6 个中低收入国家的糖尿病患病率以及财富与社会人口特征之间的关系

Gifty Marfowaa, J. Campbell, S. Nagavally, Aprill Z Dawson, R. Walker, Leonard E. Egede
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摘要

背景:随着全球糖尿病负担的持续加重,需要开展研究以了解财富的作用以及世界各地区糖尿病的相关因素。本研究的目的是调查 6 个中低收入国家的财富和糖尿病患病率及其作用,同时考虑各国糖尿病的独立相关因素。 方法:采用全球老龄化与成人健康研究(SAGE)的数据,SAGE 第 1 波。自我报告的糖尿病状况是主要因变量,财富五分位数、居住特征数量和拥有的一系列资产是自变量。逻辑回归模型检验了 6 个国家的财富与是否患有糖尿病之间的关系,以最高财富五分位数(五分位数 1)作为参照组。 结果:按国家分列的样本数包括加纳=5573、南非=4227、俄罗斯=4947、墨西哥=5448、印度=12198 和中国=15050。各国的平均年龄从 49 岁到 63 岁不等。加纳和南非的糖尿病患病率分别为 3.4% 和 9.2%。俄罗斯有 8.3%、墨西哥有 18.1%、印度有 4.9%、中国有 5.9%的样本报告患有糖尿病。在调整后的逻辑模型中,在加纳(OR 2.26;CI 1.28;4.13)、南非(OR 4.57;CI 2.25;10.32)、墨西哥(OR 2.00;CI 1.14;3.60)、印度(OR 2.45;CI 1.60;3.86)和中国(OR 2.16;CI 1.62,2.93),财富与较高的糖尿病几率相关。 结论:这些发现为我们了解财富与糖尿病之间的关系提供了更多证据。由于糖尿病一直是全球的主要死因,未来的工作应侧重于研究财富与糖尿病之间关系的内在机制,同时制定干预措施,以减轻这一影响中低收入国家社区的新兴疾病。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prevalence of Diabetes and the Relationship Between Wealth and Social Demographic Characteristics Across 6 Low-and-Middle Income Countries
BACKGROUND: As the global burden of diabetes persists, research is needed to understand the role of wealth and correlates of diabetes across regions of the world. The purpose of this study is to examine the prevalence and role of wealth and diabetes across 6 low- and middle- income countries while also accounting for independent correlates of diabetes by country. METHODS: Data from the Study on Global Ageing and Adult Health (SAGE), SAGE Wave 1 was used. Self-reported diabetes status was the primary dependent variable and wealth quintile, number of dwelling characteristics and possession of a set of assets, was the independent variable. Logistic regression models examined the relationship between wealth and presence of diabetes across 6 countries with the highest wealth quintile, quintile 1, serving as the reference group. RESULTS: Sample size by country included Ghana N = 5573, South Africa N = 4227, Russia N = 4947, Mexico N = 5448, India N = 12198, and China N = 15050. Average age across country ranged from 49 to 63 years of age. Prevalence of diabetes across country included 3.4% and 9.2% for Ghana and South Africa, respectively. In Russia, 8.3%; Mexico, 18.1%; India, 4.9%; and China, 5.9% of the sample reported having diabetes. In the adjusted logistic model, wealth was associated with higher odds of diabetes in Ghana (OR 2.26; CI 1.28; 4.13), South Africa (OR 4.57; CI 2.25; 10.32), Mexico (OR 2.00; CI 1.14; 3.60), India (OR 2.45; CI 1.60; 3.86), and China (OR 2.16; CI 1.62, 2.93). CONCLUSIONS: These findings add to the growing body of evidence in our understanding between wealth and diabetes. As diabetes persists as a leading cause of death globally, future work should focus on mechanisms underlying the relationship between wealth and diabetes while also developing interventions to mitigate his burgeoning disease affecting communities across low- and middle-income countries.
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