Latent variables may be useful in pain's assessment.
BACKGROUND
Unobserved "latent" variables have the potential to minimize "measurement error" inherent to any single clinical assessment or categorical diagnosis.


OBJECTIVE
To demonstrate the potential utility of latent variable constructs in pain's assessment.


METHODS
We created two latent variables representing depressive symptom-related pain (Pd) and its residual, "somatic" pain (Ps), from survey questions.


METHODS
The Hispanic Established Population for Epidemiological Studies in the Elderly (H-EPESE) project, a longitudinal population-based cohort study.


METHODS
Community dwelling elderly Mexican-Americans in five Southwestern U.S. states. The data were collected in the 7th HEPESE wave in 2010 (N = 1,078).


METHODS
Self-reported pain, Center for Epidemiological Studies Depression Scale (CES-D) scores, bedside cognitive performance measures, and informant-rated measures of basic and instrumental Activities of Daily Living.


RESULTS
The model showed excellent fit [χ2 = 20.37, DF = 12; p = 0.06; Comparative fit index (CFI) = 0.998; Root mean statistical error assessment (RMSEA) = 0.025]. Ps was most strongly indicated by self-reported pain-related physician visits (r = 0.48, p ≤0.001). Pd was most strongly indicated by self-reported pain-related sleep disturbances (r = 0.65, p <0.001). Both Pd and Ps were significantly independently associated with chronic pain (> one month), regional pain and pain summed across selected regions. Pd alone was significantly independently associated with self-rated health, life satisfaction, self-reported falls, Life-space, nursing home placement, the use of opiates, and a variety of sleep related disturbances. Ps was associated with the use of NSAIDS. Neither construct was associated with declaration of a resuscitation preference, mode of resuscitation preference declaration, or with opting for a "Do Not Resuscitate" (DNR) order.


CONCLUSIONS
This analysis illustrates the potential of latent variables to parse observed data into "unbiased" constructs with unique predictive profiles. The latent constructs, by definition, are devoid of measurement error that affects any subset of their indicators. Future studies could use such phenotypes as outcome measures in clinical pain management trials or associate them with potential biomarkers using powerful parametric statistical methods.
Department of Psychiatry, The University of Texas Health Science Center At San Antonio, 7703 Floyd Curl Drive MC 7792, San Antonio, TX 78229, USA. royall@uthscsa.edu.
Donald R
DR
Royall
Ricardo
R
Salazar
Raymond F
RF
Palmer
D000203:Activities of Daily Living / Q000523:psychology; D000368:Aged; D000369:Aged, 80 and over; D003863:Depression / Q000175:diagnosis / Q000523:psychology; D005260:Female; D006630:Hispanic or Latino / Q000523:psychology / Q000706:statistics & numerical data; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D010146:Pain / Q000150:complications / Q000175:diagnosis / Q000523:psychology; D010147:Pain Measurement / Q000379:methods*; D011569:Psychiatric Status Rating Scales; D012893:Sleep Wake Disorders / Q000453:epidemiology / Q000209:etiology; D015206:Southwestern United States / Q000453:epidemiology