Wildfire Smoke Exposure and Incident Dementia

Holly Elser; Timothy B Frankland; Chen Chen; Sara Y Tartof; Elizabeth Rose Mayeda; Gina S Lee; Alexander J Northrop; Jacqueline M Torres; Tarik Benmarhnia; Joan A Casey

doi:10.1001/jamaneurol.2024.4058

. 2024 Nov 25;82(1):40–48. doi: 10.1001/jamaneurol.2024.4058

Wildfire Smoke Exposure and Incident Dementia

Holly Elser ^1,², Timothy B Frankland ³, Chen Chen ⁴, Sara Y Tartof ⁵, Elizabeth Rose Mayeda ⁶, Gina S Lee ⁵, Alexander J Northrop ⁷, Jacqueline M Torres ⁸, Tarik Benmarhnia ^4,⁹, Joan A Casey ^10,^11,^✉

¹Department of Neurology, University of Pennsylvania, Philadelphia

²Editorial Fellow, JAMA Neurology

³Kaiser Permanente Hawaii Center for Integrated Health Care Research, Honolulu, Hawaii

⁴Scripps Institution of Oceanography, University of California, San Diego

⁵Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, California

⁶Department of Epidemiology, UCLA Fielding School of Public Health, University of California, Los Angeles

⁷Vagelos College of Physicians and Surgeons, Columbia University, New York, New York

⁸Department of Epidemiology & Biostatistics, University of California, San Francisco

⁹Irset Institut de Recherche en Santé, Environnement et Travail, UMR-S 1085, Inserm, University of Rennes, EHESP, Rennes, France

¹⁰Department of Environmental and Occupational Health Sciences, University of Washington School of Public Health, Seattle

¹¹Department of Epidemiology, University of Washington School of Public Health, Seattle

Accepted for Publication: October 3, 2024.

Published Online: November 25, 2024. doi:10.1001/jamaneurol.2024.4058

Correction: This article was corrected on January 13, 2025, to fix an incorrect numeral in the Results section.

^✉

Corresponding Author: Joan A. Casey, PhD, University of Washington School of Public Health, 3980 15th Ave NE, Seattle, WA 98105 (jacasey@uw.edu).

Author Contributions: Dr Casey had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Elser, Benmarhnia, Casey.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Elser, Northrop, Benmarhnia, Casey.

Critical review of the manuscript for important intellectual content: All authors.

Statistical analysis: Elser, Frankland, Chen, Benmarhnia, Casey.

Obtained funding: Casey.

Administrative, technical, or material support: Frankland, Lee, Casey.

Supervision: Tartof, Casey.

Conflict of Interest Disclosures: Mr Frankland reported grants from the US National Institutes of Health (NIH) during the conduct of the study; owning stock in AbbVie, AstraZeneca, Regeneron Pharmaceutical, and Stryker; and previously owning stock in, CVS and Pfizer outside the submitted work. Dr Tartof reported funding grants paid to her institution for work unrelated to this manuscript from Pfizer during the conduct of the study. Dr Mayeda reported personal fees for serving as a consultant for a grant from the NIH during the conduct of the study and grants from the NIH and the California Department of Public Health outside the submitted work. Ms Lee reported funding to her institution from Moderna and GlaxoSmithKline outside the submitted work. Dr Torres reported grants from the US National Institute on Aging and the US National Cancer Institute outside the submitted work. Dr Casey reported grants from the US National Institute on Aging both during the conduct of the study and outside the submitted work. No other disclosures were reported.

Funding/Support: Drs Benmarhnia, Casey, Mayeda, and Tartof and Mr Frankland were supported by the US National Institutes of Health (NIH) National Institute on Aging grant R01-AG071024. Dr Casey was also supported by the NIH National Institute for Environmental Health Sciences grant P30-ES007033.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

Disclaimer: Dr Elser is the Editorial Fellow of JAMA Neurology but was not involved in any of the decisions regarding review of the manuscript or its acceptance.

Data Sharing Statement: See Supplement 2.

Additional Contributions: The authors thank Elizabeth M. Blake, BS (University of Washington School of Public Health), for her compensated assistance in editing the manuscript and Lauren B. Wilner, BA, MPH (University of Washington School of Public Health), for her assistance in producing Figure 2.

^✉

Corresponding author.

PMCID: PMC11589856 PMID: 39585704

Key Points

Question

Is long-term exposure to wildfire smoke associated with incident dementia diagnosis?

Findings

In this open cohort study of more than 1.2 million Kaiser Permanente Southern California members, long-term exposure to wildfire and nonwildfire fine particulate matter (PM_2.5) was associated with dementia diagnosis, with stronger associations observed in potentially vulnerable subgroups.

Meaning

As climate change intensifies, interventions that reduce wildfire PM_2.5 exposure can potentially reduce the risk of dementia and support health equity.

Abstract

Importance

Long-term exposure to total fine particulate matter (PM_2.5) is a recognized dementia risk factor, but less is known about wildfire-generated PM_2.5, an increasingly common PM_2.5 source.

Objective

To assess the association between long-term wildfire and nonwildfire PM_2.5 exposure and risk of incident dementia.

Design, Setting, and Participants

This open cohort study was conducted using January 2008 to December 2019 electronic health record (EHR) data among members of Kaiser Permanente Southern California (KPSC), which serves 4.7 million people across 10 California counties. KPSC members aged 60 years or older were eligible for inclusion. Members were excluded if they did not meet eligibility criteria, if they had a dementia diagnosis before cohort entry, or if EHR data lacked address information. Data analysis was conducted from May 2023 to May 2024.

Exposures

Three-year rolling mean wildfire and nonwildfire PM_2.5 in member census tracts from January 2006 to December 2019, updated quarterly and estimated via monitoring and remote-sensing data and statistical techniques.

Main Outcome and Measures

The primary outcome was incident dementia, identified using diagnostic codes in the EHR. Odds of dementia diagnoses associated with 3-year mean wildfire and nonwildfire PM_2.5 exposure were estimated using a discrete-time approach with pooled logistic regression. Models adjusted for age, sex, race and ethnicity (considered as a social construct rather than as a biological determinant), marital status, smoking status, calendar year, and census tract–level poverty and population density. Stratified models assessed effect measure modification by age, sex, race and ethnicity, and census tract–level poverty.

Results

Among 1.64 million KPSC members aged 60 years or older during the study period, 1 223 107 members were eligible for inclusion in this study. The study population consisted of 644 766 female members (53.0%). In total, 319 521 members identified as Hispanic (26.0%), 601 334 members identified as non-Hispanic White (49.0%), and 80 993 members received a dementia diagnosis during follow-up (6.6%). In adjusted models, a 1-μg/m³ increase in the 3-year mean of wildfire PM_2.5 exposure was associated with an 18% increase in the odds of dementia diagnosis (odds ratio [OR], 1.18; 95% CI, 1.03-1.34). In comparison, a 1-μg/m³ increase in nonwildfire PM_2.5 exposure was associated with a 1% increase (OR, 1.01; 95% CI, 1.01-1.02). For wildfire PM_2.5 exposure, associations were stronger among members less than 75 years old upon cohort entry, members from racially minoritized subgroups, and those living in high-poverty vs low-poverty census tracts.

Conclusions and Relevance

In this cohort study, after adjusting for measured confounders, long-term exposure to wildfire and nonwildfire PM_2.5 over a 3-year period was associated with dementia diagnoses. As the climate changes, interventions focused on reducing wildfire PM_2.5 exposure may reduce dementia diagnoses and related inequities.

This cohort study using data from 1.2 million Kaiser Permanente Southern California members from 2008 to 2019 assesses the association between long-term wildfire and nonwildfire fine particulate matter exposure and risk of incident dementia.

Introduction

Wildfires, once rare and geographically confined, now regularly impact populations across the US.¹ Anthropogenic climate change has increased wildfire frequency and intensity, eroding gains in air quality achieved under the Clean Air Act in the Western US.^2,3,4,5 Today, wildfire-generated fine particulate matter less than 2.5 microns in diameter (PM_2.5) accounts for over 70% of total PM_2.5 exposure on poor air-quality days in California,⁶ where the 2018 wildfire season alone resulted in an estimated $149 billion in capital, health, and economic damages.⁷

Prior research suggests long-term exposure to PM_2.5, a major health-harmful component of wildfire smoke,^8,9,10 is associated with incident dementia,¹¹ with a strong biological basis for the observed association.^12,13,14 Exposure to PM_2.5 may accelerate neurodegenerative processes through enhanced production of reactive oxygen species,^15,16 altered blood-brain barrier permeability,^17,18,19 and overactivation of microglia, leading to excess production of cytotoxic factors.^12,20,21 Proposed routes of entry of PM_2.5 into the central nervous system include direct translocation via the olfactory nerve^20,22 and via peripheral circulation across the blood-brain barrier.^17,23,24 Exposure to PM_2.5 may indirectly increase dementia risk through prothrombotic physiologic changes leading to cerebrovascular dysfunction and stroke,^{25,26,27,28,29,30} which may underlie some dementia diagnoses. Although wildfires have become a dominant PM_2.5 source in California, whether long-term exposure to wildfire PM_2.5 confers similar dementia risk remains uncertain despite differences in chemical compositions, oxidative potential, and size fractions.^31,32,33

Motivated by the intensification of wildfire events in the US and globally, we examined the association of long-term wildfire and nonwildfire PM_2.5 exposure with incident dementia among older adults in Southern California. Our analysis leveraged detailed, longitudinal electronic health record (EHR) data with more than 10 years of longitudinal follow-up. This analysis explicitly considers key individual-level and community-level vulnerability factors that may impact long-term PM_2.5 exposure or the magnitude of an individual’s health response.

Methods

This open cohort study used EHR data spanning from January 2008 to December 2019 from Kaiser Permanente Southern California (KPSC), a managed care consortium, with integration of the health plan, hospitals, and physician medical groups, which serve more than 4.7 million individuals.³⁴ KPSC membership reflects the sociodemographic diversity of Southern California, with minor underrepresentation of individuals with extremely low income and individuals with high education.³⁵ The KPSC EHR catalogs longitudinal records of members’ residential address, sociodemographic characteristics, and diagnoses across care settings. This study included all KPSC members aged 60 years or older enrolled continuously for at least 1 year (allowing 90-day enrollment gaps), enrolled for 1 day in the year following their baseline year, living in a KPSC census tract, and free from dementia at cohort entry (eFigure 1 in Supplement 1). Follow-up extended from the date of cohort entry on or after January 1, 2009, through the date of dementia diagnosis, death, loss-to-follow-up, or administrative censoring on December 31, 2019. Data analysis was conducted from May 2023 to May 2024.

This study was reported per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines. Members with documentation requesting removal from all research studies were excluded. The study protocol was approved by the WCG institutional review board (IRB) and was also approved by the IRBs at KPSC, Columbia University, and the University of Washington. All IRBs waived the requirement for informed consent. Analyses were conducted using R version 4.3.2 (The R Foundation for Statistical Computing).

Wildfire Smoke Exposure

Daily mean concentrations of total PM_2.5 exposure were estimated for each Southern California census tract from 2006 to 2019 using an ensemble machine learning approach.³⁶ Predictor variables included outdoor PM_2.5 measurements from the Environmental Protection Agency Air Quality System, aerosol optical depth, plume height, meteorological variables (minimum and maximum temperature, specific humidity, wind speed and duration, precipitation, and surface shortwave radiation) extracted from the high-resolution Gridded Surface Meteorological dataset, and land use characteristics.³⁷ Daily concentrations of wildfire PM_2.5 were isolated from total PM_2.5 using the National Oceanic and Atmospheric Administration Hazard Mapping System (HMS), fire perimeter data from the California Department of Forestry and Fire Protection, and a spatiotemporal multiple imputation approach, as previously described.³⁶ Smoky tract-days were defined as days when an HMS smoke plume boundary intersected a given census tract. In brief, total PM_2.5 was first used to represent nonwildfire PM_2.5 concentrations on nonsmoky tract-days and then multiple imputation was used to estimate nonwildfire PM_2.5 concentrations on smoky tract-days. We subtracted the estimated nonwildfire PM_2.5 concentration from the total PM_2.5 concentration to obtain estimated wildfire PM_2.5 concentrations on smoky tract-days. Models achieved an R² value of 0.78 using holdout test validation overall and when restricted to lower levels of wildfire PM_2.5 (ie, less than 50 μg/m³).

As the relevant exposure period for air pollution remains unknown³⁸ and our wildfire PM_2.5 data extended from 2006 on, a 3-year mean exposure was selected. Using daily estimates, we calculated census tract–level wildfire and nonwildfire PM_2.5 concentrations as time-varying 3-year rolling means, updated quarterly. These estimates were linked to study participants based on their time-varying residential address geocoded to the census tract level.

Dementia Diagnosis

Dementia diagnoses from inpatient and outpatient visits between January 1, 2008, and December 31, 2019, were identified through the EHR using diagnostic codes from the International Classification of Diseases, Ninth and Tenth Revision (ICD-9 and ICD-10).^39,40,41,42 The outcome comprises diagnoses of Alzheimer disease, Lewy body dementia, vascular dementia, and other dementias (eTable 1 in Supplement 1). Prior research suggests sensitivity of 77% and specificity of 95% for similar diagnostic codes used to identify all-cause dementia in EHR data compared with consensus dementia diagnosis.⁴³

Covariates

EHR-derived member characteristics included age at cohort entry, sex (male or female), member-reported race and ethnicity (with categories including Hispanic, non-Hispanic Asian or Pacific Islander, non-Hispanic Black, non-Hispanic White, and other [multiple races, Native American and Alaskan Native, Pacific Islander, other, and unknown race and ethnicity]), smoking status (current, former, or never smoker), relationship status (married, domestic partner, common law marriage, divorced or separated, widowed, single, other, or unknown), and whether an interpreter was required at any health care encounters. The social constructs of race and ethnicity were included as covariates because these factors may stand in as a proxy for experiences of structural racism or social factors that may affect PM_2.5 exposure. Census tract–level covariates were obtained from the 2010 US Census and linked based on geocoded member addresses. These covariates included population density and percentage of the population living below the federal poverty threshold.⁴⁴ High-poverty census tracts were those in which 15% or more of the population lived below the federal poverty threshold. Tracts were otherwise classified as low poverty.

Statistical Analysis

A discrete-time approach with pooled logistic regression was used to estimate the odds of dementia diagnoses associated with a 1-μg/m³ increase in the 3-year mean of wildfire PM_2.5 and nonwildfire PM_2.5 concentrations. In all models, we controlled for individual-level covariates identified a priori as potential confounders, including age (natural cubic spline with 2 degrees freedom), sex, race and ethnicity, smoking status, relationship status, and whether the member required an interpreter during health care encounters. Census tract–level covariates included population density and percentage living in poverty. All models additionally included fixed effects for calendar year to address potential secular trends in PM_2.5 levels and dementia diagnoses. Models did not control for vascular risk factors, such as hypertension or high cholesterol, because these factors likely mediate, rather than confound, the association between PM_2.5 exposure and dementia.^45,46,47 All P values were 2-sided, and statistical significance was set at P = .05.

Secondary Analyses

Alternative exposure metrics were considered that captured other facets of wildfire PM_2.5 exposure in their associations with dementia.⁴⁸ These included (1) each additional week where mean wildfire PM_2.5 concentrations exceeded 5 μg/m³; (2) each IQR increase in the number of weeks wherein the mean wildfire PM_2.5 concentrations exceeded 0 μg/m³; (3) each 10-μg/m³ increase in the mean daily wildfire PM_2.5 concentration during the peak week of exposure; and (4) each additional smoke wave over a 3-year rolling exposure period. Smoke waves were defined as 2 or more consecutive days with a mean daily wildfire PM_2.5 concentration greater than 15 μg/m³.⁹ For comparability, we also estimated associations for an IQR increase in wildfire (approximately 0.1 μg/m³) and nonwildfire PM_2.5 (approximately 3 μg/m³). Because dementia risk and adverse responses to long-term PM_2.5 exposure may differ meaningfully by age,⁴⁹ sex,⁵⁰ race and ethnicity,⁵¹ and area-level poverty,⁵² subgroup analyses were conducted within strata defined by these factors. In subgroup analysis, age was dichotomized based on members’ median age upon cohort entry (younger than 75 years vs 75 years or older). For all subgroup analyses, Cochran Q statistics were calculated to assess for heterogeneity.⁵³

Sensitivity Analyses

Natural splines were used to capture potential nonlinear associations. We additionally calculated the controlled direct effect after eliminating loss to follow-up and the competing risk of death using the product of inverse probability of censoring weights and inverse probability of death weights.⁵⁴

Results

Of 1 640 220 eligible KPSC members aged 60 years or older between January 1, 2008, and January 1, 2019, 245 389 members (15.0%) were excluded because they did not satisfy criteria for continuous enrollment, and 134 111 members (8.2%) were excluded who were not 60 years old in the qualifying year. We excluded 10 274 members (0.6%) missing census tract of residence, 27 003 (1.6%) with a dementia diagnosis before cohort entry, and 339 (less than 0.1%) with missing sex data or rural-urban commuting area codes. This yielded a final study population of 1 223 107 members (eFigure 1 in Supplement 1). Over the study period, 80 884 beneficiaries (6.6%) received a dementia diagnosis, 119 435 (9.8%) died, and 156 310 (13.0%) were lost to follow-up. Most members diagnosed with dementia (69.0%) were diagnosed with nonspecific dementia (eTable 2 in Supplement 1). Approximately half of the study population were female (53.0%), identified as non-Hispanic White (49.0%), and were married or partnered (54.0%; Table). Members diagnosed with dementia during the study period were more often non-Hispanic White, widowed, or former smokers or nonsmokers and were less likely to require the use of an interpreter for health care encounters. Those with and without dementia lived in census tracts with similar population density and poverty percentages. Over the study period, the median (IQR) 3-year rolling average for wildfire PM_2.5 concentration was 0.09 μg/m³ (0.05-0.16), and the median (IQR) nonwildfire PM_2.5 concentration was 11.2 μg/m³ (9.6-12.4) (Figure 1; eTable 3 in Supplement 1).

Table. Characteristics of the Kaiser Permanente Southern California (KPSC) Study Population, 2008-2019.

Characteristic	Study population, No. (%)
	Overall (N = 1 223 107)	During follow-up
	Overall (N = 1 223 107)	Dementia-free (n = 1 142 223)	Incident dementia (n = 80 884)^a
Sex
Female	649 766 (53.0)	603 827 (53.0)	45 939 (57.0)
Male	573 341 (47.0)	538 396 (47.0)	34 945 (43.0)
Age at cohort entry, median (IQR), y	62 (60-69)	62 (60-68)	76 (70-82)
Race and ethnicity
Hispanic	319 521 (26.0)	302 737 (27.0)	16 784 (21.0)
Non-Hispanic
Asian	128 611 (11.0)	122 860 (11.0)	5751 (7.1)
Black	114 889 (9.4)	104 805 (9.2)	10 084 (12.0)
White	601 334 (49.0)	554 337 (49.0)	46 997 (58.0)
Other^b	58 752 (4.8)	57 484 (5.0)	1268 (1.6)
Relationship status
Married or partnered	662 195 (54.0)	626 862 (55.0)	35 333 (44.0)
Divorced or separated	118 585 (9.7)	110 075 (9.6)	8510 (11.0)
Single	147 649 (12.0)	142 147 (12.0)	5502 (6.8)
Widowed	163 971 (13.0)	135 213 (12.0)	28 758 (36.0)
Other or unknown	130 707 (11.0)	127 926 (11.0)	2781 (3.4)
Smoking status
Never, passive, or unknown	752 112 (61.0)	708 825 (62.0)	43 287 (54.0)
Former smoker	408 697 (33.0)	373 496 (33.0)	35 201 (44.0)
Current smoker	62 298 (5.1)	59 902 (5.2)	2396 (3.0)
Deaths during follow-up	119 435 (9.8)	114 104 (10.0)	5331 (6.6)
Lost to follow-up	156 310 (13.0)	155 954 (14.0)	356 (0.4)
Required interpreter	133 411 (11.0)	127 465 (11.0)	5946 (7.4)
Census tract–level characteristics, median (IQR)^c
Poverty, %	9 (5-16)	9 (5-16)	9 (5-16)
Population density, individuals per km²	2494 (1267-3898)	2487 (1257-3904)	2530 (1372-3869)

Open in a new tab

^{^a}

Dementia diagnoses made in the inpatient and outpatient setting between January 1, 2008, and December 31, 2019, were ascertained through the electronic health record. Diagnostic codes from the International Classification of Diseases, Ninth and Tenth Revisions were used to identify incident diagnoses of Alzheimer disease, Lewy body dementia, vascular dementia, and other dementias (eTable 1 in Supplement 1).

^{^b}

”Other” category includes individuals of multiple races, individuals of unknown race and ethnicity, individuals of other ethnicity, and Native American and Alaskan Native and Pacific Islander individuals.

^{^c}

Census tract–level covariates were obtained from the 2010 US Census based on geocoded member address.

Figure 1. — Using daily estimates across the Kaiser Permanente Southern California (KPSC) catchment area (A), census tract–level wildfire PM_2.5 concentrations (B) and nonwildfire PM_2.5 concentrations (C) were calculated as time-varying 3-year rolling means, updated quarterly. Wildfire and nonwildfire PM_2.5 exposure estimates were assigned to study participants based on their time-varying residential address geocoded to the census tract level. Shaded areas represent the mean of all quarter-specific 3-year rolling mean PM_2.5 concentrations (in μg/m³) in the KPSC catchment area, 2006-2019.

In adjusted models, an 18% increase in the odds of dementia diagnosis was observed for every 1-μg/m³ increase in 3-year average wildfire PM_2.5 concentration (odds ratio [OR], 1.18; 95% CI, 1.03-1.34) (Figure 2). For nonwildfire PM_2.5, the odds of dementia diagnosis increased by 1% for every 1-μg/m³ increase in 3-year mean exposure (OR, 1.01; 95% CI, 1.01-1.02) (Figure 2). This indicates that for the same concentration change, dementia risk associated with wildfire PM_2.5 was higher than dementia risk associated with PM_2.5 from other sources. Estimating the association for an IQR increase in wildfire PM_2.5 (0.11 μg/m³) and nonwildfire PM_2.5 (2.8 μg/m³), similar odds ratios were found (OR, 1.02; 95% CI, 1.00-1.03; and OR, 1.03; 95% CI, 1.02-1.04, respectively) (eTable 4 in Supplement 1).

Figure 2. — A discrete-time approach with pooled logistic regression was used to estimate the odds of dementia diagnoses. Models controlled for age at study entry (natural cubic spline), sex, race and ethnicity, smoking status, relationship status, whether the participant required an interpreter during health care encounters, calendar year, and census tract–level population density and poverty. Estimates are depicted overall and by subgroup. Model coefficients correspond to a 1-μg/m³ increase in wildfire and nonwildfire PM_2.5 concentrations. Overall effect estimates are provided and stratified by potential effect measure modifiers: median age at cohort entry (wildfire PM_2.5 P value for heterogeneity, <.001; nonwildfire PM_2.5 P value for heterogeneity, .49), sex (wildfire PM_2.5 P value for heterogeneity, .39; nonwildfire PM_2.5 P value for heterogeneity, .01), race and ethnicity (wildfire PM_2.5 P value for heterogeneity, .01; nonwildfire PM_2.5 P value for heterogeneity, .92), and census tract–level poverty (wildfire PM_2.5 P value for heterogeneity, .17; nonwildfire PM_2.5 P value for heterogeneity, .35). OR indicates odds ratio.

We assessed alternative wildfire PM_2.5 exposure metrics and observed an association between a 10-μg/m³ increase in wildfire PM_2.5 concentration during the peak exposure week (OR, 1.02; 95% CI, 1.00-1.05) and 1 additional smoke wave (OR, 1.03; 95% CI, 1.01-1.05) with dementia diagnosis (eTable 4 in Supplement 1). The association was weaker for an additional week where wildfire PM_2.5 was greater than 5 μg/m³ (OR, 1.01; 95% CI, 0.99-1.02) or 38 additional days where wildfire PM_2.5 was greater than 0 μg/m³ (OR, 1.01; 95% CI, 0.99-1.03).

Secondary analyses suggested stronger relative associations among younger members upon study entry (age less than 75 years vs age 75 years or more), men vs women, and those living in high-poverty vs low-poverty census tracts. However, evidence of heterogeneity was only identified for age category (P value for heterogeneity, <.001; Figure 2). Although imprecise, subgroup results suggested stronger associations among racially minoritized subgroups (Hispanic: OR, 1.09; 95% CI, 0.79-1.48; non-Hispanic Asian: OR, 1.62; 95% CI, 0.86-2.98; non-Hispanic Black: OR, 1.47; 95% CI, 0.92-2.34; non-Hispanic White: OR, 1.02; 95% CI, 0.87-1.20; P value for heterogeneity, .01; Figure 2). The “Other” group, containing individuals of multiple races, individuals of unknown race and ethnicity, individuals of other race, and Native American and Alaskan Native and Pacific Islander individuals had the highest odds of dementia per unit increase in wildfire PM_2.5 exposure (OR, 3.45; 95% CI, 1.66-6.93). For nonwildfire PM_2.5, a stronger association was observed among men than among women (P value for heterogeneity, .01) but no clear differences were observed by race and ethnicity or census tract poverty (Figure 2).

In sensitivity analyses, a nearly-linear exposure-response association was found for wildfire PM_2.5 (eFigure 2 in Supplement 1), whereas the association between nonwildfire PM_2.5 concentration and dementia diagnosis increased up to approximately 6.5 μg/m³, flattened through approximately 13 μg/m³, and then increased (eFigure 3 in Supplement 1). Results were robust when competing risk of death and loss to follow-up were eliminated (eTable 5 in Supplement 1).

Discussion

This January 2008 to December 2019 Southern California cohort study identified wildfire PM_2.5 as a potentially important risk factor for dementia. Among more than 1.2 million eligible KPSC members, each 1-μg/m³ increase in long-term wildfire PM_2.5 exposure was associated with an 18% increase in the odds of dementia diagnosis. Secondary analyses suggested that members aged less than 75 years, those from racially minoritized groups, and those living in high-poverty census tracts had heightened responses to wildfire PM_2.5 exposure. These results align with prior research consistently demonstrating that individual-level and area-level social determinants compound the risk of adverse health outcomes associated with climate-driven environmental exposures.^55,56

Past research has consistently identified an association between long-term PM_2.5 exposure and incident dementia,^38,57 with varying magnitudes of association depending on study context, outcome ascertainment, and exposure averaging period.^{11,38,58,59,60} For example, among Medicare beneficiaries aged 65 years or older, Shi and colleagues⁶¹ found that each interquartile increase in the 5-year mean PM_2.5 concentration was associated with a 6% greater risk of dementia diagnosis. Using neurologist-adjudicated dementia cases based on neuropsychological testing and magnetic resonance imaging, Semmens and colleagues⁶² found that among 3069 adults aged 75 years or older recruited across 4 US study sites in the Gingko Evaluation of Memory Study, a 2-μg/m³ increase in 20-year mean PM_2.5 exposure was associated with a 20% higher risk of dementia. Two recent meta-analyses^38,57 reported 4% greater dementia risk for each 2-μg/m³ increase and a 3% greater dementia risk for each 3-μg/m³ increase in PM_2.5, respectively. In line with these results, we estimated a 1% increase in risk for incident dementia with each 1-μg/m³ increase in the 3-year mean nonwildfire PM_2.5 concentration.

This study offers a critical extension of prior work, demonstrating increased odds of dementia associated with long-term wildfire and nonwildfire PM_2.5 among 1.2 million older Southern California residents. These results further suggest a stronger association between wildfire PM_2.5 exposure and subsequent dementia, in keeping with a strong theoretical basis suggesting unique toxic neurologic effects of wildfire PM_2.5. Wildfire PM_2.5 contains higher concentrations of oxidative and pro-inflammatory compounds,^63,64,65,66 has a smaller average particle size,³² and is generated by combustion of organic materials at substantially higher temperatures than nonwildfire PM_2.5.³³ Further, wildfire PM_2.5 concentrations tend to spike intermittently at high levels, contrasting with more consistent exposure to nonwildfire PM_2.5 throughout the year.⁴⁸ Using alternative measures of long-term wildfire PM_2.5 exposure, we found increased odds of dementia diagnosis associated with mean peak week exposure and smoke waves, but not weeks where wildfire PM_2.5 was greater than 5 μg/m³ or days where wildfire PM_2.5 was greater than 0 μg/m³. Because peak week exposure and smoke waves explicitly capture short-term increases in wildfire PM_2.5 concentration, these results suggest that these high levels may pose particular risk.

The results of this study are consistent with prior studies that have suggested dementia risk varies based on PM_2.5 components. Zhang and colleagues³¹ found agriculture, traffic, coal combustion, and wildfire-generated PM_2.5 were the individual components most strongly associated with dementia among 27 857 members older than 50 years in the Health and Retirement Study from 1998 to 2016. They observed a 5% increase in the risk of dementia for a 0.6-μg/m³ increase in wildfire-specific PM_2.5, measured only in 2017 but extrapolated across 10 years. Using data on more than 18.5 million Medicare beneficiaries from 2000 to 2017, Shi and colleagues⁶⁷ examined the association of long-term exposure to PM_2.5 subcomponents with all-cause dementia. Although this study observed associations with PM_2.5 subcomponents that also comprise wildfire PM_2.5 (including black carbon, organic matter, and sulfate), their analysis did not explicitly consider the association between wildfire PM_2.5 and incident dementia. This analysis builds on these 2 prior studies, leveraging novel long-term measures of PM_2.5 produced by wildfire events.

In subanalyses, it was found that wildfire PM_2.5 exposure was only associated with dementia diagnosis among those aged less than 75 years upon cohort entry. Possible factors contributing to this finding may include differences in time spent outdoors with higher actual wildfire PM_2.5 exposure among those aged less than 75 years; that members most susceptible to wildfire PM_2.5 exposure may have died sooner and thus were not present in the subgroup of members aged 75 years or older upon cohort entry; or lower baseline risk of dementia among younger members, which could contribute to higher effect estimates on the relative scale.

Finally, these results suggest the association between long-term wildfire PM_2.5 exposure and dementia differed substantially based on individual race and ethnicity and area poverty. In the US, environmental exposures disproportionately impact racially and economically marginalized groups,^68,69 and these groups may further experience differential health effects of wildfire PM_2.5 exposure. For example, lower-quality housing may increase smoke infiltration, and poorer families may have constrained economic choices⁷⁰ that limit their ability to pay for air filtration systems to improve air quality during smoke events.⁷¹ Future studies may wish to explicitly study these factors as effect modifiers. Members of marginalized groups may have amplified physiologic responses to environmental exposures, reflecting worse baseline health, the cumulative result of discrimination, and chronic exposure to psychosocial stressors.^{56,72,73,74,75} Consistent with this theoretical framework, the strongest associations were observed among non-Hispanic Asian, non-Hispanic Black, and Hispanic members and those living in areas characterized by high poverty. Continued focus on differential health risks from wildfire PM_2.5 exposure within subpopulations—and the mechanisms that underlie these differences—may advance health equity in a changing climate and should remain an essential focus for future scholarship.

Limitations

We estimated long-term wildfire and nonwildfire PM_2.5 exposure over a 3-year period. The causally relevant window of exposure for PM_2.5 and dementia remains unknown.³⁸ Because the neurodegenerative processes underlying dementia likely begin years before clinical symptoms emerge, future research should consider longer exposure durations. Further, the most biologically relevant measure for estimating exposure to long-term wildfire PM_2.5 has not yet been determined.⁴⁸ Notably, we estimated the association for each 1-μg/m³ increase in long-term wildfire PM_2.5 concentrations, a value larger than the IQR for the observed distribution of wildfire PM_2.5. However, in sensitivity analyses, we have also presented results using an IQR increase in both wildfire and nonwildfire PM_2.5, which are consistent with findings from our main analysis. Although wildfire smoke leads to increased concentrations of ozone and other gaseous pollutants like volatile organic compounds,^76,77 these have inconclusive associations with incident dementia.³⁸ We focused on wildfire PM_2.5, the most health-relevant component of wildfire smoke. Future work exploring specific effects of other wildfire smoke pollutants may help further characterize its health impacts.

ICD-9 and ICD-10 diagnostic codes were used to ascertain cases of incident dementia. A 2023 meta-analysis³⁸ found stronger associations between PM_2.5 and dementia in studies that used active vs passive (diagnostic code) case ascertainment. We anticipate that outcome misclassification resulting from the use of diagnostic codes did not occur systematically with respect to long-term wildfire and nonwildfire PM_2.5 exposure and therefore likely biased estimates toward the null. Reliance on diagnostic codes further precludes evaluation of dementia subtypes, which might otherwise yield novel insights into the mechanisms underlying observed associations.

Although our analysis leveraged data from more than 1.2 million KPSC beneficiaries, we lacked sufficient power to examine associations within some critical demographic subgroups (eg, Native American beneficiaries who may have elevated wildfire PM_2.5 exposure).⁴⁸ EHR data did not contain measures of behavior change in response to wildfire PM_2.5 exposure, such as masking or limiting time spent outdoors, which could plausibly mitigate dementia risk. We could not fully account for socioeconomic factors that might correlate with the ability to afford air filtration systems, receive public health messaging, or shelter indoors.⁷⁰ We aimed to minimize confounding by adjusting for sociodemographic factors in all statistical models, but the possibility of residual confounding cannot be eliminated in this observational study.

Conclusions

Among more than 1.2 million KPSC members, long-term wildfire smoke exposure was associated with subsequent dementia diagnosis. This risk was more pronounced among racially minoritized patient subgroups and among those living in high-poverty census tracts. These latter findings underscore the importance of research that considers the effects of air pollution on potentially vulnerable population subgroups and aims to identify potential strategies to mitigate inequities in air pollution exposure effects.

Supplement 1.

eFigure 1. Flow Diagram Depicting Exclusion Criteria for Members of the KPSC Study Population, 2008-2019

eFigure 2. Association of 3-Year Average Wildfire PM2.5 Exposure With Dementia Diagnosis Among KPSC Members Aged ≥60 Years, 2008-2019

eFigure 3. Association of 3-Year Average Nonwildfire PM2.5 Exposure With Dementia Diagnosis Among KPSC Members Aged ≥60 Years, 2008-2019

eTable 1. Diagnostic Codes From the International Classification of Diseases, Ninth and Tenth Revisions (ICD-9 and ICD-10) Used to Identify Dementia

eTable 2. Dementia Subtype Diagnoses Among KPSC Members Using eTable 1 Diagnostic Codes, 2008-2019

eTable 3. Association of 3-Year Average Wildfire and Nonwildfire PM2.5 Exposure With Dementia Diagnosis Among KPSC Members Aged ≥60 Years, 2008-2019^a

eTable 4. Alternative Exposure Metrics for the Association Between Wildfire PM2.5 Exposure and Dementia Diagnosis among KPSC Members Aged ≥60 Years, 2008-2019^a

eTable 5. Association of 3-Year Average Wildfire and Nonwildfire PM2.5 Exposure With Dementia Diagnosis Among KPSC Members Aged ≥60 Years, 2008-2019, Eliminating Competing Risk Due to All-Cause Mortality and Loss to Follow-Up^a

jamaneurol-e244058-s001.pdf^{(349.2KB, pdf)}

Supplement 2.

Data Sharing Statement

jamaneurol-e244058-s002.pdf^{(14.6KB, pdf)}

References

1.Burke M, Driscoll A, Heft-Neal S, Xue J, Burney J, Wara M. The changing risk and burden of wildfire in the United States. Proc Natl Acad Sci USA. 2021;118(2):e2011048118. doi: 10.1073/pnas.2011048118 [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Abatzoglou JT, Williams AP. Impact of anthropogenic climate change on wildfire across western US forests. Proc Natl Acad Sci USA. 2016;113(42):11770-11775. doi: 10.1073/pnas.1607171113 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Halofsky JE, Peterson DL, Harvey BJ. Changing wildfire, changing forests: the effects of climate change on fire regimes and vegetation in the Pacific Northwest, USA. Fire Ecology. 2020;16(1):1-26. doi: 10.1186/s42408-019-0062-8 [DOI] [Google Scholar]
4.Williams AP, Abatzoglou JT, Gershunov A, et al. Observed impacts of anthropogenic climate change on wildfire in California. Earths Future. 2019;7(8):892-910. doi: 10.1029/2019EF001210 [DOI] [Google Scholar]
5.Burke M, Childs ML, de la Cuesta B, et al. The contribution of wildfire to PM_2.5 trends in the USA. Nature. 2023;622(7984):761-766. doi: 10.1038/s41586-023-06522-6 [DOI] [PubMed] [Google Scholar]
6.Liu JC, Mickley LJ, Sulprizio MP, et al. Particulate air pollution from wildfires in the Western US under climate change. Climate Change. 2016;138(3):655-666. doi: 10.1007/s10584-016-1762-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Wang D, Guan D, Zhu S, et al. Economic footprint of California wildfires in 2018. Nat Sustainability. 2021;4(3):252-260. doi: 10.1038/s41893-020-00646-7 [DOI] [Google Scholar]
8.Reid J, Koppmann R, Eck T, Eleuterio D. A review of biomass burning emissions part II: intensive physical properties of biomass burning particles. Atmospheric Chem Phys. 2005;5(3):799-825. doi: 10.5194/acp-5-799-2005 [DOI] [Google Scholar]
9.Liu JC, Wilson A, Mickley LJ, et al. Wildfire-specific fine particulate matter and risk of hospital admissions in urban and rural counties. Epidemiology. 2017;28(1):77-85. doi: 10.1097/EDE.0000000000000556 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Xing YF, Xu YH, Shi MH, Lian YX. The impact of PM2.5 on the human respiratory system. J Thorac Dis. 2016;8(1):E69-E74. [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Weuve J, Bennett EE, Ranker L, et al. Exposure to air pollution in relation to risk of dementia and related outcomes: an updated systematic review of the epidemiological literature. Environ Health Perspect. 2021;129(9):96001. doi: 10.1289/EHP8716 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Block ML, Calderón-Garcidueñas L. Air pollution: mechanisms of neuroinflammation and CNS disease. Trends Neurosci. 2009;32(9):506-516. doi: 10.1016/j.tins.2009.05.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Heusinkveld HJ, Wahle T, Campbell A, et al. Neurodegenerative and neurological disorders by small ihaled particles. Neurotoxicology. 2016;56:94-106. doi: 10.1016/j.neuro.2016.07.007 [DOI] [PubMed] [Google Scholar]
14.Jayaraj RL, Rodriguez EA, Wang Y, Block ML. Outdoor ambient air pollution and neurodegenerative diseases: the neuroinflammation hypothesis. Curr Environ Health Rep. 2017;4(2):166-179. doi: 10.1007/s40572-017-0142-3 [DOI] [PubMed] [Google Scholar]
15.Calderón-Garcidueñas L, Mora-Tiscareño A, Ontiveros E, et al. Air pollution, cognitive deficits and brain abnormalities: a pilot study with children and dogs. Brain Cogn. 2008;68(2):117-127. doi: 10.1016/j.bandc.2008.04.008 [DOI] [PubMed] [Google Scholar]
16.Levesque S, Taetzsch T, Lull ME, et al. Diesel exhaust activates and primes microglia: air pollution, neuroinflammation, and regulation of dopaminergic neurotoxicity. Environ Health Perspect. 2011;119(8):1149-1155. doi: 10.1289/ehp.1002986 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Calderón-Garcidueñas L, Solt AC, Henríquez-Roldán C, et al. Long-term air pollution exposure is associated with neuroinflammation, an altered innate immune response, disruption of the blood-brain barrier, ultrafine particulate deposition, and accumulation of amyloid β-42 and α-synuclein in children and young adults. Toxicol Pathol. 2008;36(2):289-310. doi: 10.1177/0192623307313011 [DOI] [PubMed] [Google Scholar]
18.Jensen O, Parkin D, MacLennan R, Muir C, Skeet R, eds. Cancer Registration: Principles and Methods. International Agency for Research on Cancer, International Association of Cancer Registries; 1991. [Google Scholar]
19.Oppenheim HA, Lucero J, Guyot AC, et al. Exposure to vehicle emissions results in altered blood brain barrier permeability and expression of matrix metalloproteinases and tight junction proteins in mice. Particle Fibre Toxicol. 2013;10(1):62. doi: 10.1186/1743-8977-10-62 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Block ML, Zecca L, Hong JS. Microglia-mediated neurotoxicity: uncovering the molecular mechanisms. Nat Rev Neurosci. 2007;8(1):57-69. doi: 10.1038/nrn2038 [DOI] [PubMed] [Google Scholar]
21.Block ML, Elder A, Auten RL, et al. The outdoor air pollution and brain health workshop. Neurotoxicology. 2012;33(5):972-984. doi: 10.1016/j.neuro.2012.08.014 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Oberdörster G, Sharp Z, Atudorei V, et al. Translocation of inhaled ultrafine particles to the brain. Inhalational Toxicol. 2004;16(6-7):437-445. doi: 10.1080/08958370490439597 [DOI] [PubMed] [Google Scholar]
23.Mühlfeld C, Rothen-Rutishauser B, Blank F, Vanhecke D, Ochs M, Gehr P. Interactions of nanoparticles with pulmonary structures and cellular responses. Am J Physiol Lung Cell Mol Physiol. 2008;294(5):L817-L829. doi: 10.1152/ajplung.00442.2007 [DOI] [PubMed] [Google Scholar]
24.Lucchini RG, Dorman DC, Elder A, Veronesi B. Neurological impacts from inhalation of pollutants and the nose-brain connection. Neurotoxicology. 2012;33(4):838-841. doi: 10.1016/j.neuro.2011.12.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Ljungman PL, Mittleman MA. Ambient air pollution and stroke. Stroke. 2014;45(12):3734-3741. doi: 10.1161/STROKEAHA.114.003130 [DOI] [PMC free article] [PubMed] [Google Scholar]
26.von Bornstädt D, Kunz A, Endres M. Impact of particulate matter exposition on the risk of ischemic stroke: epidemiologic evidence and putative mechanisms. J Cereb Blood Flow Metab. 2014;34(2):215-220. doi: 10.1038/jcbfm.2013.212 [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Baumgart M, Snyder HM, Carrillo MC, Fazio S, Kim H, Johns H. Summary of the evidence on modifiable risk factors for cognitive decline and dementia: a population-based perspective. Alzheimers Dement. 2015;11(6):718-726. doi: 10.1016/j.jalz.2015.05.016 [DOI] [PubMed] [Google Scholar]
28.Shah AS, Lee KK, McAllister DA, et al. Short term exposure to air pollution and stroke: systematic review and meta-analysis. BMJ. 2015;350:h1295. doi: 10.1136/bmj.h1295 [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Shin HH, Fann N, Burnett RT, Cohen A, Hubbell BJ. Outdoor fine particles and nonfatal strokes: systematic review and meta-analysis. Epidemiology. 2014;25(6):835-842. doi: 10.1097/EDE.0000000000000162 [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Wellenius GA, Boyle LD, Wilker EH, et al. Ambient fine particulate matter alters cerebral hemodynamics in the elderly. Stroke. 2013;44(6):1532-1536. doi: 10.1161/STROKEAHA.111.000395 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Zhang B, Weuve J, Langa KM, et al. Comparison of particulate air pollution from different emission sources and incident dementia in the US. JAMA Intern Med. 2023;183(10):1080-1089. doi: 10.1001/jamainternmed.2023.3300 [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Makkonen U, Hellén H, Anttila P, Ferm M. Size distribution and chemical composition of airborne particles in south-eastern Finland during different seasons and wildfire episodes in 2006. Sci Total Environ. 2010;408(3):644-651. doi: 10.1016/j.scitotenv.2009.10.050 [DOI] [PubMed] [Google Scholar]
33.Vardoulakis S, Jalaludin BB, Morgan GG, Hanigan IC, Johnston FH. Bushfire smoke: urgent need for a national health protection strategy. Med J Aust. 2020;212(8):349-353.e1. doi: 10.5694/mja2.50511 [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Kaiser Permanente Southern California Department of Research and Evaluation . Fast facts: southern California research program. Accessed February 1, 2024. https://www.kp-scalresearch.org/aboutus/fast-facts/
35.Koebnick C, Langer-Gould AM, Gould MK, et al. Sociodemographic characteristics of members of a large, integrated health care system: comparison with US Census Bureau data. Permanente J. 2012;16(3):37-41. doi: 10.7812/TPP/12-031 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Aguilera R, Luo N, Basu R, et al. A novel ensemble-based statistical approach to estimate daily wildfire-specific PM_2.5 in California (2006-2020). Environ Int. 2023;171:107719. doi: 10.1016/j.envint.2022.107719 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Abatzoglou JT. Development of gridded surface meteorological data for ecological applications and modelling. Int J Climatology. 2013;33(1):121-131. doi: 10.1002/joc.3413 [DOI] [Google Scholar]
38.Wilker EH, Osman M, Weisskopf MG. Ambient air pollution and clinical dementia: systematic review and meta-analysis. BMJ. 2023;381:e071620. doi: 10.1136/bmj-2022-071620 [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Gilsanz P, Mayeda ER, Glymour MM, Quesenberry CP, Whitmer RA. Association between birth in a high stroke mortality state, race, and risk of dementia. JAMA Neurol. 2017;74(9):1056-1062. doi: 10.1001/jamaneurol.2017.1553 [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Mayeda ER, Glymour MM, Quesenberry CP, Whitmer RA. Inequalities in dementia incidence between six racial and ethnic groups over 14 years. Alzheimers Dement. 2016;12(3):216-224. doi: 10.1016/j.jalz.2015.12.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Whitmer RA, Sidney S, Selby J, Johnston SC, Yaffe K. Midlife cardiovascular risk factors and risk of dementia in late life. Neurology. 2005;64(2):277-281. doi: 10.1212/01.WNL.0000149519.47454.F2 [DOI] [PubMed] [Google Scholar]
42.Whitmer RA, Gustafson DR, Barrett-Connor E, Haan MN, Gunderson EP, Yaffe K. Central obesity and increased risk of dementia more than three decades later. Neurology. 2008;71(14):1057-1064. doi: 10.1212/01.wnl.0000306313.89165.ef [DOI] [PubMed] [Google Scholar]
43.Katon WJ, Lin EH, Williams LH, et al. Comorbid depression is associated with an increased risk of dementia diagnosis in patients with diabetes: a prospective cohort study. J Gen Intern Med. 2010;25(5):423-429. doi: 10.1007/s11606-009-1248-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Manson S, Schroeder J, Riper DV, Ruggles S. National Historical Geographic Information System: version 12.0 [dataset]. University of Minnesota . Accessed February 1, 2024. doi: 10.18128/D050.V12.0 [DOI]
45.de Bont J, Jaganathan S, Dahlquist M, Persson Å, Stafoggia M, Ljungman P. Ambient air pollution and cardiovascular diseases: an umbrella review of systematic reviews and meta-analyses. J Intern Med. 2022;291(6):779-800. doi: 10.1111/joim.13467 [DOI] [PMC free article] [PubMed] [Google Scholar]
46.McGuinn LA, Schneider A, McGarrah RW, et al. Association of long-term PM_2.5 exposure with traditional and novel lipid measures related to cardiovascular disease risk. Environ Int. 2019;122:193-200. doi: 10.1016/j.envint.2018.11.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Zhao M, Xu Z, Guo Q, Gan Y, Wang Q, Liu JA. Association between long-term exposure to PM_2.5 and hypertension: a systematic review and meta-analysis of observational studies. Environ Res. 2022;204(pt D):112352. doi: 10.1016/j.envres.2021.112352 [DOI] [PubMed] [Google Scholar]
48.Casey JA, Kioumourtzoglou MA, Padula A, et al. Measuring long-term exposure to wildfire PM_2.5 in California: time-varying inequities in environmental burden. Proc Natl Acad Sci USA. 2024;121(8):e2306729121. doi: 10.1073/pnas.2306729121 [DOI] [PMC free article] [PubMed] [Google Scholar]
49.Hebert LE, Weuve J, Scherr PA, Evans DA. Alzheimer disease in the United States (2010-2050) estimated using the 2010 census. Neurology. 2013;80(19):1778-1783. doi: 10.1212/WNL.0b013e31828726f5 [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Mazure CM, Swendsen J. Sex differences in Alzheimer’s disease and other dementias. Lancet Neurol. 2016;15(5):451-452. doi: 10.1016/S1474-4422(16)00067-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Mehta KM, Yeo GW. Systematic review of dementia prevalence and incidence in United States race/ethnic populations. Alzheimers Dement. 2017;13(1):72-83. doi: 10.1016/j.jalz.2016.06.2360 [DOI] [PubMed] [Google Scholar]
52.Dintica CS, Bahorik A, Xia F, Kind A, Yaffe K. Dementia risk and disadvantaged neighborhoods. JAMA Neurol. 2023;80(9):903-909. doi: 10.1001/jamaneurol.2023.2120 [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Kaufman JS, MacLehose RF. Which of these things is not like the others? Cancer. 2013;119(24):4216-4222. doi: 10.1002/cncr.28359 [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Young JG, Stensrud MJ, Tchetgen Tchetgen EJ, Hernán MA. A causal framework for classical statistical estimands in failure-time settings with competing events. Stat Med. 2020;39(8):1199-1236. doi: 10.1002/sim.8471 [DOI] [PMC free article] [PubMed] [Google Scholar]
55.Morello-Frosch R, Obasogie OK. The climate gap and the color line—racial health inequities and climate change. N Engl J Med. 2023;388(10):943-949. doi: 10.1056/NEJMsb2213250 [DOI] [PubMed] [Google Scholar]
56.Smith GS, Anjum E, Francis C, Deanes L, Acey C. Climate change, environmental disasters, and health inequities: the underlying role of structural inequalities. Curr Environ Health Rep. 2022;9(1):80-89. doi: 10.1007/s40572-022-00336-w [DOI] [PubMed] [Google Scholar]
57.Abolhasani E, Hachinski V, Ghazaleh N, Azarpazhooh MR, Mokhber N, Martin J. Air pollution and incidence of dementia: a systematic review and meta-analysis. Neurology. 2023;100(2):e242-e254. doi: 10.1212/WNL.0000000000201419 [DOI] [PubMed] [Google Scholar]
58.Tsai TL, Lin YT, Hwang BF, et al. Fine particulate matter is a potential determinant of Alzheimer’s disease: a systemic review and meta-analysis. Environ Res. 2019;177:108638. doi: 10.1016/j.envres.2019.108638 [DOI] [PubMed] [Google Scholar]
59.Shaffer RM, Blanco MN, Li G, et al. Fine particulate matter and dementia incidence in the adult changes in thought study. Environ Health Perspect. 2021;129(8):87001. doi: 10.1289/EHP9018 [DOI] [PMC free article] [PubMed] [Google Scholar]
60.Sullivan KJ, Ran X, Wu F, et al. Ambient fine particulate matter exposure and incident mild cognitive impairment and dementia. J Am Geriatr Soc. 2021;69(8):2185-2194. doi: 10.1111/jgs.17188 [DOI] [PMC free article] [PubMed] [Google Scholar]
61.Shi L, Steenland K, Li H, et al. A national cohort study (2000-2018) of long-term air pollution exposure and incident dementia in older adults in the United States. Nat Commun. 2021;12(1):6754. doi: 10.1038/s41467-021-27049-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
62.Semmens EO, Leary CS, Fitzpatrick AL, et al. Air pollution and dementia in older adults in the Ginkgo Evaluation of Memory Study. Alzheimers Dement. 2023;19(2):549-559. doi: 10.1002/alz.12654 [DOI] [PMC free article] [PubMed] [Google Scholar]
63.Verma V, Polidori A, Schauer JJ, Shafer MM, Cassee FR, Sioutas C. Physicochemical and toxicological profiles of particulate matter in Los Angeles during the October 2007 southern California wildfires. Environ Sci Technol. 2009;43(3):954-960. doi: 10.1021/es8021667 [DOI] [PubMed] [Google Scholar]
64.Balmes JR. The changing nature of wildfires: impacts on the health of the public. Clin Chest Med. 2020;41(4):771-776. doi: 10.1016/j.ccm.2020.08.006 [DOI] [PubMed] [Google Scholar]
65.Masselot P, Sera F, Schneider R, et al. Differential mortality risks associated with PM2.5 components: a multi-country, multi-city study. Epidemiology. 2022;33(2):167-175. doi: 10.1097/EDE.0000000000001455 [DOI] [PMC free article] [PubMed] [Google Scholar]
66.Wegesser TC, Pinkerton KE, Last JA. California wildfires of 2008: coarse and fine particulate matter toxicity. Environ Health Perspect. 2009;117(6):893-897. doi: 10.1289/ehp.0800166 [DOI] [PMC free article] [PubMed] [Google Scholar]
67.Shi L, Zhu Q, Wang Y, et al. Incident dementia and long-term exposure to constituents of fine particle air pollution: a national cohort study in the United States. Proc Natl Acad Sci USA. 2023;120(1):e2211282119. doi: 10.1073/pnas.2211282119 [DOI] [PMC free article] [PubMed] [Google Scholar]
68.Pulido L. A critical review of the methodology of environmental racism research. Antipode. 1996;28(2):142-159. doi: 10.1111/j.1467-8330.1996.tb00519.x [DOI] [Google Scholar]
69.Szasz A, Meuser M. Environmental inequalities: literature review and proposals for new directions in research and theory. Curr Sociol. 1997;45(3):99-120. doi: 10.1177/001139297045003006 [DOI] [Google Scholar]
70.Burke M, Heft-Neal S, Li J, et al. Exposures and behavioural responses to wildfire smoke. Nat Hum Behav. 2022;6(10):1351-1361. doi: 10.1038/s41562-022-01396-6 [DOI] [PubMed] [Google Scholar]
71.Krebs B, Neidell M. Wildfires exacerbate inequalities in indoor pollution exposure. Environ Res Lett. 2024;19(2):024043. doi: 10.1088/1748-9326/ad22b8 [DOI] [Google Scholar]
72.Laurent O, Bard D, Filleul L, Segala C. Effect of socioeconomic status on the relationship between atmospheric pollution and mortality. J Epidemiol Community Health. 2007;61(8):665-675. doi: 10.1136/jech.2006.053611 [DOI] [PMC free article] [PubMed] [Google Scholar]
73.Morello-Frosch R, Shenassa ED. The environmental “riskscape” and social inequality: implications for explaining maternal and child health disparities. Environ Health Perspect. 2006;114(8):1150-1153. doi: 10.1289/ehp.8930 [DOI] [PMC free article] [PubMed] [Google Scholar]
74.Hajat A, Hsia C, O’Neill MS. Socioeconomic disparities and air pollution exposure: a global review. Curr Environ Health Rep. 2015;2(4):440-450. doi: 10.1007/s40572-015-0069-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
75.Berberian AG, Gonzalez DJX, Cushing LJ. Racial disparities in climate change-related health effects in the United States. Curr Environ Health Rep. 2022;9(3):451-464. doi: 10.1007/s40572-022-00360-w [DOI] [PMC free article] [PubMed] [Google Scholar]
76.Buysse CE, Kaulfus A, Nair U, Jaffe DA. Relationships between particulate matter, ozone, and nitrogen oxides during urban smoke events in the western US. Environ Sci Technol. 2019;53(21):12519-12528. doi: 10.1021/acs.est.9b05241 [DOI] [PubMed] [Google Scholar]
77.Kalashnikov DA, Schnell JL, Abatzoglou JT, Swain DL, Singh D. Increasing co-occurrence of fine particulate matter and ground-level ozone extremes in the western United States. Sci Adv. 2022;8(1):eabi9386. doi: 10.1126/sciadv.abi9386 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement 1.

eFigure 1. Flow Diagram Depicting Exclusion Criteria for Members of the KPSC Study Population, 2008-2019

eFigure 2. Association of 3-Year Average Wildfire PM2.5 Exposure With Dementia Diagnosis Among KPSC Members Aged ≥60 Years, 2008-2019

eFigure 3. Association of 3-Year Average Nonwildfire PM2.5 Exposure With Dementia Diagnosis Among KPSC Members Aged ≥60 Years, 2008-2019

eTable 1. Diagnostic Codes From the International Classification of Diseases, Ninth and Tenth Revisions (ICD-9 and ICD-10) Used to Identify Dementia

eTable 2. Dementia Subtype Diagnoses Among KPSC Members Using eTable 1 Diagnostic Codes, 2008-2019

eTable 3. Association of 3-Year Average Wildfire and Nonwildfire PM2.5 Exposure With Dementia Diagnosis Among KPSC Members Aged ≥60 Years, 2008-2019^a

eTable 4. Alternative Exposure Metrics for the Association Between Wildfire PM2.5 Exposure and Dementia Diagnosis among KPSC Members Aged ≥60 Years, 2008-2019^a

jamaneurol-e244058-s001.pdf^{(349.2KB, pdf)}

Supplement 2.

Data Sharing Statement

jamaneurol-e244058-s002.pdf^{(14.6KB, pdf)}

[noi240073r1] 1.Burke M, Driscoll A, Heft-Neal S, Xue J, Burney J, Wara M. The changing risk and burden of wildfire in the United States. Proc Natl Acad Sci USA. 2021;118(2):e2011048118. doi: 10.1073/pnas.2011048118 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r2] 2.Abatzoglou JT, Williams AP. Impact of anthropogenic climate change on wildfire across western US forests. Proc Natl Acad Sci USA. 2016;113(42):11770-11775. doi: 10.1073/pnas.1607171113 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r3] 3.Halofsky JE, Peterson DL, Harvey BJ. Changing wildfire, changing forests: the effects of climate change on fire regimes and vegetation in the Pacific Northwest, USA. Fire Ecology. 2020;16(1):1-26. doi: 10.1186/s42408-019-0062-8 [DOI] [Google Scholar]

[noi240073r4] 4.Williams AP, Abatzoglou JT, Gershunov A, et al. Observed impacts of anthropogenic climate change on wildfire in California. Earths Future. 2019;7(8):892-910. doi: 10.1029/2019EF001210 [DOI] [Google Scholar]

[noi240073r5] 5.Burke M, Childs ML, de la Cuesta B, et al. The contribution of wildfire to PM_2.5 trends in the USA. Nature. 2023;622(7984):761-766. doi: 10.1038/s41586-023-06522-6 [DOI] [PubMed] [Google Scholar]

[noi240073r6] 6.Liu JC, Mickley LJ, Sulprizio MP, et al. Particulate air pollution from wildfires in the Western US under climate change. Climate Change. 2016;138(3):655-666. doi: 10.1007/s10584-016-1762-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r7] 7.Wang D, Guan D, Zhu S, et al. Economic footprint of California wildfires in 2018. Nat Sustainability. 2021;4(3):252-260. doi: 10.1038/s41893-020-00646-7 [DOI] [Google Scholar]

[noi240073r8] 8.Reid J, Koppmann R, Eck T, Eleuterio D. A review of biomass burning emissions part II: intensive physical properties of biomass burning particles. Atmospheric Chem Phys. 2005;5(3):799-825. doi: 10.5194/acp-5-799-2005 [DOI] [Google Scholar]

[noi240073r9] 9.Liu JC, Wilson A, Mickley LJ, et al. Wildfire-specific fine particulate matter and risk of hospital admissions in urban and rural counties. Epidemiology. 2017;28(1):77-85. doi: 10.1097/EDE.0000000000000556 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r10] 10.Xing YF, Xu YH, Shi MH, Lian YX. The impact of PM2.5 on the human respiratory system. J Thorac Dis. 2016;8(1):E69-E74. [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r11] 11.Weuve J, Bennett EE, Ranker L, et al. Exposure to air pollution in relation to risk of dementia and related outcomes: an updated systematic review of the epidemiological literature. Environ Health Perspect. 2021;129(9):96001. doi: 10.1289/EHP8716 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r12] 12.Block ML, Calderón-Garcidueñas L. Air pollution: mechanisms of neuroinflammation and CNS disease. Trends Neurosci. 2009;32(9):506-516. doi: 10.1016/j.tins.2009.05.009 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r13] 13.Heusinkveld HJ, Wahle T, Campbell A, et al. Neurodegenerative and neurological disorders by small ihaled particles. Neurotoxicology. 2016;56:94-106. doi: 10.1016/j.neuro.2016.07.007 [DOI] [PubMed] [Google Scholar]

[noi240073r14] 14.Jayaraj RL, Rodriguez EA, Wang Y, Block ML. Outdoor ambient air pollution and neurodegenerative diseases: the neuroinflammation hypothesis. Curr Environ Health Rep. 2017;4(2):166-179. doi: 10.1007/s40572-017-0142-3 [DOI] [PubMed] [Google Scholar]

[noi240073r15] 15.Calderón-Garcidueñas L, Mora-Tiscareño A, Ontiveros E, et al. Air pollution, cognitive deficits and brain abnormalities: a pilot study with children and dogs. Brain Cogn. 2008;68(2):117-127. doi: 10.1016/j.bandc.2008.04.008 [DOI] [PubMed] [Google Scholar]

[noi240073r16] 16.Levesque S, Taetzsch T, Lull ME, et al. Diesel exhaust activates and primes microglia: air pollution, neuroinflammation, and regulation of dopaminergic neurotoxicity. Environ Health Perspect. 2011;119(8):1149-1155. doi: 10.1289/ehp.1002986 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r17] 17.Calderón-Garcidueñas L, Solt AC, Henríquez-Roldán C, et al. Long-term air pollution exposure is associated with neuroinflammation, an altered innate immune response, disruption of the blood-brain barrier, ultrafine particulate deposition, and accumulation of amyloid β-42 and α-synuclein in children and young adults. Toxicol Pathol. 2008;36(2):289-310. doi: 10.1177/0192623307313011 [DOI] [PubMed] [Google Scholar]

[noi240073r18] 18.Jensen O, Parkin D, MacLennan R, Muir C, Skeet R, eds. Cancer Registration: Principles and Methods. International Agency for Research on Cancer, International Association of Cancer Registries; 1991. [Google Scholar]

[noi240073r19] 19.Oppenheim HA, Lucero J, Guyot AC, et al. Exposure to vehicle emissions results in altered blood brain barrier permeability and expression of matrix metalloproteinases and tight junction proteins in mice. Particle Fibre Toxicol. 2013;10(1):62. doi: 10.1186/1743-8977-10-62 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r20] 20.Block ML, Zecca L, Hong JS. Microglia-mediated neurotoxicity: uncovering the molecular mechanisms. Nat Rev Neurosci. 2007;8(1):57-69. doi: 10.1038/nrn2038 [DOI] [PubMed] [Google Scholar]

[noi240073r21] 21.Block ML, Elder A, Auten RL, et al. The outdoor air pollution and brain health workshop. Neurotoxicology. 2012;33(5):972-984. doi: 10.1016/j.neuro.2012.08.014 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r22] 22.Oberdörster G, Sharp Z, Atudorei V, et al. Translocation of inhaled ultrafine particles to the brain. Inhalational Toxicol. 2004;16(6-7):437-445. doi: 10.1080/08958370490439597 [DOI] [PubMed] [Google Scholar]

[noi240073r23] 23.Mühlfeld C, Rothen-Rutishauser B, Blank F, Vanhecke D, Ochs M, Gehr P. Interactions of nanoparticles with pulmonary structures and cellular responses. Am J Physiol Lung Cell Mol Physiol. 2008;294(5):L817-L829. doi: 10.1152/ajplung.00442.2007 [DOI] [PubMed] [Google Scholar]

[noi240073r24] 24.Lucchini RG, Dorman DC, Elder A, Veronesi B. Neurological impacts from inhalation of pollutants and the nose-brain connection. Neurotoxicology. 2012;33(4):838-841. doi: 10.1016/j.neuro.2011.12.001 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r25] 25.Ljungman PL, Mittleman MA. Ambient air pollution and stroke. Stroke. 2014;45(12):3734-3741. doi: 10.1161/STROKEAHA.114.003130 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r26] 26.von Bornstädt D, Kunz A, Endres M. Impact of particulate matter exposition on the risk of ischemic stroke: epidemiologic evidence and putative mechanisms. J Cereb Blood Flow Metab. 2014;34(2):215-220. doi: 10.1038/jcbfm.2013.212 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r27] 27.Baumgart M, Snyder HM, Carrillo MC, Fazio S, Kim H, Johns H. Summary of the evidence on modifiable risk factors for cognitive decline and dementia: a population-based perspective. Alzheimers Dement. 2015;11(6):718-726. doi: 10.1016/j.jalz.2015.05.016 [DOI] [PubMed] [Google Scholar]

[noi240073r28] 28.Shah AS, Lee KK, McAllister DA, et al. Short term exposure to air pollution and stroke: systematic review and meta-analysis. BMJ. 2015;350:h1295. doi: 10.1136/bmj.h1295 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r29] 29.Shin HH, Fann N, Burnett RT, Cohen A, Hubbell BJ. Outdoor fine particles and nonfatal strokes: systematic review and meta-analysis. Epidemiology. 2014;25(6):835-842. doi: 10.1097/EDE.0000000000000162 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r30] 30.Wellenius GA, Boyle LD, Wilker EH, et al. Ambient fine particulate matter alters cerebral hemodynamics in the elderly. Stroke. 2013;44(6):1532-1536. doi: 10.1161/STROKEAHA.111.000395 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r31] 31.Zhang B, Weuve J, Langa KM, et al. Comparison of particulate air pollution from different emission sources and incident dementia in the US. JAMA Intern Med. 2023;183(10):1080-1089. doi: 10.1001/jamainternmed.2023.3300 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r32] 32.Makkonen U, Hellén H, Anttila P, Ferm M. Size distribution and chemical composition of airborne particles in south-eastern Finland during different seasons and wildfire episodes in 2006. Sci Total Environ. 2010;408(3):644-651. doi: 10.1016/j.scitotenv.2009.10.050 [DOI] [PubMed] [Google Scholar]

[noi240073r33] 33.Vardoulakis S, Jalaludin BB, Morgan GG, Hanigan IC, Johnston FH. Bushfire smoke: urgent need for a national health protection strategy. Med J Aust. 2020;212(8):349-353.e1. doi: 10.5694/mja2.50511 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r34] 34.Kaiser Permanente Southern California Department of Research and Evaluation . Fast facts: southern California research program. Accessed February 1, 2024. https://www.kp-scalresearch.org/aboutus/fast-facts/

[noi240073r35] 35.Koebnick C, Langer-Gould AM, Gould MK, et al. Sociodemographic characteristics of members of a large, integrated health care system: comparison with US Census Bureau data. Permanente J. 2012;16(3):37-41. doi: 10.7812/TPP/12-031 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r36] 36.Aguilera R, Luo N, Basu R, et al. A novel ensemble-based statistical approach to estimate daily wildfire-specific PM_2.5 in California (2006-2020). Environ Int. 2023;171:107719. doi: 10.1016/j.envint.2022.107719 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r37] 37.Abatzoglou JT. Development of gridded surface meteorological data for ecological applications and modelling. Int J Climatology. 2013;33(1):121-131. doi: 10.1002/joc.3413 [DOI] [Google Scholar]

[noi240073r38] 38.Wilker EH, Osman M, Weisskopf MG. Ambient air pollution and clinical dementia: systematic review and meta-analysis. BMJ. 2023;381:e071620. doi: 10.1136/bmj-2022-071620 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r39] 39.Gilsanz P, Mayeda ER, Glymour MM, Quesenberry CP, Whitmer RA. Association between birth in a high stroke mortality state, race, and risk of dementia. JAMA Neurol. 2017;74(9):1056-1062. doi: 10.1001/jamaneurol.2017.1553 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r40] 40.Mayeda ER, Glymour MM, Quesenberry CP, Whitmer RA. Inequalities in dementia incidence between six racial and ethnic groups over 14 years. Alzheimers Dement. 2016;12(3):216-224. doi: 10.1016/j.jalz.2015.12.007 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r41] 41.Whitmer RA, Sidney S, Selby J, Johnston SC, Yaffe K. Midlife cardiovascular risk factors and risk of dementia in late life. Neurology. 2005;64(2):277-281. doi: 10.1212/01.WNL.0000149519.47454.F2 [DOI] [PubMed] [Google Scholar]

[noi240073r42] 42.Whitmer RA, Gustafson DR, Barrett-Connor E, Haan MN, Gunderson EP, Yaffe K. Central obesity and increased risk of dementia more than three decades later. Neurology. 2008;71(14):1057-1064. doi: 10.1212/01.wnl.0000306313.89165.ef [DOI] [PubMed] [Google Scholar]

[noi240073r43] 43.Katon WJ, Lin EH, Williams LH, et al. Comorbid depression is associated with an increased risk of dementia diagnosis in patients with diabetes: a prospective cohort study. J Gen Intern Med. 2010;25(5):423-429. doi: 10.1007/s11606-009-1248-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r44] 44.Manson S, Schroeder J, Riper DV, Ruggles S. National Historical Geographic Information System: version 12.0 [dataset]. University of Minnesota . Accessed February 1, 2024. doi: 10.18128/D050.V12.0 [DOI]

[noi240073r45] 45.de Bont J, Jaganathan S, Dahlquist M, Persson Å, Stafoggia M, Ljungman P. Ambient air pollution and cardiovascular diseases: an umbrella review of systematic reviews and meta-analyses. J Intern Med. 2022;291(6):779-800. doi: 10.1111/joim.13467 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r46] 46.McGuinn LA, Schneider A, McGarrah RW, et al. Association of long-term PM_2.5 exposure with traditional and novel lipid measures related to cardiovascular disease risk. Environ Int. 2019;122:193-200. doi: 10.1016/j.envint.2018.11.001 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r47] 47.Zhao M, Xu Z, Guo Q, Gan Y, Wang Q, Liu JA. Association between long-term exposure to PM_2.5 and hypertension: a systematic review and meta-analysis of observational studies. Environ Res. 2022;204(pt D):112352. doi: 10.1016/j.envres.2021.112352 [DOI] [PubMed] [Google Scholar]

[noi240073r48] 48.Casey JA, Kioumourtzoglou MA, Padula A, et al. Measuring long-term exposure to wildfire PM_2.5 in California: time-varying inequities in environmental burden. Proc Natl Acad Sci USA. 2024;121(8):e2306729121. doi: 10.1073/pnas.2306729121 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r49] 49.Hebert LE, Weuve J, Scherr PA, Evans DA. Alzheimer disease in the United States (2010-2050) estimated using the 2010 census. Neurology. 2013;80(19):1778-1783. doi: 10.1212/WNL.0b013e31828726f5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r50] 50.Mazure CM, Swendsen J. Sex differences in Alzheimer’s disease and other dementias. Lancet Neurol. 2016;15(5):451-452. doi: 10.1016/S1474-4422(16)00067-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r51] 51.Mehta KM, Yeo GW. Systematic review of dementia prevalence and incidence in United States race/ethnic populations. Alzheimers Dement. 2017;13(1):72-83. doi: 10.1016/j.jalz.2016.06.2360 [DOI] [PubMed] [Google Scholar]

[noi240073r52] 52.Dintica CS, Bahorik A, Xia F, Kind A, Yaffe K. Dementia risk and disadvantaged neighborhoods. JAMA Neurol. 2023;80(9):903-909. doi: 10.1001/jamaneurol.2023.2120 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r53] 53.Kaufman JS, MacLehose RF. Which of these things is not like the others? Cancer. 2013;119(24):4216-4222. doi: 10.1002/cncr.28359 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r54] 54.Young JG, Stensrud MJ, Tchetgen Tchetgen EJ, Hernán MA. A causal framework for classical statistical estimands in failure-time settings with competing events. Stat Med. 2020;39(8):1199-1236. doi: 10.1002/sim.8471 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r55] 55.Morello-Frosch R, Obasogie OK. The climate gap and the color line—racial health inequities and climate change. N Engl J Med. 2023;388(10):943-949. doi: 10.1056/NEJMsb2213250 [DOI] [PubMed] [Google Scholar]

[noi240073r56] 56.Smith GS, Anjum E, Francis C, Deanes L, Acey C. Climate change, environmental disasters, and health inequities: the underlying role of structural inequalities. Curr Environ Health Rep. 2022;9(1):80-89. doi: 10.1007/s40572-022-00336-w [DOI] [PubMed] [Google Scholar]

[noi240073r57] 57.Abolhasani E, Hachinski V, Ghazaleh N, Azarpazhooh MR, Mokhber N, Martin J. Air pollution and incidence of dementia: a systematic review and meta-analysis. Neurology. 2023;100(2):e242-e254. doi: 10.1212/WNL.0000000000201419 [DOI] [PubMed] [Google Scholar]

[noi240073r58] 58.Tsai TL, Lin YT, Hwang BF, et al. Fine particulate matter is a potential determinant of Alzheimer’s disease: a systemic review and meta-analysis. Environ Res. 2019;177:108638. doi: 10.1016/j.envres.2019.108638 [DOI] [PubMed] [Google Scholar]

[noi240073r59] 59.Shaffer RM, Blanco MN, Li G, et al. Fine particulate matter and dementia incidence in the adult changes in thought study. Environ Health Perspect. 2021;129(8):87001. doi: 10.1289/EHP9018 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r60] 60.Sullivan KJ, Ran X, Wu F, et al. Ambient fine particulate matter exposure and incident mild cognitive impairment and dementia. J Am Geriatr Soc. 2021;69(8):2185-2194. doi: 10.1111/jgs.17188 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r61] 61.Shi L, Steenland K, Li H, et al. A national cohort study (2000-2018) of long-term air pollution exposure and incident dementia in older adults in the United States. Nat Commun. 2021;12(1):6754. doi: 10.1038/s41467-021-27049-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r62] 62.Semmens EO, Leary CS, Fitzpatrick AL, et al. Air pollution and dementia in older adults in the Ginkgo Evaluation of Memory Study. Alzheimers Dement. 2023;19(2):549-559. doi: 10.1002/alz.12654 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r63] 63.Verma V, Polidori A, Schauer JJ, Shafer MM, Cassee FR, Sioutas C. Physicochemical and toxicological profiles of particulate matter in Los Angeles during the October 2007 southern California wildfires. Environ Sci Technol. 2009;43(3):954-960. doi: 10.1021/es8021667 [DOI] [PubMed] [Google Scholar]

[noi240073r64] 64.Balmes JR. The changing nature of wildfires: impacts on the health of the public. Clin Chest Med. 2020;41(4):771-776. doi: 10.1016/j.ccm.2020.08.006 [DOI] [PubMed] [Google Scholar]

[noi240073r65] 65.Masselot P, Sera F, Schneider R, et al. Differential mortality risks associated with PM2.5 components: a multi-country, multi-city study. Epidemiology. 2022;33(2):167-175. doi: 10.1097/EDE.0000000000001455 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r66] 66.Wegesser TC, Pinkerton KE, Last JA. California wildfires of 2008: coarse and fine particulate matter toxicity. Environ Health Perspect. 2009;117(6):893-897. doi: 10.1289/ehp.0800166 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r67] 67.Shi L, Zhu Q, Wang Y, et al. Incident dementia and long-term exposure to constituents of fine particle air pollution: a national cohort study in the United States. Proc Natl Acad Sci USA. 2023;120(1):e2211282119. doi: 10.1073/pnas.2211282119 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r68] 68.Pulido L. A critical review of the methodology of environmental racism research. Antipode. 1996;28(2):142-159. doi: 10.1111/j.1467-8330.1996.tb00519.x [DOI] [Google Scholar]

[noi240073r69] 69.Szasz A, Meuser M. Environmental inequalities: literature review and proposals for new directions in research and theory. Curr Sociol. 1997;45(3):99-120. doi: 10.1177/001139297045003006 [DOI] [Google Scholar]

[noi240073r70] 70.Burke M, Heft-Neal S, Li J, et al. Exposures and behavioural responses to wildfire smoke. Nat Hum Behav. 2022;6(10):1351-1361. doi: 10.1038/s41562-022-01396-6 [DOI] [PubMed] [Google Scholar]

[noi240073r71] 71.Krebs B, Neidell M. Wildfires exacerbate inequalities in indoor pollution exposure. Environ Res Lett. 2024;19(2):024043. doi: 10.1088/1748-9326/ad22b8 [DOI] [Google Scholar]

[noi240073r72] 72.Laurent O, Bard D, Filleul L, Segala C. Effect of socioeconomic status on the relationship between atmospheric pollution and mortality. J Epidemiol Community Health. 2007;61(8):665-675. doi: 10.1136/jech.2006.053611 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r73] 73.Morello-Frosch R, Shenassa ED. The environmental “riskscape” and social inequality: implications for explaining maternal and child health disparities. Environ Health Perspect. 2006;114(8):1150-1153. doi: 10.1289/ehp.8930 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r74] 74.Hajat A, Hsia C, O’Neill MS. Socioeconomic disparities and air pollution exposure: a global review. Curr Environ Health Rep. 2015;2(4):440-450. doi: 10.1007/s40572-015-0069-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r75] 75.Berberian AG, Gonzalez DJX, Cushing LJ. Racial disparities in climate change-related health effects in the United States. Curr Environ Health Rep. 2022;9(3):451-464. doi: 10.1007/s40572-022-00360-w [DOI] [PMC free article] [PubMed] [Google Scholar]

[noi240073r76] 76.Buysse CE, Kaulfus A, Nair U, Jaffe DA. Relationships between particulate matter, ozone, and nitrogen oxides during urban smoke events in the western US. Environ Sci Technol. 2019;53(21):12519-12528. doi: 10.1021/acs.est.9b05241 [DOI] [PubMed] [Google Scholar]

[noi240073r77] 77.Kalashnikov DA, Schnell JL, Abatzoglou JT, Swain DL, Singh D. Increasing co-occurrence of fine particulate matter and ground-level ozone extremes in the western United States. Sci Adv. 2022;8(1):eabi9386. doi: 10.1126/sciadv.abi9386 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Wildfire Smoke Exposure and Incident Dementia

Holly Elser, MD, PhD

Timothy B Frankland, MA

Chen Chen, PhD

Sara Y Tartof, PhD

Elizabeth Rose Mayeda, PhD

Gina S Lee, MPH

Alexander J Northrop, MD

Jacqueline M Torres, PhD

Tarik Benmarhnia, PhD

Joan A Casey, PhD

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Design, Setting, and Participants

Exposures

Main Outcome and Measures

Results

Conclusions and Relevance

Introduction

Methods

Wildfire Smoke Exposure

Dementia Diagnosis

Covariates

Statistical Analysis

Secondary Analyses

Sensitivity Analyses

Results

Table. Characteristics of the Kaiser Permanente Southern California (KPSC) Study Population, 2008-2019.

Figure 1. Mean Census Tract–Level Wildfire and Nonwildfire Fine Particulate Matter (PM2.5) Smoke Exposure, 2006-2019.

Figure 2. Association of 3-Year Mean Wildfire and Nonwildfire Fine Particulate Matter (PM2.5) Exposure With Dementia Diagnosis1 Among Kaiser Permanente Southern California (KPSC) Members Aged 60 Years or Older, 2008-2019.

Discussion

Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases

Figure 1. Mean Census Tract–Level Wildfire and Nonwildfire Fine Particulate Matter (PM_2.5) Smoke Exposure, 2006-2019.

Figure 2. Association of 3-Year Mean Wildfire and Nonwildfire Fine Particulate Matter (PM_2.5) Exposure With Dementia Diagnosis¹ Among Kaiser Permanente Southern California (KPSC) Members Aged 60 Years or Older, 2008-2019.