通过Newcomb-Benford分析检测菲律宾公共卫生监测数据中的流行病学异常
1Mathematics Division, University of Mindanao Digos College, Digos City 8002, Philippines.
Journal of public health (Oxford, England)
|May 2, 2024
概括
纽康布-本福德法 (NBL) 揭示了菲律宾疾病监测数据中的异常,大多数疾病偏离了预期的模式. 加强菲律宾综合疾病监测和应对 (PIDSR) 系统对于数据完整性和准确的公共卫生监测至关重要.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 公共卫生 公共卫生
背景情况:
- 有效的公共卫生监测对于菲律宾的疾病控制至关重要.
- 纽康-本福德定律 (Newcomb-Benford Law,简称NBL) 是一种用于检测数据集中的异常的统计方法.
- NBL在识别公共卫生数据中的不规则方面有应用.
研究的目的:
- 将NBL分析应用于2019-2023年的菲律宾流行病学数据.
- 识别疾病监测中的潜在数据质量问题和异常.
- 评估NBL对加强菲律宾综合疾病监测和应对 (PIDSR) 系统的有用性.
主要方法:
- 对各种传染病的流行病学数据进行了NBL分析.
- 使用了统计测试,包括千平方,曼蒂萨弧,平均绝对偏差 (MAD) 和扭曲因子.
- 数据涵盖了2019年至2023年的时间,涵盖了麻疹,登革热和白血病等疾病.
主要成果:
- 大多数疾病,除了麻疹,显示不符合NBL,表明潜在的异常.
- 平均绝对偏差 (MAD) 始终标记不符合疾病.
- 狂犬病数据显示出显著的偏差,而螺杆菌病数据在2021年更接近. 观察到偏差的显著年度变化.
结论:
- 与NBL的偏差表明,菲律宾监控系统中的数据质量存在问题.
- 改进PIDSR,特别是在持续不符合疾病方面,对于精确的疾病监测至关重要.
- 在公共卫生监测中,NBL是确保数据完整性和质量保证的宝贵工具.
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