@article{nokey,

title = {Social cartography and satellite-derived building coverage for post-census population estimates in difficult-to-access regions of Colombia},

author = {Lina Maria Sanchez-Cespedes and Douglas Ryan Leasure and Natalia Tejedor-Garavito and Glenn Harry Amaya Cruz and Gustavo Adolfo Garcia Velez and Andryu Enrique Mendoza and Yenny Andrea Marín Salazar and Thomas Esch and Andrew J. Tatem and Mariana Ospina Bohórquez},

url = {https://doi.org/10.1080/00324728.2023.2190151},

doi = {10.1080/00324728.2023.2190151},

year  = {2023},

date = {2023-03-28},

journal = {Population Studies},

pages = {1-18},

abstract = {Effective government services rely on accurate population numbers to allocate resources. In Colombia and globally, census enumeration is challenging in remote regions and where armed conflict is occurring. During census preparations, the Colombian National Administrative Department of Statistics conducted social cartography workshops, where community representatives estimated numbers of dwellings and people throughout their regions. We repurposed this information, combining it with remotely sensed buildings data and other geospatial data. To estimate building counts and population sizes, we developed hierarchical Bayesian models, trained using nearby full-coverage census enumerations and assessed using 10-fold cross-validation. We compared models to assess the relative contributions of community knowledge, remotely sensed buildings, and their combination to model fit. The Community model was unbiased but imprecise; the Satellite model was more precise but biased; and the Combination model was best for overall accuracy. Results reaffirmed the power of remotely sensed buildings data for population estimation and highlighted the value of incorporating local knowledge.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Impact of mobile health on maternal and child health service utilization and continuum of care in Northern Ghana},

author = {Abdul Ganiyu Kantamah and Duah Dwomoh and Susan Ama Amuasi and Winfred Dotse-Gborgbortsi and Chrysantus Kubio and Edward Anane Apraku and Jonas Kolong Timbire and Justice Nonvignon},

url = {https://doi.org/10.1038/s41598-023-29683-w

},

doi = {10.1038/s41598-023-29683-w},

year  = {2023},

date = {2023-02-21},

journal = {Scientific Reports},

volume = {13},

number = {3004},

abstract = {Maternal and child mortality are of public health concern. Most of these deaths occur in rural communities of developing countries. Technology for maternal and child health (T4MCH) is an intervention introduced to increase Maternal and Child Health (MCH) services utilization and continuum of care in some health facilities across Ghana. The objective of this study is to assess the impact of T4MCH intervention on MCH services utilization and continuum of care in the Sawla-Tuna-Kalba District in the Savannah Region of Ghana. This is a quasi-experimental study with a retrospective review of records of MCH services of women who attended antenatal services in some selected health centers in the Bole (comparison district) and Sawla-Tuna-Kalba (intervention district) of the Savannah region, Ghana. A total of 469 records were reviewed, 263 in Bole and 206 in Sawla-Tuna-Kalba. A multivariable modified Poisson and logistic regression models with augmented inverse-probability weighted regression adjustment based on propensity scores were used to quantify the impact of the intervention on service utilization and continuum of care. The implementation of T4MCH intervention increased antenatal care attendance, facility delivery, postnatal care and continuum of care by 18 percentage points (ppts) [95% CI - 17.0, 52.0], 14 ppts [95% CI 6.0%, 21.0%], 27 ppts [95% CI 15.0, 26.0] and 15.0 ppts [95% CI 8.0, 23.0] respectively compared to the control districts. The study showed that T4MCH intervention improved antenatal care, skilled delivery, postnatal services utilization, and continuum of care in health facilities in the intervention district. The intervention is recommended for a scale-up in other rural areas of Northern Ghana and the West-African sub-region.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {A subnational reproductive, maternal, newborn, child, and adolescent health and development atlas of India},

author = {Carla Pezzulo and Natalia Tejedor-Garavito and Ho Man Theophilus Chan and Ilda Dreoni and David Kerr and Samik Ghosh and Amy Bonnie and Maksym Bondarenko and Mihretab Salasibew and Andrew J. Tatem },

url = {https://www.nature.com/articles/s41597-023-01961-2},

doi = {10.1038/s41597-023-01961-2},

year  = {2023},

date = {2023-02-10},

journal = {Scientific Data},

volume = {10},

issue = {86},

abstract = {Understanding the fine scale and subnational spatial distribution of reproductive, maternal, newborn, child, and adolescent health and development indicators is crucial for targeting and increasing the efficiency of resources for public health and development planning. National governments are committed to improve the lives of their people, lift the population out of poverty and to achieve the Sustainable Development Goals. We created an open access collection of high resolution gridded and district level health and development datasets of India using mainly the 2015–16 National Family Health Survey (NFHS-4) data, and provide estimates at higher granularity than what is available in NFHS-4, to support policies with spatially detailed data. Bayesian methods for the construction of 5 km × 5 km high resolution maps were applied for a set of indicators where the data allowed (36 datasets), while for some other indicators, only district level data were produced. All data were summarised using the India district administrative boundaries. In total, 138 high resolution and district level datasets for 28 indicators were produced and made openly available.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Exploring the use of Sentinel-2 datasets and environmental variables to model wheat crop yield in smallholder arid and semi-arid farming systems},

author = {Sarchil Hama Qader and Chigozie Edson Utazi and Rhorom Priyatikanto and Peshawa Najmaddin and Emad Omer Hama-Ali and Nabaz R. Khwarahm and Andrew J. Tatem and Jadu Dash},

url = {https://doi.org/10.1016/j.scitotenv.2023.161716},

doi = {10.1016/j.scitotenv.2023.161716},

year  = {2023},

date = {2023-01-24},

urldate = {2023-01-24},

journal = {Science of The Total Environment},

volume = {869},

number = {161716},

abstract = {Low levels of agricultural productivity are associated with the persistence of food insecurity, poverty, and other socio-economic stresses. Mapping and monitoring agricultural dynamics and production in real-time at high spatial resolution are essential for ensuring food security and shaping policy interventions. However, an accurate yield estimation might be challenging in some arid and semi-arid regions since input datasets are generally scarce, and access is restricted due to security challenges. This work examines how well Sentinel-2 satellite sensor-derived data, topographic and climatic variables, can be used as covariates to accurately model and predict wheat crop yield at the farm level using statistical models in low data settings of arid and semi-arid regions, using Sulaimani governorate in Iraq as an example. We developed a covariate selection procedure that assessed the correlations between the covariates and their relationships with wheat crop yield. Potential non-linear relationships were investigated in the latter case using regression splines. In the absence of substantial non-linear relationships between the covariates and crop yield, and residual spatial autocorrelation, we fitted a Bayesian multiple linear regression model to model and predict crop yield at 10 m resolution. Out of the covariates tested, our results showed significant relationships between crop yield and mean cumulative NDVI during the growing season, mean elevation, mean end of the season, mean maximum temperature and mean the start of the season at the farm level. For in-sample prediction, we estimated an R2 value of 51 % for the model, whereas for out-of-sample prediction, this was 41 %, both of which indicate reasonable predictive performance. The calculated root-mean-square error for out-of-sample prediction was 69.80, which is less than the standard deviation of 89.23 for crop yield, further showing that the model performed well by reducing prediction variability. Besides crop yield estimates, the model produced uncertainty metrics at 10 m resolution. Overall, this study showed that Sentinel-2 data can be valuable for upscaling field measurement of crop yield in arid and semi-arid regions. In addition, the environmental covariates can strengthen the model predictive power. The method may be applicable in other areas with similar environments, particularly in conflict zones, to increase the availability of agricultural statistics.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Quality of maternal healthcare and travel time influence birthing service utilisation in Ghanaian health facilities: a geographical analysis of routine health data},

author = {Winfred Dotse-Gborgbortsi and Andrew J Tatem and Zoe Matthews and Victor A Alegana and Anthony Ofosu and Jim A Wright},

url = {http://dx.doi.org/10.1136/bmjopen-2022-066792},

doi = {10.1136/bmjopen-2022-066792},

year  = {2023},

date = {2023-01-18},

journal = {BMJ Open},

volume = {13},

issue = {1},

abstract = {Objectives: To investigate how the quality of maternal health services and travel times to health facilities affect birthing service utilisation in Eastern Region, Ghana.



Design: The study is a cross-sectional spatial interaction analysis of birth service utilisation patterns. Routine birth data were spatially linked to quality care, service demand and travel time data.



Setting: 131 Health facilities (public, private and faith-based) in 33 districts in Eastern Region, Ghana.



Participants: Women who gave birth in health facilities in the Eastern Region, Ghana in 2017.



Outcome measures: The count of women giving birth, the quality of birthing care services and the geographic coverage of birthing care services.



Results: As travel time from women’s place of residence to the health facility increased up to two (2) hours, the utilisation rate markedly decreased. Higher quality of maternal health services haves a larger, positive effect on utilisation rates than service proximity. The quality of maternal health services was higher in hospitals than in primary care facilities. Most women (88.6%) travelling via mechanised transport were within two2 hours of any birthing service. The majority (56.2%) of women were beyond the two2 -hour threshold of critical comprehensive emergency obstetric and newborn care (CEmONC) services. Few CEmONC services were in urban centres, disadvantaging rural populations.



Conclusions: To increase birthing service utilisation in Ghana, higher quality health facilities should be located closer to women, particularly in rural areas. Beyond Ghana, routinely collected birth records could be used to understand the interaction of service proximity and quality.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Prevalence of common adverse events experienced following COVID‐19 vaccination and its associated factors in Ghana: Cross‐sectional study design},

author = {Naa A. A. Boi-Dsane and Bartholomew Dzudzor and Yakubu Alhassan and Justice M. K. Aheto },

url = {https://doi.org/10.1002/hsr2.1012

},

doi = {10.1002/hsr2.1012},

year  = {2022},

date = {2022-12-24},

journal = {Health Science Reports},

volume = {6},

number = {e1012},

issue = {1},

abstract = {The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (COVID-19) pandemic devasted the general life of people and various human activities across the globe, and Ghana is of no exception. This led to development of vaccines within record time to combat morbidity and mortality associated with the virus. In Ghana, COVID-19 vaccines were introduced in addition to existing COVID-19 protocols. However, the vaccines have adverse events among those who received them. In this study, we determined the prevalence of some common adverse events of the COVID-19 vaccines and its associated sociodemographic factors in Ghana.

An online snowball cross-sectional survey was conducted between April and June 2021 among 240 people who had taken at least one dose of any of the COVID-19 vaccines approved in Ghana. The penalized binary logistic regression model was used to assess the factors associated with experience of at least one adverse event and the experience of number of adverse events using Stata version 16.

Among the 240 participants, 88.2% had experienced at least one adverse event. The most common adverse event after the first dose was pain at injection site (65.8%), headache (57.5%), tiredness (55.8%), fever (51.7%), chills (39.6%), and muscle pains (38.3%). Experience of adverse events was 16 times higher among those who took their vaccines in Ghana (adjusted odd ratio [AOR]: 16.2, 95% confidence interval [CI]: 1.98–132.56, p = 0.009), 94% less among those who took AstraZeneca (India) compared to AstraZeneca (Oxford) (AOR: 0.06, 95% CI: 0.01–0.37, p = 0.002) and 86% less among 40–49 years compared with less than 30 years old (AOR: 0.14, 95% CI: 0.03–0.58, p = 0.007).

Pain at the injection site, headache, tiredness, fever, chills, and muscle pains were the most frequently reported adverse events. The study identified country of vaccination, country of origin of AstraZeneca vaccine and age to be associated with adverse events of vaccination.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Spatially Varying Intergenerational Changes in the Prevalence of Female Genital Mutilation/Cutting in Nigeria: Lessons Learnt from a Recent Household Survey},

author = {Chibuzor Christopher Nnanatu and Adeniyi Francis Fagbamigbe and Emmanuel Afuecheta and Chigozie Edson Utazi},

editor = {Vikram Aditya},

url = {https://doi.org/10.1007/s12061-022-09497-5

},

doi = {10.1007/s12061-022-09497-5},

year  = {2022},

date = {2022-12-20},

urldate = {2023-12-20},

journal = {Applied Spatial Analysis and Policy},

abstract = {Considering the concerted investments in anti-female genital mutilation/cutting (FGM/C) campaigns championed by the Nigerian government and non-governmental organizations, research findings suggest that reduction in intergenerational (mother-to-daughter) prevalence of FGM/C in Nigeria has been very slow. What can we learn from the 2018 Nigerian Demographic and Health Survey (2018 NDHS) about the roles of the key drivers of mother-to-daughter FGM/C prevalence in Nigeria? Here, drawing upon the 2018 NDHS dataset, we provided a context-specific study on the geographical patterns and the enabling factors of intergenerational trends in FGM/C among Nigerian women aged 15 – 49 years and their daughters aged 0 – 14 years. Using Bayesian semi-parametric geo-additive regression model, we simultaneously controlled for the effects of individual-level, community-level and unobserved geographical factors. We learnt that although there has been an overall decline in mother-to-daughter prevalence of FGM/C, the practice persists in Nigeria largely due to geographical location and social norm related factors – risk was high among daughters of circumcised women and daughters of women who supported the continuation of FGM/C. We identified Kano, Kaduna, Imo and Bauchi states as the hotspots and there was an increased risk of FGM/C among daughters of women who lived in the neigbouring states of Jigawa and Yobe. Daughters of circumcised women were about 2.7 times more likely to be cut. We recommend the development of tailored community-level interventions targeting circumcised women in the hotspot states and their neighbours to ensure a total eradication of female circumcision in Nigeria by the year 2030.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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 = {Utazi CE and Aheto JM and Wigley A and Tejedor-Garavito N and Bonnie A and Nnanatu CC and Wagai J and Williams C and Setayesh H and Tatem AJ and Cutts FT},

url = {https://doi.org/10.1016/j.vaccine.2022.11.026

},

doi = {10.1016/j.vaccine.2022.11.026},

year  = {2022},

date = {2022-11-19},

urldate = {2023-11-19},

journal = {Vaccine},

volume = {41},

issue = {1},

pages = {170-181},

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 = {},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{nokey,

title = {High-resolution estimates of social distancing feasibility, mapped for urban areas in sub-Saharan Africa},

author = {Heather R. Chamberlain and Attila N. Lazar and Andrew J. Tatem },

url = {https://doi.org/10.1038/s41597-022-01799-0

},

doi = {10.1038/s41597-022-01799-0},

year  = {2022},

date = {2022-11-18},

urldate = {2023-11-18},

journal = {Scientific Data},

volume = {9},

number = {711 (2022)},

abstract = {Social distancing has been widely-implemented as a public health measure during the COVID-19 pandemic. Despite widespread application of social distancing guidance, the feasibility of people adhering to such guidance varies in different settings, influenced by population density, the built environment and a range of socio-economic factors. Social distancing constraints however have only been identified and mapped for limited areas. Here, we present an ease of social distancing index, integrating metrics on urban form and population density derived from new multi-country building footprint datasets and gridded population estimates. The index dataset provides estimates of social distancing feasibility, mapped at high-resolution for urban areas across 50 countries in sub-Saharan Africa.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {High-resolution estimates of social distancing feasibility, mapped for urban areas in sub-Saharan Africa},

author = {Heather R. Chamberlain and Attila N. Lazar and Andrew J. Tatem },

doi = {10.1038/s41597-022-01799-0},

year  = {2022},

date = {2022-11-18},

journal = {Scientific Data},

volume = {9},

number = {711},

abstract = {Social distancing has been widely-implemented as a public health measure during the COVID-19 pandemic. Despite widespread application of social distancing guidance, the feasibility of people adhering to such guidance varies in different settings, influenced by population density, the built environment and a range of socio-economic factors. Social distancing constraints however have only been identified and mapped for limited areas. Here, we present an ease of social distancing index, integrating metrics on urban form and population density derived from new multi-country building footprint datasets and gridded population estimates. The index dataset provides estimates of social distancing feasibility, mapped at high-resolution for urban areas across 50 countries in sub-Saharan Africa.},

keywords = {Africa, covid-19, NPIs},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Geographic inequalities in health intervention coverage – mapping the composite coverage index in Peru using geospatial modelling},

author = {Leonardo Z. Ferreira and C. Edson Utazi and Luis Huicho and Kristine Nilsen and Fernando P. Hartwig and Andrew J. Tatem and Aluisio J. D. Barros},

url = {https://doi.org/10.1186/s12889-022-14371-7

},

doi = {10.1186/s12889-022-14371-7},

year  = {2022},

date = {2022-11-17},

urldate = {2023-11-17},

journal = {BMC Public Health 22},

volume = {22},

number = {2104 (2022)},

abstract = {The composite coverage index (CCI) provides an integrated perspective towards universal health coverage in the context of reproductive, maternal, newborn and child health. Given the sample design of most household surveys does not provide coverage estimates below the first administrative level, approaches for achieving more granular estimates are needed. We used a model-based geostatistical approach to estimate the CCI at multiple resolutions in Peru.

We generated estimates for the eight indicators on which the CCI is based for the departments, provinces, and areas of 5 × 5 km of Peru using data from two national household surveys carried out in 2018 and 2019 plus geospatial covariates. Bayesian geostatistical models were fit using the INLA-SPDE approach. We assessed model fit using cross-validation at the survey cluster level and by comparing modelled and direct survey estimates at the department-level.

CCI coverage in the provinces along the coast was consistently higher than in the remainder of the country. Jungle areas in the north and east presented the lowest coverage levels and the largest gaps between and within provinces. The greatest inequalities were found, unsurprisingly, in the largest provinces where populations are scattered in jungle territory and are difficult to reach.

Our study highlighted provinces with high levels of inequality in CCI coverage indicating areas, mostly low-populated jungle areas, where more attention is needed. We also uncovered other areas, such as the border with Bolivia, where coverage is lower than the coastal provinces and should receive increased efforts. More generally, our results make the case for high-resolution estimates to unveil geographic inequities otherwise hidden by the usual levels of survey representativeness.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Global estimates of pregnancies at risk of Plasmodium falciparum and Plasmodium vivax infection in 2020 and changes in risk patterns since 2000},

author = {Georgia R. Gore-Langton and Jorge Canoand Hope Simpson and Andrew Tatemand Natalia Tejedor-Garavito and Adelle Wigley and Alessandra Carioli and Peter Gething and Daniel J. Weiss and Daniel Chandramohan and Patrick G. T. Walker and Matthew E. Cairns and R. Matthew Chico},

url = {https://journals.plos.org/globalpublichealth/article?id=10.1371/journal.pgph.0001061},

doi = {10.1371/journal.pgph.0001061},

year  = {2022},

date = {2022-11-09},

urldate = {2023-11-09},

journal = {PLOS Global Public Health},

abstract = {Women are at risk of severe adverse pregnancy outcomes attributable to Plasmodium spp. infection in malaria-endemic areas. Malaria control efforts since 2000 have aimed to reduce this burden of disease.

We used data from the Malaria Atlas Project and WorldPop to calculate global pregnancies at-risk of Plasmodium spp. infection. We categorised pregnancies as occurring in areas of stable and unstable P. falciparum and P. vivax transmission. We further stratified stable endemicity as hypo-endemic, meso-endemic, hyper-endemic, or holo-endemic, and estimated pregnancies at risk in 2000, 2005, 2010, 2015, 2017, and 2020.

In 2020, globally 120.4M pregnancies were at risk of P. falciparum, two-thirds (81.0M, 67.3%) were in areas of stable transmission; 85 2M pregnancies were at risk of P. vivax, 93.9% (80.0M) were in areas of stable transmission. An estimated 64.6M pregnancies were in areas with both P. falciparum and P. vivax transmission. The number of pregnancies at risk of each of P. falciparum and P. vivax worldwide decreased between 2000 and 2020, with the exception of sub-Saharan Africa, where the total number of pregnancies at risk of P. falciparum increased from 37 3M in 2000 to 52 4M in 2020.

Historic investments in malaria control have reduced the number of women at risk of malaria in pregnancy in all endemic regions except sub-Saharan Africa. Population growth in Africa has outpaced reductions in malaria prevalence. Interventions that reduce the risk of malaria in pregnancy are needed as much today as ever.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {The Population Seen from Space: When Satellite Images Come to the Rescue of the Census},

author = {Edith Darin and Mathias Kuépié and Hervé Bassinga and Gianluca Boo and Andrew J. Tatem,},

url = {https://www.cairn-int.info/journal-population-2022-3-page-437.htm},

year  = {2022},

date = {2022-10-30},

urldate = {2023-10-30},

journal = { Population},

volume = {77},

issue = {3},

pages = {437 - 464},

abstract = {Today, developing public policies requires precise knowledge of the size and characteristics of the population. To respond to this need, national statistical offices must perform counts. National censuses are the foundational data collection operations on the number of inhabitants in each country. The national population is the denominator for many development indicators (Carr-Hill, 2014). Reliably and regularly estimating this denominator is important in all domains (land use planning and development, education, democratic representation, social protection, health, etc.) and at various geographical scales (United Nations, 2017). While traditionally the publication of population sizes is organized by administrative units such as provinces or regions, this format leads to spatial discontinuities that can prove arbitrary and that do not reflect other ways of dividing a territory according to criteria such as employment (employment basin) or health (healthcare districts .},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Estimates of the number and distribution of zero-dose and under-immunised children across remote-rural, urban, and conflict-affected settings in low and middle-income countries},

author = {Adelle Wigley and Josh Lorin and Dan Hogan and C. Edson Utazi and Brittany Hagedorn and Emily Dansereau and Andrew J. Tatem and Natalia Tejedor-Garavito},

doi = {10.1371/journal.pgph.0001126},

year  = {2022},

date = {2022-10-26},

urldate = {2022-10-26},

journal = {PLOS Global Public Health},

volume = {2},

issue = {10},

pages = {e0001126},

abstract = {While there has been great success in increasing the coverage of new childhood vaccines globally, expanding routine immunization to reliably reach all children and communities has proven more challenging in many low- and middle-income countries. Achieving this requires vaccination strategies and interventions that identify and target those unvaccinated, guided by the most current and detailed data regarding their size and spatial distribution. Through the integration and harmonisation of a range of geospatial data sets, including population, vaccination coverage, travel-time, settlement type, and conflict locations. We estimated the numbers of children un- or under-vaccinated for measles and diphtheria-tetanus-pertussis, within remote-rural, urban, and conflict-affected locations. We explored how these numbers vary both nationally and sub-nationally, and assessed what proportions of children these categories captured, for 99 lower- and middle-income countries, for which data was available. We found that substantial heterogeneities exist both between and within countries. Of the total 14,030,486 children unvaccinated for DTP1, over 11% (1,656,757) of un- or under-vaccinated children were in remote-rural areas, more than 28% (2,849,671 and 1,129,915) in urban and peri-urban areas, and up to 60% in other settings, with nearly 40% found to be within 1-hour of the nearest town or city (though outside of urban/peri-urban areas). Of the total number of those unvaccinated, we estimated between 6% and 15% (826,976 to 2,068,785) to be in conflict-affected locations, based on either broad or narrow definitions of conflict. Our estimates provide insights into the inequalities in vaccination coverage, with the distributions of those unvaccinated varying significantly by country, region, and district. We demonstrate the need for further inquiry and characterisation of those unvaccinated, the thresholds used to define these, and for more country-specific and targeted approaches to defining such populations in the strategies and interventions used to reach them.},

keywords = {conflict, LMICs, vaccination, zero dose},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines},

author = {Chigozie Edson Utazi and Justice Moses K. Aheto and Ho Man Theophilus Chan and Andrew J. Tatem and Sujit K. Sahu},

doi = {10.1002/sim.9586},

year  = {2022},

date = {2022-09-21},

urldate = {2022-09-21},

journal = {Statistics in Medicine},

abstract = {Many vaccines are often administered in multiple doses to boost their effectiveness. In the case of childhood vaccines, the coverage maps of the doses and the differences between these often constitute an evidence base to guide investments in improving access to vaccination services and health system performance in low and middle-income countries. A major problem often encountered when mapping the coverage of multi-dose vaccines is the need to ensure that the coverage maps decrease monotonically with successive doses. 

The fully Bayesian model is implemented using the INLA and SPDE approaches. Using a simulation study, we find that both approaches perform comparably for out-of-sample estimation under varying point-level sample size distributions. We apply the methodology to map the coverage of the three doses of diphtheria-tetanus-pertussis vaccine using data from the 2018 Nigeria Demographic and Health Survey. The coverage maps produced using both approaches are almost indistinguishable, although the CP approach yielded more precise estimates on average in this application. We also provide estimates of zero-dose children and the dropout rates between the doses. The methodology is straightforward to implement and can be applied to other vaccines and geographical contexts.},

keywords = {Bayesian inference, vaccination},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Distance is “a big problem”: a geographic analysis of reported and modelled proximity to maternal health services in Ghana},

author = {Dotse-Gborgbortsi, Winfred and Nilsen, Kristine and Ofosu, Anthony and Matthews, Zoe and Tejedor-Garavito, Natalia and Wright, Jim and Tatem, Andrew J},

doi = {10.1186/s12884-022-04998-0},

year  = {2022},

date = {2022-08-31},

urldate = {2022-08-31},

journal = {BMC Pregnancy and Childbirth},

volume = {22},

number = {672},

abstract = {Geographic barriers to healthcare are associated with adverse maternal health outcomes. Modelling travel times using georeferenced data is becoming common in quantifying physical access. Multiple Demographic and Health Surveys ask women about distance-related problems accessing healthcare, but responses have not been evaluated against modelled travel times. This cross-sectional study aims to compare reported and modelled distance by socio-demographic characteristics and evaluate their relationship with skilled birth attendance. Also, we assess the socio-demographic factors associated with self-reported distance problems in accessing healthcare.



Distance problems and socio-demographic characteristics reported by 2210 women via the 2017 Ghana Maternal Health Survey were included in analysis. Geospatial methods were used to model travel time to the nearest health facility using roads, rivers, land cover, travel speeds, cluster locations and health facility locations. Logistic regressions were used to predict skilled birth attendance and self-reported distance problems.



Women reporting distance challenges accessing healthcare had significantly longer travel times to the nearest health facility. Poverty significantly increased the odds of reporting challenges with distance. In contrast, living in urban areas and being registered with health insurance reduced the odds of reporting distance challenges. Women with a skilled attendant at birth, four or more skilled antenatal appointments and timely skilled postnatal care had shorter travel times to the nearest health facility. Generally, less educated, poor, rural women registered with health insurance had longer travel times to their nearest health facility. After adjusting for socio-demographic characteristics, the following factors increased the odds of skilled birth attendance: wealth, health insurance, higher education, living in urban areas, and completing four or more antenatal care appointments.



Studies relying on modelled travel times to nearest facility should recognise the differential impact of geographic access to healthcare on poor rural women. Physical access to maternal health care should be scaled up in rural areas and utilisation increased by improving livelihoods.},

keywords = {Ghana, maternal health, travel time},

pubstate = {published},

tppubtype = {article}

}

@article{,

title = {Assessing spread risk of COVID-19 within and beyond China in early 2020},

author = {Lai, Shengjie and Bogoch, Isaac I and Ruktanonchai, Nick W and Watts, Alexander and Lu, Xin and Yang, Weizhong and Yu, Hongjie and Khan, Kamran and Tatem, Andrew J},

doi = {10.1016/j.dsm.2022.08.004},

year  = {2022},

date = {2022-08-26},

urldate = {2022-08-26},

journal = {Data Science and Management},

abstract = {A novel coronavirus emerged in Wuhan in late 2019 and has caused the COVID-19 pandemic announced by the World Health Organization on March 12, 2020. This study was originally conducted in January 2020 to estimate the potential risk and geographic range of COVID-19 spread within and beyond China at the early stage of the pandemic. A series of connectivity and risk analyses based on domestic and international travel networks were conducted using historical aggregated mobile phone data and air passenger itinerary data. We found that the cordon sanitaire of Wuhan was likely to have occurred during the latter stages of peak population numbers leaving the city, with travellers departing into neighbouring cities and other megacities in China. We estimated that 59,912 air passengers, of which 834 (95% uncertainty interval: 478–1349) had COVID-19 infection, travelled from Wuhan to 382 cities outside of mainland China during the two weeks prior to the city’s lockdown. Most of these destinations were located in Asia, but major hubs in Europe, the US and Australia were also prominent, with a strong correlation seen between the predicted risks of importation and the number of imported cases found. Given the limited understanding of emerging infectious diseases in the very early stages of outbreaks, our approaches and findings in assessing travel patterns and risk of transmission can help guide public health preparedness and intervention design for new COVID-19 waves caused by variants of concern and future pandemics to effectively limit transmission beyond its initial extent.},

keywords = {China, covid-19, Mobility},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Exploring methods for mapping seasonal population changes using mobile phone data},

author = {D. Woods and A. Cunningham and C. E. Utazi and M. Bondarenko and L. Shengjie and G. E. Rogers and P. Koper and C. W. Ruktanonchai and E. zu Erbach-Schoenberg and A. J. Tatem and J. Steele and A. Sorichetta},

doi = {10.1057/s41599-022-01256-8},

year  = {2022},

date = {2022-07-28},

urldate = {2022-07-28},

journal = {Humanities and Social Sciences Communications},

number = {247},

abstract = {Data accurately representing the population distribution at the subnational level within countries is critical to policy and decision makers for many applications. Call data records (CDRs) have shown great promise for this, providing much higher temporal and spatial resolutions compared to traditional data sources. For CDRs to be integrated with other data and in order to effectively inform and support policy and decision making, mobile phone user must be distributed from the cell tower level into administrative units. This can be done in different ways and it is often not considered which method produces the best representation of the underlying population distribution. Using anonymised CDRs in Namibia between 2011 and 2013, four distribution methods were assessed at multiple administrative unit levels. Estimates of user density per administrative unit were ranked for each method and compared against the corresponding census-derived population densities, using Kendall’s tau-b rank tests. Seasonal and trend decomposition using Loess (STL) and multivariate clustering was subsequently used to identify patterns of seasonal user variation and investigate how different distribution methods can impact these. Results show that the accuracy of the results of each distribution method is influenced by the considered administrative unit level. While marginal differences between methods are displayed at “coarser” level 1, the use of mobile phone tower ranges provided the most accurate results for Namibia at finer levels 2 and 3. The use of STL is helpful to recognise the impact of the underlying distribution methods on further analysis, with the degree of consensus between methods decreasing as spatial scale increases. Multivariate clustering delivers valuable insights into which units share a similar seasonal user behaviour. The higher the number of prescribed clusters, the more the results obtained using different distribution methods differ. However, two major seasonal patterns were identified across all distribution methods, levels and most cluster numbers: (a) units with a 15% user decrease in August and (b) units with a 20–30% user increase in December. Both patterns are likely to be partially linked to school holidays and people going on vacation and/or visiting relatives and friends. This study highlights the need and importance of investigating CDRs in detail before conducting subsequent analysis like seasonal and trend decomposition. In particular, CDRs need to be investigated both in terms of their area and population coverage, as well as in relation to the appropriate distribution method to use based on the spatial scale of the specific application. The use of inappropriate methods can change observed seasonal patterns and impact the derived conclusions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {How accurate are WorldPop-Global-Unconstrained gridded population data at the cell-level?: A simulation analysis in urban Namibia},

author = {Dana R. Thomson and Douglas R. Leasure and Tomas Bird and Nikos Tzavidis and Andrew J. Tatem},

doi = {10.1371/journal.pone.0271504},

year  = {2022},

date = {2022-07-21},

journal = {PLoS One},

volume = {17},

issue = {7},

abstract = {Disaggregated population counts are needed to calculate health, economic, and development indicators in Low- and Middle-Income Countries (LMICs), especially in settings of rapid urbanisation. Censuses are often outdated and inaccurate in LMIC settings, and rarely disaggregated at fine geographic scale. Modelled gridded population datasets derived from census data have become widely used by development researchers and practitioners; however, accuracy in these datasets are evaluated at the spatial scale of model input data which is generally courser than the neighbourhood or cell-level scale of many applications. We simulate a realistic synthetic 2016 population in Khomas, Namibia, a majority urban region, and introduce several realistic levels of outdatedness (over 15 years) and inaccuracy in slum, non-slum, and rural areas. We aggregate the synthetic populations by census and administrative boundaries (to mimic census data), resulting in 32 gridded population datasets that are typical of LMIC settings using the WorldPop-Global-Unconstrained gridded population approach. We evaluate the cell-level accuracy of these gridded population datasets using the original synthetic population as a reference. In our simulation, we found large cell-level errors, particularly in slum cells. These were driven by the averaging of population densities in large areal units before model training. Age, accuracy, and aggregation of the input data also played a role in these errors. We suggest incorporating finer-scale training data into gridded population models generally, and WorldPop-Global-Unconstrained in particular (e.g., from routine household surveys or slum community population counts), and use of new building footprint datasets as a covariate to improve cell-level accuracy (as done in some new WorldPop-Global-Constrained datasets). It is important to measure accuracy of gridded population datasets at spatial scales more consistent with how the data are being applied, especially if they are to be used for monitoring key development indicators at neighbourhood scales within cities.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}