Spatial and spatio-temporal methods for mapping malaria risk: a systematic review
Background Approaches in malaria risk mapping continue to advance in scope with the advent of geostatistical techniques spanning both the spatial and temporal domains. A substantive review of the merits of the methods and covariates used to map malaria risk has not been undertaken. Therefore, this r...
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BMJ Publishing Group
2020-10-01
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| Series: | BMJ Global Health |
| Online Access: | https://gh.bmj.com/content/5/10/e002919.full |
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| author | Benn Sartorius Julius Nyerere Odhiambo Chester Kalinda Peter M Macharia Robert W Snow |
| author_facet | Benn Sartorius Julius Nyerere Odhiambo Chester Kalinda Peter M Macharia Robert W Snow |
| author_sort | Benn Sartorius |
| collection | DOAJ |
| description | Background Approaches in malaria risk mapping continue to advance in scope with the advent of geostatistical techniques spanning both the spatial and temporal domains. A substantive review of the merits of the methods and covariates used to map malaria risk has not been undertaken. Therefore, this review aimed to systematically retrieve, summarise methods and examine covariates that have been used for mapping malaria risk in sub-Saharan Africa (SSA).Methods A systematic search of malaria risk mapping studies was conducted using PubMed, EBSCOhost, Web of Science and Scopus databases. The search was restricted to refereed studies published in English from January 1968 to April 2020. To ensure completeness, a manual search through the reference lists of selected studies was also undertaken. Two independent reviewers completed each of the review phases namely: identification of relevant studies based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, data extraction and methodological quality assessment using a validated scoring criterion.Results One hundred and seven studies met the inclusion criteria. The median quality score across studies was 12/16 (range: 7–16). Approximately half (44%) of the studies employed variable selection techniques prior to mapping with rainfall and temperature selected in over 50% of the studies. Malaria incidence (47%) and prevalence (35%) were the most commonly mapped outcomes, with Bayesian geostatistical models often (31%) the preferred approach to risk mapping. Additionally, 29% of the studies employed various spatial clustering methods to explore the geographical variation of malaria patterns, with Kulldorf scan statistic being the most common. Model validation was specified in 53 (50%) studies, with partitioning data into training and validation sets being the common approach.Conclusions Our review highlights the methodological diversity prominent in malaria risk mapping across SSA. To ensure reproducibility and quality science, best practices and transparent approaches should be adopted when selecting the statistical framework and covariates for malaria risk mapping. Findings underscore the need to periodically assess methods and covariates used in malaria risk mapping; to accommodate changes in data availability, data quality and innovation in statistical methodology. |
| format | Article |
| id | doaj-art-3984ce6a67d94e599fe88e50cff248b5 |
| institution | OA Journals |
| issn | 2059-7908 |
| language | English |
| publishDate | 2020-10-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Global Health |
| spelling | doaj-art-3984ce6a67d94e599fe88e50cff248b52025-08-20T01:59:00ZengBMJ Publishing GroupBMJ Global Health2059-79082020-10-0151010.1136/bmjgh-2020-002919Spatial and spatio-temporal methods for mapping malaria risk: a systematic reviewBenn Sartorius0Julius Nyerere Odhiambo1Chester Kalinda2Peter M Macharia3Robert W Snow4Centre for Tropical Medicine and Global Health,Nuffield Department of medicine, University of Oxford, Oxford, UKIgnite Global Health Research Lab, Global Research Institute, William & Mary, Williamsburg, Virginia, USA5 Bill and Joyce Cummings Institute of Global Health, University of Global Health Equity, Kigali, RwandaDepartment of Public Health, Institute of Tropical Medicine, Antwerpen, BelgiumKEMRI-Wellcome Trust Research Programme Nairobi, Nairobi, KenyaBackground Approaches in malaria risk mapping continue to advance in scope with the advent of geostatistical techniques spanning both the spatial and temporal domains. A substantive review of the merits of the methods and covariates used to map malaria risk has not been undertaken. Therefore, this review aimed to systematically retrieve, summarise methods and examine covariates that have been used for mapping malaria risk in sub-Saharan Africa (SSA).Methods A systematic search of malaria risk mapping studies was conducted using PubMed, EBSCOhost, Web of Science and Scopus databases. The search was restricted to refereed studies published in English from January 1968 to April 2020. To ensure completeness, a manual search through the reference lists of selected studies was also undertaken. Two independent reviewers completed each of the review phases namely: identification of relevant studies based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, data extraction and methodological quality assessment using a validated scoring criterion.Results One hundred and seven studies met the inclusion criteria. The median quality score across studies was 12/16 (range: 7–16). Approximately half (44%) of the studies employed variable selection techniques prior to mapping with rainfall and temperature selected in over 50% of the studies. Malaria incidence (47%) and prevalence (35%) were the most commonly mapped outcomes, with Bayesian geostatistical models often (31%) the preferred approach to risk mapping. Additionally, 29% of the studies employed various spatial clustering methods to explore the geographical variation of malaria patterns, with Kulldorf scan statistic being the most common. Model validation was specified in 53 (50%) studies, with partitioning data into training and validation sets being the common approach.Conclusions Our review highlights the methodological diversity prominent in malaria risk mapping across SSA. To ensure reproducibility and quality science, best practices and transparent approaches should be adopted when selecting the statistical framework and covariates for malaria risk mapping. Findings underscore the need to periodically assess methods and covariates used in malaria risk mapping; to accommodate changes in data availability, data quality and innovation in statistical methodology.https://gh.bmj.com/content/5/10/e002919.full |
| spellingShingle | Benn Sartorius Julius Nyerere Odhiambo Chester Kalinda Peter M Macharia Robert W Snow Spatial and spatio-temporal methods for mapping malaria risk: a systematic review BMJ Global Health |
| title | Spatial and spatio-temporal methods for mapping malaria risk: a systematic review |
| title_full | Spatial and spatio-temporal methods for mapping malaria risk: a systematic review |
| title_fullStr | Spatial and spatio-temporal methods for mapping malaria risk: a systematic review |
| title_full_unstemmed | Spatial and spatio-temporal methods for mapping malaria risk: a systematic review |
| title_short | Spatial and spatio-temporal methods for mapping malaria risk: a systematic review |
| title_sort | spatial and spatio temporal methods for mapping malaria risk a systematic review |
| url | https://gh.bmj.com/content/5/10/e002919.full |
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