religion and bmi in australia
TRANSCRIPT
ORI GIN AL PA PER
Religion and BMI in Australia
Michael A. Kortt • Brian Dollery
� Springer Science+Business Media, LLC 2012
Abstract We estimated the relationship between religion and body mass index (BMI) for
a general and representative sample of the Australia population. Data from the Household
Income Labour Dynamics survey were analysed for 9,408 adults aged 18 and older. OLS
regression analyses revealed that religious denomination was significantly related to higher
BMI, after controlling for socio-demographic, health behaviours, and psychosocial vari-
ables. ‘Baptist’ men had, on average, a 1.3 higher BMI compared to those reporting no
religious affiliation. Among women, ‘Non-Christians’ had, on average, a 1 unit lower BMI
compared to those reporting no religious affiliation while ‘Other Christian’ women
reported, on average, a 1 unit higher BMI. Our results also indicate that there was a
negative relationship between religious importance and BMI among Australian women.
Keywords Religion � Health � BMI � Obesity
Introduction
There have been relatively few studies that have examined the relationship between reli-
gion and BMI. While this question has been studied in the United States, we are not aware
of any articles that have analysed the relationship between religion and BMI in Australia.
This question is especially important given the average rise in BMI over recent decades.
Between 1989–1990 and 2004–2005, the age-standardised proportion of overweight or
obese Australian adults, based on self-reported data, increased from 38 to 53 % (ABS
2008a). This increase was most striking among obese adults, with the proportion doubling
from 9 to 18 %. More recently, it was estimated that 25 % of the adult population was
M. A. Kortt (&)Southern Cross Business School, Southern Cross University, Riverside Campus,Corner Brett and Wharf Street, Tweed Heads, NSW 2485, Australiae-mail: [email protected]
B. DollerySchool of Business, Economics and Public Policy, University of New England, Armidale, NSW,Australia
123
J Relig HealthDOI 10.1007/s10943-012-9621-x
obese based on measured height and weight data from the 2007–08 National Health Survey
(ABS 2008b). By way of comparison, it was estimated that nearly one-third of the US
population was obese based on measured height and weight data from the National Health
and Nutrition Examination Survey (Baskin et al. 2005).
The medical literature has clearly demonstrated that being overweight or obese is a risk
factor for numerous serious medical conditions, including type-2 diabetes, hypertension,
coronary heart disease, elevated cholesterol levels, depression, musculoskeletal disorders,
gallbladder disease, and several types of cancer (Bray 1992; Colditz 1992; Pi-Sunyer
1996). Moreover, evidence from the Framingham Heart Study has indicated this increased
risk of diseases can also result in relatively large decrements in life expectancy (Peeters
et al. 2003). Although a substantial amount of research has been devoted to identifying and
understanding the determinants associated with the rise in obesity, there has nonetheless
been limited research directed to examining the role that religion may play in contributing
to, or combating, the obesity epidemic.
However, there has been a growing body of evidence to suggest that there is a positive
relationship between religion and health (Ellison and Levin 1998; George et al. 2002).
Thus, from theoretical standpoint, while one might expect a priori a negative relationship
between religion and BMI, there is little empirical evidence to support this proposition. In
an effort to solve this ostensible riddle, four possible theoretical explanations have been
considered in the seminal paper by Cline and Ferraro (2006). In the first place, religion may
both condemn and serve to control certain types of aberrant behaviour, such as excessive
drinking, smoking, and pre-marital sex. However, excessive eating—or the sin of glut-
tony—may not receive the same level of condemnation and could even be viewed as an
‘accepted vice’ by religious leaders and followers (Cline and Ferraro 2006, p. 271). Sec-
ond, many religious functions and celebrations revolve around the consumption of food,
which, in turn, may create an environment conducive to excessive eating (Cline and
Ferraro 2006). Third, it is conceivable that religion may not affect BMI, but that a reverse
casual relationship exists where obese people are drawn to religion for comfort, social
support, and less stigmatisation (Cline and Ferraro 2006; Kim et al. 2003). Fourth, religion
may be practiced at home (e.g., the viewing of religious television programs) and not at the
place of worship. Cline and Ferraro (2006) refer to this activity as ‘religious media
practice’. Individuals engaged in this type of religious activity are more likely to have
access to (and consume) household food and beverages, which may be high in caloric
content and by extension contribute to weight gain. It is also possible that individuals who
engage in ‘religious media practice’ may have a preference for a more sedentary lifestyle.
In empirical terms, there is evidence to suggest a positive relationship between religion
and BMI. For example, using data from the Pawtucket Heart Health Program, Lapane et al.
(1997) observed that church members in two South-eastern New England communities
were more likely to exceed a 20 % overweight limit compared to non-church members.
Ferraro (1998) found that there were more obese adults in US states with both a larger
proportion of residents reporting religious affiliation and a higher share of Baptists. In
another study, Kim et al. (2003) reported that Conservative Protestant men had a 1.1 higher
BMI value compared with men who reported no religious affiliation. However, no sta-
tistically significant relationship between religion and BMI were observed for women. On
the other hand, Cline and Ferraro (2006) found a statistically significant positive associ-
ation between Baptist affiliation and obesity for US women. The authors also presented
evidence that women engaged in ‘religious media practice’ are more likely to be obese.
Finally, using a large cross-sectional sample (n = 16,657) of US adults 20 years and older
from the third National Health and Nutrition Examination Survey, Gillum (2006) found
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that attendance at religious services increased the odds of being overweight or obese (i.e.,
58 % among weekly attendees compared to 53 % of less regular attendees). However, this
association was no longer statistically significant after controlling for socio-demographic
factors, smoking status and health status.
In this paper, we employ available data from the nationally representative Household,
Income and Labour Dynamics in Australia (HILDA) survey to investigate the relationship
between religion and BMI for Australia. Detailed information contained in the HILDA
survey allows us to provide the first empirical results for Australia while controlling for a
rich array of socio-demographic, health behaviours and psychosocial variables. Taking
stock of the previous literature and empirical findings, we expected a priori to observe a
relationship between religion and BMI.
To preview our finding, we found that ‘Baptist’ and ‘Catholic’ men have higher BMIs.
We also observed that ‘Non-Christian’ women have lower BMIs and ‘Other Christian’
women have higher BMIs. Moreover, we also observe a negative relationship between
religious importance and BMI for Australian women.
Methods
The data used in this study came from the 2007 wave of the HILDA survey, which is the only
wave to date that contains information on religion and BMI in Australia. The major advantage
of using HILDA resides in the fact that it is one of the largest surveys in Australia to contain
information on religion and BMI, as well as detailed information on socio-demographic
attributes, health behaviours and psychosocial factors. For a detailed discussion of HILDA
and its sampling methods, see Wooden and Watson (2007). While we recognise the limita-
tions of using BMI as a measure of ‘fatness’ (Burkhauser and Cawley 2008), we are never-
theless not aware of any superior Australian datasets for the purposes of this paper.
We restricted our sample to respondents aged 18 and older. In addition, we removed
pregnant women from the sample and omitted a further 5 % of all cases due to non-
response of these respondents to some of the variables examined in this paper. Accord-
ingly, the usable sample comprised on 9,408 persons (4,562 men and 4,846 women) on
whom full information on relevant data items was available.
The dependent variable is the respondent’s body mass index (BMI), which was cal-
culated by dividing self-reported weight by self-reported height in metres squared. We are
acutely aware that the collection of anthropometric data through self-report has been
shown to be subject to error as respondents tend to under-report their weight and over-
estimate their height (Spencer et al. 2002). To adjust for this error, one option is to use the
correction equations developed by Hayes et al. (2008). As the application of these equa-
tions had very little impact on our results, we opted not to use these correction equations so
as to more readily permit the comparison of our results with those from other comparable
empirical studies.
Guided by the literature, the independent variables included in our analysis were
grouped into the following categories: socio-demographic, religious affiliation, religiosity,
health behaviours and psychosocial factors. The socio-demographic variables included in
the statistical analysis were ‘age category,’ ‘years of education,’ ‘gross financial year
income’ (covering the period between 1 July and 30 June) and indicator variables for
marital status and whether the respondent was born overseas or is an Indigenous person.
Except for the first age category (i.e., 18–29 years) and last age category (i.e., 80? years),
age was stratified into 10-year age groups with the first age group used as the reference
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category. Years of education were coded as the highest year of completed schooling, if the
respondent has no post-school qualifications (i.e., less than 8 years is coded as 8 years). Post-
school qualifications are coded into years as follows: masters/doctorate = 17 years; grad-
uate diploma/certificate = 16 years; bachelor degree = 15 years; diploma = 12 years; and
certificate = 12 years. To aid the interpretability of our results, we also divided the gross
financial year income (for gross wages and salaries) by 10,000.
Religious affiliation was divided into the following six categories—in order to facilitate
comparison with existing studies—with no religious affiliation selected as the excluded
reference group: Catholic, Protestant (incorporating Anglican, Lutheran, Presbyterian/
Reformed and Other Protestant), Non-Christian (includes Buddhism, Hinduism and Islam),
Baptist, Jewish and Other Christian (includes Brethren, Jehovah’s Witnesses and Morons).
The HILDA survey contained two measures of religiosity: (i) the respondent’s fre-
quency of attendance at religious services and (ii) the relative importance that respondents
attached to religion. Religious attendance was assessed by the question: ‘How often do you
attend religious services? Please do not include ceremonies like weddings or funerals’.
Responses to this question ranged from ‘never’ (1) to ‘everyday’ (9). With respect to
religious importance, respondents were asked to select a number on a scale ranging from
the least important thing (0) to the most important thing (10).
The following types of health behaviour were included in the analysis: physical activity,
alcohol consumption and tobacco smoking status. Physical activity was measured by the
following question: ‘In general, how often do you participate in moderate or intensive
physical activity for at least 30 min?’ Responses ranged from ‘not at all’ (1) to ‘everyday’
(6). Alcohol consumption was assessed by the following question: ‘Do you drink alcohol?’
Responses were divided into two categories (‘ex-drinker’ and ‘current drinker’), with
individuals who had never consumed alcohol chosen as the excluded reference group.
Smoking status was divided into two categories (‘smoker’ and ‘ex-smoker’), with non-
smokers selected as the reference group.
Psychosocial variables were also included in the analysis to control for psychological
distress and stress. Psychological distress was measured using the Kessler Psychological
Distress Scale (K10). Responses from the K10 are used to classify individuals into the
following risk categories (ABS 2001): ‘low’ (1), ‘moderate’ (2), ‘high’ (3) and (4) ‘very
high.’ Stress was measured by the following question: ‘How often do you feel rushed or
pressed for time?’ Responses ranged from ‘never’ (1) to ‘almost always’ (5).
Empirical Strategy
After reporting summary statistics, we estimated separate OLS regression models for men
and women in order to examine the relationship between religion and BMI. In our first
specification (Model 1), we estimated the relationship between religion and BMI while
controlling for socio-demographics variables. We then extended Model 1 to include socio-
demographic and health behaviour variables (Model 2). In our final specification (Model
3), we included socio-demographic, health behaviours and psychosocial variables. Thus,
our most extensive regression specification (Model 3) is given in Eq. (1) below:
BMI ¼ aþ b1Sþ b2Rþ b3H þ b4Pþ l ð1Þ
In Eq. (1), BMI is the respondent’s derived BMI value from self-reported height and self-
reported weight, S is a vector of socio-demographic controls (age categories, years of
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education, income, marital status, born overseas and Indigenous status), R is a vector of
religious variables (affiliation, attendance and importance), H is a vector of health
behaviours (physical activity, alcohol consumption and smoking status), P is a vector of
psychosocial variables (psychological distress and stress) and u is an i.i.d. error term.
Results
In Table 1, we report the summary statistics for the 4,562 men and 4,846 women in our
sample. Among women in our sample, 51 % are overweight or obese. Among men, 64 %
are overweight or obese. These figures are slightly lower than estimates from the Aus-
tralian Bureau of Statistics 2007–2008 National Health Survey based on measured height
and weight data (ABS 2008b). Our sample contains slightly more women (52 %) than men
(48 %) and 21 % of our sample was sample born overseas. Among women, the highest
reported religious affiliations were Protestant (36 %) and Catholic (25 %). One-quarter of
women reported no religious affiliation. Among men, the highest reported religious affil-
iations were Protestant (34 %) and Catholic (21 %). However, more men reported having
no religious affiliation (33 %). In terms of religiosity, women viewed religion as being
more important and reported, on average, a slightly higher level of attendance at religious
services.
In Table 2, we report the results from the regression models for males that were
employed to estimate the relationship between religion and BMI. Estimates for Model 1
indicate that, when compared to men with no religious affiliation, ‘Baptist’ men had a
higher BMI (b = 1.35, p \ 0.01) after controlling for socio-demographic characteristics.
‘Catholic’ men also had a higher BMI (b = 0.44, p \ 0.05). No other statistically sig-
nificant differences were observed for other religious affiliations. It is interesting to note
that for men, there was no relationship between BMI and religious importance or atten-
dance at religious services.
In Model 2, we included additional health behavioural variables (i.e., physical activity,
alcohol consumption and smoking status) and found that the size and significance of the
coefficients on the ‘Baptist’ and ‘Catholic’ variables remained largely unchanged. In
Model 3, we also included the psychosocial variables (psychological distress and stress)
and the coefficients on the ‘Baptist’ and ‘Catholic’ variables still remained largely
unchanged.
Looking across all three specifications in Table 2, it is worth noting that the ‘age effect’
for men is broadly consistent. Compared to the reference age category (18–29), there is
statistically significant positive association for each 10-year category up to and including
the ‘70–79’ age group (i.e., men in these age categories had, on average, higher BMIs
compared to men in the reference age category). However, men in the ‘80?’ age category
had, on average, a significantly lower BMI compared to the reference age category.
Table 3 reports the regression results for females. Estimates for Model 1 indicate that,
when compared to women with no religious affiliation, ‘Non-Christian’ women had lower
BMIs (b = -0.94, p \ 0.1) and ‘Other Christian’ women had higher BMIs (b = 1.11,
p \ 0.01) after controlling for socio-demographic characteristics. However, for women, no
statistically significant negative association between the level of attendance at religious
services and BMI was observed. There was, however, a statistically significant negative
association between religious importance and BMI for women (b = -0.15, p \ 0.01). The
coefficients on the ‘Non-Christian’ and ‘Other Christian’ variables remained largely
unchanged and statistically significant following the inclusion of the health behaviours
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Table 1 Sample characteristics: mean and standard deviation (SD)
Variables Persons
(n = 9,408)
Men
(n = 4,562)
Women
(n = 4,846)
Dependent
BMI 26.58 (5.12) 26.85 (4.55) 26.23 (5.59)
Underweight 0.02 0.01 0.03
Normal 0.41 0.35 0.46
Overweight 0.35 0.42 0.29
Obese 0.22 0.22 0.22
Socio-demographics
Age 18–29 0.20 0.20 0.19
Age 30–39 0.17 0.18 0.16
Age 40–49 0.21 0.21 0.22
Age 50–59 0.18 0.18 0.18
Age 60–69 0.13 0.13 0.13
Age 70–79 0.08 0.08 0.08
Age 80? 0.03 0.03 0.04
Sex 1.00 0.48 0.52
Years of education 12.11 (2.23) 12.22 (2.13) 12.01 (2.31)
Income ($) 31,354.14 (43,086.49) 40,661.62 (53,043.61) 22,592.13 (28,238.27)
Married 0.53 0.56 0.50
Born overseas 0.21 0.21 0.21
Indigenous 0.02 0.02 0.02
Religious affiliation
No religion 0.29 0.33 0.25
Catholic 0.23 0.21 0.25
Protestant 0.35 0.34 0.36
Non-Christian 0.03 0.03 0.03
Baptist 0.02 0.02 0.02
Jewish 0.003 0.003 0.003
Other Christian 0.08 0.07 0.09
Religiosity
Importance (0 = least, 10 = most) 3.62 (3.45) 3.06 (3.29) 4.14 (3.52)
Attendance
(1 = never, 9 = everyday)
2.67 (2.16) 2.45 (2.05) 2.87 (2.24)
Health behaviours
Physical activity
(1 = none, 6 = everyday)
3.64 (1.53) 3.78 (1.53) 3.50 (1.51)
Never drink 0.08 0.05 0.10
Ex-drinker 0.07 0.06 0.07
Drinker 0.85 0.89 0.82
Non smoker 0.48 0.43 0.54
Ex-smoker 0.30 0.33 0.27
Smoker 0.21 0.24 0.19
Psychosocial variables
K10 risk categories
(1 = low, 4 = very high)
1.53 (0.83) 1.48(0.79) 1.58 (0.87)
Feel rushed
(1 = never, 5 = always)
3.24 (0.95) 3.16 (0.94) 3.32 (0.95)
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Table 2 OLS regression estimates for men (n = 4,562)
Variables Model 1 Model 2 Model 3
b SE b SE b SE
Socio-demographics
Age 30–39 1.81** (0.22) 1.70** (0.22) 1.73** (0.22)
Age 40–49 2.12** (0.22) 1.92** (0.22) 1.95** (0.22)
Age 50–59 2.52** (0.23) 2.27** (0.23) 2.27** (0.23)
Age 60–69 1.90** (0.26) 1.59** (0.26) 1.58** (0.26)
Age 70–79 1.16** (0.30) 0.69* (0.30) 0.70* (0.31)
Age 80? -0.38 (0.45) -1.07* (0.45) -1.08* (0.46)
Years of education -0.23** (0.03) -0.24** (0.03) -0.24** (0.03)
Income ($) 0.03* (0.01) 0.04* (0.01) 0.04** (0.01)
Married 0.56** (0.15) 0.39** (0.15) 0.43** (0.15)
Born overseas -0.33* (0.17) -0.35* (0.17) -0.37* (0.17)
Indigenous -0.26 (0.54) -0.24 (0.53) -0.25 (0.53)
Religious affiliation
Catholic 0.44* (0.21) 0.45* (0.20) 0.45* (0.20)
Protestant 0.10 (0.18) 0.13 (0.18) 0.14 (0.18)
Non-Christian -0.26 (0.43) -0.22 (0.43) -0.22 (0.43)
Baptist 1.35** (0.49) 1.34** (0.49) 1.33** (0.49)
Jewish 0.61 (1.23) 0.53 (1.22) 0.55 (1.22)
Other Christian 0.43 (0.32) 0.39 (0.31) 0.37 (0.31)
Religiosity
Importance 0.02 (0.05) 0.01 (0.05) 0.02 (0.05)
Attendance -0.04 (0.03) -0.04 (0.03) -0.05 (0.03)
Health behaviours
Physical activity
\once per week -0.25 (0.29) -0.18 (0.29)
1–2 time a week -0.71** (0.26) -0.62* (0.27)
3 times a week -0.85** (0.28) -0.76** (0.28)
[3 times a week -1.16** (0.26) -1.06** (0.27)
Everyday -1.67** (0.28) -1.55** (0.28)
Ex-drinker -0.20 (0.40) -0.24 (0.40)
Drinker -0.11 (0.30) -0.06 (0.30)
Ex-smoker 0.63** (0.16) 0.61** (0.16)
Smoker -0.69** (0.17) -0.72** (0.18)
Psychosocial variables
K10 risk categories
Moderate 0.13 (0.17)
High 0.26 (0.23)
Very high 1.05** (0.39)
Feel rushed -0.10 (0.08)
Constant 27.57** (0.43) 29.05** (0.56) 29.03** (0.59)
R2 0.067 0.089 0.090
Dependent variable = BMI
Standard errors in parentheses
p = ?0.10, * p \ 0.05, ** p \ 0.01
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Table 3 OLS regression estimates for women (n = 4,846)
Variables Model 1 Model 2 Model 3
b SE b SE b SE
Socio-demographics
Age 30–39 1.98** (0.27) 1.87** (0.27) 1.92** (0.27)
Age 40–49 2.33** (0.26) 2.19** (0.27) 2.25** (0.27)
Age 50–59 2.97** (0.28) 2.89** (0.28) 2.89** (0.28)
Age 60–69 3.01** (0.31) 2.94** (0.31) 2.92** (0.31)
Age 70–79 2.33** (0.36) 2.10** (0.36) 2.06** (0.36)
Age 80? 0.65 (0.46) 0.11 (0.47) 0.08 (0.47)
Years of education -0.23** (0.04) -0.21** (0.04) -0.20** (0.04)
Income ($) 0.02 (0.03) 0.02 (0.03) 0.04 (0.03)
Married 0.08 (0.17) 0.06 (0.17) 0.15 (0.17)
Born overseas -1.02** (0.20) -1.09** (0.20) -1.13** (0.20)
Indigenous 1.76** (0.57) 1.84** (0.56) 1.79** (0.56)
Religious affiliation
Catholic 0.36 (0.26) 0.32 (0.25) 0.33 (0.25)
Protestant 0.08 (0.23) 0.08 (0.23) 0.08 (0.23)
Non-Christian -0.94? (0.50) -1.01* (0.49) -1.01* (0.49)
Baptist 0.24 (0.57) 0.16 (0.56) 0.20 (0.56)
Jewish -1.53 (1.37) -1.22 (1.36) -1.22 (1.36)
Other Christian 1.11** (0.37) 1.00** (0.36) 0.96** (0.36)
Religiosity
Importance -0.15** (0.05) -0.15** (0.05) -0.13* (0.05)
Attendance 0.02 (0.04) 0.01 (0.04) 0.00 (0.04)
Health behaviours
Physical activity
\once per week -0.22 (0.31) -0.12 (0.31)
1–2 time a week -0.56? (0.29) -0.44 (0.29)
3 times a week -1.43** (0.31) -1.33** (0.31)
[3 times a week -1.61** (0.30) -1.48** (0.30)
Everyday -2.54** (0.34) -2.38** (0.34)
Ex-drinker 0.84* (0.38) 0.87* (0.38)
Drinker 0.14 (0.27) 0.23 (0.27)
Ex-smoker 0.49** (0.19) 0.48* (0.19)
Smoker -0.44* (0.22) -0.53* (0.23)
Psychosocial variables
K10 risk categories
Moderate 0.41* (0.20)
High 0.57* (0.27)
Very high 1.10** (0.38)
Feel rushed -0.27** (0.09)
Constant 27.25** (0.52) 28.01** (0.61) 28.28** (0.66)
R2 0.060 0.082 0.086
Dependent variable = BMI
Standard errors in parentheses
p = ?0.10, * p \ 0.05, ** p \ 0.01
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(Model 2) and psychosocial variables (Model 3). The coefficient on the ‘religious
importance’ variable also remained largely unchanged in terms of its size and statistical
significance.
Once again, looking across all three specifications in Table 3, it is worth noting that the
‘age effect’ for women is broadly consistent. Compared to the reference age category
(18–29), there is a statistically significant positive association for each 10-year category up
to and including the ‘70–79’ age group (i.e., women in these age categories have, on
average, higher BMIs compared to the reference age category). However, women in the
‘80?’ age category had, on average, a significantly lower BMI compared to the excluded
reference age category.
Discussion
The results from this study indicate that ‘Baptist’ and ‘Catholic’ men (compared to men
with no religious affiliation) have a higher BMI. We also observed that ‘Non-Christian’
and ‘Other Christian’ women (compared to women with no religious affiliation) have lower
and higher BMIs, respectively, and that for women, in general, there is a negative rela-
tionship between religious importance and BMI.
As noted above, there have been few previous studies that have examined the impact of
religion and BMI. In contrast to Cline and Ferraro (2006), we found no evidence of a
relationship between Baptist women and BMI, although we did find that Australian Baptist
men had a higher BMI than those men reporting no religious affiliation. Our results remained
robust even after controlling for the possible mediating effects of health behaviours and
psychosocial attributes. Consistent with Kim et al. (2003) and Cline and Ferraro (2006), we
found no evidence that Australian Catholic women had a higher BMI than those women
reporting no religious affiliation. However, in contrast to Kim et al. (2003), we did not find
any relationship between Australian Protestant men and BMI. Our results may reflect, in part,
differences between Australia and US in terms of period, country and cultural effects.
In terms of religiosity, we found no evidence of a relationship between religious atten-
dance/religious importance and BMI among Australian men. This result is contrary to the
study by Ferraro (1998), who found that religious practice was more common among
overweight Americans. One possible explanation for the difference in these findings is that
the rising rate of obesity among US men (and the high public health profile of the obesity
epidemic) has led US religious institutions to condemn the ‘sin the gluttony’ and promote the
benefits associated with healthy eating. There is evidence in the US context that religious
institutions are often involved in promoting positive health messages (Steinman and Bam-
bakidis 2008). By extension, men who attend religious services are, in turn, more likely to be
exposed to the effects of positive peer re-enforcement and support from other congregation
members. Ayers et al. (2010) provides some evidence of peer effects. In their study of women
of Korean descent living in California, it was observed that religion may be helpful in the
prevention of obesity through religious-based social mechanisms.
In terms of religiosity among Australian women, we found no evidence of a relationship
between religious attendance and BMI. We did, however, find evidence of a negative
relationship between the importance of religion and BMI. Perhaps this finding reflects that
the importance women attach to religion may help to control (or at least curb) excessive
eating and alcohol consumption. Whatever the rationale, there appears to be sound grounds
for investigating further the relationship between religiosity and BMI in the Australian
milieu.
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Our study is restricted by its cross-sectional design, which limits the conclusions we can
draw about the causality between religion and BMI. The causal direction could run in a
converse fashion insofar as people with high BMIs may be drawn to religion for social
support. However, based on work by Kim et al. (2003) and Cline and Ferraro (2006), there
are sound grounds for assuming that the causal relationship runs from religion to BMI. For
example, Kim et al. (2003) examined the issue of causality by regressing height and weight
discrimination questions (as dependent variables) on independent religion variables, while
controlling for a range of socio-demographic characteristics. In their analysis, none of the
religion variables were statistically significant, which suggests that religion influences BMI
rather than the converse.
At present, the HILDA survey does not contain height and weight discrimination
questions. To investigate the direction of causality, we estimated the relationship between
the respondent’s dissatisfaction with their own body weight (1 = dissatisfied; 0 = other-
wise) and religion while controlling for a range of socio-demographic characteristics
(Table 4). If religion draws in those respondents who are dissatisfied with their weight,
then we would expect religion to have a statistically significant impact. For men, no
relationship is evident, except for the protective association observed for ‘Protestant’
Table 4 Logistic regression estimates for men and women
Variables Men (n = 4,562) Women (n = 4,846)
OR SE OR SE
Socio-demographics
Age 30–39 1.62*** (0.18) 1.15 0.12
Age 40–49 1.63*** (0.18) 1.19? 0.12
Age 50–59 1.60*** (0.19) 1.42*** 0.15
Age 60–69 1.10 (0.15) 0.96 0.11
Age 70–79 0.88 (0.14) 0.54*** 0.08
Age 80? 0.32*** (0.11) 0.24*** 0.05
Years of education 1.05*** (0.02) 1.01 0.02
Income ($) 1.01? (0.01) 1.02? 0.01
Married 1.01 (0.08) 1.16** 0.07
Born overseas 0.84** (0.07) 0.75*** 0.06
Indigenous 1.03 (0.28) 1.20 0.25
Religious affiliation
Catholic 0.93 (0.10) 1.18? 0.11
Protestant 0.84? (0.08) 1.05 0.09
Non-Christian 0.65? (0.15) 0.89 0.17
Baptist 1.21 (0.29) 1.04 0.22
Jewish 1.46 (0.86) 0.74 0.39
Other Christian 1.06 (0.17) 1.46*** 0.20
Religiosity
Importance 0.99? (0.02) 0.98 0.02
Attendance 1.03 (0.02) 0.98 0.01
Dependent variable = Dissatisfaction with body weight
Standard errors in parentheses
p = ?0.10, * p \ 0.05, ** p \ 0.01
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(OR = 0.84, p \ 0.1) and ‘Non-Christian’ men (OR = 0.65, p \ 0.1). For women,
however, we found weak statistical evidence of a positive relationship between dissatis-
faction of body weight and being ‘Catholic’ (OR = 1.18, p \ 0.1) and stronger statistical
evidence for ‘Other Christian’ women (OR = 1.46, p \ 0.01). Given that 78 % of
respondents who were dissatisfied with their weight were also classified as being over-
weight or obese, our results are at least suggestive and consistent with previous studies in
pointing to the probability that religion predominately influences BMI (rather than BMI
affecting religion).
In conclusion, we have identified a statistically significant association between religion
and BMI for Australian men and women. We observed that some religious denominations,
namely ‘Baptist’ and ‘Catholic’ men and ‘Other Christian’ women, had, on average, higher
BMIs. We also observed that ‘Non-Christian’ women, on average, had lower BMIs. Our
results also show that religious importance was negatively related to BMI in Australian
women.
Acknowledgments This article uses unit record data from the Household, Income and Labour Dynamicsin Australia (HILDA) survey. The HILDA Project was initiated and is funded by the Australian GovernmentDepartment of Families, Housing, Community Services and Indigenous Affairs (FaHCSIA) and is managedby the Melbourne Institute of Applied Economic and Social Research (MIAESR). The findings and viewsreported in this article, however, are those of the authors and should not be attributed to either FaHCSIA orthe MIAESR.
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