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The Accuracy of the Patient Health Questionnaire-9 Algorithm for Screening to Detect Major Depression: An Individual Participant Data Meta-Analysis Publisher Pubmed



He C9, 13 ; Levis B9 ; Riehm KE9 ; Saadat N9 ; Levis AW9, 13 ; Azar M9, 13 ; Rice DB9, 15 ; Krishnan A10 ; Wu Y10, 14, 17 ; Sun Y10 ; Imran M10 ; Boruff J19, 20 ; Cuijpers P21, 22 ; Gilbody S23, 24 Show All Authors
Authors
  1. He C9, 13
  2. Levis B9
  3. Riehm KE9
  4. Saadat N9
  5. Levis AW9, 13
  6. Azar M9, 13
  7. Rice DB9, 15
  8. Krishnan A10
  9. Wu Y10, 14, 17
  10. Sun Y10
  11. Imran M10
  12. Boruff J19, 20
  13. Cuijpers P21, 22
  14. Gilbody S23, 24
  15. Ioannidis JPA25, 26
  16. Kloda LA27, 28
  17. Mcmillan D24
  18. Patten SB29, 30, 31, 32
  19. Shrier I10, 14
  20. Ziegelstein RC34
  21. Akena DH36
  22. Arroll B38, 39
  23. Ayalon L40
  24. Baradaran HR44, 48
  25. Baron M10, 46
  26. Beraldi A49, 50
  27. Bombardier CH51, 52
  28. Butterworth P53, 54, 58
  29. Carter G59, 60
  30. Chagas MHN63
  31. Chan JCN10, 63, 65
  32. Cholera R14
  33. Clover K16, 18
  34. Conwell Y20
  35. De Manvan Ginkel JM22
  36. Fann JR24
  37. Fischer FH26
  38. Fung D28, 30, 32, 33
  39. Gelaye B35
  40. Goodyearsmith F37
  41. Greeno CG38
  42. Hall BJ40, 44
  43. Harrison PA45, 46
  44. Harter M47, 48
  45. Hegerl U50, 57
  46. Hides L52
  47. Hobfoll SE54, 55, 56
  48. Hudson M10, 46
  49. Hyphantis TN58
  50. Inagaki M60
  51. Ismail K61, 62
  52. Jette N30, 32, 63
  53. Khamseh ME44
  54. Kiely KM65, 66
  55. Kwan Y67
  56. Lamers F68
  57. Liu SI34, 69, 70, 71
  58. Lotrakul M72
  59. Loureiro SR63, 64
  60. Lowe B73
  61. Marsh L74
  62. Mcguire A75
  63. Mohdsidik S76
  64. Munhoz TN77
  65. Muramatsu K78
  66. Osorio FL63, 79
  67. Patel V80, 81
  68. Pence BW82
  69. Persoons P83, 84
  70. Picardi A85
  71. Reuter K86
  72. Rooney AG87
  73. Da Silva Dos Santos IS77
  74. Shaaban J88
  75. Sidebottom A11
  76. Simning A20
  77. Stafford L41, 55
  78. Sung S28, 34
  79. Tan PLL67
  80. Turner A12, 42, 43
  81. Van Weert HCPM56
  82. White J5
  83. Whooley MA2, 4, 6
  84. Winkley K7
  85. Yamada M1
  86. Thombs BD3, 9, 14, 16, 18, 46
  87. Benedetti A8, 14, 46, 89

Source: Psychotherapy and Psychosomatics Published:2020


Abstract

Background: Screening for major depression with the Patient Health Questionnaire-9 (PHQ-9) can be done using a cutoff or the PHQ-9 diagnostic algorithm. Many primary studies publish results for only one approach, and previous meta-analyses of the algorithm approach included only a subset of primary studies that collected data and could have published results. Objective: To use an individual participant data meta-analysis to evaluate the accuracy of two PHQ-9 diagnostic algorithms for detecting major depression and compare accuracy between the algorithms and the standard PHQ-9 cutoff score of ≥10. Methods: Medline, Medline In-Process and Other Non-Indexed Citations, PsycINFO, Web of Science (January 1, 2000, to February 7, 2015). Eligible studies that classified current major depression status using a validated diagnostic interview. Results: Data were included for 54 of 72 identified eligible studies (n participants = 16,688, n cases = 2,091). Among studies that used a semi-structured interview, pooled sensitivity and specificity (95% confidence interval) were 0.57 (0.49, 0.64) and 0.95 (0.94, 0.97) for the original algorithm and 0.61 (0.54, 0.68) and 0.95 (0.93, 0.96) for a modified algorithm. Algorithm sensitivity was 0.22-0.24 lower compared to fully structured interviews and 0.06-0.07 lower compared to the Mini International Neuropsychiatric Interview. Specificity was similar across reference standards. For PHQ-9 cutoff of ≥10 compared to semi-structured interviews, sensitivity and specificity (95% confidence interval) were 0.88 (0.82-0.92) and 0.86 (0.82-0.88). Conclusions: The cutoff score approach appears to be a better option than a PHQ-9 algorithm for detecting major depression. © 2019 S. Karger AG, Basel. All rights reserved.
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