Targeting Hypertension Screening in Low- and Middle-Income Countries: A Cross-Sectional Analysis of 1.2 Million Adults in 56 Countries.
Targeting Hypertension Screening in Low- and Middle-Income Countries: A Cross-Sectional Analysis of 1.2 Million Adults in 56 Countries.
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DOI：
10.1161/jaha.121.021063
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发表时间：
2021-07-06
影响因子：
5.4
通讯作者：
Geldsetzer P
中科院分区：
文献类型：
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作者：
Kirschbaum TK;Theilmann M;Sudharsanan N;Manne-Goehler J;Lemp JM;De Neve JW;Marcus ME;Ebert C;Chen S;Aryal KK;Bahendeka SK;Norov B;Damasceno A;Dorobantu M;Farzadfar F;Fattahi N;Gurung MS;Guwatudde D;Labadarios D;Lunet N;Rayzan E;Saeedi Moghaddam S;Webster J;Davies JI;Atun R;Vollmer S;Bärnighausen T;Jaacks LM;Geldsetzer P
由于低收入和中等收入国家 (LMIC) 的筛查计划通常没有资源来筛查整个人群，因此经常需要将此类工作针对易于识别的优先群体。本研究旨在确定（1）中低收入国家的高血压患病率如何随年龄、性别、体重指数和吸烟状况而变化，以及（2）这些变量的不同组合准确预测高血压的能力。我们分析了来自 56 个中低收入国家 1 170 629 名参与者的个人层面、全国代表性的数据，其中 220 636 名 (18.8%) 患有高血压。高血压的定义为收缩压≥140毫米汞柱，舒张压≥90毫米汞柱，或报告正在服用降压药物。世界各地区高血压与年龄和体重指数的正相关关系的形状各不相同。我们使用逻辑回归和随机森林模型来计算每个国家不同年龄、体重指数、性别和吸烟状况组合的受试者工作特征曲线下面积。具有所有 4 个预测变量的模型的接受者操作特征曲线下面积在国家之间的范围为 0.64 至 0.85，全球中低收入国家的国家级平均值为 0.76。从仅包含年龄的模型到包含所有 4 个预测变量的模型，受试者工作特征曲线下面积的平均绝对增量为 0.05。与单独使用年龄相比，将体重指数、性别和吸烟状况与年龄相结合，只能略微提高区分患有和不患有高血压的成年人的能力。中低收入国家的高血压筛查项目可以使用年龄作为主要变量来确定其工作目标。
As screening programs in low‐ and middle‐income countries (LMICs) often do not have the resources to screen the entire population, there is frequently a need to target such efforts to easily identifiable priority groups. This study aimed to determine (1) how hypertension prevalence in LMICs varies by age, sex, body mass index, and smoking status, and (2) the ability of different combinations of these variables to accurately predict hypertension. We analyzed individual‐level, nationally representative data from 1 170 629 participants in 56 LMICs, of whom 220 636 (18.8%) had hypertension. Hypertension was defined as systolic blood pressure ≥140 mm Hg, diastolic blood pressure ≥90 mm Hg, or reporting to be taking blood pressure–lowering medication. The shape of the positive association of hypertension with age and body mass index varied across world regions. We used logistic regression and random forest models to compute the area under the receiver operating characteristic curve in each country for different combinations of age, body mass index, sex, and smoking status. The area under the receiver operating characteristic curve for the model with all 4 predictors ranged from 0.64 to 0.85 between countries, with a country‐level mean of 0.76 across LMICs globally. The mean absolute increase in the area under the receiver operating characteristic curve from the model including only age to the model including all 4 predictors was 0.05. Adding body mass index, sex, and smoking status to age led to only a minor increase in the ability to distinguish between adults with and without hypertension compared with using age alone. Hypertension screening programs in LMICs could use age as the primary variable to target their efforts.