The present study depicting the spatial variation in the prevalence of CVDs is based on 239,729 women in their late reproductive ages (35-49 years) from 36 states/Union Territories of India.
The descriptive statistics encompassing prevalence results for various socio-demographic and behavioral indicators are portrayed in Table 1. Results from NFHS-4 data show 17.4 percent of women in the late reproductive ages of 35-49 years are currently suffering from CVDs. A higher prevalence of CVDs is found among urban women (21.9 percent), as compared to rural (14.9 percent) women. Women with more than 10 years of schooling have a higher prevalence of CVD-affected individuals (22 percent), as compared to their counterparts with less than 10 years of schooling (18.7 percent), or even those who have not had any formal education (14.7 percent). Women practising Islam (20.3 percent) or religions other than Hinduism or Islam (19.5 percent) have a higher prevalence of CVDs, whereas Hindu women have a lower CVD prevalence (16.8 percent). As the wealth index progresses from least well-off to most well-off, the prevalence of CVDs also gradually increases from 10.3 percent for the least well-off category to 22.8 percent for the most well-off category. Non-smoking women have a higher CVD prevalence (17.4 percent), as compared to their counterparts who smoke (15.9 percent), as do women who do not consume alcohol (17.5 percent), as compared to their counterparts who do (13.9 percent).
Table 1 Demographic/Socio-economic/Behavioural Characteristics and age-standardized prevalence of CVDs along with 95% CI among women in their late reproductive ages (35–49 years) in India, 2015–16
Demographic/Socio-economic/Behavioural Characteristics
|
Weighted Prevalence (Frequency)
|
Age-standardized prevalence [C.I.]
|
Age
|
|
|
35-39
|
14.5 (89535)
|
|
40-44
|
17.09 (76804)
|
|
45-49
|
20.65 (73390)
|
|
Place of Residence
|
|
|
Urban
|
21.58 (86909)
|
21.87 [21.16-22.58]
|
Rural
|
14.73 (152820)
|
14.85 [14.53-15.18]
|
Highest educational level
|
|
|
No formal education
|
14.78 (109817)
|
14.67 [14.29-15.05]
|
<10 yr schooling
|
18.21 (79402)
|
18.70 [18.19-19.21]
|
>10 yr schooling
|
20.93 (50511)
|
21.97 [21.20-22.75]
|
Religion
|
|
|
Hindus
|
16.67 (196381)
|
16.82 [16.45-17.19]
|
Muslims
|
19.85 (28919)
|
20.27 [19.47-21.07]
|
Others
|
19.31 (14429)
|
19.45 [18.46-20.45]
|
Wealth Index
|
|
|
Poorest
|
10.2 (40583)
|
10.28 [9.87-10.70]
|
Poorer
|
13.81 (44944)
|
13.93 [13.43-14.42]
|
Middle
|
17.06 (47827)
|
17.20 [16.59-17.81]
|
Richer
|
20.06 (51164)
|
20.26 [19.56-20.96]
|
Richest
|
22.64 (55210)
|
22.77 [22.04-23.50]
|
Smoke
|
|
|
No
|
17.24 (235209)
|
17.43 [17.10-17.77]
|
Yes
|
15.94 (4520)
|
15.84 [14.26-17.42]
|
Drinks alcohol
|
|
|
No
|
17.28 (235224)
|
17.46 [17.13-17.80]
|
Yes
|
14.07 (4505)
|
13.89 [12.35-15.44]
|
Total
|
17.22
|
17.39 [17.06-17.72]
|
Table 2 depicts global Moran's I results, which measures the Moran's I test statistic for spatial autocorrelation. Higher values of Moran's I imply high spatial autocorrelation in CVD among districts of India. In case of CVD data from NFHS-4, the Moran's I value of 0.49 is significantly high. Diagnosing spatial autocorrelation as an issue and the errors (residuals) being related systematically amongst themselves implies the usage of a different method to study these phenomena: the spatial regression approach.
For bivariate association between CVDs and independent variables, Moran's I values are 0.45 for women with no formal education and 0.45 for women with more than 10 years of schooling, 0.42 for rural places of residence, 0.36 for hailing from a Scheduled Caste or Other Backward Castes, signifying high spatial autocorrelation among different Indian districts w.r.t. CVD cross-classified by different socio-demographic characteristics.
Table 2 Moran’s I statistics showing spatial dependence of CVDs by different household and background characteristics across districts of India, 2015–16
Indicators
|
Univariate Moran’s I
|
Bi-variate Moran’s I
|
Urban
|
0.26
|
0.34
|
Rural
|
0.42
|
0.42
|
No formal education
|
0.43
|
0.45
|
<10 yr schooling
|
0.43
|
0.45
|
>10 yr schooling
|
0.30
|
0.33
|
SC
|
0.25
|
0.36
|
ST
|
0.11
|
0.25
|
OBC
|
0.29
|
0.36
|
Non-SC/ST/OBC
|
0.15
|
0.19
|
Consume Alcohol
|
0.08
|
0.08
|
Smoke
|
0.21
|
0.25
|
Overall
|
0.49
|
|
Table 3 presents the results of the Ordinary Least Square (OLS) model, Spatial Lag Model (SLM), and Spatial Error Model (SEM). Concentrating on the OLS model, it is found that smoking, alcohol consumption, hailing from a Scheduled Caste background, more than 10 years of schooling, as well as urban places of residence appeared as significant correlates of CVD prevalence in the country. However, it would be early to conclude before model diagnostics are taken into consideration.
Table 3 Summary of spatial error model and maximum likelihood estimation for CVD by some selected background characteristics across different districts of India, 2015-16
Indicators
|
OLS Model
Coefficients
|
Sig. value
|
Spatial Lag Model
Coefficients
|
Sig. value
|
Spatial Error Model
Coefficients
|
Sig. value
|
Urban
|
0.071
|
0.000
|
0.064
|
0.000
|
0.056
|
0.004
|
>10 yr schooling
|
0.164
|
0.000
|
0.063
|
0.027
|
0.102
|
0.013
|
SC
|
0.264
|
0.000
|
0.209
|
0.000
|
0.240
|
0.000
|
ST
|
0.074
|
0.147
|
0.125
|
0.003
|
0.120
|
0.003
|
OBC
|
0.055
|
0.278
|
0.114
|
0.006
|
0.120
|
0.003
|
Non-SC/ST/OBC
|
0.075
|
0.154
|
0.133
|
0.002
|
0.139
|
0.001
|
Consume Alcohol
|
0.092
|
0.040
|
0.050
|
0.165
|
0.029
|
0.550
|
Smoke
|
0.390
|
0.000
|
0.178
|
0.015
|
0.128
|
0.190
|
AIC
|
4582.970
|
|
4376.950
|
|
4364.270
|
|
Lambda
|
|
|
|
|
0.663
|
|
Rho
|
|
|
0.601
|
|
|
|
R-squared
|
0.173
|
|
0.449
|
|
0.470
|
|
A Moran's I score of 0.49 (p<0.001) is highly significant, indicating a strong spatial autocorrelation of the residuals. Both Lagrange Multiplier (lag) and Lagrange Multiplier (error) are significant, pointing towards spatial dependence. Also, Akaike Information Criterion (AIC), measuring the relative goodness of fit for the models, prefers the model with the least value of AIC. The co-efficient of determination (R-squared = 0.173 for OLS) does not indicate OLS to be the best fit for the given CVD data.
Once the presence of spatial dependence has been established, the spatial lag model is applied with maximum likelihood approach, the results of which are presented in table 3. An additional indicator in the form of spatial lag coefficient (Rho’ρ') appears in the model. It shows the spatial dependence inherent in the sample data, calculating the average influence of neighboring observations on a particular observation. Inclusion of this criterion and the fit of the spatial lag model also converts into a higher R-squared value. The effects of other independent variables remain virtually the same.
Next, the spatial error model has been employed. In the SEM, an additional term emerges, i.e., the co-efficient on the spatially correlated errors (Lambda 'λ'). It has been found to have a positive effect and is highly significant, too. Hence, the general fit improved, as evidenced by the R-squared value.
Both the spatial models are a marked improvement over the OLS model; among the two, the spatial error model emerging to be the most improved of the lot. Variables significant in the OLS model like alcohol consumption were no more significant in the spatial models. Other variables like urban places of residence, hailing from a Scheduled Caste or Tribe or Other Backward caste background, as well as smoking (significant in the SLM but eventually insignificant in the SEM) have all been positively correlated with CVD prevalence in the regions, controlling for selected socio-demographic and behavioural variables.
Bivariate LISA (Cluster and significance) results:
Figure 1 demonstrates bivariate LISA (cluster and significance) map of CVD prevalence across 640 districts in India. There are four hotspot clusters consisting of 73 districts concurrent with high prevalence of CVDs as well as high prevalence of women without any formal education (Fig. 1a). One of the clusters is majorly in Jammu & Kashmir, consisting of the districts like Kupwara, Bandipore, Udhampur, Doda , Kishtwar, Punch, Rajouri, Srinagar, Pulwama, Anantnag. Another cluster consists of parts of Punjab and Himachal Pradesh, with districts like Jalandhar, Una, Ludhiana, Bilaspur, Mandi. Other clusters in Haryana, Andhra Pradesh, Tamil Nadu consisted of districts like Sripotti, Sriramulu, Nellore, Thiruvallur, Kanchipuram, Thirivarur, Nagapattinam, Thottukud, Tirunelveli. So, the clusters are mainly in the northern central region, and additionally comprise of the south-eastern coastal districts. Similarly, there are 117 districts with low CVD prevalence and low percentage of women with no formal education majorly spread across western and central India like Barmer, Jalor, Udaipur, Pratapgarh, Sawai Madhopur, Mahoba, Fatehpur, Hamirpur, Chhindwara, Betul. These areas are broadly constituted by districts from northern and central Gujarat, southern Uttar Pradesh, southern and eastern Rajasthan, Chhattisgarh, southern Bihar, south-eastern Maharashtra.
Also interesting are those district clusters which have high CVD prevalence among women, but low percentage of women without formal education like Aurangabad and Nagpur. Almost 14 districts have low CVD prevalence but high percentage of women without formal education, like Nalgonda, Chittor, Kolam, Idukki.
In case of bivariate results depicting CVD among women aged between 35-49 years and those with less than 10 years of schooling (Fig. 1b), the high-high hotspot cluster in Kashmir remained almost similar to the previous scenario of high CVD prevalence concurrent with a high percentage of women without any formal education. Other districts with the same situation are Bilaspur, Solan, Ludhiana, Bulandshahr, Kaithal, Karnal, Gonda, Maharajganj, Guntur, Sri Potti, Sriramulu, Nellore, Thiruvallur, Kancheepuram, Ramanathapuram, Virudhanagar, Thottukud, Madurai, Sivaganga, Pudukottai, Thanjavur.
The 106 districts with low CVD prevalence as well as low prevalence of women with less than 10 years of schooling consisted of districts like Kachchh, Banas Kantha, Sirohi, Udaipur, Uttarkashi, Rudraprayag, Chamoli, Tehri Garhwal, Raipur, Bilaspur, Mahasamund, Hingoli, majorly constituted by southern Rajasthan, northern coastal Gujarat, almost the whole of Uttarakhand, southern Uttar Pradesh, southern Bihar, Chhattisgarh, central and eastern Maharashtra.
The high CVD prevalence along with low prevalence of women with less than 10 years of schooling consists of districts like Pune, Nagpur, Neemuch.
Low CVD prevalence along with high prevalence of women with less than 10 years of schooling is found in 19 districts, a few of which are Erode, Salem, Idukki, Hanumangarh.
On the basis of classification by caste (Fig. 1c), bivariate results between CVD among women aged between 35-49 years and women from OBCs show high prevalence of both the phenomena in 63 districts, a few of which are Kupwara, Punch, Srinagar, Ludhiana, Ambala, Baghpat, Sonipat, Panipat, Nellore, Vellore, Ramanathapuram, Virudhanagar, Madurai, Sivaganga, from Jammu, and a few districts of Punjab, Haryana, and coastal Tamil Nadu down south.
Low prevalence of CVD in 35-49 yr women as well as hailing from OBCs has been observed in Nashik, Ahmednagar, Osmanabad, Bijapur, Jaipur, Sirohi, Udaipur, Rajsamand, and a majority of the districts of Chhattisgarh.
There are 15 districts apiece with low prevalence of CVD-affected women but high percentage of women from OBCs and vice versa.
Bivariate results of CVD among women aged between 35-49 years and women from Scheduled Caste households (Fig. 1d) show high-high clusters in 56 districts like West District in Sikkim, West Garo Hills, Tawang, Bghpat, Sonipat, Jind, Kancheepuram, Tiruvallur.
Low-low clusters are mainly found in the central Indian districts like Bid, Parbhani, Hingoli, Rajnandgaon, Durg, Kabeerdham.
There are a few districts like Neemuch, Dumka, East Khasi Hills with High CVD prevalence, but low percentage of women from Scheduled Caste households.
Women aged between 35-49 years with CVD and hailing from rural areas (Fig. 1e) have high-high clusters in 70 districts like Firozpur, Jalandhar, Ludhiana, Fatehgarh, Kaithal, Guntur, Nellore, and most parts of Tamil Nadu.
Low-low clusters have been found in 112 districts like Koppal, Bellary, Bagalkot, Gadag, Bijapur etc.High CVD prevalence but low rural residential percentage has been found in 12 districts like Dumka, Jamui, Bokaro, Nagpur, Kalahandi.
In case of bivariate results of CVDs among women and women who smoke (Fig. 1f), high high clusters have been found in Prakasam, Mahbubnagar, Krishna, Guntur, Thiruvarur, Perambalu. 39 districts with low-low clusters are found, a few of which are Malappuram, Ramanagara, Gadag, Solapur, Bid, Latur, Parbhani.
Khardha, Jagatsinghpur, Nagpur, Bhadrak are a few of the 26 districts found with high CVD prevalence but low prevalence of women who smoke.
There has been a shift in the location of cold spots in this case, as compared to the previous LISA results with a majority of them shifting predominantly into the northern Deccan area, constituted by districts from southern Madhya Pradesh like Betul, west Nimar, Balaghat, Mandla, western Maharashtra like Amaravati, Aurangabad, Buldana, Solapur, Bid, Latur, Gadchiroli, Chandrapur, Chhattisgarh like Durg, Rajnandgaon, Kabeerdham, Dhamtani, Raipur, Bastar.