivariate analysis have been used along with multinomial logistic regression. Results A higher proportion of children in rural areas were severely stunted (19.6% vs. 12.5%), severe wasted (2.4% vs. 2.2%), and severe underweight (9.4% vs. 6%) than their urban counterparts. A higher proportion of rural women (9.5% vs. 5.5%) were underweight than urban women, whereas a higher proportion of urban women were obese (24.3% vs. 19.0%) than rural women. The odds of severe stunting (OR = 0.24; C.I. = 0.15–0.37), severe underweight (OR = 0.11; C.I. = 0.05–0.22) were lower among children from the richest wealth quintile than their poorest counterparts. The Relative Risk Ratio (RRR) of being overweight (RRR = 3.7; C.I. = 2.47–5.54) and Obese (RRR = 4.35; C.I. = 2.67–7.07) than normal BMI were higher among women from richest wealth quintile than women belonged to poorest wealth quintile. Conclusion This study has highlighted determinants associated with maternal and child nutritional status, whereby the child’s nutritional status was measured by stunting, wasting, and underweight, and BMI measured the mother’s nutritional status. The main risk factors for a child’s poor nutritional status include low household wealth, urban residence, and mother’s educational status. Similarly, the main risk factors for women’s poor nutritional status include increasing the women’s age, educational status, rural residence, and household wealth. Poor households should be provided special attention to improve the nutritional status among women and children in poor households.
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Background: Multimorbidity is defined as the co-occurrence of two or more than two diseases in the same person. With rising longevity, multimorbidity has become a prominent concern among the older population. Evidence from both developed and developing countries shows that older people are at much higher risk of multimorbidity, however, urban-rural differential remained scarce. Therefore, this study examines urban-rural differential in multimorbidity among older adults by decomposing the risk factors of multimorbidity and identifying the covariates that contributed to the change in multimorbidity. Methods: The study utilized information from 31,464 older adults (rural-20,725 and urban-10,739) aged 60 years and above from the recent release of the Longitudinal Ageing Study in India (LASI) wave 1 data. Descriptive, bivariate, and multivariate decomposition analysis techniques were used. Results: Overall, significant urban-rural differences were found in the prevalence of multimorbidity among older adults (difference: 16.3; p<0.001). Moreover, obese/overweight and high-risk waist circumference were found to narrow the difference in the prevalence of multimorbidity among older adults between urban and rural areas by 8% and 9.1%, respectively. Conclusion: There is a need to substantially increase the public sector investment in healthcare to address the multimorbidity among older adults, more so in urban areas, without compromising the needs of older adults in rural areas.
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Background: Functional limitation is a relevant health outcome to examine the quality of life among the elderly. In recognition of its importance, research evidence evaluating life satisfaction among older people has increased globally, but such research is minimalistic in the Indian context. Furthermore studies in the Indian context examining life satisfaction among the elderly population in the context of ADL and IADL are hard to find. Therefore, this study examines the association between functional limitations and life satisfaction among the older population in India. Methods: Data for this study was utilized from the recent release of Longitudinal Ageing Study in India (LASI) wave 1. The total sample size for the present study is 31,464 older adults aged 60 years and above. Life satisfaction was the main dependent variable categorized as 0 “high,” 1 “medium,” and 2 “low.” Descriptive statistics, along with bivariate analysis, was used to present the preliminary analysis. Apart from that, the ordered logistic regression analysis was used to carve out the results. Results: Overall, about one-third of older adults had low life satisfaction scores, and 46% of older adults had a high life satisfaction score. The low life satisfaction score was higher among older adults who reported poor self-rated health (36.7%) than those who reported good self-rated health (27.9%). For older adults who were independent for ADL, the odds of low life satisfaction score (LSS) versus the combined medium and high LSS were 1.20 times more than for older adults who were not independent for ADL [UOR: 1.20; CI: 1.14-1.26]. Conclusion: In this study, a possible association between functional limitations and life satisfaction among the elderly was explored. Both ADL and IADL were noted as factors determining life satisfaction among elderly and elderly reporting ADL and IADL had higher odds of LLS. The setting up of geriatric clinics under the Primary Health Care services would bring the necessary change as this would provide timely healthcare services to the elderly and generate a perception of overall satisfaction among the elderly as they may feel secure in the presence of better health infrastructure.
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Morbidity among the elderly has an important influence on their physical functioning and psychological well-being. Understanding the role of chronic diseases in disability among them is pertinent for policies and programmes aimed at their welfare and management of chronic diseases. This study assesses the association between chronic diseases and disability among the elderly in India. Data from the “Building a Knowledge Base on Population Ageing in India (BKPAI)” survey conducted in seven states of India in 2011 has been used for bivariate and multivariate analysis. Twenty- nine per cent of the elderly have arthritis, 21 per cent hypertension, 13 per cent cataract, 10 per cent diabetes, 7 per cent asthma and 6 per cent heart disease. Eight per cent elderly have at least one functional disability and 73 per cent have at least one physical disability. Multivariate analysis corroborated the bivariate findings that elderly persons with chronic diseases are significantly more likely to have functional and physical disability. Periodic assessment of the health status of the elderly and provision of required preventive as well as curative measures for a healthy elderly population should be a policy priority.
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Background The burden of disability and chronic morbidity among the elderly has been increasing substantially in India in recent years. Yet, the use of nationally representative data to investigate the relationship between chronic morbidity and reported disability in the country has been minimal. The objective of this study is twofold: i) to quantify the association between chronic morbidities and overall disabilities in the activities of daily living (ADLs) among elderly people in India, and ii) to understand how various chronic morbidities influence individual ADLs, specifically, walking, toileting and dressing. Methods We used data from the India Human Development Survey-II (IHDS-II) as a basis for this study. We computed the Katz Index of independence in ADL to examine the burden of disability among the elderly. Ordered logistic regression was carried out to examine the effect of chronic morbidities on: i) the disability index (where 0 = no disability; 1 = disability in 1 or 2 ADLs; and 2 = disability in 3 ADLs), and ii) disabilities in three ADLs in the population over-60 years of age in India. Results The percentage of people scoring lower Katz index (indicating severe and mild disability) in at least one of the three ADLs is very high in India (17.91% for males and 26.21% for females). Irrespective of the type of ADL, the Katz score is lower in elderly females than in elderly males. Elderly people who are illiterate and belong to the poorest wealth quintile report lower Katz scores in ADL. Both bivariate and multivariate analyses confirm that all three types of chronic morbidities are positively and significantly associated with a disability condition in the ADLs. Yet, the effects of morbidities vary greatly according to the type of disability. For instance, while diabetes affect walking (OR: 2.56; 95% CI: 2.29–2.86), and toileting (OR: 2.63; 95% CI: 2.26–3.07), high blood pressure mainly affects walking (OR: 2.29, 95% CI: 2.09–2.5) and dressing disabilities (OR: 2.13, 95% CI: 1.84–2.46). Conclusions Chronic morbidity is a decisive factor in old age disability. It is crucial to reduce chronic morbidity in a timely way to minimise the enormous associated burden of disability.
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Background: Different studies have found that socioeconomic determinants influence the prevalence of chronic diseases in older people. However, there has been relatively little research on the incidence of how social isolation may affect them. We suggest that social isolation is a serious concern for people living with chronic illnesses. Method: In this paper, we examine whether there is an increase in the propensity of being diagnosed with chronic illnesses because of a decrease in social relations for elderly Europeans. We have used a panel data for Waves 1-6 (2004-2015) of Survey on Health, Ageing and Retirement in Europe (SHARE) and logistic regressions. Besides, we have studied three geographic macro-areas (Nordic, Continental and Southern). Being diagnosed with three or more chronic diseases is considered as a dependent variable, and as social control variables we have used three isolation proxies (living alone, providing help to family, friends or neighbours and participation-club activities). Other socio-demographic variables are included (gender, age, educational level, job situation, area of location and quality of life). Results: Our results for the full sample indicate that people who participate in social activities have fewer probability of suffering from chronic diseases (OR = 0.70, 95% CI 0.54, 0.92). For people who live alone the reverse effect is observed (OR = 1.20, 95% CI 1.04, 1.39). Differences are shown by macro-areas, e.g. providing help (OR = 0.58, 95% CI 0.34, 0.97) isolation proxy is significant for the Nordic macro-area. Club-participation activities and living alone are significant for Continental and Southern macro-areas, respectively (OR = 0.65, 95% CI 0.55, 0.82; OR = 1.46, 95% CI 1.21, 1.77). Conclusions: Social isolation increases the risk of being diagnosed with chronic illnesses. That is, people with greater social participation have lower risk of suffering from multiple chronic diseases. This risk linked to isolation, together with the traditional one associated with lifestyles, should be considered in the development of new public policies.
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Background India is a populous country of about 1.3 billion. Non communicable diseases (NCDs) contribute to around 5.87 million (60%) of all deaths in India. Hence, the objectives of this paper are to find baseline information on different NCD risk factors coverage and to determine their trends in India. Methods For this systematic review, PubMed, Google and different surveillance systems were searched. Of the search results, 41 papers/survey reports were eventually assessed for eligibility. National and state representative data on NCD risk factors (for the major NCDs like cardiovascular diseases, chronic respiratory disease, cancer and diabetes) having World Health Organization(WHO) indicator definitions, covering rural and urban population, were included in the study. Thereafter, state-wise population proportion was added and divided by the total Indian population to determine the percentage of population coverage for each risk factor by the surveys. Also, the old and current data of the periodic surveys were compared to assess prevalence trends. Results Various national/state level surveys in India include single or multiple risk factors. Nationwide coverage is available for tobacco use, alcohol drinking, raised blood pressure and overweight and obesity. Periodic National Family Health Surveys provide information on selected risk factors during 2005-16 among adults aged 15-49 years. An overall significant increase was noted in overweight and obesity while decline was noted in tobacco and alcohol use during the same period. From GATS 1 (2009-10) to 2 (2016-17) also, the prevalence of tobacco consumption decreased in India. Conclusion India has a much delayed response on NCD risk factors surveillance and information of the same are sporadic and incomplete. In order to increase information comprehensiveness, standard WHO NCD risk factors questions must be incorporated in the ongoing surveys. India should also plan for cost and time effective NCD surveillance system.
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It is well known that cardiovascular disease is the leading cause of mortality in the western societies. A number of risk factors such as family history, diabetes, hypertension, obesity, diabetes, smoking and physical inactivity are responsible for a significant proportion of the overall cardiovascular risk. Interestingly, recent data suggest there is a gradient in the incidence, morbidity and mortality of cardiovascular disease across the spectrum of socioeconomic status, as this is defined by educational level, occupation or income. Additionally, dietary mediators seem to play significant role in the pathogenesis of cardiovascular disease, mediating some of the discrepancies in atherosclerosis among different socioeconomic layers. Therefore, in the present article, we aim to review the association between socioeconomic status and cardiovascular disease risk factors and the role of different dietary mediators.
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It is fair to say that the impact of globalization in the cultural sphere has most generally been viewed in a pessimistic light. Typically, it has been associated with the destruction of cultural identities, victims of the accelerating encroachment of a homogenized, westernized, consumer culture. The contemporary phase of globalization which began in the post-cold war era i.e.in 90’s, when in 1991 govt. of India followed the policy of LPG (Liberalization, privatization and globalization). Ever since then there have been numerous changes in various areas i.e. political, social and economical. Here in we will focus on social arena which largely includes the following: (i) Culture which can be in present scenario be termed as global cultural diversity, (ii) Education and health sector affected by SAP (Structural adjustment programme), (iii) Social institution i.e. family, marriage and kinship, (iii) Bazaar culture. Here in, we will be critically analyzing the above mentioned aspects and will examine how globalization is transforming the Indian society.
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Background: Chronic conditions are on rise globally and in India. Prevailing intra-urban inequities in access to healthcare services compounds the problems faced by urban poor. This paper reports the trends in self-reported prevalence of chronic conditions and health-seeking pattern among residents of a poor urban neighborhood in south India. Methods: A cross sectional survey of 1099 households (5340 individuals) was conducted using a structured questionnaire. The prevalence and health-seeking pattern for chronic conditions in general and for hypertension and diabetes in particular were assessed and compared with a survey conducted in the same community three years ago. The predictors of prevalence and health-seeking pattern were analyzed through a multivariable logistic regression analysis. Results: The overall self-reported prevalence of chronic conditions was 12%, with hypertension (7%) and diabetes (5.8%) being the common conditions. The self-reported prevalence of chronic conditions increased by 3.8 percentage point over a period of three years (OR: 1.5). Older people, women and people living below the poverty line had greater odds of having chronic conditions across the two studies compared. Majority of patients (89.3%) sought care from private health facilities indicating a decrease by 8.7 percentage points in use of government health facility compared to the earlier study (OR: 0.5). Patients seeking care from super specialty hospitals and those living below the poverty line were more likely to seek care from government health facilities. Conclusion: There is need to strengthen health services with a preferential focus on government services to assure affordable care for chronic conditions to urban poor.
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Objective: Observational studies indicate that moderate levels of alcohol consumption may reduce the risk of type 2 diabetes. In addition to providing an updated summary of the existing literature, this meta-analysis explored whether reductions in risk may be the product of misclassification bias. Research design and methods: A systematic search was undertaken, identifying studies that reported a temporal association between alcohol consumption and the risk of type 2 diabetes. No restrictions were placed upon the language or date of publication. Non-English publications were, where necessary, translated using online translation tools. Models were constructed using fractional polynomial regression to determine the best-fitting dose-response relationship between alcohol intake and type 2 diabetes, with a priori testing of sex and referent group interactions. Results: Thirty-eight studies met the selection criteria, representing 1,902,605 participants and 125,926 cases of type 2 diabetes. A conventional noncurrent drinking category was reported by 33 studies, while five reported a never-drinking category. Relative to combined abstainers, reductions in the risk of type 2 diabetes were present at all levels of alcohol intake <63 g/day, with risks increasing above this threshold. Peak risk reduction was present between 10-14 g/day at an 18% decrease in hazards. Stratification of available data revealed that reductions in risk may be specific to women only and absent in studies that adopted a never-drinking abstention category or sampled an Asian population region. Conclusions: Reductions in risk among moderate alcohol drinkers may be confined to women and non-Asian populations. Although based on a minority of studies, there is also the possibility that reductions in risk may have been overestimated by studies using a referent group contaminated by less healthy former drinkers.
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The Blinder-Oaxaca decomposition technique is widely used to identify and quantify the separate contributions of group differences in measurable characteristics, such as education, experience, marital status, and geographical differences to racial and gender gaps in outcomes. The technique cannot be used directly, however, if the outcome is binary and the coefficients are from a logit or probit model. I describe a relatively simple method of performing a decomposition that uses estimates from a logit or probit model. Expanding on the original application of the technique in Fairlie [3], I provide a more thorough discussion of how to apply the technique, an analysis of the sensitivity of the decomposition estimates to different parameters, and the calculation of standard errors. I also compare the estimates to Blinder-Oaxaca decomposition estimates and discuss an example of when the Blinder-Oaxaca technique may be problematic.