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factors associated with AF varied across studies, and we noted
that not all risk factors were considered in these studies. Overall,
AF was found to be higher among those with advanced age (19-
22, 24-26, 28-30), male gender (19-22, 30), hypertension (19,
21, 24, 28, 31), diabetes (19, 24, 28), prior myocardial infarction
(16, 21, 26, 28, 30), prior stroke (21, 24, 29), obesity (26, 29),
hyperlipidemia/hypercholesterolemia (19, 29, 30), alcohol consumption (16, 26, 28), and heart failure (24). Furthermore, ECG
and echocardiographic findings of left ventricular hypertrophy
(22, 26, 28, 30), low left ventricular ejection fraction (28), left
bundle branch block (16), left atrial diameter (30), peak early
rapid filling wave velocity (30), myocardial ischemia (21), and
ventricular premature beats (21) were found to be associated with
AF. Only six studies (19, 24, 26, 28-30) performed univariate
and multivariate analyses to establish significant disease associations, while the rest reported descriptive statistics.
DISCUSSION
To our knowledge, this is the first systematic review on global
rural AF screening and risk factors. We found that the global
pooled prevalence of AF in rural areas was 2.05% based on the
18 articles that we reviewed. This overall prevalence rate is comparable with findings from earlier systematic reviews using four
large population-based surveys (2.3%) (33) and another with
30 studies across urban or mixed urban-rural settings (2.2%)
(34); conversely, our pooled AF rate is relatively lower than that
observed in large urban screening studies (2, 12). In the three articles with mixed urban and rural participants, one study showed
that AF burden was higher among urban residents (26), while the
reverse was observed in the other two studies (23, 29). However,
the sampling methods and sample size in these studies were
modest, with a population range of 1,418 to 19,363.
AF risk factors were reported variably across studies. We
noted significant associations between rural AF and increasing
age (19-22, 24-26, 28-30). Male gender (19-22, 30), hypertension (19, 21, 24, 28, 31), diabetes (19, 24, 28), prior myocardial
ischemia/infarction (16, 21, 26, 28, 30), alcohol consumption
(16, 26, 28), prior stroke (21, 24, 29), and obesity (26, 29) were
also associated with AF. What is apparent from our review is that
none of the studies reported all the risk factors or comorbidities
that may be associated with AF. Moreover, we noted from our
review that while middle age was considered in rural screening
in 22% of studies, risk factors such as obesity and hypertension
were not always considered in determining associated risk factors
for AF (35). Furthermore, the years in which our studies were
conducted ranged from 1965 to 2016; thus, it is possible that
over time, there could have been changes in lifestyle risk factors
that could predispose rural populations to develop AF. On the
other hand, the Framingham Heart Study, for example, noted
that while the prevalence of most cardiac risk factors changes over
a span of 50 years, their associated hazards for AF changed little
(36). Our study does not allow us to comment on longitudinal
changes in risk factors over time, since none of the reviewed studies' populations had follow up over a prolonged period of time.
There is a well-known increase in the prevalence and incidence of AF with advancing age (≥60 years old). Another sigJuly 2017

nificant risk factor is male gender. Both aging and male gender
were similarly observed in the Framingham Heart Study (8) and
the Rotterdam Study (2). Moreover, the Framingham Study
also reported hypertension, diabetes, and myocardial infarction
as independent predictors of AF (8). Advancing age and male
gender would theoretically increase the risk for AF regardless
of rural or urban areas since age and sex are nonmodifiable
demographic risk factors consistently reported in studies.
Participants across studies were recruited based on age cutoffs, with the lowest cut-off being 16 years old and the highest
70 years old. This could partly explain the variance in prevalence
rates across studies. While the overall prevalence of AF in the
general population studies is 0.95%, aging plays a role, with
rates ranging from 0.1% among adults <55 years old to 9.0%
in those >80 years (37). Hence, a lower age cut-off likely results
in a lower AF prevalence rate. However, other factors may affect
AF occurrence in the population, as observed in Tanzania, where
even though the participants were ≥70 years old, the prevalence
of AF was only 0.67% (25). The authors suggested that low AF
prevalence may be due to a lower body mass index among participants, a lower incidence of ischemic heart disease, or earlier
mortality for those who developed AF (25). Ethnic background
may influence AF burden. For example, the African American
AF paradox has been observed, where there is a lower prevalence
and incidence of AF in African Americans despite a higher
prevalence of AF risk factors compared to Caucasians (38).
We noted in our analysis an inconsistent association of alcohol consumption with AF. An increase in AF risk for regular
alcohol usage or higher rates of alcohol intake was noted (16,
26, 28). However, one study found that not consuming alcohol
was a significant predictor of AF (21). Previous studies have
documented the cardiovascular benefits of low to moderate alcohol intake (39); however, heavy drinking has also been shown
to be a strong risk factor for the development of AF (40-42).
The variation in associations may be due to the differences in
alcohol consumption patterns between rural and urban areas
and across different countries and the type of alcohol consumed.
For example, Australian rural and regional areas tend to have
higher rates of alcohol use compared to urban areas (43, 44),
whereas in America, urban locales have higher alcohol consumption patterns (45).
In a small number of studies, we noted the use of primary
hospital admissions data or electronic medical records (1, 46,
47). Due to the high cost of conducting population-based
screening studies (48), administrative and medical records may
be an alternative source to establish AF prevalence in certain
populations. A few studies also focused on chronic conditions,
such as hypertension (49), stroke (50, 51), and hypertrophic
cardiomyopathy (52). Because these preexisting comorbid conditions are associated with a higher burden of AF, these studies
were excluded from prevalence calculations.
Most studies we reviewed used a 12-lead ECG to detect AF;
only the Chinese Longitudinal Healthy Longevity Survey (29)
used a single-lead ECG device. A 12-lead ECG remains the gold
standard for AF screening (53). Newer ECG devices (54-58)
for arrhythmia detection and analysis have become available.

Review of screening studies for atrial fibrillation in rural populations of 11 countries

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