Publications
1.
Chamberlain, Heather R.; Lazar, Attila N.; Tatem, Andrew J.
High-resolution estimates of social distancing feasibility, mapped for urban areas in sub-Saharan Africa Journal Article
In: Scientific Data, vol. 9, no. 711, 2022.
Abstract | Links | BibTeX | Tags: Africa, covid-19, NPIs
@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}
}
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.
2.
Pezzulo, Carla; Alegana, Victor A; Christensen, Andrew; Bakari, Omar; Tatem, Andrew
Understanding factors associated with attending secondary school in Tanzania using household survey data Journal Article
In: PLoS ONE, vol. 17, no. 2, 2022.
Abstract | Links | BibTeX | Tags: Africa, Demographic and Health Surveys, education, SDG4, Tanzania
@article{nokey,
title = {Understanding factors associated with attending secondary school in Tanzania using household survey data},
author = {Carla Pezzulo and Victor A Alegana and Andrew Christensen and Omar Bakari and Andrew Tatem},
doi = {http://dx.doi.org/10.1371/journal.pone.0263734},
year = {2022},
date = {2022-02-25},
urldate = {2022-02-25},
journal = {PLoS ONE},
volume = {17},
number = {2},
abstract = {Sustainable Development Goal (SDG) 4 aims to ensure inclusive and equitable access for all by 2030, leaving no one behind. One indicator selected to measure progress towards achievement is the participation rate of youth in education (SDG 4.3.1). Here we aim to understand drivers of school attendance using one country in East Africa as an example.
Methods
Nationally representative household survey data (2015–16 Tanzania Demographic and Health Survey) were used to explore individual, household and contextual factors associated with secondary school attendance in Tanzania. These included, age, head of household’s levels of education, gender, household wealth index and total number of children under five. Contextual factors such as average pupil to qualified teacher ratio and geographic access to school were also tested at cluster level. A two-level random intercept logistic regression model was used in exploring association of these factors with attendance in a multi-level framework.
Results
Age of household head, educational attainments of either of the head of the household or parent, child characteristics such as gender, were important predictors of secondary school attendance. Being in a richer household and with fewer siblings of lower age (under the age of 5) were associated with increased odds of attendance (OR = 0.91, CI 95%: 0.86; 0.96). Contextual factors were less likely to be associated with secondary school attendance.
Conclusions
Individual and household level factors are likely to impact secondary school attendance rates more compared to contextual factors, suggesting an increased focus of interventions at these levels is needed. Future studies should explore the impact of interventions targeting these levels. Policies should ideally promote gender equality in accessing secondary school as well as support those families where the dependency ratio is high. Strategies to reduce poverty will also increase the likelihood of attending school.},
keywords = {Africa, Demographic and Health Surveys, education, SDG4, Tanzania},
pubstate = {published},
tppubtype = {article}
}
Sustainable Development Goal (SDG) 4 aims to ensure inclusive and equitable access for all by 2030, leaving no one behind. One indicator selected to measure progress towards achievement is the participation rate of youth in education (SDG 4.3.1). Here we aim to understand drivers of school attendance using one country in East Africa as an example.
Methods
Nationally representative household survey data (2015–16 Tanzania Demographic and Health Survey) were used to explore individual, household and contextual factors associated with secondary school attendance in Tanzania. These included, age, head of household’s levels of education, gender, household wealth index and total number of children under five. Contextual factors such as average pupil to qualified teacher ratio and geographic access to school were also tested at cluster level. A two-level random intercept logistic regression model was used in exploring association of these factors with attendance in a multi-level framework.
Results
Age of household head, educational attainments of either of the head of the household or parent, child characteristics such as gender, were important predictors of secondary school attendance. Being in a richer household and with fewer siblings of lower age (under the age of 5) were associated with increased odds of attendance (OR = 0.91, CI 95%: 0.86; 0.96). Contextual factors were less likely to be associated with secondary school attendance.
Conclusions
Individual and household level factors are likely to impact secondary school attendance rates more compared to contextual factors, suggesting an increased focus of interventions at these levels is needed. Future studies should explore the impact of interventions targeting these levels. Policies should ideally promote gender equality in accessing secondary school as well as support those families where the dependency ratio is high. Strategies to reduce poverty will also increase the likelihood of attending school.
Methods
Nationally representative household survey data (2015–16 Tanzania Demographic and Health Survey) were used to explore individual, household and contextual factors associated with secondary school attendance in Tanzania. These included, age, head of household’s levels of education, gender, household wealth index and total number of children under five. Contextual factors such as average pupil to qualified teacher ratio and geographic access to school were also tested at cluster level. A two-level random intercept logistic regression model was used in exploring association of these factors with attendance in a multi-level framework.
Results
Age of household head, educational attainments of either of the head of the household or parent, child characteristics such as gender, were important predictors of secondary school attendance. Being in a richer household and with fewer siblings of lower age (under the age of 5) were associated with increased odds of attendance (OR = 0.91, CI 95%: 0.86; 0.96). Contextual factors were less likely to be associated with secondary school attendance.
Conclusions
Individual and household level factors are likely to impact secondary school attendance rates more compared to contextual factors, suggesting an increased focus of interventions at these levels is needed. Future studies should explore the impact of interventions targeting these levels. Policies should ideally promote gender equality in accessing secondary school as well as support those families where the dependency ratio is high. Strategies to reduce poverty will also increase the likelihood of attending school.
3.
Muchiri, Samuel K.; Muthee, Rose; Kiarie, Hellen; Sitienei, Joseph; Agweyu, Ambrose; Atkinson, Peter M.; Utazi, C. Edson; Tatem, Andrew J.; Alegana, Victor A.
Unmet need for COVID-19 vaccination coverage in Kenya Journal Article
In: Vaccine, vol. 40, no. 13, 2022, ISSN: 0264-410X.
Abstract | Links | BibTeX | Tags: Africa, covid-19, Kenya, travel time, vaccination
@article{nokey,
title = {Unmet need for COVID-19 vaccination coverage in Kenya},
author = {Samuel K. Muchiri and Rose Muthee and Hellen Kiarie and Joseph Sitienei and Ambrose Agweyu and Peter M. Atkinson and C. {Edson Utazi} and Andrew J. Tatem and Victor A. Alegana},
doi = {https://doi.org/10.1016/j.vaccine.2022.02.035},
issn = {0264-410X},
year = {2022},
date = {2022-02-14},
urldate = {2022-02-14},
journal = {Vaccine},
volume = {40},
number = {13},
abstract = {COVID-19 has impacted the health and livelihoods of billions of people since it emerged in 2019. Vaccination for COVID-19 is a critical intervention that is being rolled out globally to end the pandemic. Understanding the spatial inequalities in vaccination coverage and access to vaccination centres is important for planning this intervention nationally. Here, COVID-19 vaccination data, representing the number of people given at least one dose of vaccine, a list of the approved vaccination sites, population data and ancillary GIS data were used to assess vaccination coverage, using Kenya as an example. Firstly, physical access was modelled using travel time to estimate the proportion of population within 1 hour of a vaccination site. Secondly, a Bayesian conditional autoregressive (CAR) model was used to estimate the COVID-19 vaccination coverage and the same framework used to forecast coverage rates for the first quarter of 2022. Nationally, the average travel time to a designated COVID-19 vaccination site (n = 622) was 75.5 min (Range: 62.9 – 94.5 min) and over 87% of the population >18 years reside within 1 hour to a vaccination site. The COVID-19 vaccination coverage in December 2021 was 16.70% (95% CI: 16.66 – 16.74) – 4.4 million people and was forecasted to be 30.75% (95% CI: 25.04 – 36.96) – 8.1 million people by the end of March 2022. Approximately 21 million adults were still unvaccinated in December 2021 and, in the absence of accelerated vaccine uptake, over 17.2 million adults may not be vaccinated by end March 2022 nationally. Our results highlight geographic inequalities at sub-national level and are important in targeting and improving vaccination coverage in hard-to-reach populations. Similar mapping efforts could help other countries identify and increase vaccination coverage for such populations.},
keywords = {Africa, covid-19, Kenya, travel time, vaccination},
pubstate = {published},
tppubtype = {article}
}
COVID-19 has impacted the health and livelihoods of billions of people since it emerged in 2019. Vaccination for COVID-19 is a critical intervention that is being rolled out globally to end the pandemic. Understanding the spatial inequalities in vaccination coverage and access to vaccination centres is important for planning this intervention nationally. Here, COVID-19 vaccination data, representing the number of people given at least one dose of vaccine, a list of the approved vaccination sites, population data and ancillary GIS data were used to assess vaccination coverage, using Kenya as an example. Firstly, physical access was modelled using travel time to estimate the proportion of population within 1 hour of a vaccination site. Secondly, a Bayesian conditional autoregressive (CAR) model was used to estimate the COVID-19 vaccination coverage and the same framework used to forecast coverage rates for the first quarter of 2022. Nationally, the average travel time to a designated COVID-19 vaccination site (n = 622) was 75.5 min (Range: 62.9 – 94.5 min) and over 87% of the population >18 years reside within 1 hour to a vaccination site. The COVID-19 vaccination coverage in December 2021 was 16.70% (95% CI: 16.66 – 16.74) – 4.4 million people and was forecasted to be 30.75% (95% CI: 25.04 – 36.96) – 8.1 million people by the end of March 2022. Approximately 21 million adults were still unvaccinated in December 2021 and, in the absence of accelerated vaccine uptake, over 17.2 million adults may not be vaccinated by end March 2022 nationally. Our results highlight geographic inequalities at sub-national level and are important in targeting and improving vaccination coverage in hard-to-reach populations. Similar mapping efforts could help other countries identify and increase vaccination coverage for such populations.
4.
Darin, Edith; Boo, Gianluca; Tatem, Andrew J
A bottom-up population modelling approach to complement the population and housing census Conference
IUSSP , International Population Conference 2021, 2021.
Abstract | Links | BibTeX | Tags: Africa, bottom-up modelling, census, Democratic Republic of Congo
@conference{nokey,
title = {A bottom-up population modelling approach to complement the population and housing census},
author = {Darin, Edith and Boo, Gianluca and Tatem, Andrew J},
url = {https://ipc2021.popconf.org/abstracts/210325},
year = {2021},
date = {2021-12-07},
urldate = {2021-12-07},
booktitle = {IUSSP , International Population Conference 2021},
abstract = {The population and housing census provides essential demographic information for decision-making and action at local, national and international levels. However, census data in the most vulnerable countries is often outdated or partial because political instability, conflict and natural disasters prevent a national count. The bottom-up modeling approach helps supplement outdated or incomplete census data by estimating population counts and age/sex structures in approximately 100m grid cells using population data collected over a set fully enumerated places and auxiliary geospatial covariates. We present the modeling effort carried out in the Democratic Republic of Congo — the last census was carried out in 1984 — and in Burkina Faso — the last census was carried out in 2020 but only covered 70% of the country. Both models showed good predictive performance, indicated by R2 values of 0.73 and 0.63 for the respective out-of-sample predictions of population counts. The resulting bottom-up, gridded population estimates are currently used for census support and humanitarian response in both countries. This work highlights the flexibility of the bottom-up modeling approach, in terms of input population data, model specification, and aggregation of population estimates to support specific use cases.},
keywords = {Africa, bottom-up modelling, census, Democratic Republic of Congo},
pubstate = {published},
tppubtype = {conference}
}
The population and housing census provides essential demographic information for decision-making and action at local, national and international levels. However, census data in the most vulnerable countries is often outdated or partial because political instability, conflict and natural disasters prevent a national count. The bottom-up modeling approach helps supplement outdated or incomplete census data by estimating population counts and age/sex structures in approximately 100m grid cells using population data collected over a set fully enumerated places and auxiliary geospatial covariates. We present the modeling effort carried out in the Democratic Republic of Congo — the last census was carried out in 1984 — and in Burkina Faso — the last census was carried out in 2020 but only covered 70% of the country. Both models showed good predictive performance, indicated by R2 values of 0.73 and 0.63 for the respective out-of-sample predictions of population counts. The resulting bottom-up, gridded population estimates are currently used for census support and humanitarian response in both countries. This work highlights the flexibility of the bottom-up modeling approach, in terms of input population data, model specification, and aggregation of population estimates to support specific use cases.
5.
Jia, Peng; Sankoh, Osman; Tatem, Andrew J.
Mapping the environmental and socioeconomic coverage of the INDEPTH international health and demographic surveillance system network Journal Article
In: Health & Place, vol. 36, pp. 88-96, 2015, ISSN: 1353-8292.
Abstract | Links | BibTeX | Tags: Africa, Asia, Demographic surveillance sites, Health, Remote sensing
@article{JIA201588,
title = {Mapping the environmental and socioeconomic coverage of the INDEPTH international health and demographic surveillance system network},
author = {Peng Jia and Osman Sankoh and Andrew J. Tatem},
url = {https://www.sciencedirect.com/science/article/pii/S1353829215001379},
doi = {https://doi.org/10.1016/j.healthplace.2015.09.009},
issn = {1353-8292},
year = {2015},
date = {2015-01-01},
journal = {Health & Place},
volume = {36},
pages = {88-96},
abstract = {The International Network for the Demographic Evaluation of Populations and their Health (INDEPTH) has produced reliable longitudinal data about the lives of people in low- and middle-income countries (LMICs) through a global network of health and demographic surveillance system (HDSS) sites. Since reliable demographic data are scarce across many LMICs, we examine the environmental and socioeconomic (ES) similarities between existing HDSS sites and the rest of the LMICs. The HDSS sites were hierarchically grouped by the similarity of their ES conditions to quantify the ES variability between sites. The entire Africa and Asia region was classified to identify which regions were most similar to existing sites, based on available ES data. Results show that the current INDEPTH network architecture does a good job in representing ES conditions, but that great heterogeneities exist, even within individual countries. The results provide valuable information in determining the confidence with which relationships derived from present HDSS sites can be broadly extended to other areas, and to highlight areas where the new HDSS sites would improve significantly the ES coverage of the network.},
keywords = {Africa, Asia, Demographic surveillance sites, Health, Remote sensing},
pubstate = {published},
tppubtype = {article}
}
The International Network for the Demographic Evaluation of Populations and their Health (INDEPTH) has produced reliable longitudinal data about the lives of people in low- and middle-income countries (LMICs) through a global network of health and demographic surveillance system (HDSS) sites. Since reliable demographic data are scarce across many LMICs, we examine the environmental and socioeconomic (ES) similarities between existing HDSS sites and the rest of the LMICs. The HDSS sites were hierarchically grouped by the similarity of their ES conditions to quantify the ES variability between sites. The entire Africa and Asia region was classified to identify which regions were most similar to existing sites, based on available ES data. Results show that the current INDEPTH network architecture does a good job in representing ES conditions, but that great heterogeneities exist, even within individual countries. The results provide valuable information in determining the confidence with which relationships derived from present HDSS sites can be broadly extended to other areas, and to highlight areas where the new HDSS sites would improve significantly the ES coverage of the network.
6.
Linard, Catherine; Tatem, Andrew J.; Gilbert, Marius
Modelling spatial patterns of urban growth in Africa Journal Article
In: Applied Geography, vol. 44, pp. 23-32, 2013, ISSN: 0143-6228.
Abstract | Links | BibTeX | Tags: Africa, Boosted regression trees, Modelling, Spatial pattern, Urban growth
@article{LINARD201323,
title = {Modelling spatial patterns of urban growth in Africa},
author = {Catherine Linard and Andrew J. Tatem and Marius Gilbert},
url = {https://www.sciencedirect.com/science/article/pii/S0143622813001707},
doi = {https://doi.org/10.1016/j.apgeog.2013.07.009},
issn = {0143-6228},
year = {2013},
date = {2013-01-01},
journal = {Applied Geography},
volume = {44},
pages = {23-32},
abstract = {The population of Africa is predicted to double over the next 40 years, driving exceptionally high urban expansion rates that will induce significant socio-economic, environmental and health changes. In order to prepare for these changes, it is important to better understand urban growth dynamics in Africa and better predict the spatial pattern of rural-urban conversions. Previous work on urban expansion has been carried out at the city level or at the global level with a relatively coarse 5–10 km resolution. The main objective of the present paper was to develop a modelling approach at an intermediate scale in order to identify factors that influence spatial patterns of urban expansion in Africa. Boosted Regression Tree models were developed to predict the spatial pattern of rural-urban conversions in every large African city. Urban change data between circa 1990 and circa 2000 available for 20 large cities across Africa were used as training data. Results showed that the urban land in a 1 km neighbourhood and the accessibility to the city centre were the most influential variables. Results obtained were generally more accurate than results obtained using a distance-based urban expansion model and showed that the spatial pattern of small, compact and fast growing cities were easier to simulate than cities with lower population densities and a lower growth rate. The simulation method developed here will allow the production of spatially detailed urban expansion forecasts for 2020 and 2025 for Africa, data that are increasingly required by global change modellers.},
keywords = {Africa, Boosted regression trees, Modelling, Spatial pattern, Urban growth},
pubstate = {published},
tppubtype = {article}
}
The population of Africa is predicted to double over the next 40 years, driving exceptionally high urban expansion rates that will induce significant socio-economic, environmental and health changes. In order to prepare for these changes, it is important to better understand urban growth dynamics in Africa and better predict the spatial pattern of rural-urban conversions. Previous work on urban expansion has been carried out at the city level or at the global level with a relatively coarse 5–10 km resolution. The main objective of the present paper was to develop a modelling approach at an intermediate scale in order to identify factors that influence spatial patterns of urban expansion in Africa. Boosted Regression Tree models were developed to predict the spatial pattern of rural-urban conversions in every large African city. Urban change data between circa 1990 and circa 2000 available for 20 large cities across Africa were used as training data. Results showed that the urban land in a 1 km neighbourhood and the accessibility to the city centre were the most influential variables. Results obtained were generally more accurate than results obtained using a distance-based urban expansion model and showed that the spatial pattern of small, compact and fast growing cities were easier to simulate than cities with lower population densities and a lower growth rate. The simulation method developed here will allow the production of spatially detailed urban expansion forecasts for 2020 and 2025 for Africa, data that are increasingly required by global change modellers.
