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中国高血压、糖尿病和血脂异常护理流程管理中的省级差异:基于全国代表性人群调查的分析

Provincial heterogeneity in the management of care cascade for hypertension, diabetes, and dyslipidaemia in China: Analysis of nationally representative population-based survey.

作者信息

Zhao Yang, Anindya Kanya, Atun Rifat, Marthias Tiara, Han Chunlei, McPake Barbara, Duolikun Nadila, Hulse Emily, Fang Xinyue, Ding Yimin, Oldenburg Brian, Lee John Tayu

机构信息

The George Institute for Global Health, Beijing, China.

The George Institute for Global Health, University of New South Wales, Sydney, NSW, Australia.

出版信息

Front Cardiovasc Med. 2022 Aug 23;9:923249. doi: 10.3389/fcvm.2022.923249. eCollection 2022.

DOI:10.3389/fcvm.2022.923249
PMID:36093142
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9458474/
Abstract

BACKGROUND

This study aims to examine (1) province-level variations in the levels of cardiovascular disease (CVD) risk and behavioral risk for CVDs, (2) province-level variations in the management of cascade of care for hypertension, diabetes, and dyslipidaemia, and (3) the association of province-level economic development and individual factors with the quality of care for hypertension, diabetes, and dyslipidaemia.

METHODS

We used nationally representative data from the China Health and Retirement Longitudinal Study in 2015, which included 12,597 participants aged 45 years. Using a care cascade framework, we examined the quality of care provided to patients with three prevalent NCDs: hypertension, diabetes, and dyslipidaemia. The proportion of WHO CVD risk based on the World Health Organization CVD risk prediction charts, Cardiovascular Risk Score (CRS) and Behavior Risk Score (BRS) were calculated. We performed multivariable logistic regression models to determine the individual-level drivers of NCD risk variables and outcomes. To examine socio-demographic relationships with CVD risk, linear regression models were applied.

RESULTS

In total, the average CRS was 4.98 (95% CI: 4.92, 5.05), while the average BRS was 3.10 (95% confidence interval: 3.04, 3.15). The weighted mean CRS (BRS) in Fujian province ranged from 4.36 to 5.72 ( < 0.05). Most of the provinces had a greater rate of hypertension than diabetes and dyslipidaemia awareness and treatment. Northern provinces had a higher rate of awareness and treatment of all three diseases. Similar patterns of regional disparity were seen in diabetes and dyslipidaemia care cascades. There was no evidence of a better care cascade for CVDs in patients who reside in more economically advanced provinces.

CONCLUSION

Our research found significant provincial heterogeneity in the CVD risk scores and the management of the cascade of care for hypertension, diabetes, and dyslipidaemia for persons aged 45 years or more. To improve the management of cascade of care and to eliminate regional and disparities in CVD care and risk factors in China, local and population-based focused interventions are necessary.

摘要

背景

本研究旨在探讨(1)心血管疾病(CVD)风险水平及CVD行为风险的省级差异;(2)高血压、糖尿病和血脂异常的级联护理管理的省级差异;(3)省级经济发展和个体因素与高血压、糖尿病和血脂异常护理质量的关联。

方法

我们使用了2015年中国健康与养老追踪调查的全国代表性数据,其中包括12597名45岁及以上的参与者。我们采用护理级联框架,研究了为三种常见慢性病(高血压、糖尿病和血脂异常)患者提供的护理质量。根据世界卫生组织心血管疾病风险预测图表计算了基于世界卫生组织心血管疾病风险的比例、心血管风险评分(CRS)和行为风险评分(BRS)。我们进行了多变量逻辑回归模型,以确定非传染性疾病风险变量和结果的个体层面驱动因素。为了研究社会人口统计学与心血管疾病风险的关系,应用了线性回归模型。

结果

总体而言,平均CRS为4.98(95%置信区间:4.92,5.05),而平均BRS为3.10(95%置信区间:3.04,3.15)。福建省的加权平均CRS(BRS)范围为4.36至5.72(<0.05)。大多数省份高血压的知晓率和治疗率高于糖尿病和血脂异常。北方省份三种疾病的知晓率和治疗率较高。在糖尿病和血脂异常护理级联中也观察到类似的地区差异模式。没有证据表明居住在经济较发达省份的患者在心血管疾病护理级联方面更好。

结论

我们的研究发现,45岁及以上人群的心血管疾病风险评分以及高血压、糖尿病和血脂异常的级联护理管理存在显著的省级异质性。为了改善级联护理管理并消除中国心血管疾病护理及风险因素的地区差异,有必要采取基于当地和人群的重点干预措施。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe46/9458474/99f2e036a0b8/fcvm-09-923249-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe46/9458474/afcc7fd5bb80/fcvm-09-923249-g0001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe46/9458474/99f2e036a0b8/fcvm-09-923249-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe46/9458474/afcc7fd5bb80/fcvm-09-923249-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe46/9458474/97dd6e12f409/fcvm-09-923249-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe46/9458474/a18486d6595f/fcvm-09-923249-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe46/9458474/584ab0ab485e/fcvm-09-923249-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe46/9458474/99f2e036a0b8/fcvm-09-923249-g0005.jpg

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