Modelling the geographical distribution of co-infection risk from single-disease surveys
Background: The need to deliver interventions targeting multiple diseases in a cost‐effective manner calls for integrated disease control efforts. Consequently, maps are required that show where the risk of co‐infection is particularly high. Co‐infection risk is preferably estimated via Bayesian geo...
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| Published in | Statistics in medicine Vol. 30; no. 14; pp. 1761 - 1776 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Chichester, UK
John Wiley & Sons, Ltd
30.06.2011
Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Background: The need to deliver interventions targeting multiple diseases in a cost‐effective manner calls for integrated disease control efforts. Consequently, maps are required that show where the risk of co‐infection is particularly high. Co‐infection risk is preferably estimated via Bayesian geostatistical multinomial modelling, using data from surveys screening for multiple infections simultaneously. However, only few surveys have collected this type of data.
Methods: Bayesian geostatistical shared component models (allowing for covariates, disease‐specific and shared spatial and non‐spatial random effects) are proposed to model the geographical distribution and burden of co‐infection risk from single‐disease surveys. The ability of the models to capture co‐infection risk is assessed on simulated data sets based on multinomial distributions assuming light‐ and heavy‐dependent diseases, and a real data set of Schistosoma mansoni–hookworm co‐infection in the region of Man, Côte d'Ivoire. The data were restructured as if obtained from single‐disease surveys. The estimated results of co‐infection risk, together with independent and multinomial model results, were compared via different validation techniques.
Results: The results showed that shared component models result in more accurate estimates of co‐infection risk than models assuming independence in settings of heavy‐dependent diseases. The shared spatial random effects are similar to the spatial co‐infection random effects of the multinomial model for heavy‐dependent data.
Conclusions: In the absence of true co‐infection data geostatistical shared component models are able to estimate the spatial patterns and burden of co‐infection risk from single‐disease survey data, especially in settings of heavy‐dependent diseases. Copyright © 2011 John Wiley & Sons, Ltd. |
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| AbstractList | Abstract
Background: The need to deliver interventions targeting multiple diseases in a cost‐effective manner calls for integrated disease control efforts. Consequently, maps are required that show where the risk of co‐infection is particularly high. Co‐infection risk is preferably estimated via Bayesian geostatistical multinomial modelling, using data from surveys screening for multiple infections simultaneously. However, only few surveys have collected this type of data.
Methods: Bayesian geostatistical shared component models (allowing for covariates, disease‐specific and shared spatial and non‐spatial random effects) are proposed to model the geographical distribution and burden of co‐infection risk from single‐disease surveys. The ability of the models to capture co‐infection risk is assessed on simulated data sets based on multinomial distributions assuming light‐ and heavy‐dependent diseases, and a real data set of
Schistosoma mansoni
–hookworm co‐infection in the region of Man, Côte d'Ivoire. The data were restructured as if obtained from single‐disease surveys. The estimated results of co‐infection risk, together with independent and multinomial model results, were compared via different validation techniques.
Results: The results showed that shared component models result in more accurate estimates of co‐infection risk than models assuming independence in settings of heavy‐dependent diseases. The shared spatial random effects are similar to the spatial co‐infection random effects of the multinomial model for heavy‐dependent data.
Conclusions: In the absence of true co‐infection data geostatistical shared component models are able to estimate the spatial patterns and burden of co‐infection risk from single‐disease survey data, especially in settings of heavy‐dependent diseases. Copyright © 2011 John Wiley & Sons, Ltd. Background: The need to deliver interventions targeting multiple diseases in a cost‐effective manner calls for integrated disease control efforts. Consequently, maps are required that show where the risk of co‐infection is particularly high. Co‐infection risk is preferably estimated via Bayesian geostatistical multinomial modelling, using data from surveys screening for multiple infections simultaneously. However, only few surveys have collected this type of data. Methods: Bayesian geostatistical shared component models (allowing for covariates, disease‐specific and shared spatial and non‐spatial random effects) are proposed to model the geographical distribution and burden of co‐infection risk from single‐disease surveys. The ability of the models to capture co‐infection risk is assessed on simulated data sets based on multinomial distributions assuming light‐ and heavy‐dependent diseases, and a real data set of Schistosoma mansoni–hookworm co‐infection in the region of Man, Côte d'Ivoire. The data were restructured as if obtained from single‐disease surveys. The estimated results of co‐infection risk, together with independent and multinomial model results, were compared via different validation techniques. Results: The results showed that shared component models result in more accurate estimates of co‐infection risk than models assuming independence in settings of heavy‐dependent diseases. The shared spatial random effects are similar to the spatial co‐infection random effects of the multinomial model for heavy‐dependent data. Conclusions: In the absence of true co‐infection data geostatistical shared component models are able to estimate the spatial patterns and burden of co‐infection risk from single‐disease survey data, especially in settings of heavy‐dependent diseases. Copyright © 2011 John Wiley & Sons, Ltd. Background: The need to deliver interventions targeting multiple diseases in a cost-effective manner calls for integrated disease control efforts. Consequently, maps are required that show where the risk of co-infection is particularly high. Co-infection risk is preferably estimated via Bayesian geostatistical multinomial modelling, using data from surveys screening for multiple infections simultaneously. However, only few surveys have collected this type of data. Methods: Bayesian geostatistical shared component models (allowing for covariates, disease-specific and shared spatial and non-spatial random effects) are proposed to model the geographical distribution and burden of co-infection risk from single-disease surveys. The ability of the models to capture co-infection risk is assessed on simulated data sets based on multinomial distributions assuming light- and heavy-dependent diseases, and a real data set of Schistosoma mansoni-hookworm co-infection in the region of Man, Cote d'Ivoire. The data were restructured as if obtained from single-disease surveys. The estimated results of co-infection risk, together with independent and multinomial model results, were compared via different validation techniques. Results: The results showed that shared component models result in more accurate estimates of co-infection risk than models assuming independence in settings of heavy-dependent diseases. The shared spatial random effects are similar to the spatial co-infection random effects of the multinomial model for heavy-dependent data. Conclusions: In the absence of true co-infection data geostatistical shared component models are able to estimate the spatial patterns and burden of co-infection risk from single-disease survey data, especially in settings of heavy-dependent diseases. The need to deliver interventions targeting multiple diseases in a cost-effective manner calls for integrated disease control efforts. Consequently, maps are required that show where the risk of co-infection is particularly high. Co-infection risk is preferably estimated via Bayesian geostatistical multinomial modelling, using data from surveys screening for multiple infections simultaneously. However, only few surveys have collected this type of data. Bayesian geostatistical shared component models (allowing for covariates, disease-specific and shared spatial and non-spatial random effects) are proposed to model the geographical distribution and burden of co-infection risk from single-disease surveys. The ability of the models to capture co-infection risk is assessed on simulated data sets based on multinomial distributions assuming light- and heavy-dependent diseases, and a real data set of Schistosoma mansoni-hookworm co-infection in the region of Man, Cote d'Ivoire. The data were restructured as if obtained from single-disease surveys. The estimated results of co-infection risk, together with independent and multinomial model results, were compared via different validation techniques. The results showed that shared component models result in more accurate estimates of co-infection risk than models assuming independence in settings of heavy-dependent diseases. The shared spatial random effects are similar to the spatial co-infection random effects of the multinomial model for heavy-dependent data. In the absence of true co-infection data geostatistical shared component models are able to estimate the spatial patterns and burden of co-infection risk from single-disease survey data, especially in settings of heavy-dependent diseases. BACKGROUNDThe need to deliver interventions targeting multiple diseases in a cost-effective manner calls for integrated disease control efforts. Consequently, maps are required that show where the risk of co-infection is particularly high. Co-infection risk is preferably estimated via Bayesian geostatistical multinomial modelling, using data from surveys screening for multiple infections simultaneously. However, only few surveys have collected this type of data.METHODSBayesian geostatistical shared component models (allowing for covariates, disease-specific and shared spatial and non-spatial random effects) are proposed to model the geographical distribution and burden of co-infection risk from single-disease surveys. The ability of the models to capture co-infection risk is assessed on simulated data sets based on multinomial distributions assuming light- and heavy-dependent diseases, and a real data set of Schistosoma mansoni-hookworm co-infection in the region of Man, Côte d'Ivoire. The data were restructured as if obtained from single-disease surveys. The estimated results of co-infection risk, together with independent and multinomial model results, were compared via different validation techniques.RESULTSThe results showed that shared component models result in more accurate estimates of co-infection risk than models assuming independence in settings of heavy-dependent diseases. The shared spatial random effects are similar to the spatial co-infection random effects of the multinomial model for heavy-dependent data.CONCLUSIONSIn the absence of true co-infection data geostatistical shared component models are able to estimate the spatial patterns and burden of co-infection risk from single-disease survey data, especially in settings of heavy-dependent diseases. The need to deliver interventions targeting multiple diseases in a cost-effective manner calls for integrated disease control efforts. Consequently, maps are required that show where the risk of co-infection is particularly high. Co-infection risk is preferably estimated via Bayesian geostatistical multinomial modelling, using data from surveys screening for multiple infections simultaneously. However, only few surveys have collected this type of data. Bayesian geostatistical shared component models (allowing for covariates, disease-specific and shared spatial and non-spatial random effects) are proposed to model the geographical distribution and burden of co-infection risk from single-disease surveys. The ability of the models to capture co-infection risk is assessed on simulated data sets based on multinomial distributions assuming light- and heavy-dependent diseases, and a real data set of Schistosoma mansoni-hookworm co-infection in the region of Man, Côte d'Ivoire. The data were restructured as if obtained from single-disease surveys. The estimated results of co-infection risk, together with independent and multinomial model results, were compared via different validation techniques. The results showed that shared component models result in more accurate estimates of co-infection risk than models assuming independence in settings of heavy-dependent diseases. The shared spatial random effects are similar to the spatial co-infection random effects of the multinomial model for heavy-dependent data. In the absence of true co-infection data geostatistical shared component models are able to estimate the spatial patterns and burden of co-infection risk from single-disease survey data, especially in settings of heavy-dependent diseases. |
| Author | Gosoniu, L. Utzinger, J. Schur, Nadine Raso, G. Vounatsou, P. |
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| Cites_doi | 10.1007/s10654‐007‐9145‐y 10.1016/j.ijpara.2008.10.014 10.1016/j.ijpara.2005.09.003 10.1016/0035‐9203(91)90054‐3 10.1080/01621459.1962.10480664 10.1073/pnas.0601559103 10.4081/gh.2006.287 10.4081/gh.2007.257 10.1016/j.pt.2006.05.007 10.1111/1467-9876.00113 10.1136/jcp.23.6.545 10.1191/0962280205sm389oa 10.1186/1476‐072X‐5‐41 10.1111/1467‐985X.00187 10.1177/0962280207081243 10.1186/1475‐2875‐5‐99 10.1016/j.healthplace.2008.03.009 10.1017/S0031182005007432 10.3201/eid1410.080366 10.1111/1467-9868.00353 |
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| SubjectTerms | Adolescent Algorithms Bayes Theorem Bayesian analysis Bayesian geostatistics Child co-infection Communicable Diseases - epidemiology Comorbidity Computer Simulation Cote d'Ivoire - epidemiology Cross-Sectional Studies Disease control Economics Effects Endemic Diseases - statistics & numerical data geographical distribution Health Surveys Hookworm Infections - epidemiology Humans infection intervention Markov Chains Medical screening Models, Statistical Monte Carlo Method multinomial model multiple infection Prevalence Reproducibility of Results Risk Risk assessment Schistosoma Schistosomiasis mansoni - epidemiology shared component model Simulation spatial distribution Statistical Distributions Topography, Medical |
| Title | Modelling the geographical distribution of co-infection risk from single-disease surveys |
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