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Measles outbreak risk in Pakistan: exploring the potential of combining vaccination coverage and incidence data with novel data-streams to strengthen control. Epidemiol Infect 2018; 146:1575-1583. [PMID: 29860954 PMCID: PMC6090714 DOI: 10.1017/s0950268818001449] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Although measles incidence has reached historic lows in many parts of the world, the disease still causes substantial morbidity globally. Even where control programs have succeeded in driving measles locally extinct, unless vaccination coverage is maintained at extremely high levels, susceptible numbers may increase sufficiently to spark large outbreaks. Human mobility will drive potentially infectious contacts and interact with the landscape of susceptibility to determine the pattern of measles outbreaks. These interactions have proved difficult to characterise empirically. We explore the degree to which new sources of data combined with existing public health data can be used to evaluate the landscape of immunity and the role of spatial movement for measles introductions by retrospectively evaluating our ability to predict measles outbreaks in vaccinated populations. Using inferred spatial patterns of accumulation of susceptible individuals and travel data, we predicted the timing of epidemics in each district of Pakistan during a large measles outbreak in 2012–2013 with over 30 000 reported cases. We combined these data with mobility data extracted from over 40 million mobile phone subscribers during the same time frame in the country to quantify the role of connectivity in the spread of measles. We investigate how different approaches could contribute to targeting vaccination efforts to reach districts before outbreaks started. While some prediction was possible, accuracy was low and we discuss key uncertainties linked to existing data streams that impede such inference and detail what data might be necessary to robustly infer timing of epidemics.
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Utazi CE, Thorley J, Alegana VA, Ferrari MJ, Takahashi S, Metcalf CJE, Lessler J, Tatem AJ. High resolution age-structured mapping of childhood vaccination coverage in low and middle income countries. Vaccine 2018; 36:1583-1591. [PMID: 29454519 PMCID: PMC6344781 DOI: 10.1016/j.vaccine.2018.02.020] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Revised: 01/24/2018] [Accepted: 02/02/2018] [Indexed: 12/21/2022]
Abstract
BACKGROUND The expansion of childhood vaccination programs in low and middle income countries has been a substantial public health success story. Indicators of the performance of intervention programmes such as coverage levels and numbers covered are typically measured through national statistics or at the scale of large regions due to survey design, administrative convenience or operational limitations. These mask heterogeneities and 'coldspots' of low coverage that may allow diseases to persist, even if overall coverage is high. Hence, to decrease inequities and accelerate progress towards disease elimination goals, fine-scale variation in coverage should be better characterized. METHODS Using measles as an example, cluster-level Demographic and Health Surveys (DHS) data were used to map vaccination coverage at 1 km spatial resolution in Cambodia, Mozambique and Nigeria for varying age-group categories of children under five years, using Bayesian geostatistical techniques built on a suite of publicly available geospatial covariates and implemented via Markov Chain Monte Carlo (MCMC) methods. RESULTS Measles vaccination coverage was found to be strongly predicted by just 4-5 covariates in geostatistical models, with remoteness consistently selected as a key variable. The output 1 × 1 km maps revealed significant heterogeneities within the three countries that were not captured using province-level summaries. Integration with population data showed that at the time of the surveys, few districts attained the 80% coverage, that is one component of the WHO Global Vaccine Action Plan 2020 targets. CONCLUSION The elimination of vaccine-preventable diseases requires a strong evidence base to guide strategies and inform efficient use of limited resources. The approaches outlined here provide a route to moving beyond large area summaries of vaccination coverage that mask epidemiologically-important heterogeneities to detailed maps that capture subnational vulnerabilities. The output datasets are built on open data and methods, and in flexible format that can be aggregated to more operationally-relevant administrative unit levels.
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Affiliation(s)
- C Edson Utazi
- WorldPop, Department of Geography and Environment, University of Southampton, Southampton SO17 1BJ, UK; Southampton Statistical Sciences Research Institute, University of Southampton, Southampton SO17 1BJ, UK.
| | - Julia Thorley
- WorldPop, Department of Geography and Environment, University of Southampton, Southampton SO17 1BJ, UK
| | - Victor A Alegana
- WorldPop, Department of Geography and Environment, University of Southampton, Southampton SO17 1BJ, UK; Flowminder Foundation, Stockholm SE-11355, Sweden
| | - Matthew J Ferrari
- Center for Infectious Disease Dynamics, The Pennsylvania State University, State College, PA 16802, USA
| | - Saki Takahashi
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Andrew J Tatem
- WorldPop, Department of Geography and Environment, University of Southampton, Southampton SO17 1BJ, UK; Flowminder Foundation, Stockholm SE-11355, Sweden
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Buckee CO, Cardenas MIE, Corpuz J, Ghosh A, Haque F, Karim J, Mahmud AS, Maude RJ, Mensah K, Motaze NV, Nabaggala M, Metcalf CJE, Mioramalala SA, Mubiru F, Peak CM, Pramanik S, Rakotondramanga JM, Remera E, Sinha I, Sovannaroth S, Tatem AJ, Zaw W. Productive disruption: opportunities and challenges for innovation in infectious disease surveillance. BMJ Glob Health 2018. [PMID: 29527343 PMCID: PMC5841510 DOI: 10.1136/bmjgh-2017-000538] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Affiliation(s)
- Caroline O Buckee
- Department of Epidemiology, Center for Communicable Disease Dynamics, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Maria I E Cardenas
- Centro de Atención y Diagnóstico de Enfermedades Infecciosas, Universidad Industrial de Santander, Bucaramanga, Colombia
| | - June Corpuz
- Epidemiology Bureau, Department of Health of the Philippines, Manila, Philippines
| | - Arpita Ghosh
- Public Health Foundation of India, Vasant Kunj, New Delhi, India
| | - Farhana Haque
- Programme for Emerging Infections (PEI), Infectious Diseases Division (IDD), ICDDR, B, Dhaka, Bangladesh
| | - Jahirul Karim
- Disease Control Department, Directorate General of Health Services, Dhaka, Bangladesh.,Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Ayesha S Mahmud
- Department of Epidemiology, Center for Communicable Disease Dynamics, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Richard J Maude
- Department of Epidemiology, Center for Communicable Disease Dynamics, Harvard School of Public Health, Boston, Massachusetts, USA.,Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.,Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Keitly Mensah
- Dept of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | | | - Maria Nabaggala
- Infectious Disease Institute, College of Health Sciences, Makarere University, Uganda
| | | | | | - Frank Mubiru
- Infectious Disease Institute, College of Health Sciences, Makarere University, Uganda
| | - Corey M Peak
- Department of Epidemiology, Center for Communicable Disease Dynamics, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Santanu Pramanik
- Public Health Foundation of India, Vasant Kunj, New Delhi, India
| | | | | | - Ipsita Sinha
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.,Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Siv Sovannaroth
- Technical Bureau, National Malaria Control Program, Pnom Penh, Cambodia
| | - Andrew J Tatem
- WorldPop, Department of Geography and Environment, University of Southampton, Southampton, UK
| | - Win Zaw
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
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54
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Buckee CO, Cardenas MIE, Corpuz J, Ghosh A, Haque F, Karim J, Mahmud AS, Maude RJ, Mensah K, Motaze NV, Nabaggala M, Metcalf CJE, Mioramalala SA, Mubiru F, Peak CM, Pramanik S, Rakotondramanga JM, Remera E, Sinha I, Sovannaroth S, Tatem AJ, Zaw W. Productive disruption: opportunities and challenges for innovation in infectious disease surveillance. BMJ Glob Health 2018; 3:e000538. [PMID: 29527343 DOI: 10.1136/bmjgh-2017-00053] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2017] [Revised: 11/16/2017] [Accepted: 11/17/2017] [Indexed: 05/27/2023] Open
Affiliation(s)
- Caroline O Buckee
- Department of Epidemiology, Center for Communicable Disease Dynamics, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Maria I E Cardenas
- Centro de Atención y Diagnóstico de Enfermedades Infecciosas, Universidad Industrial de Santander, Bucaramanga, Colombia
| | - June Corpuz
- Epidemiology Bureau, Department of Health of the Philippines, Manila, Philippines
| | - Arpita Ghosh
- Public Health Foundation of India, Vasant Kunj, New Delhi, India
| | - Farhana Haque
- Programme for Emerging Infections (PEI), Infectious Diseases Division (IDD), ICDDR, B, Dhaka, Bangladesh
| | - Jahirul Karim
- Disease Control Department, Directorate General of Health Services, Dhaka, Bangladesh
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Ayesha S Mahmud
- Department of Epidemiology, Center for Communicable Disease Dynamics, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Richard J Maude
- Department of Epidemiology, Center for Communicable Disease Dynamics, Harvard School of Public Health, Boston, Massachusetts, USA
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Keitly Mensah
- Dept of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | | | - Maria Nabaggala
- Infectious Disease Institute, College of Health Sciences, Makarere University, Uganda
| | | | | | - Frank Mubiru
- Infectious Disease Institute, College of Health Sciences, Makarere University, Uganda
| | - Corey M Peak
- Department of Epidemiology, Center for Communicable Disease Dynamics, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Santanu Pramanik
- Public Health Foundation of India, Vasant Kunj, New Delhi, India
| | | | | | - Ipsita Sinha
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Siv Sovannaroth
- Technical Bureau, National Malaria Control Program, Pnom Penh, Cambodia
| | - Andrew J Tatem
- WorldPop, Department of Geography and Environment, University of Southampton, Southampton, UK
| | - Win Zaw
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
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Brownwright TK, Dodson ZM, van Panhuis WG. Spatial clustering of measles vaccination coverage among children in sub-Saharan Africa. BMC Public Health 2017; 17:957. [PMID: 29246217 PMCID: PMC5732449 DOI: 10.1186/s12889-017-4961-9] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2017] [Accepted: 11/29/2017] [Indexed: 11/18/2022] Open
Abstract
Background During the past two decades, vaccination programs have greatly reduced global morbidity and mortality due to measles, but recently this progress has stalled. Even in countries that report high vaccination coverage rates, transmission has continued, particularly in spatially clustered subpopulations with low vaccination coverage. Methods We examined the spatial heterogeneity of measles vaccination coverage among children aged 12–23 months in ten Sub-Saharan African countries. We used the Anselin Local Moran’s I to estimate clustering of vaccination coverage based on data from Demographic and Health Surveys conducted between 2008 and 2013. We also examined the role of sociodemographic factors to explain clustering of low vaccination. Results We detected 477 spatial clusters with low vaccination coverage, many of which were located in countries with relatively high nationwide vaccination coverage rates such as Zambia and Malawi. We also found clusters in border areas with transient populations. Clustering of low vaccination coverage was related to low health education and limited access to healthcare. Conclusions Systematically monitoring clustered populations with low vaccination coverage can inform supplemental immunization activities and strengthen elimination programs. Metrics of spatial heterogeneity should be used routinely to determine the success of immunization programs and the risk of disease persistence.
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Affiliation(s)
- Tenley K Brownwright
- Department of Epidemiology, University of Pittsburgh Graduate School of Public Health, 130 DeSoto Street, 715 Parran Hall, Pittsburgh, PA, 15261, USA
| | - Zan M Dodson
- Department of Health Policy and Management, University of Pittsburgh Graduate School of Public Health, 130 DeSoto Street, 702 Parran Hall, Pittsburgh, PA, 15261, USA
| | - Willem G van Panhuis
- Department of Epidemiology, University of Pittsburgh Graduate School of Public Health, 130 DeSoto Street, 715 Parran Hall, Pittsburgh, PA, 15261, USA.
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