Multicentric study on prevalence and risk factors for hypertension and diabetes in tribal communities in Western and Northern Maharashtra.

BACKGROUND AND OBJECTIVES: Although a number of studies have been conducted on the prevalence of hypertension (HTN) and diabetes on tribal populations in different parts of India, comparative tribe-specific information is very meager. The main objective of this study is to generate tribe-specific information on the noncommunicable disorders (NCDs) and associated risk factors in scheduled tribes (STs) in Coastal and Western Maharashtra.
METHODS: The study was conducted on 1864 (females 960) adults (≥18 years) of both sexes in four dominant tribes in the region, namely, Bhils (748), Katkaris (560), Kokana (352), and Thakars (204), using the protocols approved by the Institutional Review Board. The study areas were geographically separated by large distances (250-500 km apart). Prevalence of overweight, diabetes, HTN, and hypercholesterolemia was measured using standard field-based techniques described in our earlier publication.
RESULTS: All STs in this study are grossly underweight; the Katkaris are worst affected. The prevalence of obesity (body mass index ≥30 kg/m2), HTN (blood pressure ≥140 mmHg), diabetes (capillary blood glucose >126 mg/dl), and hypercholesterolemia (cholesterol ≥200 mg/dl) was 0.9%, 11.7%, 6.7%, and 0.6% respectively. There are no statistically significant inter-tribal differences in the prevalence of these parameters. Age and obesity appeared to be the most dominant risk factors for HTN. However, there is no clear-cut picture about the influence of risk factors on diabetes or hypercholesterolemia. INTERPRETATION AND
CONCLUSION: The prevalence of NCDs is still very low in STs, probably due to near absence of the risk factors such as obesity, sedentary lifestyle, and hyperlipidemia. STs are highly endogamous, and the study areas are separated by large distances ruling out intermingling of tribes. Yet, there is no consistent inter-tribal prevalence pattern for NCDs. Although the sample size is small, the results support the view that environmental factors are likely to be more important than host genetics in the causation of NCDs.

Introduction

Adivasis (schedules tribes [STs]), who account for 8.6% (104 million) of its population (2011 Census), are perhaps the most marginalized communities in India.[1] Although much more needs to be done, proactive government welfare programs have resulted in improving their socioeconomic and educational levels. In the past 50 years, literacy rates have shot up from 8% to 56% and the age pyramid has also improved coming closer to the rural India.[23] They live in remote difficult-to-approach mountainous jungles. However, fast acculturation that would influence disease pattern is occurring in these communities. Although infections and malnutrition still dominate their health scenario, like the rest of India, the problem of non-communicable disorders (NCDs) should no more be ignored.[4] NCDs are multifactorial and their etiologies are described more in terms of risk factors. STs are highly endogamous. Tribe-specific information on the pattern of these diseases would provide important leads about the role of genetics in the pathogenesis of these two NCDs. Although there are a number of studies on the prevalence of hypertension (HTN) and diabetes, tribe-specific information on their prevalence and the associated risk factors are very few.[56] This is true also of the large-scale multicentric survey done by the National Nutrition Monitoring Bureau (NNMB).[7]

The ST belt in Maharashtra stretches from west to east sharing borders with Gujarat in the west, Madhya Pradesh at the center, and Chhattisgarh in the east[8] [Figure 1]. There are also high-density pockets in the Western Ghats. The ST population of the state is 10 million (some 47 tribes), accounting for 9.4% of its population. However, dominant STs differ from region to region. Katkaris (KA) are the number one tribe in Raigad, Bhils (BH) in Nandurbar, Kokana (KO) in Dhule and Nandurbar and Thakars (TH) in Junnar and Khed districts of Pune.[9]

Figure 1

Study areas and concerned tribes

The main purpose of the study was to generate tribe-specific information on the prevalence of HTN and diabetes and the status of known common risk factors in the dominant tribes of Coastal and Western Maharashtra. The study is an extension of earlier publication on KAs.[10]

Methods

The methods have been described in detail in an earlier study.[10] We reproduce a brief outline and explain how the earlier work was extended.

Study areas

This community-based house-to-house cross-sectional study was conducted in high-density ST population in a few tehsils (small administrative divisions in a district) in four districts of Northern and Western Maharashtra, namely, Raigad (KAs), Pune (TH), and Dhule and Nandurbar (BH and Kokana) between July 2012 and August 2016. These are four major tribes which together account for 25% of the total ST population of the state. High ST-density tehsils where the study was performed were Akkalkuwa, Shirpur, Sindkhede (Nandurbar), Sakri (Dhule), Khed (Pune), Roha, Mangaon, and Mhasal (Raigad). According to the 2011 Census, the total tribal population of these tehsils is around 23 million; tribals account for one-third (36.2%) of the population [Figure 1]. All adult men and women above the age of ≥ 18 years who were present at the time of visits to their homes participated in the study. To avoid intermixing of the STs, the study was conducted in areas separated by geographical large distances. BH and Katkaris are 500 km apart. Other tribes are midway in between. Tribals often leave Padas for months in search of jobs. The study areas were chosen as they were close to the tribal schools where the Academy conducts annually its summer vacation educational program “Discovering Adivasi Little Scientist” in which tribal students of Class X–XII are provided research opportunities to conduct community-based research. To facilitate cooperation of the families, each study team had at least one student from the same Pada (Tribal Hamlet).

Before starting the project, student participants (“little scientists”) received 1-week training from experts in recording information in a specially designed Institutional Review Board (IRB)-approved study instrument, recording height (Ht), weight (Wt), and blood pressure (BP) using a digital BP instrument (Omron, Japan), measuring capillary blood sugar with a digital glucometer, and routine urine examination. During the training, their observations were vetted repeatedly by experts. After training, the students in groups of 3/4 conducted a house-to-house survey. The study was conducted under the supervision of the experts from the Academy's trained staff and monitored through periodic visits by a qualified medical doctor from time to time. The study design including study instruments and informed consent form which was in Marathi (local language) were approved by the IRB of the Moving Academy of Medicine and Biomedicine both from scientific and ethical angles.

To begin with, group discussions were held with members of the Padas included in the study. At the meeting, the objectives of the project were explained to the residents of the Padas. It was emphasized that the study would not interfere in any way in their daily routine and that participation would be entirely voluntary. At no stage, there would be any coercion and no incentives, financial or in kind, would be given to the participants. Later, a house-to-house survey was conducted by teams of “little scientists.” During the visit, the participants were again explained the nature of the study. IRB-approved pro formas were used to get information on sociodemographic variables, economic status, educational levels, substance abuse related to tobacco and alcohol, and dietary pattern including the main source of protein (vegetarian or nonvegetarians), and intake of green leafy vegetables and fruits from the participants. Only verbal information provided by the participants was recorded. Ht was measured on a suitable flat surface using a wall-mounted stadiometer, a prototype of Seca model SE 206 (Seca, UK). The Ht was recorded in centimeters. Digital balance (Venus), which was standardized regularly, was used to record the Wt in kilogram to the nearest decimal place. Body mass index (BMI) was calculated using Ht and Wt and expressed as mass in kg/m2. The study population was categorized as per the WHO norms into underweight (16–18.4 kg/m2), normal (BMI 18.5–24.9 kg/m2), overweight (BMI 25–29.9 kg/m2), and obese (BMI ≥30 kg/m2).[11] BP was recorded in sitting posture in the left arm using Omron digital BP apparatus (SEM-1 model, Omron Health Care Co., Ltd. Japan). BP was recorded always after 10 min of rest in millimeter of mercury to the nearest full number. All the study instruments and laboratory procedures were validated periodically by Academy's experts. Clinical data were cross-checked by the visiting consultant physician.

Classification of systolic BP (SBP) and diastolic BP (DBP) was done according to the recommendations of the Joint National Committee 7 report into normal (<120/<80 mmHg), prehypertensive (120–139/80–89 mmHg), and HTN (≥140/≥90 mmHg).[12] Fasting capillary blood glucose (CBG) was measured after an overnight fast using ACCUCHEK glucometer (Roche Diagnostics, Germany). As per the recommendations of the Indian Council of Medical Research (ICMR) working group, fasting CBG of <110 mg/dl, 110–125 mg/dl, and ≥126 mg/dl was considered as normal, impaired fasting glucose (prediabetes), and diabetic, respectively.[13] Serum cholesterol was measured on fasting samples using an enzyme-based method and expressed as mg/dl of serum. The measurements were made using autoanalyzer and Erba Lachema (Karasek, Czech Republic) enzymatic kit. Hypercholesterolemia was defined as serum cholesterol of ≥200 mg/dl.[14]

Statistical analysis

The sample size was calculated for each tribe using the following standard formula for finite population.

Where

n = Sample size with finite population

N = Population size (>100,000 for each tribe)

Z = Z-statistic for a level of confidence

p = Expected proportion

q = 1 − p

d = Precision (in proportion of 1).

No tribe-specific information is available on the prevalence of HTN and diabetes in the STs. In our earlier study, the overall prevalence of HTN and diabetes was 16% and 6%, respectively, in Katkaris (KA). These figures were used to calculate sample size for other tribes. According to the assumptions mentioned above, the sample size for HTN turns out to be 207 for each tribe. In this study, 748, 560, 352, and 204 (both males and females) from BH, KA, KO, and TH communities, respectively, participated.

Data were summarized into numbers, percentages, mean, and standard deviation. The Chi-square test was applied to assess the trend as well as gender-wise difference in study variables. Student's t-test was applied to compare the difference in means of two categories. The trend, as well as the difference, was said to be statistically significant if P < 0.05. Wherever necessary, the data were subjected to multivariate analysis using ANOVA.

SPSS (Statistical Package for the Social Sciences) version 20 software (IBM Corp Armonk, NY, USA) was used for statistical analysis.

Results

The 12 tribal Padas (tribal hamlet) where this study was conducted consisted of 1066 families, with a total population of 3467 and average women-to-men ratio of 0.97. Adults accounted for 61.65% of the population. Two-thirds of the adults (2203/3467) were registered for the study. STs have a large floating population as both men and women generally leave Padas in search of jobs in the morning and return after few days. Only those available during the study period were registered. The final total number of the participants was 1864 (960 females), which was 85% of the total adult population of the study areas. Ten families changed their mind and did not participate. They were not included in the study.

Table 1 provides the comparative information on sociocultural and educational status of the four tribes that participated in the study as reflected in the 2011 Census of the Government of India. The age pyramid of the adults who participated [Table 2] in this study is similar to that for STs in the 2011 Census. The highest proportion of younger (the age group of 18–39 years) and older populations (≥60 years) was in KA (female: 69.7% and male: 68.4%) and BH (female: 17.8% and male: 14.7%), respectively.[3]

Table 1

Sociocultural profile of the study population as per the 2011 Census

STs	Number of households	ST rank in the state	Population		Population (aged 0-6 years)		Total (%)
			Total	Female:male	Total (%)	Female:male	Literate	Workers	Cultivators
All STs	2,273,907		10,510,213	0.98	1,553,366 (14.80)	0.96	5,887,161 (56.00)	5,323,325 (50.64)	1,156,385 (21.72)
Bhils	512,708	1	2,588,659	0.98	460,973 (17.88)	0.96	1,120,155 (43.27)	1,349,147 (52.11)	195,990 (14.53)
Kokana	143,647	5	687,431	0.98	91,742 (13.34)	0.97	420,160 (61.12)	363,323 (52.85)	194,071 (53.42)
Thakurs	115,877	6	567,968	0.97	87,323 (15.37)	0.95	300,263 (52.86)	278,952 (49.11)	65,415 (23.45)
Katkaris	67,392	8	285,334	1.00	52,105 (18.25)	0.96	97,204 (34.06)	147,347 (51.64)	5,466 (3.71)

STs: Scheduled tribes, BH: Bhils, KO: Kokana, KA: Katkaris, TH: Thakar

Table 2

Age and gender distribution of the study population in comparison with the 2011 Census

Tribes	Gender	Total participant	Mean age (years)±SD	Age (years) group distribution and percentage (number of participants shown in parentheses)
				18-39	40-59	≥60
BH	Females	360	40.3±16.36	58.6 (211)	23.6 (85)	17.8 (64)
	Males	388	38.3±16.40	62.6 (243)	22.7 (88)	14.7 (57)
KA	Females	297	34.5±13.56	69.7 (207)	20.5 (61)	9.8 (29)
	Males	263	34.4±12.83	68.4 (180)	24.3 (64)	7.3 (19)
KO	Females	191	37.5±14.43	59.7 (114)	27.7 (53)	12.6 (24)
	Males	161	38.2±14.67	56.5 (91)	29.2 (47)	14.3 (23)
TH	Females	112	35.9±13.04	58.9 (66)	34.8 (39)	6.3 (7)
	Males	92	38.1±14.80	59.8 (55)	28.3 (26)	11.9 (11)
All tribes	Females	960	37.2±14.89	62.3 (598)	24.8 (238)	12.9 (124)
	Males	904	37.1±15.04	62.6 (569)	24.9 (225)	12.2 (110)
2011 Census	Females			62.8	25.8	11.4
	Males			63	27	10

BH: Bhils, KO:Kokana, KA:Katkaris, TH:Thakar, SD: Standard deviation

General features and anthropometric measurements

Information obtained on sociodemographic parameters, economic status, educational level, dietary patterns, and habits of the study population is summarized in Table 3 and compared to that in KA as reported in our earlier publication.[10] More than 90% of both men and women KA were landless “yellow card” laborers, an indication that they were below the poverty line by the criteria used by the Government of India. In other tribes, a substantial proportion (two-third) were farmers although with small holdings. Literacy rates were more or less the same in all STs, but the same was not the case for higher education. Only 2.1% KA men were college students. This figure was 15-fold higher in the rest [Table 3]. In addition, the level of alcohol addiction was higher in KA. Almost all STs are meat eaters. However, meat items were taken occasionally. Majority subsisted on daily vegetarian diet, which was also their main source for proteins. Green leafy vegetable and fruits were taken irregularly once or twice a week that too only by 60% of the families. Tobacco smoking/chewing was a widely prevalent habit being present in 39.6% and 53.9% in women and men, respectively. Twenty percent of women and half of men consumed “country-made” liquor regularly.

Table 3

Socioeconomic, educational, and dietary pattern in the study population

	Women (219)	Women (718)	χ2 (P)	Men (191)	Men (694)	χ2 (P)
Income
Below BPL (yellow card)*	212 (96.8)	572 (79.7)	34.83 (<0.0001)	183 (95.8)	546 (78.7)	27.88 (<0.0001)
Above BPL	7 (3.2)	132 (18.4)		8 (4.2)	132 (19.0)
Middle income		14 (1.9)			16 (2.3)
Occupation
Manual labor	219 (100.0)	246 (34.3)	258.55 (<0.0001)	191 (100.0)	258 (37.2)	234.01 (<0.0001)
Farmer		470 (65.5)			432 (62.2)
Salaried		2 (0.3)			4 (0.6)
Education
Illiterate	169 (77.2)	508 (70.8)	2.33 (0.13)	110 (57.6)	355 (51.2)	2.24 (0.13)
Literate	50 (22.8)	210 (29.2)		81 (42.4)	339 (48.8)
Primary	38 (17.3)	92 (43.8)		42 (22.0)	137 (40.4)
Secondary	12 (5.5)	67 (31.9)	16.95 (<0.0001)	35 (18.3)	96 (28.3)	5.28 (0.008)
Junior college	-	51 (24.3)		4 (2.1)	106 (31.3)
Tobacco habits (smoking/chewing)
No	93 (42.5)	434 (60.4)		67 (35.1)	320 (46.1)
Yes	126 (57.5)	284 (39.6)		124 (64.9)	374 (53.9)
Chewing	126	218	28.32 (<0.0001)	25 (both)	364	6.97 (0.008)
Smoking	6 (both)	66		124	10
Areca nut/gutka
No	166 (75.8)	693 (96.5)	91.7 (<0.0001)	141 (73.8)	568 (81.8)	11.94 (0.007)
Yes	53 (24.2)	25 (3.5)		59 (26.2)	126 (18.2)
Alcohol
No	179 (81.9)	652 (90.8)	12.88 (0.003)	112 (58.5)	522 (75.2)	19.45 (<0.0001)
Yes	40 (18.1)	66 (9.2)		79 (41.5)	172 (24.8)
Green leafy vegetables/week
No	45 (20.5)	05 (0.7)		16 (8.4)	6 (0.9)
Yes	174 (79.5)	713 (99.3)		175 (91.6)	688 (99.1)
Once a week	46 (21.0)	74 (10.4)	127.02 (<0.0001)	36 (18.8)	50 (7.3)	31.83 (<0.0001)
Twice a week	76 (34.7)	367 (51.5)		72 (37.7)	376 (54.7)
Thrice a week	36 (16.4)	163 (22.9)		51 (26.7)	174 (25.3)
Four times a week	16 (7.4)	109 (15.3)		16 (8.4)	88 (12.8)
Citrus fruit
No	78 (35.6)	228 (31.8)		99 (51.8)	198 (28.5)	35.44 (<0.0001)
Yes	141 (64.4)	490 (68.2)	0.97 (0.32)	92 (48.2)	496 (71.5)
Protein intake (vegetarian: pulses/milk)
No	58 (26.5)	26 (3.6)	4.01 (0.05)	84 (44)	37 (5.3)	186.28 (<0.0001)
Yes	161 (73.5)	692 (96.4)		107 (56)	657 (94.7)
Protein intake (nonvegetarian: egg, meat**)
No	51 (23.3)	122 (17.0)	4.01 (<0.05)	18 (9.4)	65 (9.4)	0.0006 (0.98)
Yes**	168 (76.7)	596 (83.0)		173 (90.6)	629 (90.6)

*BPL: Below poverty line, **Nonveg was taken once or twice a week only

Average age, Ht, Wt, and BMI were 37.2 ± 14.89 years, 149.7 ± 6.82 cm, 43.1 ± 7.56 kg, and 19.1 ± 3.34 kg/m2 in females, respectively [Table 4]. In comparison, men were taller (160.3 ± 7.41 cm) and heavier (51.5 ± 9.25 kg) and also had better BMI (20.0 ± 3.28 kg/m2). These differences were statistically highly significant [Table 4]. The mean SBP in women and men was 119.4 ± 10.65 and 124.8 ± 15.99 mmHg, respectively. DBP showed a similar trend. Average CBG in women and men was 95.4 ± 14.44 and 102.8 ± 28.91 mg/dl, respectively. A similar trend was observed with reference to average serum cholesterol (women/men 141.5 ± 37.14/137.6 ± 37.21 mg/dl).

Table 4

Comparative status of the overall laboratory parameters in both sexes

Sex	Age groups (no participants)	Mean±SD
		Age (years)	Height (cm)	Weight (kg)	BMI	SBP (mmHg)	DBP (mmHg)	CBG* (mg/dl)	Cholesterol* (mg/dl)
Females	18-39 (598)	27.5±5.88	150.1±6.88	43.1±7.16	19.1±3.16	114.8±13.91	71.6±10.61	93.4±6.49 (456)	133.5±34.99 (320)
	40-59 (238)	47.6±5.65	149.1±6.23	43.2±8.30	19.8±3.53	124.1±16.71	75.9±9.73	98.5±4.91 (139)	158.5±35.50 (93)
	>60 (124)	65.7±5.83	148.1±7.72	42.0±8.42	19.02±3.32	133.5±22.29	77.3±10.83	99.5±5.59 (75)	161.51±36.12 (49)
	Total (960)	37.2±14.89	149.7±6.82	43.1±7.56	19.1±3.34	119.4±10.65	73.3±10.65	95.4±14.44 (670)	141.5±37.14 (462)
Males	18-39 (569)	27.5±6.02	160.7±7.55	51.2±8.76	19.8±3.07	123.4±13.48	74.4±10.30	95.4±15.84 (410)	131.9±36.24 (280)
	40-59 (225)	46.7±5.68	160.0±7.25	53.5±9.72	20.9±3.57	126.2±18.46	79.8±17.08	109.8±51.15 (105)	131.5±35.85 (76)
	>60 (110)	67.1±6.88	159.0±6.92	49.5±10.1	19.5±3.44	129.2±20.88	75.0±12.4	105.2±38.26 (53)	153.0±36.46 (36)
	Total (904)	37.1±15.04	160.3±7.41	51.5±9.25	20.0±3.28	124.8±15.99	75.9±12.75	102.8±28.91 (568)	137.6±37.21 (392)
Overall females versus males	t (unpaired)	0.1442	32.159	21.521	5.865	8.627	4.786	5.830	1.763
	P	<0.89 (NS)	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.08 (NS)

NS: Not significant, BMI: Body mass index, CBG: Capillary blood glucose, SBP: Systolic blood pressure, DBP: Diastolic blood pressure, SD: Standard deviation, *Figures in parentheses denote the number of the participants in the group

Inter-tribal community status of the parameters and their comparison in women and men (one-way ANOVA) are shown in Tables 5 and 6, respectively. There was no consistent pattern. The overall picture in women showed that there were no significant differences in average Ht. Modest statistically significant differences with P = 0.02 and 0.03 were observed for Wt and BMI, respectively. In all the other parameters, the overall differences were highly statistically significant (P < 0.001). However, there was no consistent inter-tribal pattern. For example, Wt and BMI differences were observed only between BH and KA. In case of SBP, the differences were restricted to BH versus KA and KA versus TH. Although there was no tribe-specific pattern, statistically significant differences in DBP were more widespread and observed between BA and KH, BH and TH, KA and KO, KA and TH, and KO and TH. CBG was statistically higher in BA in comparison to other tribes. Statistically significant difference was also observed in serum cholesterol levels between BH and KA and KO and KA. The overall picture in men also showed a similar variable pattern [Table 6]. No tribe-specific pattern was observed in any parameter both in men and women.

Table 5

Tribe-specific anthropometric information, blood pressure, capillary blood glucose, and cholesterol in females

Age group	Participants no.	Results (mean±SD)
		Height (cm)	Weight (kg)	BMI	SBP (mmHg)	DBP (mmHg)	CBG* (mg/dl)	Cholesterol* (mg/dl)
18-39	BH (211)	150.7±6.92	44.1±7.51	19.4±3.00	112.9±12.16	70.3±9.61	84.7±15.88 (107)	155.5±32.21 (14)
	KA (207)	149.5±7.77	42.3±7.02	19.0±3487	119.0±15.63	75.1±11.63	95.6±13.07 (195)	130.5±37.09 (173)
	KO (114)	149.0±5.34	43.4±6.49	19.3±2.95	113.4±13.4	71.0±10.28	94.6±12.80 (58)	137.8±31.24 (78)
	TH (66)	150.9±6.07	42.8±7.27	18.9±3.41	110.3±11.80	65.8±6.53	93.8±11.85	128.2±32.74 (35)
40-59	BH (85)	149.3±6.54	45.7±8.68	20.5±3.77	127.1±19.80	75.4±8.70	90.9±14.64 (39)	160.2±49.52 (6)
	KA (61)	149.3±6.48	41.5±7.60	18.6±3.35	126.8±19.83	80.1±10.40	106.5±50.95 (58)	143.8±34.86 (49)
	KO (53)	148.6±6.96	45.1±7.66	20.4±2.95	126.9±20.32	76.1±10.46	95.8±8.90 (33)	168.8±32.21 (38)
	TH (39)	149.1±3.85	44.1±8.63	19.8±3.61	121.2±13.72	70.4±8.39	98.5±15.94 (39)	152.2±31.73 (20)
≥60	BH (64)	149.8±7.37	45.7±8.68	20.5±3.77	121.8±13.09	73.8±8.56	88.6±14.15 (25)	148.2±21.32 (5)
	KA (29)	146.6±6.10	40.2±7.27	18.7±3.32	145.2±23.97	85.8±11.05	101.8±14.12 (29)	147.2±31.19 (23)
	KO (24)	145.4±9.78	40.7±8.96	19.2±3.72	139.1±20.09	78.5±10.63	112.9±42.92 (15)	183.4±34.27 (15)
	TH (7)	149.3±6.59	42.4±6.02	19.0±2.09	125.8±21.68	70.4±9.71	99.5±13.3 (7)	172.5±43.67 (6)
Total	BH (360)	150.2±6.93	44.3±8.66	19.6±3.29	117±15.10	72.1±9.46	86.7±15.51 (171)	155.1±34.20 (25)
	KA (297)	149.2±7.39	41.9±7.26	18.9±3.43	123.1±18.99	77.2±11.82	98.5±26.41 (282)	134.7±36.6 (245)
	KO (191)	149.0±6.63	43.5±7.25	19.6±2.91	120.4±18.65	73.4±10.73	97.6±21.4 (106)	152.0±36.27 (131)
	TH (112)	150.2±5.46	43.3±7.68	19.2±3.42	115.1±14.34	67.7±7.02	95.8±13.59 (112)	140.4±36.34 (61)

*Figures in parentheses denote the number of participants in the group. One-way ANOVA - 18-39: Height F=2.27, P=0.079 (NS), weight F=2.32, P=0.074 (NS), BMI F=0.77, P=0.513 (NS), SBP F=10.68, P=0.001 (BH vs. KA P=0.001, KA vs. TH P=0.001, KA vs. TH P=0.001), DBP F=16.47, P=0.001 (BH vs. KA P=0.001, BA vs. TH P=0.01, KA vs. TH P=0.01), CBG F=15.71, P=0.001 (BH vs. KA P=0.001, BH vs. KO P=0.001, BH vs. TH P=0.001), cholesterol F=2.93 P=0.034 (however there were no inter tribal differences). 40–59: Height F=0.161, P=0.92 (NS), weight F=3.38, P=0.019 (BH vs. KA P=0.05), BMI F=4.08, P=0.008 (BH vs. KA P=0.01, KA vs. KO P=0.05), SBP F=0.99, P=0.397, DBP F=8.38, P=0.001 (BH vs. KA P=0.5, BH vs. KO P=0.05, KA vs. TH P=0.01, KO vs. TH P=0.05), CBG F=2.026 P=0.11 (NS), cholesterol F=3.88, P=0.01 (however there were no inter-tribal difference). ≥60: Height F=2.51, P=0.06 (NS), weight F=3.93, P=0.01 (BH vs. KA P=0.05), BMI F=2.05, P=0.11, SBP F=13.36, P<0.001 (BH vs. KN P=0.001, BH vs. KO P=0.001), DBP F=11.597, P=0.001 (BH vs. KA P=0.01, KA vs. KO P=0.05, KA vs. TH P=0.01), CBG F=3.75, P=0.015 (BH vs. KO P=0.01), cholesterol F=4.12 P=0.115 (KA vs. TH P=0.05). Overall: Height F=2.01, P=0.11 (NS), weight F=5.12 P=0.016 (BH vs. KA P=0.01), BMI F=3.01, P=0.023 (BH vs. KA P=0.05), SBP F=9.64, P=0.001 (BH vs. KA P=0.001, KA vs. TH P=0.001, KO vs. TH P=0.05), DBP F=26.98, P=0.001 (BH vs. KA P=0.001, BH vs. TH P=0.001, KA vs. KO P=0.001, KA vs. TH P<0.001, KO vs. TH P<0.001), CBG F=11.66, P<0.001 (BH vs. KA P<0.001, BH vs. KO P<0.001, BH vs. TH P<0.01), cholesterol F=7.696, P=0.001 (BH vs. KA P=0.001, KA vs. KO P=0.001). BMI: Body mass index, BH: Bhils, KO: Kokana, KA: Katkaris, TH: Thakars, CBG: Capillary blood glucose, SBP: Systolic blood pressure, DBP: Diastolic blood pressure, SD: Standard deviation, NS: Not significant

Table 6

Tribe-specific anthropometric information, blood pressure, capillary blood glucose and cholesterol on males

Age group	Participants no	Results (mean±SD)
		Height (cm)	Weight (kg)	BMI	SBP (mmHg)	DBP (mmHg)	CBG* (mg/dl)	Cholesterol* (mg/dl)
18–39	BH (243)	160.3±8.24	51.8±9.26	19.9±3.23	123.2±14.17	74.5±10.77	86.5±18.60 (96)	152.7±36.88 (18)
	KA (180)	160.4±6.54	49.8±8.21	19.3±3.10	125.1±13.63	75.4±10.71	97.1±14.66 (162)	125.1±35.93 (162)
	KO (91)	160.6±6.27	50.7±8.27	19.6±2.57	122.4±11.64	73.5±9.13	94.3±9.23 (63)	132.9±30.69 (60)
	TH (55)	162.2±7.35	54.6±8.08	20.7±2.79	120.9±12.22	72.9±8.25	95.1±10.56 (55)	148.5±37.14 (55)
40–59	BH (88)	160.3±7.52	53.2±10.00	20.7±3.84	124.8±17.73	80.7±22.0	94.7±16.22 (33)	156.5±26.21 (4)
	KA (64)	158.8±7.64	52.0±8.79	20.6±3.29	131.5±23.36	83.6±13.55	117.0±57.68 (55)	137.8±36.17 (44)
	KO (47)	160.1±6.34	55.2±10.27	21.5±3.42	124.0±13.00	77.5±9.31	117.2±54.69 (27)	165.7±32.06 (34)
	TH (26)	161.2±6.86	54.6±9.67	21.0±3.63	122.0±12.88	72.3±8.70	100.6±17.57 (26)	154.7±35.18 (12)
≥60	BH (57)	158.7±7.46	51.0±11.53	20.1±3.67	128.1±22.28	75.5±12.84	88.9±19.37 (16)	187.8±47.62 (5)
	KA (19)	160.8±6.73	47.9±9.69	18.5±3.70	134.2±25.31	77.5±14.38	112.5±37.21 (14)	120.5±20.99 (9)
	KO (23)	158.4±6.72	47.8±7.72	19.1±2.85	128.5±16.57	72.4±11.72	122.2±61.28 (12)	162.1±20.37 (16)
	TH (11)	158.5±4.45	48.0±6.30	19.1±2.66	128.5±11.02	74.2±6.13	109.9±15.66 (11)	148.3±46.19 (6)
Overall	BH (388)	160.3±8.24	51.8±9.80	20.1±3.45	124.3±16.46	76.1±14.3	88.6±18.37 (145)	159.8±38.80 (27)
	KA (263)	160.0±6.84	50.2±8.51	19.6±3.23	127.3±17.70	77.5±12.21	102.8±32.96 (231)	127.5±35.76 (215)
	KO (161)	160.1±6.36	51.6±9.13	20.1±3.01	123.7±12.92	74.4±9.72	103.4±37.09 (102)	147.3±33.61 (110)
	TH (92)	161.5±6.98	53.8±8.63	20.6±3.06	122.1±12.39	72.9±8.10	97.4±13.61 (92)	150.3±37.06 (40)

*Figures in parentheses denote the number of participants in the group. One-way ANOVA - 18-39: height F=1.04, P=0.37 (NS), weight F=2.74, P=0.06, BMI F=1.97 P=0.14 (NS), SBP F=1.97 P=0.14, DBP F=1.034 P=0.35 (NS), CBG F=14.95, P=0.001 (BH vs. KA, P=0.001; BH vs. KO, P=0.01; KA vs. KO, P=0.05), cholesterol F=5.63, P=0.004 (BH vs. KA, P<0.01; BH vs. KO, P=0.05, KA vs. KO, P=0.05). 40-59: height F=0.84, P=1.00 (NS), weight F=1.483 P=0.23 (NS), BMI F=0.68, P=0.56 (NS), SBP F=2.03 P=0.11 (NS), DBP F=3.35 P<0.02 (KA vs. TH, P=0.05); CBG F=3.35 P=0.08 (NS), cholesterol F=4.34, P=0.007 (KA vs. KO, P<0.01) (≥60): height F=0.54, P=0.66 (NS), weight F=0.87, P=0.46 (NS), BMI F=1.27, P=0.29 (NS), SBP F=0.39, P=0.75 (NS), DBP F=0.64, P=0.59 (NS) CBG F=2.07< P = 0.12 (NS), cholesterol F=6.12, P=0.002 (BH vs. KA, P=0.01; KA vs. KO, P=0.05). Overall: height F=0.99, P=0.39 (NS), weight F=3.81 P=0.01 (KA vs. TH, P=0.01), BMI F=2.51 P=0.58, SBP F=3.43, P=0.017 (KA vs. KO, P=0.05), DBP F=4.05, P=0.007 (KA vs. KH, P=0.05), CBG F=8.74, P=0.001 (BH vs. KA, P=0.001, BH vs. KO, P=0.001, KA vs. TH, P<0.01), cholesterol F=13.75, P=0.001 (BH vs. KA, P<0.001; KA vs. KO, P=0.001, KA vs. TH, P=0.01). BMI: Body mass index, BH: Bhils, KO: Kokana, KA: Katkaris, TH: Thakars, CBG: Capillary blood glucose, SBP: Systolic blood pressure, DBP: Diastolic blood pressure, SD: Standard deviation, NS: Not significant

Status of the noncommunicable disorders and related risk factors

The overall prevalence of underweight (BMI <18.5), overweight (BMI ≥25), HTN, diabetes, and hypercholesterolemia in women was 44.7%, 5.7% (of which 0.9% were obese), 10.4%, 3.7%, and 6.7%, respectively [Table 7]. In the inter-tribal comparison, the highest proportion of BH (6.7%), KA (14.5%), and KO (12.3%) showed overweight, HTN, and hypercholesterolemia, respectively, in women. The overall prevalence of diabetes was 3.7% being highest in the KA (4.3%) women. In males too [Table 8], the picture was more or less similar except that highest prevalence of hypercholesterolemia was seen in TH (12.5%) and not in the KO and the difference between the highest and the lowest levels (KA) was modestly significant statistically (P = 0.02). Chi-square for trend was statistically highly significant for HTN (P < 0.001) for age in both sexes. BMI also showed similar trend. There was no consistent pattern of the trend association with other parameters. The association was weak and not uniformly present in both sexes [Tables 7 and 8]. Chi-square analysis of the relationship of the risk factors and NCDs (HTN and diabetes) is shown in [Table 9]. Overweight emerged as a single important risk factor for HTN in both sexes. In case of diabetes, there was no correlation with any of the studied risk factors.

Table 7

Prevalence of noncommunicable disorders in females

STs	Age group (years)	Total number	No participants											Hypercholesterolemia
			BMI (kg/m2)				SBP (mmHg)			CBG (mg/dl)
			UW <18.5	No 18.5-24.9	Over weight ≥25	OB ≥30	Normal <120	Pre-HTN 12-139	HTN ≥140	Total	Normal <110	IFG 111-125	Diabetes ≥126
BH	<40	211	85	119	10	3	163	46	2	107	102	1	4	1/14
	40-59	85	31	41	13	-	40	35	10	39	35	3	1	1/6
	≥60	64	32	28	4	-	23	29	12	25	23	1	1	0/5
	All ages	360	148 (41.1)	188 (52.2)	24 (6.7)	3 (0.8)	226 (62.8)	110 (30.5)	24 (6.7)	171	160 (93.6)	5 (2.9)	6 (3.5)	2/25 (4)
KA	<40	207	100	93	14	2	121	69	17	195	178	11	6	7/170
	40-59	61	33	25	3	-	23	27	11	58	50	3	5	2/49
	≥60	29	18	10	1	1	3	11	15	28	22	5	1	1/23
	All ages	297	151 (50.8)	128 (43.1)	18 (6.1)	3 (1.0)	147 (49.5)	107 (36)	43 (14.5)	281	250 (89.0)	19 (6.8)	12 (4.3)	10/242 (4.1)
KO	<40	114	48	62	4	-	85	23	6	58	54	3	1	3/77
	40-59	53	16	35	2	1	23	19	11	33	30	3	Nil	7/38
	≥60	24	11	11	2	-	4	12	8	15	10	2	3	6/15
	All ages	191	75 (39.3)	108 (65.5)	8 (4.2)	1 (0.5)	112 (58.6)	54 (28.3)	25 (13.1)	106	94 (88.7)	8 (7.5)	4 (3.8)	16/130 (12.3)
TH	<40	66	37	27	2	1	56	9	1	66	61	4	1	1/35
	40-59	39	15	21	3	1	20	13	6	39	34	2	3	1/20
	≥60	7	3	4	-	-	4	2	1	7	6	1	Nil	1/6
	All ages	112	55 (49.1)	52 (46.4)	5 (4.5)	2 (1.8)	80 (71.5)	24 (21.4)	8 (7.1)	112	101 (90.1)	7 (6.3)	4 (3.6)	3/61 (4.9)
Total	<40	598	270	301	33	6	425	147	26	457	422	20	15	12/300
	40-59	238	95	122	21	2	106	94	38	139	124	10	5	11/113
	≥60	124	64	53	1	1	34	54	36	75	61	9	5	8/49
	All ages	960	429 (44.7)	476 (49.6)	55 (5.7)	9 (0.9)	565 (58.9)	295 (30.7)	100 (10.4)	671	607 (90.5)	39 (5.8)	25 (3.7)	31/462 (6.7)
Females and males	All ages	1864	759 (40.7)	969 (52.0)	119 (6.4)	17 (0.9)	923 (49.5)	723 (38.8)	218 (11.7)	1241	1096 (88.3)	83 (6.7)	62 (5)	6.6 (56/854)

χ2 for trend: Age versus (a) OB 5.36; P=0.02 (b) HTN 77.42, P <0.001; (c) diabetes 1.23; <0.267; (d) hypercholesterolemia 12.39, P <0.001, BMI versus (a) HTN 7.89, <0.005; diabetes 4.25, <0.04; (c) hypercholesterolemia 4.05, P=0.04. UW: Underweight, OB: Obese, HTN: Hypertension, IFG: Impaired fasting glucose, BH: Bhils, KO: Kokana, KA: Katkaris, TH: Thakars, STs: Scheduled tribes, BMI: Body mass index, SBP: Systolic blood pressure, CBG: Capillary blood glucose

Table 8

Prevalence of noncommunicable disorders in males

STs	Age group (years)	Total number	Number in each category											Hypercholesterolemia (mg/dl)
			BMI (kg/m2)				SBP (mmHg)			CBG (mg/dl)
			UW <18.5	18.5-24.9	Over weight ≥25	OB ≥30	Normal <120	Pre-HTN 12-139	HTN ≥140	Total number	Normal <110	IFG 111-125	Diabetes ≥126
BH	<40	243	94	124	25	1	96	127	20	96	87	6	3	1/18
	40-59	88	26	49	13	2	41	33	14	33	29	3	1	0/4
	≥60	57	22	28	7	1	22	23	12	16	15	-	1	2/5
	All ages	388	142 (36.6)	201 (51.8)	45 (11.6)	4 (1.0)	159 (40.9)	183 (47.2)	46 (11.9)	145	131 (90.3)	9 (6.2)	5 (3.5)	3/27 (11.1)
KA	<40	180	79	89	12	2	60	103	17	162	139	15	8	24/165
	40-59	64	18	40	6	1	20	26	18	55	40	5	10	2/44
	≥60	19	10	7	2	-	6	6	7	14	10	1	3	0/9
	All ages	263	107 (45.3)	136 (57.1)	20 (7.6)	3 (1.1)	86 (32.7)	135 (51.3)	42 (16)	231	189 (81.8)	21 (9.1)	21 (9.1)	6/218 (2.8)
KO	<40	91	37	51	3	-	45	39	7	63	60	3	-	2/61
	40-59	47	10	31	7	1	22	17	8	27	17	4	6	15/34
	≥60	23	10	13	-	-	8	8	7	12	10	-	2	1/16
	All ages	161	57 (35.4)	94 (58.4)	10 (6.2)	1 (0.6)	75 (46.6)	64 (38.8)	22 (13.7)	102	87 (85.3)	7 (6.7)	8 (7.8)	8/111 (7.2)
TH	<40	55	13	439	3	-	26	25	4	55	51	3	1	13/22
	40-59	26	6	16	4	-	9	14	3	26	21	4	1	1/12
	≥60	11	5	6	-	-	3	7	1	11	10	-	1	1/6
	All ages	92	23 (25)	61 (66.4)	7 (7.6)		38 (41.3)	46 (50)	8 (8.7)	92	82 (89.1)	7 (7.6)	3 (3.3)	5/40 (12.5)
Total	<40	569	223	303	43	3	227	294	48	376	337	27	12	10/262
	40-59	225	60	136	29	4	92	90	43	141	107	16	18	8/94
	≥60	110	47	54	9	1	39	44	27	53	45	1	7	4/36
	All ages	904	330 (36.5)	493 (54.5)	81 (9)	8 (0.9)	358 (39.6)	428 (47.3)	118 (13.1)	570	489 (85.8)	44 (9.0)	37 (6.5)	22/392 (5.6)
Females and males	All ages	1864	759 (40.7)	969 (52.0)	119 (6.4)	17 (0.9)	923 (49.5)	723 (38.8)	218 (11.7)	1241	1096 (88.3)	83 (6.7)	62 (5)	6.6 (56/854)

χ2 for trend: Age versus (a) OB 5.00; P=0.03; (b) HTN 29.92; P <0.001; (c) diabetes 9.23; P <0.002; (d) hypercholesterolemia 5.03, P=0.02; BMI versus (a) HTN 39.4, P <0.001; (b) diabetes 7.74, P <0.01; (c) hypercholesterolemia 2.85; P=0.09. UW: Underweight, OB: Obese, HTN: Hypertension, IFG: Impaired fasting glucose, KA: Katkaris, BH: Bhils, KO: Kokana, TH: Thakars, SBP: Systemic blood pressure, CBG: Capillary blood glucose, STs: Scheduled tribes, BMI: Body mass index

Table 9

Interrelationship of noncommunicable disorder and the risk factors – statistical analysis

Risk factors	Gender	Parameters	No. of participants			χ2 (P)
			Total	Normal	Affected (percentage in parentheses)
Overweight versus HTN	Female	BMI <25	905	824	81 (9.0)	14.04 (<0.001***)
		BMI >25	55	41	14 (25.5)
		BMI <23	852	776	76 (8.9)	19.13 (0.001***)
		BMI >23	108	83	25 (23.1)
	Male	BMI <25	823	740	83 (10.1)	31.65 (0.001***)
		BMI >25	81	55	26 (32.1)
		BMI <23	738	673	65 (8.8)	61.81 (<0.001***)
		BMI >23	166	113	53 (31.9)
Overweight versus diabetes	Female	BMI <25	633	610	23 (3.6)	0.87 (<0.35 NS)
		BMI >25	37	34	3 (8.1)
		BMI <23	593	572	21 (3.5)	0.89 (<0.34 NS)
		BMI >23	77	72	5 (6.5)
	Male	BMI <25	520	492	28 (5.4)	7.62 (<0.01**)
		BMI >25	48	40	8 (16.7)
		BMI <23	463	440	23 (5)	6.72 (0.034*)
		BMI >23	105	92	13 (12.4)
Overweight versus hypercholesterolemia	Female	BMI <25	433	406	27 (6.2)	1.42 (0.23 NS)
		BMI >25	29	25	4 (13.8)
		BMI <23	408	385	23 (5.6)	5.03 (0.02*)
		BMI >23	54	46	8 (14.8)
	Male	BMI <25	364	346	18 (4.9)	3.15 (0.08 NS)
		BMI >25	28	24	4 (14.3)
		BMI <23	324	310	14 (4.3)	4.56 (0.03*)
		BMI >23	68	60	8 (11.)
Cholesterol versus HTN	Female	<200	413	366	47 (11.4)	4.69 (0.03*)
		>200	30	22	8 (26.7)
	Male	<200	370	329	41 (11.1)	3.74 (0.53 NS)
		>200	22	16	6 (27.3)
Diabetes versus HTN	Female	Nondiabetes	645	582	63 (9.8)	1.53 (0.22 NS)
		Diabetic	26	21	5 (19.2)
	Male	Nondiabetes	533	477	56 (10.5)	3.24 (0.07 NS)
		Diabetic	37	29	8 (21.6)
Diabetes versus hypercholesterolemia	Female	Nondiabetic	362	345	17 (4.7)	4.54 (0.03*)
		Diabetic	14	11	3 (21.4)
	Male	Nondiabetic	297	279	18 (6.1)	0.61 (0.44 NS)
		Diabetic	24	24	0 (0)

BMI: Body mass index, NS: Not significant, HTN: Hypertension, *Significant (P<0.05); **Highly significant (P <0.01) and ***Very highly significant (P <0.001)

Awareness about the HTN was very low. Only 18 out of 904 men (1.99%) and 10 out of 960 women (1.0%) were aware that they were suffering from HTN. Situation about diabetes was worse as no affected individual was aware that he/she was suffering from the disease.

Discussion

All four STs, who participated in the study, namely, BH, KO, TH, and KA, are grossly undernourished as judged by low BMI. Almost all tribes are meat eaters. In fact, KAs eat even rodent meat, which they believe gives them longevity and strength.[15] However, this study indicates that meat is not a regular part of daily diet in any of the tribes. This may be because meat items are not regularly available and very costly. The tribes subsist mostly on an imbalanced vegetarian diet with scanty consumption of green leafy vegetables and fruits. KA is economically the poorest tribe. More than 90% females and 80% men are “yellow card holder”(people below poverty line), landless manual laborers. Literacy rates are comparable in all the tribes. However, in terms of higher education, KAs are most backward; only 2% reach junior college level. Addiction to alcohol is not a major issue in women. However, that is not the case with tobacco usage (mostly chewing) which is present in 40% women. In comparison to women, men are taller and heavier and have better BMI. The same is the case with SBP and DBP. Except for the age and serum cholesterol levels, differences in all the other parameters are highly significant statistically. However, they are modest and may therefore have little clinical significance. Average SBP tends to be more in the range of pre-HTN (SBP >120 mmHg) in both sexes. The overall prevalence of HTN, diabetes, and hypercholesterolemia is low both in women and men. Both SBP and DBP, which show similar pattern, are recorded in every participant. However, the former is a better cardiovascular risk factor.[1216] Subsequent discussion, therefore, is focused more on SBP. As no tribe-specific pattern is observed, the data are pooled for assessing the impact of risk factors.

Substance abuse is very common in the STs, a fact they want to hide. Therefore, it is often very difficult to get correct information, and prevalence is underestimated. There is hardly any tribe-specific pattern for substance abuse and data differ widely in different publications. Tobacco habits and addiction to alcohol are the two most common problems faced by tribal communities. Our results on tobacco habits are similar to figures reported in the Saharia tribe in Madhya Pradesh but much lower than 94% reported in Mishing tribes in Assam.[1718] The prevalence of alcohol addiction, in this study, is similar to that reported by Misra et al. but higher than that observed in tribes in Madhya Pradesh and Gujarat.[171819]

In this study, about 45% of tribal women and one-third of men are underweight (BMI <18.5 kg/m2). Low BMI has been reported in Adivasis in other parts of India too.[20] Furthermore, the National Family Health Survey-3 reports high prevalence of underweight in STs, both women (46.4%) men (41%).[21] These results indicate that, in comparison to urban and rural women and men in the STs are still grossly undernourished.[2223] Among different tribes, the KA have the highest proportion (14.4%; data not shown) of severely underweight (BMI <16 kg/m2) population. On the other hand, KO shows the best figures in terms of literacy, and a highest proportion are cultivators [Table 1]. These observations indicate that, whereas the KAs still continue to be at the bottom, the latter have moved up in the socioeconomic ladder.

Obesity, which is acquiring epidemic proportions in urban India, is also now an important emerging health issue in the rural India.[2425] However, the picture in not uniform, and prevalence in both urban and rural sectors shows wide regional variations. Recently, the Indian Council of Medical Research-India Diabetes (ICMR-INDIAB) has conducted multicentric study in three states (Maharashtra, Tamil Nadu, and Jharkhand) and one union territory (Chandigarh).[26] The obesity in urban area varied between 16.1 (Maharashtra) and 35.7% (Chandigarh). It was lowest (4.3%) and highest (27.9%) in rural Jharkhand and Chandigarh, respectively. Using the Asian cutoffs (obesity BMI ≥ 25), which may have implications in risk assessment for NCDs, every 11th man and 16th woman are obese in this study, an observation not supported clinically.[27] In our visits to the study areas, we have rarely seen a fat Adivasi. It is now recognized that BMI may not correspond to the same degree of fatness in different populations due, in part, to different body proportions. Therefore, it is desirable to develop different BMI cutoff points for different ethnic groups. In the meanwhile, as recommended by the WHO, international BMI cutoff points should be used to physically define fatness.[11] When this is done, only 0.9% of men and women are obese (BMI ≥30), a figure which looks more realistic. In the NNMB survey too, obesity (BMI ≥30) was not a major problem being observed in 0.1% and 0.2% women and men, respectively.[7] Obviously, obesity is still not a major health problem in STs.

In this study, the overall prevalence of overweight is 7.3% (female/male: 5.7/9.0%), a figure two times that for rural Jharkhand, which is surprising. In fact, the general characteristics of STs in this study and Jharkhand rural population are almost the same (this study/Jharkhand: age 37.3/40; Ht 154.8/155.4; Wt 47.3/46.7; BMI 19.6/19.2; SBP 122/126; DBP 75/76; and hypercholesterolemia 6.6%/3%).[26] It may be worth mentioning that Jharkhand has a substantial population of STs who account for >36% of state's rural population (9 million out of 25 million).

Diabetes is now a major global health problem and an estimated 380 million people in the world (5% of the world's population) suffer from diabetes. China, India, and the US in that order together account for half of the global burden for diabetes.[28] The overall prevalence of diabetes and impaired fasting sugar, in this study, was 5.3% (female/male: 3.7%/6.5%) and 7.3% (female/male: 5.8%/9%), respectively. The prevalence of diabetes is similar to the overall prevalence report in meta-analysis (5.9%). Overweight (BMI ≥25) did increase the overall risk for diabetes (odds ratio [OR] 3.21; P = 0.01), but the association was not the same in men and women. Overweight did not increase the risk in the latter. The prevalence of diabetes also did not show a consistent relationship to age. Chi-square analysis for trend is statistically significant only in males but not in females. In a recently conducted survey of ICMR-INDIAB in different states and union territory, urban prevalence varied from 10.9 in Maharashtra and 14.4% in Chandigarh.[26] However, rural prevalence of diabetes is only 3% in Jharkhand (lowest), which is lower than that observed (5.3%) in this study. There are a large number of publications on diabetes in STs but most of them lack quality. Of the 334 citations between 2000 and 2011, Upadhyay et al. found only seven fit for review.[6] The major problems were heterogeneity in diagnostic criteria, lack of uniformity in sampling, and restriction of the studies to certain regions. However, this study has been conducted on statistically adequate sample size in each ST community. Several reports have shown that “small-for-date babies” are more prone to develop type II diabetes when they reach adulthood.[29] Gross malnutrition, which is rampant in tribals, is one of the important causes of “small-for-date babies,” especially in the developing world. Yet in this study, the prevalence of diabetes is low in the tribals. In fact, this has also been observed in the meta-analysis by Upadhyay et al.[6]

The overall prevalence of systolic HTN in this study is 10.4% and 13.1% in women and men, respectively. It is lower than that reported in Indian urban and rural populations.[30] The prevalence is closer to that reported in the meta-analysis (16%) which reviewed data from 53 publications.[5] On the other hand, it is almost 50% of what has been reported in the second (2008–2009) NNMB survey that was conducted in nine states, with the sample size of 47,401 (44.6% women).[730] In the survey, combined prevalence (women and men) of HTN was 23.3% in tribals of Maharashtra.[7] On the other hand, every second tribal is hypertensive (prevalence of >50%) in Odisha, a figure five times that of Gujarat.[730] Even within a state, there are wide variations. Madhya Pradesh, which is the number one state accounting for 15% of the total ST population of India, has many tribes. Of the seven regions in the state where the survey was conducted, the prevalence varied between 8.7% and 33.3%. Presenting the overall mean value with such huge variations may be statistically sound; however, it would have only limited clinical implications.

Our studies, like those of Meshram et al.,[31] show a robust influence of age on the prevalence of HTN (Chi-square for trend P < 0.001, both for men and women), but that is not the case with the meta-analysis which shows only a weak correlation between age and HTN. This may be due to the fact that only four publications out of 53 in the meta-analysis had some sort of age stratification that too was restricted to only two categories, namely ≤45 and ≥45 years. In this study, overweight (OR 3.93, P < 0.001), diabetes (OR 2.351; P < 0.001), and hypercholesterolemia (OR 2.91, P < 0.001) also increased the risk for HTN. In the publication of Meshram et al., which is based on the NNMB report #25 (2009), obesity emerged as one of the risk factors for HTN in both sexes.[731]

HTN, which also shows wide regional variations globally, is a polygenetic disorder. Evidence from family studies shows BP to be moderately heritable.[32] Genome-wide association studies have identified numerous BP-related genes which are present on almost every chromosome.[33] In view of extreme human genetic diversity, it is very difficult to investigate the role of genetics in the pathogenesis of HTN. However, STs, who have been strictly following endogamy for generation since prehistoric times, could offer an excellent “natural model” to study this problem. Generation of tribe-specific information, which is one of the strengths of this study, may provide crucial leads. BH and KA are separated by a distance of >500 km. Thakurs are midway between the two. Kokanas, who spread out to nearby Gujarat, are about 200 km from BH. Large-distance geographic separations of tribes and high endogamy rule out the possibility of large-scale migration and intermixing of the tribes in this study. However, the fact that we have hardly observed any differences in the prevalence of HTN is different tribes suggest that environmental/lifestyle factors rather than genetics play a dominant role in the pathogenesis of HTN, which is perhaps the prevalent view.

Data in this study are generated by young students, albeit under supervision, as a part of our program of “discovering little scientists” which is a 2-month summer vacation research program for secondary schoolchildren. There have been lurking reservations on the veracity of such data. However, the results obtained in this study match the figures generated by high-cost, multicentric epidemiological studies sponsored by national research funding agencies, an indication that, if properly supervised, our cost-effective approach could also be used to study other community-based health issues.[132130]

Prevalence of both HTN and diabetes is progressively increasing not only in urban areas but also in the rural population. Of the numerous risk factors, overweight, hyperlipidemia, unhealthy imbalanced diet (diet rich in fat), and sedentary lifestyle are the most important risk factors for both diabetes and HTN. None of these are present in tribals in this study. No wonder the prevalence of both HTN and diabetes is still very low in STs. However, fast acculturation of the tribes could soon change the scenario, and like the rest of India, burden of NCDs would be soon substantial in STs too. Small sample size, albeit statistically sound, is a limitation of this study that may explain inconsistent inter-tribal as well as gender differences not only in the prevalence rates but also in relation to risk factors. ST is not a homogeneous community. There are some 500 STs recognized by the Government of India. Further, anthropologically, they belong to three races, namely, the Negritos, the Proto-Australoids, and the Mongoloids. Current tribal health programs are more focused on genetic disorders mainly on hemoglobinopathies and thalassemias. It is time to launch a nationwide multicentric study similar to ICMR-INDIAB, to generate baseline information on the prevalence of NCDs and associated risk factors in different populations of STs in India.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.