We used growth curve models to account for repeated observations of cognitive functioning per person ( 34 35 ); we focus on the relationship between lead exposure and both the intercept and slope of these growth curves. Following previous work ( 36 38 ), we used ordinary least squares and logistic regression models to assess the relationship of adult mediators with childhood lead exposure. A formal mediation test using the Karlson, Holm, and Breen (KHB) method was then used to decompose the relative importance of the direct and indirect pathways from childhood lead exposure to late-life cognition ( 39 40 ). More information on the measures and the statistical analysis is presented in Materials and Methods.
Our primary independent variable is a measure of childhood lead exposure in 1940. We identified HRS respondents who were exposed to lead as children by creating an interaction between two dichotomous variables (lead versus nonlead pipes and acidic/alkaline versus neutral water). Adult mediators included educational attainment (less than high school, high school, some college, and college or more), household income, wealth, and diagnoses of stroke, hypertension, and heart disease. We categorized both income and wealth into deciles that were treated as continuous variables in our models. All variables except education were time varying. We controlled for demographic and childhood covariates including sex, race/ethnicity, childhood SES, childhood health, and region of birth.
Our dependent variable is global cognitive function based on the three cognitive tests available in the HRS core survey ( 32 ): a 10-word immediate and delayed recall tests of verbal memory (0 to 20 points), a counting backward test of attention and processing speed (0 to 2 points), and a serial 7s subtraction test of working memory (0 to 5 points). A small percentage of respondents in each wave refused to participate in the cognitive tests, and to reduce sample attrition, the HRS has imputed cognitive measures for missing data ( 33 ). We used the imputed cognitive test variables released by the HRS in our analysis and standardized the summary score to have a mean of 0 and an SD of 1.
Of the 9654 HRS sample members who were successfully linked to the 1940 Census, we excluded individuals who were aged over 16 to focus on those who were children or adolescents in 1940. We excluded those who were not living in cities ( n = 3893) and who did not have valid piping and water pH data ( n = 748). We also excluded individuals who were lost to follow-up because of death or nonresponse before the baseline survey ( n = 193) and individuals with missing values on key (time-invariant) covariates ( n = 215), which yielded our final analytic sample of 1089 individuals living in 398 different cities in 1940. These participants contributed 7432 observations between 1998 and 2016 (mean follow-up time = 6.8 years). We started with the 1998 core wave because it is the first time that the sampling represents U.S. adults aged 51 and older. A chart showing the steps that we took to arrive at the analytic sample is displayed in fig. S1.
The lead concentration varies by water chemistry. For people whose municipal water has pH values less than 6.5 or greater than 8.5, we would expect lower cognitive functioning as lead levels in their bodies would have increased as lead from water lines leaches as the result of alkaline or acidic water chemistry. However, for people whose municipal water has a pH level between 6.5 and 8.5, lead municipal water pipes are less problematic because the lead is not readily leached into the water. Adapted from ( 12 ) with permission from Elsevier.
Information regarding whether cities used lead service lines comes from 30 ). Water pH data for our sample are primarily available through a report published by the U.S. Geological Survey ( 31 ), although some were identified on city websites if they included local histories of their water utility. We mapped the location of cities that used lead pipes and had acidic or alkaline water in Fig. 3 . Cities in the Northeast and Midwest were more likely to use lead pipes and less likely to have pH neutral water (i.e., acidic or alkaline) than those in the South and West.
We combine data from 10 waves of the HRS (1998 to 2016) and multiple sources of historical administrative data. The HRS was first conducted in 1992 on a national sample of persons born in 1931 to 1941. The following year, the HRS fielded the cohort born before 1924. In 1998, these cohorts were merged, and to create a sample representative of Americans of age 51 and older, two new cohorts born in 1924 to 1930 and 1942 to 1947 were enrolled and interviewed biannually thereafter. Among HRS respondents who were born by 1940 (= 20,066), 9654 were successfully matched to their household records in the 1940 U.S. Census ( 28 ) that became available to the public after a mandatory 72-year waiting period ( 29 ). The matching was done on the basis of the respondents’ first and last names (including maiden name for women), age, sex, state of birth, and the names of other people living in the household in 1940 (e.g., parents and siblings). A machine learning algorithm was used to identify the correct record from among all possible records in the 1940 Census. The algorithm was trained to minimize false-positive matches and maximize the overall linkage rate. More information on the linking procedures is described in Materials and Methods.
We hypothesize that older adults who, as children, lived in cities with lead-contaminated water will have lower cognitive function and faster cognitive decline in late life compared with peers who were not exposed to lead-contaminated water. Because childhood exposures can influence adult social positions and cardiovascular health that may, in turn, affect cognitive aging, we also hypothesize that the association between childhood lead exposure and late-life cognition will be accounted for by education, adult SES, and health.
We examine the long-term association between childhood lead exposure and trajectories of cognitive change in late life among respondents to the Health and Retirement Study (HRS); we use data from the 1998–2016 biannual survey waves ( 27 ). As the longest-running aging survey of its kind, the HRS allows for more than two decades of follow-up on cognitive functioning in late life, along with financial status and cardiovascular health that we hypothesize to be important indirect pathways shaping cognition in late life. It is also one of the only publicly available nationally representative samples of U.S. older adults that has been linked to the 1940 U.S. Census. The main advantage of using these linked data is that it allows us to identify the city in which respondents lived as children, to construct early-life measures of lead exposure from municipal drinking water systems. Given that lead was a ubiquitous and poorly regulated environmental exposure for children born across much of the 20th century, it is vital to understand the potential impact that this pollutant has had on the cognitive health of these cohorts as they age.
Early-life lead exposure may indirectly shape cognitive aging by influencing adult achievements and experiences that subsequently matter for cognitive functioning in late life. One well-established and important indirect mechanism is through educational attainment. Lead exposure has been linked to fewer years of schooling and increased risk of high school dropout ( 21 23 ); education, in turn, is closely related to adult socioeconomic status (SES). Lower levels of education often lead to occupations that involve low mental demands and stimulation, which could reduce the brain’s capacity to sustain function amid the brain pathology and neuronal losses associated with normal aging ( 24 ). Cardiovascular health represents another pathway through which childhood lead exposure could conceivably affect late-life cognition. Lead is known to disrupt not only the central nervous system but also other organ functions and systems integral to brain health including the cardiovascular system ( 19 ). Among adults, occupational and residential lead exposure has been linked to hypertension, stroke, and cardiovascular disease ( 25 26 ), which can damage blood vessels in the brain, causing lower cognitive function. Older adults with lower levels of education and poor cardiovascular health may thus enter old age with higher risk of cognitive impairment and faster decline. However, very little research has examined whether childhood lead exposure affects late-life cognition indirectly through adult education, SES, and health pathways ( 9 19 ).
Cities with lead pipes and acidic/alkaline water are marked on the map with red solid circles, and those with lead pipes and neutral water are marked on the map with red open circles. Cities using nonlead pipes regardless of water pH are marked on the map with gray open circles. Water pH level less than 6.5 or greater than 8.5 is considered acidic and alkaline water, respectively. Data source: 30 ) and the U.S. Geological Survey ( 31 ).
Conceptually, childhood lead exposure may shape cognitive aging through direct or indirect processes. It may directly influence cognitive functioning at older ages through “biological embedding” pathways ( 15 ), as shown in Fig. 2 . The argument is that lead exposure in early life can cause early brain damage ( 16 ) that may alter gene expression ( 17 ) and elevate protein production ( 18 ), leading, in turn, to increased risk of cognitive impairment in later adulthood ( 19 ). Another hypothesized direct biological pathway is through the remobilization of lead stored in bones, which can lie dormant for decades after exposure ( 19 20 ). Once lead is in the body, most of it stays in the bone until bone loss occurs (e.g., through osteoporosis); as a result of bone loss, some lead can reenter the blood and soft tissue. Remobilization of bone lead might, therefore, prompt a large increase in blood lead levels decades after exposure and directly exacerbate age-related cognitive decline.
Service lines connecting homes to street mains in the early 20th century were commonly made of lead because of its malleability and durability ( 13 ). Although some officials were concerned about the potential toxicity of lead from water that passed through lead pipes as early as the mid-19th century ( 13 ), the federal government did not begin regulating lead until the passage of the Safe Drinking Water Act in 1974. As a result, lead concentrations in tap water in that era frequently far exceeded 0.0015 parts per billion (ppb), which is currently defined by the Environmental Protection Agency (EPA) as the maximum acceptable level in drinking water; cities in 1900 had water lead concentrations, on average, 20 to 100 times greater than the current limit ( 14 ). For example, Lowell, Massachusetts had lead concentrations of 0.1608 ppb in tap water in 1900, which exceeded 100 times the current EPA standard.
Individuals can be exposed to lead through multiple sources including leaded gasoline, paint, pesticides, and air pollution; however, the dominant source of lead exposure for cohorts born in the early 20th century was public drinking water ( 10 11 ). Lead does not occur naturally in water sources. Instead, it leaches into water as it is transported through lead service lines to individual houses and buildings. The amount of lead that leaches from service lines varies by water pH, as shown in Fig. 1 . Lead solubility is greatest in acidic (pH ≤6.5) or alkaline water (pH ≥8.5); these extremes can corrode or dissolve metals substantially more easily than neutral water. For cities with lead service lines, highly acidic or alkaline water leaches more lead than neutral water, while no lead can leach into drinking water in cities that did not use lead service lines regardless of water pH ( 12 ).
No amount of lead is considered safe for human consumption. Lead is a neurotoxicant that can cause permanent damage to the developing brains of children ( 1 ). Extensive research has documented that early-life lead exposure, even at low levels, is associated with reduced attention span and poorer academic performance among children ( 2 6 ) and lower intelligence quotient among young adults ( 7 ). Recently, this literature has expanded to link childhood lead exposure to cognitive outcomes in midlife and later adulthood; lead-exposed children were significantly associated with reduced gray matter volume in areas responsible for memory function and dementia at age 45 ( 8 ) and lower language/executive function at age 64 ( 9 ). While this line of research has been critical in identifying childhood lead exposure as an important contributing factor for brain aging, previous studies have focused on regional samples that may not be widely generalizable with short periods of follow-up. It is largely unknown whether childhood lead exposure affects trajectories of cognitive functioning throughout late adulthood in a nationally representative sample.
RESULTS
Table 1 presents baseline summary statistics, stratified by lead exposure status. The mean standardized global cognitive function score is 0.61, but there are substantial differences by exposure status: Respondents from cities with lead pipes and highly acidic or alkaline water had lower cognitive scores (0.35) than their peers in places with nonlead pipes (0.59) or lead pipes and neutral water (0.68). Compared to those who were not exposed to lead in drinking water as children, the lead-exposed group had significantly higher levels of many of the risk factors that we hypothesized to be important for cognitive decline, including lower educational attainment, less household income (decile), and a higher prevalence of heart disease. The nonexposed group, while advantaged in terms of adult education and SES conditions, had a significantly higher prevalence of southern birth. The lead-exposed group was primarily born in the Northeast, had fewer female members, and far fewer Black members compared to the nonexposed group.
Percentage (N) or mean (SD) Exposed Nonexposed Full sample Lead pipes and acidic/alkaline water (7.07%; N = 77) Lead pipes and neutral water (36.18%; N = 394) Nonlead pipes (56.75%; N = 618) P value Cognitive score (0–27) 17.29 (4.02) 16.10 (4.14) 17.65 (4.08) 17.22 (3.94) P < 0.01 Standardized cognitive score 0.61 (0.87) 0.35 (0.90) 0.68 (0.89) 0.59 (0.86) P < 0.01 Lead pipes (%) 43.25 (471) Acidic/alkaline water (%) 10.47 (114) Age (57–93) 65.49 (5.01) 65.52 (4.81) 65.41 (4.95) 65.54 (5.08) P = 0.92 Female (%) 50.69 (552) 45.45 (35) 55.33 (218) 48.38 (299) P = 0.06 Race/ethnicity (%) P = 0.07 Non-Hispanic, white 88.89 (968) 97.40 (75) 89.59 (353) 87.38 (540) Non-Hispanic, Black 7.99 (87) 2.60 (2) 7.87 (31) 8.74 (54) Hispanic 2.30 (25) – 1.27 (5) 3.24 (20) Other 0.83 (9) – 1.27 (5) 0.65 (4) Childhood SES (%) P = 0.63 0 45.55 (496) 40.26 (31) 44.67 (176) 46.76 (289) 1 24.24 (264) 31.17 (24) 23.35 (92) 23.95 (148) 2 17.26 (188) 18.18 (14) 17.77 (70) 16.83 (104) 3+ 12.95 (141) 10.39 (8) 14.21 (56) 12.46 (77) Poor childhood health (%) 7.07 (77) 10.39 (8) 7.61 (30) 6.31 (39) P = 0.37 Southern born (%) 16.53 (180) 2.60 (2) 19.80 (78) 16.18 (100) P < 0.001 Educational attainment (%) P < 0.05 <High school 12.30 (134) 20.78 (16) 10.41 (41) 12.46 (77) High school 39.12 (426) 44.16 (34) 40.10 (158) 37.86 (234) Some college 20.75 (226) 12.99 (10) 19.4 (77) 22.49 (139) College+ 27.82 (303) 22.08 (17) 29.95 (118) 27.18 (168) Income (1–10) 6.24 (2.63) 5.96 (2.48) 5.90 (2.62) 6.49 (2.64) P < 0.01 Wealth (1–10) 6.43 (2.66) 6.34 (2.52) 6.44 (2.67) 6.42 (2.68) P = 0.96 Ever had stroke (%) 4.41 (48) 3.90 (3) 4.57 (18) 4.37 (27) P = 0.96 Ever had hypertension 41.05 (447) 33.77 (26) 41.88 (165) 41.42 (256) P = 0.40 Ever had heart diseases 15.98 (174) 18.18 (14) 15.99 (63) 15.70 (97) P = 0.85 These statistics are shown separately by lead exposure status. We classified respondents from cities that had lead pipes and extreme levels of water pH (acidic or alkaline), the conditions required for lead to leach into drinking water, as exposed. Because, for cities that did not use lead pipes or that had neutral water, lead cannot leach into drinking water regardless of water pH, we considered the rest of respondents unexposed. We performed significant group comparisons based on chi-square test, Kruskal-Wallis test, and t test.
P < 0.01). However, we did not find childhood lead exposure to be significantly associated with rates of cognitive decline. Results from growth curve models predicting cognitive trajectories are shown in Table 2 . Results from model 1 indicate that childhood lead exposure predicts lower levels of cognitive functioning after adjustment for age, sex, race/ethnicity, childhood SES, childhood health, and region of birth. We find that individuals exposed to lead as children had, on average, 0.41 SD lower cognitive functioning at age 72 relative to those from cities with nonlead pipes and/or lead pipes and neutral water (β = −0.408,< 0.01). However, we did not find childhood lead exposure to be significantly associated with rates of cognitive decline.
Model 1 Model 2 Model 3 Model 4 Intercept Slope Intercept Slope Intercept Slope Intercept Slope Fixed effects Age (centered) −0.050*** −0.049*** −0.062*** −0.066*** Age squared −0.001*** −0.001*** −0.001*** −0.001*** Lead 0.087 0.002 0.054 0.002 0.066 0.001 0.063 0.001 Acidic/alkaline
water 0.112 0.009 0.096 0.012 0.097 0.011 0.106 0.012 Lead X acidic/
alkaline
water −0.408** −0.006 −0.268* −0.009 −0.261* −0.006 −0.271* −0.006 Educational attainment (<high school = reference) High school 0.491*** −0.005 0.448*** −0.006 0.453*** −0.005 Some college 0.693*** −0.003 0.624*** −0.006 0.627*** −0.005 College+ 0.955*** 0.001 0.829*** −0.005 0.830*** −0.004 Income 0.019*** 0.001 0.019*** 0.001 Wealth 0.024*** 0.001* 0.023*** 0.002* Stroke −0.156** −0.009 Hypertension 0.014 0.010** Heart diseases 0.011 0.000 Intercept 0.341*** −0.389*** −0.608*** −0.599*** Variance
components Variance of
random
intercept 0.373*** 0.298*** 0.288*** 0.283*** Variance of
random
slope 0.001*** 0.001*** 0.001*** 0.001*** Residual
variance 0.313*** 0.313*** 0.312*** 0.312*** Goodness of fit AIC 15258.05 15070.48 15022.62 15004.94 BIC 15472.37 15326.28 15306.08 15329.87 All models adjust for demographic and childhood covariates including sex, childhood SES, childhood health, and region of birth. Coefficients for these demographic and childhood covariates can be found in table S1. AIC, Akaike information criterion; BIC, Bayesian information criterion. *P < 0.05; **P < 0.01; ***P < 0.001.
The negative relationship between lead exposure and baseline cognition persisted with the inclusion of educational attainment in model 2. Accounting for educational attainment reduced the magnitude of the coefficient for childhood lead exposure by 34%, but the coefficient remained statistically significant (β = −0.268, P < 0.05). Higher levels of educational attainment were associated with higher levels of cognitive functioning net of childhood and demographic covariates, with respondents with high school diplomas and college or more education having, on average, 0.491 and 0.955 SD higher levels of cognitive functioning (P < 0.001), respectively, than those with less than high school diplomas. These results suggest that about one-third of the association between childhood lead exposure and later-life cognition is accounted for by educational attainment.
Further adjusting for income, wealth, and cardiovascular health in models 3 and 4 did not eliminate the association between lead exposure and cognitive outcomes. Individuals exposed to lead as children have 0.261 and 0.271 SD lower cognitive functioning at age 72 after adjustment for the adult SES and cardiovascular health mediators (P < 0.05), respectively. While each adult SES indicator was associated with higher levels of cognitive functioning, only higher wealth was associated with a slower rate of cognitive decline (β = 0.001 and 0.002 in models 3 and 4, respectively, P < 0.05). Model 4 reveals that the magnitude of the estimated association between childhood lead exposure and baseline cognition was on par with or exceeded the magnitude of other well-established cognitive risk factors including stroke. Although the point estimate for lead was larger than the point estimate for stroke (β = −0.156, P < 0.01), for example, the coefficient for stroke is far more precise, further research is needed to replicate this finding in other samples.
Figure 4 displays the association between childhood lead exposure and cognitive trajectories over time, based on the estimates from model 1. As we saw in Table 2 , lead-exposed individuals (red solid line) initially started with lower levels of cognitive functioning, but they did not necessarily have a steeper rate of cognitive decline over time.
n = 7432 person-wave observations). Trajectories are calculated using estimates from model 1 in Table 2 , HRS, 1998 to 2016 (= 7432 person-wave observations).
Overall, the results in Table 2 suggest that childhood lead exposure has little relationship with the rates of cognitive decline, that childhood lead exposure has a sizable association with baseline cognitive functioning in later life, that about a third of that association is accounted for by educational attainment, and that very little of that association is accounted for by adult SES or cardiovascular health. To more formally explore the pathways through which childhood lead exposure might affect later-life cognitive functioning, we conducted mediation analyses (tables S2 and S3). Following previous work, in models described in table S2, we first regressed the adult SES and health mediators on childhood lead exposure, controlling for age, sex, race/ethnicity, childhood SES, childhood health, and region of birth; this provides estimates of the relationship of these adult mediators with childhood lead exposure net of covariates. We found that childhood lead exposure is unrelated to most of the adult SES and health indicators except for educational attainment. For example, lead-exposed individuals had 4.663 times greater odds of receiving less than high school education, providing partial evidence for our second hypothesis. We did not find income, wealth, stroke, hypertension, and heart diseases to be associated with childhood lead exposure. Results from the KHB method in table S3 also confirm that most of the total relationship is accounted for by the direct, rather than indirect, effect of childhood lead exposure on cognitive functioning. Only 26% of the association was explained by adult pathways, and nearly all of the indirect effect of lead exposure occurred via educational attainment.
We conducted several other supplementary analyses. First, in addition to our main specification of acidic/alkaline water, we considered different cut points in pH to measure acidic (pH ≤6.4 or 6.6) or alkaline (pH ≥8.4 or 8.6), as the exact threshold for defining highly acidic or highly alkaline water remains debatable. Using both stricter and looser specifications of acidic/alkaline, our findings are generally robust (tables S4 and S5). Second, instead of our primary specification of lead exposure, which only considers people to have been exposed if their city exclusively used lead service line pipes, we combined cities using mixed service line materials with cities using only lead pipes. Under this alternative specification, we found that childhood lead exposure no longer predicted cognition (table S6). However, combining “mixed metal” places with lead-only places may have biased our estimate of the lead’s effect toward zero because we may have included some cities with a very small share of lead pipes in the exposure group. Third, whereas our main analyses included imputed values for missing cognitive measures, we ran models using the original cognitive data without imputations; excluding respondents with imputed values did not alter our main results (table S7). Fourth, we analyzed models separately for each cognitive domain—verbal memory, working memory, and attention and processing speed (table S8). We found that childhood lead exposure is associated with baseline levels of working memory (but not decline in working memory) and that the association remained statistically significant with the inclusion of all mediators. As for baseline verbal memory, we found childhood lead exposure to be a significant predictor, but the lead coefficient was no longer significant with the inclusion of educational attainment. However, the lead coefficient with baseline verbal memory was nearly the same as in the models with the global cognitive score. We did not find childhood lead exposure to be associated with baseline attention or processing speed (or declines therein). Last, we ran models that restrict the sample to those who were born in the Northeast; the results are widely the same, although estimates were less precise. This suggests that our main findings are not driven by the geographic clustering of lead exposure in our sample.