Abstract

ObjectiveAnalyze the measurement invariance and the factor structure of the Patient Health Questionnaire-9 (PHQ-9) in the Peruvian population.MethodSecondary data analysis performed using cross-sectional data from the Health Questionnaire of the Demographic and Health Survey in Peru. Variables of interest were the PHQ-9 and demographic characteristics (sex, age group, level of education, socioeconomic status, marital status, and area of residence). Factor structure was evaluated by standard confirmatory factor analysis (CFA), and measurement invariance by multi-group CFA, using standard goodness-of-fit indices criteria for interpreting results from both CFAs. Analysis of the internal consistency (α and ω) was also pursued.ResultsData from 30,449 study participants were analyzed, 56.7% were women, average age was 40.5 years (standard deviation (SD) = 16.3), 65.9% lived in urban areas, 74.6% were married, and had 9 years of education on average (SD = 4.6). From standard CFA, a one-dimensional model presented the best fit (CFI = 0.936; RMSEA = 0.089; SRMR = 0.039). From multi-group CFA, all progressively restricted models had ΔCFI<0.01 across almost all groups by demographic characteristics. PHQ-9 reliability was optimal (α = ω = 0.87).ConclusionsThe evidence presents support for the one-dimensional model and measurement invariance of the PHQ-9 measure, allowing for reliable comparisons between sex, age groups, education level, socioeconomic status, marital status, and residence area, and recommends its use within the Peruvian population.

Highlights

  • Depression is currently one of the main causes of disability: an estimated 4.4% (322 million) people around the world suffer from depression (5.1% of women and 3.6% of men) [1]

  • In 2017, prevalence of depressive symptomatology in North America, Latin America, and the Caribbean was estimated at 15% (48.2 million) [1, 2]

  • Measurement invariance provides confidence that any difference between Patient Health Questionnaire-9 (PHQ-9) one-dimension measures across these groups comes from a real difference in depressive symptomatology and not from group-specific properties of the instrument itself

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Summary

Introduction

Depression is currently one of the main causes of disability: an estimated 4.4% (322 million) people around the world suffer from depression (5.1% of women and 3.6% of men) [1]. In Peru, depression is the second greatest cause of healthy years lost (7.4 in men and 13.7 in women) [3], and is on track to be the leading cause of years lost to disability in 2030.[4] depression is a serious public health problem requiring large-scale intervention [5]. Brief, and reliable instruments to assess depressive symptoms are needed to guarantee appropriate monitoring of intervention effectiveness [6]. The use of brief instruments for the early detection of depressive symptomatology appears to be cost-effective [7]. Likewise, comparing disease burden between groups would help to establish priorities for social interventions [8]

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