Type 2 diabetes mellitus (T2DM) and hypertension (HT) are major noncommunicable health problems in both developing and developed countries, including Thailand. Each year, a large amount of money is budgeted for treatment and care. Hill tribe people are a marginalized population in Thailand, and members of its elderly population are vulnerable to health problems due to language barriers, lifestyles, and daily dietary intake.
Methods
An analytic cross-sectional study was conducted to estimate the prevalence of T2DM and HT and to assess the factors associated with T2DM and HT. The study populations were hill tribe elderly adults aged ≥ 60 years living in Chiang Rai Province, Thailand. A simple random method was used to select the targeted hill tribe villages and participants into the study. A validated questionnaire, physical examination form, and 5-mL blood specimen were used as research instruments. Fasting plasma glucose and blood pressure were examined and used as outcome measurements. Chi-square tests and logistic regression were used for detecting the associations between variables at the significance level alpha=0.05.
Results
In total, 793 participants participated in the study; 49.6% were male, and 51.7% were aged 60-69 years. A total of 71.5% were Buddhist, 93.8% were uneducated, 62.9% were unemployed, and 89 % earned an income of < 5,000 baht/month. The overall prevalence of T2DM and HT was 16.8% and 45.5%, respectively. Approximately 9.0% individuals had comorbidity of T2DM and HT. Members of the Lahu, Yao, Karen, and Lisu tribes had a greater odds of developing T2DM than did those of the Akha tribe. Being overweight, having a parental history of T2DM, and having high cholesterol were associated with T2DM development. In contrast, those who engaged in highly physical activities and exercise had lower odds of developing T2DM than did those who did not. Regarding HT, being female, having a high dietary salt intake, being overweight, and having a parental history of HT were associated with HT development among the hill tribe elderly populations.
Conclusions
The prevalence of T2DH and HT among the hill tribe elderly populations is higher than that among the general Thai population. Public health interventions should focus on encouraging physical activity and reducing personal weight, dietary salt intake, and greasy food consumption among the hill tribe elderly.
Type 2 diabetes mellitus (T2DM) and hypertension (HT) are common noncommunicable diseases among elderly adults aged ≥ 60 years in both developing and developed countries [1]. The prevalence of T2DM and HT varies according to age, sex, and race [2, 3]. There are different factors associated with T2DM and HT in different populations, particularly among those with different lifestyles and cultures [3, 4]. Older populations are the most vulnerable to the development of T2DM and HT [5, 6]. T2DM and HT have become major causes of morbidity and mortality of elderly populations in all countries [7, 8]. The impact of T2DM and HT is not limited to physical and mental consequences; rather, it also affects family and national economics [9]. Health professionals in health care institutes must manage the maintenance of plasma glucose levels among T2DM patients and blood pressure among HT patients using different regiments of drugs for their entire lives. With these demands, there are required numbers of health professionals and large amount of financial input needed to operate the treatment and care system each year. Patients need to frequently attend a clinic to meet and receive care from a doctor. Otherwise, many complications could possibly develop, resulting in intensive and complicated methods of treatment and care.
In 2014, the WHO estimated that 422 million people worldwide were suffering from T2DM, which accounted for 8.5% of the prevalence among people over 18 years old. The prevalence is increasing among people aged > 30 years old, particularly in low- and middle-income countries. People aged ≥ 60 years old are also commonly defined as a vulnerable population for T2DM [2]. Commonly, T2DM is a disease that progresses slowly from its onset, and it may be diagnosed several years later. T2DM is a major cause of other health problems, such as blindness, kidney failure, heart attacks, stroke, and lower limb amputation. The WHO also reported that 1.6 million deaths were directly caused by diabetes, and almost half of all deaths attributable to high blood glucose occurred before the age of 70 years [2]. This finding reflects the need to regularly investigate those vulnerable to an early diagnosis and determine ways of obtaining a better prognosis. In 2016, the total T2DM prevalence among the Thai population was 9.6%: 9.1% in males and 10.1% in females. The total number of deaths caused by T2DM was 20,570 cases; in the 30-69 year age group, the number of deaths was 8,120 cases (3,610 males, 4,510 females) and in the ≥ 70 years age group, the number of deaths was 12,450 cases (4,760 males, 7,690 females). Moreover, total number of deaths attributable to high blood glucose was 35,640 cases; in the 30-69 year age group, the number of deaths was 13,810 cases (7,220 males, 6,590 females) and in the ≥ 70 years age group, the number of deaths was 21,830 cases (9,430 males, 12,400 females) [10]. The average cost of each T2DM case in attending hospital services per year was 598US$ for an independent case and 2,700US$ for a disabled case. Therefore, Thailand spends a large amount of money on the health care system annually [11].
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High blood pressure is a key risk factor for many diseases, including heart attack and stroke. In 2017, WHO estimated that more than one billion people had HT caused 12.8% of all deaths and accounted for 57 million disability-adjusted life years (DALYs) or a total of 3.7% DALYs every year [12]. Thailand reported that 29.0% of adult Thais had HT, and only 37.0% for those people who had been diagnosed had their blood pressure under control in 2017 [13]. The number of resistant HT patients in all health institutes in the entire country has increased from 3,946,902 cases in 2013 to 5,584,007 cases in 2017 [13]. The statistics represent the full picture of the situation in Thailand, but there is no information available on any specific subgroup of populations, such as the hill tribe population.
The hill tribe people are those who have migrated from the southern region of China to Thailand in the past century [14]. They are divided into six different main groups: Akha, Lahu, Karen, Hmong, Yao, and Lisu [15]. Approximately 2.5 million of the hill tribe people were living in Thailand in 2017 [16]. They have their own culture, language and lifestyles, particularly in daily cooking. Some tribes use a high volume of oil for cooking, whereas other tribes use a high volume of salt for their daily food [14, 17]. Most of them have similar cultural patterns in terms of using alcohol, particularly for religious rituals [18].
In 2017, the hill tribe elderly populations lived according to their own traditional lifestyle and living environment. They consumed drinks and foods prepared traditionally. Individual health care was mainly based on their local healing patterns. With the problems of distance, language and discrimination, their access to the Thai health care system was poor [19]. Therefore, access to modern medical care is not common, especially for those who live very far from the city. Ultimately, the findings of the study could support the development of the health care service system for the hill tribe elderly populations. The findings could also be used for the development of DM and HT prevention and control measures in these populations. Currently, there is no available information about T2DM and HT among these population groups. Therefore, the study aimed to estimate the prevalence and factors associated with DM and HT among the hill tribe elderly populations in northern Thailand.
Methods
Study design and participants
Study design
A cross-sectional study was conducted to gather information from the selected subjects.
Study setting
The study was conducted along 16 districts in Chiang Rai Province, which is located in Thailand.
Study population
The study population was comprised of hill tribe elderly adults aged ≥ 60 years old who had lived in the study setting for at least 3 years.
Eligible population
Elderly adults with the following characteristics were eligible for the study: a) being classified as a member of the hill tribe by verbal confirmation, b) being ≥ 60 years old, c) living in the study area for 3 years at the date of data collection, and d) having the ability to provide essential information. Those who had been diagnosed with type 1 diabetes mellitus, which requires daily administration of insulin, were excluded from the study.
Sample size
The sample size was calculated by Epi-Info version 7.2 (US Centers for Disease Control and Prevention, Atlanta, GA). By setting the alpha error at 0.05, the power at 0.8, the previous prevalence of T2DM among the exposed group at 18.0%, and the prevalence among the unexposed group at 0.07% [20], the sample size was calculated to be a minimum of 705 participants. Increasing the sample size by 10.0% for error resulted in 775 participants required.
Since the sample size was calculated at 775 participants, at least 130 participants were needed in each tribe.
Sample selection and preparing the participants
The list of the hill tribe villages in Chiang Rai Province was requested from the Hill Tribe Welfare and Development Center in Chiang Rai [21]. There were 749 hill tribe villages in Chiang Rai, which breakdown into 316 Lahu villages, 243 Akha villages, 63 Yao villages, 56 Hmong villages, 36 Karen villages, and 35 Lisu villages. In 2016, a total of 41,366 hill tribe families lived in Chiang Rai Province.
Permission to access the villages had been granted by the District Government Officer. Sixty hill tribe villages, or 10 villages in each tribe, were selected by a simple random method. A village headman was contacted and informed of all essential information regarding the research objective and its protocol. The list of elderly people who met the inclusion and exclusion criteria in the village was sent to the researcher. A simple random method was used to select 13 individuals in each village, after which they were invited to participate in the study. After informing the village headman about the research objectives and protocols, some tribes collected more than the minimum required sample size: Lahu (an excess of 3 participants) and Hmong (an excess of 10 participants). Those who agreed to join the project were informed of all research processes, including the preparation of NPO (nothing per oral) for at least 8 hours for the blood specimen collection on the next day (Fig. 1).
Fig. 1
Flowchart of participants’ selection. Sixty hill tribe villages were randomly selected from 749 villages, and 13-14 elderly people in each village were recruited into the study
×
Six research assistants fluent in Thai and in one of the six hill tribal languages were recruited. Selected research assistants were trained in procedures, and the required documents were completed three days before working in the field. Most of the hill tribe elderly populations do not speak Thai. Therefore, there was a need to obtain complete information using the research assistants. Recruiting young adults to help as research assistants was possible because hill tribe community members younger than 25 years old had already completed secondary level education in Thai schools.
Measurement
Research instruments
A questionnaire, physical examination form, a manual sphygmomanometer, and a 5-mL blood specimen served as research instruments. A questionnaire was developed from the review literature. After completion of the first draft, the validity was detected by the item-objective congruence (IOC) technique in which three external experts in relevant fields verified the validity. Questions with scores less than 0.5 were excluded, those with scores 0.5-0.69 were revised, and those with scores greater than 0.7 were defined as acceptable to use. The questionnaire was also tested for reliability by pilot testing it in 15 similar participants in the Ban San Ti Suk hill tribe village using the test-retest method. Questions with Cronbach’s alpha ≥ 0.5 were included in the form. Ultimately, there were 28 questions in three parts included in the questionnaire, which presented an overall Cronbach’s alpha of 0.77.
In the first parts, 13 questions were used to collect participants’ general information, such as age, sex, education, and religion. Fifteen questions were included in the second part, including questions about health behaviors such as “Do you smoke?”, “Do you drink alcohol?”, and “Do you use methamphetamine?”. In these questions, the three answer choices were “Yes”, “Ever in the past”, and “No”.
Several questions were asked regarding daily food consumption and exercise, such as “Do you usually eat a salty diet?”, “Do you favor having greasy food?”, and “Do you like to eat sweet food?” Two answer choices were provided for the questions, “Yes” and “No”. However, for exercise practices, the three answer choices were “No”, “Highly active physical work, such as farmer and labor”, and “Yes”.
Questions on medications, history of T2DM and HT, and parental history of T2DM and HT were also included. To confirm the diagnosis, all participants who responded that they had T2DM and HT were asked to present the log-book from a hospital. In Thailand, all DM and HT cases are provided individual log-books to use to collect medical information and make appointments.
The last part consisted of twenty-one items of a physical examination form, which is used at Mae Fah Luang University Hospital. This form resembles a checklist and can include more information if required. A manual sphygmomanometer was used for assessing blood pressure.
Variables and measurements
T2DM in the study was identified by the following: a) having no history of a medical diagnosis, such as type 1 diabetes mellitus, at a particularly early age after birth, b) having shown a fasting plasma glucose ≥ 126 mg/dL twice on different days [20].
Blood pressure was assessed twice in all participants with a 15-minute gap between assessments, and in case systolic and/or diastolic blood pressures were greater than 90 mmHg and/or 150 mmHg, respectively, it was assessed again after 15 minutes of rest following the 2nd assessment. Participants with 90 mmHg and/or 140 mmHg of systolic and diastolic blood pressures, respectively, were diagnosed as HT patients [22].
Body mass index (BMI) was classified into three categories, according to the WHO guidelines for Asian populations: underweight (BMI≤18.5), normal weight (BMI=18.51-22.99), and overweight (BMI ≥ 23.00) [23].
A 5-mL blood specimen was collected from a peripheral vein puncture. After blood was drawn, a 3-mL blood specimen was collected and stored in a sodium fluoride tube to detect fasting plasma glucose. Another 2-mL blood specimen was collected and stored in a clot blood clot tube for detecting lipid profiles. Uric acid, cholesterol, and triglycerides were assessed in mg/dL. Participants with uric acid ≥ 7 mg/dL, cholesterol ≥ 200 mg/dL, and triglycerides ≥ 200 mg/dL were defined as a high-level group [24]. Participants with fasting plasma glucose ≥ 126 mg/dL were asked to provide another blood specimen within a week to determine type 2 diabetes stage.
Procedures
Data gathering procedures
After the consent form was obtained, a 5-mL blood specimen was collected. Participants were asked to complete the questionnaire in a private room in the village with the help of the research assistants. A trained physician examined the physical health of all participants in a proper room. A small gift was given to participants after they completed the questionnaire.
Statistical analysis
Descriptive statistics, such as the means, minimums, maximums, standard deviations, and percentages, were used to explain the general characteristics of the participants. Chi-square tests and logistic regressions were used to detect the associations between variables at the significance level α=0.05. Logistic regression was used to detect the associations between variables in both univariate and multivariate models. The “ENTER” mode was used to select the significant variables in the model. The significance level (alpha) was set at 0.05 in both univariate and multivariate analyses. Variables that were found to be significant in the univariate analysis were retained in the multivariate analysis. In the multivariate model, the most nonsignificant variable was deleted from the model before running the second step. The model was analyzed until all remaining variables were found to be significant at an alpha level of 0.05, and the results were interpreted.
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Results
Characteristics of participants
In total, 793 participants were recruited into the study. Proportions of participants were mostly equal by sex and among the six tribes. A few people had no Thai identification card (6.1%), with an equal proportion among the tribes. The majority were aged 60-69 years (51.7%), with an average age of 70.1 years (range=60-100, SD=7.57, max=100, and min=60). The majority of the sample practiced Buddhism (71.5%) and had no education (94.8%). A few people lived alone (6.1%), and most participants were married (66.8%). Regarding economic status, 89.2% had an income of ≤ 5,000 baht/month (mean=1,129 baht, SD=1,273), and 84.9% had no debt (Table 1).
Table 1
General characteristics of the study participants
Characteristics
Number
Percent
Total
793
100.0
Sex
Male
393
49.6
Female
400
50.4
Thai ID card
Yes
745
93.9
No
48
6.1
Tribe
Akha
130
16.4
Lahu
133
16.8
Hmong
140
17.6
Yao
130
16.4
Karen
130
16.4
Lisu
130
16.4
Age (years)
60-69
410
51.7
70-79
279
35.2
≥ 80
104
13.1
Religion
Buddhism
567
71.5
Christianity
225
28.4
Islam
1
0.1
Education
None
739
93.8
Primary School
41
5.2
High School
8
1.0
Resides with
Child
559
70.5
Cousin
12
1.5
Spouse
174
21.9
Alone
48
6.1
Marital status
Single
15
1.9
Married
524
66.8
Divorced
20
2.5
Widow
226
28.8
Number of family member (persons)
1
40
5.0
2
116
14.6
3-5
301
38.0
6
336
42.4
Occupation
Unemployed (retired)
499
62.9
Farmer
252
31.8
Merchant
11
1.4
Labor
19
2.4
Other
12
1.5
Monthly family income (baht)
0
69
8.7
≤5,000
707
89.2
≥5,001
17
2.1
Debt (baht)
0
673
84.9
≤5,000
14
1.8
5,001-10,000
11
1.4
10,001-50,000
58
7.3
≥50,001
37
4.6
There were no statistical differences in the distribution of participants according to sex and tribe in three different age categories (60-69, 70-79, and ≥ 80 years). A few of the hill tribe elderly adults had the ability to communicate in Thai: 19.5% could speak, 19.5% could understand, 2.0% could read, and 1.6% could write fluently. Males had significantly better Thai communication skills than females in all four domains: speaking, understanding, reading, and writing.
The prevalence of T2DM and HT was 16.8% and 45.5%, respectively. Seventy-five participants had been diagnosed with T2DM before being recruited into the study. Among these participants, 8 (10.6%) had high fasting glucose or were unable to control blood glucose after medication. Fifty-five participants (7.7%) were detected as new T2DM cases (Table 2). However, 18 participants (1.2%) could not draw blood specimens.
Table 2
Prevalence of T2DM and HT among the participants
Chracteristics
Number
Percent
Medical history of T2DM
No
718
90.5
Yes
75
9.5
Effective control of blood glucose by daily medication
No
8
10.6
Yes
67
89.4
Fasting plasma glucose level among non-DM diagnosed
Normal
645
89.8
High (T2DM)
55
7.7
(Missing=18, 2.5%)
aPrevalence of T2DM=16.8%
Medical history of HT
No
553
69.7
Yes
240
30.3
Effective control of blood pressure by daily medication
No
91
37.9
Yes
149
62.1
Blood pressure level among non-HT diagnosed
Normal
432
78.1
High (HT)
121
21.9
bPrevalence of HT=45.5%
Having both T2DM and HT
70
9.0
a The overall prevalence of T2DM among the participants
b The overall prevalence of HT among the participants
Two hundred and forty participants (30.3%) had been diagnosed with HT, among whom 37.9% were unable to control their blood pressure after medication. After those who had no history of HT diagnosis and medication were seen, 121 participants (21.9%) were detected as new HT cases. Finally, 70 cases (9.0%) were determined to have both T2DM and HT: 36 males and 34 females (Table 2).
There was statistical significance in the proportion of participants with T2DM and HT by sex and tribe. Only the participants with T2DM showed a statistically significant difference in proportion (Table 3).
Table 3
Comparison of T2DM and HT by participants’ characteristics
Characteristic
T2DM
χ2
p-value
HT
χ2
p-value
Yes (%)
No (%)
Yes (%)
No (%)
Sex
Male
66 (17.3)
316 (82.7)
0.13
0.712
164 (41.7)
229 (58.3)
4.52
0.034*
Female
64 (16.3)
329 (83.7)
197 (49.3)
203 (50.7)
Age (years)
60-69
75 (18.8)
324 (81.2)
2.49
0.287
173 (42.2)
237 (57.8)
4.25
0.119
70-79
39 (14.3)
234 (85.7)
134 (48.0)
145 (52.0)
≥80
16 (15.5)
87 (84.5)
54 (51.9)
50 (48.1)
Tribe
Akha
11 (8.6)
117 (91.4)
24.48
<0.001*
61 (46.9)
69 (53.1)
26.45
<0.001*
Lahu
26 (19.5)
107 (80.5)
61 (45.9)
72 (54.1)
Hmong
11 (8.1)
124 (91.9)
42 (30.0)
98 (70.0)
Yao
26 (21.5)
95 (78.5)
74 (56.9)
56 (43.1)
Karen
34 (26.4)
95 (73.6)
52 (40.0)
78 (60.0)
Lisu
22 (17.1)
107 (82.9)
71 (54.6)
59 (45.4)
*Significance level at α=0.05
Health behaviors among the participants indicated that 19.7% smoked, 14.6% drank alcohol, 44.9% ate uncooked food, 23.8% chewed tobacco, and 10.1% did not exercise regularly. A comparison of health behaviors such as smoking, alcohol use, eating uncooked food, and regular exercise among the tribes showed statistically significant differences (Table 4). Additionally, there were significant sex differences in the following health behaviors: smoking; alcohol use; the consumption of uncooked food, salty food, greasy food, and sweet food; opium use; chewing tobacco; and regular exercise (Table 5).
Table 4
Characteristics of health behaviors by tribe
Health behaviors
Tribe
χ2
p-value
Total
Akha
Lahu
Hmong
Yao
Karen
Lisu
n
%
n
%
n
%
n
%
n
%
n
%
n
%
Smoking
No
486
61.3
94
19.3
70
14.4
106
21.8
72
14.8
48
9.9
96
19.8
79.02
< 0.001*
Ever in the past
151
19.0
12
7.9
33
21.9
11
7.3
29
19.2
50
33.1
16
10.6
Yes
156
19.7
24
15.4
30
19.2
23
14.7
29
18.6
32
20.5
18
11.5
Alcohol use
No
538
67.8
99
18.4
92
17.1
109
20.3
88
16.4
77
14.3
73
13.6
43.93
< 0.001*
Ever
139
17.5
13
9.4
29
20.9
14
10.1
17
12.2
25
18.0
41
29.5
Yes
116
14.6
18
15.5
12
10.3
17
14.7
25
21.6
28
24.1
16
13.8
Methamphetamine use
No
776
97.9
124
16.0
132
17.0
137
17.7
126
16.4
128
16.5
129
16.6
12.15
0.275
Ever in the past
2
0.3
0
0.0
0
0.0
0
0.0
1
50.0
1
50.0
0.0
0.0
Yes
15
1.9
6
40.0
1
6.7
3
20.0
3
20.0
1
6.7
1
6.7
Opium use
No
723
91.2
112
15.5
125
17.3
124
17.2
115
15.9
123
17.0
124
17.2
15.77
0.106
Ever in the past
54
6.8
12
22.2
6
11.1
12
22.2
12
22.2
7
13.0
5
9.3
Yes
16
2.0
6
37.5
2
12.5
4
25.0
3
18.8
0
0.0
1
6.3
Eating uncooked food
No
385
48.5
79
20.5
74
19.2
68
17.7
69
17.9
43
11.2
52
13.5
29.65
< 0.001*
Ever in the past
52
6.6
5
9.6
6
11.5
9
17.3
8
15.4
11
21.2
13
25.0
Yes
356
44.9
46
12.9
53
14.9
63
17.7
53
14.9
76
21.3
65
18.3
Chewing
No
604
76.2
70
11.6
108
17.9
135
22.4
128
21.2
100
16.6
63
10.4
159.80
< 0.001*
Yes
189
23.8
60
31.7
25
13.2
5
2.6
2
1.1
30
15.9
67
35.4
Regular exercise
No
80
10.1
22
27.5
7
8.8
15
18.8
7
8.8
22
27.5
7
8.8
37.50
< 0.001*
Yes
433
54.6
68
15.7
88
20.3
75
17.3
66
15.2
54
12.5
82
18.9
Highly active physical work
280
35.3
40
14.3
38
13.6
50
17.9
57
20.4
54
19.3
41
14.6
*Significance level at α=0.05
Table 5
Comparison of health behavior by sex
Health behvaior
Total
Male
Female
χ2
p-value
n
%
n
%
n
%
Smoking
No
486
61.3
151
31.1
335
68.9
173.52
< 0.001*
Ever in the past
151
19.0
125
82.8
26
17.2
Yes
156
19.7
117
75.0
39
25.0
Alcohol use
No
538
67.8
169
31.4
369
68.6
222.02
< 0.001*
Ever in the past
139
17.5
117
84.2
22
15.8
Yes
116
14.6
107
92.2
9
7.8
Consumption of uncooked food
No
385
48.5
106
27.5
279
72.5
145.24
< 0.001*
Ever in the past
52
6.6
37
71.2
15
28.8
Yes
356
44.9
250
70.2
106
29.8
Salty food
No
282
35.6
106
37.6
176
62.4
25.05
< 0.001*
Yes
511
64.4
287
56.2
224
43.8
Greasy food
No
297
37.5
194
65.3
103
34.7
47.12
< 0.001*
Yes
496
62.5
199
40.1
297
59.9
Sweet food
No
391
49.3
216
55.2
175
44.8
9.96
0.0016*
Yes
402
50.7
177
44.0
225
56.0
Opium use
No
723
91.2
339
46.9
384
53.1
23.95
< 0.001*
Ever in the past
54
6.8
43
79.6
11
20.4
Yes
16
2.0
11
68.8
5
31.3
Methamphetamine use
No
776
97.9
381
49.1
395
50.9
3.69
0.079
Yes
17
2.1
12
70.6
5
29.4
Chewing
No
604
76.2
313
51.8
291
48.2
5.19
0.023*
Yes
189
23.8
80
42.3
109
57.7
Regular exercise
No
433
54.6
184
42.5
249
57.5
26.05
< 0.001*
Highly active physical work
280
35.3
173
61.8
107
38.2
Yes
80
10.1
36
45.0
44
55.0
*Significance level at α=0.05
Most participants had moderate levels of health-related knowledge, attitudes, and practices. Only the distribution of attitudes by tribe showed statistical significance (Table 6).
Table 6
Comparison on knowledge, attitudes, and practices regarding health among tribes
KAP
Tribe
χ2
p-value
Total
Akha
Lahu
Hmong
Yao
Karen
Lisu
n
%
n
%
n
%
n
%
n
%
n
%
n
%
Total
377
100.0
60
15.9
76
20.2
46
12.2
70
18.6
73
19.4
52
13.8
Knowledge
Low
61
16.2
15
24.6
13
21.3
10
16.4
8
13.1
5
8.2
10
16.4
15.07
0.129
Moderate
167
44.3
24
14.4
33
19.8
21
12.6
38
22.8
31
18.6
20
12.0
High
149
39.5
21
14.1
30
20.1
15
10.1
24
16.1
37
24.8
22
14.8
Attitude
Low
53
14.1
12
22.6
5
9.4
14
26.4
14
26.4
4
7.5
5
9.4
38.04
< 0.001*
Moderate
250
66.3
44
17.6
55
22.0
25
10.0
42
16.8
44
17.6
40
16.0
High
74
19.6
4
5.4
16
21.6
7
9.5
14
18.9
25
33.8
8
10.8
Practice
Low
47
12.5
3
6.4
8
17.0
10
21.3
9
19.1
8
17.0
9
19.1
10.51
0.397
Moderate
267
70.8
44
16.5
56
21.0
27
10.1
49
18.4
54
20.2
37
13.9
High
63
16.7
13
20.6
12
19.0
9
14.3
12
19.0
11
17.5
6
9.5
*Significance level at α=0.05
With regard to the physical health and medical history among the participants, 45.0% were overweight, 6.8% were disabled persons, 15.0% had sleeping problems, 9.7% had cataracts, 28.7% had hearing problems, and 43.3% had tooth problems (Table 7).
Table 7
Physical examination and medical history
Item
Total
Male
Female
χ2
p-value
n
%
n
%
n
%
BMI
Underweight
116
14.6
62
53.4
54
46.6
3.98
0.137
Normal
320
40.4
168
52.5
152
47.5
Overweight
357
45.0
163
45.7
194
54.3
Disabled
No
739
93.2
362
49.0
377
51.0
1.42
0.232
Yes
54
6.8
31
57.4
23
42.6
Heart disease
No
724
96.1
337
46.5
387
53.5
0.37
0.538
Yes
29
3.9
16
55.2
13
44.8
History of TB diagnosis
No
757
95.5
369
48.7
388
51.3
4.41
0.036*
Yes
36
4.5
24
66.7
12
33.3
Sleeping problem
No
674
85.0
356
52.8
318
47.2
19.09
< 0.001*
Yes
119
15.0
37
31.1
82
68.9
Eye
Normal
663
83.6
328
49.5
335
50.5
0.99
0.804
Cataract
77
9.7
36
46.8
41
53.2
Pterygium
50
6.3
27
54.0
23
46.0
History of glaucoma
3
0.4
2
66.7
1
33.3
Tooth problem
No
450
56.7
234
52.0
216
48.0
2.48
0.115
Yes
343
43.3
159
46.4
184
53.6
Headache
No
557
72.1
302
54.2
275
49.4
6.55
0.010*
Yes
216
27.9
91
42.1
125
57.9
Dizziness
No
556
70.1
294
52.9
262
47.1
8.19
0.004*
Yes
237
29.9
99
41.8
138
58.2
Peptic ulcer
No
527
66.5
278
52.8
249
47.2
6.40
0.011*
Yes
266
33.5
115
43.2
151
56.8
Anorexia
No
707
89.2
371
52.5
336
47.5
22.18
< 0.001*
Yes
86
10.8
22
25.6
64
74.4
History of injury
No
713
89.9
349
48.9
364
51.1
1.05
0.305
Yes
80
10.1
44
55.0
36
45.0
History of hospital admission
No
310
39.1
143
46.1
167
53.9
2.39
0.122
Yes
483
60.9
250
51.8
233
48.2
Parental history of DM
No
515
64.9
262
50.9
253
49.1
1.01
0.313
Yes
278
35.1
131
47.1
147
52.9
Parental history of HT
No
375
47.3
190
50.7
185
49.3
0.34
0.554
Yes
418
52.7
203
48.6
215
51.4
*Significance level at α=0.05
Anzeige
There were statistically significant differences in the quality of uric acid and cholesterol according to sex, age category, and tribe. A greater proportion of males, individuals in higher age categories, and Lahu and Lisu tribe members had high uric acid levels than did females, those in younger age categories, and members of other tribes. Only age category and tribe showed significant differences on the level of triglycerides; a greater proportion of those in lower age categories had high cholesterol than those in higher age categories. A greater proportion of members of the Lahu and Akha tribes were in the high cholesterol group compared to those in the remaining tribes (Table 8).
Table 8
Classification of participants’ characteristics by biomarkers
Factors
Uric acid
χ2
p-value
Cholesterol
χ2
p-value
Triglyceride
χ2
p-value
Normal n (%)
High n (%)
Normal n (%)
High n (%)
Normal n (%)
High n (%)
Sex
Male
246 (64.4)
136 (35.6)
38.63
<0.001*
286 (74.9)
96 (25.1)
14.28
<0.001*
309 (80.9)
73 (19.1)
2.44
0.118
Female
329 (83.9)
63 (16.1)
244 (67.4)
148 (32.6)
299 (76.3)
93 (23.7)
Age (years)
60-69
311 (77.9)
88 (22.1)
6.04
0.049*
261 (65.4)
138 (34.6)
4.45
0.108*
303 (75.9)
96 (24.1)
6.58
0.037*
70-79
197 (71.1)
80 (28.9)
195 (78.9)
82 (21.1)
219 (79.1)
58 (20.9)
≥ 80
67 (68.4)
31 (31.6)
74 (78.7)
24 (21.3)
86 (87.8)
12 (12.2)
Tribe
Akha
101 (78.3)
28 (21.7)
20.19
0.018*
95 (73.6)
34 (26.4)
17.05
0.004*
99 (76.7)
30 (23.3)
8.86
0.114
Lahu
113 (85.0)
20 (15.0)
100 (75.2)
33 (24.8)
96 (72.2)
37 (27.8)
Hmong
81 (64.3)
45 (35.7)
93 (73.8)
33 (26.2)
100 (79.4)
26 (20.6)
Yao
96 (74.4)
33 (25.6)
82 (90.0)
47 (9.1)
98 (75.9)
31 (24.1)
Karen
99 (76.7)
30 (23.3)
72 (55.8)
57 (44.2)
111 (86.0)
18 (14.0)
Lisu
85 (66.4)
43 (33.6)
88 (68.8)
40 (31.2)
104 (81.3)
24 (18.7)
*Significance level at α=0.05
In the multivariate model, five factors were associated with T2DM: tribe, exercise, BMI, parental history of T2DM, and triglycerides. The Lahu, Yao, Karen, and Lisu tribes had greater odds of developing T2DM than the Akha tribe, with ORadj=2.89 (95%CI=1.32-6.33), ORadj=3.47 (95%CI=1.58-7.62), ORadj=5.03 (95%CI=2.35-10.78), and ORadj=2.73 (95%CI=1.22-6.07) respectively. Those who were overweight had greater odds of developing T2DM than those with normal weight, with ORadj=2.08 (95%CI=1.32-3.27). Those who had a parental history of T2DM had greater odds of developing T2DM than those who did not, with ORadj=1.55 (95%CI=1.17-2.10). Those with high cholesterol had greater odds of developing T2DM than those with low cholesterol, with ORadj=1.73 (95%CI=1.10-2.73). Those who engaged in high levels of physical activity and exercise had lower odds of developing T2DM than those who did not, with ORadj=0.48 (95%CI=0.25-0.91) and ORadj=0.45 (95%CI=0.24-0.83), respectively (Table 9).
Table 9
Factors associated with T2DM in univariate and multivariate analyses (n = 775)**
Factors
T2DM
OR
95%CI
p-value
ORadj
95%CI
p-value
Yes
No
n
%
n
%
Sex
Mal
66
17.3
316
82.7
1.00
Female
64
16.3
329
83.7
0.93
1.02 -2.02
0.712
Tribe
Akha
11
8.6
117
91.4
1.00
1.00
Lahu
26
19.5
107
80.5
2.58
1.37-4.85
0.013*
2.89
1.32-6.33
0.008*
Hmong
11
8.1
124
91.9
0.94
0.45-1.96
0.896
0.91
0.35-2.31
0.845
Yao
26
21.5
95
78.5
2.91
1.54-5.48
0.006*
3.47
1.58-7.62
0.002*
Karen
34
26.4
95
73.6
3.80
2.06-7.03
< 0.001*
5.03
2.35-10.78
< 0.001*
Lisu
22
17.1
107
82.9
2.18
1.14-4.13
0.046*
2.73
1.22-6.07
0.014*
Age (year)
60-69
75
18.8
324
81.2
1.00
70-79
39
14.3
234
85.7
0.72
0.50-1.02
0.127
≥ 80
16
15.5
87
84.5
0.79
0.48-1.30
0.444
Smoking
No
78
16.4
398
83.6
1.00
Ever in the past
34
23.1
113
76.9
1.53
1.04-2.24
0.064*
Yes
18
11.8
134
88.2
0.68
0.43-1.08
0.177
Alcohol use
No
79
15.0
447
85.0
1.00
Ever in the past
26
19.3
109
80.7
1.35
0.89-2.03
0.230
Yes
25
21.9
89
78.1
1.58
1.04 -2.42
0.072*
Salty food
No
151
53.5
131
46.5
1.00
Yes
266
52.1
245
47.9
0.94
0.70-1.26
0.687
Greasy food
No
155
52.2
142
47.8
1.00
Yes
258
52.0
238
48.0
0.99
0.74-1.32
0.962
Sweet food
No
202
51.7
189
48.3
1.00
Yes
184
45.8
218
54.2
0.78
0.59-1.04
0.097
Exercise
No
21
26.6
58
73.4
1.00
1.00
Highly active physical work
45
16.7
225
83.3
0.55
0.33- 0.90
0.050*
0.48
0.25-0.91
0.024*
Yes
64
15.0
362
85.0
0.48
0.30- 0.78
0.013*
0.45
0.24-0.83
0.011*
BMI
Normal
39
12.6
271
87.4
1.00
1.00
Underweight
13
11.4
101
88.6
0.89
0.51-1.56
0.743
0.90
0.45-1.80
0.773
Overweight
78
22.2
273
77.8
1.98
1.39- 2.82
0.001*
2.08
1.32-3.27
0.001*
Parental history of DM
No
217
42.1
298
57.9
1.00
1.00
Yes
149
53.6
129
46.4
1.58
1.18-2.12
0.002*
1.55
1.17-2.10
0.001*
Hypertension
No
70
12.9
282
80.1
1.00
Yes
60
14.2
363
85.8
1.50
1.02- 2.19
0.035*
Headache
No
95
16.9
467
83.1
1.00
Yes
35
16.4
178
83.6
0.96
0.67-1.38
0.875
Dizziness
No
86
15.9
456
84.1
1.00
Yes
44
18.9
189
81.1
1.23
0.88-1.72
0.303
Cholesterol
Normal
90
17.3
430
82.7
1.00
High
38
16.1
198
83.9
0.91
0.64-1.29
0.682
Triglyceride
Normal
88
14.9
504
85.1
1.00
1.00
High
40
24.4
124
75.6
1.84
1.29-2.63
0.004*
1.73
1.10-2.73
0.017*
*Significance level at α=0.05 **18 participants could not provide blood specimens
Four factors were found to be associated with HT after controlling for all possible confounding variables: sex, dietary salt intake, BMI, and parental history of HT. Females had greater odds of developing HT than males, with ORadj=1.29 (95%CI=1.01-1.68). Those who had dietary salt intake had greater odds of developing HT than those who did not, with ORadj=1.48 (95%CI=1.14-2.00). Those who were overweight had greater odds of developing HT than those with normal weight, with ORadj=1.37 (95%CI=1.01-1.90), and those who had a parental history of HT had greater odds of developing HT than those who did not, with ORadj=3.38 (95%CI=2.81-4.48) (Table 10).
Table 10
Factors associated with HT in univariate and multivariate analyses
Factors
HT
OR
95%CI
p-value
ORAdj
95%CI
p-value
Yes
No
n
%
n
%
Sex
Male
164
41.7
229
58.3
1.00
1.00
Female
197
49.3
203
50.7
1.35
1.02-1.79
0.034*
1.29
1.01-1.68
0.031*
Tribe
Akha
61
46.9
69
53.1
1.00
Lahu
61
45.9
72
54.1
0.95
0.59-1.55
0.863
Hmong
42
30.0
98
70.0
0.48
0.29-0.79
0.004*
Yao
74
56.9
56
43.1
1.49
0.91-2.43
0.107
Karen
52
40.0
78
60.0
0.75
0.46-1.23
0.261
Lisu
71
54.6
59
45.4
1.36
0.83-2.21
0.215
Age (years)
60-69
173
42.2
237
57.8
1.00
70-79
134
48.0
145
52.0
1.26
0.93-1.71
0.131
≥80
54
51.9
50
48.1
1.48
0.96-2.27
0.075
Smoking
No
233
47.9
253
52.1
1.00
Ever in the past
65
43.0
86
57.0
0.82
0.56-1.18
0.293
Yes
63
40.4
93
59.6
0.73
0.51-1.06
0.100
Alcohol use
No
249
46.3
289
53.7
1.00
Ever in the past
64
46.0
75
54.0
0.99
0.68-1.44
0.960
Yes
48
41.4
68
58.6
0.81
0.54-1.23
0.337
Salty food
No
138
48.9
144
51.1
1.00
1.00
Yes
307
60.1
204
39.9
1.57
1.17-2.01
0.002*
1.48
1.14-2.00
0.001*
Greasy food
No
136
45.8
161
54.2
1.00
Yes
241
48.6
255
51.4
1.11
0.83-1.49
0.582
Sweet food
No
202
51.7
189
48.3
1.00
Yes
197
49.0
205
51.0
0.89
0.68-1.18
0.454
Regular Exercise
Yes
36
45.0
44
55.0
1.00
Highly active physical work
113
40.5
166
59.5
0.83
0.50-1.37
0.472
No
212
48.8
222
51.2
1.16
0.72-1.88
0.527
BMI
Normal
112
35.0
208
65.0
1.00
1.00
Underweight
42
36.2
74
63.8
1.05
0.67-1.64
0.816
2.56
0.70 – 1.70
0.696
Overweight
207
58.0
150
42.0
2.56
1.87- 3.49
< 0.001*
1.37
1.01 – 1.90
< 0.001*
Parental history of HT
No
155
41.3
220
58.7
1.00
1.00
Yes
302
72.2
116
27.8
3.69
2.74-4.97
< 0.001*
3.38
2.81-4.48
< 0.001*
Diabetes mellitus
No
282
43.7
363
56.3
1.00
Yes
70
53.8
60
46.2
1.50
1.02- 2.19
0.035*
Headache
No
249
43.2
328
56.8
1.00
Yes
112
51.9
104
48.1
1.41
1.03-1.94
0.029*
Dizziness
No
238
42.8
318
57.2
1.00
Yes
123
51.9
114
48.1
1.44
1.06-1.95
0.019*
Cholesterol
Normal
238
44.9
292
55.1
1.00
High
115
47.1
129
52.9
1.09
0.80-1.48
0.564
Triglyceride
Normal
262
43.1
346
56.9
1.00
High
91
54.8
75
45.2
1.60
1.13- 2.26
0.007*
*Significance level at α=0.05
Discussion
Members of the hill tribe elderly population are living with a high burden of T2DM and HT in Thailand. There are several factors associated with HT and T2DM, such as behaviors related to daily living, culture and food practices. Most members of the hill tribe elderly population have no education and low economic status. Very few have Thai ID cards, which is usually used to access all public services in Thailand, including health care services [17]. Only one-fourth of the participants were able to speak and understand Thai, and a few people could read and write in Thai. The prevalence of T2DM and HT was 16.8% and 45.5%, respectively, of which 7.7% and 21.9% represented the incident rates for T2DM and HT, respectively. Moreover, 9.3% of T2DM participants and 37.9% of HT participants could not control their plasma glucose and blood pressure after having daily medication. The comorbidity rate was approximately one-fourth of the participants who used alcohol and smoked. The participants had a high frequency of consumption of dietary salt (64.4%), greasy food (62.5%), sweet food (50.7%) and uncooked food (44.9%). Five factors were found to be significantly associated with T2DM: tribe, exercise, BMI, parental history of T2DM, and triglycerides. Another four factors were found to be significantly associated with HT: sex, dietary salt intake, BMI, and parental history of HT.
Anzeige
The results of our study revealed very interesting information on the prevalence of T2DM among the hill tribe elderly populations in Thailand at 16.8%, which is 1.75 times higher than that of the Thai population [11]. We also found significant differences in prevalence among the various tribes. Meanwhile, the prevalence of HT was 45.5%, which is almost 1.6 times greater than that of the general Thai elderly population [13]. Among the participants with HT in the hill tribe elderly population, 21.9% did not know that they had HT. In taking a closer look into tribal differences, more than half of the Yao and Lisu participants had HT. This phenomenon could be attributed to the differences in culture and lifestyle among the hill tribe people, who consume alcohol and foods that are highly sweetened and salty and do not exercise regularly.
In our study, the comorbidity rate of T2DM and HT is higher than that in an Indian sample in a study of Jaya et al. [25]. However, the T2DM prevalence of our study sample is similar to that of a sample from a study conducted by Mohamed et al. [26] among the ethnic groups in northern Sudan, with a T2DM prevalence of 18.7%. Dhiraj et al. [27] reported that in different tribes of the population, there were different burdens of T2DM in the sub-Himalayan region of India. This information supports the finding that the hill tribe people in Thailand originate from Tibet [14, 16], which is close to those living in the sub-Himalayan region of India. Therefore, the T2DM and HT prevalence among the 6 hill tribes in Thailand are possibly different.
A study using a mass database in Korea reported that regular and frequent exercise led to reduced T2DM mortality and morbidly rates, particularly in the elderly population [28]. A study in Saudi Arabia also reported that sufficient physical exercise was a protective factor against T2DM development [29]. This result is similar to the finding of our study that regular exercise and highly active physical work serve as protective factors against T2DM among the hill tribe elderly populations in Thailand. Regarding BMI, Kulaya et al. [30] reported that increasing BMI was identified as a major risk factor for T2DM in the Thai population. In a study of Asian Americans in the United States, a BMI< 23 or overweight was detected as a risk factor for T2DM development [31]. Moreover, a case-control study aimed at assessing the association between BMI and T2DM in the Mid-Atlantic region found a heavy association between increasing BMI and T2DM, after controlling for all confounding factors [32]. However, in a study among Afro-Trinidadians in the United States in 2016, no significant difference in BMI was found between those who had T2DM and those who did not [33]. In our study, it was found that increasing BMI or overweight was a risk factor for T2DM in the hill tribe elderly populations.
Many studies [34‐36] have reported that having a parental or family history of diabetes or first-degree relatives with diabetes was associated with the development of T2DM, which is consistent with the findings of our study. Triglyceride levels are another factor related to the development of T2DM. A retrospective longitudinal large-scale study conducted between the year 2000 and 2012 found that every 10 mg/dL increase in triglyceride levels significantly increased the risk of T2DM by 4.0% in the United States [37]. In addition, Ming et al. [38] reported that an increase in triglycerides was a risk factor for type 2 diabetes among those living in rural China. These studies present findings similar to those of this study, such that higher triglyceride levels are a risk factor for T2DM. Different tribes or races also have significant associations with T2DM. The studies of Vitor [39] and Diego et al. [40], which were conducted in the United States using different study designs, revealed that differences in the races of parents had an impact on the development of HT in their children. However, in our study, there was no significant difference in HT prevalence among the tribes.
Anzeige
Jugal et al. [41] reported that there were several factors associated with HT among those living in rural Delhi, India, such as older age, alcohol use, education and cholesterol levels. However, sex was not found to be associated with HT. On the other hand, Saswata et al. [42] reported that females had a greater chance of developing HT than males in a study conducted in western India. Daily food consumption is one of the predictors for HT. Daily consumption of salty foods is one of the risk factors of HT. This finding is supported by several studies [43‐45] that show that dietary salt intake was highly associated with HT development in developing and developed countries and in urban and rural areas. In this study, we also found that dietary salt intake among the hill tribe elderly populations was a significant risk factor for HT development. Another factor related to HT is BMI. Alicja et al. [46] reported that both men and women had an increased risk of HT with increasing BMI, particularly among the elderly populations. A rural Chinese cohort study in 2016 [47] and a study in Bangladesh in 2017 [48] confirmed that the increase in BMI had a significant association with HT development. These findings coincide with those of our study, which revealed that an increase in BMI was associated with a greater odds of HT development among the hill tribe elderly populations in Thailand.
The study of Ghada et al. [49] in Egypt showed a strong association between a family history of HT and the development of HT in one’s offspring. A family history has been detected as a risk factor for HT among young adults and the elderly population in several countries [50‐52].
Some limitations have been identified in this study, such as misunderstanding the NPO techniques before drawing blood specimens, language, and the inability to draw blood specimens in some people. Since some targeted hill tribe villages are located far away from the city, traveling to the study setting very early in the day to collect blood specimens was sometimes not practical. Other limitations included unclear information on the research procedure and not drinking and eating food for at least 8 hours before having blood drawn. Sometimes there was no cooperation from the participants, which may have occurred because they clearly did not understand the importance of laboratory interpretations. Moreover, most hill tribe elderly adults are not educated. This finding coincides with those of studies by Apidechkul et al. [53] and Apidechul [54], who reported that a high proportion of the Akha elderly population and the Lahu people were in the illiterate group. This finding could explain participants’ limited understanding of the research information and lack of cooperation with the procedure.
The researchers could not draw blood from a few participants (1.26%) because of their individual peripheral vein characteristics. However, nobody refused to provide information and a specimen. Because this lack of data would affect the predictive statistical model (logistic regressions), these participants were excluded from the analysis to ensure the accuracy of the results. Furthermore, some participants had been diagnosed as T2DM and HT before starting the study, which could possibly impact the findings of the study, particularly their knowledge, attitudes and practices, which are common limitations of the cross-sectional study design. Concerning this point, knowledge of and attitudes toward DM and HT were not included in the prediction model. Moreover, if we look closely, only attitude is significantly different among the tribes. Additionally, the number of Lahu (excess of 3participants) and Hmong (excess of 10 participants) participants exceeded the minimum requirement for the sample size due to miscommunication between the researcher and community headman. However, these excess data did not impact the results of study but rather supported the power of the tests.
Conducting research with the hill tribe people, particularly among the elderly population, required researchers to be clearly knowledgeable about the condition before reaching them. Additionally, having research assistants who were fluent in both Thai and the local hill tribe languages was an advantage for obtaining information.
Conclusions
The hill tribe elderly populations in Thailand are living with a high burden of T2DM and HT. T2DM and HT screening programs in these populations should be implemented regularly to detect early-stage and new cases. There is an urgent need to develop proper health behavior change models to reduce BMI and the consumption of dietary salt and greasy foods among the elderly populations. Moreover, a program to encourage physical exercise is also necessary. Otherwise, Thailand must budget large amounts of money to provide care and treatment for these populations in the near future.
Acknowledgements
The author would like to thank all the participants for kindly providing all essential information regarding the research procedures. The author is also grateful to all research assistants from the Center of Excellence for the Hill tribe Health Research for their help in data collection. The author would like to thank The National Research Council of Thailand and Mae Fah Luang University, Thailand in support the grant.
Funding
This research was supported by the National Research Council of Thailand and Mae Fah Lung University, Thailand (Grant Number 77-2015).
Availability of data and materials
The raw data supporting these findings can be found in the Additional file 1.
Ethics approval and consent to participate
Consent to participate, all study instruments and procedures were approved by the Ethics Committee for Human Research, Mae Fah Laung University, Chiang Rai, Thailand (No. REH-58087). All participants received an oral and written explanation and provided their consent before a voluntary agreement was witnessed and documented by signature or fingerprint.
Competing interests
The author declares that he has no competing interests.
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