BACKGROUND Dementia is a leading cause of global death and disability. High-quality data describing dementia prevalence and burden remain scarce in sub-Saharan Africa. Health and Aging in Africa: A Longitudinal Study in South Africa (HAALSI) fills evidence gaps with longitudinal data on cognition, biomarkers, and everyday function in a population-based cohort of Black South Africans, aged 40 years and older, in a rural subdistrict. This study uses consensus diagnoses and prediction algorithms to estimate dementia prevalence. METHODS Data were from eligible HAALSI Wave 2 respondents aged 50 years or older (n=3662) and were collected between September, 2019, and January, 2020. An enriched sub-cohort (ie, including a high proportion of individuals with cognitive impairment; n=632) completed a battery of rigorous neuropsychological and clinical assessments and received expert classification of cognitively unimpaired, mild cognitive impairment, or dementia. Logistic regression was used to predict dementia status within the sub-cohort using predictor variables from the parent HAALSI wave. Coefficients were applied to the parent cohort to obtain dementia probability scores and calculate dementia prevalence. Optimal probability cut points to classify individual cases were selected for each model. FINDINGS When the sub-cohort was reweighted to reflect the full HAALSI population, the estimated prevalence of dementia was 18% (95% CI 15-22), with steep age gradients. Four models of increasing complexity showed good discrimination between dementia and non-dementia (area under receiver operating characteristic curves 0·78-0·84; classification accuracy 74-81%). Model-based dementia prevalence estimates aligned closely with weighted prevalence; model performance was consistent in cross-validated datasets. INTERPRETATION HAALSI is among the first studies to use algorithmic methods to describe dementia prevalence in a population-based sample in South Africa. These efforts could provide a foundation to expand understanding of dementia epidemiology in a region of the world experiencing rapid population ageing. FUNDING National Institute on Aging.

中文翻译：

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使用远程诊断和算法建模估计痴呆患病率：南非农村地区基于人群的研究。


背景 痴呆症是全球死亡和残疾的主要原因。在撒哈拉以南非洲，描述痴呆患病率和负担的高质量数据仍然稀缺。非洲的健康与老龄化：南非的一项纵向研究 （HAALSI） 用农村分区 40 岁及以上的南非黑人群体的认知、生物标志物和日常功能的纵向数据填补了证据空白。本研究使用共识诊断和预测算法来估计痴呆患病率。方法 数据来自 50 岁或以上 （n=3662） 的合格 HAALSI Wave 2 受访者，收集于 2019 年 9 月至 2020 年 1 月期间。一个丰富的亚队列（即，包括高比例的认知障碍个体;n=632）完成了一系列严格的神经心理学和临床评估，并接受了认知未受损、轻度认知障碍或痴呆的专家分类。使用 Logistic 回归来使用来自父 HAALSI 波的预测变量预测子队列内的痴呆状态。将系数应用于父队列以获得痴呆概率评分并计算痴呆患病率。为每个模型选择最佳概率割点来对单个案例进行分类。结果 当对子队列进行重新加权以反映整个HAALSI人群时，估计的痴呆患病率为18%（95%CI 15-22），年龄梯度陡峭。四个复杂性增加的模型显示出痴呆和非痴呆之间的良好区分 （受试者工作特征曲线下面积 0·78-0·84;分类准确率 74-81%）。 基于模型的痴呆患病率估计与加权患病率密切相关;模型性能在交叉验证的数据集中是一致的。解释 HAALSI 是最早使用算法方法在南非人群样本中描述痴呆患病率的研究之一。这些努力可以为在世界上经历人口快速老龄化的地区扩大对痴呆流行病学的理解奠定基础。资助 国家老龄化研究所。