Including information about co-morbidity in estimates of disease burden: results from the World Health Organization World Mental Health Surveys.
BACKGROUND
The methodology commonly used to estimate disease burden, featuring ratings of severity of individual conditions, has been criticized for ignoring co-morbidity. A methodology that addresses this problem is proposed and illustrated here with data from the World Health Organization World Mental Health Surveys. Although the analysis is based on self-reports about one's own conditions in a community survey, the logic applies equally well to analysis of hypothetical vignettes describing co-morbid condition profiles.


METHODS
Face-to-face interviews in 13 countries (six developing, nine developed; n=31 067; response rate=69.6%) assessed 10 classes of chronic physical and nine of mental conditions. A visual analog scale (VAS) was used to assess overall perceived health. Multiple regression analysis with interactions for co-morbidity was used to estimate associations of conditions with VAS. Simulation was used to estimate condition-specific effects.


RESULTS
The best-fitting model included condition main effects and interactions of types by numbers of conditions. Neurological conditions, insomnia and major depression were rated most severe. Adjustment for co-morbidity reduced condition-specific estimates with substantial between-condition variation (0.24-0.70 ratios of condition-specific estimates with and without adjustment for co-morbidity). The societal-level burden rankings were quite different from the individual-level rankings, with the highest societal-level rankings associated with conditions having high prevalence rather than high individual-level severity.


CONCLUSIONS
Plausible estimates of disorder-specific effects on VAS can be obtained using methods that adjust for co-morbidity. These adjustments substantially influence condition-specific ratings.
Alonso
J
J
Health Services Research Unit, Institut Municipal d'Investigació Mèdica (IMIM-Hospital del Mar), Barcelona, Spain.
Vilagut
G
G
Chatterji
S
S
Heeringa
S
S
Schoenbaum
M
M
Bedirhan Üstün
T
T
Rojas-Farreras
S
S
Angermeyer
M
M
Bromet
E
E
Bruffaerts
R
R
de Girolamo
G
G
Gureje
O
O
Haro
J M
JM
Karam
A N
AN
Kovess
V
V
Levinson
D
D
Liu
Z
Z
Medina-Mora
M E
ME
Ormel
J
J
Posada-Villa
J
J
Uda
H
H
Kessler
R C
RC
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000704:Analysis of Variance; D002908:Chronic Disease / Q000453:epidemiology*; D015897:Comorbidity; D017281:Cost of Illness; D003431:Cross-Cultural Comparison; D003865:Major Depressive Disorder / Q000453:epidemiology; D005260:Female; D015276:Health Care Rationing / Q000706:statistics & numerical data; D006291:Health Policy; D006305:Health Status Indicators; D006306:Health Surveys / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D001523:Mental Disorders / Q000453:epidemiology*; D008875:Middle Aged; D009422:Nervous System Diseases / Q000453:epidemiology; D010147:Pain Measurement; D007319:Sleep Initiation and Maintenance Disorders / Q000453:epidemiology; D019472:Universal Health Insurance / Q000706:statistics & numerical data; D014944:World Health Organization; D055815:Young Adult