Impact of Social Determinants of Health on Patients with Complex Diabetes Who Are Served by National Safety-Net Health Centers
Impact of Social Determinants of Health on Patients with Complex Diabetes Who Are Served by National Safety-Net Health Centers
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DOI：
10.3122/jabfm.2016.03.150226
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发表时间：
2016-05-01
影响因子：
2.9
通讯作者：
Weir, Rosy Chang
中科院分区：
文献类型：
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作者：
Li, Vivian;McBurnie, Mary Ann;Weir, Rosy Chang
目的：最近的研究表明，越来越需要了解健康的社会决定因素（SDHs）在塑造个人的健康状况和结果的贡献。我们研究了糖尿病患者的安全网中心，并评估其疾病的复杂性，人口统计学特征，合并症，保险状况，和主要语言与他们的HbA 1c水平随时间推移的关联。方法：成人糖尿病患者至少有3个不同的初级保健访问之间的2006年1月1日，2013年12月31日，在CHARN数据仓库中确定。这些患者被分为4组：未诊断为心血管疾病（CVD）或抑郁症的患者;患有CVD但未患抑郁症的患者;患有抑郁症但未患CVD的患者;以及患有CVD和抑郁症的患者。Charlson评分;人口统计学特征，如年龄、性别和种族/民族;以及SDH，如主要语言和保险状况被用作预测因子。结果指标为HbA 1c。假设检验采用3-水平分层线性models.Results：基线HbA 1c在4个糖尿病组和种族/民族之间差异显着。HbA 1c随时间的变化量因保险状态而异。持续投保的患者倾向于基线HbA 1c较低，增幅较小。与讲英语的患者相比，讲汉语的患者的基线HbA 1c往往较低，但随着时间的推移增加较大。有各种意想不到的关联：与糖尿病组相比，其他更复杂的组基线时平均HbA 1c往往较低;女性基线时的测量值往往较低;年龄较大和Charlson评分较高与HbA 1c较低相关。结论：在模型中，基线HbA 1c值和随时间的变化仍存在无法解释的变异性。SDH，如保险状况和主要语言，与HbA 1c相关，结果表明，这些关系随安全网中心糖尿病患者的疾病状况而变化。重要的是要认识到，在复杂的患者中，人口统计学和SDH指标之间存在复杂的关系，在正确建模和理解这些关系方面还有很多工作要做。我们还建议优先收集SDH，并为安全网患者提供服务数据，这将有助于进行更全面的研究。
Objective: Recent research demonstrates an increased need to understand the contribution of social determinants of health (SDHs) in shaping an individual's health status and outcomes. We studied patients with diabetes in safety-net centers and evaluated associations of their disease complexity, demographic characteristics, comorbidities, insurance status, and primary language with their HbA1c level over time.Methods: Adult patients with diabetes with at least 3 distinct primary care visits between January 1, 2006, and December 31, 2013, were identified in the CHARN data warehouse. These patients were categorized into 4 groups: those without a diagnosis of cardiovascular disease (CVD) or depression; those with CVD but not depression; those with depression but not CVD; and those with CVD and depression. Charlson score; demographic characteristics such as age, sex, and race/ethnicity; and SDHs such as primary language and insurance status were used as predictors. The outcome measure was HbA1c. Hypothesis testing was conducted using 3-level hierarchical linear models.Results: Baseline HbA1c differed significantly across the 4 diabetes groups and by race/ethnicity. The amount of HbA1c change over time differed by insurance status. Patients who were continuously insured tended to have lower baseline HbA1c and a smaller increase. Chinese-speaking patients tended to have lower baseline HbA1c but a larger increase over time compared with English speakers. There were various unexpected associations: compared with the diabetes-only group, mean HbA1c tended to be lower among the other more complex groups at baseline; women tended to have lower measures at baseline; older age and higher Charlson scores were associated with lower HbA1c.Conclusions: There is still unexplained variability relating to both baseline HbA1c values and change over time in the model. SDHs, such as insurance status and primary language, are associated with HbA1c, and results suggest that these relationships vary with disease status among patients with diabetes in safety-net centers. It is important to recognize that there are complex relationships among demographic and SDH measures in complex patients, and there is work to be done in correctly modeling and understanding these relationships. We also recommend prioritizing the collection of SDH and enabling services data for safety-net patients that would be instrumental in conducting a more comprehensive study.