Review of screening studies for atrial fibrillation in rural populations of 11 countries Alex I. Gavino, MD, and Craig S. McLachlan, PhD, MPH&TM
Atrial fibrillation (AF) is a common cardiac arrhythmia, and pathological burden can be influenced by environmental factors. The rural environment may influence the burden of AF, although no systematic review studies have been conducted to address this issue. We performed a systematic review of AF screening studies conducted in rural global populations to determine the burden, risk factors, and screening methods surrounding AF in these settings. Out of the 1792 articles gathered from a keyword search of medical databases and reference lists, 18 publications from 11 countries were included in our analysis. The pooled prevalence of AF across the studies was 2.05% (95% confidence interval, 1.97%–2.13%) and ranged from 0.3% to 10.87%. Only one study utilized a handheld electrocardiogram to screen AF, while the rest used the 12-lead electrocardiogram. AF risk factors reported across studies varied and included increasing age, male gender, hypertension, diabetes, prior myocardial infarction or stroke, obesity, hyperlipidemia/hypercholesterolemia, alcohol consumption, and heart failure. However, none of the studies assessed all risk factors. We suggest that future research on AF in rural communities examine a complete checklist of AF risk factors to better understand their influence on AF burden and development. This will aid in understanding rural prevention strategies and the management of detected AF cases specific to rural areas. At present, the burden of AF in rural communities is poorly understood and has been underreported.
F
actors specific to atrial fibrillation (AF) risk are known and include aging populations, poorer health status, and possibly a higher prevalence of related cardiovascular risk factors (1–3). Whether rural background is a risk factor for AF prevalence has not been explored. There is an emerging interest in improving the detection of AF in communities using novel technologies (4). Screening for AF in rural communities remains a challenge partly due to lack of access to services, as well as limited knowledge of emerging screening devices and access to automated software to detect AF. Location, demographics, and socioeconomic circumstances (5) may contribute to the burden of AF in rural communities. Detection rates for new cases of AF in urban areas are improved if screening programs are in place for AF screening (1.64%) compared to routine care (1.04%) (6). Additionally, AF detection in communities will also depend on the type of screening utilized and the length of time the screening is conducted. If screening is conducted over days as opposed to min280
utes or seconds, the diagnostic yield will be higher, particularly for paroxysmal AF. Most reported data on AF screening programs are based on epidemiological studies in urban populations (2, 7–13). We performed a systematic review of literature for AF screening to describe the methods used for AF detection in rural environments, to describe the risk factors included in these studies, and to report on the overall global rural population prevalence of AF. METHODS Multiple databases (PubMed/MEDLINE, EMBASE, Cumulative Index to Nursing and Allied Health Literature [CINAHL], Scopus) were searched from inception through March 15, 2016. Keywords for the search strategy included (atrial fibrillation) AND (screening OR detection OR diagnosis OR identification OR prevalence OR incidence) AND (rural OR regional OR remote). In addition, we manually searched reference lists, gray literature, and the Internet to find other potential studies. We reviewed the titles and abstracts of retrieved articles based on our predefined selection criteria. Inclusion criteria included a study design that was either a cross-sectional population study or a prospective cohort. Additionally, the studies had to be conducted in a rural setting and report AF prevalence. Studies were excluded if they 1) were case-control studies or randomized controlled trials, 2) were retrospective database reviews (i.e., electronic medical records, general practice database, hospital records), 3) were conducted in exclusively urban areas, or 4) measured AF prevalence among participants with a specific comorbidity such as stroke, hypertension, obesity, or other cardiac diseases. The primary outcome was the prevalence of AF in a general rural population. Secondary outcomes were AF risk factors and screening methods used. After initial screening, full-text articles of potential studies were obtained for further review. When we were unable to retrieve the full text despite doing an exhaustive Internet search and contacting the authors, the studies were excluded. In a number of cases From Rural Clinical School, Faculty of Medicine, University of New South Wales, Sydney, Australia. Corresponding author: Alex I. Gavino, MD, Rural Clinical School, Faculty of Medicine, University of New South Wales, Samuels Building, Level 3, Sydney, NSW 2052, Australia (e-mail:
[email protected];
[email protected]. edu.au; Twitter handle: @alexgavino). Proc (Bayl Univ Med Cent) 2017;30(3):280–285
where the rurality of the study location was not explicitly stated in the paper, we contacted the corresponding authors to confirm whether their study was conducted in a rural, urban, or mixed setting. Nineteen of the 26 authors (73%) whom we contacted responded to our inquiry. Studies that were conducted in mixed populations but separately reported AF prevalence in rural and urban areas met our inclusion criteria. The following information was extracted from the manuscript and recorded in a table: authors’ name, year of publication, country of origin, study design, number of participants, age range, median age, gender distribution, screening strategy, and overall AF prevalence. Studies not published in English were translated using Google Translator. If data were missing in the paper of interest, the study authors were contacted for this missing information. In one instance where the AF prevalence was not reported in the original paper, we used data from a subsequent systematic review. The overall prevalence rate was estimated using the proportion of identified AF across all studies. A binomial confidence interval was calculated for the overall prevalence. Subgroup analysis considered screening duration (i.e., screening period of 1 year). Studies that did not specify screening duration were excluded from the subgroup analysis. RESULTS After removal of duplicate records, a total of 1424 articles were screened against our inclusion and exclusion criteria. We reviewed the full text of 75 articles and identified 18 studies to include in our final analysis (Figure 1). These 18 rural AF
Figure 1. Study selection and stratification. July 2017
screening and prevalence studies published between 1965 and 2016 were conducted in 11 different countries: Australia (n = 2), China (n = 3), Ghana (n = 1), Greece (n = 2), Ireland (n = 1), Japan (n = 4), Korea (n = 1), Spain (n = 1), Tanzania (n = 1), Thailand (n = 1), and USA (n = 1). Our search did not yield any studies from South American countries. There were 7 prospective cohort studies and 11 cross-sectional studies (Table 1) (14–31). Participants were recruited either from population-based screening programs, annual health checks, or general practitioner clinics. Seventeen out of the 18 studies (94.4%) utilized a 12-lead electrocardiogram (ECG) to detect AF, while one study used a single-lead handheld ECG device (29). Two studies also measured the heart or pulse rate in conjunction with the 12-lead ECG (27, 31). The total number of participants was 123,923, while the sample size for each study ranged from 720 to 41,436 individuals. There were different cut-offs for participants’ age: ≥16, 18, 20, 21, 35, 40, 50, 60, 65, and 70 years old. Men comprised 39% (n = 48,336) of the total, with a frequency range of 33.6% to 52.0%. Two studies from China (26, 29) compared AF burden for both urban and rural residents. One study from Australia (23) was conducted in four regional towns in Victoria. However, data from one town was excluded since it is considered a metropolitan area despite being regional. The overall prevalence of AF from all studies was 2.05% (95% confidence interval, 1.97%–2.13%) and ranged from 0.3% to 10.87%. The lowest AF prevalence was observed in a community setting in Ghana, while the highest prevalence was from opportunistic screening in general practice clinics in Ireland. Follow-up was done in the Australian Busselton Health Study from 1966 to 1981 (16) and the Shibata Study of Japan (15). The Kurashiki Annual Medical Survey Study in 2006 (19) had a subsequent publication that found the overall incidence of newly documented AF to be 9.3 per 1000 patient-years between 2006 and 2007 (32). Pooled prevalence for studies with a screening period of ≤1 year was higher (2.25%) than that in studies with >1 year of screening duration (1.61%). The factors that were more commonly considered in the analyses of published studies were gender, age, alcohol and smoking history, physical activity, history of hypertension, diabetes, hyperlipidemia, myocardial infarction, cardiac disease, liver disease, chronic kidney disease, body mass index, blood pressure, and ECG and echocardiogram measurements (e.g., left ventricular hypertrophy, left ventricular ejection fraction, left atrial dimensions). Blood tests analyzed included hematocrit, liver assays (alanine transaminase, aspartate transaminase), lipid profile (total cholesterol, high-density lipoprotein, low-density lipoprotein, triglycerides), renal function tests (creatinine, glomerular filtration rate), glucose measurements (fasting blood sugar, glycosylated hemoglobin A1C), and cardiac biomarkers (brain natriuretic peptide, high-sensitivity C-reactive protein). Significant risk
Review of screening studies for atrial fibrillation in rural populations of 11 countries
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Tanzania
China
Ghana
China
Dewhurst, 2012 (25)
Li, 2013 (26)
Koopman, 2014 (27)
Sun, 2015 (28)
Cross-sectional
Community-based longitudinal cohort
Community-based cross-sectional
Cross-sectional
Community-based cross-sectional
Cross-sectional
Cross-sectional observational
Cross-sectional
Population-based prospective
Population-based prospective cohort
920 (52%) 364 (43%)
– 62.6 ± 14.2 69.2 ± 6.3 72.0 ± 11.3 –
≥60 25–74 ≥60 ≥40 ≥18
1770 841a
72.5 ± 5.7 – – 58.3 ± 20.5 77.8 – 66.0 (median) 53.8 80.4 ± 10.8 60.2 74.0 (median)
≥65 ≥40 ≥18 ≥20 ≥70 ≥35 ≥50 ≥35 ≥65 ≥21 ≥65
1450b 2232 11,806b
11,341 1153b 7262
4053
924
1155
720 2917
26,472
41,436
963
bIncludes
12 L ECG
12 L ECG
12 L ECG
12 L ECG
12 L ECG
Screening method
3390 (45%)
1576
536 (47%)
5172 (46%)
480 (52%)
5136 (44%)
976 (44%)
596 (52%)
658 (45%)
1325 (45%)
332 (46%)
9162 (35%)
Radial pulse palpation and 12 L ECG
12 L ECG
Handheld single-lead ECG
ECG
Heart rate and 12 L ECG
ECG
12 L ECG
12 L ECG
12 L ECG
12 L ECG
12 L ECG
12 L ECG
13,963 (34%) 12 L ECG
324 (34%)
960 (42%)
–
≥40
2299
5129
2466 (48%)
Male (%)
–
Mean age
≥16
Sample Age range size (years)
includes participants with ECG measurements. data from rural areas only; breakdown of rural versus urban provided by the authors. cAge adjusted. 12 L indicates 12-lead; ECG, electrocardiogram.
aOnly
Ireland
Greece
Ntaios, 2012 (24)
Smyth, 2016 (31)
Community-based longitudinal
Australia
Carrington, 2012 (23)
China
Japan
Shibata, 2011 (22)
Korea
Greece
Ninios, 2010 (21)
Chei, 2015 (29)
Japan
Ohsawa, 2009 (20)
Park, 2015 (30)
Cross-sectional
Japan
Iguchi, 2008 (19)
Population-based prospective cohort
Population-based cross-sectional
Prospective cohort Cross-sectional
Australia
Lake, 1989 (16)
Prospective cohort
Thailand
Japan
Tanaka, 1985 (15)
Cross-sectional
Assantachai, 2002 (18)
USA
Ostrander, 1965 (14)
Study design
Segura Fragoso, 1999 (17) Spain
Country
First author, year (ref)
Table 1. Study characteristics and outcomes
26
260
52
32
52
52
36
52
3–4
78
72
156
32
–
156
780
364
52
Screening period (weeks)
790 (10.87%)
54 (1.3%)
(4.6%)
139 (1.2%)
3 (0.3%)
101 (0.86%) (0.67%)c
15 (0.67%) (0.64%)c
45 (3.9%)
33 (2.23%)
1.50%
36 (5%)
413 (1.56%)
677 (1.6%)
21 (2.2%)
15 (1.8%)
47 (2.7%)
28 (1.20%)
22 (0.42%)
Overall prevalence
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 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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Mobile single-lead handheld or wearable ECG devices provide a high diagnostic yield because they can be worn for an extended period and allow for repeat screening (59–63). A potential limitation of our review is that only one study explored the prevalence of paroxysmal AF (PAF) across rural populations (26). It is accepted that the standard 12-lead ECG has a low sensitivity for PAF detection. PAF is largely asymptomatic, although it carries an identical risk for stroke as persistent AF (64, 65). We have thus identified the burden of PAF in rural areas globally as unknown. In summary, our review explored published studies from 1965 to 2016 (more than five decades) on rural AF. We found that the overall prevalence of global rural community AF was 2.05% and ranged from 0.3% to 10.87%. Information on the burden of rural AF is necessary for better provision of care and distribution of resources in rural populations (66). The 12-lead ECG was the primary screening tool for AF. However, the emergence of novel mobile ECG technologies for rhythm monitoring and interpretation may facilitate better AF detection in resource-limited areas. Reported AF risk factors varied across studies, and none of the studies assessed all potential risk factors. We also acknowledge there may be paradoxes in rural populations. For example, we noted reduced associations for AF and male gender in Tanzania (25) and China (29), and for age and AF burden in Tanzania (25). We suggest that future screening for AF in rural communities may better understand the true burden of rural AF across rural regions of different countries. Indeed, an examination of a complete checklist of AF risk factors may facilitate a better understanding of their association with AF burden. 1.
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