Mapping adolescent first births within three east African countries using data from Demographic and Health Surveys: exploring geospatial methods to inform policy

Background: Early adolescent pregnancy presents a major barrier to the health and wellbeing of young women and their children. Previous studies suggest geographic heterogeneity in adolescent births, with clear “hot spots” experiencing very high prevalence of teenage pregnancy. As the reduction of adolescent pregnancy is a priority in many countries, further detailed information of the geographical areas where they most commonly occur is of value to national and district level policy makers. The aim of this study is to develop a comprehensive assessment of the geographical distribution of adolescent first births in Uganda, Kenya and Tanzania using Demographic and Household (DHS) data using descriptive, spatial analysis and spatial modelling methods.Methods: The most recent Demographic and Health Surveys (DHS) among women aged 20 to 29 in Tanzania, Kenya, and Uganda were utilised. Analyses were carried out on first births occurring before the age of 20 years, but were disaggregated in to three age groups: &lt;16, 16/17 and 18/19 years. In addition to basic descriptive choropleths, prevalence maps were created from the GPS-located cluster data utilising adaptive bandwidth kernel density estimates. To map adolescent first birth at district level with estimates of uncertainty, a Bayesian hierarchical regression modelling approach was used, employing the Integrated Nested Laplace Approximation (INLA) technique.Results: The findings show marked geographic heterogeneity among adolescent first births, particularly among those under 16 years. Disparities are greater in Kenya and Uganda than Tanzania. The INLA analysis which produces estimates from smaller areas suggest “pockets” of high prevalence of first births, with marked differences between neighbouring districts. Many of these high prevalence areas can be linked with underlying poverty.Conclusions: There is marked geographic heterogeneity in the prevalence of adolescent first births in East Africa, particularly in the youngest age groups. Geospatial techniques can identify these inequalities and provide policy-makers with the information needed to target areas of high prevalence and focus scarce resources where they are most needed.<br/

Identifying malaria transmission foci for elimination using human mobility data

Humans move frequently and tend to carry parasites among areas with endemic malaria and into areas where local transmission is unsustainable. Human-mediated parasite mobility can thus sustain parasite populations in areas where they would otherwise be absent. Data describing human mobility and malaria epidemiology can help classify landscapes into parasite demographic sources and sinks, ecological concepts that have parallels in malaria control discussions of transmission foci. By linking transmission to parasite flow, it is possible to stratify landscapes for malaria control and elimination, as sources are disproportionately important to the regional persistence of malaria parasites. Here, we identify putative malaria sources and sinks for pre-elimination Namibia using malaria parasite rate (PR) maps and call data records from mobile phones, using a steady-state analysis of a malaria transmission model to infer where infections most likely occurred. We also examined how the landscape of transmission and burden changed from the pre-elimination setting by comparing the location and extent of predicted pre-elimination transmission foci with modeled incidence for 2009. This comparison suggests that while transmission was spatially focal pre-elimination, the spatial distribution of cases changed as burden declined. The changing spatial distribution of burden could be due to importation, with cases focused around importation hotspots, or due to heterogeneous application of elimination effort. While this framework is an important step towards understanding progressive changes in malaria distribution and the role of subnational transmission dynamics in a policy-relevant way, future work should account for international parasite movement, utilize real time surveillance data, and relax the steady state assumption required by the presented model

Making SDGs work for climate change hotspots

The impacts of climate change on people's livelihoods have been widely documented. It is expected that climate and environmental change will hamper poverty reduction, or even exacerbate poverty in some or all of its dimensions. Changes in the biophysical environment, such as droughts, flooding, water quantity and quality, and degrading ecosystems, are expected to affect opportunities for people to generate income. These changes, combined with a deficiency in coping strategies and innovation to adapt to particular climate change threats, are in turn likely to lead to increased economic and social vulnerability of households and communities, especially amongst the poorest

Mapping spatial and temporal inequalities in utilisation of maternal and newborn care in five East African countries

Historically, maternal and newborn health (MNH) outcomes used to monitor progress in achieving global and national targets have been measured at an aggregate level, showing vast inequalities between and within countries. To ensure no one is left behind in improving health, researchers have called for the spatial and temporal disaggregation of MNH data. This thesis aims to generate high spatial resolution data over time that can be used to monitor progress in reducing inequalities amongst utilisation of key MNH services in the East African Community (EAC) region, including Burundi, Kenya, Rwanda, Tanzania, and Uganda.Following a ‘three-paper’ format, the first paper in this thesis employs a hierarchical mixed effects logistic regression framework, to estimate the odds of: 1) skilled birth attendance (SBA), 2) receiving 4+ antenatal care (ANC) visits, and 3) receiving a postnatal health check-up (PNC) within 48 hours of delivery. Model results are applied to an 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. Across all outcomes, decreasing wealth and education levels are associated with lower odds of obtaining MNH care, while increasing geographic inaccessibility scores are associated with the strongest effect in lowering odds of obtaining care observed across outcomes, with the widest disparities observed among skilled birth attendance.The second paper explores temporal trends in absolute and relative spatial inequalities in utilisation of these MNH services between 1990 and 2015. A Bayesian framework is employed to generate sub-national estimates of utilisation of SBA, ANC, and PNC over several time points. Absolute change in estimates over time is reported, as well as relative change in ratios of the best- to-worst performing districts per country. Across all countries, the greatest spatial equality is observed among ANC, while SBA and PNC tend to have greater spatial variability. Lastly, while progress has been made to reduce coverage gaps between districts, improvement in PNC coverage has stagnated and should be monitored closely over the coming decades.The final paper comprising this work explores the trade-off between increasing spatial resolution in model inputs and resulting model uncertainty, with aims of understanding the optimal spatial resolution to report health outcomes. Prevalence of childbirth via c-section is estimated in Tanzania, using geospatial covariates at varying levels of spatial coarseness within a Bayesian model framework. Uncertainty in posterior outcomes is reported as the distribution of 95% credible intervals at each spatial resolution, and visualised at the spatial resolution with the greatest model precision. Overall, higher spatial resolution input increases model uncertainty, while model precision is best approximated at the highest spatial resolution, suggesting an important policy trade-off between identifying concealed spatial heterogeneities in health indicators.This thesis makes substantive contributions to the literature by outlining where spatial inequalities in key MNH services are occurring within the EAC region and how these disparities are evolving over time. This work also makes methodological contributions by demonstrating how spatial approaches can be used to monitor health indicators, as well as exploring uncertainty in the application of these techniques, with important implications in communicating results to policy makers. These techniques can be applied across health and development outcomes, notably across Sustainable Development Goal indictors, ensuring “no one left behind” by 2030

Using Google Location History data to quantify fine-scale human mobility

Abstract Background Human mobility is fundamental to understanding global issues in the health and social sciences such as disease spread and displacements from disasters and conflicts. Detailed mobility data across spatial and temporal scales are difficult to collect, however, with movements varying from short, repeated movements to work or school, to rare migratory movements across national borders. While typical sources of mobility data such as travel history surveys and GPS tracker data can inform different typologies of movement, almost no source of readily obtainable data can address all types of movement at once. Methods Here, we collect Google Location History (GLH) data and examine it as a novel source of information that could link fine scale mobility with rare, long distance and international trips, as it uniquely spans large temporal scales with high spatial granularity. These data are passively collected by Android smartphones, which reach increasingly broad audiences, becoming the most common operating system for accessing the Internet worldwide in 2017. We validate GLH data against GPS tracker data collected from Android users in the United Kingdom to assess the feasibility of using GLH data to inform human movement. Results We find that GLH data span very long temporal periods (over a year on average in our sample), are spatially equivalent to GPS tracker data within 100 m, and capture more international movement than survey data. We also find GLH data avoid compliance concerns seen with GPS trackers and bias in self-reported travel, as GLH is passively collected. We discuss some settings where GLH data could provide novel insights, including infrastructure planning, infectious disease control, and response to catastrophic events, and discuss advantages and disadvantages of using GLH data to inform human mobility patterns. Conclusions GLH data are a greatly underutilized and novel dataset for understanding human movement. While biases exist in populations with GLH data, Android phones are becoming the first and only device purchased to access the Internet and various web services in many middle and lower income settings, making these data increasingly appropriate for a wide range of scientific questions

Using Google location history data to quantify fine-scale human mobility

Background: Human mobility is fundamental to understanding global issues in the health and social sciences such as disease spread and displacements from disasters and conflicts. Detailed mobility data across spatial and temporal scales are difficult to collect, however, with movements varying from short, repeated movements to work or school, to rare migratory movements across national borders. While typical sources of mobility data such as travel history surveys and GPS tracker data can inform different typologies of movement, almost no source of readily obtainable data can address all types of movement at once. Methods: Here, we collect Google Location History (GLH) data and examine it as a novel source of information that could link fine scale mobility with rare, long distance and international trips, as it uniquely spans large temporal scales with high spatial granularity. These data are passively collected by Android smartphones, which reach increasingly broad audiences, becoming the most common operating system for accessing the Internet worldwide in 2017. We validate GLH data against GPS tracker data collected from Android users in the United Kingdom to assess the feasibility of using GLH data to inform human movement. Results: We find that GLH data span very long temporal periods (over a year on average in our sample), are spatially equivalent to GPS tracker data within 100m, and capture more international movement than survey data. We also find GLH data avoid compliance concerns seen with GPS trackers and bias in self-reported travel, as GLH is passively collected. We discuss some settings where GLH data could provide novel insights, including infrastructure planning, infectious disease control, and response to catastrophic events, and discuss advantages and disadvantages of using GLH data to inform human mobility patterns. Conclusions: GLH data are a greatly underutilized and novel dataset for understanding human movement. While biases exist in populations with GLH data, Android phones are becoming the first and only device purchased to access the Internet and various web services in many middle and lower income settings, making these data increasingly appropriate for a wide range of scientific questions.</p

MOESM2 of Using Google Location History data to quantify fine-scale human mobility

Additional file 2. Literature review data

MOESM1 of Using Google Location History data to quantify fine-scale human mobility

Additional file 1. Study materials