@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}

}

@article{https://doi.org/10.1111/tmi.13460,

title = {Spatial inequalities in skilled attendance at birth in Ghana: a multilevel analysis integrating health facility databases with household survey data},

author = {Winfred Dotse-Gborgbortsi and Andrew J. Tatem and Victor Alegana and C. Edson Utazi and Corrine Warren Ruktanonchai and Jim Wright},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/tmi.13460},

doi = {https://doi.org/10.1111/tmi.13460},

year  = {2020},

date = {2020-01-01},

journal = {Tropical Medicine & International Health},

volume = {25},

number = {9},

pages = {1044-1054},

abstract = {Abstract Objective This study aimed at using survey data to predict skilled attendance at birth (SBA) across Ghana from healthcare quality and health facility accessibility. Methods Through a cross-sectional, observational study, we used a random intercept mixed effects multilevel logistic modelling approach to estimate the odds of having SBA and then applied model estimates to spatial layers to assess the probability of SBA at high-spatial resolution across Ghana. We combined data from the Demographic and Health Survey (DHS), routine birth registers, a service provision assessment of emergency obstetric care services, gridded population estimates and modelled travel time to health facilities. Results Within an hour's travel, 97.1% of women sampled in the DHS could access any health facility, 96.6% could reach a facility providing birthing services, and 86.2% could reach a secondary hospital. After controlling for characteristics of individual women, living in an urban area and close proximity to a health facility with high-quality services were significant positive determinants of SBA uptake. The estimated variance suggests significant effects of cluster and region on SBA as 7.1% of the residual variation in the propensity to use SBA is attributed to unobserved regional characteristics and 16.5% between clusters within regions. Conclusion Given the expansion of primary care facilities in Ghana, this study suggests that higher quality healthcare services, as opposed to closer proximity of facilities to women, is needed to widen SBA uptake and improve maternal health.},

keywords = {accouchement qualifié, EmONC, financement EmONC, GIS, maternal health, quality care, santé maternelle, skilled birth attendance, soins de qualité, temps de trajet, travel time},

pubstate = {published},

tppubtype = {article}

}

@article{UTAZI20203062,

title = {Geospatial variation in measles vaccine coverage through routine and campaign strategies in Nigeria: Analysis of recent household surveys},

author = {C. Edson Utazi and John Wagai and Oliver Pannell and Felicity T. Cutts and Dale A. Rhoda and Matthew J. Ferrari and Boubacar Dieng and Joseph Oteri and M. Carolina Danovaro-Holliday and Adeyemi Adeniran and Andrew J. Tatem},

url = {https://www.sciencedirect.com/science/article/pii/S0264410X20303017},

doi = {https://doi.org/10.1016/j.vaccine.2020.02.070},

issn = {0264-410X},

year  = {2020},

date = {2020-01-01},

journal = {Vaccine},

volume = {38},

number = {14},

pages = {3062-3071},

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.},

keywords = {Geospatial analysis, Measles vaccine, Post-campaign coverage survey, Routine immunization, Supplementary immunization activities},

pubstate = {published},

tppubtype = {article}

}

@article{Utazi2019,

title = {Mapping vaccination coverage to explore the effects of delivery mechanisms and inform vaccination strategies},

author = {C. Edson Utazi and Julia Thorley and Victor A. Alegana and Matthew J. Ferrari and Saki Takahashi and C. Jessica E. Metcalf and Justin Lessler and Felicity T. Cutts and Andrew J. Tatem},

url = {https://doi.org/10.1038/s41467-019-09611-1},

doi = {10.1038/s41467-019-09611-1},

issn = {2041-1723},

year  = {2019},

date = {2019-04-09},

journal = {Nature Communications},

volume = {10},

number = {1},

pages = {1633},

abstract = {The success of vaccination programs depends largely on the mechanisms used in vaccine delivery. National immunization programs offer childhood vaccines through fixed and outreach services within the health system and often, additional supplementary immunization activities (SIAs) are undertaken to fill gaps and boost coverage. Here, we map predicted coverage at 1thinspacetexttimesthinspace1thinspacekm spatial resolution in five low- and middle-income countries to identify areas that are under-vaccinated via each delivery method using Demographic and Health Surveys data. We compare estimates of the coverage of the third dose of diphtheria-tetanus-pertussis-containing vaccine (DTP3), which is typically delivered through routine immunization (RI), with those of measles-containing vaccine (MCV) for which SIAs are also undertaken. We find that SIAs have boosted MCV coverage in some places, but not in others, particularly where RI had been deficient, as depicted by DTP coverage. The modelling approaches outlined here can help to guide geographical prioritization and strategy design.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1177/0962280218797362,

title = {A spatial regression model for the disaggregation of areal unit based data to high-resolution grids with application to vaccination coverage mapping},

author = {CE Utazi and J Thorley and VA Alegana and MJ Ferrari and K Nilsen and S Takahashi and CJE Metcalf and J Lessler and AJ Tatem},

url = {https://doi.org/10.1177/0962280218797362},

doi = {10.1177/0962280218797362},

year  = {2019},

date = {2019-01-01},

journal = {Statistical Methods in Medical Research},

volume = {28},

number = {10-11},

pages = {3226-3241},

abstract = {The growing demand for spatially detailed data to advance the Sustainable Development Goals agenda of ‘leaving no one behind’ has resulted in a shift in focus from aggregate national and province-based metrics to small areas and high-resolution grids in the health and development arena. Vaccination coverage is customarily measured through aggregate-level statistics, which mask fine-scale heterogeneities and ‘coldspots’ of low coverage. This paper develops a methodology for high-resolution mapping of vaccination coverage using areal data in settings where point-referenced survey data are inaccessible. The proposed methodology is a binomial spatial regression model with a logit link and a combination of covariate data and random effects modelling two levels of spatial autocorrelation in the linear predictor. The principal aspect of the model is the melding of the misaligned areal data and the prediction grid points using the regression component and each of the conditional autoregressive and the Gaussian spatial process random effects. The Bayesian model is fitted using the INLA-SPDE approach. We demonstrate the predictive ability of the model using simulated data sets. The results obtained indicate a good predictive performance by the model, with correlations of between 0.66 and 0.98 obtained at the grid level between true and predicted values. The methodology is applied to predicting the coverage of measles and diphtheria-tetanus-pertussis vaccinations at 5 × 5 km2 in Afghanistan and Pakistan using subnational Demographic and Health Surveys data. The predicted maps are used to highlight vaccination coldspots and assess progress towards coverage targets to facilitate the implementation of more geographically precise interventions. The proposed methodology can be readily applied to wider disaggregation problems in related contexts, including mapping other health and development indicators.},

note = {PMID: 30229698},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Utazie000611,

title = {Geographic coverage of demographic surveillance systems for characterising the drivers of childhood mortality in sub-Saharan Africa},

author = {C Edson Utazi and Sujit K Sahu and Peter M Atkinson and Natalia Tejedor-Garavito and Christopher T Lloyd and Andrew J Tatem},

url = {https://gh.bmj.com/content/3/2/e000611},

doi = {10.1136/bmjgh-2017-000611},

year  = {2018},

date = {2018-01-01},

journal = {BMJ Global Health},

volume = {3},

number = {2},

publisher = {BMJ Specialist Journals},

abstract = {A major focus of international health and development goals is the reduction of mortality rates in children under 5 years of age. Achieving this requires understanding the drivers of mortality and how they vary geographically to facilitate the targeting and prioritisation of appropriate interventions. Much of our knowledge on the causes of, and trends in, childhood mortality come from longitudinal demographic surveillance sites, with a renewed focus recently on the establishment and growth of networks of sites from which standardised outputs can facilitate broader understanding of processes. To ensure that the collective outputs from surveillance sites can be used to derive a comprehensive understanding and monitoring system for driving policy on tackling childhood mortality, confidence is needed that existing and planned networks of sites are providing a reliable and representative picture of the geographical variation in factors associated with mortality. Here, we assembled subnational data on childhood mortality as well as key factors known to be associated with it from household surveys in 27 sub-Saharan African countries. We then mapped the locations of existing longitudinal demographic surveillance sites to assess the extent of current coverage of the range of factors, identifying where gaps exist. The results highlight regions with unique combinations of factors associated with childhood mortality that are poorly represented by the current distribution of sites, such as southern Mali, central Nigeria and southern Zambia. Finally, we determined where the establishment of new surveillance systems could improve coverage.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{UTAZI20181583,

title = {High resolution age-structured mapping of childhood vaccination coverage in low and middle income countries},

author = {C. Edson Utazi and Julia Thorley and Victor A. Alegana and Matthew J. Ferrari and Saki Takahashi and C. Jessica E. Metcalf and Justin Lessler and Andrew J. Tatem},

url = {https://www.sciencedirect.com/science/article/pii/S0264410X18301944},

doi = {https://doi.org/10.1016/j.vaccine.2018.02.020},

issn = {0264-410X},

year  = {2018},

date = {2018-01-01},

journal = {Vaccine},

volume = {36},

number = {12},

pages = {1583-1591},

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.},

keywords = {Bayesian geostatistics, Coverage heterogeneities, Demographic and Health Surveys, Measles vaccine},

pubstate = {published},

tppubtype = {article}

}

@article{UTAZI2016161,

title = {A probabilistic predictive Bayesian approach for determining the representativeness of health and demographic surveillance networks},

author = {C. Edson Utazi and Sujit K. Sahu and Peter M. Atkinson and Natalia Tejedor and Andrew J. Tatem},

url = {https://www.sciencedirect.com/science/article/pii/S2211675316300240},

doi = {https://doi.org/10.1016/j.spasta.2016.05.006},

issn = {2211-6753},

year  = {2016},

date = {2016-01-01},

journal = {Spatial Statistics},

volume = {17},

pages = {161-178},

abstract = {Health and demographic surveillance systems, formed into networks of sites, are increasingly being established to circumvent unreliable national civil registration systems for estimates of mortality and its determinants in low income countries. Health outcomes, as measured by morbidity and mortality, generally correlate strongly with socioeconomic and environmental characteristics. Therefore, to enable comparison between sites, understand which sites can be grouped and where additional sites would aid understanding of rates and determinants, determining the environmental and socioeconomic representativeness of networks becomes important. This paper proposes a full Bayesian methodology for assessing current representativeness and consequently, identification of future sites, focusing on the INDEPTH network in sub-Saharan Africa as an example. Using socioeconomic and environmental data from the current network of 39 sites, we develop a multi-dimensional finite Gaussian mixture model for clustering the existing sites. Using the fitted model we obtain the posterior predictive probability distribution for cluster membership of each 1×1 km grid cell in Africa. The maximum of the posterior predictive probability distribution for each grid cell is proposed as the criterion for representativeness of the network for that particular grid cell. We demonstrate the conceptual superiority and practical appeal of the proposed Bayesian probabilistic method over previously applied deterministic clustering methods. As an example of the potential utility and application of the method, we also suggest optimal site selection methods for possible additions to the network.},

keywords = {Bayesian inference, BIC, Central clustering, Finite Gaussian mixture model, Gibbs sampling, Predictive clustering},

pubstate = {published},

tppubtype = {article}

}