1
|
Odhiambo JN, Kalinda C, Macharia PM, Snow RW, Sartorius B. Spatial and spatio-temporal methods for mapping malaria risk: a systematic review. BMJ Glob Health 2021; 5:bmjgh-2020-002919. [PMID: 33023880 PMCID: PMC7537142 DOI: 10.1136/bmjgh-2020-002919] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 08/23/2020] [Accepted: 08/24/2020] [Indexed: 12/21/2022] Open
Abstract
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.
Collapse
Affiliation(s)
| | - Chester Kalinda
- Discipline of Public Health Medicine, University of KwaZulu-Natal, Durban, South Africa.,Faculty of Agriculture and Natural Resources, University of Namibia, Windhoek, Namibia
| | - Peter M Macharia
- Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya
| | - Robert W Snow
- Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya.,Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| | - Benn Sartorius
- Discipline of Public Health Medicine, University of KwaZulu-Natal, Durban, South Africa.,Department of Disease Control, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
| |
Collapse
|
2
|
Stevens GA, Alkema L, Black RE, Boerma JT, Collins GS, Ezzati M, Grove JT, Hogan DR, Hogan MC, Horton R, Lawn JE, Marušić A, Mathers CD, Murray CJL, Rudan I, Salomon JA, Simpson PJ, Vos T, Welch V. Guidelines for Accurate and Transparent Health Estimates Reporting: the GATHER statement. Lancet 2016; 388:e19-e23. [PMID: 27371184 DOI: 10.1016/s0140-6736(16)30388-9] [Citation(s) in RCA: 678] [Impact Index Per Article: 84.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Measurements of health indicators are rarely available for every population and period of interest, and available data may not be comparable. The Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) define best reporting practices for studies that calculate health estimates for multiple populations (in time or space) using multiple information sources. Health estimates that fall within the scope of GATHER include all quantitative population-level estimates (including global, regional, national, or subnational estimates) of health indicators, including indicators of health status, incidence and prevalence of diseases, injuries, and disability and functioning; and indicators of health determinants, including health behaviours and health exposures. GATHER comprises a checklist of 18 items that are essential for best reporting practice. A more detailed explanation and elaboration document, describing the interpretation and rationale of each reporting item along with examples of good reporting, is available on the GATHER website.
Collapse
Affiliation(s)
- Gretchen A Stevens
- Department of Information, Evidence and Research, World Health Organization, Geneva, Switzerland.
| | - Leontine Alkema
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA, USA
| | - Robert E Black
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - J Ties Boerma
- Department of Information, Evidence and Research, World Health Organization, Geneva, Switzerland
| | - Gary S Collins
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, UK
| | - Majid Ezzati
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - John T Grove
- Global Development Program, Bill & Melinda Gates Foundation, Seattle, WA, USA
| | - Daniel R Hogan
- Department of Information, Evidence and Research, World Health Organization, Geneva, Switzerland
| | | | | | - Joy E Lawn
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Ana Marušić
- Department of Research in Biomedicine and Health and Cochrane Croatia, University of Split School of Medicine, Split, Croatia
| | - Colin D Mathers
- Department of Information, Evidence and Research, World Health Organization, Geneva, Switzerland
| | | | - Igor Rudan
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UK
| | - Joshua A Salomon
- Department of Global Health and Population, Harvard T H Chan School of Public Health, Boston, MA, USA
| | | | - Theo Vos
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Vivian Welch
- Bruyére Research Institute, Ottawa, ON, Canada; Centre for Global Health, University of Ottawa, Ottawa, ON, Canada
| |
Collapse
|
3
|
Stevens GA, Alkema L, Black RE, Boerma JT, Collins GS, Ezzati M, Grove JT, Hogan DR, Hogan MC, Horton R, Lawn JE, Marušić A, Mathers CD, Murray CJL, Rudan I, Salomon JA, Simpson PJ, Vos T, Welch V. Guidelines for Accurate and Transparent Health Estimates Reporting: the GATHER statement. PLoS Med 2016; 13:e1002056. [PMID: 27351744 PMCID: PMC4924581 DOI: 10.1371/journal.pmed.1002056] [Citation(s) in RCA: 174] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
Gretchen Stevens and colleagues present the GATHER statement, which seeks to promote good practice in the reporting of global health estimates.
Collapse
Affiliation(s)
- Gretchen A. Stevens
- Department of Information, Evidence and Research, World Health Organization, Geneva, Switzerland
- * E-mail:
| | - Leontine Alkema
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, Massachusetts, United States of America
| | - Robert E. Black
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - J. Ties Boerma
- Department of Information, Evidence and Research, World Health Organization, Geneva, Switzerland
| | - Gary S. Collins
- Nuffield Department of Orthopaedics, Rheumatology & Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, United Kingdom
| | - Majid Ezzati
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - John T. Grove
- Global Development Program, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America
| | - Daniel R. Hogan
- Department of Information, Evidence and Research, World Health Organization, Geneva, Switzerland
| | - Margaret C. Hogan
- Independent consultant, Seattle, Washington, United States of America
| | | | - Joy E. Lawn
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Ana Marušić
- Department of Research in Biomedicine and Health and Cochrane Croatia, University of Split School of Medicine, Split, Croatia
| | - Colin D. Mathers
- Department of Information, Evidence and Research, World Health Organization, Geneva, Switzerland
| | - Christopher J. L. Murray
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America
| | - Igor Rudan
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, Scotland, United Kingdom
| | - Joshua A. Salomon
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | | | - Theo Vos
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, Scotland, United Kingdom
| | - Vivian Welch
- Bruyére Research Institute, Bruyére Continuing Care, Ottawa, Ontario, Canada
- Centre for Global Health, University of Ottawa, Ottawa, Ontario, Canada
| | | |
Collapse
|
4
|
Davies J, Yudkin JS, Atun R. Liberating data: the crucial weapon in the fight against NCDs. Lancet Diabetes Endocrinol 2016; 4:197-198. [PMID: 26827114 DOI: 10.1016/s2213-8587(16)00037-1] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2016] [Accepted: 01/21/2016] [Indexed: 10/22/2022]
Affiliation(s)
- Justine Davies
- The Lancet Diabetes & Endocrinology, London EC2Y 5AS, UK.
| | - John S Yudkin
- Division of Medicine, University College London, London, UK
| | - Rifat Atun
- Harvard School of Public Health, Harvard University, Boston, MA, USA
| |
Collapse
|
5
|
Affiliation(s)
- Rifat Atun
- Harvard University, Boston, MA 02115, USA.
| |
Collapse
|
6
|
Abstract
Background: Joint United Nations Programme on HIV/AIDS (UNAIDS) and Murray et al. have both produced sets of estimates for worldwide HIV incidence, prevalence and mortality. Understanding differences in these estimates can strengthen the interpretation of each. Methods: We describe differences in the two sets of estimates. Where possible, we have drawn on additional published data to which estimates can be compared. Findings: UNAIDS estimates that there were 6 million more people living with HIV (PLHIV) in 2013 (35 million) compared with the Murray et al. estimates (29 million). Murray et al. estimate that new infections and AIDS deaths have declined more gradually than does UNAIDS. Just under one third of the difference in PLHIV is in Africa, where Murray et al. have relied more on estimates of adult mortality trends than on data on survival times. Another third of the difference is in North America, Europe, Central Asia and Australasia. Here Murray et al. estimates of new infections are substantially lower than the number of new HIV/AIDS diagnoses reported by countries, whereas published UNAIDS estimate tend to be greater. The remaining differences are in Latin America and Asia where the data upon which the UNAIDS methods currently rely are more sparse, whereas the mortality data leveraged by Murray et al. may be stronger. In this region, however, anomalies appear to exist between the both sets of estimates and other data. Interpretation: Both estimates indicate that approximately 30 million PLHIV and that antiretroviral therapy has driven large reductions in mortality. Both estimates are useful but show instructive discrepancies with additional data sources. We find little evidence to suggest that either set of estimates can be considered systematically more accurate. Further work should seek to build estimates on as wide a base of data as possible.
Collapse
|