Ocaña-Riola Ricardo
Escuela Andaluza de Salud Pública, Campus Universitario de Cartuja, Cuesta del Observatorio, 4, Apdo de Correos 2070, 18080 Granada, Spain.
Stat Med. 2007 Oct 30;26(24):4489-504. doi: 10.1002/sim.2861.
The ongoing spread of spatial analysis techniques for small areas has facilitated the publication of mortality and morbidity Atlases based on time periods that group information spanning several years. Although this is a widespread practice, this paper proves that the use of count data aggregated over time for disease mapping may give inappropriate area-specific relative risk. As a result, both decision-making and healthcare policies could be affected by inappropriate model specifications using aggregated information over time. The Poisson distribution properties were used in order to quantify the bias in area-specific relative risk estimation due to count data aggregated over time. A hierarchical Bayesian model with spatio-temporal random structure is proposed as an alternative to smoothing relative risk if the period of study need to span several years. Methods discussed in this paper were applied to a small-area survey on male mortality from all causes in Southern Spain for the period 1985-1999. The results suggest that particular caution should be taken when interpreting risk maps based on clustered annual data that use models with no temporal structure to smooth out the rates.
小区域空间分析技术的不断推广，促进了基于数年信息汇总时间段的死亡率和发病率地图集的出版。尽管这是一种普遍做法，但本文证明，将随时间汇总的计数数据用于疾病绘图可能会得出不适当的特定区域相对风险。因此，决策和医疗政策可能会受到使用随时间汇总信息的不适当模型规范的影响。利用泊松分布特性来量化由于随时间汇总的计数数据导致的特定区域相对风险估计中的偏差。如果研究期需要跨越数年，提出了一种具有时空随机结构的分层贝叶斯模型作为平滑相对风险的替代方法。本文讨论的方法应用于1985 - 1999年西班牙南部男性全因死亡率的小区域调查。结果表明，在解释基于使用无时间结构模型平滑发病率的聚类年度数据的风险地图时应格外谨慎。
