Department of Global Health Systems and Development, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America.
PLoS One. 2012;7(8):e42861. doi: 10.1371/journal.pone.0042861. Epub 2012 Aug 22.
As some malaria control programs shift focus from disease control to transmission reduction, there is a need for transmission data to monitor progress. At lower levels of transmission, it becomes increasingly more difficult to measure precisely, for example through entomological studies. Many programs conduct regular cross sectional parasite prevalence surveys, and have access to malaria treatment data routinely collected by ministries of health, often in health management information systems. However, by themselves, these data are poor measures of transmission. In this paper, we propose an approach for combining annual parasite incidence and treatment data with cross-sectional parasite prevalence and treatment seeking survey data to estimate the incidence of new infections in the human population, also known as the force of infection. The approach is based on extension of a reversible catalytic model. The accuracy of the estimates from this model appears to be highly dependent on levels of detectability and treatment in the community, indicating the importance of information on private sector treatment seeking and access to effective and appropriate treatment.
随着一些疟疾控制项目的重点从疾病控制转移到减少传播，需要有传播数据来监测进展。在传播水平较低的情况下，要准确测量变得越来越困难，例如通过昆虫学研究。许多项目定期进行横断面寄生虫患病率调查，并可以获得卫生部常规收集的疟疾治疗数据，这些数据通常在卫生管理信息系统中。然而，仅凭这些数据并不能很好地衡量传播情况。在本文中，我们提出了一种方法，将年度寄生虫发病率和治疗数据与横断面寄生虫患病率和治疗寻求调查数据相结合，以估计人群中新感染的发病率，也称为感染强度。该方法基于可逆催化模型的扩展。该模型估计值的准确性似乎高度依赖于社区中的可检测性和治疗水平，这表明有关私营部门寻求治疗和获得有效和适当治疗的信息的重要性。
