Population Impact of Potentially Modifiable Risk Factors for Stroke
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VIEW THE COMPANIONExposure of populations to potentially modifiable risk factors for stroke may impact both the incidence and outcome of stroke. The magnitude of the impact is determined by 4 factors: the prevalence of the risk factors in the population, the strength of the associations between the risk factors and the incidence and outcome of stroke, the causal role that can be attributed to the risk factors on the incidence and outcome of stroke, and how the incidence and outcome of stroke is measured. A measure of the impact of stroke risk factors on the population, which incorporates the baseline population prevalence of risk factors and the relative risk of stroke associated with risk factors, is the population attributable fraction (PAF).1,2
The Population Attributable Fraction
The PAF is the fraction of all cases of stroke, or outcomes of stroke, in a population that is attributable to exposure to a specific risk factor.1,2 It is calculated by subtracting the expected (E) number of cases among a nonexposed population from the observed (O) number of cases, and dividing by the observed number of cases (O−E/O).2
The word attributable infers that the risk factors are causal and, therefore, that the PAF is also the estimated fraction of all cases of stroke that would not have occurred if there had been no exposure.2 There are caveats to this inference. The PAF is usually derived from epidemiological cohort and case-control studies that are inherently prone to systematic and random error associated with the study design and analysis. Despite adjusting for all measured risk factors and potential confounding factors, risk factors may still not be independent of each other (eg, removing a risk factor may affect exposure to other risk factors), and estimates of the relative risk and PAF may remain biased because of residual and unmeasured confounding.3
Causal Attribution
Causal attribution of the burden of stroke (incidence and adverse outcomes) to risk factors for stroke can be estimated by several approaches or models.4–10
One approach is categorical attribution, in which stroke is attributed to a single risk factor, or group of risk factors, according to a defined system. The system has a set of mutually exclusive and collectively comprehensive categories and a set of rules or criteria for assigning stroke events to one of the risk factor categories.4–6 The advantage of categorical attribution is that it is simple and appealing because the total of the summary measure of risk (100%) equals the sum of the contributions of a set of mutually exclusive causal risk factors. Categorical attribution to risk factors, however, overlooks the fact that stroke can have multiple causal risk factors, and there is no universally accepted valid classification system for attributing a case of stroke to a specific single causal risk factor. Hence, this method of simply adding up the adjusted PAFs of the individual factors will overestimate the total PAF, and may exceed 100%, suggesting the impossible situation in which >100% of strokes (or stroke-related adverse outcomes) could be prevented.11
To calculate a total PAF from multiple causal risk factors, an alternative approach is to add each risk factor to the statistical model in every possible order and to calculate the average PAF for all risk factor permutations.9,10 This approach, used by the INTERSTROKE investigators12 (Study of the Importance of Conventional and Emerging Risk Factors of Stroke in Different Regions and Ethnic Groups of the World; see below), ensures that adding multivariable estimates for individual risk factors equals the PAF for the composite of these risk factors and allows measurement of the independent proportion of the PAF that each risk factor contributes to the overall PAF for all risk factors. However, it may also underestimate the effect of removing some individual risk factors on the burden of disease.
Another approach for assessing causal attribution is counterfactual analysis, whereby the contribution of one or a group of risk factors to stroke incidence or outcome is estimated by comparing the magnitude of the actual (or factual) burden of stroke with the magnitude that would be expected in some alternative hypothetical scenario (the counterfactual), including the absence of, or reduction in, the risk factor of interest.4–6 For example, the counterfactual distribution of the risk of stroke could be a theoretical minimum exposure distribution (ie, a distribution that would result in the lowest population risk, irrespective of whether achievable in practice), the plausible minimum risk, the feasible minimum, and the cost-effective minimum risk.13 The GBD study (Global Burden of Disease) 201314 and 201615 (see below) used a theoretical minimum risk exposure level as the counterfactual to estimate the burden of stroke (as measured in disability-adjusted life-years [DALYs] lost because of stroke) associated with any exposure to various risks that exceeded this level. The theoretical minimum risk exposure level for a risk factor was defined as the optimal level of exposure that minimized risk for everyone in the population.14 For example, the theoretical minimum risk exposure level for systolic blood pressure (SBP) was set at <120 mm Hg, which should minimize stroke risk and be theoretically possible to achieve. The counterfactual approach can, therefore, provide a measure of the burden of stroke attributable to risk factors as defined by the difference between the burden observed and the burden that would have been observed under an alternative population distribution of exposure. It also provides a measure of the avoidable burden, as defined by the reduction in the future burden of disease if the current levels of exposure to a risk factor were reduced to those specified by the counterfactual distribution of exposure. It thus enables estimates of the potential gains in population health by varying degrees of risk reduction across risk factors. As there are 2 factors that are independent of the type of counterfactual—the duration of the counterfactual (eg, number of years of theoretical minimum exposure) and the time during which changes in population health under the counterfactual are evaluated—models have been developed that facilitate counterfactual analysis with varying durations.4,6,13
Search Strategy
I searched the Cochrane Library, PubMed, and MEDLINE using the search term “stroke” in combinations with the terms “risk factors,” “exposures,” “modifiable,” “impact,” “population,” “attributable risk,” “population attributable fraction,” “burden,” “incidence,” “outcome,” “epidemiology,” “systematic review,” and “meta-analysis” for articles published between January 2014 and May 2019. I also searched the reference lists of articles identified by the search. I selected mainly articles published in the past 5 years but included older key publications.
Impact of Modifiable Risk Factors on the Incidence of Stroke
All Stroke
Several case-control and cohort studies have estimated the PAF of risk factors for stroke, and some have also estimated the PAF of risk factors for ischemic and hemorrhagic stroke subtypes.12,16–23 The summary data from the most contemporary and comprehensive studies for all stroke, and for ischemic and hemorrhagic stroke, are shown in Tables 1 through 3, respectively.
The largest and most comprehensive international study was the INTERSTROKE study, which reported that among 13 447 cases of acute first-ever stroke and 13 472 age- and sex-matched controls with no history of stroke that were assessed in 32 countries between 2007 and 2015, there were 10 potentially modifiable risk factors that were associated independently and significantly with all stroke and were collectively associated with 90.7% (99% CI, 88.7%–92.4%) of the PAF of stroke.12 The 10 factors were a self-reported history of hypertension or blood pressure ≥140/90 mm Hg, a lack of regular physical activity, high Apo (apolipoprotein) B/ApoA1 ratio, unhealthy cardiovascular diet (as measured by a low score on the modified Alternative Healthy Eating Index), increased waist-to-hip ratio, psychosocial factors, current smoking, cardiac causes, high or heavy episodic alcohol consumption, and diabetes mellitus (Table 1).12
| Risk Factor | PAF of First Stroke, % | |||
|---|---|---|---|---|
| 32 Countries INTERSTROKE12 | Sub-Saharan Africa SIREN23 | Indonesia National Survey20 | Rotterdam Cohort18 | |
| 13 447 Cases | 2118 Cases | 722 330 People | 6844 People | |
| 13 472 Controls | 2118 Controls | 1020 Strokes | ||
| PAF (99% CI) | PAF (95% CI) | PAF (95% CI) | PAF (95% CI) | |
| Hypertension | 47.9 (45.1 to 50.6) | 90.8 (87.9 to 93.7) | 37.2 (34.8 to 39.6) M | 36 (26 to 49) |
| 38.9 (34.7 to 42.9) F | ||||
| Baseline age >50 y | 58.8 (48.8 to 68.7) | |||
| Physical inactivity | 35.8 (27.7 to 44.7) | 2.4 (0.7 to 4.1) | ||
| Dyslipidemia | 35.8 (25.3 to 46.2) | 3.9 (1.1 to 7.0) M | ||
| 6.9 (1.3 to 12.5) F | ||||
| Apo B/Apo A1 ratio: T3 vs T1 | 26.8 (22.2 to 31.9) | |||
| Diet (mAHEI score) | ||||
| T3 vs T1 | 23.2 (18.2 to 28.9) | |||
| Regular meat | 31.1 (13.3 to 48.9) | |||
| Low green vegetables | 18.2 (14.1 to 22.3) | |||
| Added salt at table | 5.3 (3.3 to 7.3) | |||
| Regular sugar | 4.4 (−2.4 to 11.2) | |||
| Waist-to-hip ratio | ||||
| T3 vs T1 | 18.6 (13.3 to 25.3) | |||
| Elevated | 26.5 (12.9 to 40.2) | 5.4 (3.2 to 7.4) M | ||
| 8.2 (3.4 to 12.5) F | ||||
| BMI: 25–30 kg/m2 | 1 (0 to 92) | |||
| Psychosocial factors | 17.4 (13.1 to 22.6) | |||
| Stress | 11.6 (6.6 to 16.7) | |||
| Smoking: current | 12.4 (10.2 to 14.9) | 2.3 (1.5 to 3.1) | 16.7 (11.7 to 21.8) M | 16 (10 to 26) |
| 0.6 (0.2 to 1.0) F | ||||
| Cardiac causes | 9.1 (8.0 to 10.2) | 4.3 (0.6 to 7.9) | ||
| Atrial fibrillation | 2 (1 to 5) | |||
| Coronary disease | 1 (0 to 8) | |||
| Alcohol: high/heavy episodic | 5.8 (3.4 to 9.7) | |||
| Diabetes mellitus | 3.9 (1.9 to 7.6) | 22.1 (17.8 to 26.4) | 3.8 (2.6 to 5.0) M | 4 (2 to 7) |
| 7.1 (3.9 to 10.5) F | ||||
| Family history of CVD | 9.2 (−0.5 to 18.9) | |||
| Education: some vs none | 23.5 (3.1 to 44.0) | |||
| Income >$100 USD/mo | 14.9 (3.8 to 26.0) | |||
| Composite PAF | 90.7 (88.7 to 92.4) | 98.2 (97.2 to 99.0) | 51 (41 to 62) | |
Apo indicates apolipoprotein; BMI, body mass index; CVD, cardiovascular disease; F, female; INTERSTROKE, Study of the Importance of Conventional and Emerging Risk Factors of Stroke in Different Regions and Ethnic Groups of the World; M, male; mAHEI, modified Alternative Healthy Eating Index; PAF, population attributable fraction; and SIREN, Stroke Investigative Research and Educational Network.
The methods and criteria for diagnosing stroke and measuring each risk factor were standardized throughout the INTERSTROKE study, and the results were reasonably consistent across major regions of the world, ethnic groups, men and women, and age groups. However, there were important regional variations in the relative and absolute importance of several risk factors for stroke.12 The PAF for the composite of all 10 risk factors for all stroke was the lowest in Africa (PAF, 82.7% [99% CI, 65.0–92.5]) and the highest in Southeast Asia (97.4% [90.2–99.3]).12 Hypertension was significantly associated with all stroke in all regions (PAF ranging from 39% in Western Europe, North America, Australia to 60% in Southeast Asia), but the PAF associated with current smoking ranged from 4% in Africa to 18% in Western Europe, North America, and Australia; the PAF for waist-to-hip ratio ranged from 3% in Eastern and Central Europe and the Middle East to 37% in Western Europe, North America, Australia, and Southeast Asia; and the PAF for physical inactivity ranged from 5% in Africa to 60% in China.12 Other risk factors for stroke in some countries (eg, developing countries) that were not reported in the INTERSTROKE study include infections (rheumatic heart disease, infective endocarditis, HIV, syphilis, tuberculosis, mycotic aneurysm, malaria, and schistosomiasis), sickle cell disease, and Takayasu disease.
Africa was represented by 973 cases of stroke and 973 controls in the INTERSTROKE study.12 More recently, 2118 cases of stroke and 2118 age- and sex-matched stroke-free controls from 15 sites in Sub-Saharan Africa (Nigeria and Ghana) were enrolled in the SIREN (Stroke Investigative Research and Educational Network) study.23 Like INTERSTROKE, the SIREN study also reported that nearly all (98.2% [95% CI, 97.2–99.0]) of the adjusted PAFs of stroke was associated with potentially modifiable risk factors for stroke.23 The 11 risk factors, in descending order of PAF, were hypertension, dyslipidemia, regular meat consumption, elevated waist-to-hip ratio, diabetes mellitus, low green leafy vegetable consumption, stress, added salt at the table, cardiac disease, physical inactivity, and current cigarette smoking (Table 1).23 Hypertension was by far the most potent risk factor, being associated with 91% (88%–94%) of the PAF of stroke among individuals of African descent. Hypertension, dyslipidemia, diabetes mellitus, stress, and low consumption of green leafy vegetables were associated with stroke irrespective of age, whereas regular meat consumption, added table salt, and current cigarette smoking were significantly associated with stroke in participants ≥50 years of age, and cardiac diseases were significantly more associated with stroke in individuals <50 years of age.23 In the INTERSTROKE study, diet was also a stronger risk factor for stroke in older individuals, and hypertension, waist-to-hip ratio, and cardiac causes were stronger risk factors among younger individuals.12
Ischemic Stroke
In the INTERSTROKE study, the 10 risk factors for all stroke (Table 1) were also associated independently and significantly with ischemic stroke (Table 2).12 The PAF of the combination of the 10 risk factors (for all stroke) was 91.5% (99% CI, 89.4–93.2) for ischemic stroke.12 Smoking, diabetes mellitus, cardiac causes, and Apos were significantly more associated with ischemic stroke than hemorrhagic stroke.12 The PAF of ischemic stroke associated with ApoB/ApoA1 was higher (67.6%) in Southeast Asia than in Western Europe, North America, and Australia (24.8%), whereas the PAF of ischemic stroke for atrial fibrillation was lower (3.1%) in South Asia than in Western Europe, North America, and Australia (17.1%).12
| Risk Factor | PAF of First Ischemic Stroke, % | |||
|---|---|---|---|---|
| 32 Countries INTERSTROKE12 | Sub-Saharan Africa SIREN23 | Indonesia National Survey20 | Rotterdam Cohort18 | |
| 13 447 Cases | 2118 Cases | 722 330 People | 6844 People | |
| 13 472 Controls | 2118 Controls | 610 Ischemic Strokes | ||
| PAF (99% CI) | PAF (95% CI) | PAF (95% CI) | PAF (95% CI) | |
| Hypertension | 45.7 (42.4 to 49.0) | 86.6 (81.6 to 91.6) | 29.3 (25.1 to 33.0) M | 33 (20 to 49) |
| 37.3 (30.8 to 43.9) F | ||||
| Baseline age >50 y | 61.2 (48.6 to 73.9) | |||
| Physical inactivity | 33.4 (24.2 to 44.0) | 2.5 (0.5 to 4.6) | ||
| Dyslipidemia | 37.6 (24.3 to 50.8) | 10.1 (5.6 to 15.0) M | 3 (0 to 82) | |
| 10.1 (0.4 to 19.9) F | ||||
| Apo B/Apo A1 ratio: T3 vs T1 | 34.0 (29.0 to 39.3) | |||
| Diet (mAHEI score) | ||||
| T3 vs T1 | 22.4 (17.0 to 29.0) | |||
| Regular meat | 27.7 (3.5 to 52.0) | |||
| Low green vegetable | 17.5 (12.2 to 22.9) | |||
| Added salt at table | 4.7 (2.6 to 6.8) | |||
| Regular sugar | 7.3 (−0.4 to 15.1) | |||
| Waist-to-hip ratio | ||||
| T3 vs T1 | 20.4 (14.3 to 28.2) | |||
| Elevated | 30.4 (13.1 to 47.8) | 10.6 (7.2 to 14.2) M | ||
| 15.1 (7.7 to 22.2) F | ||||
| BMI >25 kg/m2 | 12 (5 to 27) | |||
| Psychosocial factors | 15.1 (10.3 to 21.5) | |||
| Stress | 11.4 (4.4 to 18.3) | |||
| Smoking: current | 15.1 (12.8 to 17.8) | 1.5 (0.4 to 2.5) | 25.1 (16.6 to 33.3) M | 16 (8 to 30) |
| 0.6 (0.1 to 1.3) F | ||||
| Cardiac causes | 9.1 (8.0 to 10.2) | 7.4 (3.1 to 11.8) | ||
| Atrial fibrillation | 0 (0 to 16) | |||
| Coronary disease | 3 (1 to 8) | |||
| Alcohol: high/heavy episodic | 4.6 (2.0 to 10.0) | |||
| Diabetes mellitus | 7.5 (5.0 to 11.1) | 26.2 (20.8 to 31.6) | 5.3 (3.6 to 7.6) M | 3 (1 to 8) |
| 6.0 (1.2 to 12.7) F | ||||
| Family history of CVD | 10.1 (−0.9 to 21.0) | |||
| Education: some vs none | 23.8 (0.0 to 47.5) | |||
| Income >$100 USD/mo | 19.0 (7.1 to 30.9) | |||
| Composite PAF | 91.5 (89.4 to 93.2) | 97.7 (96.2 to 98.8) | 55 (41 to 68) | |
Apo indicates apolipoprotein; BMI, body mass index; CVD, cardiovascular disease; F, female; INTERSTROKE, Study of the Importance of Conventional and Emerging Risk Factors of Stroke in Different Regions and Ethnic Groups of the World; M, male; mAHEI, modified Alternative Healthy Eating Index; PAF, population attributable fraction; and SIREN, Stroke Investigative Research and Educational Network.
| Risk Factor | PAF of First Hemorrhagic Stroke, % | |||
|---|---|---|---|---|
| 32 Countries INTERSTROKE12 | Sub-Saharan Africa SIREN23 | Indonesia National Survey20 | Rotterdam Cohort18 | |
| 13,447 Cases | 2118 Cases | 722 330 People | 6844 People | |
| 13,472 Controls | 2118 Controls | 103 Hemorrhagic Strokes | ||
| PAF (99% CI) | PAF (95% CI) | PAF (95% CI) | PAF (95% CI) | |
| Hypertension | 56.4 (52.0 to 60.6) | 96.6 (94.4 to 98.9) | 47.3 (43.1 to 51.3) M | 24 (4 to 73) |
| 46.6 (39.7 to 53.2) F | ||||
| Baseline age >50 y | 49.7 (31.3 to 68.1) | |||
| Physical inactivity | 34.6 (21.3 to 50.7) | 2.1 (−1.6 to 5.8) | ||
| Dyslipidemia | 32.7 (11.2 to 54.1) | −2.6 (−7.5 to 2.3) M | 31 (11 to 63) | |
| 5.7 (−5.0 to 16.2) F | ||||
| Apo B/Apo A1 ratio: T3 vs T1 | 1.2 (0.0 to 98.3) | |||
| Diet (mAHEI score) | ||||
| T3 vs T1 | 24.5 (16.5 to 34.8) | |||
| Regular meat | 38.8 (3.8 to 73.9) | |||
| Low green vegetable | 20.7 (13.1 to 28.2) | |||
| Added salt at table | 6.6 (2.3-11.0) | |||
| Regular sugar | −6.3 (−27.3 to 14.7) | |||
| Waist-to-hip ratio | ||||
| T3 vs T1 | 13.1 (6.4 to 25.1) | |||
| Elevated | 16.4 (−7.2 to 40.1) | 3.8 (0.7 to 7.46) M | ||
| 1.9 (−5.4 to 9.6) F | ||||
| Psychosocial factors | 24.7 (18.1 to 32.8) | |||
| Stress | 14.0 (6.6 to 21.4) | |||
| Smoking: current | 3.6 (0.9 to 13.0) | 3.8 (2.3 to 5.2) | 10.6 (1.2 to 19.6) M | 40 (22 to 60) |
| 0.6 (0.1 to 1.4) F | ||||
| Cardiac causes | 1.4 (0.6 to 3.4) | −6.0 (−18.1 to 6.2) | ||
| Alcohol: high/heavy episodic | 9.8 (6.4 to 14.8) | |||
| Diabetes mellitus | −7.0 (−11 to −3.0) | 14.6 (5.9 to 23.3) | 2.3 (0.5 to 4.3) M | 3 (0 to 26) |
| 8.4 (2.0 to 17.1) F | ||||
| Family history CVD | 7.5 (−13.5 to 28.5) | |||
| Education (some vs none) | 30.9 (−14.2 to 76.1) | |||
| Income >$100 USD/mo | 19.0 (7.1 to 30.9) | |||
| Composite PAF | 87.1 (82.2 to 90.8) | 99.3 (98.5 to 99.9) | 70 (45 to 87) | |
Apo indicates apolipoprotein; CVD, cardiovascular disease; F, female; INTERSTROKE, Study of the Importance of Conventional and Emerging Risk Factors of Stroke in Different Regions and Ethnic Groups of the World; M, male; mAHEI, modified Alternative Healthy Eating Index; PAF, population attributable fraction; and SIREN, Stroke Investigative Research and Educational Network.
In the SIREN study, ischemic strokes were commonly of lacunar subtype followed by large artery atherosclerosis and cardioembolic strokes.23 Ten of the 11 factors for all stroke in the SIREN study were associated with the occurrence of ischemic stroke (Table 2), of which diabetes mellitus, cardiac disease, and dyslipidemia were more associated with ischemic than hemorrhagic stroke.
Hemorrhagic Stroke
In the INTERSTROKE study, the PAR of the combination of the 10 risk factors for all stroke was 87.1% (82.2–90.8) for intracerebral hemorrhage.12 Seven of the 10 risk factors for all stroke (hypertension, regular physical activity, unhealthy diet, increased waist-to-hip ratio, psychosocial factors, cardiac disease, and alcohol consumption) were associated significantly with intracerebral hemorrhage, and hypertension was more associated with intracerebral hemorrhage than ischemic stroke.12
In the SIREN study, 6 of the 11 factors for all stroke were associated with hemorrhagic stroke.23 Hypertension and current alcohol use were significantly associated with hemorrhagic stroke compared with ischemic stroke.
Impact of Modifiable Risk Factors on the Outcome of Stroke
Risk factors for stroke may not only contribute to the causes and incidence of stroke but also to the outcome of stroke. The outcome of stroke can be measured and quantified in terms of several metrics, which include mortality, disability, years lived with disability, DALYs, and recurrent stroke.
Mortality
Most studies of risk factors for mortality after stroke have measured risk factors in patients with acute stroke and are, therefore, prone to reverse causality if the stroke may have induced changes in risk factors. Among the few studies that examined the effect of risk factors measured before stroke on outcomes after stroke, the population-based Rotterdam Study enrolled 14 235 participants >55 years of age without previous stroke and recorded their risk factor profile.24 During follow-up, 1237 participants experienced a first-ever stroke and were matched with 4928 stroke-free participants (4 per stroke case). This cohort was followed for mortality over the next 6 years, during which 919 (74%) participants with stroke died and 2654 (54%) stroke-free participants died. The proportion of deaths after stroke that were attributable to preexistent vascular risk factors (the combined PAF) was 27% (95% CI, 14%–45%) among participants with stroke and 19% (95% CI, 12%–29%) among stroke-free participants (Table 4).24 The main risk factors for death after stroke were smoking (PAF, 13%), diabetes mellitus (6% [4%–10%]), and atrial fibrillation (6%).23 These results are supported by the WHI cohort study (Women’s Health Initiative) of 159 587 postmenopausal women aged 50 to 79 years who were stroke-free at baseline (1993/1998), of whom 3173 developed an incident stroke before 2005 and 1111 (35%) died during follow-up to 2010.25 Modifiable factors before stroke that were associated with greater poststroke mortality included smoking (versus nonsmoker; hazard ratio, 2.13 [95% CI, 1.53–3.00]), diabetes mellitus (hazard ratio, 1.28 [95% CI, 1.01–1.64]), physical inactivity (versus >150 minutes of exercise per week; hazard ratio, 1.39 [95% CI, 1.09–1.78]), and being underweight before the stroke (hazard ratio, 2.02 [95% CI, 0.98–4.16]).25
| Study | PAF of Stroke-Related Deaths, % (95% CI) | PAF of Stroke-Related DALYs, % (95% Uncertainty Interval) | |
|---|---|---|---|
| Rotterdam Cohort Study24 | GBD Study 201314 | GBD 20006 | |
| 14 235 People | |||
| 1237 Strokes (919 Deaths) | |||
| 4928 Controls (2654 Deaths) | |||
| Risk factor | |||
| High blood pressure | 6 (0–57) | 64.1 (61.3–65.8) | 62 |
| Diet low in fruits | 35.6 (26.5–42.0) | 11* | |
| High BMI | 23.5 (20.7–26.1) | 13 | |
| Underweight | 1 (0–2) | ||
| Diet high in sodium | 22.6 (12.5–33.0) | ||
| Smoking | 13 (6–25) | 20.7 (18.2–22.7) | 12 |
| Diet low in vegetables | 20.0 (17.0–22.4) | 11* | |
| Ambient PM2.5 pollution | 16.9 (16.6–17) | ||
| Household air pollution from solid fuels | 15.7 (14.5–16.4) | ||
| Diet low in whole grains | 15.0 (12.5–16.9) | ||
| High fasting plasma glucose | 11.7 (7.6–15.7) | ||
| Diabetes mellitus | 6 (4–10) | ||
| Low physical activity | 7.7 (5.6–9.2) | 7 | |
| Low glomerular filtration rate | 7.1 (6.4–7.8) | ||
| Alcohol use | 7.0 (5.6–8.0) | 4 | |
| Lead exposure | 6.6 (4.8–8.4) | ||
| High total cholesterol | 4.5 (3.0–6.6) | 18 | |
| Low HDL cholesterol | 2 (0–10) | ||
| Secondhand smoke | 2.2 (2.1–2.2) | ||
| Diet high in sugar-sweetened beverages | 0.3 (0.2–0.4) | ||
| Atrial fibrillation | 6 (3–9) | ||
| All factors | 27 (14–45) | 90.5 (88.5–92.2) | 70 (adjusted) |
PM2.5 refers to atmospheric particulate matter (PM) that have a diameter of less than 2.5 micrometers. BMI indicates body mass index; DALY, disability-adjusted life-year; F, female; GBD, Global Burden of Disease; HDL, high-density lipoprotein; M, male; and PAF, population attributable fraction.
*
Low fruit and vegetable intake contributed 11% of the PAF of stroke in GBD 2000.
Death or Disability
The PROSCIS study (Prospective Cohort With Incident Stroke) study reported that among 507 patients with first ischemic stroke who were followed prospectively, 104 (20.5%) had died (n=24; 4.7%) or were functionally impaired (n=80; 15.8%) after 1 year.26 The PAFs of independent prognostic factors for death or disability were age ≥75 years (17.3%), education <10 years (10.4%), National Institutes of Health Stroke Scale score >4 points (10.9%), prestroke physical disability (18.5%), and diabetes mellitus (7.6%). Validation in an independent cohort of 200 patients, of whom 39 (19.5%) were dead or disabled at 1 year, reported similar ranking of risk factors by population impact.26
DALYs
DALYs lost represent the sum of years of life lost and years lived with disability. One DALY is, therefore, equivalent to 1 year of healthy life (free of disease or disability) lost. Because DALYs quantify mortality and disability associated with stroke in one metric, DALYs are considered a more comprehensive measure of stroke outcome than mortality and disability alone.
The GBD Study 2013 estimated the PAF of stroke-related DALYs associated with 17 potentially causal and modifiable risk factors for stroke between 1990 and 2013 from a meta-analysis of 3 inputs: (1) the reported prevalence of population exposure to stroke risk factors in 188 countries, (2) metaregression estimates of relative risks associated with individual risk factors in published cohort and intervention studies, and (3) the theoretical minimum risk exposure level (see above).14 These input data were used to estimate the PAF, which is the proportion of the burden of stroke (stroke-related DALYs) that could have been prevented if the risk factor was at the counterfactual (optimal) theoretical minimum level of risk factor exposure. The PAF was then multiplied by the stroke-specific disease burden metric (DALYs, number of deaths or years lived with disability) to obtain the burden of stroke attributable to each risk-outcome pair for each time point, accompanied by 95% uncertainty intervals that incorporate sampling error and model estimation error.
The GBD Study 2013 estimated that 90.5% (95% uncertainty interval, 88.5–92.2) of the global burden of stroke, as measured in DALYs, was attributable to the combined effect of the 17 modifiable risk factors analyzed.14 The leading risk factors were high SBP ≥120 mm Hg (PAF, 64.1%), diet low in fruits (35.6%), high body mass index (23.5%), diet high in sodium (22.6%), and smoking (20.7%; Table 4).14 The GBD study also identified ambient and household air pollution, low glomerular filtration rate, and lead exposure as important additional modifiable stroke risk factors.14 The PAF of all risk factors increased from 1990 to 2013 (except for secondhand smoking and household air pollution from solid fuels). As with the PAF of stroke risk factors for stroke incidence,12,16–23 the PAF of stroke risk factors for stroke-related DALYs differed between countries and regions.14
Updated estimates from the GBD study through to 2016 have also indicated that about 90% (88.8% [95% uncertainty intervals, 86.5–90.9]) of stroke DALYs can be attributed to modifiable risk factors (Table 5).15 Metabolic risks (high SBP, high body mass index, high fasting plasma glucose, high total cholesterol, and low glomerular filtration rate) accounted for 72.1% (66.4–77.3), behavioral factors (smoking, poor diet, and low physical activity) for 66.3% (59.3–73.1), and environmental risks (air pollution and lead exposure) for 28.1% (25.3–30.9) of stroke DALYs.15
| Pathological stroke subtype | PAF of Stroke-Related DALYs (%) in Global Burden of Disease Study 201615 | |
|---|---|---|
| Ischemic Stroke, % | Hemorrhagic Stroke % | |
| 95% Uncertainty Interval | 95% Uncertainty Interval | |
| Risk factor | ||
| High SBP | 54.2 (43.4–62.6) M | 60.2 (50.6–68.7) M |
| 54.2 (43.4–63.2) F | 59.2 (49.6–68.0) F | |
| Dietary risks | 48.8 (37.4–59.6) M | 56.1 (44.3–67.4) M |
| 44.0 (33.2–54.4) F | 52.6 (41.2–63.8) F | |
| Tobacco | 28.4 (23.5–33.2) M | 34.0 (28.3–29.5) M |
| 11.2 (9.2–13.4) F | 14.2 (11.8–16.9) F | |
| High total cholesterol | 21.5 (13.5–34.6) M | Not significant |
| 23.5 (13.2–39.6) F | ||
| Air pollution | 21.2 (18.4–23.9) M | 26.7 (23.7–29.7) M |
| 20.2 (17.8–22.7) F | 27.8 (25.0–30.6) F | |
| High fasting plasma glucose | 18.7 (10.0–30.9) M | 16.6 (10.4–24.5) M |
| 17.8 (9.5–30.7) F | 16.3 (10.4–24.4) F | |
| High body mass index | 16.5 (9.5–24.9) M | 26.7 (16.1–38.6) M |
| 18.8 (12.0–26.7) F | 30.7 (20.8–41.6) F | |
| Low physical activity | 10.5 (1.8–19.8) M | Not significant |
| 9.7 (1.8–18.4) F | ||
| Alcohol and drug use | 10.3 (6.0–14.7) M | 24.0 (18.0–29.8) M |
| 6.9 (3.2–10.4) F | ||
| Impaired kidney function | 8.1 (6.7–9.6) M | 8.3 (6.7–9.9) M |
| 9.2 (7.2–11.0) F | 9.1 (7.5–10.8) F | |
| Other environmental risks | Not significant: M | 4.0 (1.8–6.8) M |
| 1.9 (0.7–3.6) F | 2.4 (0.8–4.7) F | |
| Occupational risks | Not significant | 3.2 (2.2–4.2) M |
| 2.3 (1.6–3.0) F | ||
| All factors | 87.9 (84.1–91.6) | 89.5 (87.1–91.6) |
DALY indicates disability-adjusted life-year; F, female; M, male; PAF, population attributable fraction; and SBP, systolic blood pressure.
The GBD 2013 and 2016 study results complement the earlier Comparative Risk Assessment module of the GBD 2000 Study, which reported that, among 14 epidemiological subregions of the world, the combination of high blood pressure (PAF, 62%), high cholesterol (18%), high body mass index (13%), tobacco (12%), low fruit and vegetable intake (11%), physical inactivity (7%), and alcohol abuse jointly contributed 70% (adjusted) to 76% (unadjusted) of the PAF of stroke-related DALYs (Table 4).6
Discussion
The population impact of potentially modifiable risk factors for stroke on the incidence and outcome of stroke is substantial in all of the studies reviewed above, despite differences in their populations, designs, risk factor prevalence, outcomes, and analyses. About 90% of all strokes can be attributable to modifiable risk factors, after accounting for joint effects of combinations of risk factors. The remaining 10%, or so, of strokes are likely attributable to independent effects of genetic factors, unmeasured and unknown risk factors, and gene-environment interactions.27
The ranking of the major risk factors for stroke by population impact is reasonably consistent among studies, despite considerable regional variation in the nature and importance of the various risk factors for stroke. Hypertension is ubiquitously the major causal risk factor for stroke incidence and outcome, accounting for at least a third of the incidence and DALYs of stroke in developed countries, and more than two-thirds in developing countries, despite variations in the definition of hypertension in different studies. In INTERSTROKE, hypertension was defined by a self-reported history of hypertension or blood pressure ≥140/90 mm Hg12; in the SIREN study, hypertension was defined as a self-reported history of hypertension, blood pressure ≥140/90 mm Hg, or use of antihypertensive drugs,23 and in GBD, high SBP was defined as SBP ≥120 mm Hg.14,15 The high population impact of hypertension on stroke incidence is plausible, given that high blood pressure underpins many of the major causes of stroke; hypertension is a causal risk factor for atherosclerosis and atrial fibrillation (embolic ischemic stroke), intracranial small vessel disease (lacunar ischemic stroke and deep intracerebral hemorrhage), and the formation of intracranial aneurysms (subarachnoid hemorrhage). The population impact of hypertension on stroke outcome is plausible too, given that a substantial proportion of strokes due to embolism from the heart (atrial fibrillation caused by hypertensive heart disease), hypertensive intracerebral hemorrhage, and aneurysmal subarachnoid hemorrhage are fatal or disabling.
Tobacco smoking also continues to impact upon the burden of stroke, particularly in developing countries, and atrial fibrillation and renal impairment threaten as the age and life expectancy of population increases, particularly in developed countries.28 Poor diet (high intake of salt, meat, and sugar; low intake of fruit, green leafy vegetables, and grains), physical inactivity, obesity, diabetes mellitus, and air pollution have emerged as major modifiable determinants of the burden of stroke. Genetic factors are also relevant, increasing the risk of incident stroke by about one-third, while lifestyle factors increase the risk of stroke by about two-thirds, independent of genetic risk stratums.27
Stroke is caused by a constellation of diseases that can largely be attributed to modifiable lifestyle, behavior, and socioeconomic and psychosocial factors. It should, therefore, be largely preventable.
Strategies to prevent stroke include the high-risk and population-wide mass approaches.29–33 The high-risk approach aims to identify individuals at risk of stroke and to lower the prevalence and level of their causal risk factors, and risk, accordingly. It is usually implemented by clinicians and nurses who identify individuals at risk of stroke by several means. First, by eliciting a history of previous stroke or cardiovascular disease. Effective secondary prevention treatments for these individuals will minimize their individual risk of recurrent stroke. However, the impact of secondary stroke prevention on the global burden of stroke is likely to be modest because recurrent strokes only contribute about one-quarter of all strokes; most strokes are first-ever strokes.33–35 Second, individuals at high risk may be identified by assessing risk factors at screening visits and incorporating them into absolute risk prediction equations, such as the Framingham Stroke Risk Scores and QStroke score.36–39 Those predicted at high risk of stroke may be offered blood pressure and cholesterol-lowering medication, or the multidrug polypill, in addition to general lifestyle and dietary advice (eg, smoking cessation).40 This approach is reasonable if screening assessments are simple and identify thresholds for prescribing prophylactic medication. However, such general health checks have had no effect on stroke rates in randomized trials in 107 421 persons (relative risk, 1.05 [95% CI, 0.95–1.17]; I2=53%).41,42 This may be because most strokes occur in individuals at low or moderate risk; large numbers of people at low risk are likely to produce more cases than small numbers at high risk.29 Hence, a low absolute risk score should not reassure individuals or their doctor of immunity from stroke.
Complementary strategies of primary stroke prevention, which target people at all levels of risk, and, therefore, all ages, across the whole population are, therefore, needed. The population (mass) approach aims to lower the mean level of causal risk factors throughout the population to lower the prevalence of disease.29,43 It is usually implemented by governments, health organizations, and public health physicians via policies such as taxation of tobacco and alcohol, standardized packaging of tobacco products, smoke-free and exercise-friendly public spaces, and reducing salt, saturated fat, and trans fats in processed foods.32,44–46
The World Health Organization has suggested a series of best buy interventions to reduce risk factors that are cost-effective and feasible to implement in low- and middle-income health systems.47 These include tobacco and alcohol taxes, advertising bans and warnings, reductions in salt and trans fat intakes, and promotion of physical activity. Bloom et al47 conclude “Interventions in this area will undeniably be costly. But inaction is likely to be far more costly.”44
A further initiative to enable individuals of all ages and stroke risk across the world to assess, modify, and monitor their absolute risk of stroke is via mobile technology (mHealth) and the smartphone Stroke Riskometer app.48–50
The considerable, yet preventable, burden of stroke attributable to modifiable risk factors for stroke highlighted above reinforces the importance of a harmonious, 2-tiered approach to stroke prevention. Education about stroke risk and healthy lifestyle behaviors should be applied to the whole population, irrespective of their genetic risk, while simple, inexpensive screening for a history of vascular disease and presence of modifiable causal risk factors may identify those for whom prophylactic drug therapy may complement reinforced lifestyle advice. Concurrently, improved social and environmental factors, including reduced population exposure to air pollution, tobacco, salt, and poverty, require immediate government intervention.
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Received: 22 March 2019
Revision received: 13 May 2019
Accepted: 20 May 2019
Published online: 12 February 2020
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Dr Hankey has received honoraria from the American Heart Association for serving as an associate editor of Circulation and from Bayer for lecturing about stroke prevention in atrial fibrillation at sponsored scientific symposia.
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