A Comparative Study of the Spatial Distribution of Schistosomiasis in Mali in 1984-1989 and 2004-2006

被引:80
作者
Clements, Archie C. A. [1 ,2 ]
Bosque-Oliva, Elisa [3 ]
Sacko, Moussa [4 ]
Landoure, Aly [4 ]
Dembele, Robert [5 ]
Traore, Mamadou [6 ]
Coulibaly, Godefroy [4 ]
Gabrielli, Albis F. [7 ]
Fenwick, Alan [3 ]
Brooker, Simon [8 ,9 ]
机构
[1] Univ Queensland, Sch Populat Hlth, Herston, Qld, Australia
[2] Queensland Inst Med Res, Australian Ctr Int & Trop Hlth, Herston, Qld 4006, Australia
[3] Univ London Imperial Coll Sci Technol & Med, Schistosomiasis Control Initiat, London, England
[4] Inst Natl Rech Sante Publ, Bamako, Mali
[5] Minist Sante, Programme Natl Lutte Schistosomiase, Bamako, Mali
[6] Univ Bamako, Dept Enseignement & Rech Sante Publ, Fac Med Pharm & Odontostomatol, Bamako, Mali
[7] World Hlth Org, Dept Control Neglected Trop Dis, Prevent Chemotherapy & Transmiss Control, Geneva, Switzerland
[8] London Sch Hyg & Trop Med, London WC1, England
[9] KEMRI Wellcome Trust Collaborat Programme, Malaria Publ Hlth & Epidemiol Grp, Nairobi, Kenya
基金
英国惠康基金;
关键词
URINARY SCHISTOSOMIASIS; WEST-AFRICA; RISK MODELS; PREVALENCE; IMPLEMENTATION; PREDICTION; CHILDREN; TANZANIA; EPIDEMIOLOGY; TRANSMISSION;
D O I
10.1371/journal.pntd.0000431
中图分类号
R51 [传染病];
学科分类号
100401 ;
摘要
Background: We investigated changes in the spatial distribution of schistosomiasis in Mali following a decade of donor-funded control and a further 12 years without control. Methodology/Principal Findings: National pre-intervention cross-sectional schistosomiasis surveys were conducted in Mali in 1984-1989 (in communities) and again in 2004-2006 (in schools). Bayesian geostatistical models were built separately for each time period and on the datasets combined across time periods. In the former, data from one period were used to predict prevalence of schistosome infections for the other period, and in the latter, the models were used to determine whether spatial autocorrelation and covariate effects were consistent across periods. Schistosoma haematobium prevalence was 25.7% in 1984-1989 and 38.3% in 2004-2006; S. mansoni prevalence was 7.4% in 1984-1989 and 6.7% in 2004-2006 (note the models showed no significant difference in mean prevalence of either infection between time periods). Prevalence of both infections showed a focal spatial pattern and negative associations with distance from perennial waterbodies, which was consistent across time periods. Spatial models developed using 1984-1989 data were able to predict the distributions of both schistosome species in 2004-2006 (area under the receiver operating characteristic curve was typically >0.7) and vice versa. Conclusions/Significance: A decade after the apparently successful conclusion of a donor-funded schistosomiasis control programme from 1982-1992, national prevalence of schistosomiasis had rebounded to pre-intervention levels. Clusters of schistosome infections occurred in generally the same areas accross time periods, although the precise locations varied. To achieve long-term control, it is essential to plan for sustainability of ongoing interventions, including stengthening endemic country health systems.
引用
收藏
页数:11
相关论文
共 33 条
[1]  
BRINKMANN UK, 1988, TROP MED PARASITOL, V39, P157
[2]  
BRINKMANN UK, 1988, TROP MED PARASITOL, V39, P167
[3]   Tools from ecology: useful for evaluating infection risk models? [J].
Brooker, S ;
Hay, SI ;
Bundy, DAP .
TRENDS IN PARASITOLOGY, 2002, 18 (02) :70-74
[4]   Predicting the distribution of urinary schistosomiasis in Tanzania using satellite sensor data [J].
Brooker, S ;
Hay, SI ;
Issae, W ;
Hall, A ;
Kihamia, CM ;
Lwambo, NJS ;
Wint, W ;
Rogers, DJ ;
Bundy, DAP .
TROPICAL MEDICINE & INTERNATIONAL HEALTH, 2001, 6 (12) :998-1007
[5]   Spatial epidemiology of human schistosomiasis in Africa: risk models, transmission dynamics and control [J].
Brooker, Simon .
TRANSACTIONS OF THE ROYAL SOCIETY OF TROPICAL MEDICINE AND HYGIENE, 2007, 101 (01) :1-8
[6]   Spatial epidemiology of Plasmodium vivax, Afghanistan [J].
Brooker, Simon ;
Leslie, Toby ;
Kolaczinski, Kate ;
Mohsen, Engineer ;
Mehboob, Najeebullah ;
Saleheen, Sarah ;
Khudonazarov, Juma ;
Freeman, Tim ;
Clements, Archie ;
Rowland, Mark ;
Kolaczinski, Jan .
EMERGING INFECTIOUS DISEASES, 2006, 12 (10) :1600-1602
[7]   Bayesian spatial analysis of a national urinary schistosomiasis questionnaire to assist geographic targeting of schistosomiasis control in Tanzania, East Africa [J].
Clements, A. C. A. ;
Brooker, S. ;
Nyandindi, U. ;
Fenwick, A. ;
Blair, L. .
INTERNATIONAL JOURNAL FOR PARASITOLOGY, 2008, 38 (3-4) :401-415
[8]   Bayesian geostatistical prediction of the intensity of infection with Schistosoma mansoni in East Africa [J].
Clements, A. C. A. ;
Moyeed, R. ;
Brooker, S. .
PARASITOLOGY, 2006, 133 :711-719
[9]   Bayesian spatial analysis and disease mapping: tools to enhance planning and implementation of a schistosomiasis control programme in Tanzania [J].
Clements, ACA ;
Lwambo, NJS ;
Blair, L ;
Nyandindi, U ;
Kaatano, G ;
Kinung'hi, S ;
Webster, JP ;
Fenwick, A ;
Brooker, S .
TROPICAL MEDICINE & INTERNATIONAL HEALTH, 2006, 11 (04) :490-503
[10]   Mapping the probability of schistosomiasis and associated uncertainty, West Africa [J].
Clements, Archie C. A. ;
Garba, Amadou ;
Sacko, Moussa ;
Toure, Seydou ;
Dembele, Robert ;
Landoure, Aly ;
Bosque-Oliva, Elisa ;
Gabrielli, Albis F. ;
Fenwick, Alan .
EMERGING INFECTIOUS DISEASES, 2008, 14 (10) :1629-1632