Publications
1.
Pezzulo, Carla; Tejedor-Garavito, Natalia; Chan, Ho Man Theophilus; Dreoni, Ilda; Kerr, David; Ghosh, Samik; Bonnie, Amy; Bondarenko, Maksym; Salasibew, Mihretab; Tatem, Andrew J.
A subnational reproductive, maternal, newborn, child, and adolescent health and development atlas of India Journal Article
In: Scientific Data, vol. 10, iss. 86, 2023.
Abstract | Links | BibTeX | Tags:
@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}
}
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.
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.
Steele, Jessica E; Pezzulo, Carla; Albert, Maximilian; Brooks, Christopher J; zu Erbach-Schoenberg, Elisabeth; O’Connor, Siobh'an B; Sundsoy, Paal R; Engo-Monsen, Kenth; Nilsen, Kristine; Graupe, Bonita; Nyachhyon, Rajesh Lal; Silpakar, Pradeep; and Tatem, Andrew J
Mobility and phone call behavior explain patterns in poverty at high-resolution across multiple settings Journal Article
In: Humanities and Social Sciences Communications, vol. 8, no. 1, 2021.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Mobility and phone call behavior explain patterns in poverty at high-resolution across multiple settings},
author = {Steele, Jessica E and Pezzulo, Carla and Albert, Maximilian and Brooks, Christopher J and zu Erbach-Schoenberg, Elisabeth and O’Connor, Siobh{'a}n B and Sunds{o}y, P{aa}l R and Eng{o}-Monsen, Kenth and Nilsen, Kristine and Graupe, Bonita and Nyachhyon, Rajesh Lal and Silpakar, Pradeep and and Tatem, Andrew J },
doi = {https://doi.org/10.1057/s41599-021-00953-0},
year = {2021},
date = {2021-11-22},
journal = {Humanities and Social Sciences Communications},
volume = {8},
number = {1},
abstract = {Call detail records (CDRs) from mobile phone metadata are a promising data source for mapping poverty indicators in low- and middle-income countries. These data provide information on social networks, call behavior, and mobility patterns in a population, which are correlated with measures of socioeconomic status. CDRs are passively collected and provide information with high spatial and temporal resolution. Identifying features from these data that are generalizable and able to predict poverty and wealth beyond a single context could promote broader usage of mobile data, contribute to a reduction in the cost of socioeconomic data collection and processing, as well as complement existing census and survey-based methods of poverty estimation with improved temporal resolution. This is especially important within the context of the sustainable development goals (SDGs), where poverty and related health indicators are to be reduced significantly across subnational geographies by 2030. Here we utilize measures of cell phone user behavior derived from three CDR datasets within a Bayesian modeling framework to map poverty and wealth patterns across Namibia, Nepal, and Bangladesh. We demonstrate five metrics of user mobility and call behavior that are able to explain between 50% and 65% of the variance in socioeconomic status nationally for these three countries. These key metrics prove useful in very different contexts and can be readily provided as part of an existing CDR platform or software package. This paper provides a key contribution in this regard by identifying such metrics relevant to estimating poverty. We highlight the inclusion of ancillary data and local context as an important factor in understanding model outputs when targeting poverty alleviation strategies.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Call detail records (CDRs) from mobile phone metadata are a promising data source for mapping poverty indicators in low- and middle-income countries. These data provide information on social networks, call behavior, and mobility patterns in a population, which are correlated with measures of socioeconomic status. CDRs are passively collected and provide information with high spatial and temporal resolution. Identifying features from these data that are generalizable and able to predict poverty and wealth beyond a single context could promote broader usage of mobile data, contribute to a reduction in the cost of socioeconomic data collection and processing, as well as complement existing census and survey-based methods of poverty estimation with improved temporal resolution. This is especially important within the context of the sustainable development goals (SDGs), where poverty and related health indicators are to be reduced significantly across subnational geographies by 2030. Here we utilize measures of cell phone user behavior derived from three CDR datasets within a Bayesian modeling framework to map poverty and wealth patterns across Namibia, Nepal, and Bangladesh. We demonstrate five metrics of user mobility and call behavior that are able to explain between 50% and 65% of the variance in socioeconomic status nationally for these three countries. These key metrics prove useful in very different contexts and can be readily provided as part of an existing CDR platform or software package. This paper provides a key contribution in this regard by identifying such metrics relevant to estimating poverty. We highlight the inclusion of ancillary data and local context as an important factor in understanding model outputs when targeting poverty alleviation strategies.
4.
Alegana, VA; Pezzulo, Carla; Tatem, AJ; Omar, B; and Christensen, Andrew
Mapping out-of-school adolescents and youths in low- and middle-income countries Journal Article
In: Humanities and Social Sciences Communications, vol. 8, no. 213, 2021.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Mapping out-of-school adolescents and youths in low- and middle-income countries},
author = {Alegana, VA and Pezzulo, Carla and Tatem, AJ and Omar, B and and Christensen, Andrew},
doi = {https://doi.org/10.1057/s41599-021-00892-w},
year = {2021},
date = {2021-09-15},
journal = {Humanities and Social Sciences Communications},
volume = {8},
number = {213},
abstract = {Education is a human right and a driver of development, but, is still not accessible for a vast number of adolescents and school-age-youths. Out-of-school adolescents and youth rates (SDG 4.3.1) in lower and middle-income countries have been at a virtual halt for almost a decade. Thus, there is an increasing need to understand geographic variation on accessibility and school attendance to aid in reducing inequalities in education. Here, the aim was to estimate physical accessibility and secondary school non-attendance amongst adolescents and school-age youths in Tanzania, Cambodia, and the Dominican Republic. Community cluster survey data were triangulated with the spatial location of secondary schools, non-proprietary geospatial data and fine-scale population maps to estimate accessibility to all levels of secondary school education and the number of out-of-school. School attendance rates for the three countries were derived from nationally representative household survey data, and a Bayesian model-based geostatistical framework was used to estimate school attendance at high resolution. Results show a sub-national variation in accessibility and secondary school attendance rates for the three countries considered. Attendance was associated with distance to the nearest school (R2 > 70%). These findings suggest increasing the number of secondary schools could reduce the long-distance commuted to school in low-income and middle-income countries. Future work could extend these findings to fine-scale optimisation models for school location, intervention planning, and understanding barriers associated with secondary school non-attendance at the household level.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Education is a human right and a driver of development, but, is still not accessible for a vast number of adolescents and school-age-youths. Out-of-school adolescents and youth rates (SDG 4.3.1) in lower and middle-income countries have been at a virtual halt for almost a decade. Thus, there is an increasing need to understand geographic variation on accessibility and school attendance to aid in reducing inequalities in education. Here, the aim was to estimate physical accessibility and secondary school non-attendance amongst adolescents and school-age youths in Tanzania, Cambodia, and the Dominican Republic. Community cluster survey data were triangulated with the spatial location of secondary schools, non-proprietary geospatial data and fine-scale population maps to estimate accessibility to all levels of secondary school education and the number of out-of-school. School attendance rates for the three countries were derived from nationally representative household survey data, and a Bayesian model-based geostatistical framework was used to estimate school attendance at high resolution. Results show a sub-national variation in accessibility and secondary school attendance rates for the three countries considered. Attendance was associated with distance to the nearest school (R2 > 70%). These findings suggest increasing the number of secondary schools could reduce the long-distance commuted to school in low-income and middle-income countries. Future work could extend these findings to fine-scale optimisation models for school location, intervention planning, and understanding barriers associated with secondary school non-attendance at the household level.
5.
Pezzulo, Carla; Nilsen, Kristine; Carioli, Alessandra; Tejedor-Garavito, Natalia; Hanspal, Sophie E; Hilber, Theodor; James, William H M; Ruktanonchai, Corrine W; Alegana, Victor; Sorichetta, Alessandro; Wigley, Adelle S; Hornby, Graeme M; Matthews, Zoe; Tatem, Andrew J
In: The Lancet Global Health, vol. 9, no. 6, pp. e802-e812, 2021, ISSN: 2214-109X.
Abstract | Links | BibTeX | Tags:
@article{PEZZULO2021e802,
title = {Geographical distribution of fertility rates in 70 low-income, lower-middle-income, and upper-middle-income countries, 2010–16: a subnational analysis of cross-sectional surveys},
author = {Carla Pezzulo and Kristine Nilsen and Alessandra Carioli and Natalia Tejedor-Garavito and Sophie E Hanspal and Theodor Hilber and William H M James and Corrine W Ruktanonchai and Victor Alegana and Alessandro Sorichetta and Adelle S Wigley and Graeme M Hornby and Zoe Matthews and Andrew J Tatem},
url = {https://www.sciencedirect.com/science/article/pii/S2214109X21000826},
doi = {https://doi.org/10.1016/S2214-109X(21)00082-6},
issn = {2214-109X},
year = {2021},
date = {2021-01-01},
journal = {The Lancet Global Health},
volume = {9},
number = {6},
pages = {e802-e812},
abstract = {Summary
Background
Understanding subnational variation in age-specific fertility rates (ASFRs) and total fertility rates (TFRs), and geographical clustering of high fertility and its determinants in low-income and middle-income countries, is increasingly needed for geographical targeting and prioritising of policy. We aimed to identify variation in fertility rates, to describe patterns of key selected fertility determinants in areas of high fertility.
Methods
We did a subnational analysis of ASFRs and TFRs from the most recent publicly available and nationally representative cross-sectional Demographic and Health Surveys and Multiple Indicator Cluster Surveys collected between 2010 and 2016 for 70 low-income, lower-middle-income, and upper-middle-income countries, across 932 administrative units. We assessed the degree of global spatial autocorrelation by using Moran's I statistic and did a spatial cluster analysis using the Getis-Ord Gi* local statistic to examine the geographical clustering of fertility and key selected fertility determinants. Descriptive analysis was used to investigate the distribution of ASFRs and of selected determinants in each cluster.
Findings
TFR varied from below replacement (2·1 children per women) in 36 of the 932 subnational regions (mainly located in India, Myanmar, Colombia, and Armenia), to rates of 8 and higher in 14 subnational regions, located in sub-Saharan Africa and Afghanistan. Areas with high-fertility clusters were mostly associated with areas of low prevalence of women with secondary or higher education, low use of contraception, and high unmet needs for family planning, although exceptions existed.
Interpretation
Substantial within-country variation in the distribution of fertility rates highlights the need for tailored programmes and strategies in high-fertility cluster areas to increase the use of contraception and access to secondary education, and to reduce unmet need for family planning.
Funding
Wellcome Trust, the UK Foreign, Commonwealth and Development Office, and the Bill & Melinda Gates Foundation.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Summary
Background
Understanding subnational variation in age-specific fertility rates (ASFRs) and total fertility rates (TFRs), and geographical clustering of high fertility and its determinants in low-income and middle-income countries, is increasingly needed for geographical targeting and prioritising of policy. We aimed to identify variation in fertility rates, to describe patterns of key selected fertility determinants in areas of high fertility.
Methods
We did a subnational analysis of ASFRs and TFRs from the most recent publicly available and nationally representative cross-sectional Demographic and Health Surveys and Multiple Indicator Cluster Surveys collected between 2010 and 2016 for 70 low-income, lower-middle-income, and upper-middle-income countries, across 932 administrative units. We assessed the degree of global spatial autocorrelation by using Moran's I statistic and did a spatial cluster analysis using the Getis-Ord Gi* local statistic to examine the geographical clustering of fertility and key selected fertility determinants. Descriptive analysis was used to investigate the distribution of ASFRs and of selected determinants in each cluster.
Findings
TFR varied from below replacement (2·1 children per women) in 36 of the 932 subnational regions (mainly located in India, Myanmar, Colombia, and Armenia), to rates of 8 and higher in 14 subnational regions, located in sub-Saharan Africa and Afghanistan. Areas with high-fertility clusters were mostly associated with areas of low prevalence of women with secondary or higher education, low use of contraception, and high unmet needs for family planning, although exceptions existed.
Interpretation
Substantial within-country variation in the distribution of fertility rates highlights the need for tailored programmes and strategies in high-fertility cluster areas to increase the use of contraception and access to secondary education, and to reduce unmet need for family planning.
Funding
Wellcome Trust, the UK Foreign, Commonwealth and Development Office, and the Bill & Melinda Gates Foundation.
Background
Understanding subnational variation in age-specific fertility rates (ASFRs) and total fertility rates (TFRs), and geographical clustering of high fertility and its determinants in low-income and middle-income countries, is increasingly needed for geographical targeting and prioritising of policy. We aimed to identify variation in fertility rates, to describe patterns of key selected fertility determinants in areas of high fertility.
Methods
We did a subnational analysis of ASFRs and TFRs from the most recent publicly available and nationally representative cross-sectional Demographic and Health Surveys and Multiple Indicator Cluster Surveys collected between 2010 and 2016 for 70 low-income, lower-middle-income, and upper-middle-income countries, across 932 administrative units. We assessed the degree of global spatial autocorrelation by using Moran's I statistic and did a spatial cluster analysis using the Getis-Ord Gi* local statistic to examine the geographical clustering of fertility and key selected fertility determinants. Descriptive analysis was used to investigate the distribution of ASFRs and of selected determinants in each cluster.
Findings
TFR varied from below replacement (2·1 children per women) in 36 of the 932 subnational regions (mainly located in India, Myanmar, Colombia, and Armenia), to rates of 8 and higher in 14 subnational regions, located in sub-Saharan Africa and Afghanistan. Areas with high-fertility clusters were mostly associated with areas of low prevalence of women with secondary or higher education, low use of contraception, and high unmet needs for family planning, although exceptions existed.
Interpretation
Substantial within-country variation in the distribution of fertility rates highlights the need for tailored programmes and strategies in high-fertility cluster areas to increase the use of contraception and access to secondary education, and to reduce unmet need for family planning.
Funding
Wellcome Trust, the UK Foreign, Commonwealth and Development Office, and the Bill & Melinda Gates Foundation.
6.
Wigley, A. S.; Tejedor-Garavito, N.; Alegana, V.; Carioli, A.; Ruktanonchai, C. W.; Pezzulo, C.; Matthews, Z.; Tatem, A. J.; Nilsen, K.
Measuring the availability and geographical accessibility of maternal health services across sub-Saharan Africa Journal Article
In: BMC Medicine, vol. 18, no. 1, pp. 237, 2020, ISSN: 1741-7015.
Abstract | Links | BibTeX | Tags:
@article{Wigley2020,
title = {Measuring the availability and geographical accessibility of maternal health services across sub-Saharan Africa},
author = {A. S. Wigley and N. Tejedor-Garavito and V. Alegana and A. Carioli and C. W. Ruktanonchai and C. Pezzulo and Z. Matthews and A. J. Tatem and K. Nilsen},
url = {https://doi.org/10.1186/s12916-020-01707-6},
doi = {10.1186/s12916-020-01707-6},
issn = {1741-7015},
year = {2020},
date = {2020-09-08},
journal = {BMC Medicine},
volume = {18},
number = {1},
pages = {237},
abstract = {With universal health coverage a key component of the 2030 Sustainable Development Goals, targeted monitoring is crucial for reducing inequalities in the provision of services. However, monitoring largely occurs at the national level, masking sub-national variation. Here, we estimate indicators for measuring the availability and geographical accessibility of services, at national and sub-national levels across sub-Saharan Africa, to show how data at varying spatial scales and input data can considerably impact monitoring outcomes.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
With universal health coverage a key component of the 2030 Sustainable Development Goals, targeted monitoring is crucial for reducing inequalities in the provision of services. However, monitoring largely occurs at the national level, masking sub-national variation. Here, we estimate indicators for measuring the availability and geographical accessibility of services, at national and sub-national levels across sub-Saharan Africa, to show how data at varying spatial scales and input data can considerably impact monitoring outcomes.
7.
Lai, Shengjie; zu Erbach-Schoenberg, Elisabeth; Pezzulo, Carla; Ruktanonchai, Nick W.; Sorichetta, Alessandro; Steele, Jessica; Li, Tracey; Dooley, Claire A.; Tatem, Andrew J.
Exploring the use of mobile phone data for national migration statistics Journal Article
In: Palgrave Communications, vol. 5, no. 1, pp. 34, 2019, ISSN: 2055-1045.
Abstract | Links | BibTeX | Tags:
@article{Lai2019,
title = {Exploring the use of mobile phone data for national migration statistics},
author = {Shengjie Lai and Elisabeth zu Erbach-Schoenberg and Carla Pezzulo and Nick W. Ruktanonchai and Alessandro Sorichetta and Jessica Steele and Tracey Li and Claire A. Dooley and Andrew J. Tatem},
url = {https://doi.org/10.1057/s41599-019-0242-9},
doi = {10.1057/s41599-019-0242-9},
issn = {2055-1045},
year = {2019},
date = {2019-03-26},
journal = {Palgrave Communications},
volume = {5},
number = {1},
pages = {34},
abstract = {Statistics on internal migration are important for keeping estimates of subnational population numbers up-to-date, as well as urban planning, infrastructure development, and impact assessment, among other applications. However, migration flow statistics typically remain constrained by the logistics of infrequent censuses or surveys. The penetration rate of mobile phones is now high across the globe with rapid recent increases in ownership in low-income countries. Analyzing the changing spatiotemporal distribution of mobile phone users through anonymized call detail records (CDRs) offers the possibility to measure migration at multiple temporal and spatial scales. Based on a dataset of 72 billion anonymized CDRs in Namibia from October 2010 to April 2014, we explore how internal migration estimates can be derived and modeled from CDRs at subnational and annual scales, and how precision and accuracy of these estimates compare to census-derived migration statistics. We also demonstrate the use of CDRs to assess how migration patterns change over time, with a finer temporal resolution compared with censuses. Moreover, we show how gravity-type spatial interaction models built using CDRs can accurately capture migration flows. The results highlight that estimates of migration flows made using mobile phone data is a promising avenue for complementing more traditional national migration statistics and obtaining more timely and local data.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Statistics on internal migration are important for keeping estimates of subnational population numbers up-to-date, as well as urban planning, infrastructure development, and impact assessment, among other applications. However, migration flow statistics typically remain constrained by the logistics of infrequent censuses or surveys. The penetration rate of mobile phones is now high across the globe with rapid recent increases in ownership in low-income countries. Analyzing the changing spatiotemporal distribution of mobile phone users through anonymized call detail records (CDRs) offers the possibility to measure migration at multiple temporal and spatial scales. Based on a dataset of 72 billion anonymized CDRs in Namibia from October 2010 to April 2014, we explore how internal migration estimates can be derived and modeled from CDRs at subnational and annual scales, and how precision and accuracy of these estimates compare to census-derived migration statistics. We also demonstrate the use of CDRs to assess how migration patterns change over time, with a finer temporal resolution compared with censuses. Moreover, we show how gravity-type spatial interaction models built using CDRs can accurately capture migration flows. The results highlight that estimates of migration flows made using mobile phone data is a promising avenue for complementing more traditional national migration statistics and obtaining more timely and local data.
8.
James, W. H. M.; Tejedor-Garavito, N.; Hanspal, S. E.; Campbell-Sutton, A.; Hornby, G. M.; Pezzulo, C.; Nilsen, K.; Sorichetta, A.; Ruktanonchai, C. W.; Carioli, A.; Kerr, D.; Matthews, Z.; Tatem, A. J.
Gridded birth and pregnancy datasets for Africa, Latin America and the Caribbean Journal Article
In: Scientific Data, vol. 5, no. 1, pp. 180090, 2018, ISSN: 2052-4463.
Abstract | Links | BibTeX | Tags:
@article{James2018,
title = {Gridded birth and pregnancy datasets for Africa, Latin America and the Caribbean},
author = {W. H. M. James and N. Tejedor-Garavito and S. E. Hanspal and A. Campbell-Sutton and G. M. Hornby and C. Pezzulo and K. Nilsen and A. Sorichetta and C. W. Ruktanonchai and A. Carioli and D. Kerr and Z. Matthews and A. J. Tatem},
url = {https://doi.org/10.1038/sdata.2018.90},
doi = {10.1038/sdata.2018.90},
issn = {2052-4463},
year = {2018},
date = {2018-05-22},
journal = {Scientific Data},
volume = {5},
number = {1},
pages = {180090},
abstract = {Understanding the fine scale spatial distribution of births and pregnancies is crucial for informing planning decisions related to public health. This is especially important in lower income countries where infectious disease is a major concern for pregnant women and new-borns, as highlighted by the recent Zika virus epidemic. Despite this, the spatial detail of basic data on the numbers and distribution of births and pregnancies is often of a coarse resolution and difficult to obtain, with no co-ordination between countries and organisations to create one consistent set of subnational estimates. To begin to address this issue, under the framework of the WorldPop program, an open access archive of high resolution gridded birth and pregnancy distribution datasets for all African, Latin America and Caribbean countries has been created. Datasets were produced using the most recent and finest level census and official population estimate data available and are at a resolution of 30 arc seconds (approximately 1thinspacekm at the equator). All products are available through WorldPop.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Understanding the fine scale spatial distribution of births and pregnancies is crucial for informing planning decisions related to public health. This is especially important in lower income countries where infectious disease is a major concern for pregnant women and new-borns, as highlighted by the recent Zika virus epidemic. Despite this, the spatial detail of basic data on the numbers and distribution of births and pregnancies is often of a coarse resolution and difficult to obtain, with no co-ordination between countries and organisations to create one consistent set of subnational estimates. To begin to address this issue, under the framework of the WorldPop program, an open access archive of high resolution gridded birth and pregnancy distribution datasets for all African, Latin America and Caribbean countries has been created. Datasets were produced using the most recent and finest level census and official population estimate data available and are at a resolution of 30 arc seconds (approximately 1thinspacekm at the equator). All products are available through WorldPop.
9.
Pezzulo, Carla; Hornby, Graeme M.; Sorichetta, Alessandro; Gaughan, Andrea E.; Linard, Catherine; Bird, Tomas J.; Kerr, David; Lloyd, Christopher T.; Tatem, Andrew J.
Sub-national mapping of population pyramids and dependency ratios in Africa and Asia Journal Article
In: Scientific Data, vol. 4, no. 1, pp. 170089, 2017, ISSN: 2052-4463.
Abstract | Links | BibTeX | Tags:
@article{Pezzulo2017,
title = {Sub-national mapping of population pyramids and dependency ratios in Africa and Asia},
author = {Carla Pezzulo and Graeme M. Hornby and Alessandro Sorichetta and Andrea E. Gaughan and Catherine Linard and Tomas J. Bird and David Kerr and Christopher T. Lloyd and Andrew J. Tatem},
url = {https://doi.org/10.1038/sdata.2017.89},
doi = {10.1038/sdata.2017.89},
issn = {2052-4463},
year = {2017},
date = {2017-07-19},
journal = {Scientific Data},
volume = {4},
number = {1},
pages = {170089},
abstract = {The age group composition of populations varies substantially across continents and within countries, and is linked to levels of development, health status and poverty. The subnational variability in the shape of the population pyramid as well as the respective dependency ratio are reflective of the different levels of development of a country and are drivers for a country's economic prospects and health burdens. Whether measured as the ratio between those of working age and those young and old who are dependent upon them, or through separate young and old-age metrics, dependency ratios are often highly heterogeneous between and within countries. Assessments of subnational dependency ratio and age structure patterns have been undertaken for specific countries and across high income regions, but to a lesser extent across the low income regions. In the framework of the WorldPop Project, through the assembly of over 100 million records across 6,389 subnational administrative units, subnational dependency ratio and high resolution gridded age/sex group datasets were produced for 87 countries in Africa and Asia.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The age group composition of populations varies substantially across continents and within countries, and is linked to levels of development, health status and poverty. The subnational variability in the shape of the population pyramid as well as the respective dependency ratio are reflective of the different levels of development of a country and are drivers for a country's economic prospects and health burdens. Whether measured as the ratio between those of working age and those young and old who are dependent upon them, or through separate young and old-age metrics, dependency ratios are often highly heterogeneous between and within countries. Assessments of subnational dependency ratio and age structure patterns have been undertaken for specific countries and across high income regions, but to a lesser extent across the low income regions. In the framework of the WorldPop Project, through the assembly of over 100 million records across 6,389 subnational administrative units, subnational dependency ratio and high resolution gridded age/sex group datasets were produced for 87 countries in Africa and Asia.
10.
Alegana, Victor A.; Wright, Jim; Pezzulo, Carla; Tatem, Andrew J.; Atkinson, Peter M.
Treatment-seeking behaviour in low- and middle-income countries estimated using a Bayesian model Journal Article
In: BMC Medical Research Methodology, vol. 17, no. 1, pp. 67, 2017, ISSN: 1471-2288.
Abstract | Links | BibTeX | Tags:
@article{Alegana2017,
title = {Treatment-seeking behaviour in low- and middle-income countries estimated using a Bayesian model},
author = {Victor A. Alegana and Jim Wright and Carla Pezzulo and Andrew J. Tatem and Peter M. Atkinson},
url = {https://doi.org/10.1186/s12874-017-0346-0},
doi = {10.1186/s12874-017-0346-0},
issn = {1471-2288},
year = {2017},
date = {2017-04-20},
journal = {BMC Medical Research Methodology},
volume = {17},
number = {1},
pages = {67},
abstract = {Seeking treatment in formal healthcare for uncomplicated infections is vital to combating disease in low- and middle-income countries (LMICs). Healthcare treatment-seeking behaviour varies within and between communities and is modified by socio-economic, demographic, and physical factors. As a result, it remains a challenge to quantify healthcare treatment-seeking behaviour using a metric that is comparable across communities. Here, we present an application for transforming individual categorical responses (actions related to fever) to a continuous probabilistic estimate of fever treatment for one country in Sub-Saharan Africa (SSA).},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Seeking treatment in formal healthcare for uncomplicated infections is vital to combating disease in low- and middle-income countries (LMICs). Healthcare treatment-seeking behaviour varies within and between communities and is modified by socio-economic, demographic, and physical factors. As a result, it remains a challenge to quantify healthcare treatment-seeking behaviour using a metric that is comparable across communities. Here, we present an application for transforming individual categorical responses (actions related to fever) to a continuous probabilistic estimate of fever treatment for one country in Sub-Saharan Africa (SSA).
11.
Bosco, C.; Alegana, V.; Bird, T.; Pezzulo, C.; Bengtsson, L.; Sorichetta, A.; Steele, J.; Hornby, G.; Ruktanonchai, C.; Ruktanonchai, N.; Wetter, E.; Tatem, A. J.
Exploring the high-resolution mapping of gender-disaggregated development indicators Journal Article
In: Journal of The Royal Society Interface, vol. 14, no. 129, pp. 20160825, 2017.
Abstract | Links | BibTeX | Tags:
@article{doi:10.1098/rsif.2016.0825,
title = {Exploring the high-resolution mapping of gender-disaggregated development indicators},
author = {C. Bosco and V. Alegana and T. Bird and C. Pezzulo and L. Bengtsson and A. Sorichetta and J. Steele and G. Hornby and C. Ruktanonchai and N. Ruktanonchai and E. Wetter and A. J. Tatem},
url = {https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2016.0825},
doi = {10.1098/rsif.2016.0825},
year = {2017},
date = {2017-01-01},
journal = {Journal of The Royal Society Interface},
volume = {14},
number = {129},
pages = {20160825},
abstract = {Improved understanding of geographical variation and inequity in health status, wealth and access to resources within countries is increasingly being recognized as central to meeting development goals. Development and health indicators assessed at national or subnational scale can often conceal important inequities, with the rural poor often least well represented. The ability to target limited resources is fundamental, especially in an international context where funding for health and development comes under pressure. This has recently prompted the exploration of the potential of spatial interpolation methods based on geolocated clusters from national household survey data for the high-resolution mapping of features such as population age structures, vaccination coverage and access to sanitation. It remains unclear, however, how predictable these different factors are across different settings, variables and between demographic groups. Here we test the accuracy of spatial interpolation methods in producing gender-disaggregated high-resolution maps of the rates of literacy, stunting and the use of modern contraceptive methods from a combination of geolocated demographic and health surveys cluster data and geospatial covariates. Bayesian geostatistical and machine learning modelling methods were tested across four low-income countries and varying gridded environmental and socio-economic covariate datasets to build 1×1 km spatial resolution maps with uncertainty estimates. Results show the potential of the approach in producing high-resolution maps of key gender-disaggregated socio-economic indicators, with explained variance through cross-validation being as high as 74–75% for female literacy in Nigeria and Kenya, and in the 50–70% range for many other variables. However, substantial variations by both country and variable were seen, with many variables showing poor mapping accuracies in the range of 2–30% explained variance using both geostatistical and machine learning approaches. The analyses offer a robust basis for the construction of timely maps with levels of detail that support geographically stratified decision-making and the monitoring of progress towards development goals. However, the great variability in results between countries and variables highlights the challenges in applying these interpolation methods universally across multiple countries, and the importance of validation and quantifying uncertainty if this is undertaken.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Improved understanding of geographical variation and inequity in health status, wealth and access to resources within countries is increasingly being recognized as central to meeting development goals. Development and health indicators assessed at national or subnational scale can often conceal important inequities, with the rural poor often least well represented. The ability to target limited resources is fundamental, especially in an international context where funding for health and development comes under pressure. This has recently prompted the exploration of the potential of spatial interpolation methods based on geolocated clusters from national household survey data for the high-resolution mapping of features such as population age structures, vaccination coverage and access to sanitation. It remains unclear, however, how predictable these different factors are across different settings, variables and between demographic groups. Here we test the accuracy of spatial interpolation methods in producing gender-disaggregated high-resolution maps of the rates of literacy, stunting and the use of modern contraceptive methods from a combination of geolocated demographic and health surveys cluster data and geospatial covariates. Bayesian geostatistical and machine learning modelling methods were tested across four low-income countries and varying gridded environmental and socio-economic covariate datasets to build 1×1 km spatial resolution maps with uncertainty estimates. Results show the potential of the approach in producing high-resolution maps of key gender-disaggregated socio-economic indicators, with explained variance through cross-validation being as high as 74–75% for female literacy in Nigeria and Kenya, and in the 50–70% range for many other variables. However, substantial variations by both country and variable were seen, with many variables showing poor mapping accuracies in the range of 2–30% explained variance using both geostatistical and machine learning approaches. The analyses offer a robust basis for the construction of timely maps with levels of detail that support geographically stratified decision-making and the monitoring of progress towards development goals. However, the great variability in results between countries and variables highlights the challenges in applying these interpolation methods universally across multiple countries, and the importance of validation and quantifying uncertainty if this is undertaken.
12.
Steele, Jessica E.; Sundsøy, Pål Roe; Pezzulo, Carla; Alegana, Victor A.; Bird, Tomas J.; Blumenstock, Joshua; Bjelland, Johannes; Engø-Monsen, Kenth; Montjoye, Yves-Alexandre; Iqbal, Asif M.; Hadiuzzaman, Khandakar N.; Lu, Xin; Wetter, Erik; Tatem, Andrew J.; Bengtsson, Linus
Mapping poverty using mobile phone and satellite data Journal Article
In: Journal of The Royal Society Interface, vol. 14, no. 127, pp. 20160690, 2017.
Abstract | Links | BibTeX | Tags:
@article{doi:10.1098/rsif.2016.0690,
title = {Mapping poverty using mobile phone and satellite data},
author = {Jessica E. Steele and Pål Roe Sundsøy and Carla Pezzulo and Victor A. Alegana and Tomas J. Bird and Joshua Blumenstock and Johannes Bjelland and Kenth Engø-Monsen and Yves-Alexandre Montjoye and Asif M. Iqbal and Khandakar N. Hadiuzzaman and Xin Lu and Erik Wetter and Andrew J. Tatem and Linus Bengtsson},
url = {https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2016.0690},
doi = {10.1098/rsif.2016.0690},
year = {2017},
date = {2017-01-01},
journal = {Journal of The Royal Society Interface},
volume = {14},
number = {127},
pages = {20160690},
abstract = {Poverty is one of the most important determinants of adverse health outcomes globally, a major cause of societal instability and one of the largest causes of lost human potential. Traditional approaches to measuring and targeting poverty rely heavily on census data, which in most low- and middle-income countries (LMICs) are unavailable or out-of-date. Alternate measures are needed to complement and update estimates between censuses. This study demonstrates how public and private data sources that are commonly available for LMICs can be used to provide novel insight into the spatial distribution of poverty. We evaluate the relative value of modelling three traditional poverty measures using aggregate data from mobile operators and widely available geospatial data. Taken together, models combining these data sources provide the best predictive power (highest r2 = 0.78) and lowest error, but generally models employing mobile data only yield comparable results, offering the potential to measure poverty more frequently and at finer granularity. Stratifying models into urban and rural areas highlights the advantage of using mobile data in urban areas and different data in different contexts. The findings indicate the possibility to estimate and continually monitor poverty rates at high spatial resolution in countries with limited capacity to support traditional methods of data collection.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Poverty is one of the most important determinants of adverse health outcomes globally, a major cause of societal instability and one of the largest causes of lost human potential. Traditional approaches to measuring and targeting poverty rely heavily on census data, which in most low- and middle-income countries (LMICs) are unavailable or out-of-date. Alternate measures are needed to complement and update estimates between censuses. This study demonstrates how public and private data sources that are commonly available for LMICs can be used to provide novel insight into the spatial distribution of poverty. We evaluate the relative value of modelling three traditional poverty measures using aggregate data from mobile operators and widely available geospatial data. Taken together, models combining these data sources provide the best predictive power (highest r2 = 0.78) and lowest error, but generally models employing mobile data only yield comparable results, offering the potential to measure poverty more frequently and at finer granularity. Stratifying models into urban and rural areas highlights the advantage of using mobile data in urban areas and different data in different contexts. The findings indicate the possibility to estimate and continually monitor poverty rates at high spatial resolution in countries with limited capacity to support traditional methods of data collection.
13.
Erbach-Schoenberg, Elisabeth; Alegana, Victor A.; Sorichetta, Alessandro; Linard, Catherine; Lourenço, Christoper; Ruktanonchai, Nick W.; Graupe, Bonita; Bird, Tomas J.; Pezzulo, Carla; Wesolowski, Amy; Tatem, Andrew J.
Dynamic denominators: the impact of seasonally varying population numbers on disease incidence estimates Journal Article
In: Population Health Metrics, vol. 14, no. 1, pp. 35, 2016, ISSN: 1478-7954.
Abstract | Links | BibTeX | Tags:
@article{zuErbach-Schoenberg2016,
title = {Dynamic denominators: the impact of seasonally varying population numbers on disease incidence estimates},
author = {Elisabeth Erbach-Schoenberg and Victor A. Alegana and Alessandro Sorichetta and Catherine Linard and Christoper Lourenço and Nick W. Ruktanonchai and Bonita Graupe and Tomas J. Bird and Carla Pezzulo and Amy Wesolowski and Andrew J. Tatem},
url = {https://doi.org/10.1186/s12963-016-0106-0},
doi = {10.1186/s12963-016-0106-0},
issn = {1478-7954},
year = {2016},
date = {2016-10-12},
journal = {Population Health Metrics},
volume = {14},
number = {1},
pages = {35},
abstract = {Reliable health metrics are crucial for accurately assessing disease burden and planning interventions. Many health indicators are measured through passive surveillance systems and are reliant on accurate estimates of denominators to transform case counts into incidence measures. These denominator estimates generally come from national censuses and use large area growth rates to estimate annual changes. Typically, they do not account for any seasonal fluctuations and thus assume a static denominator population. Many recent studies have highlighted the dynamic nature of human populations through quantitative analyses of mobile phone call data records and a range of other sources, emphasizing seasonal changes. In this study, we use mobile phone data to capture patterns of short-term human population movement and to map dynamism in population densities.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Reliable health metrics are crucial for accurately assessing disease burden and planning interventions. Many health indicators are measured through passive surveillance systems and are reliant on accurate estimates of denominators to transform case counts into incidence measures. These denominator estimates generally come from national censuses and use large area growth rates to estimate annual changes. Typically, they do not account for any seasonal fluctuations and thus assume a static denominator population. Many recent studies have highlighted the dynamic nature of human populations through quantitative analyses of mobile phone call data records and a range of other sources, emphasizing seasonal changes. In this study, we use mobile phone data to capture patterns of short-term human population movement and to map dynamism in population densities.
14.
Sorichetta, Alessandro; Bird, Tom J.; Ruktanonchai, Nick W.; Erbach-Schoenberg, Elisabeth; Pezzulo, Carla; Tejedor, Natalia; Waldock, Ian C.; Sadler, Jason D.; Garcia, Andres J.; Sedda, Luigi; Tatem, Andrew J.
Mapping internal connectivity through human migration in malaria endemic countries Journal Article
In: Scientific Data, vol. 3, no. 1, pp. 160066, 2016, ISSN: 2052-4463.
Abstract | Links | BibTeX | Tags:
@article{Sorichetta2016,
title = {Mapping internal connectivity through human migration in malaria endemic countries},
author = {Alessandro Sorichetta and Tom J. Bird and Nick W. Ruktanonchai and Elisabeth Erbach-Schoenberg and Carla Pezzulo and Natalia Tejedor and Ian C. Waldock and Jason D. Sadler and Andres J. Garcia and Luigi Sedda and Andrew J. Tatem},
url = {https://doi.org/10.1038/sdata.2016.66},
doi = {10.1038/sdata.2016.66},
issn = {2052-4463},
year = {2016},
date = {2016-08-16},
journal = {Scientific Data},
volume = {3},
number = {1},
pages = {160066},
abstract = {Human mobility continues to increase in terms of volumes and reach, producing growing global connectivity. This connectivity hampers efforts to eliminate infectious diseases such as malaria through reintroductions of pathogens, and thus accounting for it becomes important in designing global, continental, regional, and national strategies. Recent works have shown that census-derived migration data provides a good proxy for internal connectivity, in terms of relative strengths of movement between administrative units, across temporal scales. To support global malaria eradication strategy efforts, here we describe the construction of an open access archive of estimated internal migration flows in endemic countries built through pooling of census microdata. These connectivity datasets, described here along with the approaches and methods used to create and validate them, are available both through the WorldPop website and the WorldPop Dataverse Repository.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Human mobility continues to increase in terms of volumes and reach, producing growing global connectivity. This connectivity hampers efforts to eliminate infectious diseases such as malaria through reintroductions of pathogens, and thus accounting for it becomes important in designing global, continental, regional, and national strategies. Recent works have shown that census-derived migration data provides a good proxy for internal connectivity, in terms of relative strengths of movement between administrative units, across temporal scales. To support global malaria eradication strategy efforts, here we describe the construction of an open access archive of estimated internal migration flows in endemic countries built through pooling of census microdata. These connectivity datasets, described here along with the approaches and methods used to create and validate them, are available both through the WorldPop website and the WorldPop Dataverse Repository.
15.
Ruktanonchai, Corrine W.; Ruktanonchai, Nick W.; Nove, Andrea; Lopes, Sofia; Pezzulo, Carla; Bosco, Claudio; Alegana, Victor A.; Burgert, Clara R.; Ayiko, Rogers; Charles, Andrew SEK; Lambert, Nkurunziza; Msechu, Esther; Kathini, Esther; Matthews, Zoë; Tatem, Andrew J.
Equality in Maternal and Newborn Health: Modelling Geographic Disparities in Utilisation of Care in Five East African Countries Journal Article
In: PLOS ONE, vol. 11, no. 8, pp. 1-17, 2016.
Abstract | Links | BibTeX | Tags:
@article{10.1371/journal.pone.0162006,
title = {Equality in Maternal and Newborn Health: Modelling Geographic Disparities in Utilisation of Care in Five East African Countries},
author = {Corrine W. Ruktanonchai and Nick W. Ruktanonchai and Andrea Nove and Sofia Lopes and Carla Pezzulo and Claudio Bosco and Victor A. Alegana and Clara R. Burgert and Rogers Ayiko and Andrew SEK Charles and Nkurunziza Lambert and Esther Msechu and Esther Kathini and Zoë Matthews and Andrew J. Tatem},
url = {https://doi.org/10.1371/journal.pone.0162006},
doi = {10.1371/journal.pone.0162006},
year = {2016},
date = {2016-01-01},
journal = {PLOS ONE},
volume = {11},
number = {8},
pages = {1-17},
publisher = {Public Library of Science},
abstract = {Background Geographic accessibility to health facilities represents a fundamental barrier to utilisation of maternal and newborn health (MNH) services, driving historically hidden spatial pockets of localized inequalities. Here, we examine utilisation of MNH care as an emergent property of accessibility, highlighting high-resolution spatial heterogeneity and sub-national inequalities in receiving care before, during, and after delivery throughout five East African countries. Methods We calculated a geographic inaccessibility score to the nearest health facility at 300 x 300 m using a dataset of 9,314 facilities throughout Burundi, Kenya, Rwanda, Tanzania and Uganda. Using Demographic and Health Surveys data, we utilised hierarchical mixed effects logistic regression to examine the odds of: 1) skilled birth attendance, 2) receiving 4+ antenatal care visits at time of delivery, and 3) receiving a postnatal health check-up within 48 hours of delivery. We applied model results onto the accessibility surface to visualise the probabilities of obtaining MNH care at both high-resolution and sub-national levels after adjusting for live births in 2015. Results Across all outcomes, decreasing wealth and education levels were associated with lower odds of obtaining MNH care. Increasing geographic inaccessibility scores were associated with the strongest effect in lowering odds of obtaining care observed across outcomes, with the widest disparities observed among skilled birth attendance. Specifically, for each increase in the inaccessibility score to the nearest health facility, the odds of having skilled birth attendance at delivery was reduced by over 75% (0.24; CI: 0.19–0.3), while the odds of receiving antenatal care decreased by nearly 25% (0.74; CI: 0.61–0.89) and 40% for obtaining postnatal care (0.58; CI: 0.45–0.75). Conclusions Overall, these results suggest decreasing accessibility to the nearest health facility significantly deterred utilisation of all maternal health care services. These results demonstrate how spatial approaches can inform policy efforts and promote evidence-based decision-making, and are particularly pertinent as the world shifts into the Sustainable Goals Development era, where sub-national applications will become increasingly useful in identifying and reducing persistent inequalities.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background Geographic accessibility to health facilities represents a fundamental barrier to utilisation of maternal and newborn health (MNH) services, driving historically hidden spatial pockets of localized inequalities. Here, we examine utilisation of MNH care as an emergent property of accessibility, highlighting high-resolution spatial heterogeneity and sub-national inequalities in receiving care before, during, and after delivery throughout five East African countries. Methods We calculated a geographic inaccessibility score to the nearest health facility at 300 x 300 m using a dataset of 9,314 facilities throughout Burundi, Kenya, Rwanda, Tanzania and Uganda. Using Demographic and Health Surveys data, we utilised hierarchical mixed effects logistic regression to examine the odds of: 1) skilled birth attendance, 2) receiving 4+ antenatal care visits at time of delivery, and 3) receiving a postnatal health check-up within 48 hours of delivery. We applied model results onto the accessibility surface to visualise the probabilities of obtaining MNH care at both high-resolution and sub-national levels after adjusting for live births in 2015. Results Across all outcomes, decreasing wealth and education levels were associated with lower odds of obtaining MNH care. Increasing geographic inaccessibility scores were associated with the strongest effect in lowering odds of obtaining care observed across outcomes, with the widest disparities observed among skilled birth attendance. Specifically, for each increase in the inaccessibility score to the nearest health facility, the odds of having skilled birth attendance at delivery was reduced by over 75% (0.24; CI: 0.19–0.3), while the odds of receiving antenatal care decreased by nearly 25% (0.74; CI: 0.61–0.89) and 40% for obtaining postnatal care (0.58; CI: 0.45–0.75). Conclusions Overall, these results suggest decreasing accessibility to the nearest health facility significantly deterred utilisation of all maternal health care services. These results demonstrate how spatial approaches can inform policy efforts and promote evidence-based decision-making, and are particularly pertinent as the world shifts into the Sustainable Goals Development era, where sub-national applications will become increasingly useful in identifying and reducing persistent inequalities.
16.
Sedda, Luigi; Tatem, Andrew J.; Morley, David W.; Atkinson, Peter M.; Wardrop, Nicola A.; Pezzulo, Carla; Sorichetta, Alessandro; Kuleszo, Joanna; Rogers, David J.
Poverty, health and satellite-derived vegetation indices: their inter-spatial relationship in West Africa Journal Article
In: International Health, vol. 7, no. 2, pp. 99-106, 2015, ISSN: 1876-3413.
Abstract | Links | BibTeX | Tags:
@article{10.1093/inthealth/ihv005,
title = {Poverty, health and satellite-derived vegetation indices: their inter-spatial relationship in West Africa},
author = {Luigi Sedda and Andrew J. Tatem and David W. Morley and Peter M. Atkinson and Nicola A. Wardrop and Carla Pezzulo and Alessandro Sorichetta and Joanna Kuleszo and David J. Rogers},
url = {https://doi.org/10.1093/inthealth/ihv005},
doi = {10.1093/inthealth/ihv005},
issn = {1876-3413},
year = {2015},
date = {2015-01-01},
journal = {International Health},
volume = {7},
number = {2},
pages = {99-106},
abstract = {Previous analyses have shown the individual correlations between poverty, health and satellite-derived vegetation indices such as the normalized difference vegetation index (NDVI). However, generally these analyses did not explore the statistical interconnections between poverty, health outcomes and NDVI.In this research aspatial methods (principal component analysis) and spatial models (variography, factorial kriging and cokriging) were applied to investigate the correlations and spatial relationships between intensity of poverty, health (expressed as child mortality and undernutrition), and NDVI for a large area of West Africa.This research showed that the intensity of poverty (and hence child mortality and nutrition) varies inversely with NDVI. From the spatial point-of-view, similarities in the spatial variation of intensity of poverty and NDVI were found.These results highlight the utility of satellite-based metrics for poverty models including health and ecological components and, in general for large scale analysis, estimation and optimisation of multidimensional poverty metrics. However, it also stresses the need for further studies on the causes of the association between NDVI, health and poverty. Once these relationships are confirmed and better understood, the presence of this ecological component in poverty metrics has the potential to facilitate the analysis of the impacts of climate change on the rural populations afflicted by poverty and child mortality.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Previous analyses have shown the individual correlations between poverty, health and satellite-derived vegetation indices such as the normalized difference vegetation index (NDVI). However, generally these analyses did not explore the statistical interconnections between poverty, health outcomes and NDVI.In this research aspatial methods (principal component analysis) and spatial models (variography, factorial kriging and cokriging) were applied to investigate the correlations and spatial relationships between intensity of poverty, health (expressed as child mortality and undernutrition), and NDVI for a large area of West Africa.This research showed that the intensity of poverty (and hence child mortality and nutrition) varies inversely with NDVI. From the spatial point-of-view, similarities in the spatial variation of intensity of poverty and NDVI were found.These results highlight the utility of satellite-based metrics for poverty models including health and ecological components and, in general for large scale analysis, estimation and optimisation of multidimensional poverty metrics. However, it also stresses the need for further studies on the causes of the association between NDVI, health and poverty. Once these relationships are confirmed and better understood, the presence of this ecological component in poverty metrics has the potential to facilitate the analysis of the impacts of climate change on the rural populations afflicted by poverty and child mortality.
17.
Alegana, V. A.; Atkinson, P. M.; Pezzulo, C.; Sorichetta, A.; Weiss, D.; Bird, T.; Erbach-Schoenberg, E.; Tatem, A. J.
Fine resolution mapping of population age-structures for health and development applications Journal Article
In: Journal of The Royal Society Interface, vol. 12, no. 105, pp. 20150073, 2015.
Abstract | Links | BibTeX | Tags:
@article{doi:10.1098/rsif.2015.0073,
title = {Fine resolution mapping of population age-structures for health and development applications},
author = {V. A. Alegana and P. M. Atkinson and C. Pezzulo and A. Sorichetta and D. Weiss and T. Bird and E. Erbach-Schoenberg and A. J. Tatem},
url = {https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2015.0073},
doi = {10.1098/rsif.2015.0073},
year = {2015},
date = {2015-01-01},
journal = {Journal of The Royal Society Interface},
volume = {12},
number = {105},
pages = {20150073},
abstract = {The age-group composition of populations varies considerably across the world, and obtaining accurate, spatially detailed estimates of numbers of children under 5 years is important in designing vaccination strategies, educational planning or maternal healthcare delivery. Traditionally, such estimates are derived from population censuses, but these can often be unreliable, outdated and of coarse resolution for resource-poor settings. Focusing on Nigeria, we use nationally representative household surveys and their cluster locations to predict the proportion of the under-five population in 1 × 1 km using a Bayesian hierarchical spatio-temporal model. Results showed that land cover, travel time to major settlements, night-time lights and vegetation index were good predictors and that accounting for fine-scale variation, rather than assuming a uniform proportion of under 5 year olds can result in significant differences in health metrics. The largest gaps in estimated bednet and vaccination coverage were in Kano, Katsina and Jigawa. Geolocated household surveys are a valuable resource for providing detailed, contemporary and regularly updated population age-structure data in the absence of recent census data. By combining these with covariate layers, age-structure maps of unprecedented detail can be produced to guide the targeting of interventions in resource-poor settings.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The age-group composition of populations varies considerably across the world, and obtaining accurate, spatially detailed estimates of numbers of children under 5 years is important in designing vaccination strategies, educational planning or maternal healthcare delivery. Traditionally, such estimates are derived from population censuses, but these can often be unreliable, outdated and of coarse resolution for resource-poor settings. Focusing on Nigeria, we use nationally representative household surveys and their cluster locations to predict the proportion of the under-five population in 1 × 1 km using a Bayesian hierarchical spatio-temporal model. Results showed that land cover, travel time to major settlements, night-time lights and vegetation index were good predictors and that accounting for fine-scale variation, rather than assuming a uniform proportion of under 5 year olds can result in significant differences in health metrics. The largest gaps in estimated bednet and vaccination coverage were in Kano, Katsina and Jigawa. Geolocated household surveys are a valuable resource for providing detailed, contemporary and regularly updated population age-structure data in the absence of recent census data. By combining these with covariate layers, age-structure maps of unprecedented detail can be produced to guide the targeting of interventions in resource-poor settings.
