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The interaction between social determinants of health and cervical cancer survival: A systematic review

医学 宫颈癌 检查表 观察研究 医疗补助 婚姻状况 人口 家庭医学 批判性评价 梅德林 公共卫生 科克伦图书馆 系统回顾 生存曲线 癌症 老年学 内科学 荟萃分析 医疗保健 替代医学 环境卫生 病理 心理学 政治学 法学 经济 认知心理学 经济增长
作者
Kellen Cristine Tjioe,Marisol Miranda Galvis,Marian Johnson,Gagan Agrawal,E. Andrew Balas,Jorge E. Cortés
出处
期刊:Gynecologic Oncology [Elsevier BV]
卷期号:181: 141-154 被引量:9
标识
DOI:10.1016/j.ygyno.2023.12.020
摘要

Abstract

Objective

This systematic review aimed to investigate what are the most relevant social determinants of health (SDH), how they are measured, how they interact among themselves and what is their impact on the outcomes of cervical cancer patients.

Methods

Search was performed in PubMed, Scopus, Web of Science, Embase, Cochrane, and Google Scholar databases from January 2001 to September 2022. The protocol was registered at PROSPERO (CRD42022346854). We followed the PICOS strategy: Population– Patients treated for cervical cancer in the United States; Intervention – Any SDH; Comparison- None; Outcome measures- Cancer treatment outcomes related to the survival of the patients; Types of studies- Observational studies. Two reviewers extracted the data following the PRISMA guidelines. Joanna Briggs Institute Critical Appraisal Checklist for Analytical Cross-Sectional Studies was used for risk of bias (ROB) assessment.

Results

Twenty-four studies were included (22 had low and 2 had moderate ROB). Most manuscripts analyzed data from public registries (83.3%) and only one SDH (54.17%). The SDH category of Neighborhood was not included in any study. Although the SDH were measured differently across the studies, not being married, receiving treatment at a low-volume hospital, and having public insurance (Medicaid or Medicare) or not being insured was associated with shorter survival of cervical cancer patients in most studies.

Conclusions

There is a deficit in the number of studies comprehensively assessing the impact of SDH on cervical cancer treatment-related outcomes. Marital status, hospital volume and health insurance status are potential predictors of worse outcome.
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