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. Author manuscript; available in PMC: 2024 Nov 24.
Published in final edited form as: J Appl Dev Psychol. 2023 Nov 24;89:101604. doi: 10.1016/j.appdev.2023.101604

The Association of Sleep Duration with Grade Point Averages and Absences Among 9th Graders in Georgia, USA

Zerleen S Quader a,#, Henok Mulatu Teferi a,#, Cassandra Bryan a, Rachel Weingart b, Gina Marie Mathew c, Lauren Hale c, Dayna A Johnson a, Julie A Gazmararian a,*
PMCID: PMC10732583  NIHMSID: NIHMS1947206  PMID: 38130707

Abstract

This study aimed to examine the association between sleep measures (self-reported sleep duration and weekend catch-up sleep) and grade point average (GPA) and absences among 9th grade students from two racially and economically diverse high schools in a semi-rural county of north-central Georgia. Linear and Poisson regression models estimated the association between sleep measures and GPA and absences (separately), respectively. Analyses adjusted for gender, race/ethnicity, free/reduced-price school lunch status, and parental education. Sleep duration was significantly associated with both GPA and absences, such that for every one additional hour of sleep, GPA increased by 0.8 percentage points (b=0.8, 95% CI:0.1,1.5) while the number of absences was lower by 6% (b=−0.05; OR=0.94, 95% CI:0.91,0.98). Weekend catch-up sleep was also significantly and positively associated with absences (b=0.04; OR=1.04, 95% CI; 1.02, 1.07). Increasing sleep may be a strategy to improve GPA and reduce absences among teenagers. Future research should identify effective measures to lengthen sleep.

Keywords: Adolescent sleep, educational outcomes, Georgia, Sleep duration

Introduction

The Healthy People 2030 initiative seeks to increase the proportion of high school students who get sufficient sleep (Office of Disease Prevention and Health Promotion, n.d.). Adolescence is a time of burgeoning growth and changes in health behaviors, such as sleep quantity, quality, and physiological and emotional changes related to sleep (Tarokh et al., 2016). More than two-thirds of U.S adolescents are sleeping less than the recommended 8 to 10 hours on school nights, and between 50–90% of American high school students are chronically sleep-deprived (Basch et al., 2014; Hirshkowitz et al., 2015; Keyes et al., 2015; National Sleep Foundation, 2015.; Paruthi et al., 2016; Wheaton et al., 2018). Basch and colleagues found that one quarter of HS students slept fewer than 6.5 hours on school nights (Basch et al., 2014). Determinants of sleep deprivation among high school students include physiological, social, and environmental factors, including circadian phase delay, bedtime autonomy, early school start times, academic pressures, screen time, and social networking (Carskadon, 2011; Foti et al., 2011). Sleeping the recommended number of hours is associated with better mental and physical health outcomes (Paruthi et al., 2016), while sleep deprivation is associated with increased risk of accidents, injuries, hypertension, obesity, diabetes, and depression (Chaput et al., 2016; Owens et al., 2014; Paruthi et al., 2016). Insufficient sleep in youth is also associated with learning, attention, and behavior problems (Chaput et al., 2016; Paruthi et al., 2016). These factors are critical to adolescents’ behavioral and physical development and may affect educational outcomes.

Sleep deprivation also affects adolescent development by causing impaired cognitive functioning including poorer decision making and decreased alertness, attention span, creativity, and working memory (Beebe, 2016; Durmer & Dinges, 2005; Lo et al., 2016; Wolfson & Carskadon, 2003). The neuropsychological model (Lim & Dinges, 2010) posits that the impairment in complex cognitive skills such as decision making, working memory, and judgment during sleep loss is driven by reduced metabolic activity within the prefrontal cortex and other neural regions that are vital to executive function (Thomas et al., 2000). These neural changes during sleep loss may lead to decreased academic performance and attendance during development.

There are nearly two decades of evidence supporting an association between short sleep and impaired academic performance in adolescents (Chung & Cheung, 2008; Hysing et al., 2016; Lee et al., 2015; Ng et al., 2009; Peiró-Velert et al., 2014; Perez-Lloret et al., 2013). Moreover, limited research shows an association between short sleep duration and poorer school attendance in adolescents (Hysing et al., 2015; Singh et al., 2018). However, such research has been conducted in ethnically homogenous samples and used self-reported school performance measures (Ng et al., 2009; Perez-Lloret et al., 2013). Therefore, more research is needed, particularly examining these associations in diverse populations (Beebe, 2016; Jackson et al., 2020).

There is a growing body of work connecting low socioeconomic status (SES) to higher levels of sleep deficiency among children and adolescents (Marco et al., 2011; Owens et al., 2014). Students living in poverty are also often behind academically, and rural areas may have fewer community resources to support students (Miller & Votruba-Drzal, 2015). However, some evidence suggests the opposite; that is, rural students have more access to community resources associated with increased educational attainment (Byun et al., 2012). There is also evidence indicating that sleep patterns differ by region and location. For example, sleep deficiency may be worse in southern states, as well as in urban areas with more sleep disturbances (Keyes et al., 2015). Sleep plays an important role in adolescent health and development and represents a modifiable health behavior. Examining sleep and education in different settings can help tailor interventions and reduce existing disparities. Sleep duration in adolescence is associated with academic performance, but the association with absences is less clear. Moreover, there is a lack of research in diverse communities. Therefore, the objective of this study was to examine the associations of sleep duration and weekend catch-up sleep with grade point average (GPA) and absences among racially and economically diverse 9th graders attending two high schools in Georgia.

Methods

Data Source and Study Population

Data are from students attending two racially and economically diverse high schools in a semi-rural county of north-central Georgia. All 9th-grade students were eligible to participate and were recruited during the 2019–2020 school year. Because of the COVID-19 pandemic, data collection ended when schools closed in March 2020. Of 1,122 students who were eligible and invited to participate in the study, 617 students completed the study (54% participation rate). Over half of the students were non-Hispanic White, followed by 19% Hispanic and 15% non-Hispanic Black (Table 1). Approximately half were eligible for free/reduced-price school lunch (FRL). Twenty-six percent of students’ parents had at most a high school degree, and 60% had some college education or greater. Parental consent and student assent were obtained prior to data collection. Previously validated/established survey instruments were used to develop a student survey to measure desired constructs, including sleep duration and quality, stress, self-rated health, and other covariates. All surveys were administered online via the Qualtrics survey platform. The study protocol was approved by the Emory University Institutional Review Board. Four hundred thirty-three students had completed all sleep questions in the survey. Three students were missing data on parental education (a covariate) and were excluded. The final analytic sample included 430 students. There were no differences in racial/ethnic composition, eligibility for FRL, or gender distribution between the analytic sample and the original study sample of 617 students.

Table 1.

Demographic characteristics, Effects of Sleep on Education and Health Outcomes Among Adolescents, 9th grade students, 2019 – 2020 school year (N = 430)

N %
Race/ethnicity a
 Asian 20 4.7
 Black 65 15.1
 Hispanic 82 19.1
 Multiracial 21 4.9
 White 242 56.3
FRL Eligible b
 No 218 50.7
 Yes 212 49.3
Gender
 Female 222 51.6
 Male 208 48.4
Parental Education
 < High school 57 13.3
 High school graduate 112 26.1
 Some college 84 19.5
 College or greater 177 41.2
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a

School-reported race/ethnicity

b

Free/reduced lunch eligible

Measures

Exposure: Sleep Duration

Sleep duration, the average number of hours of sleep per night, was calculated based on student-reported bed and wake times on weekdays and weekends in response to items from the Student Sleep Habits Survey (Wolfson et al., 2003). Weeknight and weekend hours were weighted to calculate an overall weekly average sleep duration: ((Weeknight sleep duration * 5) + (Weekend sleep duration * 2)) / 7. Other dimensions of sleep were also measured in the student survey and examined in sensitivity analyses. Subjective daytime sleepiness was calculated as a summed score of students’ responses to 16 questions from the Cleveland Adolescent Sleepiness Questionnaire and assessed with a five-point Likert scale (Spilsbury et al., 2007). Sleep sufficiency was based on responses to the survey question, “In general, do you feel you usually get too much/ enough/ too little sleep?” Social jetlag, a measure of inconsistent sleep timing between the school week and weekend, was calculated as the absolute difference between the midpoints of weekend and school night sleep (Wittmann et al., 2006). Lastly, weekend catchup sleep was calculated by subtracting weekday sleep duration from weekend sleep duration.

Outcome: Academic outcomes

Grade point average and number of absences for each student were obtained from the county school district. GPA was cumulative across the full school year, with a maximum GPA of 100. Student attendance was also only measured until the transition to online learning, and the maximum number of days attended was 162. Days absent included both excused and unexcused absences for each student.

Covariates

Analyses adjusted for student self-reported race/ethnicity (Asian, Black, Hispanic, Multiracial, or White), gender (male or female), FRL, and highest level of parental education (less than high school, high school graduate, some college, or college or greater).

Analyses

Descriptive statistics were procured for demographic characteristics, sleep duration, GPA, and absences. Unadjusted and adjusted linear and Poisson regression models were conducted to examine the associations of sleep duration with GPA and absences, respectively. Analyses also explored the interaction between sleep duration and race/ethnicity on each outcome; however, sample sizes were too small to interpret stratified results. A stratified regression analysis by FRL eligibility was also conducted to examine the role of FRL eligibility as a moderator in the association between sleep duration and each of the outcomes. Additional analyses also explored associations of several other dimensions of self-reported sleep, including subjective daytime sleepiness, sleep sufficiency, and social jetlag with outcomes, as well as only Fall 2019 GPA instead of cumulative GPA as an outcome. The association between weekend catchup sleep and GPA and absences was also examined.

Results

Overall, students reported sleeping for an average of 7.1 hours per night (SD 1.2 hours), and there were no significant differences in sleep duration by race/ethnicity, FRL eligibility, gender, or parental education (Table 2). The average GPA of the sample was 84.8. Non-Hispanic Black and multiracial students, FRL-eligible students, male students, and students whose parents had high school degrees or less had the lowest GPAs compared to their references. Students were absent, on average, five days of the school year. Non-Hispanic White and Black students had the most absences, along with FRL-eligible students.

Table 2.

Mean sleep duration, GPA, and number of absences by demographic characteristics, Effects of Sleep on Education and Health Outcomes Among Adolescents, 9th grade students, 2019 – 2020 school year (N = 430)

Sleep duration a GPA b Absences c
Mean SD p- value d Mean SD p-value Mean SD p-value
Total 7.1 1.2 84.8 8.9 5.1 5.3
Race/ethnicity e
 Asian 7.1 1.1 89.8 5.7 3.4 3.2
 Black 6.9 1.2 0.110 82.5 4.8 0.008 5.1 5.5  0.048
 Hispanic 7.4 1.1 84.9 8.3 4.0 4.7
 Multiracial 5.8 1.6 81.9 13.6 4.5 3.2
 White 7.2 1.1 85.3 8.7 5.7 5.6
FRL Eligible f
 No 7.2 1.1 0.851 86.5 8.2 <0.0001 4.6 4.1  0.033
 Yes 7.1 1.2 83.1 9.3 5.7 6.2
Gender
 Female 7.2 1.1 0.179 86.4 8.4 0.001 5.5 5.4 0.103
 Male 7.1 1.2 83.1 9.1 4.7 5.1
Parental Education
 < High school 7.3 1.0 83.6 11.1 6.3 6.5
 High school graduate 7.0 1.2 0.680 82.1 9.8 0.001 5.3 5.6 0.235
 Some college 7.1 1.2 85.7 7.2 4.9 5.2
 College or greater 7.2 1.2 86.5 7.9 4.7 4.5
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a

Hours/day.

b

Grade Point Average; based on fall 2019 and spring 2020 grades.

c

Out of 162 total possible school days.

d

Overall ANOVA test.

e

School-reported race/ethnicity.

f

Free/reduced lunch eligible.

The unadjusted linear regression model showed a higher average GPA of 1.1 percentage points for every one-hour increase in sleep duration (b=1.1, 95% CI; 0.4, 1.8) (Table 3). After adjusting for covariates, the association between sleep duration and GPA remained statistically significant, with an average increase of 0.8 percentage points in students’ GPA for every one-hour longer in sleep duration (b=0.8, 95% CI; 0.1, 1.5). Additional analyses estimating the association between sleep duration and fall 2019 GPAs yielded similar results (data available upon request).

Table 3.

Regression models examining sleep duration and mean GPA and Absences, Effects of Sleep on Education and Health Outcomes Among Adolescents, 9th grade students, 2019 – 2020 school year (N = 430)

GPA (Linear regression) Absences (Poisson regression)
Unadjusted Adjusted a Unadjusted Adjusted
b 95% CI b 95% CI b OR 95% CI b OR 95% CI
Intercept 77.1 (71.9, 82.3) * 74.1 (68.6, 79.6) * 2.0 7.7 (5.9, 9.9) 2.5 12.3 (9.3,16.4)
Sleep duration 1.1 (0.4, 1.8) * 0.8 (0.1, 1.5) * −0.05 0.94 (0.9, 0.97) −0.05 0.94 (0.91,0.98)
Race/Ethnicity b
 Asian 5.5 (1.8, 9.3) * −0.7 0.5 (0.4,0.7) *
 Black −2.3 (−4.6, 0.02) −0.2 0.8 (0.7,0.9) *
 Hispanic 1.2 (−0.99, 3.4) −0.6 0.5 (0.5,0.6) *
 Multiracial −3.2 (−6.9, 0.5) −0.3 0.7 (0.6,0.9) *
 White Ref -- Ref -- --
FRL Eligible c
 No 3.5* (1.9, 5.2) −0.3 0.8 (0.9,0.8) *
 Yes Ref -- Ref -- --
Gender
 Female 3.3 (1.7, 4.9) * 0.2 1.2 (1.1,1.3) *
 Male Ref -- Ref -- --
Parental
Education
 < High school Ref -- Ref
 High school graduate −0.9 (−3.7, 1.7) −0.2 0.8 (0.7,0.9) *
 Some college 2.2 (−0.6, 5.1) −0.4 0.7 (0.6,0.8) *
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The unadjusted Poisson regression models indicated that the number of predicted absences was 6% lower for every 1 hr longer sleep duration (b=−0.05; OR=0.94, 95% CI; 0.91, 0.97). After adjusting for covariates, sleep duration remained significantly associated with absences with equivalent effect sizes (b=−0.05; OR=0.94, 95% CI;0.91, 0.98; Table 3).

While the association between weekend catchup sleep and GPA was not significant, we identified a positive and statistically significant association between weekend catchup sleep and absences after adjusting for covariates (b=0.04; OR=1.04, 95% CI; 1.02, 1.07; Table 4).

Table 4.

Regression models examining weekend catchup sleep and mean GPA and Absences, Effects of Sleep on Education and Health Outcomes Among Adolescents. 9th grade students, 2019–2020 school year (N = 430).

GPA (Linear regression)
 Absences (Poisson regression)
Unadjusted Adjusteda Unadjusted Adjusted
b 95% CI b 95% CI b OR 95% CI b OR 95% CI
Intercept 84.9 (83.5. 86.4) 83.1 (80.7. 85.4) 1.5 4.5 (4.2.4.8) 2.0 7.4 (6.4.8.6)
Weekend catchup sleep −0.O4 (−0.5. 0.4) −0.1 (−0.5. 0.4) 0.1 1.1 (1.0.1.08) 0.O4 1.04 (1.02.1.07)
Race/Ethnicity b
 Asian 5.4 (1.6, 9.2) −0.6 0.5 (0.4,0.7)
 Black −2.4 (−4.8, −0.2) −0.2 0.8 (0.7. 0.9)
 Hispanic 1.3 (−0.8, 3.6) −0.6 0.6 (0.5. 0.64)
 Multiracial −3.5 (−7.3, 0.1) −0.3 0.7 (0.6. 0.9)
 White Ref - Ref - -
FRL Eligible c
 No 3.5 (1.9, 5.2) −0.3 0.7 (0.6,0.8)
 Yes Ref - Ref - -
Gender
 Female 3.4 (1.9, 5.0) 0.1 1.2 (1.1,1.3)
 Male Ref -
Parental Education
 < High school Ref - Ref
 High school graduate −1.1 (−3.8, 1.7) −0.2 0.8 (0.7,0.9)
 Some college 2.2 (−0.7, 5.0) −0.3 0.7 (0.6,0.8)
 College or greater 3.2 (0.5, 5.7) −0.4 0.7 (0.6,0.8)
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OR: Odds Ratio.

*

p < 0.05.

a

Adjusted for race/ethnicity, free/reduced lunch, gender, parental education.

b

School-reported race/ethnicity.

c

Free/reduced lunch eligible.

A stratified analysis based on FRL eligibility revealed variations in the relationships between sleep duration, GPA, and absences across groups (FRL eligible vs. FRL non-eligible). When examining FRL non-eligible students, a statistically significant and positive correlation was found between sleep duration and GPA (b=1.0, 95% CI; 0.11,2.02). However, the analysis among FRL-eligible students did not indicate a significant association (b=0.5, 95% CI; −0.40, 1.49). Regarding the association between sleep duration and absences, a statistically significant and negative association (b=−0.1; OR=0.9, 95% CI; 0.8, 0.94) was observed among FRL-eligible students, but this relationship was not significant among FRL non-eligible students (b= 0.01; OR=1.0, 95% CI; 0.9, 1.1; Table 5).

Table 5.

Regression models examining the moderation effect of free and reduced lunch eligibility in the association between sleep duration and GPA and Absences. Effects of Sleep on Education and Health Outcomes Among Adolescents, 9th-grade students, 2019–2020 school year (N = 430).

GPA (linear regression) Absences (Poisson regression)
Unadjusted Adjusteda Unadjusted Adjusteda
b 95% CI b 95% CI b OR 95% CI b OR 95% CI
Overall
 Sleep duration 0.8 (−0.1,1.8) 0.5 (−0.4, 1.5) −0.1 0.9 (0.8, 0.93) * −0.1 0.9 (0.8, 0.94) *
 bFRL (reference - yes) −0.3 (10.4, 9.9) −0.1 (−9.9, 9.5) −1.1 0.3 (0.2, 0.6) −1.0 0.3 (0.2, 0.6) *
 Sleep duration x FRL 0.5 (−0.9, 1.9) 0.5 (−0.8, 1.9) 0.1 1.1 (1.05, 1.2) * 0.1 1.1 (1.0, 1.2)
bFRL Eligible
 Sleep duration 0.8 (0.2, 1.8) 05 (−0.4, 1.4) −0.1 0.9 (0.8, 0.93) −0.1 0.9 (0.8, 0.94)
bFRL non-eiigibie
 Sleep duration 1.3 (0.3, 2.3) * 1.1 (0.1, 2.0) 0.01 1.0 (0.9, 1.1) 0.01 1.0 (0.9, 1.1)
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OR; Odds ratio.

a

Adjusted for race/ethnicity, gender, and parental education.

b

Free/reduced lunch eligible.

*

p <0.05.

Discussion

While prior research supports the association between sleep and academic performance, there is significant variability in the sleep measures and outcomes of interest, limited diversity in study samples, and fewer studies have examined sleep in relation to absenteeism (Dewald et al., 2010; Hershner, 2020). The results of this cross-sectional analysis of 9th grade students in a semi-rural county of north-central Georgia suggest that after adjustment for sociodemographic characteristics, sleep duration is positively associated with GPA and negatively associated with absenteeism in a diverse sample of students. This study supports the importance of sleep for academic performance in a diverse population and provides additional evidence on the less-studied topic of student absenteeism.

Results from analyses examining sleep duration and GPA are consistent with previous studies of sleep and academic outcomes. One of the first large studies examining sleep and academic performance among U.S. adolescents found that those with lower grades self-reported going to bed later and sleeping fewer hours every week (Wolfson & Carskadon, 1998). However, this study was conducted in one school district in Rhode Island, with over 90% of sample identifying as White, and low levels of FRL eligibility (Wolfson & Carskadon, 1998). In contrast, the current study sample was over 40% non-White and almost half were FRL eligible. A literature review on adolescent school start times, sleep, and academic and health outcomes concluded that in general, cross-sectional studies support the notion that insufficient sleep is associated with poorer academic performance; but a few studies have not found an association (Wheaton et al., 2016). Prospective studies examining this association are limited, and have also shown mixed results between sleep duration and grades, with some studies suggesting no link between sleep and school performance (Shochat et al., 2014). However, differences between studies’ measurements of sleep (e.g., weekday sleep duration, circadian preference, or sleep quality), use of self-reported sleep, as well as variability in grading, schools, and covariate adjustment may explain the inconsistent results.

The association between sleep duration and absenteeism is less clear. The causes of absenteeism are complex, and though illness is often the most frequently reported reason for absences, they are most likely driven by various individual, family, and social factors (Allen et al., 2018; Allison et al., 2019). In line with prior studies, this study identified a statistically significant association between sleep duration and absenteeism. A study conducted among 16–19 year old students in Norway found that short sleep duration and sleep deficiency were associated with higher odds of school absences (Hysing et al., 2016), and another study of middle school students in Ohio found that students with high rates of absenteeism reported higher levels of daytime sleepiness (Drake et al., 2003). Other studies have focused specifically on school start times and academic outcomes, and have found that earlier start times are related to more tardiness and more absences (Wheaton et al., 2016). The current study population had relatively low levels of absenteeism, with only 30 students in the sample (7%) who fell within the Every Student Succeeds Act criteria of chronic absenteeism (absent for more than 10% of the academic year); the national average was approximately 15% in 2017–2018 (U.S. Department of Education. “Chronic Absenteeism in the Nation’s Schools.,” n.d.).‘

This study had several strengths and some limitations. First, it included a racially and economically diverse study population from a semi-rural area, representing an understudied population. Grades and attendance were school-district-reported rather than self-reported, reducing self-report bias for the outcome variables. Finally, multiple measures of sleep duration and quality were included in the student survey and examined as sensitivity analyses to understand if different aspects of sleep were associated with grades or absenteeism. This study’s limitations include the fact that sleep data were self-reported, and due to the COVID-19 pandemic, grade and attendance data may not be consistent with those from prior years. Specifically, shortly after the completion of the student survey, pandemic-related school closures and transitions to remote learning occurred, and annual standardized testing was not conducted as usual. In addition, the modest effect size observed for the association between sleep duration and GPA in this study poses challenges in determining its clinical significance. Finally, the analyses were cross-sectional in design, and analyses of longitudinal changes in sleep and educational outcomes are warranted in future research.

Conclusions

As sleep deficiency continues to be a major problem for U.S. adolescents, understanding the role of sleep as a common yet modifiable risk factor with the potential to improve educational outcomes and reduce disparities in education is important. Furthermore, sleep has long been recognized as an important contribution to optimal child and adolescent brain development (Galván, 2020). This work supports the need for larger-scale longitudinal and intervention studies to better identify appropriate methods to improve sleep among teenagers. For example, delaying school start times has been proposed as potential policy change to help address insufficient sleep among adolescents and improve educational and health outcomes (Adolescent Sleep Working Group et al., 2014). This research also suggests that sleep-related interventions may be a strategy for improving educational outcomes among teenagers, which could have longer-term implications for their well-being and development. This study also provides a foundation for future research to explore the impact of sleep duration on academic outcomes within diverse subgroups.

Supplementary Material

1

Highlights.

This study focuses on the importance of sleep during adolescence and contributes to the existing research examining the role of sleep in academic outcomes, in a particularly underexamined population of racially/ethnically and socioeconomically diverse adolescents in a semi-rural area.

This study adds to literature by further examining whether sleep influences chronic absenteeism among students.

Findings from the study may have important implications about opportunities to address adolescent sleep health and improve long term educational and health outcomes.

Sleep-related interventions may be a strategy for improving educational outcomes among teenagers yet may need to be tailored to specific populations.

Funding:

Funding was provided by the National Institute of Child Health and Human Development (Grant No. R21HD097491) and the National Heart, Lung, and Blood Institute (Grant Nos. T32HL130025 and 1F31HL156426–01A1).

Footnotes

Conflict of Interest Declaration: Dr. Hale is Chair of the National Sleep Foundation and between 2015 and 2020 received an honorarium for her role as Editor-in-Chief of Sleep Health. She occasionally receives honoraria for other speaking engagements and is a paid consultant for the Alliance for Sleep by Idorsia. The other authors have no conflicts of interest to disclose.

Informed consent: Informed consent was obtained from parent and/or legal guardian for study participation, and student assent was obtained prior to data collection. The study protocol was approved by Emory University IRB (IRB00111438).

Ethical approval: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Data Availability Statement:

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

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Associated Data

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

Supplementary Materials

1
NIHMS1947206-supplement-1.docx (23.3KB, docx)

Data Availability Statement

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

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