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首页> 外文期刊>PLoS Computational Biology >Deploying digital health data to optimize influenza surveillance at national and local scales
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Deploying digital health data to optimize influenza surveillance at national and local scales

机译：部署数字健康数据以在国家和地方范围内优化流感监测

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Author summary Influenza contributes substantially to global morbidity and mortality each year, and epidemiological surveillance for influenza is typically conducted by sentinel physicians and health care providers recruited to report cases of influenza-like illness. While population coverage and representativeness, and geographic distribution are considered during sentinel provider recruitment, systems cannot always achieve these standards due to the administrative burdens of data collection. We present spatial estimates of influenza disease burden across United States counties by leveraging the volume and fine spatial resolution of medical claims data, and existing socio-environmental hypotheses about the determinants of influenza disease disease burden. Using medical claims as a testbed, this study adds to literature on the optimization of surveillance system design by considering conditions of limited reporting and spatial aggregation. We highlight the importance of considering sampling biases and reporting locations when interpreting surveillance data, and suggest that local mobility and regional policies may be critical to understanding the spatial distribution of reported influenza-like illness.
机译：作者摘要每年，流感在很大程度上导致全球发病率和死亡率，并且流行性感冒的流行病学监测通常由招募报告流感样疾病病例的前哨医师和卫生保健提供者进行。虽然在征聘哨兵提供者时考虑了人口覆盖率和代表性以及地理分布，但是由于数据收集的行政负担，系统无法始终达到这些标准。我们利用医疗索赔数据的数量和精细的空间分辨率，以及有关流感疾病疾病负担决定因素的现有社会环境假设，对美国各州的流感疾病负担进行空间估算。这项研究使用医疗索赔作为测试平台，通过考虑有限报告和空间聚集的条件，为监视系统设计的优化添加了文献。我们强调了在解释监测数据时考虑抽样偏差和报告位置的重要性，并建议地方流动性和区域政策对于理解报告的流感样疾病的空间分布可能至关重要。

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