Villarreal-Zegarra David, Al-Kassab-Córdova Ali, Otazú-Alfaro Sharlyn, Cabieses Baltica
Instituto Peruano de Orientación Psicológica, Lima, Peru.
Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, UT, United States.
SSM Popul Health. 2024 Nov 15;29:101724. doi: 10.1016/j.ssmph.2024.101724. eCollection 2025 Mar.
Globally, evidence indicates that poverty and geographical setting influence the prevalence of depressive symptoms and access to treatment. Therefore, this study aimed to evaluate the socioeconomic and spatial distribution of depressive symptoms and treatment in Peru.
We conducted an observational study based on the analysis of secondary data derived from the Peruvian Demographic and Health Surveys for 2014-2021. Using the Patient Health Questionnaire-9 on depressive symptoms, we estimated the Erreygers concentration index (ECI) to identify socioeconomic inequality in depressive symptoms and access to treatment. Spatial analyses were conducted using Global Moran's I, Kriging interpolation, hotspot analysis (Getis-Ord-Gi∗), and the Bernoulli-based Kulldorff spatial analysis.
The surveys included a total of 113,392 participants. Depressive symptoms exhibited only negative ECI values throughout the 2014-2021 period (pro-poor distribution), whereas access to treatment only displayed positive ECI values (pro-rich distribution). We identified two and four significant clusters in the southeastern areas of Peru in 2014 and 2021, respectively.
Depressive symptoms were concentrated among the poorest, whereas access to treatment was remarkably concentrated among the wealthiest groups. A clustered spatial pattern was observed, and similar high-risk areas were identified. Social policies that address unequal socioeconomic and spatial distribution in depressive symptoms and treatment are required.
全球范围内，有证据表明贫困和地理环境会影响抑郁症状的患病率以及治疗机会。因此，本研究旨在评估秘鲁抑郁症状及治疗的社会经济和空间分布情况。
我们基于对2014 - 2021年秘鲁人口与健康调查得出的二手数据进行了一项观察性研究。使用患者健康问卷 - 9来评估抑郁症状，我们估算了埃尔雷格斯集中指数（ECI），以确定抑郁症状及治疗方面的社会经济不平等情况。使用全局莫兰指数（Global Moran's I）、克里金插值法、热点分析（Getis - Ord - Gi∗）以及基于伯努利分布的库尔朵夫空间分析进行空间分析。
这些调查总共纳入了113,392名参与者。在2014 - 2021年期间，抑郁症状仅呈现负的ECI值（有利于穷人的分布），而治疗机会仅呈现正的ECI值（有利于富人的分布）。我们分别在2014年和2021年在秘鲁东南部地区发现了两个和四个显著的聚集区。
抑郁症状集中在最贫困人群中，而治疗机会则显著集中在最富人群中。观察到了聚集的空间模式，并确定了类似的高风险区域。需要制定社会政策来解决抑郁症状及治疗方面社会经济和空间分布不平等的问题。
