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

Estimating uncertainty in geospatial modelling at multiple spatial resolutions: the pattern of delivery via caesarean section in Tanzania

Visualising maternal and newborn health (MNH) outcomes at fine spatial resolutions is crucial to ensuring the most vulnerable women and children are not left behind in improving health. Disaggregated data on life-saving MNH interventions remain difficult to obtain, however, necessitating the use of Bayesian geostatistical models to map outcomes at small geographical areas. While these methods have improved model parameter estimates and precision among spatially correlated health outcomes and allowed for the quantification of uncertainty, few studies have examined the trade-off between higher spatial resolution modelling and how associated uncertainty propagates. Here, we explored the trade-offbetween model outcomes and associated uncertainty at increasing spatial resolutions by quantifying the posterior distribution of delivery via caesarean section (c-section) in Tanzania. Overall, in modelling delivery via c-section at multiple spatial resolutions, we demonstrated poverty to be negatively correlated across spatial resolutions, suggesting important disparities in obtaining life-saving obstetric surgery persist across sociodemographic factors. Lastly, we found that while uncertainty increased with higher spatial resolution input, model precision was best approximated at the highest spatial resolution, suggesting an important policy trade-off between identifying concealed spatial heterogeneities in health indicators

Spatial inequalities in skilled attendance at birth in Ghana: A multilevel analysis integrating health facility databases with household survey data

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

Temporal trends in spatial inequalities of maternal and newborn health services among four East African countries, 1999 - 2015

Background: Sub-Saharan Africa continues to account for the highest regional maternal mortality ratio (MMR) in the world, at just under 550 maternal deaths per 100,000 live births in 2015, compared to a global rate of 216 deaths. Spatial inequalities in access to life-saving maternal and newborn health (MNH) services persist within sub-Saharan Africa, however, with varied improvement over the past two decades. While previous research within the East African Community (EAC) region has examined utilisation of MNH care as an emergent property of geographic accessibility, no research has examined how these spatial inequalities have evolved over time at similar spatial scales. Methods: Here, we analysed temporal trends of spatial inequalities in utilisation of antenatal care (ANC), skilled birth attendance (SBA), and postnatal care (PNC) among four East African countries. Specifically, we used Bayesian spatial statistics to generate district-level estimates of these services for several time points using Demographic and Health Surveys data in Kenya, Tanzania, Rwanda, and Uganda. We examined temporal trends of both absolute and relative indices over time, including the absolute difference between estimates, as well as change in performance ratios of the best-to-worst performing districts per country. Results: Across all countries, we found the greatest spatial equality in ANC, while SBA and PNC tended to have greater spatial variability. In particular, Rwanda represented the only country to consistently increase coverage and reduce spatial inequalities across all services. Conversely, Tanzania had noticeable reductions in ANC coverage throughout most of the country, with some areas experiencing as much as a 55% reduction. Encouragingly, however, we found that performance gaps between districts have generally decreased or remained stably low across all countries, suggesting countries are making improvements to reduce spatial inequalities in these services. Conclusions: We found that while the region is generally making progress in reducing spatial gaps across districts, improvement in PNC coverage has stagnated, and should be monitored closely over the coming decades. This study is the first to report temporal trends in district-level estimates in MNH services across the EAC region, and these findings establish an important baseline of evidence for the Sustainable Development Goal era. <br/