Education, Socioeconomic Status, and Intelligence in Childhood and Stroke Risk in Later Life: A Meta-analysis : Epidemiology

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Social Epidemiology

Education, Socioeconomic Status, and Intelligence in Childhood and Stroke Risk in Later Life

A Meta-analysis

McHutchison, Caroline A.a,b; Backhouse, Ellen V.a; Cvoro, Veraa; Shenkin, Susan D.b,c; Wardlaw, Joanna M.a,b

Author Information
Epidemiology 28(4):p 608-618, July 2017. | DOI: 10.1097/EDE.0000000000000675


Stroke is the second most common cause of death worldwide1,2 and the most common cause of dependency in adults in the developed world.3 The absolute number of strokes per year, prevalence, and global burden are large and increasing.2 Among the 16.7 million people who have a first stroke each year worldwide,4 a third will die and a third of survivors will have cognitive impairment or dementia.5 Reduction in stroke is therefore likely also to reduce dementia.

Vascular risk factors in adulthood, such as hypertension, diabetes, and smoking, are associated with increased risk of stroke and are established targets for prevention, but only explain part of the variance in stroke risk.6 Poor socioeconomic status (SES) in adulthood,7 and declining cognition8 in later life are recognized to increase stroke risk. Factors such as childhood education, SES, and intelligence (IQ) have been associated with increased risk of cardiovascular disease,9–12 chronic adult diseases,13 and death from stroke in adulthood,14 but with mixed findings, limited information specifically on stroke14,15 and no meta-analysis to determine overall effects. Two reviews9,14 found associations between low childhood SES and increased risk of stroke in some but not all included studies, but did not perform a meta-analysis.

To address the hypothesis that less education, lower SES, or lower IQ in childhood affect stroke risk in later life, and if so by how much, we performed a systematic review and meta-analysis of all available data.


We used the Meta-analysis of Observational Studies in Epidemiology (MOOSE)16 and Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines17 and prospectively registered the protocol, including search methods, with PROSPERO (registration number: CRD42015016701).

Search Methods

We developed and tested a search strategy (see eDocument 1;, which details the search strategy used for MEDLINE) with an experienced librarian to identify studies that examined early life factors (education, childhood SES, and childhood/premorbid IQ) and cerebrovascular disease including clinically overt (i.e., stroke, cognitive impairment, depression) or “silent” features detected on imaging or pathology (see Backhouse et al.18). Here we report the data on stroke.

We searched MEDLINE (1966 to present), PsycINFO (1806 to present), and EMBASE (1980 to present) for papers published up until end November 2015 using OVID SP UI03.16.00.110. We checked reference lists of identified papers and hand searched the previous five years of Stroke, Neurology, and International Journal of Epidemiology. We contacted authors for clarification of results where required but not routinely due to resource availability.

A single reviewer screened each title and abstract; uncertain papers were cross-checked by the other reviewer or examined in more detail. Eligible full text articles were assessed independently by both reviewers. Disagreements regarding inclusion were discussed between the two reviewers and the review team.

We compared duplicate publications on the same cohort for each early life factor. Based on sample size (largest), publication year (most recent), and relevance of outcome reported, a single paper per study was selected to avoid duplication of participants.

Study Criteria

Studies that provided data on stroke in adulthood in relation to one or more of the three early life factors were included. We included general IQ measures collected up to early adulthood. Estimates of IQ in youth, determined using a valid tool in older subjects (e.g., National Adult Reading Test (NART),19 Wechsler Test of Adult Reading (WTAR),20 etc.) were also included based on their strong relationship to childhood IQ.21 Indicators of childhood SES such as parental education or occupation and housing conditions collected postpartum were included. All measures of education (years, highest level attained) were included.

“Stroke” was defined as a diagnosis based on clinical examination, neuroimaging, self-reported, or a diagnosis extracted from centralized health statistics (e.g., hospital and death registers etc.). Ischemic stroke, being the commonest type, was the primary outcome. We included studies examining a combination of ischemic, hemorrhagic stroke, and transient ischemic attack (TIA) as these samples consisted predominantly of ischemic stroke patients. We excluded studies of hemorrhagic stroke or TIA only as these were too small and infrequent.

We considered papers written in any language, containing relevant primary data and published in full in peer-reviewed journals. We excluded review articles, abstract-only publications, animal studies, children ages <18 years only, stroke mortality only, fewer than 50 stroke cases or that focused on a specific patient subpopulation (e.g., Parkinson’s Disease etc.).

Data Extraction and Quality Assessment

One of two reviewers performed data extraction, with each extraction cross-checked by the other reviewer; a sample were cross-checked by the remaining members of the review team. We extracted data on study design and setting, participants, definitions of early life factors and stroke, numbers with and without stroke, the relevant statistic (odds ratio [OR], hazard ratio [HR], relative risk [RR] etc.), and any confounding variables such as hypertension, adult SES, etc.

We quality assessed the included studies,22 based on six factors; representativeness of study sample, attrition, description, and reliable measurement of early life factor and stroke, inclusion of confounding variables and appropriateness of statistical analysis. Each aspect was rated on a four-point scale (yes/4, partly, no, and unsure/1), with maximum score (best quality) of 24.

Statistical Analysis

We grouped the included papers by reported outcome statistic (e.g., HR, OR, RR, or mean) for meta-analysis. We chose the outcome variable with the most data, and that represented a similar level of education where more than one was offered. We defined “low education” as 8–10 years of full time education or below and “high education” as 11 years and above. Most papers reporting childhood SES used father’s or head of household’s occupation, classed as manual or nonmanual, to indicate low or high SES which we therefore used in meta-analysis. We used parental education level and financial troubles in childhood (yes/no) where father’s occupation was not available. Most papers reporting childhood IQ reported a score on a specific test or compared quartiles. We used the study’s definition of lower versus higher IQ. We compared the lowest and highest IQ when no overall HR was reported.

We standardized all results to represent a reference level of high education, IQ, or SES. HRs, ORs, RRs, and mean differences (MD) were used to compute the overall effect and 95% confidence intervals (CIs) using Review Manager V.5.3.14. Where no ratio was reported, we used frequency data to calculate the OR. Where possible, we used outcomes adjusted for vascular risk factors or other key variables (listed in footnotes in figures). We estimated absolute differences in stroke rates per 1,000 population using the rates by education, childhood SES, or IQ category where reported. To account for an expected high level of heterogeneity, we used a random effects model. We assessed heterogeneity using the I2 statistic and publication bias using a funnel plot.

We prespecified subgroups (adjusted versus unadjusted results, participant age and sex hospital/outpatient clinic, including case–control design, versus population sampling method, outcome measurement, and first only versus recurrent/unspecified stroke) to test whether any of these modified the association with education, IQ, or childhood SES in sensitivity analyses and also conducted meta-regressions using the “metafor” package23 in R24 to examine if any of these subgroups explained any variance.


The search identified 19,180 titles and abstracts, after removal of duplicates (Figure 1), from which we identified 1,217 full text articles. Of these, 905 were excluded, the main reasons being inappropriate outcome (e.g., not reporting stroke separately from other disease outcomes), followed by absence of analysis of early life factor in direct relation to stroke. We identified 90 papers which examined early life factors and risk of stroke (see Table and eTable 1a–c;, which includes details of included papers). Although most Continents were represented by one or more studies, most studies took place in North America or Europe.

Summary of Included Papers (for Individual Details See eTable 1;
PRISMA flowchart of search and study selection.

Quality Assessment and Publication Bias

The quality of the included papers was good, with scores from 18 to 24/24 (median = 22). Sample representativeness was the lowest scoring subscale, with statistical analysis being the highest scoring (see eFigure 3;, which shows frequencies by subscale).

All analyses were examined for publication bias. Little evidence of was found in analyses where there were sufficient numbers of studies for publication bias to be accurately assessed (see eFigure 4a–c;, which show funnel plots for assessment of publication bias).

Education and Risk of Stroke in Adulthood

We included 79 studies,11,25–102 reported in 108 papers (total n = 2,881,067), examining education and risk of stroke (see eTable 1a;, which includes details of included papers). Thirty-one papers66–68,70–72,74–77,80–89,91–101 reported adjusted results, of which 1668,70,72,76,81,83,84,86,89,92-96,99,100 adjusted for vascular risk factors.

Twenty-five papers41–65 reported frequencies of stroke by duration of education. We used these frequencies to calculate unadjusted ORs, which we analyzed with 19 other papers66–84 that reported ORs (total 73,886 strokes and 2,324,944 stroke-free participants). Mean years of education was reported in 16 papers25–40 (3,209 strokes and 19,712 stroke-free participants), 13 reported HRs11,90–101 (7,871 strokes and over 207,336 stroke-free participants), and five reported RRs85–89 (56,593 strokes and over 18,061 stroke-free participants [note that in all analyses the true denominator is larger than the number shown because not all studies reported the number of stroke-free participants]).

Overall, less versus more education was associated with an increased risk of stroke (OR = 1.35, 95% CI = 1.24, 1.48, I2 = 96%; MD = 0.66, 95% CI = 0.31, 1.01, I2 = 77%; HR = 1.33, 95% CI = 1.17, 1.53, I2 = 70%; RR = 1.35, 95% CI = 1.09, 1.67, I2 = 90%; Figure 2A, B). Based on included studies with relevant data (n = 46), this relative increase of 33%–39% was equivalent to an absolute increase in stroke of 3.5/1,000, that is, (when expressed as whole numbers) for every 2,000 people who only completed education up to high school level (or 11 years), 7 more will have a stroke compared with people with college or university education (>11 years). There was between-study heterogeneity for all comparisons, ranging from 76% to 96%. One study102 reported a stroke prevalence ratio for those with higher education (PR = 0.9, 95% CI = 0.6, 1.1; 229 strokes and 2,786 stroke-free participants), so could not be included in the meta-analysis.

A, Education attainment (low versus high) and risk of stroke, risk ratio (OR, HR, and RR), random effects model (risk ratio <1 indicates low education decreases risk of stroke; >1 = low education increases risk of stroke). B, Education attainment in mean years of education in persons with and without stroke, random effects model for the mean differences (negative mean difference = lower education decreases risk of stroke and positive mean difference = higher education decreases risk of stroke). Figure is available in color online.

In sensitivity analyses, we examined education and stroke risk (see eFigure 5a–j;, which includes forest plots for sensitivity analyses). Use of adjusted versus unadjusted risk ratios, participants’ age, recruitment through hospitals/outpatient clinics, including case–control design, versus population cohorts, stroke ascertainment using clinical examination/neuroimaging versus self-report/centralized health data, participant sex and first only versus recurrent/unspecified stroke, did not explain between-study heterogeneity (See eFigure 5d–j; Meta-regression, possible on 41 studies, did not identify any study or participant characteristics that altered the relationship between education and stroke risk.

Childhood SES and Risk of Stroke in Adulthood

Ten studies,11,63,84,89,98,103–107 reported in 15 publications (total n = 1,354,899), examined childhood SES and stroke (see eTable 1b;, which includes details of included papers). Over 13,210 stroke and 1,318,962 stroke-free participants were included ages 30–70 years old at the time of follow-up or stroke. One study103 did not provide the number of stroke and stroke-free participants (total n = 11,106). Frequencies were used to calculate ORs for five studies.63,89,104–106 One paper adjusted for vascular risk factors,84 one for adult SES,98 and two for demographic variables.98,107

We meta-analyzed three studies11,98,107 reporting HRs (3,240 strokes, 73,644 stroke-free participants) and six63,84,89,104–106 reporting ORs (9,970 strokes, 1,245,318 stroke-free participants) for childhood SES and stroke. Subjects with lower childhood SES (i.e., father’s occupation manual) had an increased risk of stroke compared with those with higher (i.e., nonmanual father’s occupation) SES (HR = 1.31, 95% CI = 1.03, 1.68, I2 = 88%; OR = 1.28, 95% CI = 1.12, 1.46, I2 = 56%; Figure 3). Based on included studies with relevant data (n = 6), this relative increase of 28%–32% was equivalent to an absolute increase of 0.3/1,000 strokes, i.e., (when expressed as whole numbers) for every 10,000 people whose fathers had manual jobs, three more will have a stroke compared with people whose father’s had nonmanual jobs. Removal of one large study105 reduced the heterogeneity substantially (OR = 1.36, 95% CI = 1.30, 1.42, I2 = 0%).

Childhood socioeconomic status (low versus high) and risk of stroke, risk ratio (HR, OR), random effects model (risk ratio <1 indicates low SES decreases risk of stroke; >1 = low SES increases risk of stroke). Figure is available in color online.

One study103 reported the stroke rate by 10,000 person-years by father’s occupational class so could not be meta-analyzed, but showed that lower versus higher father’s occupational class was associated with a higher stroke rate (7.8 per 10,000/yr vs. 2.3 per 10,000/yr; P = 0.001).

Meta-regression on five studies found that studies ascertaining stroke using centralized health statistics versus direct examination were more likely to find that lower SES increased stroke risk (β = −0.21; P = 0.005).

Childhood IQ and Risk of Stroke in Adulthood

Nine studies,11–13,39,63,108–111 reported in 13 studies (total n = 1,209,952), examined childhood/premorbid IQ and stroke (see eTable 1c;, which includes details of included studies). Five studies11,12,63,108,110 adjusted for confounders, of which three adjusted for vascular risk factors.11,63,110

Six studies11–13,63,108,110 measured IQ in childhood/early adulthood and three39,109,111 estimated premorbid IQ in adulthood using the NART resulting in 9,685 strokes and 1,200,264 stroke-free participants ages 36–77 years old at the time of follow-up or stroke.

We meta-analyzed five studies11,12,63,108,110 (9,087 strokes, 1,191,612 stroke-free participants) reporting HRs and three39,109,111 (322 strokes, 305 stroke-free participants) reporting the average IQ score and risk of stroke. In subjects with a lower versus higher childhood IQ, stroke risk was increased (HR = 1.17, 95% CI = 1.00, 1.37, I2 = 55%; MD = 6.83, 95% CI = 2.11, 11.55, I2 = 72%; Figure 4A, B). Based on included studies reporting the relevant data (n = 3), this 17% increase in relative risk was equivalent to an increase in absolute risk of 0.3/1,000, that is, (when expressed as whole numbers) for every 10,000 people whose childhood IQ was lower than the population average, three more will have a stroke compared with children whose childhood IQ was higher than the average. The remaining study13 reported an OR which could not be included in the meta-analyses. Consistent with the other studies, it demonstrated that higher IQ was associated with decreased stroke risk (OR = 0.85, 95% CI = 0.74, 0.99) after adjusting for demographic and SES factors. Removal of one large study12 reduced heterogeneity to zero (overall HR = 1.09, 95% CI = 1.08, 1.10).

A, Childhood/premorbid IQ (low versus high) and risk of stroke, hazard ratio, random effects model (hazard ratio <1 indicates low IQ decreases risk of stroke; >1 = low IQ increases risk of stroke). B, Childhood/premorbid IQ by mean IQ score in persons with and without stroke, random effects model for the mean difference (negative mean difference = lower IQ decreases risk of stroke and positive mean difference = lower IQ increases risk of stroke). Figure is available in color online.


This meta-analysis of all available literature shows these early life factors to be consistently associated with increased stroke risk in adulthood. Having <11 versus ≥11 years of education was associated with an increase in relative risk of stroke of about a third. Similarly, lower childhood SES and IQ were both associated with 17%–32% relative increases in stroke risk. These relative risks translated to absolute increases in stroke risk in adulthood of approximately 3.5/1,000 for lower versus higher education and 0.3/1,000 for lower versus higher childhood SES and IQ. These apparently small absolute effects are substantial when considered in terms of the huge global burden of stroke—for example, for every 2,000 people living in a country where, say, most people leave full time education after high school, there may be about seven more strokes over a lifetime than, say, in a country where many people continue in education beyond high school level. Or, for every 10,000 people in a country where most fathers had manual jobs, there may be about three more strokes in a lifetime than in a country where many people’s fathers had nonmanual jobs. In a country of 50 million people most of whom leave education before or at completion of high school, 175,000 more of them might expect to have a stroke in their lifetime than in another country of 50 million people most of whom complete a college or university education. These simple illustrations are only intended to provide estimates of the potential magnitude of absolute effects, as they do not account for age at stroke (which may be younger in those exposed to the higher risk), and should be viewed with caution.

Our findings build on previous reviews9,14 suggesting that poorer conditions in childhood were associated with a higher stroke risk in adulthood. However, there has been no previous comprehensive meta-analysis of any of the three factors and stroke risk such as performed here.

Many studies included relatively young participants yet the median age for stroke is 72 and incidence rises with age.112 Most participants in studies of childhood IQ and SES were younger than the typical age of stroke, so further research is needed to determine whether the risk increases further with age. A study (n = 470) published after our search cut-off113 found that poorer childhood SES was associated with a higher stroke risk in subjects of mean age = 66.5. Despite the paucity of studies examining older participants, there is clear evidence that early life factors contribute to the risk of stroke, even in younger adults.

Many studies did not adjust for known risk factors, such as hypertension, smoking, or diabetes. In those that did, there was considerable variation in the number and type of risk factors. Although risk factor-adjusted results showed a lower stroke risk compared with unadjusted results, the overall effect remained significant. Other factors, for example, birth weight or childhood nutrition were not considered as they were unavailable for most studies.

Differences in the measurement of early life factors and stroke ascertainment may have contributed to heterogeneity. Many larger studies relied on centralized health statistics, which may be less specific than clinical examination and neuroimaging, although may avoid other source of bias. Studies using centralized health statistics showed a lower stroke risk compared with those using clinical examination and neuroimaging. Additionally, we focused on ischemic stroke due to the small number of studies with >50 strokes in hemorrhagic stroke studies. One review suggested that the effect of childhood SES was higher in hemorrhagic stroke but did not perform a meta-analysis.9

It is likely that the early life factors are inter-related but very few studies presented information on more than one factor simultaneously. Many studies use education as a proxy for childhood IQ, as those with higher IQ are more likely to pursue higher education. The closeness of the two variables has been widely debated.114 Only four identified studies included more than one early life factor, of which two accounted for early life factors in analysis11,63 and found that the relationship between lower childhood IQ and increased stroke risk in middle-age men remained when controlling for education. Other studies suggest that a strong association between childhood IQ and later life disease risk even when accounting for other childhood factors such as SES.10,12 Childhood SES is related to risk of death in adulthood independent of adult SES,115 and the childhood SES relationship to adult stroke risk remained in the one study that adjusted for adult SES.98

Our systematic review had limitations. We lacked resources to contact authors for missing data. Due to available data, absolute risks were calculated on a subset of papers, a further reason for caution. The sensitivity analyses and meta-regressions were not able to account for much of the heterogeneity. Although some studies were performed in the Asia Pacific Region and Africa, most studies were based in Europe or North America; social and educational disparities may vary in other world regions but lack of relevant data precluded a sensitivity analysis of racial or geographical differences.

There were also strengths. The importance of quality assessment is widely recognized116; however, there is no agreed method of assessment.22,117 Nonetheless, two reviewers assessed each study independently using an established scale,22 finding high study quality with minimal bias and strong statistical methodology. We registered the protocol, used state-of-the-art methodology, found all articles thought to be relevant and included non-English papers, thus the review included approximately 164,683 strokes and over 5 million stroke-free participants, with at least 1.2 million subjects or more in analysis of SES and IQ.


There are several possible explanations for the observed relationships. Individuals with higher childhood SES and intelligence may pursue and have better access to higher education, hence higher paid, safer jobs. This may encourage better health and lifestyle behaviors including exercise, diet, and self-management of vascular risk factors, resulting in decreased stroke risk. Alternatively, positive factors in early life may be associated with greater brain resilience, allowing more subclinical vascular brain damage to be sustained before clinical stroke occurs; further research is required to determine if this latter hypothesis is likely.

We show a consistent relationship between lower childhood/premorbid IQ, SES and education, and increased risk of stroke. Disparities in health and social equality have been widely discussed (e.g., Marmot Report118). Because stroke is a risk factor for dementia,119 future studies should examine the literature to see whether the early life factors influence risk of dementia. Additionally, future studies should distinguish between lifestyle and vascular risk factors, examine the individual and combined effects of intelligence, SES and education to determine the independent contribution of each factor to stroke (and dementia) in later life.


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