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The impacts of climate change on people’s livelihoods have been widely documented.1 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.

Timely and precise detection of emerging infections is imperative for effective outbreak management and disease control. Human mobility significantly influences the spatial transmission dynamics of infectious diseases. Spatial sampling, integrating the spatial structure of the target, holds promise as an approach for testing allocation in detecting infections, and leveraging information on individuals' movement and contact behavior can enhance targeting precision. This study introduces a spatial sampling framework informed by spatiotemporal analysis of human mobility data, aiming to optimize the allocation of testing resources for detecting emerging infections. Mobility patterns, derived from clustering point-of-interest and travel data, are integrated into four spatial sampling approaches at the community level. We evaluate the proposed mobility-based spatial sampling by analyzing both actual and simulated outbreaks, considering scenarios of transmissibility, intervention timing, and population density in cities. Results indicate that leveraging inter-community movement data and initial case locations, the proposed Case Flow Intensity (CFI) and Case Transmission Intensity (CTI)-informed spatial sampling enhances community-level testing efficiency by reducing the number of individuals screened while maintaining a high accuracy rate in infection identification. Furthermore, the prompt application of CFI and CTI within cities is crucial for effective detection, especially in highly contagious infections within densely populated areas. With the widespread use of human mobility data for infectious disease responses, the proposed theoretical framework extends spatiotemporal data analysis of mobility patterns into spatial sampling, providing a cost-effective solution to optimize testing resource deployment for containing emerging infectious diseases., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Targeted public health interventions for an emerging epidemic are essential for preventing pandemics. During 2020-2022, China invested significant efforts in strict zero-COVID measures to contain outbreaks of varying scales caused by different SARS-CoV-2 variants. Based on a multi-year empirical dataset containing 131 outbreaks observed in China from April 2020 to May 2022 and simulated scenarios, we ranked the relative intervention effectiveness by their reduction in instantaneous reproduction number. We found that, overall, social distancing measures (38% reduction, 95% prediction interval 31-45%), face masks (30%, 17-42%) and close contact tracing (28%, 24-31%) were most effective. Contact tracing was crucial in containing outbreaks during the initial phases, while social distancing measures became increasingly prominent as the spread persisted. In addition, infections with higher transmissibility and a shorter latent period posed more challenges for these measures. Our findings provide quantitative evidence on the effects of public-health measures for zeroing out emerging contagions in different contexts., (© 2023. Springer Nature Limited.)

Concentrated animal-feeding operations (CAFOs) emit pollution into surrounding areas, and previous research has found associations with poor health outcomes. The objective of this study was to investigate if home proximity to poultry CAFOs during pregnancy is associated with adverse birth outcomes, including preterm birth (PTB) and low birth weight (LBW). This study includes births occurring on the Eastern Shore, Virginia, from 2002 to 2015 (N = 5768). A buffer model considering CAFOs within 1 km, 2 km, and 5 km of the maternal residence and an inverse distance weighted (IDW) approach were used to estimate proximity to CAFOs. Associations between proximity to poultry CAFOs and adverse birth outcomes were determined by using regression models, adjusting for available covariates. We found a -52.8 g (-95.8, -9.8) change in birthweight and a -1.51 (-2.78, -0.25) change in gestational days for the highest tertile of inverse distance to CAFOs. Infants born with a maternal residence with at least one CAFO within a 5 km buffer weighed -47 g (-94.1, -1.7) less than infants with no CAFOs within a 5 km buffer of the maternal address. More specific measures of exposure pathways via air and water should be used in future studies to refine mediators of the association found in the present study., Competing Interests: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Background: Modern transportation plays a key role in the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and new variants. However, little is known about the exact transmission risk of the virus on airplanes., Methods: Using the itinerary and epidemiological data of coronavirus disease 2019 (COVID-19) cases and close contacts on domestic airplanes departing from Wuhan city in China before the lockdown on 23 January 2020, we estimated the upper and lower bounds of overall transmission risk of COVID-19 among travelers., Results: In total, 175 index cases were identified among 5797 passengers on 177 airplanes. The upper and lower attack rates (ARs) of a seat were 0.60% (34/5622, 95% confidence interval [CI] .43-.84%) and 0.33% (18/5400, 95% CI .21-.53%), respectively. In the upper- and lower-bound risk estimates, each index case infected 0.19 (SD 0.45) and 0.10 (SD 0.32) cases, respectively. The seats immediately adjacent to the index cases had an AR of 9.2% (95% CI 5.7-14.4%), with a relative risk 27.8 (95% CI 14.4-53.7) compared to other seats in the upper limit estimation. The middle seat had the highest AR (0.7%, 95% CI .4%-1.2%). The upper-bound AR increased from 0.7% (95% CI 0.5%-1.0%) to 1.2% (95% CI .4-3.3%) when the co-travel time increased from 2.0 hours to 3.3 hours., Conclusions: The ARs among travelers varied by seat distance from the index case and joint travel time, but the variation was not significant between the types of aircraft. The overall risk of SARS-CoV-2 transmission during domestic travel on planes was relatively low. These findings can improve our understanding of COVID-19 spread during travel and inform response efforts in the pandemic., (© The Author(s) 2021. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.)

Non-pharmaceutical interventions (NPIs) and vaccination are two fundamental approaches for mitigating the coronavirus disease 2019 (COVID-19) pandemic. However, the real-world impact of NPIs versus vaccination, or a combination of both, on COVID-19 remains uncertain. To address this, we built a Bayesian inference model to assess the changing effect of NPIs and vaccination on reducing COVID-19 transmission, based on a large-scale dataset including epidemiological parameters, virus variants, vaccines, and climate factors in Europe from August 2020 to October 2021. We found that (1) the combined effect of NPIs and vaccination resulted in a 53% (95% confidence interval: 42-62%) reduction in reproduction number by October 2021, whereas NPIs and vaccination reduced the transmission by 35% and 38%, respectively; (2) compared with vaccination, the change of NPI effect was less sensitive to emerging variants; (3) the relative effect of NPIs declined 12% from May 2021 due to a lower stringency and the introduction of vaccination strategies. Our results demonstrate that NPIs were complementary to vaccination in an effort to reduce COVID-19 transmission, and the relaxation of NPIs might depend on vaccination rates, control targets, and vaccine effectiveness concerning extant and emerging variants., (© 2022. The Author(s).)

Background: Previous work has determined an association between proximity to active surface mining within Central Appalachia and an increased risk of preterm birth (PTB) and low birthweight (LBW). Multiple potential exposure pathways may exist; however, including inhalation of particulate matter (airshed exposure), or exposure to impacted surface waters (watershed exposure). We hypothesize that this relationship is mediated by exposure to contaminants along one or both of these pathways., Methods: We geolocated 194,084 birth records through health departments in WV, KY, VA, and TN between 1990 and 2015. We performed a mediation analysis, iteratively including within our models: (a) the percent of active surface mining within 5 km of maternal residence during gestation; (b) the cumulative surface mining airshed trajectories experienced during gestation; and (c) the percent of active surface mining occurring within the watershed of residency during gestation., Results: Our baseline models found that active surface mining was associated with an increased odds of PTB (1.09, 1.05-1.13) and LBW (1.06, 1.02-1.11), controlling for individual-level predictors. When mediators were added to the baseline model, the association between active mining and birth outcomes became nonsignificant (PTB: 0.48, 0.14-1.58; LBW 0.78, 0.19-3.00), whereas the association between PTB and LBW remained significant by airshed exposure (PTB: 1.14, 1.11-1.18; LBW: 1.06, 1.03-1.10)., Conclusions: Our results found that surface mining airsheds at least partially explained the association between active mining and adverse birth outcomes, consistent with a hypothesis of mediation, while mediation via the watershed pathway was less evident., Competing Interests: The authors declare that they have no conflicts of interest with regard to the content of this report., (Copyright © 2022 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of The Environmental Epidemiology. All rights reserved.)

Governments worldwide have rapidly deployed non-pharmaceutical interventions (NPIs) to mitigate the COVID-19 pandemic. However, the effect of these individual NPI measures across space and time has yet to be sufficiently assessed, especially with the increase of policy fatigue and the urge for NPI relaxation in the vaccination era. Using the decay ratio in the suppression of COVID-19 infections and multi-source big data, we investigated the changing performance of different NPIs across waves from global and regional levels (in 133 countries) to national and subnational (in the United States of America [USA]) scales before the implementation of mass vaccination. The synergistic effectiveness of all NPIs for reducing COVID-19 infections declined along waves, from 95.4% in the first wave to 56.0% in the third wave recently at the global level and similarly from 83.3% to 58.7% at the USA national level, while it had fluctuating performance across waves on regional and subnational scales. Regardless of geographical scale, gathering restrictions and facial coverings played significant roles in epidemic mitigation before the vaccine rollout. Our findings have important implications for continued tailoring and implementation of NPI strategies, together with vaccination, to mitigate future COVID-19 waves, caused by new variants, and other emerging respiratory infectious diseases., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Author(s).)

Public and school holidays have important impacts on population mobility and dynamics across multiple spatial and temporal scales, subsequently affecting the transmission dynamics of infectious diseases and many socioeconomic activities. However, worldwide data on public and school holidays for understanding their changes across regions and years have not been assembled into a single, open-source and multitemporal dataset. To address this gap, an open access archive of data on public and school holidays in 2010-2019 across the globe at daily, weekly, and monthly timescales was constructed. Airline passenger volumes across 90 countries from 2010 to 2018 were also assembled to illustrate the usage of the holiday data for understanding the changing spatiotemporal patterns of population movements., (© 2022. The Author(s).)

Background: Household survey data are frequently used to measure reproductive, maternal, newborn, child and adolescent health (RMNCAH) service utilisation in low and middle income countries. However, these surveys are typically only undertaken every 5 years and tend to be representative of larger geographical administrative units. Investments in district health management information systems (DHMIS) have increased the capability of countries to collect continuous information on the provision of RMNCAH services at health facilities. However, reliable and recent data on population distributions and demographics at subnational levels necessary to construct RMNCAH coverage indicators are often missing. One solution is to use spatially disaggregated gridded datasets containing modelled estimates of population counts. Here, we provide an overview of various approaches to the production of gridded demographic datasets and outline their potential and their limitations. Further, we show how gridded population estimates can be used as alternative denominators to produce RMNCAH coverage metrics in combination with data from DHMIS, using childhood vaccination as examples., Methods: We constructed indicators on the percentage of children one year old for diphtheria, pertussis and tetanus vaccine dose 3 (DTP3) and measles vaccine dose (MCV1) in Zambia and Nigeria at district levels. For the numerators, information on vaccines doses was obtained from each country's respective DHMIS. For the denominators, the number of children was obtained from 3 different sources including national population projections and aggregated gridded estimates derived using top-down and bottom-up geospatial methods., Results: In Zambia, vaccination estimates utilising the bottom-up approach to population estimation substantially reduced the number of districts with > 100% coverage of DTP3 and MCV1 compared to estimates using population projection and the top-down method. In Nigeria, results were mixed with bottom-up estimates having a higher number of districts > 100% and estimates using population projections performing better particularly in the South., Conclusions: Gridded demographic data utilising traditional and novel data sources obtained from remote sensing offer new potential in the absence of up to date census information in the estimation of RMNCAH indicators. However, the usefulness of gridded demographic data is dependent on several factors including the availability and detail of input data., (© 2021. The Author(s).)

Understanding seasonal human mobility at subnational scales has important implications across sciences, from urban planning efforts to disease modelling and control. Assessing how, when, and where populations move over the course of the year, however, requires spatially and temporally resolved datasets spanning large periods of time, which can be rare, contain sensitive information, or may be proprietary. Here, we aim to explore how a set of broadly available covariates can describe typical seasonal subnational mobility in Kenya pre-COVID-19, therefore enabling better modelling of seasonal mobility across low- and middle-income country (LMIC) settings in non-pandemic settings. To do this, we used the Google Aggregated Mobility Research Dataset, containing anonymized mobility flows aggregated over users who have turned on the Location History setting, which is off by default. We combined this with socioeconomic and geospatial covariates from 2018 to 2019 to quantify seasonal changes in domestic and international mobility patterns across years. We undertook a spatiotemporal analysis within a Bayesian framework to identify relevant geospatial and socioeconomic covariates explaining human movement patterns, while accounting for spatial and temporal autocorrelations. Typical pre-pandemic mobility patterns in Kenya mostly consisted of shorter, within-county trips, followed by longer domestic travel between counties and international travel, which is important in establishing how mobility patterns changed post-pandemic. Mobility peaked in August and December, closely corresponding to school holiday seasons, which was found to be an important predictor in our model. We further found that socioeconomic variables including urbanicity, poverty, and female education strongly explained mobility patterns, in addition to geospatial covariates such as accessibility to major population centres and temperature. These findings derived from novel data sources elucidate broad spatiotemporal patterns of how populations move within and beyond Kenya, and can be easily generalized to other LMIC settings before the COVID-19 pandemic. Understanding such pre-pandemic mobility patterns provides a crucial baseline to interpret both how these patterns have changed as a result of the pandemic, as well as whether human mobility patterns have been permanently altered once the pandemic subsides. Our findings outline key correlates of mobility using broadly available covariates, alleviating the data bottlenecks of highly sensitive and proprietary mobile phone datasets, which many researchers do not have access to. These results further provide novel insight on monitoring mobility proxies in the context of disease surveillance and control efforts through LMIC settings., (© 2021. The Author(s).)

Travel restrictions and physical distancing have been implemented across the world to mitigate the coronavirus disease 2019 (COVID-19) pandemic, but studies are needed to understand their effectiveness across regions and time. Based on the population mobility metrics derived from mobile phone geolocation data across 135 countries or territories during the first wave of the pandemic in 2020, we built a metapopulation epidemiological model to measure the effect of travel and contact restrictions on containing COVID-19 outbreaks across regions. We found that if these interventions had not been deployed, the cumulative number of cases could have shown a 97-fold (interquartile range 79-116) increase, as of May 31, 2020. However, their effectiveness depended upon the timing, duration, and intensity of the interventions, with variations in case severity seen across populations, regions, and seasons. Additionally, before effective vaccines are widely available and herd immunity is achieved, our results emphasize that a certain degree of physical distancing at the relaxation of the intervention stage will likely be needed to avoid rapid resurgences and subsequent lockdowns., (© 2021 THE AUTHORS.)

The coronavirus disease 2019 (COVID-19) pandemic has posed substantial challenges to the formulation of preventive interventions, particularly since the effects of physical distancing measures and upcoming vaccines on reducing susceptible social contacts and eventually halting transmission remain unclear. Here, using anonymized mobile geolocation data in China, we devise a mobility-associated social contact index to quantify the impact of both physical distancing and vaccination measures in a unified way. Building on this index, our epidemiological model reveals that vaccination combined with physical distancing can contain resurgences without relying on stay-at-home restrictions, whereas a gradual vaccination process alone cannot achieve this. Further, for cities with medium population density, vaccination can reduce the duration of physical distancing by 36% to 78%, whereas for cities with high population density, infection numbers can be well-controlled through moderate physical distancing. These findings improve our understanding of the joint effects of vaccination and physical distancing with respect to a city's population density and social contact patterns.

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., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Background: The COVID-19 pandemic has posed an ongoing global crisis, but how the virus spread across the world remains poorly understood. This is of vital importance for informing current and future pandemic response strategies., Methods: We performed two independent analyses, travel network-based epidemiological modelling and Bayesian phylogeographic inference, to investigate the intercontinental spread of COVID-19., Results: Both approaches revealed two distinct phases of COVID-19 spread by the end of March 2020. In the first phase, COVID-19 largely circulated in China during mid-to-late January 2020 and was interrupted by containment measures in China. In the second and predominant phase extending from late February to mid-March, unrestricted movements between countries outside of China facilitated intercontinental spread, with Europe as a major source. Phylogenetic analyses also revealed that the dominant strains circulating in the USA were introduced from Europe. However, stringent restrictions on international travel across the world since late March have substantially reduced intercontinental transmission., Conclusions: Our analyses highlight that heterogeneities in international travel have shaped the spatiotemporal characteristics of the pandemic. Unrestricted travel caused a large number of COVID-19 exportations from Europe to other continents between late February and mid-March, which facilitated the COVID-19 pandemic. Targeted restrictions on international travel from countries with widespread community transmission, together with improved capacity in testing, genetic sequencing and contact tracing, can inform timely strategies for mitigating and containing ongoing and future waves of COVID-19 pandemic., (© International Society of Travel Medicine 2020. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

As rates of new coronavirus disease 2019 (COVID-19) cases decline across Europe owing to nonpharmaceutical interventions such as social distancing policies and lockdown measures, countries require guidance on how to ease restrictions while minimizing the risk of resurgent outbreaks. We use mobility and case data to quantify how coordinated exit strategies could delay continental resurgence and limit community transmission of COVID-19. We find that a resurgent continental epidemic could occur as many as 5 weeks earlier when well-connected countries with stringent existing interventions end their interventions prematurely. Further, we find that appropriate coordination can greatly improve the likelihood of eliminating community transmission throughout Europe. In particular, synchronizing intermittent lockdowns across Europe means that half as many lockdown periods would be required to end continent-wide community transmission., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Background: 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., Methods: Availability was estimated using the World Health Organization guidelines for monitoring emergency obstetric care, defined as the number of hospitals per 500,000 population. Geographical accessibility was estimated using the Lancet Commission on Global Surgery, defined as the proportion of pregnancies within 2 h of the nearest hospital. These were calculated using geo-located hospital data for sub-Saharan Africa, with their associated travel times, along with small area estimates of population and pregnancies. The results of the availability analysis were then compared to the results of the accessibility analysis, to highlight differences between the availability and geographical accessibility of services., Results: Despite most countries meeting the targets at the national level, we identified substantial sub-national variation, with 58% of the countries having at least one administrative unit not meeting the availability target at province level and 95% at district level. Similarly, 56% of the countries were found to have at least one province not meeting the accessibility target, increasing to 74% at the district level. When comparing both availability and accessibility within countries, most countries were found to meet both targets; however sub-nationally, many countries fail to meet one or the other., Conclusion: While many of the countries met the targets at the national level, we found large within-country variation. Monitoring under the current guidelines, using national averages, can mask these areas of need, with potential consequences for vulnerable women and children. It is imperative therefore that indicators for monitoring the availability and geographical accessibility of health care reflect this need, if targets for universal health coverage are to be met by 2030.

Objective: This study aimed at using survey data to predict skilled attendance at birth (SBA) across Ghana from healthcare quality and health facility accessibility., Methods: Through a cross-sectional, observational study, we used a random intercept mixed effects multilevel logistic modelling approach to estimate the odds of having SBA and 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., Results: Within 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., Conclusion: Given 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., (© 2020 The Authors Tropical Medicine & International Health Published by John Wiley & Sons Ltd.)

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-off between 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., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ.)

Background: Existence of inequalities in quality and access to healthcare services at subnational levels has been identified despite a decline in maternal and perinatal mortality rates at national levels, leading to the need to investigate such conditions using geographical analysis. The need to assess the accuracy of global demographic distribution datasets at all subnational levels arises from the current emphasis on subnational monitoring of maternal and perinatal health progress, by the new targets stated in the Sustainable Development Goals., Methods: The analysis involved comparison of four models generated using Worldpop methods, incorporating region-specific input data, as measured through the Community Level Intervention for Pre-eclampsia (CLIP) project. Normalised root mean square error was used to determine and compare the models' prediction errors at different administrative unit levels., Results: The models' prediction errors are lower at higher administrative unit levels. All datasets showed the same pattern for both the live birth and pregnancy estimates. The effect of improving spatial resolution and accuracy of input data was more prominent at higher administrative unit levels., Conclusion: The validation successfully highlighted the impact of spatial resolution and accuracy of maternal and perinatal health data in modelling estimates of pregnancies and live births. There is a need for more data collection techniques that conduct comprehensive censuses like the CLIP project. It is also imperative for such projects to take advantage of the power of mapping tools at their disposal to fill the gaps in the availability of datasets for populated areas., Competing Interests: Competing interests: None declared.

Introduction: Adolescent pregnancy is associated with significant risks and disadvantages for young women and girls and their children. A clear understanding of population subgroups with particularly high prevalence of first births in adolescence is vital if appropriate national responses are to be developed. This paper aims to provide detailed data on socioeconomic and geographic inequities in first births to adolescents in Nepal, including wealth quintile, education, rural/urban residence and geographic region. A key element is the use of geospatial modelling to develop estimates for the prevalence of adolescent births at the district level., Methods: The study uses data from the 2011 Nepal Demographic and Health Survey. Initial cross-tabulations present disaggregated data by socioeconomic status and basic geographic region. Estimates of prevalence of adolescent first births at the district level are creating by regression modelling using the Integrated Nested Laplace Approximation package in R software., Results: Our findings show that 40% of women had given birth before the age of 20 years, with 5% giving birth before 16 years. First births to adolescents remain common among poorer, less educated and rural women. Geographic disparities are striking, with estimates for the percentage of women giving birth before 20 years ranging from 35% to 53% by region. District level estimates showed even more marked differentials (26%-67% had given birth by 20 years), with marked heterogeneity even within regions. In some districts, estimates for the prevalence of first birth among the youngest age groups (<16 years) are high., Conclusion: Important geographic and socioeconomic inequities exist in adolescent first births. In some districts and within some subgroups, there remain high levels of adolescent first births, including births to very young adolescents. The use of Bayesian geospatial modelling techniques can be used by policymakers to target resources., Competing Interests: Competing interests: None declared.

Competing Interests: Competing interests: None declared.

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.

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.

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 1 km at the equator). All products are available through WorldPop.

This correspondence argues and offers recommendations for how Geographic Information System (GIS) applied to maternal and newborn health data could potentially be used as part of the broader efforts for ending preventable maternal and newborn mortality. These recommendations were generated from a technical consultation on reporting and mapping maternal deaths that was held in Washington, DC from January 12 to 13, 2015 and hosted by the United States Agency for International Development's (USAID) global Maternal and Child Survival Program (MCSP). Approximately 72 participants from over 25 global health organizations, government agencies, donors, universities, and other groups participated in the meeting.The meeting placed emphases on how improved use of mapping could contribute to the post-2015 United Nation's Sustainable Development Goals (SDGs), agenda in general and to contribute to better maternal and neonatal health outcomes in particular. Researchers and policy makers have been calling for more equitable improvement in Maternal and Newborn Health (MNH), specifically addressing hard-to-reach populations at sub-national levels. Data visualization using mapping and geospatial analyses play a significant role in addressing the emerging need for improved spatial investigation at subnational scale. This correspondence identifies key challenges and recommendations so GIS may be better applied to maternal health programs in resource poor settings. The challenges and recommendations are broadly grouped into three categories: ancillary geospatial and MNH data sources, technical and human resources needs and community participation.

Background: An increase in effective malaria control since 2000 has contributed to a decline in global malaria morbidity and mortality. Knowing when and how existing interventions could be combined to maximise their impact on malaria vectors can provide valuable information for national malaria control programs in different malaria endemic settings. Here, we assess the effect of indoor residual spraying on malaria vector densities in a high malaria endemic setting in eastern Uganda as part of a cohort study where the use of long-lasting insecticidal nets (LLINs) was high., Methods: Anopheles mosquitoes were sampled monthly using CDC light traps in 107 households selected randomly. Information on the use of malaria interventions in households was also gathered and recorded via a questionnaire. A Bayesian spatio-temporal model was then used to estimate mosquito densities adjusting for climatic and ecological variables and interventions., Results: Anopheles gambiae (sensu lato) were most abundant (89.1%; n = 119,008) compared to An. funestus (sensu lato) (10.1%, n = 13,529). Modelling results suggest that the addition of indoor residual spraying (bendiocarb) in an area with high coverage of permethrin-impregnated LLINs (99%) was associated with a major decrease in mosquito vector densities. The impact on An. funestus (s.l.) (Rate Ratio 0.1508; 97.5% CI: 0.0144-0.8495) was twice as great as for An. gambiae (s.l.) (RR 0.5941; 97.5% CI: 0.1432-0.8577)., Conclusions: High coverage of active ingredients on walls depressed vector populations in intense malaria transmission settings. Sustained use of combined interventions would have a long-term impact on mosquito densities, limiting infectious biting.

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., Competing Interests: The authors have declared that no competing interests exist.

Zika virus is a mosquito-borne pathogen that is rapidly spreading across the Americas. Due to associations between Zika virus infection and a range of fetal maladies(1,2), the epidemic trajectory of this viral infection poses a significant concern for the nearly 15 million children born in the Americas each year. Ascertaining the portion of this population that is truly at risk is an important priority. One recent estimate(3) suggested that 5.42 million childbearing women live in areas of the Americas that are suitable for Zika occurrence. To improve on that estimate, which did not take into account the protective effects of herd immunity, we developed a new approach that combines classic results from epidemiological theory with seroprevalence data and highly spatially resolved data about drivers of transmission to make location-specific projections of epidemic attack rates. Our results suggest that 1.65 (1.45-2.06) million childbearing women and 93.4 (81.6-117.1) million people in total could become infected before the first wave of the epidemic concludes. Based on current estimates of rates of adverse fetal outcomes among infected women(2,4,5), these results suggest that tens of thousands of pregnancies could be negatively impacted by the first wave of the epidemic. These projections constitute a revised upper limit of populations at risk in the current Zika epidemic, and our approach offers a new way to make rapid assessments of the threat posed by emerging infectious diseases more generally.

Zika virus was discovered in Uganda in 1947 and is transmitted by Aedes mosquitoes, which also act as vectors for dengue and chikungunya viruses throughout much of the tropical world. In 2007, an outbreak in the Federated States of Micronesia sparked public health concern. In 2013, the virus began to spread across other parts of Oceania and in 2015, a large outbreak in Latin America began in Brazil. Possible associations with microcephaly and Guillain-Barré syndrome observed in this outbreak have raised concerns about continued global spread of Zika virus, prompting its declaration as a Public Health Emergency of International Concern by the World Health Organization. We conducted species distribution modelling to map environmental suitability for Zika. We show a large portion of tropical and sub-tropical regions globally have suitable environmental conditions with over 2.17 billion people inhabiting these areas.

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.

Objectives: We investigated the differences in clinical characteristics and musculoskeletal (MSK) conditions by cocaine use., Methods: HealthStreet, a community engagement program assessed 7936 adults for medical conditions., Results: Among 6145 African Americans (77%) and 1791 (23%) Caucasians, 15.5 % and 25% reported cocaine use, respectively. African-American cocaine users were older (p < .001) and more likely to report hypertension (p < .001) and HIV (p <.001) than Caucasian users. Compared to non-users, back pain was reported by 42% of African Americans (p < .001) and 48% of Caucasian cocaine users (p < .003). African-American cocaine users had significantly higher odds of back pain, 1.60 (95% CI 1.27, 2.04) and arthritis, 1.40 (95% CI 1.19, 1.64) than Caucasians., Conclusions: Cocaine users are more likely to endorse MSK conditions than non-users. Racial disparities among users existed; however, how race affects health among users requires further research.

Background: Utilization of spatial statistics and Geographic Information Systems (GIS) technologies remain underrepresented in the community-engagement literature, despite its potential role in informing community outreach efforts and in identifying populations enthusiastic to participate in biomedical and health research. Such techniques are capable not only of examining the epidemiological relationship between the environment and a disease, but can also focus limited resources and strategically inform where on the landscape outreach efforts may be optimized., Methods: These analyses present several spatial statistical techniques among the HealthStreet population, a community-engaged organization with aims to link underrepresented populations to medical and social care as well as opportunities to participate in University-sponsored research. Local Indicators of Spatial Association (LISA) and Getis-Ord Gi*(d) statistics are utilized to examine where cancer-related "hot spots" exist among minority and non-minority HealthStreet respondents within Alachua County, Florida, United States (US). Interest in research is also reported, by minority status and lifetime history of cancer., Results: Overall, spatial clustering of cancer was observed to vary by minority status, suggesting disparities may exist among minorities and non-minorities in regards to where cancer is occurring. Specifically, significant hot spots of cancer were observed among non-minorities in more urban areas throughout Alachua County, Florida, US while more rural clusters were observed among minority members, specifically west and southwest of urban city limits., Conclusions: These results may help focus future outreach efforts to include underrepresented populations in health research, as well as focus preventative and palliative oncological care. Further, global community engaged studies and community outreach efforts outside of the United States may use similar methods to focus limited resources and recruit underrepresented populations into health research.