[Skip to Content]
[Skip to Content Landing]

Identification of Modifiable Social and Behavioral Factors Associated With Childhood Cognitive Performance

Educational Objective To identify modifiable factors associated with childhood cognitive performance.
1 Credit CME
Key Points

Question  Are specific exposures associated with childhood cognitive performance and are inequities in these exposures associated with racial disparities in cognitive test scores?

Findings  In this cohort study of 1055 mother-child dyads, 24 of 155 prenatal and postnatal exposures were associated with childhood cognitive performance; models that included all of the exposures fully accounted for the racial disparities in cognitive test scores. Modifiable exposures included breastfeeding, parental educational levels, fostering of cognitive growth during mother-child interactions, parenting stress, and maternal reading ability, which together were associated with 0.5% of a standard deviation difference in cognitive test scores.

Meaning  The study’s results indicated that addressing inequities in the early environment could help to reduce racial disparities in childhood cognitive performance.

Abstract

Importance  Inequities in social environments are likely associated with a large portion of racial disparities in childhood cognitive performance. Identification of the specific exposures associated with cognitive development is needed to inform prevention efforts.

Objective  To identify modifiable factors associated with childhood cognitive performance.

Design, Setting, and Participants  This longitudinal pregnancy cohort study included 1503 mother-child dyads who were enrolled in the University of Tennessee Health Science Center–Conditions Affecting Neurodevelopment and Learning in Early Life study between December 1, 2006, and July 31, 2011, and assessed annually until the children were aged 4 to 6 years. The analytic sample comprised 1055 mother-child dyads. A total of 155 prenatal, perinatal, and postnatal exposures were included to evaluate environment-wide associations. Participants comprised a community-based sample of pregnant women who were recruited between 16 weeks and 28 weeks of gestation from 4 hospitals in Shelby County, Tennessee. Women with high-risk pregnancies were excluded. Data were analyzed from June 1, 2018, to April 15, 2019.

Exposures  Individual and neighborhood socioeconomic position, family structure, maternal mental health, nutrition, delivery complications, birth outcomes, and parenting behaviors.

Main Outcomes and Measures  Child’s full-scale IQ measured by the Stanford-Binet Intelligence Scales, Fifth Edition, at age 4 to 6 years.

Results  Of 1055 children included in the analytic sample, 532 (50.4%) were female. Among mothers, the mean (SD) age was 26.0 (5.6) years; 676 mothers (64.1%) were Black, and 623 mothers (59.0%) had an educational level of high school or less. Twenty-four factors were retained in the least absolute shrinkage and selection operator regression analysis and full models adjusted for potential confounding. Associations were noted between child cognitive performance and parental education and breastfeeding; for each increase of 1.0 SD in exposure, positive associations were found with cognitive growth fostering from observed parent-child interactions (β = 1.12; 95% CI, 0.24-2.00) and maternal reading ability (β = 1.42; 95% CI, 0.16-2.68), and negative associations were found with parenting stress (β = −1.04; 95% CI, −1.86 to −0.21). A moderate increase in these beneficial exposures was associated with a notable improvement in estimated cognitive test scores using marginal means (0.5% of an SD). Black children experienced fewer beneficial cognitive performance exposures; in a model including all 24 exposures and covariates, no racial disparity was observed in cognitive performance (95% CIs for race included the null).

Conclusions and Relevance  The prospective analysis identified multiple beneficial and modifiable cognitive performance exposures that were associated with mean differences in cognitive performance by race. The findings from this observational study may help guide experimental studies focused on reducing racial disparities in childhood cognitive performance.

Sign in to take quiz and track your certificates

Buy This Activity

JN Learning™ is the home for CME and MOC from the JAMA Network. Search by specialty or US state and earn AMA PRA Category 1 Credit(s)™ from articles, audio, Clinical Challenges and more. Learn more about CME/MOC

CME Disclosure Statement: Unless noted, all individuals in control of content reported no relevant financial relationships. If applicable, all relevant financial relationships have been mitigated.

Article Information

Accepted for Publication: June 2, 2020.

Corresponding Author: Kaja Z. LeWinn, ScD, MS, Weill Institute for Neurosciences, Department of Psychiatry, University of California, San Francisco, 401 Parnassus Ave, San Francisco, CA 94143 (kaja.lewinn@ucsf.edu).

Published Online: September 21, 2020. doi:10.1001/jamapediatrics.2020.2904

Author Contributions: Dr LeWinn and Ms Batra had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: LeWinn, Bush, Tylavsky, Rehkopf.

Acquisition, analysis, or interpretation of data: LeWinn, Bush, Batra, Tylavsky.

Drafting of the manuscript: LeWinn, Batra, Rehkopf.

Critical revision of the manuscript for important intellectual content: LeWinn, Bush, Tylavsky, Rehkopf.

Statistical analysis: LeWinn, Batra.

Obtained funding: LeWinn, Bush, Tylavsky.

Administrative, technical, or material support: LeWinn, Bush, Tylavsky.

Supervision: LeWinn, Rehkopf.

Conflict of Interest Disclosures: Dr LeWinn reported receiving grants from the Urban Child Institute during the conduct of the study. Dr Bush reported receiving grants from the University of California, San Francisco, and the Urban Child Institute during the conduct of the study. Dr Tylavsky reported receiving grants from the National Institutes of Health and the Urban Child Institute and during the conduct of the study. No other disclosures were reported.

Funding/Support: This study was funded by the Urban Child Institute in Memphis, Tennessee (Drs LeWinn, Bush, and Tylavsky); the Urban Child Institute also funded the Conditions Affecting Neurodevelopment and Learning in Early Life (CANDLE) study.

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

Additional Contributions: Robert Davis, MD, of the Center for Biomedical Informatics, University of Tennessee Health Science Center, and Nancy Adler, MD, of the Center for Health and Community, University of California, San Francisco, provided insightful comments and suggestions that greatly improved our manuscript. Neither contributor received compensation. We are grateful for the participation of families enrolled in the Conditions Affecting Neurodevelopment and Learning in Early Life (CANDLE), as well as the dedication of CANDLE research staff and investigators.

References
1.
Martin  LT , Kubzansky  LD , LeWinn  KZ , Lipsitt  LP , Satz  P , Buka  SL .  Childhood cognitive performance and risk of generalized anxiety disorder.   Int J Epidemiol. 2007;36(4):769-775. doi:10.1093/ije/dym063 PubMedGoogle Scholar
2.
Batty  GD , Deary  IJ .  Early life intelligence and adult health.   BMJ. 2004;329(7466):585-586. doi:10.1136/bmj.329.7466.585 PubMedGoogle Scholar
3.
Lager  A , Bremberg  S , Vagero  D .  The association of early IQ and education with mortality: 65 year longitudinal study in Malmo, Sweden.   BMJ. 2009;339:b5282. doi:10.1136/bmj.b5282 PubMedGoogle Scholar
4.
Kendler  KS , Turkheimer  E , Ohlsson  H , Sundquist  J , Sundquist  K .  Family environment and the malleability of cognitive ability: a Swedish national home-reared and adopted-away cosibling control study.   Proc Natl Acad Sci U S A. 2015;112(15):4612-4617. doi:10.1073/pnas.1417106112 PubMedGoogle Scholar
5.
Nelson  CA  III , Zeanah  CH , Fox  NA , Marshall  PJ , Smyke  AT , Guthrie  D .  Cognitive recovery in socially deprived young children: the Bucharest Early Intervention Project.   Science. 2007;318(5858):1937-1940. doi:10.1126/science.1143921 PubMedGoogle Scholar
6.
Ritchie  SJ , Tucker-Drob  EM .  How much does education improve intelligence? a meta-analysis.   Psychol Sci. 2018;29(8):1358-1369. doi:10.1177/0956797618774253 PubMedGoogle Scholar
7.
Campbell  FA , Ramey  CT , Pungello  E , Sparling  J , Miller-Johnson  S .  Early childhood education: young adult outcomes from the Abecedarian Project.   Appl Dev Sci. 2002;6(1):42-57. doi:10.1207/S1532480XADS0601_05 Google Scholar
8.
Turkheimer  E , Haley  A , Waldron  M , D’Onofrio  B , Gottesman  II .  Socioeconomic status modifies heritability of IQ in young children.   Psychol Sci. 2003;14(6):623-628. doi:10.1046/j.0956-7976.2003.psci_1475.x PubMedGoogle Scholar
9.
Tucker-Drob  EM , Bates  TC .  Large cross-national differences in gene × socioeconomic status interaction on intelligence.   Psychol Sci. 2016;27(2):138-149. doi:10.1177/0956797615612727 PubMedGoogle Scholar
10.
Tucker-Drob  EM , Briley  DA , Harden  KP .  Genetic and environmental influences on cognition across development and context.   Curr Dir Psychol Sci. 2013;22(5):349-355. doi:10.1177/0963721413485087 PubMedGoogle Scholar
11.
Del Carmen Ruiz  J , Quackenboss  JJ , Tulve  NS .  Contributions of a child’s built, natural, and social environments to their general cognitive ability: a systematic scoping review.   PLoS One. 2016;11(2):e0147741. doi:10.1371/journal.pone.0147741 PubMedGoogle Scholar
12.
Lee  VE , Burkam  DT .  Inequality at the Starting Gate: Social Background Differences in Achievement as Children Begin School. Economic Policy Institute; 2002.
13.
Canfield  RL , Henderson  CR  Jr , Cory-Slechta  DA , Cox  C , Jusko  TA , Lanphear  BP .  Intellectual impairment in children with blood lead concentrations below 10 microg per deciliter.   N Engl J Med. 2003;348(16):1517-1526. doi:10.1056/NEJMoa022848 PubMedGoogle Scholar
14.
Perera  FP , Li  Z , Whyatt  R ,  et al.  Prenatal airborne polycyclic aromatic hydrocarbon exposure and child IQ at age 5 years.   Pediatrics. 2009;124(2):e195-e202. doi:10.1542/peds.2008-3506 PubMedGoogle Scholar
15.
Liu  J , Lewis  G .  Environmental toxicity and poor cognitive outcomes in children and adults.   J Environ Health. 2014;76(6):130-138.PubMedGoogle Scholar
16.
Northstone  K , Joinson  C , Emmett  P , Ness  A , Paus  T .  Are dietary patterns in childhood associated with IQ at 8 years of age? a population-based cohort study.   J Epidemiol Community Health. 2012;66(7):624-628. doi:10.1136/jech.2010.111955 PubMedGoogle Scholar
17.
Freitas-Vilela  AA , Pearson  RM , Emmett  P ,  et al.  Maternal dietary patterns during pregnancy and intelligence quotients in the offspring at 8 years of age: findings from the ALSPAC cohort.   Matern Child Nutr. 2018;14(1):e12431. doi:10.1111/mcn.12431 PubMedGoogle Scholar
18.
Lawlor  DA , Najman  JM , Batty  GD , O’Callaghan  MJ , Williams  GM , Bor  W .  Early life predictors of childhood intelligence: findings from the Mater-University study of pregnancy and its outcomes.   Paediatr Perinat Epidemiol. 2006;20(2):148-162. doi:10.1111/j.1365-3016.2006.00704.x PubMedGoogle Scholar
19.
Eriksen  H-LF , Kesmodel  US , Underbjerg  M , Kilburn  TR , Bertrand  J , Mortensen  EL .  Predictors of intelligence at the age of 5: family, pregnancy and birth characteristics, postnatal influences, and postnatal growth.   PLoS One. 2013;8(11):e79200. doi:10.1371/journal.pone.0079200 PubMedGoogle Scholar
20.
Koenen  KC , Moffitt  TE , Caspi  A , Taylor  A , Purcell  S .  Domestic violence is associated with environmental suppression of IQ in young children.   Dev Psychopathol. 2003;15(2):297-311. doi:10.1017/S0954579403000166 PubMedGoogle Scholar
21.
van Bakel  HJA , Riksen-Walraven  JM .  Parenting and development of one-year-olds: links with parental, contextual, and child characteristics.   Child Dev. 2002;73(1):256-273. doi:10.1111/1467-8624.00404 PubMedGoogle Scholar
22.
Patel  CJ , Rehkopf  DH , Leppert  JT ,  et al.  Systematic evaluation of environmental and behavioural factors associated with all-cause mortality in the United States National Health and Nutrition Examination Survey.   Int J Epidemiol. 2013;42(6):1795-1810. doi:10.1093/ije/dyt208 PubMedGoogle Scholar
23.
Patel  CJ , Ioannidis  JPA , Cullen  MR , Rehkopf  DH .  Systematic assessment of the correlations of household income with infectious, biochemical, physiological, and environmental factors in the United States, 1999-2006.   Am J Epidemiol. 2015;181(3):171-179. doi:10.1093/aje/kwu277 PubMedGoogle Scholar
24.
Sontag-Padilla  L , Burns  RM , Shih  RA ,  et al.  The Urban Child Institute CANDLE Study: Methodological Overview and Baseline Sample Description RAND Corporation; 2015. doi:10.7249/RR1336
25.
Wechsler  D .  Wechsler Abbreviated Scale of Intelligence, Second Edition (WASI-II). NCS Pearson; 2011.
26.
Roid  GH .  Stanford-Binet Intelligence Scales, Fifth Edition. Riverside Publishing; 2003.
27.
Benjamini  Y , Hochberg  Y .  Controlling the false discovery rate: a practical and powerful approach to multiple testing.   J R Stat Soc Series B Stat Methodol. 1995;57(1):289–300. doi:10.1111/j.2517-6161.1995.tb02031.xGoogle Scholar
28.
Glickman  ME , Rao  SR , Schultz  MR .  False discovery rate control is a recommended alternative to Bonferroni-type adjustments in health studies.   J Clin Epidemiol. 2014;67(8):850-857. doi:10.1016/j.jclinepi.2014.03.012 PubMedGoogle Scholar
29.
Tibshirani  R .  Regression shrinkage and selection via the lasso.   J R Stat Soc Series B Stat Methodol. 1996;58(1):267–288. doi:10.1111/j.2517-6161.1996.tb02080.xGoogle Scholar
30.
Van Houwelingen  JC .  Shrinkage and penalized likelihood as methods to improve predictive accuracy.   Stat Neerl. 2001;55(1):17–34. doi:10.1111/1467-9574.00154 Google Scholar
31.
Zhao  P , Yu  B .  On model selection consistency of lasso.   J Mach Learn Res. 2006;7:2541–2563.Google Scholar
32.
Burgette  LF , Reiter  JP .  Multiple imputation for missing data via sequential regression trees.   Am J Epidemiol. 2010;172(9):1070-1076. doi:10.1093/aje/kwq260 PubMedGoogle Scholar
33.
Stekhoven  DJ , Buhlmann  P .  MissForest—non-parametric missing value imputation for mixed-type data.   Bioinformatics. 2012;28(1):112-118. doi:10.1093/bioinformatics/btr597 PubMedGoogle Scholar
34.
Chow  GC .  Tests of equality between sets of coefficients in two linear regressions.   Econometrica. 1960;28(3):591–605. doi:10.2307/1910133 Google Scholar
35.
Organisation for Economic Co-operation and Development.  Society at a Glance 2014: OECD Social Indicators. OECD Publishing; 2014.
36.
Buckley  JP , Doherty  BT , Keil  AP , Engel  SM .  Statistical approaches for estimating sex-specific effects in endocrine disruptors research.   Environ Health Perspect. 2017;125(6):067013. doi:10.1289/EHP334 PubMedGoogle Scholar
37.
Christian  K , Morrison  FJ , Bryant  FB .  Predicting kindergarten academic skills: interactions among child care, maternal education, and family literacy environments.   Early Child Res Q. 1998;13(3):501–521. doi:10.1016/S0885-2006(99)80054-4 Google Scholar
38.
Roberts  J , Jurgens  J , Burchinal  M .  The role of home literacy practices in preschool children’s language and emergent literacy skills.   J Speech Lang Hear Res. 2005;48(2):345-359. doi:10.1044/1092-4388(2005/024) PubMedGoogle Scholar
39.
Green  CM , Berkule  SB , Dreyer  BP ,  et al.  Maternal literacy and associations between education and the cognitive home environment in low-income families.   Arch Pediatr Adolesc Med. 2009;163(9):832-837. doi:10.1001/archpediatrics.2009.136 PubMedGoogle Scholar
40.
Sayegh  P , Arentoft  A , Thaler  NS , Dean  AC , Thames  AD .  Quality of education predicts performance on the Wide Range Achievement Test–4th Edition Word Reading subtest.   Arch Clin Neuropsychol. 2014;29(8):731-736. doi:10.1093/arclin/acu059 PubMedGoogle Scholar
41.
Baer  J , Kutner  M , Sabatini  J , White  S.  Basic Reading Skills and the Literacy of America’s Least Literate Adults: Results From the 2003 National Assessment of Adult Literacy (NAAL) Supplemental Studies. NCES 2009-481. National Center for Education Statistics; 2009. Accessed December 27, 2019. https://eric.ed.gov/?id=ED505187
42.
Ladson-Billings  G .  From the achievement gap to the education debt: understanding achievement in U.S. schools.   Educ Res. 2006;35(7):3-12. doi:10.3102/0013189X035007003 Google Scholar
43.
Margo  RA .  Race and Schooling in the South, 1880-1950: An Economic History. University of Chicago Press; 1990. doi:10.7208/chicago/9780226505015.001.0001
44.
Bornstein  MH .  How infant and mother jointly contribute to developing cognitive competence in the child.   Proc Natl Acad Sci U S A. 1985;82(21):7470-7473. doi:10.1073/pnas.82.21.7470 PubMedGoogle Scholar
45.
Fewell  RR , Deutscher  B .  Contributions of receptive vocabulary and maternal style: variables to later verbal ability and reading in low-birthweight children.   Top Early Child Special Educ. 2002;22(4):181–190. doi:10.1177/027112140202200401 Google Scholar
46.
Herbers  JE , Cutuli  JJ , Lafavor  TL ,  et al.  Direct and indirect effects of parenting on the academic functioning of young homeless children.   Early Educ Dev. 2011;22(1):77–104. doi:10.1080/10409280903507261 Google Scholar
47.
Craig  F , Operto  FF , De Giacomo  A ,  et al.  Parenting stress among parents of children with neurodevelopmental disorders.   Psychiatry Res. 2016;242:121-129. doi:10.1016/j.psychres.2016.05.016 PubMedGoogle Scholar
48.
Estes  A , Munson  J , Dawson  G , Koehler  E , Zhou  X-H , Abbott  R .  Parenting stress and psychological functioning among mothers of preschool children with autism and developmental delay.   Autism. 2009;13(4):375-387. doi:10.1177/1362361309105658 PubMedGoogle Scholar
49.
Baker  BL , Blacher  J , Crnic  KA , Edelbrock  C .  Behavior problems and parenting stress in families of three-year-old children with and without developmental delays.   Am J Ment Retard. 2002;107(6):433-444. doi:10.1352/0895-8017(2002)107<0433:BPAPSI>2.0.CO;2 PubMedGoogle Scholar
50.
Neece  CL , Green  SA , Baker  BL .  Parenting stress and child behavior problems: a transactional relationship across time.   Am J Intellect Dev Disabil. 2012;117(1):48-66. doi:10.1352/1944-7558-117.1.48 PubMedGoogle Scholar
51.
Tachibana  Y , Fukushima  A , Saito  H ,  et al.  A new mother-child play activity program to decrease parenting stress and improve child cognitive abilities: a cluster randomized controlled trial.   PLoS One. 2012;7(7):e38238. doi:10.1371/journal.pone.0038238 PubMedGoogle Scholar
52.
Burgdorf  V , Szabo  M , Abbott  MJ .  The effect of mindfulness interventions for parents on parenting stress and youth psychological outcomes: a systematic review and meta-analysis.   Front Psychol. 2019;10:1336. doi:10.3389/fpsyg.2019.01336 PubMedGoogle Scholar
53.
Lee  JJ , Wedow  R , Okbay  A ,  et al; 23andMe Research Team; COGENT (Cognitive Genomics Consortium); Social Science Genetic Association Consortium.  Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals.   Nat Genet. 2018;50(8):1112-1121. doi:10.1038/s41588-018-0147-3 PubMedGoogle Scholar
54.
Cheesman  R , Hunjan  A , Coleman  JRI ,  et al.  Comparison of adopted and non-adopted individuals reveals gene-environment interplay for education in the UK Biobank.   Psychol Sci. 2020;31(5):582-591. doi:10.1177/0956797620904450PubMedGoogle Scholar
55.
Cortes  KE .  Do bans on affirmative action hurt minority students? evidence from the Texas Top 10% Plan.   Econ Educ Rev. 2010;29(6):1110-1124. doi:10.1016/j.econedurev.2010.06.004 Google Scholar
56.
Blume  GH , Long  MC .  Changes in levels of affirmative action in college admissions in response to statewide bans and judicial rulings.   Educ Eval Policy Anal. 2014;36(2):228-252. doi:10.3102/0162373713508810 Google Scholar
57.
Pitre  CC .  Improving African American student outcomes: understanding educational achievement and strategies to close opportunity gaps.   West J Black Stud. 2014;38(4):209-217.Google Scholar
58.
Berkner  L , Chavez  L.  Access to Postsecondary Education for the 1992 High School Graduates. Postsecondary Education Descriptive Analysis Reports. Statistical Analysis Report. Office of Educational Research and Improvement, US Department of Education; 1997. Accessed May 21, 2020. https://eric.ed.gov/?id=ED413854
59.
Carnevale  AP , Strohl  J . Separate and Unequal: How Higher Education Reinforces the Intergenerational Reproduction of White Racial Privilege. Center on Education and the Workforce, Georgetown Public Policy Institute; July 2013. Accessed April 1, 2019. https://cew.georgetown.edu/wp-content/uploads/SeparateUnequal.FR_.pdf
60.
Kaufman  P , Chen  X . Projected postsecondary outcomes of 1992 high school graduates. National Center for Education Statistics working paper 1999-15. June 1999. Accessed April 15, 2019. https://nces.ed.gov/pubs99/199915.pdf
61.
Wagmiller  RL , Adelman  RM . Childhood and intergenerational poverty: the long-term consequences of growing up poor. National Center for Children in Poverty. Accessed April 15, 2019. http://www.nccp.org/publications/pub_909.html
62.
Anderson  JW , Johnstone  BM , Remley  DT .  Breast-feeding and cognitive development: a meta-analysis.   Am J Clin Nutr. 1999;70(4):525-535. doi:10.1093/ajcn/70.4.525 PubMedGoogle Scholar
63.
Kramer  MS , Aboud  F , Mironova  E ,  et al; Promotion of Breastfeeding Intervention Trial (PROBIT) Study Group.  Breastfeeding and child cognitive development: new evidence from a large randomized trial.   Arch Gen Psychiatry. 2008;65(5):578-584. doi:10.1001/archpsyc.65.5.578 PubMedGoogle Scholar
64.
Deave  T , Heron  J , Evans  J , Emond  A .  The impact of maternal depression in pregnancy on early child development.   BJOG. 2008;115(8):1043-1051. doi:10.1111/j.1471-0528.2008.01752.x PubMedGoogle Scholar
65.
Cassidy-Bushrow  AE , Sitarik  AR , Havstad  S ,  et al.  Burden of higher lead exposure in African-Americans starts in utero and persists into childhood.   Environ Int. 2017;108:221-227. doi:10.1016/j.envint.2017.08.021 PubMedGoogle Scholar
AMA CME Accreditation Information

Credit Designation Statement: The American Medical Association designates this Journal-based CME activity activity for a maximum of 1.00  AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

Successful completion of this CME activity, which includes participation in the evaluation component, enables the participant to earn up to:

  • 1.00 Medical Knowledge MOC points in the American Board of Internal Medicine's (ABIM) Maintenance of Certification (MOC) program;;
  • 1.00 Self-Assessment points in the American Board of Otolaryngology – Head and Neck Surgery’s (ABOHNS) Continuing Certification program;
  • 1.00 MOC points in the American Board of Pediatrics’ (ABP) Maintenance of Certification (MOC) program;
  • 1.00 Lifelong Learning points in the American Board of Pathology’s (ABPath) Continuing Certification program; and
  • 1.00 CME points in the American Board of Surgery’s (ABS) Continuing Certification program

It is the CME activity provider's responsibility to submit participant completion information to ACCME for the purpose of granting MOC credit.

Close
Want full access to the AMA Ed Hub?
After you sign up for AMA Membership, make sure you sign in or create a Physician account with the AMA in order to access all learning activities on the AMA Ed Hub
Buy this activity
Close
Want full access to the AMA Ed Hub?
After you sign up for AMA Membership, make sure you sign in or create a Physician account with the AMA in order to access all learning activities on the AMA Ed Hub
Buy this activity
Close
With a personal account, you can:
  • Access free activities and track your credits
  • Personalize content alerts
  • Customize your interests
  • Fully personalize your learning experience
Education Center Collection Sign In Modal Right
Close

Name Your Search

Save Search
With a personal account, you can:
  • Access free activities and track your credits
  • Personalize content alerts
  • Customize your interests
  • Fully personalize your learning experience
Close
Close

Lookup An Activity

or

My Saved Searches

You currently have no searches saved.

Close

My Saved Courses

You currently have no courses saved.

Close