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01-07-2015 | Uitgave 7/2015

Quality of Life Research 7/2015

Health perceptions and symptom burden in primary care: measuring health using audio computer-assisted self-interviews

Tijdschrift:
Quality of Life Research > Uitgave 7/2015
Auteurs:
Keiki Hinami, Jennifer Smith, Catherine D. Deamant, Romina Kee, Diana Garcia, William E. Trick

Abstract

Purpose

To assess the relationships among somatic symptoms and health perception measures in data collected from the implementation of audio computer-assisted self-interview (ACASI) technology in a primary care clinic of a safety-net healthcare system.

Methods

We approached 2,848 English- or Spanish-speaking patients to complete an ACASI-administered survey before their clinic appointment between April 2011 and July 2012. We administered the National Institutes of Health Patient-Reported Outcomes Measurement Information System (PROMIS) Global Health-10 assessing General Self-Rated Health (GSRH), Global Physical and Mental Health; Memorial Symptom Assessment Scale (MSAS) assessing symptom burden; and the Patient Health Questionnaire-2 (PHQ-2). We calculated population attributable fractions (PAF) of symptoms on poorly perceived health.

Results

Participation rate was 90 %, but 51 % of observations were analyzable. Mean age was 57 years; 53 % were non-Hispanic black; and 20 % completed the survey in Spanish. All but 2 % reported at least one symptom most commonly lack of energy (87 %) and pain (83 %). The MSAS was well correlated with PHQ-2 (r = 0.65) and Global Physical Health (r = −0.65), but less with GSRH (r = −0.49). All negative health perception measures were largely attributable to lack of energy and pain, while depression-range PHQ-2 was attributable also to less prevalent symptoms including decreased appetite and sexual disinterest.

Conclusions

Symptom burden was less correlated with GSRH than with other measures of poor health perception. Fatigue and pain contributed the highest PAF to all measures of perceived poor health. Success with collecting PROMs in a resource-limited clinical setting demonstrates that the implementation of ACASI technology is feasible.

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