Air quality improvement and cognitive decline in community-dwelling older women in the United States: A longitudinal cohort study.

BACKGROUND: Late-life exposure to ambient air pollution is a modifiable risk factor for dementia, but epidemiological studies have shown inconsistent evidence for cognitive decline. Air quality (AQ) improvement has been associated with improved cardiopulmonary health and decreased mortality, but to the best of our knowledge, no studies have examined the association with cognitive function. We examined whether AQ improvement was associated with slower rate of cognitive decline in older women aged 74 to 92 years. METHODS AND
FINDINGS: We studied a cohort of 2,232 women residing in the 48 contiguous US states that were recruited from more than 40 study sites located in 24 states and Washington, DC from the Women's Health Initiative (WHI) Memory Study (WHIMS)-Epidemiology of Cognitive Health Outcomes (WHIMS-ECHO) study. They were predominantly non-Hispanic White women and were dementia free at baseline in 2008 to 2012. Measures of annual (2008 to 2018) cognitive function included the modified Telephone Interview for Cognitive Status (TICSm) and the telephone-based California Verbal Learning Test (CVLT). We used regionalized universal kriging models to estimate annual concentrations (1996 to 2012) of fine particulate matter (PM2.5) and nitrogen dioxide (NO2) at residential locations. Estimates were aggregated to the 3-year average immediately preceding (recent exposure) and 10 years prior to (remote exposure) WHIMS-ECHO enrollment. Individual-level improved AQ was calculated as the reduction from remote to recent exposures. Linear mixed effect models were used to examine the associations between improved AQ and the rates of cognitive declines in TICSm and CVLT trajectories, adjusting for sociodemographic (age; geographic region; race/ethnicity; education; income; and employment), lifestyle (physical activity; smoking; and alcohol), and clinical characteristics (prior hormone use; hormone therapy assignment; depression; cardiovascular disease (CVD); hypercholesterolemia; hypertension; diabetes; and body mass index [BMI]). For both PM2.5 and NO2, AQ improved significantly over the 10 years before WHIMS-ECHO enrollment. During a median of 6.2 (interquartile range [IQR] = 5.0) years of follow-up, declines in both general cognitive status (β = -0.42/year, 95% CI: -0.44, -0.40) and episodic memory (β = -0.59/year, 95% CI: -0.64, -0.54) were observed. Greater AQ improvement was associated with slower decline in TICSm (βPM2.5improvement = 0.026 per year for improved PM2.5 by each IQR = 1.79 μg/m3 reduction, 95% CI: 0.001, 0.05; βNO2improvement = 0.034 per year for improved NO2 by each IQR = 3.92 parts per billion [ppb] reduction, 95% CI: 0.01, 0.06) and CVLT (βPM2.5 improvement = 0.070 per year for improved PM2.5 by each IQR = 1.79 μg/m3 reduction, 95% CI: 0.02, 0.12; βNO2improvement = 0.060 per year for improved NO2 by each IQR = 3.97 ppb reduction, 95% CI: 0.005, 0.12) after adjusting for covariates. The respective associations with TICSm and CVLT were equivalent to the slower decline rate found with 0.9 to 1.2 and1.4 to 1.6 years of younger age and did not significantly differ by age, region, education, Apolipoprotein E (ApoE) e4 genotypes, or cardiovascular risk factors. The main limitations of this study include measurement error in exposure estimates, potential unmeasured confounding, and limited generalizability.
CONCLUSIONS: In this study, we found that greater improvement in long-term AQ in late life was associated with slower cognitive declines in older women. This novel observation strengthens the epidemiologic evidence of an association between air pollution and cognitive aging.

Introduction

A growing body of epidemiological evidence supports late-life exposure to ambient air pollutants as an important modifiable risk factor for dementia [1]. These human data converge with neurotoxicological studies that demonstrate increased early markers of neurodegenerative disease (accumulation of amyloid-β and phosphorylation of tau), changes in hippocampal neuronal morphology, and increased cognitive deficits in animals with inhaled exposures to particles [2-8]. Neuroimaging studies in humans have also reported associations between increased fine particulate matter (PM2.5; aerodynamic diameter <2.5 μm) and nitrogen dioxide (NO2) and smaller brain volumes in gray matter [9-16], including areas vulnerable to Alzheimer disease (AD) neuropathologies [17,18]. Despite the suggestive evidence for a possible continuum of air pollution neurotoxicity on brain aging processes [19], the reported associations between exposures and cognitive decline have been mixed [20].

Since National Ambient Air Quality Standards were first put into effect 50 years ago, significant reductions in air pollution levels have been seen throughout the US [21]. During the period of 2000 to 2010, annual averages of PM2.5 and NO2 decreased by 27% and 35%, respectively [22]. Long-term reduction in the ambient levels of these air pollutants have been linked with increased life expectancy [23], reduced mortality [24], and improved respiratory health (lung function growth, decreased bronchitic symptoms, and lower asthma incidence) [25-27].

Decreasing air pollution levels across the nation provide the ideal environmental context for a quasi-experimental approach [28] to studying the potential benefits of improved air quality (AQ) on maintaining brain health of older people. To the best of our knowledge, no previous studies have explored whether AQ improvement may be associated with cognitive function. We leveraged a nationwide cohort of community-dwelling older women with individual-level air pollution exposure estimates (1996 to 2012) and annual assessments of late-life cognitive function (2008 to 2018). We hypothesized that improved AQ, as indicated by reductions in PM2.5 and NO2, was associated with slower rate of cognitive decline in older women. We further explored whether the putative associations might differ by age, region, education, Apolipoprotein E (ApoE) e4 genotype, and cardiovascular risk factors.

Methods

Study design and population

We conducted a longitudinal study on a geographically diverse cohort of community-dwelling older women (N = 2,880; aged 74 to 92 years) enrolled in the Women’s Health Initiative (WHI) Memory Study (WHIMS)-Epidemiology of Cognitive Health Outcomes (WHIMS-ECHO) study. WHIMS-ECHO began in 2008 as an extension study of WHIMS [29]—an ancillary study to the Women’s Health Initiative hormone therapy (WHI-HT) trials (1993 to 2004). WHIMS participants (N = 7,479) were community-dwelling postmenopausal women without dementia (aged ≥65 years) who resided in the 48 contiguous US states and were recruited from more than 40 study sites located in 24 states and Washington, DC. WHIMS-ECHO participants received annual neuropsychological assessments via centralized telephone-administered cognitive interviews conducted by trained and certified staff. The analyses were restricted to women without prevalent dementia at WHIMS-ECHO enrollment and with follow-up visits and complete data on AQ measures and relevant covariates.

Our study did not employ a prospective protocol. Analyses were first planned and performed in April 2020, and before the submission, the completed manuscript was revised by 2 anonymous reviewers assigned by the WHI Publications and Presentations Committee. During the peer review process, we added a partially adjusted model in our main analyses and ad hoc analyses to explore the potential impact of nonlinear cognitive trajectory and nonlinear AQ improvement effects on cognitive trajectory slope. The Institutional Review Board at the University of Southern California reviewed and approved all study protocols. Written informed consent was obtained from all participants as part of WHI-HT, WHIMS, and WHIMS-ECHO studies. This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist).

Measures of general cognitive status and episodic memory

General cognitive status was assessed with modified Telephone Interview for Cognitive Status (TICSm), which is a widely used screening tool for cognitive impairment in older adults [30].The TICSm includes 16 cognitive test items from different cognitive function domains, including recall of a list of words, generating a list of nouns as quickly as possible, naming common items, and counting backwards. The TICSm score (0 to 50) was defined as the total number of correct responses, with test items with multiple parts contributing a corresponding number of points and higher scores indicating better cognitive functioning. Episodic memory, one of the most sensitive cognitive indicators for early detection of AD [31], was assessed by the telephone-based California Verbal Learning Test (CVLT) [32]. Participants were read a 16-item list of words from 4 semantically related categories and were instructed to immediately repeat back as many words as could be remembered. We used the modified version of the CVLT with 3 immediate free recall trials. The CVLT score was defined as the total number of correct responses across 3 learning trials (ranged from 0 to 48), with higher scores representing better performance. In the present study, we used all longitudinal data collected from telephone-based assessments until June 2018 [29].

Estimation of air pollution exposure

Participants’ residential addresses were prospectively collected at each WHI assessment since its inception in 1993, updated at least biannually, and then geocoded using standardized procedures [33]. The exact date of address change was used in analyses when available; otherwise, the date when the change in residence was ascertained was used. We used validated regionalized national universal kriging models with partial least squares regression of geographic covariates and US Environmental Protection Agency (EPA) monitoring data to estimate annual mean concentrations of PM2.5 (in μg/m3) and NO2 (in parts per billion [ppb]; a proxy of traffic-related air pollutants) at each of these residential addresses. Over 300 geographic covariates covering land use, vegetative index, proximity to features, etc., were used in the historical models for pre-1999 PM2.5 estimation or in the national models for post-1999 PM2.5 estimation (average cross-validation R2 = 0.88) [34,35]. Over 400 geographic covariates representing proximity and buffer measures as well as satellite-derived NO2 data were used to estimate NO2 (average cross-validation R2 = 0.85) [36]. These annual estimates were then aggregated to the 3-year average at the WHIMS-ECHO enrollment (referred to as recent exposure) and the corresponding 3-year average 10 years earlier (referred to as remote exposure), accounting for residential mobility within each 3-year time window. The individual-level measure of long-term AQ improvement over the 10-year period was defined as the reduction from remote to recent exposures (Fig 1). We focused on AQ improvement over the 10 years prior to recruitment in order to avoid methodological concerns on the temporality between the defined period of AQ improvement and the concurrently observed slower cognitive decline during the follow-up.

Fig 1

Flowchart of study population and demonstration of study timeline.

(A) Flowchart of study population. (B) Demonstration of study timeline.a aThe exposure time windows may vary slightly depending on each individual’s WHIMS-ECHO enrollment time. CVLT, California Verbal Learning Test; TICSm, modified Telephone Interview for Cognitive Status; WHI-CT (HT), Women’s Health Initiative-Clinical Trial (Hormone Therapy); WHIMS-ECHO, Women’s Health Initiative Memory Study-Epidemiology of Cognitive Health Outcomes.

Covariate data

Participants completed structured questionnaires at WHI inception to provide information on demographics (geographic region where participants resided, age, and race/ethnicity [non-Hispanic Black, non-Hispanic White, or others including Hispanic/Latino and missing]), socioeconomic factors (education, family income, and employment status), and lifestyle factors (smoking status, alcohol intake, and physical activity). Clinical characteristics included body mass index (BMI; calculated from measured height and weight), self-reported use of any postmenopausal hormone treatment, depressive symptoms (assessed using the Center for Epidemiologic Studies Depression Scale short form), and self-reported histories of cardiovascular diseases (CVDs; e.g., heart problems, problems with blood circulation, or blood clots), hypercholesterolemia, hypertension, and diabetes mellitus. Good reliability and validity of both the self-reported medical histories and the physical measures have been documented [37-39]. Lifestyle factors and clinical covariates (BMI; blood pressure; and incident CVD events) were also updated before the WHIMS-ECHO enrollment. Socioeconomic characteristics of residential neighborhoods were characterized using US Census tract-level residential data and estimated at both WHI inception and WHIMS-ECHO enrollment [40]. ApoE e4 genotype data were obtained for a subset of women (n = 1,611). Details on covariates are available in S1–S5 Texts.

Statistical analyses

We used linear mixed effect models to examine whether AQ improvement before WHIMS-ECHO enrollment was associated with average decline rates in the TICSm and CVLT trajectories during the follow-up. Each model included a product term of time and AQ improvement, with years since WHIMS-ECHO enrollment used as the timescale (S6 Text). To account for selective attrition over the WHIMS-ECHO follow-up, models were adjusted for time-varying propensity scores (S7 Text). Potential confounders included demographic variables, individual- and neighborhood-level socioeconomic characteristics, lifestyle factors, and clinical characteristics at the WHI inception. A random effect for the WHI clinic sites (n = 39, S1 Fig) and an indicator of WHIMS-ECHO enrollment year were also included in all models to control for spatial confounding and temporal trends, respectively.

Sensitivity analyses were conducted to evaluate the robustness of our findings. First, to address possible residual confounding due to temporal misspecification of potential confounders (lifestyle factors, neighborhood socioeconomic characteristics, and clinical attributes), we re-fitted the linear mixed effects models with adjustment of either the measures updated before WHIMS-ECHO enrollment or the changes in these relevant covariates since WHI inception. Second, we applied multiple imputation to address missing data on AQ measures or covariates (S8 Text). Third, to examine whether our findings could be explained by regression to the mean in AQ measures, we further adjusted for recent or remote exposures. Fourth, to explore whether our findings could be explained by cerebrovascular risk, we excluded women with prevalent or incident stroke during the WHIMS-ECHO follow-up. Fifth, we excluded incident dementia cases to further explore whether any observed associations with AQ improvement could be explained by underlying dementia risk (S9 Text). Sixth, we conducted additional analyses with interactions between the measures of AQ improvement and a quadratic term of follow-up year in the adjusted models to examine whether AQ improvement was associated with nonlinear changes in cognitive function. We then evaluated whether the associations between AQ improvement and linear trajectory slopes were sensitive to incorporating the nonlinear changes of cognitive function in the analyses with a quadratic term of follow-up year. Finally, we assessed the nonlinear AQ improvement effect on cognitive trajectory slope by examining the interaction between a quadratic term of AQ improvement and follow-up year.

We also explored whether the putative slower cognitive decline associated with improved AQ might differ by age, education, geographical region, ApoE e4 genotypes, and cardiovascular risk factors, using a product term of the AQ improvement indicator, follow-up time, and each potential effect modifier.

All statistical analyses were performed using R version 3.6.2 and SAS 9.4 for Windows. All tests were interpreted at the 0.05 significance level using a 2-sided alternative hypothesis.

Results

We excluded 346 women with prevalent dementia at WHIMS-ECHO enrollment or without follow-up visits, resulting in a sample of 2,534 women with at least 2 modified TICSm measures of general cognitive status (Fig 1). For the analyses on episodic memory decline, we further excluded 587 women without repeated measures of episodic memory assessed by CVLT. For both sets of analyses, we also excluded women with missing data on AQ measures or relevant covariates. This resulted in a final analytic sample of 2,232 women for the analyses on general cognitive status assessed by TICSm, a subset of which (n = 1,721) was used for the analyses on episodic memory assessed by CVLT (Fig 1).

Compared to the women excluded due to missing AQ measures or relevant covariates (Fig 1), women included in our analyses were younger with higher socioeconomic status and more likely to reside in the Northeast, self-identify as non-Hispanic White, and drink alcohol and have ApoE e4 genotype (Table 1). Compared to those excluded due to not having repeated CVLT measures (Fig 1), women with repeated CVLT measures were more likely to be younger, have higher education and income, and currently drink alcohol, but less likely to report having hypertension and carry the ApoE e4 genotype (S1 Table). Mean AQ improved significantly with reduced ambient levels for both PM2.5 (Mean ± SD: 13.3 ± 2.7 to 10.6 ± 2.0 μg/m3; p < 0.001) and NO2 (15.7 ± 7.2 to 10.4 ± 4.9 ppb; p < 0.001) over the 10 years before WHIMS-ECHO enrollment. Women residing in locations with initially high ambient air pollutants tended to experience greater AQ improvement for both PM2.5 (correlation = 0.67; p < 0.001) and NO2 (correlation = 0.80; p < 0.001) (S2 Table). Overall, non-Hispanic White women experienced less AQ improvement, while women who were older than 80, reported higher income, or resided in the Northeast and West experienced greater reductions in ambient PM2.5 and NO2, as compared to their counterparts (Table 2). Women with ApoE e4 genotype experienced less reduction in ambient PM2.5, while nondrinkers experienced less reduction in ambient NO2, as compared to their counterparts (Table 2).

Table 1

Distribution of population characteristics in the WHIMS-ECHO cohort, stratified by women included versus excluded.

	Analytic sample for TICSm analyses				Analytic sample for CVLT analyses
Characteristics	Study sample (N = 2,534)a	Included (N = 2,232)	Excluded (N = 302)a	p b	Study sample (N = 1,947)a	Included (N = 1,721)	Excluded (N = 226)a	p b
Region				<0.001				<0.001
Northeast	773 (30.5%)	718 (32.2%)	55 (18.2%)		593 (30.5%)	551 (32.0%)	42 (18.6%)
South	540 (21.3%)	443 (19.8%)	97 (32.1%)		402 (20.6%)	329 (19.1%)	73 (32.3%)
Midwest	618 (24.4%)	549 (24.6%)	69 (22.8%)		482 (24.8%)	428 (24.9%)	54 (23.9%)
West	603 (23.8%)	522 (23.4%)	81 (26.8%)		470 (24.1%)	413 (24.0%)	57 (25.2%)
Age				0.04				0.02
≤80 years	979 (38.6%)	879 (39.4%)	100 (33.1%)		808 (41.5%)	731 (42.5%)	77 (34.1%)
>80 years	1,555 (61.4%)	1,353 (60.6%)	202 (66.9%)		1,139 (58.5%)	990 (57.5%)	149 (65.9%)
Ethnicity				<0.001				<0.001
Black (not Hispanic)	160 (6.3%)	116 (5.2%)	44 (14.6%)		120 (6.2%)	86 (5.0%)	34 (15.0%)
White (not Hispanic)	2,262 (89.3%)	2,042 (91.5%)	220 (72.8%)		1,742 (89.5%)	1,576 (91.6%)	166 (73.5%)
Other or missing	112 (4.4%)	74 (3.3%)	38 (12.6%)		85 (4.4%)	59 (3.4%)	26 (11.5%)
Education				0.03				0.02
≤High school or GED	660 (26.1%)	564 (25.3%)	96 (32.2%)		476 (24.5%)	406 (23.6%)	70 (31.6%)
>High school but <4 years of college	975 (38.5%)	864 (38.7%)	111 (37.2%)		733 (37.7%)	651 (37.8%)	82 (36.9%)
≥4 years of college	895 (35.4%)	804 (36.0%)	91 (30.5%)		734 (37.8%)	664 (38.6%)	70 (31.5%)
Employment				0.69				0.72
Currently working	388 (15.4%)	348 (15.6%)	40 (13.7%)		310 (16.0%)	279 (16.2%)	31 (14.1%)
Not working	238 (9.4%)	210 (9.4%)	28 (9.6%)		188 (9.7%)	166 (9.6%)	22 (10.0%)
Retired	1,899 (75.2%)	1,674 (75.0%)	225 (76.8%)		1,443 (74.3%)	1,276 (74.1%)	167 (75.9%)
Income (US$)				0.03				0.02
<9,999	94 (3.7%)	74 (3.3%)	20 (6.6%)		67 (3.4%)	52 (3.0%)	15 (6.6%)
10,000 to 34,999	1,140 (45.0%)	1,000 (44.8%)	140 (46.4%)		849 (43.6%)	742 (43.1%)	107 (47.3%)
35,000 to 74,999	927 (36.6%)	824 (36.9%)	103 (34.1%)		741 (38.1%)	663 (38.5%)	78 (34.5%)
75,000 or more	242 (9.6%)	220 (9.9%)	22 (7.3%)		204 (10.5%)	186 (10.8%)	18 (8.0%)
Do not know	131 (5.2%)	114 (5.1%)	17 (5.6%)		86 (4.4%)	78 (4.5%)	8 (3.5%)
Smoking status				0.38				0.23
Never smoked	1,396 (55.6%)	1,239 (55.5%)	157 (55.9%)		1,074 (55.7%)	954 (55.4%)	120 (57.7%)
Past smoker	996 (39.6%)	890 (39.9%)	106 (37.7%)		763 (39.6%)	689 (40.0%)	74 (35.6%)
Current smoker	121 (4.8%)	103 (4.6%)	18 (6.4%)		92 (4.8%)	78 (4.5%)	14 (6.7%)
Alcohol use				0.004				0.002
Nondrinker	307 (12.2%)	261 (11.7%)	46 (16.0%)		227 (11.7%)	191 (11.1%)	36 (16.7%)
Past drinker	436 (17.3%)	372 (16.7%)	64 (22.3%)		313 (16.2%)	266 (15.5%)	47 (21.8%)
<1 drink per day	1,466 (58.2%)	1,315 (58.9%)	151 (52.6%)		1,150 (59.4%)	1,036 (60.2%)	114 (52.8%)
≥1 drink per day	310 (12.3%)	284 (12.7%)	26 (9.1%)		247 (12.8%)	228 (13.2%)	19 (8.8%)
Moderate or strenuous physical activities ≥20 minutes				0.25				0.17
No activity	1,387 (54.8%)	1,207 (54.1%)	180 (60.0%)		1,053 (54.1%)	917 (53.3%)	136 (60.7%)
Some activity	138 (5.5%)	124 (5.6%)	14 (4.7%)		102 (5.2%)	91 (5.3%)	11 (4.9%)
2 to 4 episodes/week	534 (21.1%)	481 (21.6%)	53 (17.7%)		413 (21.2%)	376 (21.8%)	37 (16.5%)
>4 episodes/week	473 (18.7%)	420 (18.8%)	53 (17.7%)		377 (19.4%)	337 (19.6%)	40 (17.9%)
BMI (kg/m 2 )				0.36				0.67
<25	701 (27.8%)	619 (27.7%)	82 (28.3%)		538 (27.7%)	476 (27.7%)	62 (28.4%)
25 to 29	931 (36.9%)	815 (36.5%)	116 (40.0%)		718 (37.0%)	633 (36.8%)	85 (39.0%)
≥30	890 (35.3%)	798 (35.8%)	92 (31.7%)		683 (35.2%)	612 (35.6%)	71 (32.6%)
Hypertension				0.96				0.56
No	1,646 (65.5%)	1,461 (65.5%)	185 (65.6%)		1,289 (66.7%)	1,152 (66.9%)	137 (64.9%)
Yes	868 (34.5%)	771 (34.5%)	97 (34.4%)		643 (33.3%)	569 (33.1%)	74 (35.1%)
Hypercholesterolemia				0.06				0.10
No	2,063 (82.6%)	1,855 (83.1%)	208 (78.5%)		1,595 (83.1%)	1,438 (83.6%)	157 (78.9%)
Yes	434 (17.4%)	377 (16.9%)	57 (21.5%)		325 (16.9%)	283 (16.4%)	42 (21.1%)
Diabetes				0.41				0.12
No	2,433 (96.1%)	2,143 (96.0%)	290 (97.0%)		1,876 (96.4%)	1,655 (96.2%)	221 (98.2%)
Yes	98 (3.9%)	89 (4.0%)	9 (3.0%)		70 (3.6%)	66 (3.8%)	4 (1.8%)
CVD history				0.25				0.30
No	2,143 (85.7%)	1,907 (85.4%)	236 (88.1%)		1,652 (86.0%)	1,476 (85.8%)	176 (88.4%)
Yes	357 (14.3%)	325 (14.6%)	32 (11.9%)		268 (14.0%)	245 (14.2%)	23 (11.6%)
Any prior postmenopausal hormone treatment				0.31				0.51
No	1,373 (54.2%)	1,218 (54.6%)	155 (51.5%)		1,052 (54.1%)	935 (54.3%)	117 (52.0%)
Yes	1,160 (45.8%)	1,014 (45.4%)	146 (48.5%)		894 (45.9%)	786 (45.7%)	108 (48.0%)
WHI hormone therapy assignment				0.75				0.94
CEE alone placebo	459 (18.1%)	400 (17.9%)	59 (19.5%)		346 (17.8%)	306 (17.8%)	40 (17.7%)
CEE alone	461 (18.2%)	403 (18.1%)	58 (19.2%)		345 (17.7%)	302 (17.5%)	43 (19.0%)
CEE+MPA placebo	832 (32.8%)	733 (32.8%)	99 (32.8%)		654 (33.6%)	578 (33.6%)	76 (33.6%)
CEE+MPA	782 (30.9%)	696 (31.2%)	86 (28.5%)		602 (30.9%)	535 (31.1%)	67 (29.6%)
ApoE c				0.06				0.04
e2/2+e2/3+e3/3	1,382 (77.5%)	1,239 (76.9%)	143 (83.1%)		1,097 (79.3%)	984 (78.5%)	113 (86.3%)
e2/4+e3/4+e4/4	401 (22.5%)	372 (23.1%)	29 (16.9%)		287 (20.7%)	269 (21.5%)	18 (13.7%)

aNumbers in the samples may not add up to total due to missing data.

bp-Values were calculated using chi-squared tests.

cNumbers in the samples with ApoE genotyping did not add up to the total due to missing data.

ApoE, Apolipoprotein E; BMI, body mass index; CEE, conjugated equine estrogens; CVD, cardiovascular disease; CVLT, California Verbal Learning Test; GED, general educational development; MPA, medroxyprogesterone acetate; NO2, nitrogen dioxide; PM2.5, fine particulate matter; SD, standard deviation; TICSm, modified Telephone Interview for Cognitive Status; WHI, Women’s Health Initiative; WHIMS-ECHO, Women’s Health Initiative Memory Study-Epidemiology of Cognitive Health Outcomes.

Table 2

Distribution of AQ measures by population characteristics in the WHIMS-ECHO cohort, 1998 to 2018.

	Analytic sample for TICSm analyses					Analytic sample for CVLT analyses
		AQ improvement in PM2.5 (μg/m3)a		AQ improvement in NO2 (ppb)a			AQ improvement in PM2.5 (μg/m3)a		AQ improvement in NO2 (ppb)a
Population characteristics	N	Mean ± SD	p b	Mean ± SD	p b	N	Mean ± SD	p b	Mean ± SD	p b
Overall	2,232	2.73 ± 1.63		5.27 ± 3.46		1,721	2.78 ± 1.68		5.34 ± 3.49
Region			<0.001		<0.001			<0.001		<0.001
Northeast	718	3.01 ± 0.95		5.72 ± 3.26		551	3.02 ± 0.92		5.73 ± 3.23
South	443	2.50 ± 1.28		4.92 ± 3.08		329	2.54 ± 1.26		5.02 ± 3.08
Midwest	549	2.15 ± 1.22		4.36 ± 2.32		428	2.12 ± 1.21		4.36 ± 2.25
West	522	3.18 ± 2.55		5.89 ± 4.60		413	3.35 ± 2.63		6.08 ± 4.73
Age			0.009		0.004			0.002		0.002
≤80 years	879	2.62 ± 1.50		5.00 ± 3.23		731	2.64 ± 1.54		5.03 ± 3.32
>80 years	1,353	2.81 ± 1.71		5.44 ± 3.58		990	2.89 ± 1.76		5.56 ± 3.60
Ethnicity			<0.001		<0.001			<0.001		<0.001
Black (not Hispanic)	116	3.28 ± 1.39		6.84 ± 2.67		86	3.49 ± 1.31		6.91 ± 2.64
White (not Hispanic)	2,042	2.68 ± 1.63		5.13 ± 3.47		1,576	2.72 ± 1.67		5.21 ± 3.51
Other	74	3.42 ± 1.84		6.42 ± 3.30		59	3.51 ± 1.94		6.46 ± 3.39
Education			0.04		0.09			0.06		0.48
≤High school or GED	564	2.64 ± 1.49		5.08 ± 3.22		406	2.65 ± 1.51		5.17 ± 3.32
>High school but <4 years of college	864	2.69 ± 1.77		5.19 ± 3.62		651	2.75 ± 1.82		5.34 ± 3.74
≥4 years of college	804	2.85 ± 1.57		5.47 ± 3.43		664	2.89 ± 1.63		5.44 ± 3.35
Employment			0.81		0.33			0.84		0.49
Currently working	348	2.78 ± 1.63		5.51 ± 3.52		279	2.81 ± 1.68		5.57 ± 3.62
Not working	210	2.76 ± 1.69		5.13 ± 3.64		166	2.84 ± 1.76		5.32 ± 3.77
Retired	1,674	2.72 ± 1.63		5.23 ± 3.42		1,276	2.77 ± 1.67		5.29 ± 3.43
Income (US$)			0.01		0.04			0.04		0.03
<9,999	74	2.73 ± 1.98		5.02 ± 3.93		52	2.72 ± 2.10		5.00 ± 3.97
10,000 to 34,999	1,000	2.64 ± 1.66		5.18 ± 3.54		742	2.73 ± 1.70		5.30 ± 3.50
35,000 to 74,999	824	2.78 ± 1.55		5.22 ± 3.24		663	2.79 ± 1.61		5.21 ± 3.37
75,000 or more	220	3.07 ± 1.73		5.95 ± 3.88		186	3.10 ± 1.76		6.10 ± 3.91
Do not know	114	2.55 ± 1.43		5.11 ± 2.88		78	2.48 ± 1.36		5.17 ± 2.80
Smoking status			0.98		0.66			0.80		0.67
Never smoked	1,239	2.74 ± 1.67		5.27 ± 3.38		954	2.80 ± 1.71		5.38 ± 3.44
Past smoker	890	2.73 ± 1.59		5.23 ± 3.56		689	2.75 ± 1.65		5.25 ± 3.57
Current smoker	103	2.76 ± 1.54		5.55 ± 3.52		78	2.83 ± 1.56		5.52 ± 3.42
Alcohol use			0.48		<0.001			0.54		0.006
Nondrinker	261	2.59 ± 1.72		4.60 ± 3.18		191	2.62 ± 1.78		4.63 ± 3.30
Past drinker	372	2.75 ± 1.67		5.37 ± 3.78		266	2.85 ± 1.71		5.52 ± 3.92
<1 drink per day	1,315	2.76 ± 1.60		5.46 ± 3.41		1,036	2.79 ± 1.65		5.49 ± 3.43
≥1 drink per day	284	2.71 ± 1.67		4.85 ± 3.36		228	2.79 ± 1.67		5.02 ± 3.35
Moderate or strenuous physical activities ≥20 minutes			0.82		0.25			0.74		0.21
No activity	1,207	2.71 ± 1.63		5.28 ± 3.41		917	2.78 ± 1.68		5.42 ± 3.51
Some activity	124	2.76 ± 1.24		5.56 ± 3.18		91	2.75 ± 1.23		5.58 ± 3.16
2 to 4 episodes/week	481	2.79 ± 1.68		5.38 ± 3.54		376	2.85 ± 1.74		5.40 ± 3.51
>4 episodes/week	420	2.72 ± 1.69		4.99 ± 3.56		337	2.72 ± 1.70		4.98 ± 3.51
BMI (kg/m 2 )			0.43		0.48			0.91		0.09
<25	619	2.80 ± 1.64		5.19 ± 3.37		476	2.81 ± 1.70		5.10 ± 3.34
25 to 29	815	2.74 ± 1.64		5.21 ± 3.25		633	2.76 ± 1.68		5.30 ± 3.29
≥30	798	2.68 ± 1.62		5.38 ± 3.72		612	2.78 ± 1.66		5.56 ± 3.79
Hypertension			0.82		0.52			0.63		0.31
No	1,461	2.73 ± 1.65		5.23 ± 3.52		1,152	2.77 ± 1.71		5.28 ± 3.54
Yes	771	2.75 ± 1.60		5.33 ± 3.33		569	2.81 ± 1.61		5.46 ± 3.38
Hypercholesterolemia			0.12		0.97			0.44		0.97
No	1,855	2.71 ± 1.65		5.26 ± 3.53		1,438	2.77 ± 1.70		5.34 ± 3.57
Yes	377	2.85 ± 1.56		5.27 ± 3.04		283	2.85 ± 1.56		5.34 ± 3.09
Diabetes			0.27		0.42			0.07		0.11
No	2,143	2.73 ± 1.64		5.25 ± 3.47		1,655	2.77 ± 1.68		5.31 ± 3.50
Yes	89	2.92 ± 1.50		5.56 ± 3.16		66	3.15 ± 1.48		6.02 ± 3.28
CVD history			0.95		0.94			0.95		0.83
No	1,907	2.74 ± 1.65		5.27 ± 3.52		1,476	2.78 ± 1.70		5.34 ± 3.58
Yes	325	2.73 ± 1.53		5.25 ± 3.04		245	2.78 ± 1.52		5.29 ± 2.94
Any prior postmenopausal hormone treatment			0.84		0.22			0.90		0.29
No	1,218	2.73 ± 1.44		5.35 ± 3.31		935	2.79 ± 1.47		5.42 ± 3.29
Yes	1,014	2.74 ± 1.84		5.17 ± 3.62		786	2.78 ± 1.90		5.24 ± 3.72
WHI hormone therapy assignment			0.13		0.02			0.03		0.09
CEE alone placebo	400	2.81 ± 1.74		5.24 ± 3.33		306	2.89 ± 1.84		5.35 ± 3.43
CEE alone	403	2.57 ± 1.65		4.80 ± 3.30		302	2.53 ± 1.69		4.88 ± 3.37
CEE+MPA placebo	733	2.77 ± 1.55		5.45 ± 3.53		578	2.84 ± 1.58		5.47 ± 3.46
CEE+MPA	696	2.75 ± 1.64		5.35 ± 3.52		535	2.79 ± 1.66		5.44 ± 3.61
ApoE			0.01		0.92			0.047		0.87
e2/2+e2/3+e3/3	1,239	2.81 ± 1.55		5.33 ± 3.46		984	2.82 ± 1.58		5.39 ± 3.45
e2/4+e3/4+e4/4	372	2.56 ± 1.77		5.31 ± 3.63		269	2.59 ± 1.88		5.35 ± 3.73

aRecent exposures were the 3-year average exposures estimated at the WHIMS-ECHO enrollment. Remote exposures were the 3-year average exposures estimated 10 years before the WHIMS-ECHO enrollment. AQ improvement was defined as reduction from the remote to recent exposures over the 10-year period.

bp-Values were calculated using ANOVA F-tests for mean exposures.

ApoE, Apolipoprotein E; AQ, air quality; BMI, body mass index; CEE, conjugated equine estrogens; CVD, cardiovascular disease; CVLT, California Verbal Learning Test; GED, general educational development; MPA, medroxyprogesterone acetate; NO2, nitrogen dioxide; PM2.5, fine particulate matter; ppb, parts per billion; SD, standard deviation; TICSm, modified Telephone Interview for Cognitive Status; WHI, Women’s Health Initiative; WHIMS-ECHO, Women’s Health Initiative Memory Study-Epidemiology of Cognitive Health Outcomes.

During a median 6.2 (interquartile range [IQR] = 5) years of follow-up, women had a median 7 (IQR = 5) interviews for TICSm tests and 6 (IQR = 3) interviews for CVLT tests. The means of the cognitive test scores did not differ much over the visits (S3 Table), while significant cognitive declines (TICSm slope = −0.42/year; 95% CI: −0.44, −0.40; CVLT slope = −0.59/year; 95% CI: −0.64, −0.54) were observed (Fig 2). Non-Hispanic Black women or women older than 80 had lower mean cognitive scores at baseline for both TICSm and CVLT (S4 Table). Women residing in the Midwest and who had higher education or income, were currently employed, currently drinking alcohol, did not have hypertension, and did not carry the ApoE e4 genotype had higher mean cognitive scores on both TICSm and CVLT at baseline, compared to their counterparts (S4 Table). The distributions of cognitive scores at the last visit were similar across these population characteristics, except that some differences were no longer significant (S4 Table).

Fig 2

Estimated cognitive trajectory over time with different levels of AQ improvement in WHIMS-ECHO cohort.

(A) Associations on general cognitive ability decline (N = 2,232). (B) Associations on episodic memory decline (N = 1,721). Estimated TICSm score (panel A) or CVLT score (panel B) change over time for low (25th percentile), median, and high (75th percentile) level of AQ improvement in PM2.5 or NO2 in the WHIMS-ECHO cohort. The estimated TICSm scores or CVLT scores were calculated using parameter estimates derived from Model III of Table 3, which were adjusted for spatial random effect, WHIMS-ECHO enrollment year, age, follow-up year, age interaction with follow-up year, time-varying propensity scores, demographic variables (geographic region and race/ethnicity), socioeconomic factors (education, income, and employment status) and neighborhood characteristics, lifestyle factors (smoking, drinking, and physical activities), prior hormone use, hormone therapy assignment, cardiovascular risk factors (hypertension, diabetes, and hypercholesterolemia), depression, BMI, and CVD histories. AQ, air quality; BMI, body mass index; CVD, cardiovascular disease; CVLT, California Verbal Learning Test; NO2, nitrogen dioxide; PM2.5, fine particulate matter; TICSm, modified Telephone Interview for Cognitive Status; WHIMS-ECHO, Women’s Health Initiative Memory Study-Epidemiology of Cognitive Health Outcomes.

Table 3

Summary of the associations between AQ improvement and cognitive declines in the WHIMS-ECHO cohort.

(A) Associations with declines in general cognitive ability (N = 2,232)
	AQ improvement in PM 2.5 a			AQ improvement in NO 2 a
Models	β b	95% CI	p c	β b	95% CI	p c
Model I d	0.026	0.001, 0.05	0.04	0.034	0.01, 0.06	0.006
Model II e	0.026	0.002, 0.05	0.04	0.034	0.01, 0.06	0.005
Model III f	0.026	0.001, 0.05	0.04	0.034	0.01, 0.06	0.005
(B) Associations with declines in episodic memory (N = 1,721)
	AQ improvement in PM 2.5 a			AQ improvement in NO 2 a
Models	β b	95% CI	p c	β b	95% CI	p c
Model I d	0.072	0.02, 0.13	0.01	0.059	0.004, 0.11	0.03
Model II e	0.071	0.02, 0.13	0.01	0.060	0.006, 0.12	0.03
Model III f	0.070	0.02, 0.12	0.01	0.060	0.005, 0.12	0.03

aRecent exposures were the 3-year average exposures estimated at the WHIMS-ECHO enrollment. Remote exposures were the 3-year average exposures estimated 10 years before the WHIMS-ECHO enrollment. AQ improvement was defined as reduction from the remote to recent exposures over the 10-year period.

bβ (95% CI) = regression coefficient (95% CI) estimating the increase in TICSm score or CVLT score per year for each IQR increase of AQ improvement (IQRPM2.5 = 1.79 μg/m3 for both analytic samples; IQRNO2 = 3.92 ppb for TICSm analytic sample and 3.97 ppb for CVLT analytic sample). Positive coefficients represent slower decline associated with greater AQ improvement.

cp-Values were calculated using Wald t tests.

dModel I: adjusted for spatial random effect, WHIMS-ECHO enrollment year, age, follow-up year, age interaction with follow-up year, and time-varying propensity scores.

eModel II: adjusted for those in Model I, as well as demographic variables (geographic region and race/ethnicity), socioeconomic factors (education, income, and employment status) and neighborhood socioeconomic characteristics, and lifestyle factors (smoking, drinking, and physical activities).

fModel III: adjusted for those in Model II, as well as prior hormone use, hormone therapy assignment, cardiovascular risk factors (hypertension, diabetes, and hypercholesterolemia), depression, BMI, and CVD histories.

AQ, air quality; BMI, body mass index; CVD, cardiovascular disease; CVLT, California Verbal Learning Test; IQR, interquartile range; NO2, nitrogen dioxide; PM2.5, fine particulate matter; TICSm, modified Telephone Interview for Cognitive Status; WHIMS-ECHO, Women’s Health Initiative Memory Study-Epidemiology of Cognitive Health Outcomes.

Residing in locations with greater AQ improvement was associated with slower rates of decline in both general cognitive status and episodic memory (Table 3). Based on fully adjusted models (Table 3, Model III), each IQR increment of improved AQ in PM2.5 and NO2 (IQRPM2.5 = 1.79 μg/m3 for both analytic samples; IQRNO2 = 3.92 ppb for TICSm and 3.97 ppb for CVLT analyses) was associated with diminished cognitive declines over time by 0.026 to 0.034/year in TICSm (PM2.5: (β = 0.026/year, 95% CI: 0.001, 0.05; NO2: β = 0.034/year; 95% CI: 0.01, 0.06) and by 0.060 to 0.070/year in CVLT (PM2.5: β = 0.070/year, 95% CI: 0.02, 0.12; NO2: β = 0.060/year; 95% CI: 0.005, 0.12). These putative benefits suggested by the respective associations with TICSm and CVLT were equivalent to slower cognitive declines in women who were 0.9 to 1.2 years and 1.4 to 1.6 years younger at WHIMS-ECHO enrollment.

The associations between greater AQ improvement and slower cognitive declines remained robust in sensitivity analyses adjusting for potential confounders updated prior to the WHIMS-ECHO enrollment or temporal changes in relevant covariates from WHI inception (S5 Table). Using multiple imputation to include women with missing air pollution and covariate data, the associations were slightly attenuated, but remained statistically significant (S6 Table). The associations between greater AQ improvement and slower cognitive declines were strengthened in models further adjusting for recent or remote exposures (S7 Table). The results were largely unchanged after excluding prevalent or incident stroke cases (S8 Table). Excluding incident dementia cases (n = 398) resulted in a 41% to 81% reduction in the strength of the associations with slower declines in general cognitive status, which were no longer significant. For episodic memory decline, there was a 17% to 22% reduction in effect estimates, which remained statistically significant (S8 Table).

We did not find significant associations between AQ improvement and quadratic change in cognitive function (all p-values > 0.10, S9 Table). With the quadratic term of follow-up year included in models, the associations between AQ improvement and TICSm trajectory slope were similar to the estimates in the main analyses, while the estimated associations with CVLT trajectory slopes were slightly attenuated for both PM2.5and NO2 improvement (S10 Table). Except for some evidence supporting the quadratic term for PM2.5 improvement on TICSm trajectory slope (p = 0.04 for the interaction between the quadratic term of PM2.5 improvement and follow-up year, S11 Table), the overall results did not suggest nonlinear effects of AQ improvement on cognitive trajectory slopes (S11 Table).

We found no statistical evidence to suggest that the observed associations substantially differed by age, education, geographic region, ApoE e4 genotypes, or common cardiovascular risk factors after adjusting for multiple comparisons (false discovery rate corrected p-values > 0.05 for all tests, Figs 3 and 4).

Fig 3

Estimated associationsa between AQ improvementb and cognitive ability declinec, stratified by population characteristics.

The bars and whisker represent the regression coefficient beta and corresponding 95% CIs. aAssociation was represented by beta, the regression coefficient estimating the increase in TICSm score per year for each IQR increase of AQ improvement (IQRPM2.5 = 1.79 μg/m3; IQRNO2 = 3.92 ppb), adjusting for spatial random effect, WHIMS-ECHO enrollment year, age, follow-up year, age interaction with follow-up year, time-varying propensity scores, demographic variables (geographic region and race/ethnicity), socioeconomic factors (education, income, and employment status) and neighborhood characteristics, lifestyle factors (smoking, drinking, and physical activities), prior hormone use, hormone therapy assignment, cardiovascular risk factors (hypertension, diabetes, and hypercholesterolemia), depression, BMI, and CVD histories. bRecent exposures were the 3-year average exposures estimated at the WHIMS-ECHO enrollment. Remote exposures were the 3-year average exposures estimated 10 years before the WHIMS-ECHO enrollment. AQ improvement was defined by reduction from remote to recent exposures over the 10-year period. cp-Value was calculated using Wald t test for the interaction between AQ improvement and each subgroup unadjusted for multiple comparison. After controlling for multiple comparison using Benjamini–Hochberg approach, false discovery rate corrected p-values > 0.05 for all interaction tests. ApoE, Apolipoprotein E; AQ, air quality; BMI, body mass index; CVD, cardiovascular disease; GED, general educational development; IQR, interquartile range; NO2, nitrogen dioxide; PM2.5, fine particulate matter; ppb, parts per billion; TICSm, modified Telephone Interview for Cognitive Status; WHIMS-ECHO, Women’s Health Initiative Memory Study-Epidemiology of Cognitive Health Outcomes.

Fig 4

Estimated associationsa between AQ improvementb and episodic memory declinec, stratified by population characteristics.

The bars and whisker represent the regression coefficient beta and corresponding 95% CIs. aAssociation was represented by beta, the regression coefficient estimating the increase in CVLT score per year for each IQR increase of AQ improvement (IQRPM2.5 = 1.79 μg/m3; IQRNO2 = 3.97 ppb), adjusting for spatial random effect, WHIMS-ECHO enrollment year, age, follow-up year, age interaction with follow-up year, time-varying propensity scores, demographic variables (geographic region and race/ethnicity), socioeconomic factors (education, income, and employment status) and neighborhood characteristics, lifestyle factors (smoking, drinking, and physical activities), prior hormone use, hormone therapy assignment, cardiovascular risk factors (hypertension, diabetes, and hypercholesterolemia), depression, BMI, and CVD histories. bRecent exposures were the 3-year average exposures estimated at the WHIMS-ECHO enrollment. Remote exposures were the 3-year average exposures estimated 10 years before the WHIMS-ECHO enrollment. AQ improvement was defined by reduction from remote to recent exposures over the 10-year period. cp-Value was calculated using Wald t test for the interaction between AQ improvement and each subgroup unadjusted for multiple comparison. After controlling for multiple comparison using Benjamini–Hochberg approach, false discovery rate corrected p-values > 0.05 for all interaction tests. ApoE, Apolipoprotein E; AQ, air quality; BMI, body mass index; CVD, cardiovascular disease; CVLT, California Verbal Learning Test; GED, general educational development; IQR, interquartile range; NO2, nitrogen dioxide; PM2.5, fine particulate matter; WHIMS-ECHO, Women’s Health Initiative Memory Study-Epidemiology of Cognitive Health Outcomes.

Discussion

In this geographically diverse cohort of community-dwelling older women with up to 20 years of follow-up, we found that living in locations with 10-year improvements in ambient AQ in late life was associated with slower cognitive declines. The estimated associations, equivalent to slower declines in general cognitive status observed in women 0.9 to 1.2 years younger or in episodic memory observed in women 1.4 to 1.6 years younger, remained after adjusting for sociodemographics (age; geographic region; race/ethnicity; education; income; employment status; and neighborhood socioeconomic characteristics), lifestyle factors (smoking; alcohol; and physical activity), or clinical characteristics (BMI; depressive symptoms; diabetes; hypercholesterolemia; hypertension; CVD; and hormone therapy). The findings were robust in our sensitivity analyses accounting for various sources of spatiotemporal confounding and were largely unchanged after excluding women with prevalent or incident stroke. Excluding incident dementia cases greatly reduced the associations with slower declines in general cognitive status, but the associations with slower decline in episodic memory were modestly attenuated and remained statistically significant. There was no strong evidence that the slower declines in cognition associated with improved AQ differed by age, education, geographic region, APoE e4 genotype, or cardiovascular risk factors. To the best of our knowledge, this study provides the first epidemiologic evidence supporting the potential benefit of improved AQ on slowing cognitive aging.

To the best of our knowledge, our study adds novel epidemiologic data strengthening the evidence between late-life exposure to ambient air pollution and cognitive decline. Evidence from epidemiological and neurotoxicological studies point to a possible continuum of air pollution neurotoxicity on brain aging. Within the WHIMS suite of studies, we have found that PM2.5 is associated with progression of gray matter atrophy in brain areas vulnerable to AD [18,41], episodic memory decline [41,42], and increased risk of clinically significant cognitive impairment [43], demonstrating this continuum in older women. In the ALzheimer and FAmilies cohort, investigators also found that higher exposure to PM was associated with lower cortical thickness in brain regions linked to AD in middle-aged men and women [17]. However, longitudinal epidemiological studies investigating the associations of cognitive declines with PM2.5 and NO2 have produced mixed results, including no associations [44-49], significant adverse effects [41,42,50-52], and significant positive associations (suggesting that PM2.5 improved cognition function) [53]. In the present study, we found that improved AQ was significantly associated with slower declines in both general cognitive status and episodic memory. Although these findings alone do not prove causality, using a quasi-experimental design, our novel findings along with other supportive evidence from human studies and animal models strengthen the evidence of late-life exposure to ambient air pollution in its contribution to the progression of cognitive aging.

Our study findings have several important public health implications. First, we found that slower cognitive decline was associated with long-term reduction in ambient PM2.5 and NO2 levels. In the Children’s Health Study, investigators also showed that the benefits of AQ improvement in both PM2.5 and NO2 was associated with improved respiratory health [25-27]. These findings may imply that the observed health benefits of AQ improvement are due to the overall reduced ambient air pollution levels, rather than driven by specific control programs to mitigate either PM2.5 or NO2 in the US. Second, although the Clean Air Act mandates that the EPA sets AQ standards that provide a safe margin for susceptible populations [54], our analyses (Fig 3) revealed similar associations comparing subgroups of older women. This observation suggests the resulting benefit of slower cognitive decline associated with AQ improvement in late life may be universal in older women, including those already at greater risk for cognitive decline (e.g., women with high-risk cardiovascular profiles; ApoE e4 carriers). Third, the EPA’s projection that the AQ and health benefits achieved as a result of the Clean Air Act Amendments of 1990 are valued at approximately US$2 trillion in 2020 [55] was likely underestimated as it did not include the assessment of brain health, which costs the US economy US$159 to US$215 billion [56]. Fourth, our findings call for future studies to determine whether risk reductions may still be seen with improvements at lower-exposure levels. Increased dementia risks associated with low-level exposures had been reported in Sweden and Canada (range of mean PM2.5: 7.6 to 10.4 μg/m3; range of mean NO2: 12.1 to 16.2 ppb) [57-60]. In a subset of WHIMS participants, we previously found that exposure to late-life PM2.5 at levels below the current EPA regulatory standard of 12 μg/m3 was associated with gray matter atrophy in brain areas vulnerable to AD [18]. Therefore, it is imperative to know whether further improvement in low-exposure air pollution levels already in compliance with the current standard still translate to slower cognitive decline. Last, our findings may provide the impetus for future research to consider how AQ improvement could potentially benefit brain development, as studies have found that higher levels of air pollution may hinder cognitive development in children [61,62].

The underlying neurobiological processes driving the observed slower cognitive declines associated with improved AQ are unclear. In our sensitivity analyses excluding women with stroke, the slower cognitive declines with improved AQ were largely unchanged, suggesting that reducing clinical stroke in late life may not make significant contributions to the observed associations. Neuroimaging studies have not shown consistent associations between air pollution and MRI-based measurements of subclinical cerebrovascular disease [9,11,63-65]. By contrast, excluding dementia cases resulted in a substantial attenuation in the estimated associations with TICSm declines, suggesting that the putative benefit of AQ improvement on slowing declines in general cognitive status may be operating by slowing the neuropathological processes near the clinical stage. On the other hand, there was a modest attenuation in the estimated associations with CVLT declines after excluding dementia cases, suggesting the observed associations of slower episodic memory decline with AQ improvement were only partly explained by the underlying neuropathological processes leading to clinical dementia. The remaining association observed among women without dementia implied the possibility of neuroprotective mechanisms underlying the putative benefits on brain health associated with improved AQ. Put together, these results point to the possibility that AQ improvement may benefit the continuum of pathological brain aging. Longitudinal studies with high-quality data on PET scan and fluid-based biomarkers are needed to better understand the underlying neuropathological processes amendable to improved AQ in late life, such as preserving brain volume or maintaining function of neural networks.

We acknowledge several study limitations. First, cognition was assessed using telephone-based interviews rather than the “gold standard” method of face-to-face administration. However, telephone-based cognitive assessment has been shown to be both reliable and valid and may improve study validity by increasing retention and data completeness [66]. Second, we estimated the ambient levels of air pollution at residential locations and detailed information on time-activity patterns were not available. However, the use of individual-level estimates of reduced exposures to ambient air pollutants is appropriate for studying the public health benefits [23,25-27] since these pollutants are regulated by the EPA. Third, the errors in predicting the air pollution estimates may have varied throughout the 10-year period and may have contributed uncertainty to our analyses. Fourth, we could not completely rule out unmeasured confounding by other environmental factors (e.g., noise and green space) with longitudinal changes that may be concurrent with improved AQ. However, noise levels have been increasing [67], and green space has been decreasing over time due to increasing urbanization [68], so it is unlikely they would contribute to the health benefits of long-term AQ improvement. Fifth, regression to the mean may be a concern due to measuring changes in AQ across only 2 time periods, which may have attenuated the estimated associations between AQ improvement and cognitive decline, as suggested by the results of our sensitivity analyses further adjusting for either recent or remote exposures (S7 Table). Sixth, because our study sample came from a nonrandom selective process (Fig 1), we could not completely rule out the possibility of selection biases. Last, our findings cannot be generalizable to men or younger women.

Our study had several major methodological strengths. First, the geographic diversity coupled with the extended longitudinal follow-up provided an ideal environmental context to explore the health benefits of AQ improvement across the US. Second, the estimated benefits were based on within-cohort comparisons, instead of cross-cohort comparisons done in previous studies [25-27], greatly reducing possible confounding stemming from between-cohort differences [69]. Third, using individual-level estimates of ambient air pollution to define improved AQ further reduces the spatial confounding arising from using county-/city-/community-level estimates as was done in previous studies [23-27]. Last, our analyses also accounted for different sources of spatiotemporal confounding, such as adjustment of WHIMS-ECHO enrollment year, including a random effect of clinic centers, and further adjusting for temporal changes in relevant covariates that may correlate with AQ improvement.

In conclusion, we found that long-term AQ improvement during late life was robustly associated with slower rates of cognitive declines among older women. The associations were similar for both PM2.5 and NO2 and did not differ by age, geographic region, education, or cardiovascular risk factors. Future studies are needed to improve our understanding on the underlying neuropathological processes that may be amendable by reducing exposure to late-life ambient air pollution.

STROBE Statement—Checklist of items that should be included in reports of cohort studies.

STROBE, Strengthening the Reporting of Observational Studies in Epidemiology.

(DOCX)

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Covariates assessed at WHI inception.

WHI, Women’s Health Initiative.

(DOCX)

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Assessment of US Census tract-level socioeconomic characteristics of residential neighborhood.

(DOCX)

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Assessment of covariates at the WHIMS-ECHO enrollment.

WHIMS-ECHO, Women’s Health Initiative Memory Study-Epidemiology of Cognitive Health Outcomes.

(DOCX)

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Assessment of longitudinal changes in covariates from the WHI inception to WHIMS-ECHO enrollment.

WHI, Women’s Health Initiative; WHIMS-ECHO, Women’s Health Initiative Memory Study-Epidemiology of Cognitive Health Outcomes.

(DOCX)

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APoE genotype data.

APoE, Apolipoprotein E.

(DOCX)

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Equations of 3-level linear mixed effect models.

(DOCX)

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Time-varying propensity score approach to adjust for selective attrition due to loss to follow-up.

(DOCX)

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Multiple imputation.

(DOCX)

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Ascertainment of probable dementia.

(DOCX)

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Map of US with region, state, and clinic sites of WHIMS participants.

The direct link to the base layer of the map used in this figure: https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/USA_States_Generalized/FeatureServer. Maps were created using ArcGIS software by Esri. ArcGIS and ArcMap are the intellectual property of Esri and are used herein under license. Copyright Esri. All rights reserved. For more information about Esri software, please visit www.esri.com. WHIMS, Women’s Health Initiative Memory Study.

(TIFF)

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Comparing study samples with repeated CVLT measures versus excluded due to no repeated CVLT measures.

CVLT, California Verbal Learning Test.

(DOCX)

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Pearson correlations between AQ measures.

AQ, air quality.

(DOCX)

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Distribution of cognitive test scores by visit.

(DOCX)

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Distribution of cognitive scores at baseline and last visits by population characteristics.

(DOCX)

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Summary of sensitivity analyses for the associations between AQ improvements and cognitive decline, with adjustment of covariates assessed at the WHIMS-ECHO enrollment or Changes from WHI inception to WHIMS-ECHO enrollment.

AQ, air quality; WHI, Women’s Health Initiative; WHIMS-ECHO, Women’s Health Initiative Memory Study-Epidemiology of Cognitive Health Outcomes.

(DOCX)

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Summary of the associations between AQ improvements and cognitive decline, with missing AQ measures and covariates imputed using multiple imputation.

AQ, air quality.

(DOCX)

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Summary of the associations between AQ measures and cognitive decline, with single exposure or multiple exposures in one model.

AQ, air quality.

(DOCX)

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Summary of the associations between AQ improvements and cognitive decline, excluding women with dementia or stroke.

AQ, air quality.

(DOCX)

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Summary of the associations between AQ improvement and quadratic term of cognitive decline.

AQ, air quality.

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Summary of the associations between AQ improvement and cognitive declines, with the nonlinear change in cognitive trajectories.

AQ, air quality.

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Summary of the associations with cognitive trajectory slopes, evaluating nonlinear associations with AQ improvement.

AQ, air quality.

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26 Feb 2021

Dear Dr Younan,

Thank you for submitting your manuscript entitled "Association of Air Quality Improvement with Slower Decline of Cognitive Function in Older Women" for consideration by PLOS Medicine.

Your manuscript has now been evaluated by the PLOS Medicine editorial staff as well as by an academic editor with relevant expertise and I am writing to let you know that we would like to send your submission out for external peer review.

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Feel free to email us at plosmedicine@plos.org if you have any queries relating to your submission.

Kind regards,

Dr Raffaella Bosurgi

Executive Editor

PLOS Medicine

27 Aug 2021

Dear Dr. Younan,

Thank you very much for submitting your manuscript "Association of Air Quality Improvement with Slower Decline of Cognitive Function in Older Women" (PMEDICINE-D-21-00961R1) for consideration at PLOS Medicine.

Your paper was evaluated by three independent reviewers, including a statistical reviewer, and was discussed among all the editors here and with an Academic Editor with relevant expertise. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.

In addition, we request that you upload any figures associated with your paper as individual TIF or EPS files with 300dpi resolution at resubmission; please read our figure guidelines for more information on our requirements: http://journals.plos.org/plosmedicine/s/figures. While revising your submission, please upload your figure files to the PACE digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at PLOSMedicine@plos.org.

We expect to receive your revised manuscript by Sep 17 2021 11:59PM. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

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Your article can be found in the "Submissions Needing Revision" folder.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

We look forward to receiving your revised manuscript.

Sincerely,

Louise Gaynor-Brook, MBBS PhD

Associate Editor

PLOS Medicine

plosmedicine.org

-----------------------------------------------------------

Comments from the Academic Editor:

This is a very good attempt at trying to relate improvement in environmental pollution with cognitive decline. They have tried to control demographic, lifestyle and clinical factors. It is very likely that not all confounding factors can be accounted for in such a study. The question that comes up is, do the people living in an environment that is improving differ systematically from those who live in an environment that is not? One can account for edu and income, but is that enough? Do they have a generally healthy lifestyle? Do they exercise more? Do they eat healthily? Some of these are difficult to control for?

The other issue is that the improvement in the environment occurred over the 10 years prior to the recruitment. Has that improvement continued through the study? Did other areas catch up?

Requests from the editors:

General comments:

Throughout the paper, please adapt reference call-outs to the following style: "... Alzheimer’s disease (AD) neuropathologies [17,18].” (noting the absence of spaces within the square brackets).

Please avoid using ‘average’ - please specify.

Please replace "subject" with participant, patient, individual, or person.

Thank you for providing a data availability statement. PLOS Medicine requires that the de-identified data underlying the specific results in a published article be made available, without restrictions on access, in a public repository or as Supporting Information at the time of article publication, provided it is legal and ethical to do so. If the data will be freely available upon request, please state the owner of the data set and specific contact information for data requests (web or email address). Please note that a study author cannot be the contact person for the data.

Title: Please revise your title according to PLOS Medicine's style. Your title must be nondeclarative. Please place the study design in the subtitle (ie, after a colon). We suggest “Association between air quality improvement and changes in cognitive function in community-dwelling older women: A longitudinal cohort study“

Abstract:

Abstract Background: Please revise to “Air quality (AQ) has been associated with…”, and please temper assertions of primacy by adding “to the best of our knowledge” or similar. The final sentence should clearly state the study question.

Abstract Methods and Findings:

Please provide brief demographic details of the study population (e.g. sex, age, ethnicity, etc)

Please clarify what is represented by the units used for the numbers presented in the abstract e.g. (βPM2.5=0.026/year per IQR=1.79 µg/m3). It may be easier to describe this.

Please revise the sentence beginning “ Individual-level improved AQ calculated…”

Line 57 - Please make clear that the estimates were 3-year averages

Please specify the important dependent variables that are adjusted for in the analyses.

Please clarify whether the results presented are adjusted analyses, and please specify the comparison group.

Please define ppb at first use

In the last sentence of the Abstract Methods and Findings section, please describe 2-3 of the main limitation(s) of the study's methodology."

Abstract Conclusions:

Please begin your Abstract Conclusions with "In this study, we observed ..." or similar, to summarize the main findings from your study, and expand a little on the implications of your study without overstating your conclusions.

Author Summary:

At this stage, we ask that you include a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract. Please see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary

In the final bullet point of ‘What Do These Findings Mean?’, please describe the main limitations of the study in non-technical language.

Introduction:

Please indicate whether your study is novel and how you determined that (being careful to temper assertions of primacy by adding “to the best of our knowledge” or similar). If there has been a systematic review of the evidence related to your study, please refer to and reference that review and indicate whether it supports the need for your study.

Methods:

Did your study have a prospective protocol or analysis plan? Please state this (either way) early in the Methods section. If a prospective analysis plan (from your funding proposal, IRB or other ethics committee submission, study protocol, or other planning document written before analyzing the data) was used in designing the study, please include the relevant prospectively written document with your revised manuscript as a Supporting Information file to be published alongside your study, and cite it in the Methods section. A legend for this file should be included at the end of your manuscript. If no such document exists, please make sure that the Methods section transparently describes when analyses were planned, and if/when reported analyses differed from those that were planned. Changes in the analysis-- including those made in response to peer review comments-- should be identified as such in the Methods section of the paper, with rationale. If a reported analysis was performed based on an interesting but unanticipated pattern in the data, please be clear that the analysis was data-driven.

Thank you for providing your STROBE guideline. Please add the following statement, or similar, to the Methods: "This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist)." When completing the checklist, please use section and paragraph numbers, rather than page numbers which will likely no longer correspond to the appropriate sections after copy-editing.

Results:

Please incorporate S1 Table into your Results section as Table 1, showing the baseline characteristics of the study population.

Line 214 - please clarify what is represented by ‘±2.7’ (presumably SD)

Line 230 - please be careful to avoid causative language; please revise sentence beginning “Older women residing…” to “Residing in locations with greater AQ improvement was associated with slower rates of decline... “ or similar

Line 232 - please revise sentence beginning “For each IQR increment of improved AQ…” to “Each IQR increment … was associated with…“ or similar

Discussion:

Line 347 - please temper assertions of primacy by adding “to the best of our knowledge” or similar. Please also revise the use of ‘causal’ on lines 347 and 362; we suggest ‘strengthen the evidence' or similar.

Figures:

Please consider avoiding the use of red and green together in order to make your figure more accessible to those with colour blindness (e.g. Fig 2).

Please indicate in the figure caption the meaning of the bars and whiskers in Figure 3.

Please define all abbreviations used in the figure legend of each figure.

Tables:

Please define all abbreviations used in the table legend of each table.

When a p value is given, please specify the statistical test used to determine it in the table legend.

Table 2, S3 Table - Please also provide the unadjusted analyses/models.

S5 Table - please clarify how the models differ in the table legend.

References:

Please ensure that journal name abbreviations match those found in the National Center for Biotechnology Information (NCBI) databases, and are appropriately formatted and capitalised.

Please also see https://journals.plos.org/plosmedicine/s/submission-guidelines#loc-references for further details on reference formatting.

Comments from the reviewers:

Reviewer #1: This study attempts to leverage a nationwide cohort of community dwelling older women with individual-level air pollution exposure estimates (1996-2012) and annual assessments of late-life cognitive function (2008−2018). The authors hypothesise that improved AQ, as indicated by reductions in PM2.5 and NO2, is associated with slower rate of cognitive decline in older women.

Comments:

The STROBE checklist has been suitably provided in the supplementary material.

"We conducted a longitudinal study on a geographically diverse cohort of community dwelling older women (N=2880; aged 74-92) enrolled in the Women's Health Initiative (WHI) Memory Study (WHIMS)-Epidemiology of Cognitive Health Outcomes (WHIMS-ECHO) study ... This resulted in a final analytic sample of 2232 women for the analyses on general cognitive status, a subset of which (n=1721) was used for the analyses on episodic memory (Fig 1A)."

Can the authors please comment on whether they believe this cohort to be representative of the wider population? I.E. Are there any potential causes of bias that should be highlighted?

"WHIMS-ECHO participants received annual neuropsychological assessments via centralized telephone-administered cognitive interviews conducted by trained and certified staff."

Can the authors please discuss the potential for self-reporting bias in the study limitations?

It is noted that, with respect to covariates: "Good reliability and validity of both the self-reported medical histories and the physical measures have been documented (37-39)."

"Participants were asked 16 questions with items assessing several cognitive functions. The TICSm score (0-50) was defined as the total number of correct responses, with higher scores indicating better cognitive functioning."

Can these questions and scoring computation please be provided in the supplementary material?

"In the present study, we used all longitudinal data collected from telephone based assessments until June 2018 (29)."

Can this be extended to 2020?

"We used regionalized universal kriging models to estimate annual concentrations (1996-2012) of fine particulate matter (PM2.5) and nitrogen dioxide (NO2) at residential locations. Estimates were averaged over 3-year periods immediately preceding (recent exposure) and 10 years prior to (remote exposure) WHIMS ECHO enrollment."

Did the authors consider including a more current AQ assessment, i.e. after enrolment, as well as these two time points?

"We used linear mixed effect models to examine whether AQ improvement before WHIMS-ECHO enrollment was associated with average decline rates in the TICSm and CVLT trajectories during the follow-up".

This is a technically appropriate methodology, assuming linearity holds.

Overall, this is a clearly written article, with excellent communication of information via figures.

The authors have included an extensive array of potential confounders within the model, and completed a thorough set of sensitivity analyses demonstrating the robustness of the study outcomes.

Furthermore, the study limitations have been acknowledged suitably within the discussion section of the manuscript.

Reviewer #2: The study of Younan et al examines whether air pollution improvement of NO2 and PM2.5 over a 10-years period, is associated with cognitive improvement in elderly women from US. They used data from 2,000 women enrolled in the Women's Health Initiative Memory Study-Epidemiology of Cognitive Health Outcomes who provided on general cognitive status and episodic memory decline. Authors observed that greater air quality improvement for NO2 and PM2.5 was associated with slower decline in both outcomes. The study is well-designed and well-written. Main strengths include the repeated outcomes measurements and the large sample size. Hereby I include some comments:

Major comments:

- Indoor air quality is not considered in the present study and this may have a great impact in the association since elderly women spent most of the time indoors.

- Authors are aware that they are not considering other features of the urban environment such as green spaces nor noise. It would be nice to better explain why the authors think they do not have affected the associations observed (also refer to some papers that have seen an association between noise and dementia such as Yu et al Epidemiology. 2020 Nov 1;31(6):771-778). I suggest to draw a digital acyclic graph (DAG) including noise, green spaces, and other features of the urban environment to better understand their potential contribution to the studied associations.

- I am missing a table with a description of the outcomes and the number of interviews performed for each woman (I think this is not mentioned in the manuscript; also report the mean and SD in the text of the number of interviews). It would be also informative to show the distribution of each test in each interview. Have the authors checked whether the number of interviews affected the association? (major number of interviews �  stronger associations?). A table including the scores at baseline and the scores in the last interview would be also an asset and comparing this (or the distribution of the outcomes) with women's characteristics (age, BMI, education etc).

- I would include the results of the imputed dataset as main results and move the complete case ones to the supplementary material.

- It is not clear to me how the authors have selected the confounders since some of them are not associated with air quality improvement as shown in Table 1 (if the based on the association with exposure and outcome) and some can act as potential mediators (e.g. physical activity can mediate the association between urban design - air pollution - physical activity - dementia; cardiovascular health since some of the indicators such as obesity have been associated with air pollution exposure; or mental health). The DAG can help in visualizing which covariates act as confounders and which one can act as potential mediators or effect modifiers.

- In figure 3B it seems that BMI modifies the association between air quality but this is not mentioned.

- Clinical sites are firstly mentioned in the statistical analysis. A good description of them should be included in the study population and also in Table 1 as another population characteristic.

- I would include the association between remote and recent NO2 and PM2.5 exposure with each of the outcomes as main analysis and then report the air quality improvement in the same table similar to Table S5 (very difficult to understand what each Models mean).

- Please, discuss how the estimation of air pollution can have varied throughout the 10 years period and how this may have affected the results (and perform some analyses if needed).

Minor comments

- Introduction and discussion - it would be nice to discuss some equivalent analysis between air pollution and cognition development in children such as the study conducted in Barcelona schools (PLoS Med. 2015 Mar 3;12(3):e1001792).

- How deaths are treated in the analysis?

- Use the same wording in the text and in Figure 1 regarding the outcomes assessed.

- I would not say 'Older women' in sentences 216 and 230 because it seems the authors are comparing women within the study based on their age. I would just say 'Women'.

- Include the confounding variables in the models in Figure 2 footnote.

- In table 2, I would change Model I and Model II for Minimally adjusted model and Fully adjusted model. If authors select confounders based on the DAG, then the minimally adjusted model is no longer needed.

Reviewer #3: This article reports a detailed evaluation of whether improvement in air quality is associated with cognitive change in older women using the Women's Health Initiative study dataset. The authors have linked this to geocoded measures of environment and air quality over time. Air pollution is a risk factor for cognitive decline and dementia, so this is an important study because it evaluates whether risk reversal is associated with any cognitive benefit. The investigation is rigorous and well considered. Authors are careful not to claim causality as the study is observational and it is possible that there are unmeasured confounders. There are some areas of the methodology that were unclear such as:

1. It is unclear how exposure to air pollution over time was estimated at the level of the individual participant, particularly when they moved residence during the study. Can the authors please explain in sufficient detail for full replication of methods, exactly how an individual's 'air pollution reduction' score was derived?

2. What is the validity and reliability of the environmental measures and air pollution measures that were used in this study?

3. Did the authors take into consideration where participants spent their time during each day or did they only examine residential location of participants? for example, an individual may live in one area but work in another and effectively spend more time away from their residential location.

4. When participants moved, how was time treated in the statistical modelling. i.e what was the unit of time - was it days, weeks, months or a year?

5. The geographical regions would not be well understood outside of the USA. it is suggested that these are better described in the method or supplement. Similarly the education levels are not described in widely understood terms. In the US 'College' refers to what other countries call 'University' or 'tertiary' level education. To ensure wider relevance it would be useful to define this variable more clearly.

6. More information is needed on the APOE genotyping and how this variable was classified and treated in statistical analyses as there are different practices in the literature eg. some studies exclude certain combinations of e2, e3, and e4 alleles or combine e4 homozygous and e4 heterozygous. APOE e4 status should also be reported in the descriptive statistics.

7. Can the authors provide any information on how the missing data on the CVLT would have biased results?

8. Were these the only two cognitive measures collected by the WHIMS study? Other papers mention the 3MSE which is an extended Mini-Mental State Examination.

Any attachments provided with reviews can be seen via the following link:

[LINK]

14 Oct 2021

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

18 Nov 2021

Dear Dr. Younan,

Thank you very much for re-submitting your manuscript "Association between air quality improvement and slower cognitive decline in community-dwelling older women: A longitudinal cohort study" (PMEDICINE-D-21-00961R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by two reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

We hope to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

Please note, when your manuscript is accepted, an uncorrected proof of your manuscript will be published online ahead of the final version, unless you've already opted out via the online submission form. If, for any reason, you do not want an earlier version of your manuscript published online or are unsure if you have already indicated as such, please let the journal staff know immediately at plosmedicine@plos.org.

If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org.

We look forward to receiving the revised manuscript by Nov 25 2021 11:59PM.

Sincerely,

Callam Davidson (on behalf of Louise Gaynor-Brook)

Associate Editor

PLOS Medicine

plosmedicine.org

------------------------------------------------------------

Requests from Editors:

Your title should be nondeclarative. Please update to ‘Air quality and cognitive decline in community-dwelling older women in the United States: A longitudinal cohort study’.

The Data Availability Statement (DAS) requires revision. If the data are owned by a third party but freely available upon request, please note this and state the owner of the data set and contact information for data requests (web or email address). The URL you have provided in your statement appears to be a broken link.

Line 53: Please update to ‘in older women aged 74-92 years’.

Please include basic information about the nationwide remit of the WHIMS-ECHO cohort in your abstract methods and findings (i.e. ‘Participants resided in the 48 contiguous U.S. states and were recruited from more than 40 study sites located in 24 states and Washington, D.C.’).

Your Author Summary should not recycle text from the abstract as far as is possible, please revisit and ensure you have not copied text directly across (e.g. bullet point 1).

Please consider relocating some of the text describing your study population from the methods to the results section. The methods section ought to describe the WHIMS study and define inclusion/exclusion criteria, while the final analytic sample and its demographic characteristics would be better placed in the results.

Your rebuttal letter notes that you updated some of your analyses based on reviewer comments. Changes in the analysis, including those made in response to peer review comments, should be identified as such in the Methods section of the paper, with rationale (this can be included in the Study design and population subsection).

Line 360: Ensure footnote ‘f’ begins on a new line.

Line 467: Please update ‘studies’ to ‘at least one study’ as you only cite one study.

Please remove your ‘Financial disclosure’, ‘Competing interests’, and ‘Data statement’ from the end of the main text; all of this information will be captured as metadata based on your responses to the submission form questions (so please ensure all relevant information is included in your answers).

Similarly, the ‘Ethical approval’ statement (line 545) can be removed as this is already included in your Methods section.

Please check the formatting of reference 50.

You have included a map in your updated supplementary materials. Please confirm that the appropriate usage rights apply to the use of this map. Please see our guidelines for map images: https://journals.plos.org/plosmedicine/s/figures#loc-maps

Comments from Reviewers:

Reviewer #1: The authors have satisfactorily considered and responded to each comment in turn, providing clarifications and amending the manuscript where required.

Reviewer #2: The authors properly replied to all my concerns. I do not have any other additional comment.

Any attachments provided with reviews can be seen via the following link:

[LINK]

25 Nov 2021

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

15 Dec 2021

Dear Dr Younan,

On behalf of my colleagues and the Academic Editor, Prof. Perminder Sachdev, I am pleased to inform you that we have agreed to publish your manuscript "Air quality improvement and cognitive decline in community-dwelling older women in the United States: A longitudinal cohort study" (PMEDICINE-D-21-00961R3) in PLOS Medicine.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. Please be aware that it may take several days for you to receive this email; during this time no action is required by you. Once you have received these formatting requests, please note that your manuscript will not be scheduled for publication until you have made the required changes. Given our busy publication schedule, we are planning to publish your paper in early February 2022 (the exact date will be communicated to you once confirmed).

In the meantime, please log into Editorial Manager at http://www.editorialmanager.com/pmedicine/, click the "Update My Information" link at the top of the page, and update your user information to ensure an efficient production process.

PRESS

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To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Thank you again for submitting to PLOS Medicine. We look forward to publishing your paper.

Sincerely,

Louise Gaynor-Brook, MBBS PhD

Associate Editor, PLOS Medicine