1636
45. Cohen JA, Institute of Medicine (U.S.) Round Table on Health
Disparities. Challenges and Successes in Reducing Health
Disparities. Washington, DC: National Academies Press, 2008.
46. Dow WH, Schoeni RF, Adler NE, Stewart J. Evaluating the
evidence base: policies and interventions to address
International Journal of Epidemiology, 2015, Vol. 44, No. 5
socioeconomic status gradients in health. Ann N Y Acad Sci
2010;1186:240–51.
47. Phelan JC, Link BG, Tehranifar P. Social conditions as fundamental causes of health inequalities: theory, evidence, and policy
implications. J Health Soc Behav 2010;51:S28–S40.
International Journal of Epidemiology, 2015, 1636–1647
Commentary: The salience
doi: 10.1093/ije/dyv182
Advance Access Publication Date: 22 October 2015
of socioeconomic status in
assessing cardiovascular disease and risk in
low- and middle-income countries
Catherine Kreatsoulas,1 Daniel J Corsi2 and SV Subramanian3*
1
Harvard T.H. Chan School of Public Health, Boston, MA, USA, 2Ottawa Hospital Research Institute,
Ottawa, ON, Canada and 3Department of Social and Behavioral Sciences, Harvard T.H. Chan School of
Public Health, Boston, MA, USA
*Corresponding author. Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, 677
Huntington Avenue, Kresge Building Rm 716, Boston, MA 02115-6096, USA. E-mail: [email protected]
Introduction
Emerging data suggest that cardiovascular disease and risk
factor (CVD/RF) prevalence trends are rising in
Bangladesh and other low- and middle-income countries
(LMICs).1 It is clear, however, that average trends and
patterns mask the fact that the distribution of CVD/RF is
uneven across different socioeconomic groups,2,3 with the
prevalence being substantially higher among high socioeconomic groups. Harshfield and colleagues report that the
prevalences of hypertension/hyperglycaemia are substantially higher among the high-socioeconomic status (SES)
individuals in Bangladesh.4 Whereas early prevention efforts may be ideal in all population groups, as is seen in the
global effort to implement universal health coverage,5 this
remains a challenge when allocating scant resources, particularly as the threat of communicable diseases still
remains high in many of the LMICs.2,6–8
In this commentary, we start by pointing to a methodological concern related to the analysis by Harshfield and
colleagues, resulting in the distortion of CVD/RF prevalence estimates. We then take this opportunity to present a
systematic review of the existing literature of SES and
CVD/RF in Bangladesh and underscore the importance of
collecting systematic national surveillance data. With an
increasing number of studies considering SES in assessments of CVD/RF, we present a rationale to consider both
the socioeconomic gradient of CVD/RF and the percentage
of the total CVD/RF burden attributable to each SES
group. In the final section of this commentary, we highlight
the explicit need to consider gender in assessing SES and
CVD/RF in LMICs, and especially in countries characterized by substantial gender inequalities.9
Correction to Harshfields’ and colleagues’
CVD/RF prevalence estimates
The analysis conducted by Harshfield and colleagues contains some notable concerns related to the sample sizes
along with incorrect adjustments for survey design and sampling weights. Together, these errors have distorted the
CVD/RF prevalence estimates reported by the authors,
which we clarify below. The study is based on the 2011
Bangladesh Demographic and Health Survey (BDHS),
which consists of a representative sample of households in
Bangladesh. In one-third of households, ever-married men,
ages 15–54 years, were invited to participate in a men’s
health survey; however, all men and women aged 35 years
and older were invited to participate in a biomarker survey
that required participants to have blood pressure and blood
glucose measured. There is a discrepancy in how Harshfield
and colleagues report the number of men and women eligible for blood pressure and glucose measurement compared
C The Author 2015; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association
V
International Journal of Epidemiology, 2015, Vol. 44, No. 5
with the number of individuals who participated and on
whom valid and complete data were obtained.
The final BDHS survey report indicates a total sample
size of 4311 women and 4524 men aged 35years and older,
who were eligible for the biomarker component,10 of
which 92% of women and 86% of men participated in
blood pressure measurement, and 89% of women and
83% of men participated in blood glucose measurement.
The sample sizes of 4311 women and 4523 men reported
(and used incorrectly as denominators) by Harshfield and
colleagues represent the eligible population and not the
participating population (note a further discrepancy of
n ¼ 1 in men’s sample size between Harshfield et al. and
the BDHS report). Consequently, the incorrect sample sizes
comprising the denominators of the authors’ calculations
result in an underestimation of the reported prevalences of
hypertension and hyperglycemia.
We illustrate the magnitude of this error in Table 1,
which reproduces part of Harshfield’s and colleagues
Table 3. For instance, the prevalence of medical hypertension is reported as 19.6% in men and 33.6% in women,
whereas we calculated the prevalence as 21.6% in men and
35.2% in women based on the correct sample size and
with adjustments for survey sampling weights and survey
design characteristics. We draw the attention of the reader
to this point mainly as clarification, as it appears that these
1637
errors affect the prevalence estimates and do not, as far as
we can determine, influence the SES risk factor associations or inferences drawn by the study authors.
CVD/RF and SES in Bangladesh: A
Systematic Review
Following a previous critical review of studies investigating
the association between SES and CVD/RF in India,2 we
conducted a similar systematic review for Bangladesh.
Using MEDLINE and EMBASE databases, we reviewed
260 articles that specifically matched our search terms
(Box 1) and identified a total of 67 published articles that
assessed the association of seven CVD/RF (hypertension,
cholesterol, diabetes, obesity, body mass index, physical
inactivity, diet and smoking) with SES factors (including
education and social class) in Bangladesh for the period
January 1980 to June 2015. We excluded studies whose
primary outcome was mortality and Bangladeshi populations not residing in Bangladesh. Individual study details
extracted from the studies included in this systematic review are outlined in Table 2.
Of the 67 studies, 84% (n ¼ 56) were cross-sectional
studies, 3% (n ¼ 2) were longitudinal studies and 12%
(n ¼ 8) were non-systematic reviews or commentaries. The
sample sizes of the studies varied widely from n ¼ 32 to
Table 1. Corrected prevalence estimates of selected blood pressure and blood glucose outcomes by sex, Bangladesh
Demographic Health Survey 2011
Risk factor
Hypertension (measured)
Hypertension (medical)
Hyperglycaemia (measured)
Hyperglycaemia (medical)
Overall (n ¼ 7304)
Men (n ¼ 3618)
Women (n ¼ 3686)
n
%
n
%
n
%
1548
2139
363
594
20.7
28.5
4.7
7.8
614
823
178
289
16.2
21.6
4.5
7.4
934
1316
185
305
25.1
35.2
4.8
8.2
P
<0.001
<0.001
0.640
0.180
Unweighted counts, frequencies adjusted for survey weights and survey design characteristics. Definitions of measured/medical hypertension and hyperglycaemia given by Harshfield et al.
Box 1: Systematic review search terms
We conducted a cursory search on MEDLINE of the following MeSH terms: [‘Neoplasms’ OR ‘Cardiovascular Diseases’
OR ‘Wounds and Injuries’ OR ‘Hypertension’ OR ‘Diabetes Mellitus’ OR ‘Obesity’ OR ‘Chronic Obstructive Pulmonary
Disease’ OR ‘Respiratory Tract Diseases’ OR ‘Schizophrenia’ OR ‘Psychotic Disorders’ OR ‘Mental Disorders’ OR
‘Depressive Disorder’ AND ‘Bangladesh’] which yielded 1087 articles, of which 209 (19%) were indexed as ‘cardiovascular disease’.
We systematically searched the following MeSH terms in the MEDLINE and EMBASE databases: (‘Socioeconomic factors’ OR ‘education’ OR ‘social class’) AND (‘Cardiovascular diseases’ OR ‘Obesity’ OR ‘Hypertension’ OR ‘Diabetes mellitus’ OR ‘Cholesterol’ OR ‘Physical inactivity’ OR ‘Smoking’ OR ‘Motor activity’ OR ‘Physical activity’ OR ‘Diet’) AND
‘Bangladesh’. In addition, we supplemented relevant articles from the reference lists of identified articles in our search.
Study design
Cross-sectional
Cross-sectional
Cross-sectional
Cross-sectional
Cross-sectional
Cross-sectional
Cross-sectional
Cross-sectional
Cross-sectional
Review
Cross-sectional
Cross-sectional
Cross-sectional
Review
Commentary
Cross-sectional
Cross-sectional
Cross-sectional
Cross-sectional
Cross-sectional
Primary author
Abdullah AS et al.14
Abdullah M, Wheeler EF15
Abdullah M et al.16
Akter J et al.17
Akter S et al.18
Akther A et al.19
Alam K, Mahal A20
Asghar S et al.21
Bhowmik B et al.22
Bleich S et al.23
Chen Y et al.24
Choudhury K et al.12
Choudhury KN25
Cohen N26
de Beyer et al.27
Efroymson D et al.28
Fatema K et al.29
Fischer F30
Ge W et al.31
Heck JE et al.32
11 170
562
17 749
742 41
63 708
234
Women and men, no
stratification
Women and men, stratified
Women only
Women and men, stratified
Women and men, no
stratification
Women and men, no
stratification
Women and men, no
stratification
Women and men, stratified
Women and men, no
stratification
11 116
6618
Women and men, stratified
Women and men, no
stratification
Women and men, no
stratification
Women and men, no
stratification
Women and men, no
stratification
Women and men, no
stratification
Women only
Women and men, no
stratification
2293
952
1360
111
7541
888
53
2008
2813
Sample size Sex analysis
Obesity, hypertension, tobacco
smoking
Obesity, diet, tobacco smoking, BMI
indices
Hypertension, cholesterol
Cardiovascular disease, tobacco
smoking, NCD
Diet, tobacco smoking
Diet, tobacco smoking
Cardiovascular, hypertension, diabetes mellitus, cholesterol, BMI
indices, diet, tobacco smoking
Tobacco smoking
Hypertension, diet, tobacco smoking,
BMI indicies
Tobacco smoking
Obesity, hypertension, physical inactivity, BMI indices, diabetes
mellitus
Obesity, BMI indices
Obesity, hypertension, diabetes mellitus, BMI indices
Obesity, hypertension, diabetes
mellitus
Obesity, hypertension, diabetes mellitus, physical inactivity, tobacco
smoking
Cardiovascular disease, angina
Diet
Diet, season
Tobacco smoking
CVD/RF
Table 2. Studies in systematic review of cardiovascular risk factors and socioeconomic factors in Bangladesh
Education, wealth, occupation
Education, household income, social
class, marital status
Education
Education, household income
Education, household income, wealth
Education, marital status, national income, occupation, religion
Education, household income, living
space
Education, household income, wealth,
marital status
Education
Education, household income
Education, social class
Education, household income, marital
status, occupation, living space
Education, household income,
occupation
Education, household income, wealth,
marital status
Education, occupation
Education, household income, marital
status, occupation
Living space
Household income, occupation
SES factor
(Continued)
Positive
Not specified
Negative
Negative
Negative
Positive
Not specified
Negative
Negative
Not specified
Not specified
Positive
Positive
Positive
Positive
Positive
Positive
Not specified
Positive
Negative
CVD/RF–SES
relationship
1638
International Journal of Epidemiology, 2015, Vol. 44, No. 5
Cross-sectional
Cross-sectional
Cross-sectional
Hussain A et al.33
Hossain MG et al.34
Huda N et al.35
Review
Cross-sectional
Cross-sectional
Cross-sectional
Cross-sectional
Cross-sectional
Cross-sectional
Longitudinal
prospective
Cross-sectional
Cross-sectional
Cross-sectional
Cross-sectional
Cross-sectional
Islam AKMM et al.38
Islam MZ et al.39
Islam MZ et al.40
Islam N et al.13
Islam SMS et al.41
Jesmin S et al.42
Jesmin S et al.43
Jiang J et al.44
Khan M et al.46
Khan MHM, Kraemer A47
Khan MMH et al.48
Khanam MA et al.49
Kabir MA45
Review
Islam AKMM37
Hypertension study group36 Cross-sectional
Study design
Primary author
Table . Continued
29 960
12 155
7543
3634
3771
10 389
3447
1535
515
191
2008
191
500
10 115
322
5000
Men only
Women and men, stratified
Women only
Women and men, no
stratification
Men only
Women only
Women only
Women only
Women and men, no
stratification
Women and men, stratified
Women only
Women and men, stratified
Women only
Women and men, stratified
Women and men, stratified
Sample size Sex analysis
Hypertension
Tobacco smoking
Diabetes mellitus
BMI indices
Hypertension, cholesterol, diabetes
mellitus, physical inactivity, tobacco smoking, BMI indices
Hypertension, cholesterol, diabetes
mellitus, BMI indices, metabolic
syndrome
Obesity, hypertension, diabetes
mellitus
Obesity, hypertension, diet, tobacco
smoking, BMI indices
Tobacco smoking
Diet, BMI indices
Tobacco smoking
Hypertension, diabetes mellitus, physical inactivity, tobacco smoking,
BMI indices
Obesity, hypertension, cholesterol,
diabetes mellitus, physical inactivity, diet, tobacco smoking
Hypertension, metabolic syndrome
Obesity, diet, BMI indices, diet
Cardiovascular disease, hypertension,
diabetes mellitus, BMI indices
Obesity, BMI indices
Obesity, hypertension, tobacco smoking, BMI indices
CVD/RF
Education, household income, occupation, living space
Education, wealth, marital status
Education, ever-married, occupation,
childhood residence, previous
residence
Education, marital status, occupation,
religion, birth place
Education, wealth
Education, household income
Education, household income
Education, household income, land
ownership, house living area
Education, household income, social
class, occupation
Education, household income
Education, household income,
occupation
Education, household income, marital
status, religion
Marital status
Education, household income, wealth,
social class, marital status,
occupation
Education, martial status
Household income
SES factor
(Continued)
Negative
Negative
Positive
Positive
Negative
Not specified
Positive
Mixed
Not specified
Positive
Negative
Positive
Positive
Not specified
Not specified
Positive
Not specified
Not specified
CVD/RF–SES
relationship
International Journal of Epidemiology, 2015, Vol. 44, No. 5
1639
Randomized
controlled trial
Cross-sectional
Cross-sectional
Review, case study
Cross-sectional
Cross-sectional
Cross-sectional
Cross-sectional
Cross-sectional
Cross-sectional
Cross-sectional
Cross-sectional
Cross-sectional
Cross-sectional
Cross-sectional
Cross-sectional
Cross-sectional
Systematic review
Cross-sectional
Kibriya MG et al.50
Kostova D et al.52
Mahtab H, Habib SH53
Mamun AA, Finlay JE54
Minh HV et al.55
Mollah AS et al.56
Mumu SJ et al.57
Palipudi KM et al.58
Parr JD59
Rahim MA et al.60
Rahman M et al.61
Rahman M et al.62
Rahman M et al.63
Razzaque A et al.64
Roy A et al.65
Saleh F et al.66
Saquib N et al.67
Saquib N et al.68
Kishore J et al.51
Study design
Primary author
Table . Continued
402
2590
508
7786
1620
1000
1555
33 665
9629
400
106
4063
12 057
9565
36 51 921
32
Women and men, no
stratification
Women and men, no
stratification
Men only
Women and men, no
stratification
Women and men, no
stratification
Women and men, stratified
Women and men, no
stratification
Women and men, no
stratification
Men only
Women and men, no
stratification
Women and men, no
stratification
Women and men, no
stratification
Women and men, no
stratification
Women and men, stratified
Women only
Women only
Women and men, no
stratification
Women and men, no
stratification
Sample size Sex analysis
Education, household income
Education, household income,
occupation
Education, wealth, occupation
Education
Education, wealth, social class, water
and toilet access
Education
Education, wealth, per capita GDP
Education, wealth, occupation
SES factor
Education, household income,
occupation
Education, household income, living
space, religion
Cardiovascular disease, obesity,
Education, wealth, marital status, livhypertension, diabetes mellitus
ing space, number of household
members
Obesity, hypertension, diabetes melli- Education, household income, occutus, BMI indices
pation, living space
Hypertension, physical inactivity, diet, Education
tobacco smoking
Tobacco smoking, stroke
Household income
Diabetes mellitus, physical inactivity, Education, household income, occudiet, tobacco smoking
pation, living space
Cardiovascular disease, hypertension, Age
diabetes mellitus
Obesity, diabetes mellitus
Education, household income, wealth,
marital status
Cardiovascular disease, obesity,
hypertension, cholesterol, diabetes
mellitus, NCD
Hypertension, diabetes mellitus, BMI
indices
Tobacco smoking
Cardiovascular disease, hypertension,
diabetes mellitus, NCD
Hypertension, cholesterol, diabetes
mellitus, stroke
Diabetes mellitus, physical inactivity,
diet, BMI indices
Tobacco smoking
Tobacco smoking
Diabetes mellitus
Obesity, diet
Obesity, hypertension, cholesterol,
diabetes mellitus, BMI indices
Tobacco smoking
CVD/RF
(Continued)
Positive
Positive
Negative
Not specified
Negative
Positive
Positive
Negative
Negative
Not specified
Negative
Positive
Positive
Negative
Negative
Positive
Positive
Negative
Positive
CVD/RF–SES
relationship
1640
International Journal of Epidemiology, 2015, Vol. 44, No. 5
Cross-sectional
Cross-sectional
Cross-sectional
Cross-sectional
Review
Longitudinal prospective 20 033
Cross-sectional
Cross-sectional
Cross-sectional
Sayeed MA et al.70
Sayeed MA et al.71
Sayeed MA et al.72
Shafique S et al.73
Wasay M et al.74
Wu F et al.75
Zaman MM et al.76
Zaman MM et al.77
Zaman MM et al.78
1271
447
815
282 182
2361
1052
1287
4923
Cross-sectional
Sayeed MA et al.69
Women and men, stratified
Women and men, stratified
Women and men, stratified
Women and men, no
stratification
Women only
Women and men, no
stratification
Women and men, stratified
Women and men, stratified
Women and men, no
stratification
Sample size Sex analysis
Study design
Primary author
Table . Continued
Hypertension, diabetes mellitus, BMI
indices
Obesity, diet
Stroke, tobacco smoking, diabetes,
obesity, cardiovascular disease
Obesity, hypertension, diabetes mellitus, tobacco smoking, BMI indices
Hypertension, cholesterol, diabetes
mellitus, diet, tobacco smoking,
BMI indices
Obesity, hypertension, diabetes mellitus, tobacco smoking
Obesity, hypertension, diet, tobacco
smoking, BMI indices
Obesity, hypertension, cholesterol,
diabetes mellitus, physical inactivity, BMI indices
Obesity, hypertension, cholesterol,
diabetes mellitus, physical inactivity, BMI indices
Obesity, hypertension, BMI indices
CVD/RF
Education
Household income, religion, drinkingwater source, cooking oil, household items
Education
Education, religion
Education, living space
Education, socioeconomic status
Household income, social class, living
space
Social class
Education, household income,
occupation
Education, household income, wealth,
social class
SES factor
Mixed
Not specified
Mixed
Negative
Mixed
Positive
Positive
Positive
Positive
Positive
CVD/RF–SES
relationship
International Journal of Epidemiology, 2015, Vol. 44, No. 5
1641
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International Journal of Epidemiology, 2015, Vol. 44, No. 5
Table 3. Prevalence, odds ratios (OR) and percentage of total burden of hypertension, hyperglycaemia and overweight among
the poorest and richest 20% of men and women in Bangladesh, 2011
Risk factor
Men (n ¼ 3618)
Women (n ¼ 3686)
Prevalence
Hypertension
(medical)
Hyperglycaemia
(medical)
Overweight
OR
Poorest 20%
Richest 20%
13.7
33.1
2.9
0.9
% of burden
Prevalence
OR
In poorest
20%
In richest
20%
Poorest
20%
Richest
20%
3.1
12.6
31.9
26.8
47.0
17.3
7.0
7.8
48.7
3.6
23.8
34.4
2.0
54.5
5.5
n ¼ 3 651 921. In all, 15 studies included only urban populations, 11 studies included only rural populations and
36 studies included both urban and rural Bangladeshi
populations.
Among the published studies, the most commonly
studied CVD/RF included measures of obesity (n ¼ 37
studies, 55%) and hypertension (n ¼ 36 studies, 54%), followed by smoking (n ¼ 28 studies, 42%) and diabetes
(n ¼ 27 studies, 40%). A quarter of the published studies
reported relationships with dietary factors (n ¼ 18 studies,
27%) and 11 studies (16%) reported results on cholesterol
and physical activity.
Of the 58 studies that reported on sex, 4/58 (7%)
included only males, 10/58 (17%) studies included only
females and, although 44/58 (76%) studies reported results
on both males and females, only 16/44 studies (36%)
stratified any results by sex.
The quality of measurement varied between the studies,
where 25/59 (42%) studies relied strictly on self-report
of risk factors, 9/59 (15%) studies relied on trained personnel and/or laboratory tests and 25/59 (42%) studies used a
combination of self-report and some measured outcomes.
Important to note, whereas five studies looked at access to
health care/ knowledge or some type of adherence to therapy, none of the studies differentiated between medically
controlled versus medically treated risk factors and this important nuance is often missed, not only in epidemiological
studies but also in clinical research. The proportion of individuals who are receiving medical treatment but are poorly
controlled (their biomarker values are still not within normal ranges) are not differentiated in the outcomes of the
studies reviewed. This discrepancy may be an issue of adherence, in combination with physician/patient perceptions, social beliefs, education and income, all important
factors in LMICs.11
Of the 67 studies analysed, although all studies mentioned SES, 14 (21%) did not report their results by SES.
% of burden
In poorest
20%
In richest
20%
2.4
14.5
28.2
16.7
5.4
8.2
42.5
41.2
12.2
5.9
49.3
Of the remaining 53 studies, 30 (57%) found a positive
association between higher prevalence of CVD/RF in
higher SES groups. Among the studies that reported results
by SES, positive SES-CVD/RF associations were found for
obesity (94%, n ¼ 29), hypertension (96%, n ¼ 8), diabetes
(95%, n ¼ 21) and dyslipidaemia (100%, n ¼ 3). Poor diet
as a risk factor for CVD was seen consistently among
lower SES groups (100%, n ¼ 5). Negative CVD-SES associations, where higher SES groups report a lower prevalence of CVD/RF, were consistently reported with
smoking/ tobacco use (95%, n ¼ 19), although cigarette
smoking specifically was greater among higher-SES
groups.12,13
Following Harshfield and colleagues, we examined the
strength of SES-CVD associations according to urban/rural
locality. Wide variability was found in the strength of the
associations reported for CVD/RF and SES in urban compared to rural populations even of similar education and
social class; however, this may be related to the quality of
the sampling and design of the individual studies.
For example, many of the prevalence estimates of CVD/RF
were obtained from hospital/clinic populations, which inherently contain selection bias.
Regardless of individual study nuances, with the exception of smoking and poor diet, this systematic review indicates that there is a robust association between greater
levels of CVD/RF among higher-SES groups in Bangladesh,
demonstrating that CVD/RF conditions disproportionally
affect the socially advantaged. The findings of this review
strongly support a previous study in India, which reported
greater prevalence of CVD/RF and CVD among high-SES
groups in India.2 With this in mind, it is important to note
that high SES groups represent overall a small proportion
of the total population of Bangladesh. By contextualizing
these findings we caution policy makers to resist ‘jumping
the gun’ and that they do not shift focus from the public
health issues that affect the broader population.2
International Journal of Epidemiology, 2015, Vol. 44, No. 5
Putting equity at the heart of the CVD/RF
discourse
Concern has been raised over the anticipated increase in
the prevalence of CVD/RF and NCDs in Bangladesh and
other LMIC.1 Evidence on the secular increases in such
conditions, however, have typically focused on the mean
rates at the country level rather than how CVD/RF are distributed within in the population, particularly along the
SES dimension. There is a need to understand variations in
the burden of disease within poor and rich segments of the
population in order to inform health policies and
target resource allocation to the proportional burden of
disease on different population groups, to improve health
equity.7
We take this opportunity to expand upon findings
related to the positive association between SESCVD/RF in
Bangladesh. The study by Harshfield et al. clearly demonstrates a greater prevalence of hypertension and hyperglycaemia among the wealthiest groups in Bangladesh
compared with the poorest, with the likelihood of having
these risk factors two to four times greater among individuals in the highest wealth quintile compared with the lowest. Although this metric provides one extremely useful
starting point to assess which groups are more likely to be
affected by CVD/RF, it does not fully capture the extent to
which the total burden of CVD/RF may be concentrated
within certain population groups.7 Using data from the
BDHS, we illustrate a more complete metric which captures the extent to which the burden of CVD/RF falls
among the richest or poorest 20% of the population.
Among the poorest and richest 20% of men and women
in Bangladesh, we present the prevalence, a measure of the
number of existing cases at a given time, and the odds ratio
(OR) indicating strength of association, comparing the
proportion of cases among the poorest and richest for
hypertension, hyperglycaemia and overweight in men and
women (Table 3). It is surprising that Harshfield and colleagues do not consider overweight/obesity in their analysis
(although these data are also available in the BDHS) as
overweight/obesity are well-established as both a ‘disease’
and a major CVD/RF.79 Our analyses demonstrate that
both the prevalence and the proportion of the total burden
of the three CVD/RF were greater among the richest population groups in Bangladesh. The latter metric in particular
is important for equity analyses and disease priority setting
in this context, demonstrating, for example, that nearly
half of the burden of hyperglycaemia and overweight is
concentrated among the richest 20% of population, compared with less than 10% among the poorest 20% of the
population. Priorities to address CVD/RF in settings such
as Bangladesh would only benefit a minority of the poor,
despite advocates suggesting that such disease and risk
1643
factors are
groups.2,7,8
increasingly
concentrated
among
these
Need for a gender perspective in SES and
CVD/RF studies
Studies often report results by sex, yielding important
information, but the richer SES information lies in accounting for gender and not just sex. While sex and gender
are two terms often used interchangeably, the concepts
they represent are not; sex is used to capture the biological
differences between males and females, whereas gender is a
construct that embodies the social roles, relationships, beliefs, norms, attitudes, behaviours, values and relative
power that society ascribes to the two sexes on a differential basis.80 Women, particularly in LMICs, have limited
access to health care because of many of the components
that constitute gender, such as cultural beliefs, socioeconomic circumstances, illiteracy, disempowerment, accessibility and negative cultural and religious customs, to name
a few.81 Women’s health has been primarily focused on reproduction and issues related to maternal health,82 and
considerably less attention has been focused on viewing
their health and well-being from a more intrinsic
perspective.81
In many of the studies we reviewed, including the study
by Harshfield et al., risk factor patterns are relatively consistent across the socioeconomic measures of wealth and
education in men, but these trends are not consistent in
women, particularly along the education metric. For example, in the study by Harshfield et al., there are no differences in household wealth between men and women in the
study (P ¼ 0.548). However, over 55% of women and just
over one-third of men (35%) have no education, compared
with only 4% of women and 14% of men who have higher
education (P < 0.001). This discrepancy highlights an important and all too common gender difference, particularly
among LMICs. The greater variation commonly seen
among women in higher SES groups could be attributed to:
artefact; an issue of power (often there are low numbers of
women in higher SES groups); or bias within the development of the wealth metric, which is based on household
assets and inherently incorporates aspects of men’s wealth,
underestimating wealth in women, with no real measures
to adequately capture household circumstances, social capital or area gender equity, factors important when measuring SES in women.83 The measurement of gender factors in
Bangladesh are particularly salient, as recent health reforms aimed to reduce gender inequity have been implemented as an impetus for health improvement.84
Whereas important aspects of gender remain underexplored in health research, particularly in LMICs where
1644
social roles between men and women can vary greatly,
there is often important information overlooked in the sex
analyses as well. Study authors often report their findings
adjusted according to age and sex but, beyond reporting
sex differences, rarely are any insights explored. In the
publicly available BDHS dataset used by Harshfield et al.
and others, the average age of the population is relatively
older (mean age 51 years) and it is difficult to interpret
CVD risk factors findings among women without accounting for menopausal status which, although is possible to
determine using data from the BDHS, was not considered
by the authors. It has been well established from large epidemiological studies (mostly of HIC) that the prevalence of
CVD risk factors such as hypertension and hyperglycaemia
increase with age. However, CVD risk factors in women
that manifest in the premenopausal stage are associated
with a higher risk for adverse CVD events compared with
risk factors that develop postmenopausally.85 Although
study authors may have adjusted the analysis by age, not
stratifying or adjusting by menopausal status may mask
the magnitude of the effect size of CVD/RF among younger
women.
Generally speaking, information about cardiovascular
health in women in the developing world is lacking or neglected,9,86 and among the few studies of women in LMICs
they are reported to receive less appropriate management
than men, with the greatest disparities seen among the
lower-SES groups.86 In addition to sex and gender differences in the manifestation of disease, there is a worldwide
perception that cardiovascular disease is ‘a man’s disease’
and it is difficult to gauge how much this perception affects the health literacy of patients and healthcare providers
alike.
Health improvement efforts in Bangladesh are almost
exclusively centred around issues of maternal health and
reproduction, with great strides already achieved,87,88 but
the incidence of pre-eclampsia, a serious condition in
pregnancy that characterized by hypertension, remains a
concern ( 9%).88 An association between women with
pre-eclampsia and an increased risk of major adverse cardiovascular events later in life has been well established.89
The example of pre-eclampsia also underscores the lifecourse aspects of health and disease, where the benefits of
controlling hypertension during pregnancy may be important for securing maternal and child health in the short term
and preventing CVD later in life.
Concluding remarks
In summary, the findings by Harshfield and colleagues that
the prevalences of hypertension and hyperglycaemia are
substantially higher among high-SES groups in Bangladesh
International Journal of Epidemiology, 2015, Vol. 44, No. 5
(a resource-poor country) are critical and need to be
emphasized, as the influential research community and
policy makers increase their support to focus on noncommunicable diseases (NCDs).90–92 Indeed, a focus on
CVD/RF need not be pitched against the continuing
agenda of communicable diseases in such settings; resource
constraints are likely to force policy makers to prioritize
and channel resources towards addressing disease burden
that afflicts the majority of the population, and especially
the poor. As was observed in India,8 a public policy focus
on addressing CVD/RF in Bangladesh will invariably benefit the high-SES groups at the expense of the low-SES
groups and the poor. With 43.3% and 76.5% of population living on less than $1.25/day and $2/day, respectively,
and with such burden perhaps falling more on women,
Bangladesh needs to focus on disease burdens that are
more common among this group.
Conflict of interest: None of the authors have any conflicts
of interest to report.
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