Education and cognitive function among older adults in Brazil and Mexico

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Tipo de produção
article
Data de publicação
2023
Título da Revista
ISSN da Revista
Título do Volume
Editora
WILEY
Autores
AVILA, Jaqueline Contrera
BERTOLA, Laiss
OBREGON, Alejandra Michaels
FERRI, Cleusa Pinheiro
WONG, Rebeca
Citação
ALZHEIMER'S & DEMENTIA: DIAGNOSIS, ASSESSMENT & DISEASE MONITORING, v.15, n.3, article ID e12470, 9p, 2023
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
Education is protective against cognitive impairment. We used nationally representative data from Mexico and Brazil to assess the association between education and cognitive function. The sample included adults & GE; 50 years from the Brazilian Longitudinal Study of Aging (ELSI) and the Mexican Health and Aging Study (MHAS). Participants were classified as cognitively impaired or not impaired. We used logistic regression models to estimate the association between education and cognitive function. Education level was higher in MHAS than in ELSI. Participants with at least 1 year of education were less likely to have cognitive impairment than those with no formal education in both cohorts. Men in ELSI had higher odds for cognitive impairment compared to men in MHAS. In both cohorts, higher educational level was associated with lower odds of cognitive impairment compared to no formal education. Sex was an effect modifier in MHAS but not in ELSI.HIGHLIGHTSCognitive test batteries were harmonized using a regression-based approach.Even very low levels of education were associated with reduced odds of cognitive impairment compared to no formal education.Brazilians were more likely to have cognitive impairment than Mexicans given the same education level.The differences in the association of education with cognition between Brazil and Mexico were only observed among men.
Palavras-chave
cognition, ELSI, harmonization, MHAS, sex differences
Referências
  1. Aguilar-Navarro SG, 2007, SALUD PUBLICA MEXICO, V49, P256, DOI 10.1590/S0036-36342007000400005
  2. [Anonymous], 2023, COUNTRIES LATIN AM C
  3. [Anonymous], GATEWAY GLOBAL AGING
  4. [Anonymous], 1971, EDUCACAO M LEI 5 962
  5. [Anonymous], SIGI 2020 REGIONAL R, DOI [10.1787/CB7D45D1-EN, DOI 10.1787/CB7D45D1-EN]
  6. Ardila A., 2020, SAGE HDB EVOL PSYCHO, DOI [10.4135/9781529739442.n23, DOI 10.4135/9781529739442.N23]
  7. Bartels C, 2010, BMC NEUROSCI, V11, DOI 10.1186/1471-2202-11-118
  8. Bloomberg M, 2021, LANCET PUBLIC HEALTH, V6, pe106, DOI 10.1016/S2468-2667(20)30258-9
  9. Brasil, 1946, CONSTITUICAO ESTADOS
  10. Brasil-Ministerio da Educacao, MAP AN BRAS
  11. Cuentame de Mexico, AN
  12. Díaz-Venegas C, 2019, AGING MENT HEALTH, V23, P1586, DOI 10.1080/13607863.2018.1501663
  13. Downer Brian, 2021, Real Datos Espacio, V12, P90
  14. Farmer Mary E., 1995, Annals of Epidemiology, V5, P1, DOI 10.1016/1047-2797(94)00047-W
  15. Lima-Costa MF, 2018, AM J EPIDEMIOL, V187, P1345, DOI 10.1093/aje/kwx387
  16. INEGI, ENC INT 2015
  17. Instituto Paulo Montenegro de Acao Educativa, 2016, IND ALF FUNC INAF
  18. Jorm AF, 2004, INT PSYCHOGERIATR, V16, P275, DOI 10.1017/S1041610204000390
  19. Kobayashi LC, 2021, J GERONTOL B-PSYCHOL, V76, P1767, DOI 10.1093/geronb/gbaa205
  20. Launer LJ, 1999, NEUROLOGY, V52, P78, DOI 10.1212/WNL.52.1.78
  21. Livingston G, 2020, LANCET, V396, P413, DOI 10.1016/S0140-6736(20)30367-6
  22. Lyketsos CG, 1999, AM J PSYCHIAT, V156, P58, DOI 10.1176/ajp.156.1.58
  23. Manly JJ, 2005, ARCH NEUROL-CHICAGO, V62, P1739, DOI 10.1001/archneur.62.11.1739
  24. Mantri S, 2019, INT J GERIATR PSYCH, V34, P855, DOI 10.1002/gps.5075
  25. Mukadam N, 2019, LANCET GLOB HEALTH, V7, pE596, DOI 10.1016/S2214-109X(19)30074-9
  26. Neumann LTV, 2018, GERONTOLOGIST, V58, P611, DOI 10.1093/geront/gny019
  27. Nichols E, 2023, ALZHEIMERS DEMENT, V19, P1009, DOI 10.1002/alz.12740
  28. Opdebeeck C, 2016, AGING NEUROPSYCHOL C, V23, P40, DOI 10.1080/13825585.2015.1041450
  29. Organization for Economic Co-operation and Development (OECD), 2019, COUNTR NOT MEX SKILL
  30. Our World in Data, LLECE MEAN PERF MATH
  31. Our World in Data, 1990, LEARN OUTC MIN VS AD
  32. Prince M., 2015, ALZHEIMERS DIS INT, DOI [10.1111/j.0963-7214.2004.00293.x, DOI 10.1111/J.0963-7214.2004.00293.X]
  33. Prince M, 2013, ALZHEIMERS DEMENT, V9, P63, DOI 10.1016/j.jalz.2012.11.007
  34. Prince M, 2012, LANCET, V380, P50, DOI 10.1016/S0140-6736(12)60399-7
  35. R Development Core Team, 2019, R LANG ENV STAT COMP, P2
  36. Robins M., 2020, CAUSAL INFERENCE WHA
  37. Roser M., QUALITY ED ND
  38. Roser M, 2016, OUR WORLD DATA GLOBA
  39. Santibanez L., 2005, ED MEXICO CHALLENGES
  40. Seblova D, 2020, AGEING RES REV, V58, DOI 10.1016/j.arr.2019.101005
  41. Sheena BS, 2022, LANCET GASTROENTEROL, V7, P796, DOI [10.1016/S2468-1253(22)00124-8, 10.1016/S2468-2667(21)00249-8]
  42. Steffick DE., 2000, DOCUMENTATION AFFECT
  43. Stern Y, 2020, ALZHEIMERS DEMENT, V16, P1305, DOI 10.1016/j.jalz.2018.07.219
  44. Suemoto CK., RISK FACTORS DEMENTI, DOI [10.1002/ALZ.12820, DOI 10.1002/ALZ.12820]
  45. UNESCO, 2016, ED ALL 2000 2015 ACH
  46. United Nations Development Programme, HUM DEV REP 2020 NEX
  47. van Buuren S, 2011, J STAT SOFTW, V45, P1
  48. van Hooren SAH, 2007, AGING NEUROPSYCHOL C, V14, P40, DOI 10.1080/138255890969483
  49. Wilson RS, 2009, NEUROLOGY, V72, P460, DOI 10.1212/01.wnl.0000341782.71418.6c
  50. Wong R, 2017, INT J EPIDEMIOL, V46, DOI 10.1093/ije/dyu263