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Starnes JR, Rogers A, Wamae J, Okoth V, Mudhune SA, Omondi A, Were V, Baraza Awino D, Lefebvre CH, Yap S, Otieno Odhong T, Vill B, Were L, Wamai R. Childhood mortality and associated factors in Migori County, Kenya: evidence from a cross-sectional survey. BMJ Open 2023; 13:e074056. [PMID: 37607788 PMCID: PMC10445361 DOI: 10.1136/bmjopen-2023-074056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 08/08/2023] [Indexed: 08/24/2023] Open
Abstract
OBJECTIVES The under-five mortality (U5M) rate in Kenya (41 per 1000 live births) remains significantly above international goals (25 per 1000 live births). This is further exacerbated by regional inequalities in mortality. We aimed to describe U5M in Migori County, Kenya, and identify associated factors that can serve as programming targets. DESIGN Cross-sectional observational survey. SETTING Areas served by the Lwala Community Alliance and control areas in Migori County, Kenya. PARTICIPANTS This study included 15 199 children born to respondents during the 18 years preceding the survey. PRIMARY AND SECONDARY OUTCOME MEASURES The primary outcome was mortality in the first 5 years of life. The survey was powered to detect a 10% change in various health metrics over time with 80% power. RESULTS A total of 15 199 children were included in the primary analyses, and 230 (1.5%) were deceased before the fifth birthday. The U5M rate from 2016 to 2021 was 32.2 per 1000 live births. Factors associated with U5M included year of birth (HR 0.926, p<0.001), female sex (HR 0.702, p=0.01), parental marriage (HR 0.642, p=0.036), multiple gestation pregnancy (HR 2.776, p<0.001), birth spacing less than 18 months (HR 1.894, p=0.005), indoor smoke exposure (HR 1.916, p=0.027) and previous familial contribution to the National Hospital Insurance Fund (HR 0.553, p=0.009). The most common cause of death was malaria. CONCLUSIONS We describe factors associated with childhood mortality in a Kenyan community using survival analyses of complete birth histories. Mortality rates will serve as the baseline for future programme evaluation as a part of a 10-year study design. This provides both the hyperlocal information needed to improve programming and generalisable conclusions for other organisations working in similar environments.
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Affiliation(s)
- Joseph R Starnes
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Lwala Community Alliance, Rongo, Kenya
| | | | | | | | | | - Alyn Omondi
- Adaptive Model for Research and Empowerment of Communities in Africa, Kisumu, Kenya
| | - Vincent Were
- Kenya Medical Research Institute, Nairobi, Kenya
| | | | - Christina Hope Lefebvre
- Department of Cultures, Societies, and Global Studies, Northeastern University, Boston, Massachusetts, USA
| | - Samantha Yap
- Department of Cultures, Societies, and Global Studies, Northeastern University, Boston, Massachusetts, USA
| | - Tom Otieno Odhong
- Department of Health Services, Migori County Government, Migori, Kenya
| | - Beffy Vill
- Department of Health Services, Migori County Government, Migori, Kenya
| | - Lawrence Were
- Department of Global Health, Boston University, Boston, Massachusetts, USA
| | - Richard Wamai
- Department of Cultures, Societies, and Global Studies, Northeastern University, Boston, Massachusetts, USA
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Modelling and Analysis of a Measles Epidemic Model with the Constant Proportional Caputo Operator. Symmetry (Basel) 2023. [DOI: 10.3390/sym15020468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023] Open
Abstract
Despite the existence of a secure and reliable immunization, measles, also known as rubeola, continues to be a leading cause of fatalities globally, especially in underdeveloped nations. For investigation and observation of the dynamical transmission of the disease with the influence of vaccination, we proposed a novel fractional order measles model with a constant proportional (CP) Caputo operator. We analysed the proposed model’s positivity, boundedness, well-posedness, and biological viability. Reproductive and strength numbers were also verified to examine how the illness dynamically behaves in society. For local and global stability analysis, we introduced the Lyapunov function with first and second derivatives. In order to evaluate the fractional integral operator, we used different techniques to invert the PC and CPC operators. We also used our suggested model’s fractional differential equations to derive the eigenfunctions of the CPC operator. There is a detailed discussion of additional analysis on the CPC and Hilfer generalised proportional operators. Employing the Laplace with the Adomian decomposition technique, we simulated a system of fractional differential equations numerically. Finally, numerical results and simulations were derived with the proposed measles model. The intricate and vital study of systems with symmetry is one of the many applications of contemporary fractional mathematical control. A strong tool that makes it possible to create numerical answers to a given fractional differential equation methodically is symmetry analysis. It is discovered that the proposed fractional order model provides a more realistic way of understanding the dynamics of a measles epidemic.
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Abstract
In this work, we replaced the integer derivative with Caputo derivative to model the transmission dynamics of measles in an epidemic situation. We began by recalling some results on the local and global stability of the measles-free equilibrium point as well as the local stability of the endemic equilibrium point. We computed the basic reproduction number of the fractional model and found that is it equal to the one in the integer model when the fractional order ν = 1. We then performed a sensitivity analysis using the global method. Indeed, we computed the partial rank correlation coefficient (PRCC) between each model parameter and the basic reproduction number R0 as well as each variable state. We then demonstrated that the fractional model admits a unique solution and that it is globally stable using the Ulam–Hyers stability criterion. Simulations using the Adams-type predictor–corrector iterative scheme were conducted to validate our theoretical results and to see the impact of the variation of the fractional order on the quantitative disease dynamics.
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Mkopi A, Mtenga S, Festo C, Mhalu G, Shabani J, Tillya R, Masemo A, Kheir K, Nassor M, Mwengee W, Lyimo D, Masanja H. Factors affecting non-coverage of measles-rubella vaccination among children aged 9-59 months in Tanzania. Vaccine 2021; 39:6041-6049. [PMID: 34531077 DOI: 10.1016/j.vaccine.2021.09.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 08/21/2021] [Accepted: 09/03/2021] [Indexed: 11/17/2022]
Abstract
Globally, measles remains a major cause of child mortality, and rubella is the leading cause of birth defects among all infectious diseases. In 2012, the World Health Assembly endorsed the Global Vaccine Action Plan that set a target to eliminate Measles-Rubella (MR) in five of the six World Health Organization (WHO) regions by 2020. This was cross-sectional study employed both quantitative and qualitative research methods. The sample size was calculated to provide overall, age- and sex-specific coverage estimates for MR vaccine among children aged between 9 and 59 months at the national level. Using desired precision of ±5% with an expected coverage of 95%, a total of 15,235 households were required. The age of children, a child who had received the MR vaccine before the campaign, household wealth quintile, the age of caregivers, and their marital status were associated with non-coverage of MR vaccination among children aged 9-59 months in Tanzania. Nationally, an estimated 88.2% (95% CI: 87.3-89%) of children aged 9-59 months received the MR campaign dose, as assessed by caregivers' recall. These estimates revealed slightly higher coverage in Zanzibar 89.6% (95% CI: 84.7-93%) compared to Mainland Tanzania 88.1% (95% CI 87.2-88.9%). These associated factors revealed causes of unvaccinated children and may be some of the reasons for Tanzania's failure to meet the MR campaign target of 95 percent vaccination coverage. Thus, vaccine development must increase programmatic oversight in order to improve immunization activities and communication strategies in Tanzanian areas with low MR coverage.
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Affiliation(s)
| | - Sally Mtenga
- Ifakara Health Institute, Dar es Salaam, Tanzania
| | | | - Grace Mhalu
- Ifakara Health Institute, Dar es Salaam, Tanzania
| | | | | | - Ame Masemo
- Zanzibar Health Research Institute, Zanzibar, Tanzania
| | - Khamis Kheir
- Zanzibar Health Research Institute, Zanzibar, Tanzania
| | - Mohamed Nassor
- World Health Organization, Tanzania Country Office, Dar es Salaam, Tanzania
| | - William Mwengee
- World Health Organization, Tanzania Country Office, Dar es Salaam, Tanzania
| | - Dafrossa Lyimo
- Ministry of Health, Community Development, Gender, Elderly, and Children, Dar es Salaam, Tanzania
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5
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Hu Y, Liang H, Chen F, Shen L, Pan X, Wang Y, Chen Y, Lv H. Evaluating the vaccination coverage: validity of household-hold vaccination booklet and caregiver's recall. Hum Vaccin Immunother 2021; 17:3034-3041. [PMID: 33825657 DOI: 10.1080/21645515.2021.1906151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022] Open
Abstract
BACKGROUND We compared results from household data sources to medical record sources by using data from a vaccination coverage survey. METHODS Vaccination coverage (VC) was calculated based on parental recall, household vaccination booklet, and Zhejiang provincial immunization information system (ZJIIS). We evaluated the accuracy of VC based on household sources (vaccination booklet and recall) assuming the medical record was accurate. Concordance, sensitivity, specificity, positive predictive value, and negative predictive value were estimated as well as the Kappa statistic was also used to evaluate the agreement between data sources. RESULTS Among the 1,800 children identified in the household survey, all were registered in ZJIIS. VC estimated using the vaccination booklet alone was substantially lower than that based on medical records (net bias 3.4-16.7% in different age groups). VC based on parental recall ranged from 2.5% below (among children aged 1 year) to 16.7% points above (among children aged 6 years) than those based on medical records. Concordance was lowest for card estimates (32.5-45.5%). Sensitivity was <60% for all household sources, except for recall source. Specificity was lowest for recall estimates (14.5-42.6%). Positive predictive value was >75%, while negative predictive value was <50%, for all household sources. Kappa statistics generally indicated poor agreement between household and medical record sources. CONCLUSIONS Household-retained vaccination booklets and parental recall were insufficient sources for evaluating the VC. Our findings emphasized the importance of taking interventions to make the vaccination booklet more consistent with the records from medical resource.
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Affiliation(s)
- Yu Hu
- Institute of Immunization and Prevention, Zhejiang Center for Disease Control and Prevention, Hangzhou, China
| | - Hu Liang
- Institute of Immunization and Prevention, Zhejiang Center for Disease Control and Prevention, Hangzhou, China
| | - Fuxing Chen
- Institute of Immunization and Prevention, Zhejiang Center for Disease Control and Prevention, Hangzhou, China
| | - Linzhi Shen
- Institute of Immunization and Prevention, Zhejiang Center for Disease Control and Prevention, Hangzhou, China
| | - Xuejiao Pan
- Institute of Immunization and Prevention, Zhejiang Center for Disease Control and Prevention, Hangzhou, China
| | - Ying Wang
- Institute of Immunization and Prevention, Zhejiang Center for Disease Control and Prevention, Hangzhou, China
| | - Yaping Chen
- Institute of Immunization and Prevention, Zhejiang Center for Disease Control and Prevention, Hangzhou, China
| | - Huakun Lv
- Institute of Immunization and Prevention, Zhejiang Center for Disease Control and Prevention, Hangzhou, China
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Allan S, Adetifa IMO, Abbas K. Inequities in childhood immunisation coverage associated with socioeconomic, geographic, maternal, child, and place of birth characteristics in Kenya. BMC Infect Dis 2021; 21:553. [PMID: 34112096 PMCID: PMC8192222 DOI: 10.1186/s12879-021-06271-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 05/31/2021] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND The global Immunisation Agenda 2030 highlights coverage and equity as a strategic priority goal to reach high equitable immunisation coverage at national levels and in all districts. We estimated inequities in full immunisation coverage associated with socioeconomic, geographic, maternal, child, and place of birth characteristics among children aged 12-23 months in Kenya. METHODS We analysed full immunisation coverage (1-dose BCG, 3-dose DTP-HepB-Hib (diphtheria, tetanus, pertussis, hepatitis B and Haemophilus influenzae type B), 3-dose polio, 1-dose measles, and 3-dose pneumococcal vaccines) of 3943 children aged 12-23 months from the 2014 Kenya Demographic and Health Survey. We disaggregated mean coverage by socioeconomic (household wealth, religion, ethnicity), geographic (place of residence, province), maternal (maternal age at birth, maternal education, maternal marital status, maternal household head status), child (sex of child, birth order), and place of birth characteristics, and estimated inequities in full immunisation coverage using bivariate and multivariate logistic regression. RESULTS Immunisation coverage ranged from 82% [81-84] for the third dose of polio to 97.4% [96.7-98.2] for the first dose of DTP-HepB-Hib, while full immunisation coverage was 68% [66-71] in 2014. After controlling for other background characteristics through multivariate logistic regression, children of mothers with primary school education or higher have at least 54% higher odds of being fully immunised compared to children of mothers with no education. Children born in clinical settings had 41% higher odds of being fully immunised compared to children born in home settings. Children in the Coast, Western, Central, and Eastern regions had at least 74% higher odds of being fully immunised compared to children in the North Eastern region, while children in urban areas had 26% lower odds of full immunisation compared to children in rural areas. Children in the middle and richer wealth quintile households were 43-57% more likely to have full immunisation coverage compared to children in the poorest wealth quintile households. Children who were sixth born or higher had 37% lower odds of full immunisation compared to first-born children. CONCLUSIONS Children of mothers with no education, born in home settings, in regions with limited health infrastructure, living in poorer households, and of higher birth order are associated with lower rates of full immunisation. Targeted programmes to reach under-immunised children in these subpopulations will lower the inequities in childhood immunisation coverage in Kenya.
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Affiliation(s)
- Simon Allan
- Gavi, the Vaccine Alliance, Geneva, Switzerland
- London School of Hygiene and Tropical Medicine, London, UK
| | - Ifedayo M. O. Adetifa
- London School of Hygiene and Tropical Medicine, London, UK
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | - Kaja Abbas
- London School of Hygiene and Tropical Medicine, London, UK
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7
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Mburu CN, Ojal J, Chebet R, Akech D, Karia B, Tuju J, Sigilai A, Abbas K, Jit M, Funk S, Smits G, van Gageldonk PGM, van der Klis FRM, Tabu C, Nokes DJ, Scott J, Flasche S, Adetifa I. The importance of supplementary immunisation activities to prevent measles outbreaks during the COVID-19 pandemic in Kenya. BMC Med 2021; 19:35. [PMID: 33531015 PMCID: PMC7854026 DOI: 10.1186/s12916-021-01906-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 01/11/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND The COVID-19 pandemic has disrupted routine measles immunisation and supplementary immunisation activities (SIAs) in most countries including Kenya. We assessed the risk of measles outbreaks during the pandemic in Kenya as a case study for the African Region. METHODS Combining measles serological data, local contact patterns, and vaccination coverage into a cohort model, we predicted the age-adjusted population immunity in Kenya and estimated the probability of outbreaks when contact-reducing COVID-19 interventions are lifted. We considered various scenarios for reduced measles vaccination coverage from April 2020. RESULTS In February 2020, when a scheduled SIA was postponed, population immunity was close to the herd immunity threshold and the probability of a large outbreak was 34% (8-54). As the COVID-19 contact restrictions are nearly fully eased, from December 2020, the probability of a large measles outbreak will increase to 38% (19-54), 46% (30-59), and 54% (43-64) assuming a 15%, 50%, and 100% reduction in measles vaccination coverage. By December 2021, this risk increases further to 43% (25-56), 54% (43-63), and 67% (59-72) for the same coverage scenarios respectively. However, the increased risk of a measles outbreak following the lifting of all restrictions can be overcome by conducting a SIA with ≥ 95% coverage in under-fives. CONCLUSION While contact restrictions sufficient for SAR-CoV-2 control temporarily reduce measles transmissibility and the risk of an outbreak from a measles immunity gap, this risk rises rapidly once these restrictions are lifted. Implementing delayed SIAs will be critical for prevention of measles outbreaks given the roll-back of contact restrictions in Kenya.
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Affiliation(s)
- C N Mburu
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya.
- Department of Infectious Diseases Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
| | - J Ojal
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
- Department of Infectious Diseases Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - R Chebet
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | - D Akech
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | - B Karia
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | - J Tuju
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | - A Sigilai
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | - K Abbas
- Department of Infectious Diseases Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - M Jit
- Department of Infectious Diseases Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - S Funk
- Department of Infectious Diseases Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - G Smits
- Department of Immunosurveillance, Centre for Infectious Diseases Control, National Institute of Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - P G M van Gageldonk
- Department of Immunosurveillance, Centre for Infectious Diseases Control, National Institute of Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - F R M van der Klis
- Department of Immunosurveillance, Centre for Infectious Diseases Control, National Institute of Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - C Tabu
- National Vaccine and Immunisation Programme, Ministry of Health, Nairobi, Kenya
| | - D J Nokes
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
- School of Life Sciences and Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry, UK
| | - Jag Scott
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
- Department of Infectious Diseases Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - S Flasche
- Department of Infectious Diseases Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Imo Adetifa
- KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya.
- Department of Infectious Diseases Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
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Utazi CE, Wagai J, Pannell O, Cutts FT, Rhoda DA, Ferrari MJ, Dieng B, Oteri J, Danovaro-Holliday MC, Adeniran A, Tatem AJ. Geospatial variation in measles vaccine coverage through routine and campaign strategies in Nigeria: Analysis of recent household surveys. Vaccine 2020; 38:3062-3071. [PMID: 32122718 PMCID: PMC7079337 DOI: 10.1016/j.vaccine.2020.02.070] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 01/31/2020] [Accepted: 02/03/2020] [Indexed: 11/21/2022]
Abstract
Measles vaccination campaigns are conducted regularly in many low- and middle-income countries to boost measles control efforts and accelerate progress towards elimination. National and sometimes first-level administrative division campaign coverage may be estimated through post-campaign coverage surveys (PCCS). However, these large-area estimates mask significant geographic inequities in coverage at more granular levels. Here, we undertake a geospatial analysis of the Nigeria 2017-18 PCCS data to produce coverage estimates at 1 × 1 km resolution and the district level using binomial spatial regression models built on a suite of geospatial covariates and implemented in a Bayesian framework via the INLA-SPDE approach. We investigate the individual and combined performance of the campaign and routine immunization (RI) by mapping various indicators of coverage for children aged 9-59 months. Additionally, we compare estimated coverage before the campaign at 1 × 1 km and the district level with predicted coverage maps produced using other surveys conducted in 2013 and 2016-17. Coverage during the campaign was generally higher and more homogeneous than RI coverage but geospatial differences in the campaign's reach of previously unvaccinated children are shown. Persistent areas of low coverage highlight the need for improved RI performance. The results can help to guide the conduct of future campaigns, improve vaccination monitoring and measles elimination efforts. Moreover, the approaches used here can be readily extended to other countries.
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Affiliation(s)
- C Edson Utazi
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK; Southampton Statistical Sciences Research Institute, University of Southampton, Southampton SO17 1BJ, UK.
| | - John Wagai
- World Health Organization Consultant, Abuja, Nigeria
| | - Oliver Pannell
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK
| | - Felicity T Cutts
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | | | - Matthew J Ferrari
- Center for Infectious Disease Dynamics, The Pennsylvania State University, State College, PA, 16802, USA
| | | | - Joseph Oteri
- National Primary Health Care Development Agency, Abuja, Nigeria
| | | | | | - Andrew J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK
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Edson Utazi C, Wagai J, Pannell O, Cutts FT, Rhoda DA, Ferrari MJ, Dieng B, Oteri J, Carolina Danovaro-Holliday M, Adeniran A, Tatem AJ. WITHDRAWN: Geospatial variation in measles vaccine coverage through routine and campaign strategies in Nigeria: analysis of recent household surveys. Vaccine X 2020. [DOI: 10.1016/j.jvacx.2020.100056] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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10
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Cutts FT, Dansereau E, Ferrari MJ, Hanson M, McCarthy KA, Metcalf CJE, Takahashi S, Tatem AJ, Thakkar N, Truelove S, Utazi E, Wesolowski A, Winter AK. Using models to shape measles control and elimination strategies in low- and middle-income countries: A review of recent applications. Vaccine 2020; 38:979-992. [PMID: 31787412 PMCID: PMC6996156 DOI: 10.1016/j.vaccine.2019.11.020] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Revised: 11/07/2019] [Accepted: 11/08/2019] [Indexed: 01/30/2023]
Abstract
After many decades of vaccination, measles epidemiology varies greatly between and within countries. National immunization programs are therefore encouraged to conduct regular situation analyses and to leverage models to adapt interventions to local needs. Here, we review applications of models to develop locally tailored interventions to support control and elimination efforts. In general, statistical and semi-mechanistic transmission models can be used to synthesize information from vaccination coverage, measles incidence, demographic, and/or serological data, offering a means to estimate the spatial and age-specific distribution of measles susceptibility. These estimates complete the picture provided by vaccination coverage alone, by accounting for natural immunity. Dynamic transmission models can then be used to evaluate the relative impact of candidate interventions for measles control and elimination and the expected future epidemiology. In most countries, models predict substantial numbers of susceptible individuals outside the age range of routine vaccination, which affects outbreak risk and necessitates additional intervention to achieve elimination. More effective use of models to inform both vaccination program planning and evaluation requires the development of training to enhance broader understanding of models and where feasible, building capacity for modelling in-country, pipelines for rapid evaluation of model predictions using surveillance data, and clear protocols for incorporating model results into decision-making.
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Affiliation(s)
- F T Cutts
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
| | - E Dansereau
- Vaccine Delivery, Global Development, The Bill & Melinda Gates Foundation, Seattle, WA, USA
| | - M J Ferrari
- Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA, USA
| | - M Hanson
- Vaccine Delivery, Global Development, The Bill & Melinda Gates Foundation, Seattle, WA, USA
| | - K A McCarthy
- Institute for Disease Modeling, 3150 139th Ave SE, Bellevue, WA 98005, USA
| | - C J E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - S Takahashi
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA; Department of Medicine, University of California San Francisco, San Francisco, CA 94143, USA
| | - A J Tatem
- WorldPop, Department of Geography and Environmental Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK
| | - N Thakkar
- Institute for Disease Modeling, 3150 139th Ave SE, Bellevue, WA 98005, USA
| | - S Truelove
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - E Utazi
- WorldPop, Department of Geography and Environmental Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK
| | - A Wesolowski
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - A K Winter
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
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11
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Danovaro-Holliday MC, Dansereau E, Rhoda DA, Brown DW, Cutts FT, Gacic-Dobo M. Collecting and using reliable vaccination coverage survey estimates: Summary and recommendations from the "Meeting to share lessons learnt from the roll-out of the updated WHO Vaccination Coverage Cluster Survey Reference Manual and to set an operational research agenda around vaccination coverage surveys", Geneva, 18-21 April 2017. Vaccine 2018; 36:5150-5159. [PMID: 30041880 PMCID: PMC6099121 DOI: 10.1016/j.vaccine.2018.07.019] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Revised: 07/07/2018] [Accepted: 07/09/2018] [Indexed: 11/30/2022]
Abstract
Household surveys are frequently used as means of vaccination coverage measurement, but obtaining accurate survey estimates present several challenges. In 2015, the World Health Organization (WHO) released a working draft of its updated Vaccination Coverage Survey Reference Manual that moved well beyond the traditional Expanded Program on Immunization (EPI) survey design. In April 2017, WHO convened a four-day meeting, to review lessons learned using the updated manual and to define an agenda for operational research about vaccination coverage surveys. About 70 stakeholders, including EPI managers and participants from 10 countries that have used the updated Survey Manual, survey experts, statisticians, partners, representatives from WHO regional offices and headquarters, and providers of technical assistance discussed methodological issues from sampling to accurately ascertaining a person's vaccination status, optimizing data collection and data management and conducting appropriate analyses. Participants also discussed data sharing and how to best survey data for immunization decision-making. The lessons learned from the use of the updated WHO Survey Manual related mainly to operational issues to implement better quality vaccination coverage surveys. It resulted in a list of 23 recommendations for WHO, donors and partners, immunization programs, and household surveys that collect immunization data. Similarly, 14 research topics, categorized in six themes (overall survey conduction, sampling, vaccination ascertainment, data collection, data analysis and use, and inclusion of questions on knowledge, attitudes and practices) were prioritized. Top areas of further work included improving our understanding of the accuracy of caregiver recall when documented evidence of vaccination is not available, improving engagement and coordination between immunization programs and entities conducting multi-purpose household surveys such as Demographic and Health Survey and Multiple Cluster Indicator Survey, improving mechanisms for sharing vaccination survey datasets and documentation, and making better use of survey results to translate data into knowledge for decision-making. This manuscript summarizes the meeting proceedings and provides an update of actions taken by WHO since this meeting.
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Affiliation(s)
- M Carolina Danovaro-Holliday
- Expanded Programme on Immunization (EPI), Department of Immunization, Vaccines and Biologicals (IVB), World Health Organization (WHO), Geneva, Switzerland.
| | - Emily Dansereau
- Expanded Programme on Immunization (EPI), Department of Immunization, Vaccines and Biologicals (IVB), World Health Organization (WHO), Geneva, Switzerland
| | | | - David W Brown
- Brown Consulting Group Int'l LLC, Cornelius, NC, USA
| | | | - Marta Gacic-Dobo
- Expanded Programme on Immunization (EPI), Department of Immunization, Vaccines and Biologicals (IVB), World Health Organization (WHO), Geneva, Switzerland
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