Diagnosis is often based on the exceedance or not of continuous health indicators of a predefined cut-off value, so as to classify patients into positives and negatives for the disease under investigation. In this paper, we investigate the effects of dichotomization of spatially-referenced continuous outcome variables on geostatistical inference. Although this issue has been extensively studied in other fields, dichotomization is still a common practice in epidemiological studies. Furthermore, the effects of this practice in the context of prevalence mapping have not been fully understood. Here, we demonstrate how spatial correlation affects the loss of information due to dichotomization, how linear geostatistical models can be used to map disease prevalence and thus avoid dichotomization, and finally, how dichotomization affects our predictive inference on prevalence. To pursue these objectives, we develop a metric, based on the composite likelihood, which can be used to quantify the potential loss of information after dichotomization without requiring the fitting of Binomial geostatistical models. Through a simulation study and two applications on disease mapping in Africa, we show that, as thresholds used for dichotomization move further away from the mean of the underlying process, the performance of binomial geostatistical models deteriorates substantially. We also find that dichotomization can lead to the loss of fine scale features of disease prevalence and increased uncertainty in the parameter estimates, especially in the presence of a large noise to signal ratio. These findings strongly support the conclusions from previous studies that dichotomization should be always avoided whenever feasible.

诊断通常基于是否超过了预定的临界值的连续健康指标，以便将患者分为所研究疾病的阳性和阴性。在本文中，我们研究了将空间参考的连续结果变量二分法对地统计推断的影响。尽管在其他领域对此问题进行了广泛的研究，但是二分法仍然是流行病学研究中的普遍做法。此外，这种方法在患病率作图方面的效果还没有得到充分的了解。在这里，我们演示了空间相关性如何影响由于二分法导致的信息丢失，如何使用线性地统计学模型来绘制疾病流行率并避免二分法，最后，二分法如何影响我们对患病率的预测性推断。为了实现这些目标，我们在复合可能性的基础上开发了一种度量标准，该度量标准可用于量化二分法后信息的潜在损失，而无需拟合二项式地统计模型。通过模拟研究和在非洲疾病映射上的两个应用，我们表明，随着用于二分法的阈值进一步远离基本过程的均值，二项式地统计模型的性能大大降低。我们还发现，二分法会导致疾病流行的精细尺度特征的丧失，以及参数估计中不确定性的增加，尤其是在存在较大的信噪比的情况下。