Design and Analysis of Elimination Surveys for Neglected Tropical Diseases

被引:29
作者
Fronterre, Claudio [1 ]
Amoah, Benjamin [1 ]
Giorgi, Emanuele [1 ]
Stanton, Michelle C. [1 ]
Diggle, Peter J. [1 ]
机构
[1] Univ Lancaster, Ctr Hlth Informat Comp & Stat, Lancaster, England
基金
比尔及梅琳达.盖茨基金会;
关键词
disease mapping; elimination surveys; geostatistics; neglected tropical diseases; predictions;
D O I
10.1093/infdis/jiz554
中图分类号
R392 [医学免疫学]; Q939.91 [免疫学];
学科分类号
100102 ;
摘要
As neglected tropical diseases approach elimination status, there is a need to develop efficient sampling strategies for confirmation (or not) that elimination criteria have been met. This is an inherently difficult task because the relative precision of a prevalence estimate deteriorates as prevalence decreases, and classic survey sampling strategies based on random sampling therefore require increasingly large sample sizes. More efficient strategies for survey design and analysis can be obtained by exploiting any spatial correlation in prevalence within a model-based geostatistics framework. This framework can be used for constructing predictive probability maps that can inform in-country decision makers of the likelihood that their elimination target has been met, and where to invest in additional sampling. We evaluated our methodology using a case study of lymphatic filariasis in Ghana, demonstrating that a geostatistical approach outperforms approaches currently used to determine an evaluation unit's elimination status.
引用
收藏
页码:S554 / S560
页数:7
相关论文
共 9 条
  • [1] [Anonymous], 1960, Spatial variation. stochastic models and their application to some problems in forest surveys and other sampling investigations
  • [2] Adaptive geostatistical design and analysis for prevalence surveys
    Chipeta, Michael G.
    Terlouw, Dianne J.
    Phiri, Kamija S.
    Diggle, Peter J.
    [J]. SPATIAL STATISTICS, 2016, 15 : 70 - 84
  • [3] COOMBS C.H., 1964, THEORY DATA
  • [4] Diggle P., 2019, Model-Based Geostatistics for Global Public Health: Methods and Applications
  • [5] Diggle PJ, 2011, EPIDEMIOL RES INT, V2011, P1, DOI [10.1155/2011/608719, DOI 10.1155/2011/608719]
  • [6] PrevMap: An R Package for Prevalence Mapping
    Giorgi, Emanuele
    Diggle, Peter J.
    [J]. JOURNAL OF STATISTICAL SOFTWARE, 2017, 78 (08): : 1 - 29
  • [7] Quantifying the value of surveillance data for improving model predictions of lymphatic filariasis elimination
    Michael, Edwin
    Sharma, Swarnali
    Smith, Morgan E.
    Touloupou, Panayiota
    Giardina, Federica
    Prada, Joaquin M.
    Stolk, Wilma A.
    Hollingsworth, Deirdre
    de Vlas, Sake J.
    [J]. PLOS NEGLECTED TROPICAL DISEASES, 2018, 12 (10):
  • [8] Predicting lymphatic filariasis transmission and elimination dynamics using a multi-model ensemble framework
    Smith, Morgan E.
    Singh, Brajendra K.
    Irvine, Michael A.
    Stolk, Wilma A.
    Subramanian, Swaminathan
    Hollingsworth, T. Deirdre
    Michael, Edwin
    [J]. EPIDEMICS, 2017, 18 : 16 - 28
  • [9] World Health Organization, 2011, Monitoring and epidemiological assessment of mass drug administration in the global programme to eliminate lymphatic filariasis: a manual for national elimination programmes