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首页> 外文期刊>JMIR public health and surveillance. >Monitoring Information-Seeking Patterns and Obesity Prevalence in Africa With Internet Search Data: Observational Study
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Monitoring Information-Seeking Patterns and Obesity Prevalence in Africa With Internet Search Data: Observational Study

机译：在非洲监测中寻求信息的模式和肥胖普遍存在互联网搜索数据：观察研究

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Background The prevalence of chronic conditions such as obesity, hypertension, and diabetes is increasing in African countries. Many chronic diseases have been linked to risk factors such as poor diet and physical inactivity. Data for these behavioral risk factors are usually obtained from surveys, which can be delayed by years. Behavioral data from digital sources, including social media and search engines, could be used for timely monitoring of behavioral risk factors. Objective The objective of our study was to propose the use of digital data from internet sources for monitoring changes in behavioral risk factors in Africa. Methods We obtained the adjusted volume of search queries submitted to Google for 108 terms related to diet, exercise, and disease from 2010 to 2016. We also obtained the obesity and overweight prevalence for 52 African countries from the World Health Organization (WHO) for the same period. Machine learning algorithms (ie, random forest, support vector machine, Bayes generalized linear model, gradient boosting, and an ensemble of the individual methods) were used to identify search terms and patterns that correlate with changes in obesity and overweight prevalence across Africa. Out-of-sample predictions were used to assess and validate the model performance. Results The study included 52 African countries. In 2016, the WHO reported an overweight prevalence ranging from 20.9% (95% credible interval [CI] 17.1%-25.0%) to 66.8% (95% CI 62.4%-71.0%) and an obesity prevalence ranging from 4.5% (95% CI 2.9%-6.5%) to 32.5% (95% CI 27.2%-38.1%) in Africa. The highest obesity and overweight prevalence were noted in the northern and southern regions. Google searches for diet-, exercise-, and obesity-related terms explained 97.3% (root-mean-square error [RMSE] 1.15) of the variation in obesity prevalence across all 52 countries. Similarly, the search data explained 96.6% (RMSE 2.26) of the variation in the overweight prevalence. The search terms yoga, exercise, and gym were most correlated with changes in obesity and overweight prevalence in countries with the highest prevalence. Conclusions Information-seeking patterns for diet- and exercise-related terms could indicate changes in attitudes toward and engagement in risk factors or healthy behaviors. These trends could capture population changes in risk factor prevalence, inform digital and physical interventions, and supplement official data from surveys.
机译：背景技术在非洲国家的慢性病症如慢性病症的患病率正在增加。许多慢性疾病已与危险因素有关，例如饮食差和身体不活跃。这些行为风险因素的数据通常从调查中获得，这可能会延迟多年。来自数字来源的行为数据包括社交媒体和搜索引擎，可用于及时监测行为风险因素。目的是我们研究的目的是提出使用来自互联网来源的数字数据来监测非洲行为风险因素的变化。方法我们从2010年到2016年从饮食，运动和疾病中提交给Google的调整后的搜索查询量，从2010年到2016年。我们还获得了来自世界卫生组织（世卫组织）的52个非洲国家的肥胖和超重患病率同期。机器学习算法（即随机林，支持向量机，贝叶斯广义线性模型，梯度提升和各个方法的集合）用于识别与非洲肥胖和超重普遍性的变化相关的搜索条款和模式。使用超出样本预测来评估和验证模型性能。结果该研究包括52个非洲国家。 2016年，世界卫生组织报道了超过20.9％（95％的可靠间隔[CI] 17.1％-25.0％）至66.8％（95％的CI 62.4％-71.0％）和肥胖患病率从4.5％（95 ％CI 2.9％-6.5％）非洲的32.5％（95％CI 27.2％-38.1％）。北部和南部地区指出了最高肥胖和超重普遍性。谷歌搜索饮食，运动和肥胖相关的术语，解释了所有52个国家肥胖症普遍性的变化的97.3％（根均方误差[RMSE] 1.15）。类似地，搜索数据解释了超重普遍性的变化的96.6％（RMSE 2.26）。搜索条件瑜伽，锻炼和健身房与最高普遍性的国家的肥胖和超重普遍性的变化相关。结论寻求与饮食和举办术语的信息模式可能表明危险因素或健康行为的态度变化。这些趋势可以捕捉危险因素普遍性，通知数字和物理干预的人口变化，并补充来自调查的官方数据。

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