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Global Public Health: An International Journal for Research, Policy and Practice Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/rgph20
Religious affiliation and disparities in risk of non-communicable diseases and health behaviours: Findings from the fourth Thai National Health Examination Survey a
a
Wit Wichaidit , Rassamee Sangthong , Virasakdi a
a
b
Chongsuvivatwong , Edward McNeil , Suwat Chariyalertsak , c
d
Pattapong Kessomboon , Surasak Taneepanichskul , Panwadee e
e
Putwatana , Wichai Aekplakorn & The Thai National Health Examination Survey IV Study Group a
Epidemiology Unit, Faculty of Medicine, Prince of Songkla University, Hatyai, Thailand b
Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
c
Faculty of Medicine, KhonKaen University, KhonKaen, Thailand
d
College of Public Health Sciences, Chulalongkorn University, Bangkok, Thailand e
Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand Published online: 01 Apr 2014.
To cite this article: Wit Wichaidit, Rassamee Sangthong, Virasakdi Chongsuvivatwong, Edward McNeil, Suwat Chariyalertsak, Pattapong Kessomboon, Surasak Taneepanichskul, Panwadee Putwatana, Wichai Aekplakorn & The Thai National Health Examination Survey IV Study Group (2014) Religious affiliation and disparities in risk of non-communicable diseases and health behaviours: Findings from the fourth Thai National Health Examination Survey, Global Public Health: An International Journal for Research, Policy and Practice, 9:4, 426-435, DOI: 10.1080/17441692.2014.894549 To link to this article: http://dx.doi.org/10.1080/17441692.2014.894549
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Global Public Health, 2014 Vol. 9, No. 4, 426–435, http://dx.doi.org/10.1080/17441692.2014.894549
Downloaded by [Umeå University Library] at 16:19 15 November 2014
Religious affiliation and disparities in risk of non-communicable diseases and health behaviours: Findings from the fourth Thai National Health Examination Survey Wit Wichaidita*, Rassamee Sangthonga, Virasakdi Chongsuvivatwonga, Edward McNeila, Suwat Chariyalertsakb, Pattapong Kessomboonc, Surasak Taneepanichskuld, Panwadee Putwatanae, Wichai Aekplakorne and The Thai National Health Examination Survey IV Study Group a
Epidemiology Unit, Faculty of Medicine, Prince of Songkla University, Hatyai, Thailand; bFaculty of Medicine, Chiang Mai University, Chiang Mai, Thailand; cFaculty of Medicine, KhonKaen University, KhonKaen, Thailand; dCollege of Public Health Sciences, Chulalongkorn University, Bangkok, Thailand; eFaculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand (Received 3 July 2013; accepted 4 February 2014) This study aims to compare the health-related behaviours and risk of non-communicable diseases (NCDs) between Muslims and non-Muslims in Thailand, a predominantly Buddhist country in which Muslims are the second largest religious group. Data from the fourth Thai National Health Examination Survey (NHES IV) conducted in 2009 were used to run multivariate survey logistic regression models with adjustment for age, gender and socio-economic status indicators. Data from 20,450 respondents, of whom 807 (3.9%) were Muslims, were included in the study. Muslims were significantly more likely to have daily consumption of deep-fried food (adjusted odds ratio [OR] = 1.35; 95% confidence interval [CI] = 1.15−1.58) and packaged snacks (adjusted OR = 1.55; 95% CI = 1.30−1.86), and have inadequate control of hypercholesterolemia (adjusted OR = 2.95; 95% CI = 1.30−6.68). In conclusion, we found disparity in the majority of risk factors for NCDs between Muslim and non-Muslim Thais. Keywords: health disparities; non-communicable diseases; global health; population health; Thailand
Introduction Non-communicable diseases (NCDs) currently account for 43% of the global burden of disease, and the share is expected to grow during this decade (World Health Organization [WHO], 2013). Recently, concerns have been raised with regard to the high risk of cardiovascular disease (CVD) and other NCDs in Muslim populations worldwide (Rashid, 2011). Globally, studies have shown distinct characteristics of Muslims in terms of eating behaviours (Abraham & Birmingham, 2008; Bakhotmah, 2011; Mussap, 2009), perception of weight management (Ludwig, Cox, & Ellahi, 2011), smoking (Mufunda et al., 2007; Williams, Nazroo, Kooner, & Steptoe, 2010), physical activity (Horne, Skelton, Speed, & Todd, 2012; Kahan, 2011), stress level (Williams et al., 2010), awareness of diseases and myths associated with them (Fatima, Karoli, Chandra, & *Corresponding author. Email: [email protected] © 2014 Taylor & Francis
Downloaded by [Umeå University Library] at 16:19 15 November 2014
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Naqvi, 2011; Rai & Kishore, 2009) and health inequalities (Karlsen & Nazroo, 2010). Factors influencing health disparities in different parts of the world vary depending on study settings and context (Adler & Newman, 2002; Eshetu & Woldesenbet, 2011) due to the effect of religion and culture on health and dietary behaviours (Abraham & Birmingham, 2008; Ludwig et al., 2011; Mussap, 2009) and the influence of religiosity on clinical care (Al-Yousefi, 2012). Therefore, it is important to consider that health behaviours and outcomes of Muslims may differ in each country. Thailand, a middle-income country in south-east Asia, is a predominantly Buddhist nation where Muslims are the second most significant religious group constituting approximately 4.5% of the total population (National Statistical Office, 2005). Muslims in Thailand belong to a variety of ethnicities, including indigenous Thais, Malay, Indonesian, Chinese and Indian (Department of Religious Affairs, 2009). A previous study conducted in the lower southern region of Thailand showed that Muslims had significantly lower levels of high-density lipoprotein (HDL) cholesterol and a higher prevalence of hypertension, even when adjusted for other variables (Yipintsoi, Lim, & Jintapakorn, 2005). There is very little evidence showing associations between religious affiliation and health behaviours as well as health outcomes. It is also important to see whether these differences exist in other parts of the country where Muslims are the minority, thus a nation-wide study is needed for verification. Moreover, disparities in the demographic characteristics of Muslims need to be taken into account as their confounding effects may be substantial. In 2012, our group was requested by Thai Muslim leaders who were concerned about Muslim health to investigate whether Thai Muslims had higher prevalence of unhealthy behaviours related to CVDs. In response, we visited the fourth Thai National Health Examination Survey (NHES IV) data-set and conducted this analysis. The objective of this study was to compare the health behaviours and risks of NCDs between Muslims and non-Muslims in Thailand. Methods Data collection The fourth Thai National Health Examination Survey 2009 was a nationally representative cross-sectional survey conducted by randomly sampling five provinces in each of the four regions of Thailand (North, Northeast, Central, South) plus the capital city, Bangkok. The overall response rate was 93% (Aekplakorn et al., 2011). The Muslimmajority provinces in the lower southern part of Thailand were not included in the survey. Study tools The NHES IV consisted of a questionnaire interview, physical examination and laboratory testing of blood specimens. Demographic and socio-economic characteristics included age, gender, marital status, religion, education, place of residence (urban vs. rural), employment and income. Physical examination included measurement of weight, height, waist circumference, hip circumference, arm length (for those aged 60 years and above) and blood pressure measurement. Laboratory testing of blood specimens included complete blood count, fasting plasma glucose, clotted blood creatinine and clotted blood lipid profile (cholesterol, triglyceride, HDL cholesterol and low-density lipoprotein cholesterol). Health-related lifestyle factors in the study included physical activity, smoking status, body mass index (BMI), eating behaviours and dietary intake. Level of physical activity
Downloaded by [Umeå University Library] at 16:19 15 November 2014
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W. Wichaidit et al.
was determined using the Global Physical Activity Questionnaire (GPAQ) version 2 (WHO, 2005), which covered physical activity at work, during travel to and from places and recreational activities. The length and level of vigour of each activity were then converted to metabolic equivalent task values, and levels of physical activity were determined based on these values. Smoking status was self-reported and classified according to the amount and frequency of tobacco consumption. Non-smokers included those who never consumed any tobacco product or had smoked less than 100 cigarettes, pipes, cigars or hand-rolled cigarettes in their lifetime. Ex-smokers included those who used to smoke cigarettes, pipes, cigars or hand-rolled cigarettes but had stopped prior to the survey. Current smokers included those who smoked on occasion within the past 30 days or daily at the time of the survey. Regular or daily smokers only included current smokers who indicated that they smoked on a daily basis. BMI was determined based on physical examination results, calculated as weight in kilograms divided by the square of height in centimetres. BMI values of ≥25 and ≥30 were used as cut points for being overweight and category-I obese, respectively. Information on frequency of dietary intake was obtained with a food frequency questionnaire (FFQ), which asked the respondent to self-report the frequency of consuming each of the 44 items of food and drink, not including fruits and vegetables. The consumption frequencies were: none, 1 times/day. For fruits and vegetables, respondents were asked the number of days per week and times per day that they consumed fruits and vegetables, and the number of standard servings of fruits and vegetables normally consumed. Standard food measurement tools and a handbook of photographic food atlas containing actual sizes of a variety of foods were used to increase the accuracy of intake estimation. Data on health status and history of NCDs were also self-reported. Participants were asked whether they had been told by health care staff or a physician that they had hypertension, diabetes, hypercholesterolemia, CVD or chronic obstructive pulmonary disease and whether they ever had stroke or partial paralysis (weakness of the arms or legs). Participants who answered ‘yes’ to any of these questions were asked additional questions on treatment and management of each disease. Statistical analysis Data from the interview and physical examination of those aged 15 years and above were included in the analysis, which was performed using R statistical software (R Development Core Team, 2011) and Epicalc package (Chongsuvivatwong, 2008). For the analysis, occupations of the respondents were grouped into four categories: Category 1 – unskilled workers: general labourers and agricultural workers; Category 2 – skilled workers: supervisor/driver, technician/merchant, clerk, services and soldier/police; Category 3 – white collar: academics/professionals, legislators and politicians; and Category 4 – unemployed and other occupations. In this study, a crude distinction between Muslims and non-Muslims was made in order to highlight the health outcomes and health behaviours of Muslim Thais. In Thailand, the non-Muslim population is predominantly Buddhist, and more than 99% are either Buddhist or Muslim (National Statistical Office, 2012). Descriptive statistical analysis focused on comparison between health behaviours and self-reported history of NCDs among Muslim and non-Muslim Thais. The survey
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package (Lumley, 2010) for R was used to adjust for the sampling weights. Statistical significances in the difference in prevalence of outcome by religion and sex were tested using Pearson’s chi-square with Rao–Scott adjustment. Multivariate logistic regression models were fit to determine the associations between health behaviours and NCDs, which were the outcome variables and religious affiliation. Age, sex, income, education, type of occupation, place of residence (urban vs. rural) and region were included as covariates in the multivariate models.
Downloaded by [Umeå University Library] at 16:19 15 November 2014
Results A total of 20,450 respondents were included in the analysis. The respondents were predominantly Buddhists (n = 19,431; 95.0%), followed by Muslims (n = 807; 3.9%), Christians (n = 155; 0.8%) and other or no religion (n = 16; 0.1%). Table 1 shows that the majority of the Muslims in Thailand were congregated in Bangkok and the southern region. On average, Muslims spent around 0.6 more years in education compared to nonMuslims, but earned about 373 Baht (approximately 12 US dollars) less per month. The proportion of those who had other or no occupation was higher among Muslims while the
Table 1. Estimated prevalence (%) and mean values between Muslim Thais and non-Muslim Thais populations for basic demographic and socio-economic characteristics (n = 20,409; numbers are adjusted for sampling weight with Survey package in R). Item
Muslims % ± SE (n = 807)
Non-Muslims % ± SE (n = 19,602)
Region North 0.2 ± 0.2% 19.1 Central 2.3 ± 2.3% 25.6 Northeast 0.3 ± 0.2% 34.7 South 51.3 ± 29.5% 11.7 Bangkok 46.0 ± 30.1% 9.0 Gender (% male) 46.3 ± 1.5% 48.8 Age (years, mean ± SE) 40.2 ± 1.9 43.7 Years of education (mean ± SE) 8.0 ± 0.3 7.3 Monthly personal income (baht, mean ± SE) 6461.3 ± 1202.7 6834.3 Occupation Category 1: unskilled 37.1 ± 6.5% 50.0 Category 2: skilled worker 22.5 ± 3.3% 26.0 Category 3: white collar 4.1 ± 0.6% 2.4 Others/No occupation 36.3 ± 3.4% 21.6 Use of insurance scheme [n(yes), %( yes); multiple schemes allowed] Universal coverage 82.2 ± 4.7% 80.3 Social security 11.0 ± 2.6% 9.9 Civil servants medical benefit scheme 5.9 ± 2.32% 8.0 State enterprise employee benefit scheme 0.5 ± 0.3% 0.7 Private insurance 3.9 ± 0.7% 10.3 Community fund 1.7 ± 0.2% 2.2 Other sources 3.8 ± 0.5% 3.4 *As calculated by Pearson’s chi-square with Rao–Scott adjustment. a Statistically significant at 95% level of confidence.
P-value*
± ± ± ± ± ± ± ± ±
9.2% 10.8% 13.7% 6.4% 8.7% 0.4% 0.4 0.2 432.7
Global Public Health: An International Journal for Research, Policy and Practice Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/rgph20
Religious affiliation and disparities in risk of non-communicable diseases and health behaviours: Findings from the fourth Thai National Health Examination Survey a
a
Wit Wichaidit , Rassamee Sangthong , Virasakdi a
a
b
Chongsuvivatwong , Edward McNeil , Suwat Chariyalertsak , c
d
Pattapong Kessomboon , Surasak Taneepanichskul , Panwadee e
e
Putwatana , Wichai Aekplakorn & The Thai National Health Examination Survey IV Study Group a
Epidemiology Unit, Faculty of Medicine, Prince of Songkla University, Hatyai, Thailand b
Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
c
Faculty of Medicine, KhonKaen University, KhonKaen, Thailand
d
College of Public Health Sciences, Chulalongkorn University, Bangkok, Thailand e
Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand Published online: 01 Apr 2014.
To cite this article: Wit Wichaidit, Rassamee Sangthong, Virasakdi Chongsuvivatwong, Edward McNeil, Suwat Chariyalertsak, Pattapong Kessomboon, Surasak Taneepanichskul, Panwadee Putwatana, Wichai Aekplakorn & The Thai National Health Examination Survey IV Study Group (2014) Religious affiliation and disparities in risk of non-communicable diseases and health behaviours: Findings from the fourth Thai National Health Examination Survey, Global Public Health: An International Journal for Research, Policy and Practice, 9:4, 426-435, DOI: 10.1080/17441692.2014.894549 To link to this article: http://dx.doi.org/10.1080/17441692.2014.894549
PLEASE SCROLL DOWN FOR ARTICLE
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Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/termsand-conditions
Global Public Health, 2014 Vol. 9, No. 4, 426–435, http://dx.doi.org/10.1080/17441692.2014.894549
Downloaded by [Umeå University Library] at 16:19 15 November 2014
Religious affiliation and disparities in risk of non-communicable diseases and health behaviours: Findings from the fourth Thai National Health Examination Survey Wit Wichaidita*, Rassamee Sangthonga, Virasakdi Chongsuvivatwonga, Edward McNeila, Suwat Chariyalertsakb, Pattapong Kessomboonc, Surasak Taneepanichskuld, Panwadee Putwatanae, Wichai Aekplakorne and The Thai National Health Examination Survey IV Study Group a
Epidemiology Unit, Faculty of Medicine, Prince of Songkla University, Hatyai, Thailand; bFaculty of Medicine, Chiang Mai University, Chiang Mai, Thailand; cFaculty of Medicine, KhonKaen University, KhonKaen, Thailand; dCollege of Public Health Sciences, Chulalongkorn University, Bangkok, Thailand; eFaculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand (Received 3 July 2013; accepted 4 February 2014) This study aims to compare the health-related behaviours and risk of non-communicable diseases (NCDs) between Muslims and non-Muslims in Thailand, a predominantly Buddhist country in which Muslims are the second largest religious group. Data from the fourth Thai National Health Examination Survey (NHES IV) conducted in 2009 were used to run multivariate survey logistic regression models with adjustment for age, gender and socio-economic status indicators. Data from 20,450 respondents, of whom 807 (3.9%) were Muslims, were included in the study. Muslims were significantly more likely to have daily consumption of deep-fried food (adjusted odds ratio [OR] = 1.35; 95% confidence interval [CI] = 1.15−1.58) and packaged snacks (adjusted OR = 1.55; 95% CI = 1.30−1.86), and have inadequate control of hypercholesterolemia (adjusted OR = 2.95; 95% CI = 1.30−6.68). In conclusion, we found disparity in the majority of risk factors for NCDs between Muslim and non-Muslim Thais. Keywords: health disparities; non-communicable diseases; global health; population health; Thailand
Introduction Non-communicable diseases (NCDs) currently account for 43% of the global burden of disease, and the share is expected to grow during this decade (World Health Organization [WHO], 2013). Recently, concerns have been raised with regard to the high risk of cardiovascular disease (CVD) and other NCDs in Muslim populations worldwide (Rashid, 2011). Globally, studies have shown distinct characteristics of Muslims in terms of eating behaviours (Abraham & Birmingham, 2008; Bakhotmah, 2011; Mussap, 2009), perception of weight management (Ludwig, Cox, & Ellahi, 2011), smoking (Mufunda et al., 2007; Williams, Nazroo, Kooner, & Steptoe, 2010), physical activity (Horne, Skelton, Speed, & Todd, 2012; Kahan, 2011), stress level (Williams et al., 2010), awareness of diseases and myths associated with them (Fatima, Karoli, Chandra, & *Corresponding author. Email: [email protected] © 2014 Taylor & Francis
Downloaded by [Umeå University Library] at 16:19 15 November 2014
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Naqvi, 2011; Rai & Kishore, 2009) and health inequalities (Karlsen & Nazroo, 2010). Factors influencing health disparities in different parts of the world vary depending on study settings and context (Adler & Newman, 2002; Eshetu & Woldesenbet, 2011) due to the effect of religion and culture on health and dietary behaviours (Abraham & Birmingham, 2008; Ludwig et al., 2011; Mussap, 2009) and the influence of religiosity on clinical care (Al-Yousefi, 2012). Therefore, it is important to consider that health behaviours and outcomes of Muslims may differ in each country. Thailand, a middle-income country in south-east Asia, is a predominantly Buddhist nation where Muslims are the second most significant religious group constituting approximately 4.5% of the total population (National Statistical Office, 2005). Muslims in Thailand belong to a variety of ethnicities, including indigenous Thais, Malay, Indonesian, Chinese and Indian (Department of Religious Affairs, 2009). A previous study conducted in the lower southern region of Thailand showed that Muslims had significantly lower levels of high-density lipoprotein (HDL) cholesterol and a higher prevalence of hypertension, even when adjusted for other variables (Yipintsoi, Lim, & Jintapakorn, 2005). There is very little evidence showing associations between religious affiliation and health behaviours as well as health outcomes. It is also important to see whether these differences exist in other parts of the country where Muslims are the minority, thus a nation-wide study is needed for verification. Moreover, disparities in the demographic characteristics of Muslims need to be taken into account as their confounding effects may be substantial. In 2012, our group was requested by Thai Muslim leaders who were concerned about Muslim health to investigate whether Thai Muslims had higher prevalence of unhealthy behaviours related to CVDs. In response, we visited the fourth Thai National Health Examination Survey (NHES IV) data-set and conducted this analysis. The objective of this study was to compare the health behaviours and risks of NCDs between Muslims and non-Muslims in Thailand. Methods Data collection The fourth Thai National Health Examination Survey 2009 was a nationally representative cross-sectional survey conducted by randomly sampling five provinces in each of the four regions of Thailand (North, Northeast, Central, South) plus the capital city, Bangkok. The overall response rate was 93% (Aekplakorn et al., 2011). The Muslimmajority provinces in the lower southern part of Thailand were not included in the survey. Study tools The NHES IV consisted of a questionnaire interview, physical examination and laboratory testing of blood specimens. Demographic and socio-economic characteristics included age, gender, marital status, religion, education, place of residence (urban vs. rural), employment and income. Physical examination included measurement of weight, height, waist circumference, hip circumference, arm length (for those aged 60 years and above) and blood pressure measurement. Laboratory testing of blood specimens included complete blood count, fasting plasma glucose, clotted blood creatinine and clotted blood lipid profile (cholesterol, triglyceride, HDL cholesterol and low-density lipoprotein cholesterol). Health-related lifestyle factors in the study included physical activity, smoking status, body mass index (BMI), eating behaviours and dietary intake. Level of physical activity
Downloaded by [Umeå University Library] at 16:19 15 November 2014
428
W. Wichaidit et al.
was determined using the Global Physical Activity Questionnaire (GPAQ) version 2 (WHO, 2005), which covered physical activity at work, during travel to and from places and recreational activities. The length and level of vigour of each activity were then converted to metabolic equivalent task values, and levels of physical activity were determined based on these values. Smoking status was self-reported and classified according to the amount and frequency of tobacco consumption. Non-smokers included those who never consumed any tobacco product or had smoked less than 100 cigarettes, pipes, cigars or hand-rolled cigarettes in their lifetime. Ex-smokers included those who used to smoke cigarettes, pipes, cigars or hand-rolled cigarettes but had stopped prior to the survey. Current smokers included those who smoked on occasion within the past 30 days or daily at the time of the survey. Regular or daily smokers only included current smokers who indicated that they smoked on a daily basis. BMI was determined based on physical examination results, calculated as weight in kilograms divided by the square of height in centimetres. BMI values of ≥25 and ≥30 were used as cut points for being overweight and category-I obese, respectively. Information on frequency of dietary intake was obtained with a food frequency questionnaire (FFQ), which asked the respondent to self-report the frequency of consuming each of the 44 items of food and drink, not including fruits and vegetables. The consumption frequencies were: none, 1 times/day. For fruits and vegetables, respondents were asked the number of days per week and times per day that they consumed fruits and vegetables, and the number of standard servings of fruits and vegetables normally consumed. Standard food measurement tools and a handbook of photographic food atlas containing actual sizes of a variety of foods were used to increase the accuracy of intake estimation. Data on health status and history of NCDs were also self-reported. Participants were asked whether they had been told by health care staff or a physician that they had hypertension, diabetes, hypercholesterolemia, CVD or chronic obstructive pulmonary disease and whether they ever had stroke or partial paralysis (weakness of the arms or legs). Participants who answered ‘yes’ to any of these questions were asked additional questions on treatment and management of each disease. Statistical analysis Data from the interview and physical examination of those aged 15 years and above were included in the analysis, which was performed using R statistical software (R Development Core Team, 2011) and Epicalc package (Chongsuvivatwong, 2008). For the analysis, occupations of the respondents were grouped into four categories: Category 1 – unskilled workers: general labourers and agricultural workers; Category 2 – skilled workers: supervisor/driver, technician/merchant, clerk, services and soldier/police; Category 3 – white collar: academics/professionals, legislators and politicians; and Category 4 – unemployed and other occupations. In this study, a crude distinction between Muslims and non-Muslims was made in order to highlight the health outcomes and health behaviours of Muslim Thais. In Thailand, the non-Muslim population is predominantly Buddhist, and more than 99% are either Buddhist or Muslim (National Statistical Office, 2012). Descriptive statistical analysis focused on comparison between health behaviours and self-reported history of NCDs among Muslim and non-Muslim Thais. The survey
Global Public Health
429
package (Lumley, 2010) for R was used to adjust for the sampling weights. Statistical significances in the difference in prevalence of outcome by religion and sex were tested using Pearson’s chi-square with Rao–Scott adjustment. Multivariate logistic regression models were fit to determine the associations between health behaviours and NCDs, which were the outcome variables and religious affiliation. Age, sex, income, education, type of occupation, place of residence (urban vs. rural) and region were included as covariates in the multivariate models.
Downloaded by [Umeå University Library] at 16:19 15 November 2014
Results A total of 20,450 respondents were included in the analysis. The respondents were predominantly Buddhists (n = 19,431; 95.0%), followed by Muslims (n = 807; 3.9%), Christians (n = 155; 0.8%) and other or no religion (n = 16; 0.1%). Table 1 shows that the majority of the Muslims in Thailand were congregated in Bangkok and the southern region. On average, Muslims spent around 0.6 more years in education compared to nonMuslims, but earned about 373 Baht (approximately 12 US dollars) less per month. The proportion of those who had other or no occupation was higher among Muslims while the
Table 1. Estimated prevalence (%) and mean values between Muslim Thais and non-Muslim Thais populations for basic demographic and socio-economic characteristics (n = 20,409; numbers are adjusted for sampling weight with Survey package in R). Item
Muslims % ± SE (n = 807)
Non-Muslims % ± SE (n = 19,602)
Region North 0.2 ± 0.2% 19.1 Central 2.3 ± 2.3% 25.6 Northeast 0.3 ± 0.2% 34.7 South 51.3 ± 29.5% 11.7 Bangkok 46.0 ± 30.1% 9.0 Gender (% male) 46.3 ± 1.5% 48.8 Age (years, mean ± SE) 40.2 ± 1.9 43.7 Years of education (mean ± SE) 8.0 ± 0.3 7.3 Monthly personal income (baht, mean ± SE) 6461.3 ± 1202.7 6834.3 Occupation Category 1: unskilled 37.1 ± 6.5% 50.0 Category 2: skilled worker 22.5 ± 3.3% 26.0 Category 3: white collar 4.1 ± 0.6% 2.4 Others/No occupation 36.3 ± 3.4% 21.6 Use of insurance scheme [n(yes), %( yes); multiple schemes allowed] Universal coverage 82.2 ± 4.7% 80.3 Social security 11.0 ± 2.6% 9.9 Civil servants medical benefit scheme 5.9 ± 2.32% 8.0 State enterprise employee benefit scheme 0.5 ± 0.3% 0.7 Private insurance 3.9 ± 0.7% 10.3 Community fund 1.7 ± 0.2% 2.2 Other sources 3.8 ± 0.5% 3.4 *As calculated by Pearson’s chi-square with Rao–Scott adjustment. a Statistically significant at 95% level of confidence.
P-value*
± ± ± ± ± ± ± ± ±
9.2% 10.8% 13.7% 6.4% 8.7% 0.4% 0.4 0.2 432.7
