Publications
1.
Utazi, C. Edson; Aheto, Justice M. K.; Wigley, Adelle; Tejedor-Garavito, Natalia; Bonnie, Amy; Nnanatu, Chris; Wagai, John; Williams, Cheryl; Setayesh, Hamidrez; J.Tatem, Andrew
In: Vaccine, 2022.
Abstract | Links | BibTeX | Tags: Measles vaccination, Nigeria, zero dose
@article{nokey,
title = {Mapping the distribution of zero-dose children to assess the performance of vaccine delivery strategies and their relationships with measles incidence in Nigeria},
author = {C. Edson Utazi and Justice M. K. Aheto and Adelle Wigley and Natalia Tejedor-Garavito and Amy Bonnie and Chris Nnanatu and John Wagai and Cheryl Williams and Hamidrez Setayesh and Andrew J.Tatem},
doi = {10.1016/j.vaccine.2022.11.026},
year = {2022},
date = {2022-11-19},
urldate = {2022-11-19},
journal = {Vaccine},
abstract = {Geographically precise identification and targeting of populations at risk of vaccine-preventable diseases has gained renewed attention within the global health community over the last few years. District level estimates of vaccination coverage and corresponding zero-dose prevalence constitute a potentially useful evidence base to evaluate the performance of vaccination strategies. These estimates are also valuable for identifying missed communities, hence enabling targeted interventions and better resource allocation. Here, we fit Bayesian geostatistical models to map the routine coverage of the first doses of diphtheria-tetanus-pertussis vaccine (DTP1) and measles-containing vaccine (MCV1) and corresponding zero-dose estimates in Nigeria at 1x1 km resolution and the district level using geospatial data sets. We also map MCV1 coverage before and after the 2019 measles vaccination campaign in the northern states to further explore variations in routine vaccine coverage and to evaluate the effectiveness of both routine immunization (RI) and campaigns in reaching zero-dose children. Additionally, we map the spatial distributions of reported measles cases during 2018 to 2020 and explore their relationships with MCV zero-dose prevalence to highlight the public health implications of varying performance of vaccination strategies across the country. Our analysis revealed strong similarities between the spatial distributions of DTP and MCV zero dose prevalence, with districts with the highest prevalence concentrated mostly in the northwest and the northeast, but also in other areas such as Lagos state and the Federal Capital Territory. Although the 2019 campaign reduced MCV zero-dose prevalence substantially in the north, pockets of vulnerabilities remained in areas that had among the highest prevalence prior to the campaign. Importantly, we found strong correlations between measles case counts and MCV RI zero-dose estimates, which provides a strong indication that measles incidence in the country is mostly affected by RI coverage. Our analyses reveal an urgent and highly significant need to strengthen the country’s RI program as a longer-term measure for disease control, whilst ensuring effective campaigns in the short term.},
keywords = {Measles vaccination, Nigeria, zero dose},
pubstate = {published},
tppubtype = {article}
}
Geographically precise identification and targeting of populations at risk of vaccine-preventable diseases has gained renewed attention within the global health community over the last few years. District level estimates of vaccination coverage and corresponding zero-dose prevalence constitute a potentially useful evidence base to evaluate the performance of vaccination strategies. These estimates are also valuable for identifying missed communities, hence enabling targeted interventions and better resource allocation. Here, we fit Bayesian geostatistical models to map the routine coverage of the first doses of diphtheria-tetanus-pertussis vaccine (DTP1) and measles-containing vaccine (MCV1) and corresponding zero-dose estimates in Nigeria at 1x1 km resolution and the district level using geospatial data sets. We also map MCV1 coverage before and after the 2019 measles vaccination campaign in the northern states to further explore variations in routine vaccine coverage and to evaluate the effectiveness of both routine immunization (RI) and campaigns in reaching zero-dose children. Additionally, we map the spatial distributions of reported measles cases during 2018 to 2020 and explore their relationships with MCV zero-dose prevalence to highlight the public health implications of varying performance of vaccination strategies across the country. Our analysis revealed strong similarities between the spatial distributions of DTP and MCV zero dose prevalence, with districts with the highest prevalence concentrated mostly in the northwest and the northeast, but also in other areas such as Lagos state and the Federal Capital Territory. Although the 2019 campaign reduced MCV zero-dose prevalence substantially in the north, pockets of vulnerabilities remained in areas that had among the highest prevalence prior to the campaign. Importantly, we found strong correlations between measles case counts and MCV RI zero-dose estimates, which provides a strong indication that measles incidence in the country is mostly affected by RI coverage. Our analyses reveal an urgent and highly significant need to strengthen the country’s RI program as a longer-term measure for disease control, whilst ensuring effective campaigns in the short term.
2.
Cutts, F. T.; Dansereau, E.; Ferrari, M. J.; Hanson, M.; McCarthy, K. A.; Metcalf, C. J. E.; Takahashi, S.; Tatem, A. J.; Thakkar, N.; Truelove, S.; Utazi, E.; Wesolowski, A.; Winter, A. K.
Using models to shape measles control and elimination strategies in low- and middle-income countries: A review of recent applications Journal Article
In: Vaccine, vol. 38, no. 5, pp. 979-992, 2020, ISSN: 0264-410X.
Abstract | Links | BibTeX | Tags: Elimination, Epidemiology, Mathematical models, Measles, Measles vaccination, Rubella
@article{CUTTS2020979,
title = {Using models to shape measles control and elimination strategies in low- and middle-income countries: A review of recent applications},
author = {F. T. Cutts and E. Dansereau and M. J. Ferrari and M. Hanson and K. A. McCarthy and C. J. E. Metcalf and S. Takahashi and A. J. Tatem and N. Thakkar and S. Truelove and E. Utazi and A. Wesolowski and A. K. Winter},
url = {https://www.sciencedirect.com/science/article/pii/S0264410X19315439},
doi = {https://doi.org/10.1016/j.vaccine.2019.11.020},
issn = {0264-410X},
year = {2020},
date = {2020-01-01},
journal = {Vaccine},
volume = {38},
number = {5},
pages = {979-992},
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.},
keywords = {Elimination, Epidemiology, Mathematical models, Measles, Measles vaccination, Rubella},
pubstate = {published},
tppubtype = {article}
}
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.
