Association Between Extreme Heat and Externalizing Symptoms in Pre- and Early Adolescence: Findings From the ABCD Study

Sara Briker; Kate T Tran; Elina Visoki; Joshua H Gordon; Kevin W Hoffman; Ran Barzilay

doi:10.1016/j.jaacop.2024.09.009

. 2024 Sep 27;3(3):713–724. doi: 10.1016/j.jaacop.2024.09.009

Association Between Extreme Heat and Externalizing Symptoms in Pre- and Early Adolescence: Findings From the ABCD Study

Sara Briker ^a, Kate T Tran ^b,^c, Elina Visoki ^b,^c, Joshua H Gordon ^a, Kevin W Hoffman ^a, Ran Barzilay ^a,^b,^c,^∗

PMCID: PMC12414308 PMID: 40922773

Abstract

Objective

Considering the growing threat of climate change and the current youth mental health crisis, data are needed on the relationship between climate and youth mental health. Hot weather contributes to the mental health burden, specifically aggression. We studied associations between extreme heat and externalizing symptoms or suicidal behavior among US preadolescents.

Method

We analyzed data from the Adolescent Brain Cognitive Development (ABCD) Study. Participants (N = 8,120, mean age 9.89 years at baseline, 48.40% female, 23.81% Black, 18.17% Hispanic) were assessed longitudinally between 2016 and 2020 across 21 sites. We estimated exposure to extreme heat (measured around the study site) as the number of days in the month of study visit with a maximum temperature ≥90°F (32.2°C) based on National Centers for Environmental Information data. We characterized exposure to extreme heat across racial/ethnic groups. We used mixed-effects regression models to test associations of extreme heat with externalizing symptoms (parent-report) and suicide attempts (self-report), assessed in a validated clinical interview. Models adjusted for demographics (age, sex, race, ethnicity, and household income) and neighborhood characteristics (gross residential density, population density, national walkability index, and fraction of grass, forest, and built land use).

Results

Exposure to extreme heat was less prevalent among non-Hispanic White participants (5.2 days/mo) compared to non-Hispanic Black and to Hispanic youth (7.2 and 7.4 days/mo, respectively). Extreme heat showed a small but significant association with externalizing symptoms (incidence rate ratio [IRR]=1.06, 95% CI = 1.04-1.08, p < .001). The association did not change when adjusting for demographics, and remained similar when further adjusting for neighborhood characteristics (IRR = 1.05, 95% CI = 1.00-1.12, p = .04). Sensitivity analyses using extreme heat at the participants’ home address level in the 6 days prior to study visit, available only for ABCD baseline assessment, revealed similar findings. Extreme heat was not associated with suicide attempts (odds ratio = 0.94, 95% CI = 0.77-1.14, p = .52).

Conclusion

Our findings add to the literature on the association between extreme heat and externalizing symptoms, and suggest that this association already exists in preadolescence. Future studies are warranted to better understand the mechanisms linking hot weather and mental health and its related racial/ethnic disparities.

Study preregistration information

Association between extreme heat and mental health in early adolescence; https://osf.io/ph7y2/.

Diversity & Inclusion Statement

We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure that the study questionnaires were prepared in an inclusive way. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. We actively worked to promote sex and gender balance in our author group.

We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work. While citing references scientifically relevant for this work, we also actively worked to promote sex and gender balance in our reference list.

Key words: extreme heat, adolescents, mental health, climate change, ABCD Study

Plain language summary

Using data from the ABCD Study that follows 8,120 children from childhood to adolescence, this study found an association of exposure to extreme heat, defined by days of the month with ≥ 90°F (32.2°C), with behavioral symptoms in preadolescents aged 10-12 years across the US. In addition, the study found that youth of color are exposed to more extreme heat, which might contribute to youth mental health disparities. While the association observed between heat and behavioral problems was small in magnitude, this large-scale study adds to the evidence of the negative contribution of climate change to youth mental health in the US. More research is needed to uncover mechanisms that explain how climate change affects the mental health of young people and its related disparities.

“The heat was beginning to scorch my cheeks; beads of sweat were gathering in my eyebrows. … and I had the same disagreeable sensations—especially in my forehead, where all the veins seemed to be bursting through the skin.”

Albert Camus, The Stranger, 1942 ¹

Environmental exposures are critical to understanding youth mental health²^,³ and its related disparities.⁴ High temperatures and heat waves are associated with adverse mental health conditions in children and adolescents.5, 6, 7 The impact of heat on health will likely worsen as average global temperatures continue to rise because of climate change.⁸ However, although the evidence for the association between heat and mental health is growing, few longitudinal studies have been conducted, especially among youth.⁶ In fact, a scoping review of 371 articles exploring the impact of climate change on children’s health, including mental health, emphasized the need for more longitudinal studies.⁶

Exposure to hot weather has been correlated with violence and aggression in adults.9, 10, 11 One proposed mechanism for this association is the temperature–aggression theory, which posits that exposure to high temperatures increases violence by causing people to feel uncomfortable and therefore act more aggressively.¹² For example, a recent pooled analysis across 100 cities in the United States found a significant association between increased temperatures and firearm shootings.¹¹ In addition, studies have also noted an association between increases in ambient temperature and self-inflicted injury¹³ and suicide deaths14, 15, 16, 17 in both adults and youth. A meta-analysis of 41 studies examining the association between temperature and mental health outcomes found a 2.2% increase in the risk of mental health–related mortality for every 1°C increase in temperature.¹⁷ Specifically in children, a study examining emergency room visits in California found that during warmer season months, there was a 6.7% increase in emergency room visits for self-inflicted injuries or suicide for every 5.6°C increase in temperature in the 6-to 18-year-old age group during the months of May through October.¹³ However, only a single study has examined the association between increased temperatures and externalizing symptoms in adolescents; in a 3-year longitudinal study of 1287 urban California youth aged 9 to 18 years, aggressive behavior in youth increased significantly with exposure to increased temperatures.¹⁸

As the evidence that suggests a link between the environment and mental health continues to grow, it is important to acknowledge factors that can potentially confound this association. Temperature itself is affected by the built environment, including neighborhood characteristics; this concept is known as the urban heat effect.¹⁹ Dense infrastructure can lead to an increase in air temperature in urban environments,¹⁹ whereas green space can contribute to a cooling effect.20, 21, 22 Other environmental factors, in addition to temperature, also play a role in the mental health burden. For instance, higher neighborhood walkability has been associated with improved mental health, including decreased depressive symptoms in older adults²³ and postpartum women.²⁴ The effects of residential density can also affect mental health in youth, specifically externalizing symptoms; for example, some studies have identified an association between environmental ambient noise and hyperactivity in children.²⁵^,²⁶

When considering the impact of the built environment on ambient temperature, it is critically important to acknowledge how racism and structural inequities continue to affect the built environment of American cities. For instance, the historical practice of redlining provides just one profound illustration of the lasting effects of structural racism. Although redlining, which consisted of banks “refusing to give mortgages to African Americans or extracting unusually severe terms from them with subprime loans” was outlawed with the 1968 Fair Housing Act, the consequences of this discrimination continue to be visible today.²⁷ In fact, a 2020 study of 108 US urban areas found that historically redlined areas had higher land surface temperatures in more than 90% of cases, with a temperature differential within some cities reaching as high as 7°C.²⁸ Furthermore, a study using 2017 US Census data from 175 urbanized areas found that in three-fourths of the cities analyzed, people of color (defined as all participants who identify as Hispanic and all who do not identify as White alone) have statistically significant higher daytime surface urban heat island exposure than non-Hispanic White people.²⁹ Given these findings, it is prudent to recognize that historically marginalized communities will likely bear a disproportionate burden of the health consequences of climate change.³⁰

In the current work, we aimed to add to the evidence on the relationship between extreme heat and externalizing symptoms in a large cohort of preadolescents. We leveraged observational data from the Adolescent Brain Cognitive Development (ABCD) Study that included thousands of youths from across the United States. The ABCD Study longitudinally follows participants from preadolescence, collecting data on mental health³¹ that can be linked with geocoded external data resources.³² In addition to the heat-externalizing symptom association, considering that suicide is the second leading cause of death in youth between 10 and 14 years of age,³³ we also tested the association between extreme heat and suicide attempts, because acts of aggression toward self can manifest as suicide attempts, and externalizing symptoms have been found to be predictors of suicidal behavior in adolescents.³⁴ We hypothesized that exposure to a greater number of days with extreme heat in the past month would be associated with greater externalizing symptoms and increased suicidal behavior. Given previous evidence suggesting an impact of structural racism on heat exposure,28, 29, 30 we also explored differential exposure to extreme heat across race and ethnicity.

Method

Participants

We included youth from the ABCD Study, which aims to identify biological and environmental factors that have an impact on or alter developmental trajectories, by tracking human brain and behavior development from childhood through adolescence. To achieve this goal, the ABCD Study recruited a cohort of children aged 9 to 10 years at baseline, ascertained through their school systems, and followed them longitudinally.³⁵ Participants in the ABCD Study were enrolled at 21 sites, with a catchment area encompassing over 20% of the entire US population in this age group. Because our exposure variable (exposure to extreme heat in the month of the ABCD Study assessment) was determined by proximity to the ABCD Study site, we excluded youth living more than 50 miles from their study site. In our main analysis, we included participants (N = 8,120) from the ABCD Study who were assessed during the months of April to October from 2016 to 2020. The distribution of participants per month is shown in Figure S1, available online. Participants assessed between April and October were overall similar in demographics compared to those assessed in cooler months (November, December, January, February, and March), with slightly greater representation of Black youth (22.8% vs 19.8%) and lower representation of Hispanic youth (17.6% vs 23.0%), with no difference in clinical characteristics (Table S1, available online). We included baseline, 1-year, and 2-year follow-up data from the ABCD Study data release 4.0 (https://abcdstudy.org), with the exception of 3 neighborhood variables (grass, forest, and built land use) that were included only in release 5.1. All participants gave assent, and parents or caregivers signed informed consent. The ABCD Study protocol was approved by the University of California, San Diego Institutional Review Board, and was exempted from a full review by the University of Pennsylvania Institutional Review Board.

Exposures

The main exposure was extreme heat, which we defined as the number of days with a maximum temperature of 90°F (32.2°C) or greater during the month of the clinical assessment (potential range, 0-31). The cut-off of 90°F was chosen based on the definition of a heat wave, as was done previously.36, 37, 38 In addition, we specifically used monthly data because of the availability of the data in the form of days per month with temperatures ≥90°C. We focused our analyses on the non-winter months of April to October, as we expected a low number of extreme heat days in winter. We defined winter as the months of November to March, because, based on the US Climate Normals from 1981 to 2010,³⁹ those are the coldest months of the year in the United States. Temperatures at each ABCD Study site from 2016 to 2022 were obtained from the Global Summaries of the Month dataset,⁴⁰ using data from the geographically closest weather station. The 21 ABCD sites and their corresponding weather stations are described in Table S2, available online. Notably, the longest distance between an ABCD Study site and its corresponding weather station was 11 km, which is well within the catchment for each ABCD Study site (80 km).³⁵

Obtaining Temperature Data and Integration With ABCD Study Data

To obtain data for extreme heat, we identified a weather station from the National Centers for Environmental Information (NCEI) to pair to each ABCD Study site, and we used R software to download temperature data from the NCEI Global Summary of the Month, Version 1(GSOM) dataset for each of the identified weather stations.

To identify 1 NCEI weather station to pair to each ABCD Study site, we used the NCEI GSOM website (https://www.ncei.noaa.gov/access/search/data-search/global-summary-of-the-month)⁴⁰ to search for weather stations near each ABCD Study site. On the search page, we selected the variable “number days with maximum temperature greater than 90F” in the “what” search box, input the zip code of the ABCD Study site in the “where” search box, and used a date range of 01/01/2016 to 12/01/2022. In cases in which there was no weather station present in the automatically defined area, we manually expanded the search area. To expand the search area, we increased the north and west longitudes and latitudes by 0.02 and decreased the south and east longitudes and latitudes by 0.02 using the “find the location using map” feature. We repeated this process until we identified at least 1 weather station for each ABCD study site. In addition, if an ABCD study site had more than 1 weather station identified, we used the weather station closest in distance to the respective ABCD site, with the distance determined using the geosphere package in R. Using this search method, we identified 1 weather station for each of the 21 ABCD study sites (Figure 1). These temperature data were downloaded using R software and merged with the ABCD Study data.

Geographic Distribution of the Adolescent Brain Cognitive Development (ABCD) Study Sites, With Temperature Data From Their Associated Weather Stations

***Note:****Map of the 21 ABCD Study sites with mean and SD for extreme heat (≥90°F) days per month from April to October, 2016 to 2020.*

To maximize the odds that the exposure (extreme heat) occurred before the outcome measure (ie, externalizing symptoms or suicide attempts), temperature data from the preceding month was used for study visits conducted in the first half of the month (days 1-14).

Comparison of Exposure to Extreme Heat Across Race and Ethnicity

We explored potential differential exposure to extreme heat based on race/ethnicity. For this purpose, we compared mean extreme heat exposure (days ≥90°F per month) between the largest caregiver-reported race and ethnicity groups (Hispanic, non-Hispanic Black, and non-Hispanic White, based on caregiver report of ethnicity and race) using 1-way analysis of variance (ANOVA). Thereafter, we performed the Tukey honestly significance difference post hoc test (with a false discovery rate [FDR] correction for multiple tests) to explore the difference in extreme heat exposure between pairs of racial/ethnic groups.

Outcomes

Externalizing symptom count (lifetime or current) was based on the parent and/or caregiver computerized Kiddie–Structured Assessment for Affective Disorders and Schizophrenia for Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (KSADS-5).⁴¹ Externalizing symptoms included the hyperactivity symptoms of attention-deficit/hyperactivity disorder (ADHD), oppositional defiant disorder (ODD) symptoms, and conduct disorder (CD) symptoms, consistent with previous work⁴² (Table S3, available online). Parents reported whether symptoms were observed in the past, and, in addition, whether symptoms were occurring currently. Lifetime externalizing symptom count was defined as the total number of symptoms that the participant endorsed as occurring in the past or in the present.

The suicide attempt outcome (binary “yes” or “no” measure) was assessed as part of the KSADS-5.⁴³ Previous work in the ABCD Study and other youth samples showed poor agreement between youth and caregiver responses on suicidality⁴⁴^,⁴⁵; therefore, we referred only to youth report.⁴⁵

Covariates

Models included age, sex (assigned at birth), and household income. Household income was collected as an ordinal variable, from below $5,000 (1) to above $200,000 (10) annually, and we scaled this measure before including it in the mixed models. Given the observed difference of exposure to extreme heat, we also included race and ethnicity in our multivariable models.

To address the potential confounding effects of neighborhood characteristics, we used address-derived geocoded variables from the ABCD Study including gross residential density, population density, national walkability index, and fraction of grass, forest, and built land use in the models. These geocoded measures were determined by the ABCD Study using Smart Location Database from the US Environmental Protection Agency⁴⁶ and using information from the 2010 census,⁴⁷ at the census–tract resolution.⁴⁸ We chose these address-derived geocoded variables as covariates because we wanted to minimize the possibility that any identified association between extreme heat and mental health could be explained by other aspects of the environment. Because residential density,⁴⁹ built environment,¹⁹ and green space 20, 21, 22 all relate to urban heat effect, we included the ABCD Study variables of gross residential density (ABCD variable reshist_addr1_d1a), population density (ABCD variable reshist_addr1_popdensity as covariates), and neighborhood measures of grass, forest, and built land use (ABCD variables reshist_addr1_urbsat_grass, reshist_addr1_urbsat_forest, reshist_addr1_urbsat_built) as covariates. In addition, we included walkability index (reshist_addr1_walkindex) as a covariate because these data were readily available in the ABCD Study data and because higher neighborhood walkability has been associated with improved mental health, including decreased depressive symptoms in older adults²³ and postpartum women.²⁴ All neighborhood measures were scaled prior to their inclusion in the mixed models.

Statistical Analysis

The statistical analysis plan and hypotheses were preregistered on the Open Science Framework in November, 2022 (https://osf.io/ph7y2/). Statistical analyses were conducted after preregistration from November 2022 to May 2024. We used the R statistical software version 4.1.2 (R Project for Statistical Computing) for our data analyses. Mean (SD) and frequency (percentage) were reported for descriptive purposes. Statistical significance was set at p < .05. The code for the present analysis is available at https://github.com/barzilab1/ABCD_climate_mentalhealth.

Mixed-Effects Models

Multivariable mixed-effects models (referred to as mixed models in this study) were used to test the association between the number of extreme heat days (≥90°F) per month as the exposure and lifetime externalizing symptoms (count variable) or suicide attempt (binary variable) as the outcome. We used Poisson mixed models for externalizing symptoms and logistic mixed models for suicide attempt. All mixed models took the hierarchical and longitudinal nature of the ABCD Study data by including a 4-level hierarchy, where the longitudinal data was first nested according to participant, then according to family, and finally according to research site. Moreover, we assumed a similar time trend across all participants and allowed only random intercepts in the mixed models. The mixed models included 8,120 participants who had assessments at least once across the 3 timepoints (baseline, 1-year follow-up, and 2-year follow-up). For each outcome, we ran a set of 3 mixed models. In model 1, we did not control for any covariates. In model 2, we covaried for age, sex, race, ethnicity, and household income. In model 3, we further covaried for neighborhood characteristics including gross residential density, population density, national walkability index, and fraction of grass, forest, and built land use.

Missing Data

The mixed models used listwise deletion of missing data by time point, such that a missing value affected only the time point in which data were missing, therefore avoiding participant-level listwise deletion. For example, if a participant was missing data at 1 timepoint (baseline, 1-year follow-up, or 2-year follow-up), the mixed models would only delete the data of the missing observation, and data from the other 2 timepoints would still be used.

Sensitivity Analysis

A separate analysis was performed using only data from before the COVID-19 pandemic (2016-2019). We also conducted a separate analysis limiting the data to assessments conducted from May to September to further focus on the time of year when extreme heat is most prevalent. In addition, to determine the temporal specificity of the association, we conducted separate analyses to test the associations between extreme heat and current externalizing symptoms (instead of lifetime, as in main analyses). In addition, to confirm our results, we included a sensitivity analysis in which we included only the 11 ABCD Study sites for which we could identify a corresponding weather station using the default search parameters on the NCEI website, without manually expanding the search area (Supplement 1, available online). The 11 ABCD sites and their weather stations are described in Table S4, available online. The differences in race and ethnicity between the 11 sites identified without expanding the search area (included in the sensitivity analysis) and the 10 sites not included in the sensitivity analysis are described in Table S5, available online.

Finally, we conducted 2 robustness tests by substituting our exposure variable. First, we used temperature measurements based on the participants’ geocoded home addresses, as opposed to temperature measurements at the ABCD study sites (as in the main analyses). To determine the extreme heat exposure in the robustness testing, we used the ABCD measure “reshist_addr1_scanweektemp_t1-6,” which denotes the maximum temperature in degrees Celsius at a participant’s primary residence (addr1) in the 6 days prior (t1-t6) to the date of the ABCD Study visit. From this ABCD measure, we created a new variable denoting the number of days in which the maximum temperature was ≥90°F (32.2°C) at the participant’s home address in the 6 days prior to the study visit. Because this ABCD measure was available only for baseline assessments and not for the 3 timepoints, we used this variable only for robustness testing and not for the main analyses. Second, we used the same exposure as in the main analyses, but instead of using the continuous count variable of number of days with extreme heat, we converted the exposure to a 3-level categorical variable of no exposure (0 days of extreme heat), low exposure (1-5 days of extreme heat), and high exposure (>5 days of extreme heat).

Results

Characteristics of Study Participants

Of the total 11,876 participants recruited to the ABCD Study, we identified 8,120 participants who were clinically assessed in at least 1 of the 3 assessment waves (baseline, 1-year follow-up, or 2-year follow-up) between April and October in 2016 to 2020. Among those, 6,478 participants were assessed at baseline, 5,601 were assessed at the 1-year follow-up, and 4,618 were assessed at the 2-year follow-up assessment. Participants’ mean age was 9.89, 10.93, and 12.03 years at baseline, 1-year follow-up, and 2-year follow-up assessments, respectively. Throughout all the assessments included in our analysis, the mean number of extreme heat days per month was lowest at the 1-year follow-up assessment (5.85 days/mo) and highest at the 2-year follow-up (6.34 days/mo). Table 1 describes the characteristics of study participants. Table S6 (available online) describes the demographics of the study participants, stratified by ABCD Study site. Table S7 (available online) details the range of extreme heat days per month at each corresponding weather station.

Table 1.

Characteristics of Participants Included in This Study Across Three ABCD Study Assessments

	Baseline assessment	1-Year assessment	2-Year assessment
	(n = 6,478)	(n = 5,601)	(n = 4,618)
Age at assessment, y, mean (SD)	9.89 (0.62)	10.93 (0.65)	12.03 (0.67)
Sex, female, n (%)	3,138 (48.44)	2,685 (47.94)	2,222 (48.12)
Race, n (%)^a
American Indian/Alaska Native	216 (3.33)	190 (3.39)	168 (3.64)
Asian	388 (5.99)	317 (5.66)	277 (6.00)
Black	1,572 (24.27)	1,252 (22.35)	881 (19.08)
Mixed race	782 (12.07)	662 (11.82)	588 (12.73)
Native Hawaiian and other Pacific Islander	41 (0.63)	30 (0.54)	33 (0.71)
Other race(s)	382 (5.90)	298 (5.32)	273 (5.91)
White	4,703 (72.60)	4,221 (75.36)	3,615 (78.28)
Hispanic ethnicity, n (%)^a	1,185 (18.29)	960 (17.14)	856 (18.54)
Household income, n (%)^b
<$25k	951 (14.68)	695 (12.41)	457 (9.90)
$25k-$49,999	840 (12.97)	706 (12.60)	539 (11.67)
$50k-$99,999	1,665 (25.70)	1,371 (24.48)	1,185 (25.66)
$100k and above	2,456 (37.91)	2,373 (42.37)	2,066 (44.74)
Extreme heat days in the month around ABCD Study visit, mean (SD)^c	5.87 (7.63)	5.85 (7.66)	6.34 (8.23)
Lifetime externalizing symptoms, mean (SD)^d	5.35 (6.21)	9.22 (6.50)	9.33 (6.32)
Current externalizing symptoms, mean (SD)^d	1.49 (3.66)	1.46 (3.68)	1.09 (2.95)
Lifetime suicide attempts, n (%)	82 (1.27)	62 (1.11)	55 (1.19)
Current suicide attempts, n (%)	25 (0.39)	16 (0.29)	—^e

Open in a new tab

Note: ABCD Study = Adolescent Brain Cognitive Development Study.

According to parent identification of the participants.

Income was collected as an ordinal variable from <$5,000 (1) through >$200,000 (10) per year.

Defined as the number of days per month with maximum temperature ≥90°F; continuous variable ranging from 0 to 31.

Child externalizing symptom count (reported as past or current) based on the parent and/or caregiver–validated and computerized Kiddie–Structured Assessment for Affective Disorders and Schizophrenia for Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (KSADS-5); included attention-deficit/hyperactivity disorder symptoms, oppositional defiant disorder symptoms, and conduct disorder symptoms.

This number was less than 15 and is suppressed to mitigate risk of identifiability of participants.

Differential Exposure to Extreme Heat Among Different Racial and Ethnic Groups

To explore the potential implications of exposure to extreme heat for health equity, we compared mean extreme heat exposure (days ≥90°F per month) between participants with different caregiver-reported race and ethnicity. Overall, there was a significant difference in extreme heat exposure between participants with different caregiver-reported race and ethnicity (F_2,17,619 = 145.9, p < .001) (Table S8, available online). Participants who identified as non-Hispanic White experienced significantly fewer mean monthly extreme heat days (5.2 extreme heat days/mo) compared to Hispanic (7.4 extreme heat days/mo, p < .001) and non-Hispanic Black (7.2 extreme heat days/mo, p < .001) participants, with no significant differences in extreme heat exposure between Hispanic and non-Hispanic Black participants (p = .76) (Figure 2).

Average Monthly Extreme Heat Exposure (Days ≥90°F per Month) Stratified by Self-Identified Race

***Note:****Graph shows the mean and SD for monthly extreme heat exposure (days per month with a temperature ≥90°F), stratified by caregiver-reported race and ethnicity.*

Multivariable Analyses of the Association Between Extreme Heat and Externalizing Symptoms

There was a significant association between extreme heat (days ≥90°F per month) and lifetime endorsement of externalizing symptoms (incidence rate ratio [IRR] =1.06, 95% CI = 1.04-1.08, p < .001) (Table 2, model 1). This association did not change when covarying for age, sex, race, ethnicity, and household income) (Table 2, model 2), and when further co-varying for geocoded neighborhood characteristics (residential density, population density, national walkability index and fraction of grass, forest, and built land use) (Table 2, model 3). The association between extreme heat (days ≥90°F per month) and lifetime endorsement of externalizing symptoms in each ABCD Study site is visualized in Figure 3. Notably, the direction of the association was positive in 17 of 21 ABCD Study sites.

Table 2.

Association Between Number of Extreme Heat Days per Month and Lifetime Externalizing Symptoms

Predictors	Model 1 (n = 5,415)^a			Model 2 (n = 4,975)^b			Model 3 (n = 4,012)^c
(Intercept)	IRR	95% CI	p	IRR	95% CI	p	IRR	95% CI	p
	3.04	2.66-3.48	<.001	3.82	3.39-4.30	<.001	2.73	2.44-3.07	<.001
Absolute extreme heat, days^d	1.06	1.04-1.08	<.001	1.06	1.04-1.08	<.001	1.06	1.00-1.12	.044
Age at assessment, y				1.05	1.03-1.06	<.001	0.92	0.84-1.00	.051
Sex, female				0.60	0.56-0.65	<.001	0.58	0.52-0.64	<.001
Black race				1.28	1.15-1.43	<.001	1.20	1.04-1.39	.01
Hispanic ethnicity				0.90	0.80-1.02	.098	0.74	0.63-0.86	<.001
Household income				0.92	0.89-0.95	<.001	0.83	0.79-0.89	<.001
Gross residential density							1.08	0.96-1.22	.20
National walkability index							1.10	1.03-1.18	.008
Population density							0.91	0.79-1.04	.15
Neighborhood grass land use							1.02	0.93-1.11	.66
Neighborhood forest land use							1.06	0.92-1.22	.42
Neighborhood built land use							1.01	0.87-1.18	.91

Open in a new tab

Note: Three mixed-effects Poisson regression models were run to disentangle the role of extreme heat (independent variable) in association with lifetime externalizing symptoms (dependent variable). All models took the 4-level hierarchical and 3-assessment longitudinal nature of the Adolescent Brain Cognitive Development (ABCD) Study data into account (data were nested first according to participant, then according to family, and finally according to the research site). All models assumed a similar time trend across all participants and allowed only random intercepts in all mixed models. IRR = incidence rate ratio.

Base model with no covariates.

Model 2 covaried for age, sex, and household income.

Model 3 covaried for age, sex, household income, and scaled (z-scored) neighborhood characteristics.

Defined as the number of days per month with maximum temperature ≥90°F; continuous variable ranging from 0 to 31.

Association Between Number of Extreme Heat Days per Month and Lifetime Externalizing Symptoms by Adolescent Brain Cognitive Development (ABCD) Study Site

***Note:***Top panel (A) visualizes dot plots and regression lines of the association between extreme heat days (temperature ≥90°F) per month and lifetime externalizing symptoms for each of the 21 ABCD Study sites included in this study. Note that 17 of the 21 ABCD Study sites demonstrated a positive trend. Bottom panel (B) visualizes the aggregated association between extreme heat days and lifetime externalizing symptoms across all sites.

Multivariable Analyses of the Association Between Extreme Heat and Suicide Attempts

There was no association between extreme heat and lifetime suicide attempts among study participants (odds ratio = 0.94, 95% CI = 0.77-1.14, p = .52) (Table S9).

Sensitivity Analyses

To evaluate the potential impact of the COVID-19 pandemic, we conducted sensitivity analyses for all models, excluding assessments conducted in 2020. The association between extreme heat and externalizing symptoms remained similar in direction and statistical significance to that from the main analysis (Table S10, available online).

To further focus on the time of year when extreme heat is most prevalent, we reran the main analyses and included only those participants who were assessed in the months of May to September. The association between extreme heat and lifetime externalizing symptoms was similar in direction to that in the main analyses in all models, and statistically significant in the unadjusted model and the model covarying for demographics, but not significant when further adjusting for neighborhood measures (p = .058) (Table S11, available online).

To determine the temporal specificity of the association between extreme heat and externalizing symptoms, we used current externalizing symptoms as the dependent variable (instead of lifetime, as in the main analyses). The association between extreme heat and current externalizing symptoms was similar to that in the main analyses in the unadjusted model and in the model adjusting for demographics (IRR = 1.04, 95% CI = 1.01-1.07, p = .007), but was no longer observed when further adjusting for neighborhood characteristics (Table S12, available online).

When including only the 11 ABCD Study sites for which we could identify a corresponding weather station using the default search parameters on the NCEI website (without manually expanding the search area to identify 21 sites as in the main analyses), the association between extreme heat and externalizing symptoms was significant when covarying for demographics (IRR = 1.05, 95% CI = 1.02-1.08, p < .001). This association did not remain significant when further covarying for neighborhood characteristics (Table S13, available online).

To assess potential measurement error secondary to using measures of extreme heat based on ABCD study sites, as opposed to being based on the participants’ home addresses, we conducted a secondary analysis using the exposure of extreme heat at the participant’s geocoded home address in the 6 days prior to the ABCD Study baseline assessment. The association between extreme heat at the participants’ home addresses and externalizing symptoms was similar in direction and statistical significance to that of the main analyses in all models (for the fully adjusted model, IRR = 1.06, 95% CI = 1.01-1.11, p = .021) (Table 14, available online).

Finally, we used a 3-level categorical definition of our exposure variable instead of the continuous count of days with extreme heat from the main analyses (Table 15, available online). Compared to no exposure to extreme heat (0 days in the month of ABCD Study visit), exposure to more than 5 days of extreme heat was associated with externalizing symptoms when adjusting for demographics (IRR = 1.07, 95% CI = 1.03-1.12, p = .002), but not when further adjusting for neighborhood covariates (IRR = 1.0, 95% CI = 0.88-1.14, p = .99). Exposure to 1 to 5 days of extreme heat was not associated with externalizing symptoms.

Discussion

We report a small but significant association between the number of extreme heat days per month and externalizing symptoms in a large, diverse youth cohort from 21 sites across the United States. Importantly, the association remained significant when covarying for individual demographics and multiple geocoded neighborhood characteristics. Our results align with the existing literature that suggests an association between higher ambient temperatures and worse mental health measures,5, 6, 7^,⁵⁰ specifically aggression.9, 10, 11, 12 However, in contrast to previous studies that note an association between higher temperatures and self-inflicted injuries¹³ and suicide deaths,14, 15, 16, 17 we did not observe an association between extreme heat and suicide attempts. The national scope of our study is particularly important and adds to the relatively limited number of national and international studies that have evaluated the impact of climate on youth mental health.⁵¹

Leveraging both the large size and diversity of the ABCD Study cohort, we were able to show that the association between exposure to extreme heat and externalizing symptoms remained significant after accounting for demographics including age, sex, race, ethnicity, and household income. This suggests that our findings are broadly applicable to youth in the United States. Of note, we also report a disproportionate burden of extreme heat exposure across race/ethnicity, with significantly lower exposure in participants who identified as non-Hispanic White (mean of 5.2 extreme heat days/mo), and significantly higher exposure in participants who identified as Hispanic or non-Hispanic Black (mean of 7.4 and 7.2 extreme heat days/mo, respectively). This observation aligns with previous studies that have shown that historically redlined communities and communities of color experience higher temperatures than White communities.²⁸^,²⁹ Our findings underscore the need for more research on the relationship between climate change and mental health in youth in general, and specifically in historically marginalized populations.

A key strength of our study is that, by also using the available geocoded data, we were able to show that the association between extreme heat and externalizing symptoms remained statistically significant after further accounting for neighborhood factors including gross residential density, population density, walkability index, and fraction of grass, forest, and built land use. This finding suggests that at least some of the association between extreme heat and externalizing symptoms is independent of neighborhood characteristics. Nonetheless, in some of the sensitivity analyses that we conducted, the extreme heat–externalizing symptoms association was attenuated when adjusting for neighborhood factors, and did not reach statistical significance in some models. This attenuation suggests that built environmental factors may have a confounding effect on the association between heat and youth mental health. This confounding effect may be explained by urban heat islands, whereby the presence of dense infrastructure secondary to high population density can lead to an increase in air temperature in urban environments,¹⁹ and the presence of green space can have an opposite effect.20, 21, 22 In addition, neighborhood factors, such as environmental ambient noise, may be independently associated with externalizing symptoms, including hyperactivity in children.²⁵^,²⁶ We hope that the current study will promote more research investigating the potential mechanisms underpinning the impact of heat on youth mental health.

By examining the association between extreme heat and externalizing symptoms within each ABCD Study site, we found a positive trend (ie, more heat with more externalizing symptoms) in 17 of the 21 ABCD Study sites. The presence of a positive association in the majority of sites suggests that the effect is widespread and not driven by a single study site. Interestingly, 2 of the study sites that exhibited a negative association between externalizing symptoms and extreme heat were located in southern California, whereas 1 of the sites was located in New York State. Although we do not know the reason why these 3 sites differed from the others, it is possible that those 3 sites either share characteristics that are unique compared with those of other sites or that share factors that are confounding the association. For example, the relative lower humidity in the southwestern United States, including southern California,⁵² may make extreme heat more tolerable,⁸ thereby diminishing the association. It is also possible that access to air conditioning, which was not assessed in the ABCD Study, confounded the heat–externalizing symptoms association. Future works that address different weather conditions across ABCD Study sites may benefit from linking external weather stations data with ABCD Study data at the site level.

In contrast to our hypothesis and previous studies that report an association between higher temperatures and self-inflicted injury¹³ and suicide deaths14, 15, 16, 17 in both adults and youth, we did not observe an association between extreme heat and self-reported suicide attempts. This difference may be a result of the low total number of suicide attempts in our dataset, due to the young age of the study population, leading to insufficient power to detect an association. Future research into this association in the ABCD Study is warranted, because an increase in suicidal behavior is expected as the study participants grow older.⁵³ The lack of an expected association between extreme heat and suicide attempts in our results may also be explained by variations in study populations. For example, the cited studies examined either self-inflicted injuries or suicide attempts that led to an emergency department visit,¹³ self-inflicted injuries or suicide registered in the National Center for Health Statistics (NCHS),¹⁶ or suicide attempts that resulted in death.¹⁴^,¹⁵ However, there is evidence that distinct differences exist between individuals who make medically serious suicide attempts and those who make non-medically serious suicide attempts.54, 55, 56 As our study examined self-reported suicide attempts in a non-clinical sample (the ABCD Study cohort was recruited through school systems, as opposed to medical settings), it is likely that the participants in our study had more non–medically serious suicide attempts, making them a study population different from the previously cited study populations. Furthermore, because of developmental differences in suicide attempts across age groups,⁵⁷ differences in the age range of participants offers another potential explanation, as none of the previously cited studies focused specifically on preadolescence or early adolescence.

Our results should be interpreted with caution, given some limitations. First, we were limited in spatial granularity, as our main analyses used temperatures that were recorded at the weather stations near the ABCD Study sites, and not at the individual participants’ addresses. It is possible that the heat experienced by participants differed depending on where they lived in relation to the ABCD Study sites. Of note, we excluded youth living more than 50 miles from their study site. Also of note, we provide sensitivity analyses that use extreme heat at the home address (available only for baseline ABCD Study assessment) as the exposure that demonstrated findings similar to those in the main analyses. Second, we used lifetime endorsement of symptoms in our analyses; however, we do not have data regarding whether the participants had lived in other areas during their lives, which may have confounded the results. Notably, when testing association of extreme heat with current symptoms, we observed a significant association between extreme heat and symptoms in the model adjusting for demographics, which did not remain significant when further adjusting for neighborhood characteristics. We suggest that this is due to the low prevalence of current symptoms, as the ABCD Study includes a community sample that is not currently (ie, at time of assessment) experiencing substantial symptom burden. More studies are needed in clinical populations to test whether change in heat relates to change in symptoms in youth with substantial externalizing symptoms. Third, we did not have data for other key factors that may have an impact on heat exposure, including home air conditioning, access to air conditioned transportation, and time spent outdoors. More research is needed on how these factors and other environmental conditions affect the relationship between heat exposure and youth mental health. Notably, the ABCD Study continues to expand measures of participants’ environments,⁵⁵ which is likely to offer more opportunities in the future to delineate the relationship between heat exposure and brain and behavior outcomes. Fourth, although we adjusted for household income in our models, we acknowledge that financial well-being depends on other factors beyond income, which were not accounted for in our study. More research is needed on how other socioeconomic measures (eg, income-to-needs ratio) affect association of heat with mental health. Fifth, our study was also limited in the exposure resolution, because extreme heat was recorded as the number of days per month. The participants may have had differential exposure depending on whether their study visit took place at the beginning of the month or the end of the month. Of note, we attempted to mitigate this limitation by using temperature data from the previous month for visits that took place in the first half of the month. Sixth, although the ABCD study does consist of participants from a variety of locations and backgrounds, White individuals make up the majority of participants at most of the study sites. Given the evidence suggesting that Black and Hispanic communities in the United States are disproportionately affected by extreme heat exposure,²⁸^,²⁹ it would be beneficial to conduct a similar analysis using a dataset that includes more individuals from historically marginalized communities. Finally, although we included longitudinal data from 3 individual timepoints, we cannot be that the exposures occurred before the outcomes, and cannot infer causality between exposure to heat and mental health symptoms.

In conclusion, our study used a multi-site longitudinal cohort to identify a small but significant association between extreme heat and externalizing symptoms in preadolescence and early adolescence. Although this study is descriptive and does not address the mechanism through which heat affects youth mental health, the results support the importance of investigating the contribution of extreme heat to youth mental health on a larger scale, especially given the projected global increases in temperature.⁸ Future work is needed to explore this relationship in more granularity, as well as to elucidate factors that can mitigate the impact of extreme heat on adolescents’ mental health.

CRediT authorship contribution statement

Sara Briker: Writing – review & editing, Writing – original draft, Investigation, Conceptualization. Kate T. Tran: Writing – review & editing, Software, Methodology, Investigation, Formal analysis, Data curation. Elina Visoki: Writing – review & editing, Methodology, Investigation, Formal analysis, Data curation. Joshua H. Gordon: Writing – review & editing, Investigation. Kevin W. Hoffman: Writing – review & editing, Investigation. Ran Barzilay: Writing – review & editing, Writing – original draft, Supervision, Resources, Investigation, Funding acquisition, Conceptualization.

Footnotes

This study was supported by the National Institute of Mental Health grants K23MH120437 (RB), P50MH115838 (RB), R25MH119043 (JG, KH), the American Foundation of Suicide Prevention (AFSP) grant SRG-0-006-22 (RB), and the Lifespan Brain Institute of Children’s Hospital of Philadelphia and Penn Medicine, University of Pennsylvania.

The ABCD Study protocol was approved by the University of California, San Diego Institutional Review Board (IRB), and was exempted from a full review by the University of Pennsylvania IRB.

Consent has been provided for descriptions of specific patient information.

This work has been prospectively registered: https://osf.io/ph7y2/?view_only=0085b06443eb47149bfd2f6b802715d5.

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development^SM (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children aged 9-10 and follow them over 10 years into early adulthood. The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.

Data Sharing: Deidentified participant data and Analytic Code, Study Protocol, and Statistical Analysis Plan supporting documents available at https://nda.nih.gov/study.html?id=2313. Data will be available with publication. This research used ABCD Study data. Access to ABCD data may be granted to bone fide researchers based on NIH decision. The data will be made available for any purposes without investigator support.

Kate T. Tran served as the statistical expert for this research.

The authors thank Tyler M. Moore, PhD, of the University of Pennsylvania, for his support in statistical analyses.

Disclosure: Ran Barzilay has served on the scientific board and reports stock ownership in ‘Taliaz Health’, with no conflict of interest relevant to this work. Elina Visoki’s spouse is a shareholder and executive of ‘Kidas,’ with no conflict of interest relevant to this work. Sara Briker, Kate T. Tran, Joshua H. Gordon, and Kevin W. Hoffman have reported no biomedical financial interests or potential conflicts of interest.

Supplemental Material

mmc1.docx^{(228.6KB, docx)}

References

1.The Stranger. Albert CamusCamus A. The Stranger. Knopf. 1993 [Google Scholar]
2.Barzilay R., Pries L.K., Moore T.M., et al. Exposome and trans-syndromal developmental trajectories toward psychosis. Biol Psychiatry Glob Open Sci. 2022;2(3):197–205. doi: 10.1016/j.bpsgos.2022.05.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
3.van Kamp I., Persson Waye K., Kanninen K., et al. Early environmental quality and life-course mental health effects: the Equal-Life project. Environ Epidemiol. 2022;6(1) doi: 10.1097/EE9.0000000000000183. [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Barzilay R., Michel N. Editorial: understanding adolescent mental health disparities through the lens of environmental stress exposure. J Am Acad Child Adolesc Psychiatry. published online ahead of print July 26, 2024 doi: 10.1016/j.jaac.2024.07.909. [DOI] [PubMed] [Google Scholar]
5.Cianconi P., Betrò S., Janiri L. The impact of climate change on mental health: a systematic descriptive review. Front Psychiatry. 2020;11 doi: 10.3389/fpsyt.2020.00074. [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Helldén D., Andersson C., Nilsson M., Ebi K.L., Friberg P., Alfvén T. Climate change and child health: a scoping review and an expanded conceptual framework. Lancet Planet Health. 2021;5(3):e164–e175. doi: 10.1016/S2542-5196(20)30274-6. [DOI] [PubMed] [Google Scholar]
7.Sugg M.M., Dixon P.G., Runkle J.D. Crisis support-seeking behavior and temperature in the United States: is there an association in young adults and adolescents? Sci Total Environ. 2019;669:400–411. doi: 10.1016/j.scitotenv.2019.02.434. [DOI] [PubMed] [Google Scholar]
8.Centers for Disease Control and Prevention Climate change and extreme heat: what you can do to prepare. 2016. https://www.cdc.gov/climateandhealth/pubs/extreme-heat-guidebook.pdf
9.Anderson C., Anderson K., Dorr N., DeNeve K., Flanagan M. Temperature and aggression. Adv Exp Soc Psychol. 2000;32:63–133. [Google Scholar]
10.Xu R., Xiong X., Abramson M.J., Li S., Guo Y. Ambient temperature and intentional homicide: a multi-city case-crossover study in the US. Environ Int. 2020;143 doi: 10.1016/j.envint.2020.105992. [DOI] [PubMed] [Google Scholar]
11.Lyons V.H., Gause E.L., Spangler K.R., Wellenius G.A., Jay J. Analysis of daily ambient temperature and firearm violence in 100 US cities. JAMA Netw Open. 2022;5(12) doi: 10.1001/jamanetworkopen.2022.47207. [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Mahendran R., Xu R., Li S., Guo Y. Interpersonal violence associated with hot weather. Lancet Planet Health. 2021;5(9):e571–e572. doi: 10.1016/S2542-5196(21)00210-2. [DOI] [PubMed] [Google Scholar]
13.Basu R., Gavin L., Pearson D., Ebisu K., Malig B. Examining the association between apparent temperature and mental health-related emergency room visits in California. Am J Epidemiol. 2018;187(4):726–735. doi: 10.1093/aje/kwx295. [DOI] [PubMed] [Google Scholar]
14.Parks R.M., Bennett J.E., Tamura-Wicks H., et al. Anomalously warm temperatures are associated with increased injury deaths. Nat Med. 2020;26(1):65–70. doi: 10.1038/s41591-019-0721-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Burke M., González F., Baylis P., et al. Higher temperatures increase suicide rates in the United States and Mexico. Nat Clim Change. 2018;8(8):723–729. doi: 10.1038/s41558-018-0222-x. [DOI] [Google Scholar]
16.Dixon P.G., Kalkstein A.J. Where are weather-suicide associations valid? An examination of nine US counties with varying seasonality. Int J Biometeorol. 2018;62(5):685–697. doi: 10.1007/s00484-016-1265-1. [DOI] [PubMed] [Google Scholar]
17.Liu J., Varghese B.M., Hansen A., et al. Is there an association between hot weather and poor mental health outcomes? A systematic review and meta-analysis. Environ Int. 2021;153 doi: 10.1016/j.envint.2021.106533. [DOI] [PubMed] [Google Scholar]
18.Younan D., Li L., Tuvblad C., et al. Long-term ambient temperature and externalizing behaviors in adolescents. Am J Epidemiol. 2018;187(9):1931–1941. doi: 10.1093/aje/kwy104. [DOI] [PMC free article] [PubMed] [Google Scholar]
19.US Environmental Protection Agency Reducing urban heat islands: compendium of strategies— urban heat island basics. 2008. https://www.epa.gov/heat-islands/
20.Aram F., Higueras García E., Solgi E., Mansournia S., García E.H. Urban green space cooling effect in cities. green space cooling effect in cities. Heliyon. 2019;5:1339. doi: 10.1016/j.heliyon.2019. [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Ng E., Chen L., Wang Y., Yuan C. A study on the cooling effects of greening in a high-density city: an experience from Hong Kong. Build Environ. 2012;47(1):256–271. doi: 10.1016/j.buildenv.2011.07.014. [DOI] [Google Scholar]
22.Skelhorn C., Lindley S., Levermore G. Vol. 121. Landsc Urban Plan; 2014. pp. 129–140. (The impact of vegetation types on air and surface temperatures in a temperate city: a fine scale assessment in Manchester, UK). [DOI] [Google Scholar]
23.Berke E.M., Gottlieb L.M., Moudon A.V., Larson E.B. Protective association between neighborhood walkability and depression in older men. J Am Geriatr Soc. 2007;55(4):526–533. doi: 10.1111/j.1532-5415.2007.01108.x. [DOI] [PubMed] [Google Scholar]
24.Zhang Y., Tayarani M., Wang S., et al. Identifying urban built environment factors in pregnancy care and maternal mental health outcomes. BMC Pregnancy Childbirth. 2021;21(1) doi: 10.1186/s12884-021-04056-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Schubert M., Hegewald J., Freiberg A., et al. Behavioral and emotional disorders and transportation noise among children and adolescents: a systematic review and meta-analysis. Int J Environ Res Public Health. 2019;16(18) doi: 10.3390/ijerph16183336. [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Lim J., Kweon K., Kim H.W., Cho S.W., Park J., Sim C.S. Negative impact of noise and noise sensitivity on mental health in childhood. Noise Health. 2018;20(96):199–211. doi: 10.4103/nah.NAH_9_18. [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Rothstein R. Liveright Publishing; 2017. The Color of Law: A Forgotten History of How Our Government Segregated America. [Google Scholar]
28.Hoffman J.S., Shandas V., Pendleton N. The effects of historical housing policies on resident exposure to intra-urban heat: a study of 108 US urban areas. Climate. 2020;8(1):12. doi: 10.3390/cli8010012. [DOI] [Google Scholar]
29.Hsu A., Sheriff G., Chakraborty T., Manya D. Disproportionate exposure to urban heat island intensity across major US cities. Nat Commun. 2021;12(1):2721. doi: 10.1038/s41467-021-22799-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Berberian A.G., Gonzalez D.J.X., Cushing L.J. 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]
31.Karcher N.R., Barch D.M. The ABCD Study: understanding the development of risk for mental and physical health outcomes. Neuropsychopharmacology. 2021;46(1):131–142. doi: 10.1038/s41386-020-0736-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Fan C.C., Marshall A., Smolker H., et al. Adolescent Brain Cognitive Development (ABCD) Study linked external data (LED): protocol and practices for geocoding and assignment of environmental data. Dev Cogn Neurosci. 2021;52 doi: 10.1016/j.dcn.2021.101030. [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Curtin S.C., Tejada-Vera B., Bastian B.A. Deaths: leading causes for 2021. 2024. https://stacks.cdc.gov/view/cdc/147882 [PubMed]
34.Soto-Sanz V., Castellví P., Piqueras J.A., et al. Internalizing and externalizing symptoms and suicidal behaviour in young people: a systematic review and meta-analysis of longitudinal studies. Acta Psychiatr Scand. 2019;140(1):5–19. doi: 10.1111/acps.13036. [DOI] [PubMed] [Google Scholar]
35.Garavan H., Bartsch H., Conway K., et al. Recruiting the ABCD sample: design considerations and procedures. Dev Cogn Neurosci. 2018;32:16–22. doi: 10.1016/j.dcn.2018.04.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Centers for Disease Control and Prevention Picture of America Heat-Related Illness Fact Sheet. https://www.cdc.gov/pictureofamerica/pdfs/picture_of_america_heat-related_illness.pdf
37.Khatana S.A.M., Werner R.M., Groeneveld P.W. Association of extreme heat with all-cause mortality in the contiguous US, 2008-2017. JAMA Netw Open. 2022;5(5) doi: 10.1001/jamanetworkopen.2022.12957. [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Sabrin S., Zech W.C., Nazari R., Karimi M. Understanding occupational heat exposure in the United States and proposing a quantifying stress index. Int Arch Occup Environ Health. 2021;94(8):1983–2000. doi: 10.1007/s00420-021-01711-0. [DOI] [PubMed] [Google Scholar]
39.Osborne S. What’s the coldest day of the year? (1981–2010 Normals) November 18, 2014. https://www.climate.gov/news-features/featured-images/whats-coldest-day-year-1981%E2%80%932010-normals
40.Lawrimore J.H., Ray R., Applequist S., Korzeniewski B., Menne M.J. Published online 2022. Global Summary of the Month (GSOM), Version 1. [DOI] [Google Scholar]
41.Kaufman J., Birmaher B., Brent D., et al. Schedule for Affective Disorders and Schizophrenia for School-Age Children—Present and Lifetime Version (K-SADS-PL): initial reliability and validity data. J Am Acad Child Adolesc Psychiatry. 1997;36(7):980–988. doi: 10.1097/00004583-199707000-00021. [DOI] [PubMed] [Google Scholar]
42.Shoval G., Visoki E., Moore T.M., et al. Evaluation of attention-deficit/hyperactivity disorder medications, externalizing symptoms, and suicidality in children. JAMA Netw Open. 2021;4(6) doi: 10.1001/jamanetworkopen.2021.11342. [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Janiri D., Doucet G.E., Pompili M., et al. Risk and protective factors for childhood suicidality: a US population-based study. Lancet Psychiatry. 2020;7(4):317–326. doi: 10.1016/S2215-0366(20)30049-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
44.DeVille D.C., Whalen D., Breslin F.J., et al. Prevalence and family-related factors associated with suicidal ideation, suicide attempts, and self-injury in children aged 9 to 10 years. JAMA Netw Open. 2020;3(2) doi: 10.1001/jamanetworkopen.2019.20956. [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Jones J.D., Boyd R.C., Calkins M.E., et al. Parent-adolescent agreement about adolescents’ suicidal thoughts. Pediatrics. 2019;143(2) doi: 10.1542/peds.2018-1771. [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Environmental Protection Agency Smart location mapping. 2022. https://www.epa.gov/smartgrowth/smart-location-mapping#SLD
47.Adolescent Brain Cognitive Development Study (ABCD) data release 4.0: linked external data (LED) Published online 2021. [DOI] [Google Scholar]
48.Cardenas-Iniguez C., Schachner J.N., Ip K.I., et al. Building towards an adolescent neural urbanome: expanding environmental measures using linked external data (LED) in the ABCD Study. Dev Cogn Neurosci. 2024;65 doi: 10.1016/j.dcn.2023.101338. [DOI] [PMC free article] [PubMed] [Google Scholar]
49.Liang Z., Wu S., Wang Y., et al. The relationship between urban form and heat island intensity along the urban development gradients. Sci Total Environ. 2020;708 doi: 10.1016/j.scitotenv.2019.135011. [DOI] [PubMed] [Google Scholar]
50.Charlson F., Ali S., Benmarhnia T., et al. Climate change and mental health: a scoping review. Public Health. 2021;18:4486. doi: 10.3390/ijerph. [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Aylward B., Cunsolo A., Vriezen R., Harper S.L. Climate change is impacting mental health in North America: a systematic scoping review of the hazards, exposures, vulnerabilities, risks and responses. Int Rev Psychiatry. 2022;34(1):34–50. doi: 10.1080/09540261.2022.2029368. [DOI] [PubMed] [Google Scholar]
52.Seager R., Lis N., Feldman J., et al. Whither the 100th meridian? The once and future physical and human geography of America’s arid–humid divide. Part I: the story so far. Earth Interact. 2018;22(5):1–22. doi: 10.1175/EI-D-17-0011.1. [DOI] [Google Scholar]
53.Cha C.B., Franz P.J., Guzmán E., Glenn C.R., Kleiman E.M., Nock M.K. Annual research review: suicide among youth—epidemiology, (potential) etiology, and treatment. J Child Psychol Psychiatry. 2018;59(4):460–482. doi: 10.1111/jcpp.12831. [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Horesh N., Levi Y., Apter A. Medically serious vs non-serious suicide attempts: relationships of lethality and intent to clinical and interpersonal characteristics. J Affect Disord. 2012;136(3):286–293. doi: 10.1016/j.jad.2011.11.035. [DOI] [PubMed] [Google Scholar]
55.Levi Y., Horesh N., Fischel T., Treves I., Or E., Apter A. Mental pain and its communication in medically serious suicide attempts: an “impossible situation.”. J Affect Disord. 2008;111(2-3):244–250. doi: 10.1016/j.jad.2008.02.022. [DOI] [PubMed] [Google Scholar]
56.Swahn M.H., Potter L.B. Factors associated with the medical severity of suicide attempts in youths and young adults. Suicide Life Threat Behav. 2002;32:21–29. doi: 10.1521/suli.32.1.5.21.24214. [DOI] [PubMed] [Google Scholar]
57.Lee J., Bang Y.S., Min S., et al. Characteristics of adolescents who visit the emergency department following suicide attempts: comparison study between adolescents and adults. BMC Psychiatry. 2019;19(1):231. doi: 10.1186/s12888-019-2213-5. [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

Supplemental Material

mmc1.docx^{(228.6KB, docx)}

[bib1] 1.The Stranger. Albert CamusCamus A. The Stranger. Knopf. 1993 [Google Scholar]

[bib2] 2.Barzilay R., Pries L.K., Moore T.M., et al. Exposome and trans-syndromal developmental trajectories toward psychosis. Biol Psychiatry Glob Open Sci. 2022;2(3):197–205. doi: 10.1016/j.bpsgos.2022.05.001. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib3] 3.van Kamp I., Persson Waye K., Kanninen K., et al. Early environmental quality and life-course mental health effects: the Equal-Life project. Environ Epidemiol. 2022;6(1) doi: 10.1097/EE9.0000000000000183. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib4] 4.Barzilay R., Michel N. Editorial: understanding adolescent mental health disparities through the lens of environmental stress exposure. J Am Acad Child Adolesc Psychiatry. published online ahead of print July 26, 2024 doi: 10.1016/j.jaac.2024.07.909. [DOI] [PubMed] [Google Scholar]

[bib5] 5.Cianconi P., Betrò S., Janiri L. The impact of climate change on mental health: a systematic descriptive review. Front Psychiatry. 2020;11 doi: 10.3389/fpsyt.2020.00074. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib6] 6.Helldén D., Andersson C., Nilsson M., Ebi K.L., Friberg P., Alfvén T. Climate change and child health: a scoping review and an expanded conceptual framework. Lancet Planet Health. 2021;5(3):e164–e175. doi: 10.1016/S2542-5196(20)30274-6. [DOI] [PubMed] [Google Scholar]

[bib7] 7.Sugg M.M., Dixon P.G., Runkle J.D. Crisis support-seeking behavior and temperature in the United States: is there an association in young adults and adolescents? Sci Total Environ. 2019;669:400–411. doi: 10.1016/j.scitotenv.2019.02.434. [DOI] [PubMed] [Google Scholar]

[bib8] 8.Centers for Disease Control and Prevention Climate change and extreme heat: what you can do to prepare. 2016. https://www.cdc.gov/climateandhealth/pubs/extreme-heat-guidebook.pdf

[bib9] 9.Anderson C., Anderson K., Dorr N., DeNeve K., Flanagan M. Temperature and aggression. Adv Exp Soc Psychol. 2000;32:63–133. [Google Scholar]

[bib10] 10.Xu R., Xiong X., Abramson M.J., Li S., Guo Y. Ambient temperature and intentional homicide: a multi-city case-crossover study in the US. Environ Int. 2020;143 doi: 10.1016/j.envint.2020.105992. [DOI] [PubMed] [Google Scholar]

[bib11] 11.Lyons V.H., Gause E.L., Spangler K.R., Wellenius G.A., Jay J. Analysis of daily ambient temperature and firearm violence in 100 US cities. JAMA Netw Open. 2022;5(12) doi: 10.1001/jamanetworkopen.2022.47207. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib12] 12.Mahendran R., Xu R., Li S., Guo Y. Interpersonal violence associated with hot weather. Lancet Planet Health. 2021;5(9):e571–e572. doi: 10.1016/S2542-5196(21)00210-2. [DOI] [PubMed] [Google Scholar]

[bib13] 13.Basu R., Gavin L., Pearson D., Ebisu K., Malig B. Examining the association between apparent temperature and mental health-related emergency room visits in California. Am J Epidemiol. 2018;187(4):726–735. doi: 10.1093/aje/kwx295. [DOI] [PubMed] [Google Scholar]

[bib14] 14.Parks R.M., Bennett J.E., Tamura-Wicks H., et al. Anomalously warm temperatures are associated with increased injury deaths. Nat Med. 2020;26(1):65–70. doi: 10.1038/s41591-019-0721-y. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib15] 15.Burke M., González F., Baylis P., et al. Higher temperatures increase suicide rates in the United States and Mexico. Nat Clim Change. 2018;8(8):723–729. doi: 10.1038/s41558-018-0222-x. [DOI] [Google Scholar]

[bib16] 16.Dixon P.G., Kalkstein A.J. Where are weather-suicide associations valid? An examination of nine US counties with varying seasonality. Int J Biometeorol. 2018;62(5):685–697. doi: 10.1007/s00484-016-1265-1. [DOI] [PubMed] [Google Scholar]

[bib17] 17.Liu J., Varghese B.M., Hansen A., et al. Is there an association between hot weather and poor mental health outcomes? A systematic review and meta-analysis. Environ Int. 2021;153 doi: 10.1016/j.envint.2021.106533. [DOI] [PubMed] [Google Scholar]

[bib18] 18.Younan D., Li L., Tuvblad C., et al. Long-term ambient temperature and externalizing behaviors in adolescents. Am J Epidemiol. 2018;187(9):1931–1941. doi: 10.1093/aje/kwy104. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib19] 19.US Environmental Protection Agency Reducing urban heat islands: compendium of strategies— urban heat island basics. 2008. https://www.epa.gov/heat-islands/

[bib20] 20.Aram F., Higueras García E., Solgi E., Mansournia S., García E.H. Urban green space cooling effect in cities. green space cooling effect in cities. Heliyon. 2019;5:1339. doi: 10.1016/j.heliyon.2019. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib21] 21.Ng E., Chen L., Wang Y., Yuan C. A study on the cooling effects of greening in a high-density city: an experience from Hong Kong. Build Environ. 2012;47(1):256–271. doi: 10.1016/j.buildenv.2011.07.014. [DOI] [Google Scholar]

[bib22] 22.Skelhorn C., Lindley S., Levermore G. Vol. 121. Landsc Urban Plan; 2014. pp. 129–140. (The impact of vegetation types on air and surface temperatures in a temperate city: a fine scale assessment in Manchester, UK). [DOI] [Google Scholar]

[bib23] 23.Berke E.M., Gottlieb L.M., Moudon A.V., Larson E.B. Protective association between neighborhood walkability and depression in older men. J Am Geriatr Soc. 2007;55(4):526–533. doi: 10.1111/j.1532-5415.2007.01108.x. [DOI] [PubMed] [Google Scholar]

[bib24] 24.Zhang Y., Tayarani M., Wang S., et al. Identifying urban built environment factors in pregnancy care and maternal mental health outcomes. BMC Pregnancy Childbirth. 2021;21(1) doi: 10.1186/s12884-021-04056-1. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib25] 25.Schubert M., Hegewald J., Freiberg A., et al. Behavioral and emotional disorders and transportation noise among children and adolescents: a systematic review and meta-analysis. Int J Environ Res Public Health. 2019;16(18) doi: 10.3390/ijerph16183336. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib26] 26.Lim J., Kweon K., Kim H.W., Cho S.W., Park J., Sim C.S. Negative impact of noise and noise sensitivity on mental health in childhood. Noise Health. 2018;20(96):199–211. doi: 10.4103/nah.NAH_9_18. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib27] 27.Rothstein R. Liveright Publishing; 2017. The Color of Law: A Forgotten History of How Our Government Segregated America. [Google Scholar]

[bib28] 28.Hoffman J.S., Shandas V., Pendleton N. The effects of historical housing policies on resident exposure to intra-urban heat: a study of 108 US urban areas. Climate. 2020;8(1):12. doi: 10.3390/cli8010012. [DOI] [Google Scholar]

[bib29] 29.Hsu A., Sheriff G., Chakraborty T., Manya D. Disproportionate exposure to urban heat island intensity across major US cities. Nat Commun. 2021;12(1):2721. doi: 10.1038/s41467-021-22799-5. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib30] 30.Berberian A.G., Gonzalez D.J.X., Cushing L.J. 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]

[bib31] 31.Karcher N.R., Barch D.M. The ABCD Study: understanding the development of risk for mental and physical health outcomes. Neuropsychopharmacology. 2021;46(1):131–142. doi: 10.1038/s41386-020-0736-6. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib32] 32.Fan C.C., Marshall A., Smolker H., et al. Adolescent Brain Cognitive Development (ABCD) Study linked external data (LED): protocol and practices for geocoding and assignment of environmental data. Dev Cogn Neurosci. 2021;52 doi: 10.1016/j.dcn.2021.101030. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib33] 33.Curtin S.C., Tejada-Vera B., Bastian B.A. Deaths: leading causes for 2021. 2024. https://stacks.cdc.gov/view/cdc/147882 [PubMed]

[bib34] 34.Soto-Sanz V., Castellví P., Piqueras J.A., et al. Internalizing and externalizing symptoms and suicidal behaviour in young people: a systematic review and meta-analysis of longitudinal studies. Acta Psychiatr Scand. 2019;140(1):5–19. doi: 10.1111/acps.13036. [DOI] [PubMed] [Google Scholar]

[bib35] 35.Garavan H., Bartsch H., Conway K., et al. Recruiting the ABCD sample: design considerations and procedures. Dev Cogn Neurosci. 2018;32:16–22. doi: 10.1016/j.dcn.2018.04.004. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib36] 36.Centers for Disease Control and Prevention Picture of America Heat-Related Illness Fact Sheet. https://www.cdc.gov/pictureofamerica/pdfs/picture_of_america_heat-related_illness.pdf

[bib37] 37.Khatana S.A.M., Werner R.M., Groeneveld P.W. Association of extreme heat with all-cause mortality in the contiguous US, 2008-2017. JAMA Netw Open. 2022;5(5) doi: 10.1001/jamanetworkopen.2022.12957. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib38] 38.Sabrin S., Zech W.C., Nazari R., Karimi M. Understanding occupational heat exposure in the United States and proposing a quantifying stress index. Int Arch Occup Environ Health. 2021;94(8):1983–2000. doi: 10.1007/s00420-021-01711-0. [DOI] [PubMed] [Google Scholar]

[bib39] 39.Osborne S. What’s the coldest day of the year? (1981–2010 Normals) November 18, 2014. https://www.climate.gov/news-features/featured-images/whats-coldest-day-year-1981%E2%80%932010-normals

[bib40] 40.Lawrimore J.H., Ray R., Applequist S., Korzeniewski B., Menne M.J. Published online 2022. Global Summary of the Month (GSOM), Version 1. [DOI] [Google Scholar]

[bib41] 41.Kaufman J., Birmaher B., Brent D., et al. Schedule for Affective Disorders and Schizophrenia for School-Age Children—Present and Lifetime Version (K-SADS-PL): initial reliability and validity data. J Am Acad Child Adolesc Psychiatry. 1997;36(7):980–988. doi: 10.1097/00004583-199707000-00021. [DOI] [PubMed] [Google Scholar]

[bib42] 42.Shoval G., Visoki E., Moore T.M., et al. Evaluation of attention-deficit/hyperactivity disorder medications, externalizing symptoms, and suicidality in children. JAMA Netw Open. 2021;4(6) doi: 10.1001/jamanetworkopen.2021.11342. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib43] 43.Janiri D., Doucet G.E., Pompili M., et al. Risk and protective factors for childhood suicidality: a US population-based study. Lancet Psychiatry. 2020;7(4):317–326. doi: 10.1016/S2215-0366(20)30049-3. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib44] 44.DeVille D.C., Whalen D., Breslin F.J., et al. Prevalence and family-related factors associated with suicidal ideation, suicide attempts, and self-injury in children aged 9 to 10 years. JAMA Netw Open. 2020;3(2) doi: 10.1001/jamanetworkopen.2019.20956. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib45] 45.Jones J.D., Boyd R.C., Calkins M.E., et al. Parent-adolescent agreement about adolescents’ suicidal thoughts. Pediatrics. 2019;143(2) doi: 10.1542/peds.2018-1771. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib46] 46.Environmental Protection Agency Smart location mapping. 2022. https://www.epa.gov/smartgrowth/smart-location-mapping#SLD

[bib47] 47.Adolescent Brain Cognitive Development Study (ABCD) data release 4.0: linked external data (LED) Published online 2021. [DOI] [Google Scholar]

[bib48] 48.Cardenas-Iniguez C., Schachner J.N., Ip K.I., et al. Building towards an adolescent neural urbanome: expanding environmental measures using linked external data (LED) in the ABCD Study. Dev Cogn Neurosci. 2024;65 doi: 10.1016/j.dcn.2023.101338. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib49] 49.Liang Z., Wu S., Wang Y., et al. The relationship between urban form and heat island intensity along the urban development gradients. Sci Total Environ. 2020;708 doi: 10.1016/j.scitotenv.2019.135011. [DOI] [PubMed] [Google Scholar]

[bib50] 50.Charlson F., Ali S., Benmarhnia T., et al. Climate change and mental health: a scoping review. Public Health. 2021;18:4486. doi: 10.3390/ijerph. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib51] 51.Aylward B., Cunsolo A., Vriezen R., Harper S.L. Climate change is impacting mental health in North America: a systematic scoping review of the hazards, exposures, vulnerabilities, risks and responses. Int Rev Psychiatry. 2022;34(1):34–50. doi: 10.1080/09540261.2022.2029368. [DOI] [PubMed] [Google Scholar]

[bib52] 52.Seager R., Lis N., Feldman J., et al. Whither the 100th meridian? The once and future physical and human geography of America’s arid–humid divide. Part I: the story so far. Earth Interact. 2018;22(5):1–22. doi: 10.1175/EI-D-17-0011.1. [DOI] [Google Scholar]

[bib53] 53.Cha C.B., Franz P.J., Guzmán E., Glenn C.R., Kleiman E.M., Nock M.K. Annual research review: suicide among youth—epidemiology, (potential) etiology, and treatment. J Child Psychol Psychiatry. 2018;59(4):460–482. doi: 10.1111/jcpp.12831. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib54] 54.Horesh N., Levi Y., Apter A. Medically serious vs non-serious suicide attempts: relationships of lethality and intent to clinical and interpersonal characteristics. J Affect Disord. 2012;136(3):286–293. doi: 10.1016/j.jad.2011.11.035. [DOI] [PubMed] [Google Scholar]

[bib55] 55.Levi Y., Horesh N., Fischel T., Treves I., Or E., Apter A. Mental pain and its communication in medically serious suicide attempts: an “impossible situation.”. J Affect Disord. 2008;111(2-3):244–250. doi: 10.1016/j.jad.2008.02.022. [DOI] [PubMed] [Google Scholar]

[bib56] 56.Swahn M.H., Potter L.B. Factors associated with the medical severity of suicide attempts in youths and young adults. Suicide Life Threat Behav. 2002;32:21–29. doi: 10.1521/suli.32.1.5.21.24214. [DOI] [PubMed] [Google Scholar]

[bib57] 57.Lee J., Bang Y.S., Min S., et al. Characteristics of adolescents who visit the emergency department following suicide attempts: comparison study between adolescents and adults. BMC Psychiatry. 2019;19(1):231. doi: 10.1186/s12888-019-2213-5. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Association Between Extreme Heat and Externalizing Symptoms in Pre- and Early Adolescence: Findings From the ABCD Study

Sara Briker, MD

Kate T Tran, MPH

Elina Visoki, MSc

Joshua H Gordon, MD, PhD

Kevin W Hoffman, MD, PhD

Ran Barzilay, MD, PhD

Abstract

Objective

Method

Results

Conclusion

Study preregistration information

Diversity & Inclusion Statement

Plain language summary

Method

Participants

Exposures

Obtaining Temperature Data and Integration With ABCD Study Data

Figure 1.

Comparison of Exposure to Extreme Heat Across Race and Ethnicity

Outcomes

Covariates

Statistical Analysis

Mixed-Effects Models

Missing Data

Sensitivity Analysis

Results

Characteristics of Study Participants

Table 1.

Differential Exposure to Extreme Heat Among Different Racial and Ethnic Groups

Figure 2.

Multivariable Analyses of the Association Between Extreme Heat and Externalizing Symptoms

Table 2.

Figure 3.

Multivariable Analyses of the Association Between Extreme Heat and Suicide Attempts

Sensitivity Analyses

Discussion

CRediT authorship contribution statement

Footnotes

Supplemental Material

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases