Using remotely sensed night-time light as a proxy for poverty in Africa

The synergy between protected area effectiveness and economic growth

Protected areas conserve biodiversity and ecosystem functions but might impede local economic growth. Understanding the global patterns and predictors of different relationships between protected area effectiveness and neighboring community economic growth can inform better implementation of the Kunming-Montreal Global Biodiversity Framework. We assessed 10,143 protected areas globally with matched samples to address the non-random location of protected areas. Our results show that protected areas resist human-induced land cover changes and do not limit nightlight increases in neighboring settlements. This result is robust, using different matching techniques, parameter settings, and selection of covariates. We identify four types of relationships between land cover changes and nightlight changes for each protected area: "synergy," "retreat," and two tradeoff relationships. About half of the protected areas (47.5%) retain their natural land cover and do so despite an increase of nightlights in the neighboring communities. This synergy relationship is the most common globally but varies between biomes and continents. Synergy is less frequent in the Amazon, Southeast Asia, and some developing areas, where most biodiversity resides and which suffer more from poverty. Smaller protected areas and those with better access to cities, moderate road density, and better baseline economic conditions have a higher probability of reaching synergy. Our results are promising, as the expansion of protected areas and increased species protection will rely more on conserving the human-modified landscape with smaller protected areas. Future interventions should address local development and biodiversity conservation together to achieve more co-benefits.

Mapping the potential distribution of the principal vector of Crimean-Congo haemorrhagic fever virus Hyalomma marginatum in the Old World

Crimean-Congo haemorrhagic fever (CCHF) is the most widely distributed tick-borne viral disease in humans and is caused by the Crimean-Congo haemorrhagic fever virus (CCHFV). The virus has a broader distribution, expanding from western China and South Asia to the Middle East, southeast Europe, and Africa. The historical known distribution of the CCHFV vector Hyalomma marginatum in Europe includes most of the Mediterranean and the Balkan countries, Ukraine, and southern Russia. Further expansion of its potential distribution may have occurred in and out of the Mediterranean region. This study updated the distributional map of the principal vector of CCHFV, H. marginatum, in the Old World using an ecological niche modeling approach based on occurrence records from the Global Biodiversity Information Facility (GBIF) and a set of covariates. The model predicted higher suitability of H. marginatum occurrences in diverse regions of Africa and Asia. Furthermore, the model estimated the environmental suitability of H. marginatum across Europe. On a continental scale, the model anticipated a widespread potential distribution encompassing the southern, western, central, and eastern parts of Europe, reaching as far north as the southern regions of Scandinavian countries. The distribution of H. marginatum also covered countries across Central Europe where the species is not autochthonous. All models were statistically robust and performed better than random expectations (p < 0.001). Based on the model results, climatic conditions could hamper the successful overwintering of H. marginatum and their survival as adults in many regions of the Old World. Regular updates of the models are still required to continually assess the areas at risk using up-to-date occurrence and climatic data in present-day and future conditions.

Quantification of Urbanization Using Night-Time Light Intensity in Relation to Women's Overnutrition in Bangladesh

Urbanization is accelerating in developing countries, which are simultaneously experiencing a rise in the prevalence of overnutrition (i.e., overweight and obesity), specifically among women. Since urbanization is a dynamic process, a continuous measure may better represent it when examining its association with overnutrition. However, most previous research has used a rural-urban dichotomy-based urbanization measure. This study utilized satellite-based night-time light intensity (NTLI) data to measure urbanization and evaluate its association with body weight in reproductive-aged (15-49) women in Bangladesh. Multilevel models estimated the association between residential area NTLI and women's body mass index (BMI) or overnutrition status using data from the latest Bangladesh Demographic and Health Survey (BDHS 2017-18). Higher area-level NTLI was associated with a higher BMI and increased odds of being overweight and obese in women. Living in areas with moderate NTL intensities was not linked with women's BMI measures, whereas living in areas with high NTL intensities was associated with a higher BMI or higher odds of being overweight and obese. The predictive nature of NTLI suggests that it could be used to study the relationship between urbanization and overnutrition prevalence in Bangladesh, though more longitudinal research is needed. This research emphasizes the necessity for preventive efforts to offset the expected public health implications of urbanization.

How Poor Is Your Sample? A Simple Approach for Estimating the Relative Economic Status of Small and Nonrepresentative Samples

BACKGROUND

Asset-based indices of living standards, or wealth indices, are widely used proxies for economic status; however, such indices are not readily available for small and nonrepresentative samples.

METHODS

We describe a simple out-of-sample prediction approach that uses estimates from large and representative "reference" samples to calculate measures of relative economic status (e.g., wealth index scores) for small and/or nonrepresentative "target" samples. The method relies on the availability of common variables and assumptions about comparable associations between these variables and the underlying construct of interest (e.g., household wealth). We provide 2 sample applications that use Demographic and Health Surveys (DHS) from 5 countries as reference samples. Using ordinary least squares regression, we estimate associations between household characteristics and the DHS wealth index. We use parameter estimates to predict wealth index scores for small nonrepresentative target samples. Comparisons of wealth distributions in the reference and target samples highlight selection effects.

RESULTS

Applications of the approach to diverse populations, including populations at high risk of HIV infection and households with orphaned and separated children, demonstrate its usefulness for characterizing the economic status of small and nonrepresentative samples relative to existing reference samples. Women and men in northern Tanzania at high risk of HIV infection were concentrated in the upper half of the wealth distribution. By contrast, the relative distribution of household wealth among households with orphaned and separated children varied greatly across countries and rural versus urban settings.

CONCLUSIONS

Public health professionals who implement, manage, and evaluate programs in low- and middle-income countries may find this approach applicable because of the simplicity of the estimation methods, low marginal cost of primary data acquisition, and availability of established measures of relative economic status in many publicly available household surveys (e.g., those administered by the DHS Program, World Bank, International Labour Organization, and UNICEF).

Geostatistical modelling of the distribution, risk and burden of podoconiosis in Kenya

BACKGROUND

Understanding and accurately predicting the environmental limits, population at risk and burden of podoconiosis are critical for delivering targeted and equitable prevention and treatment services, planning control and elimination programs and implementing tailored case finding and surveillance activities.

METHODS

This is secondary analysis of a nationwide podoconiosis mapping survey in Kenya. We combined national representative prevalence survey data of podoconiosis with climate and environmental data, overlayed with population figures in a geostatistical modelling framework, to predict the environmental suitability, population living in at-risk areas and number of cases of podoconiosis in Kenya.

RESULTS

In 2020, the number of people living with podoconiosis in Kenya was estimated to be 9344 (95% uncertainty interval 4222 to 17 962). The distribution of podoconiosis varies by geography and three regions (Eastern, Nyanza and Western) represent >90% of the absolute number of cases. High environmental suitability for podoconiosis was predicted in four regions of Kenya (Coastal, Eastern, Nyanza and Western). In total, 2.2 million people live in at-risk areas and 4.2% of the total landmass of Kenya is environmentally predisposed for podoconiosis.

CONCLUSIONS

The burden of podoconiosis is relatively low in Kenya and is mostly restricted to certain small geographical areas. Our results will help guide targeted prevention and treatment approaches through local planning, spatial targeting and tailored surveillance activities.

Incidence of dog bite injuries and its associated factors in Punjab province of Pakistan

Dog bites are a major cause for transmission of rabies virus to humans. Pakistan ranks fifth among most rabies affected countries in the world. There are a few regional (ecological) studies that investigated factors that explain geographic disparities in incidence of dog bite injuries. The main objective of this research was to document findings of spatial exploratory data analysis of incidence of reported cases of dog bite in Punjab province of Pakistan (2016-2019). In addition, we have quantified the association between incidence of dog bites and a set of selected socio-economic and demographic variables. District-wise data about reported cases of dog bites from 2016 to 2019 were used to map annual crude incidence per 100,000 of population. There was an obvious spatial variation in incidence of dog bites but there was no evidence of spatial autocorrelation. The risk of dog bite attacks was relatively higher in districts with low human population density (per sq. km), poor literacy rate, more rural population (% of total population), and lower median nighttime lights.

A review of explainable AI in the satellite data, deep machine learning, and human poverty domain

Recent advances in artificial intelligence and deep machine learning have created a step change in how to measure human development indicators, in particular asset-based poverty. The combination of satellite imagery and deep machine learning now has the capability to estimate some types of poverty at a level close to what is achieved with traditional household surveys. An increasingly important issue beyond static estimations is whether this technology can contribute to scientific discovery and, consequently, new knowledge in the poverty and welfare domain. A foundation for achieving scientific insights is domain knowledge, which in turn translates into explainability and scientific consistency. We perform an integrative literature review focusing on three core elements relevant in this context-transparency, interpretability, and explainability-and investigate how they relate to the poverty, machine learning, and satellite imagery nexus. Our inclusion criteria for papers are that they cover poverty/wealth prediction, using survey data as the basis for the ground truth poverty/wealth estimates, be applicable to both urban and rural settings, use satellite images as the basis for at least some of the inputs (features), and the method should include deep neural networks. Our review of 32 papers shows that the status of the three core elements of explainable machine learning (transparency, interpretability, and domain knowledge) is varied and does not completely fulfill the requirements set up for scientific insights and discoveries. We argue that explainability is essential to support wider dissemination and acceptance of this research in the development community and that explainability means more than just interpretability.

Empirical dynamic modeling of the association between ambient PM2.5 and under-five mortality across 2851 counties in Mainland China, 1999-2012

BACKGROUND

Ambient fine particulate matter (PM_2.5) pollution has been associated with mortality from various diseases, however, its association with under-five mortality rate (U5MR) has remained largely unknown.

METHODS

Based on the U5MR data across 2851 counties in Mainland China from 1999 to 2012, we employed approximate Bayesian latent Gaussian models to assess the association between ambient PM_2.5 and U5MR at the county level for the whole nation and sub-regions. GDP growth rate, normalized difference vegetation index (NDVI), temperature, and night-time light were included as covariates using a smoothing function. We further implemented an empirical dynamic model (EDM) to explore the potential causal relationship between PM_2.5 and U5MR.

RESULTS

We observed a declining trend in U5MR in most counties throughout the study period. Spatial heterogeneity in U5MR was observed. Nationwide analysis suggested that each 10 µg/m³ increase in annual concentration of PM_2.5 was associated with an increase of 1.2 (95% CI: 1.0 - 1.3) per 1000 live births in U5MR. Regional analyses showed that the strongest positive association was located in the Northeastern part of China [1.8 (95% CI: 1.4 - 2.1)]. The EDM showed a significant causal association between PM_2.5 and U5MR, with an embedding dimension of 5 and 7, and nonlinear values θ of 4 and 6, respectively.

CONCLUSION

China exhibited a downward trend in U5MR from 1999 to 2012, with spatial heterogeneity observed across the country. Our analysis reveals a positive association between PM_2.5 and U5MR, which may support a causal relationship.

Predictive Risk Mapping of Schistosomiasis in Madagascar Using Ecological Niche Modeling and Precision Mapping

Schistosomiasis is a neglected tropical disease (NTD) found throughout tropical and subtropical Africa. In Madagascar, the condition is widespread and endemic in 74% of all administrative districts in the country. Despite the significant burden of the disease, high-resolution risk maps have yet to be produced to guide national control programs. This study used an ecological niche modeling (ENM) and precision mapping approach to estimate environmental suitability and disease transmission risk. The results show that suitability for schistosomiasis is widespread and covers 264,781 km2 (102,232 sq miles). Covariates of significance to the model were the accessibility to cities, distance to water, enhanced vegetation index (EVI), annual mean temperature, land surface temperature (LST), clay content, and annual precipitation. Disease transmission risk is greatest in the central highlands, tropical east coast, arid-southwest, and northwest. An estimated 14.9 million people could be at risk of schistosomiasis; 11.4 million reside in rural areas, while 3.5 million are in urban areas. This study provides valuable insight into the geography of schistosomiasis in Madagascar and its potential risk to human populations. Because of the focal nature of the disease, these maps can inform national surveillance programs while improving understanding of areas in need of medical interventions.

Distribution of tungiasis in latin America: Identification of areas for potential disease transmission using an ecological niche model

Background

Tungiasis is a neglected tropical disease (NTD) found in Sub-Saharan Africa and Latin America. Despite the high frequency in marginalized populations, little information is available on the geography and estimates of the population at risk in endemic regions. Here we used a geostatistical model to map the potential geographic distribution of areas suitable for tungiasis transmission in Latin America and estimated the at-risk population.

Methods

We developed an ecological niche model (ENM) using tungiasis occurrence records and remotely sensed environmental and socioeconomic data. The potential geographic distribution was then compared to the current population distribution of the region to derive the total population living in urban and rural areas.

Findings

We identified a total of 138 records of occurrences of tungiasis in Latin America, ranging from Mexico to Argentina; 27 reports were not included in the modeling, due to missing detailed geographic information. The occurrences with detailed geographic information (n = 112) included 17 countries in Latin America and the Caribbean. The locations were in environments that primarily consisted of forests (29%), croplands (16•5%), and shrublands (10•9%). We predicted environmentally suitable areas for tungiasis transmission in 45 countries. The estimated human population living in these areas is 450,546,547 with urban centers accounting for 347,007,103 and rural areas 103,539,444. Countries with significant ecological suitability and documented occurrences include Brazil, Colombia, Mexico, Argentina, Bolivia, Ecuador, French Guyana, Guatemala, Haiti, Paraguay, Peru, Trinidad and Tobago, and Venezuela.

Interpretation

This is the first study mapping the potential distribution of tungiasis in Latin America, evidencing the need for population-based studies and elaboration of integrated control measures.

Funding

This project was supported in part by an appointment to the Research Participation Program at the Centers for Disease Control and Prevention administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and the Centers for Disease Control and Prevention.

Remote Sensing of Urban Poverty and Gentrification

In the past few decades, most urban areas in the world have been facing the pressure of an increasing population living in poverty. A recent study has shown that up to 80% of the population of some cities in Africa fall under the poverty line. Other studies have shown that poverty is one of the main contributors to residents’ poor health and social conflict. Reducing the number of people living in poverty and improving their living conditions have become some of the main tasks for many nations and international organizations. On the other hand, urban gentrification has been taking place in the poor neighborhoods of all major cities in the world. Although gentrification can reduce the poverty rate and increase the GDP and tax revenue of cities and potentially bring opportunities for poor communities, it displaces the original residents of the neighborhoods, negatively impacting their living and access to social services. In order to support the sustainable development of cities and communities and improve residents’ welfare, it is essential to identify the location, scale, and dynamics of urban poverty and gentrification, and remote sensing can play a key role in this. This paper reviews, summarizes, and evaluates state-of-the-art approaches for identifying and mapping urban poverty and gentrification with remote sensing, GIS, and machine learning techniques. It also discusses the pros and cons of remote sensing approaches in comparison with traditional approaches. With remote sensing approaches, both spatial and temporal resolutions for the identification of poverty and gentrification have been dramatically increased, while the economic cost is significantly reduced.

Poverty Mapping in the Dian-Gui-Qian Contiguous Extremely Poor Area of Southwest China Based on Multi-Source Geospatial Data

Accurate information on the spatial distribution of poverty is of great significance to the formulation and implementation of the government’s targeted poverty alleviation policy. Traditional poverty mapping is mainly based on household survey data and statistical data, which cannot describe the spatial distribution of poverty well. This paper presents a study of mapping the integrated poverty index (IPI) in the Dian-Gui-Qian contiguous extremely poor area of southwest China. Based on multiple independent spatial variables extracted from NPP/VIIRS nighttime light (NTL) remote sensing data, digital elevation model (DEM), land cover information, open street map, and city accessibility data, eight algorithms were employed and compared to determine the optimal model for IPI estimation. Among these machine learning algorithms, traditional multiple linear regression had the lowest accuracy compared with the other seven machine learning algorithms and XGBoost showed the best performance. Feature selection was performed to reduce overfitting and five variables were finally selected. The final developed XGBoost model achieved an MAE of 0.0454 and an R2 of 0.68. The IPI map derived from the developed XGBoost model characterized the spatial pattern of poverty in the Dian-Gui-Qian contiguous extremely poor area well, which provided a good reference for the poverty alleviation work and public resources allocation in the study area. This study can also serve as a template for poverty mapping in other areas using remote sensing data.

Spatial and epidemiological drivers of Plasmodium falciparum malaria among adults in the Democratic Republic of the Congo

Background

Adults are frequently infected with malaria and may serve as a reservoir for further transmission, yet we know relatively little about risk factors for adult infections. In this study, we assessed malaria risk factors among adults using samples from the nationally representative, cross-sectional 2013–2014 Demographic and Health Survey (DHS) conducted in the Democratic Republic of the Congo (DRC). We further explored differences in risk factors by urbanicity.

Methods

Plasmodium falciparum infection was determined by PCR. Covariates were drawn from the DHS to model individual, community and environmental-level risk factors for infection. Additionally, we used deep sequencing data to estimate the community-level proportions of drug-resistant infections and included these estimates as potential risk factors. All identified factors were assessed for differences in associations by urbanicity.

Results

A total of 16 126 adults were included. Overall prevalence of malaria was 30.3% (SE=1.1) by PCR; province-level prevalence ranged from 6.7% to 58.3%. Only 17% of individuals lived in households with at least one bed-net for every two people, as recommended by the WHO. Protective factors included increasing within-household bed-net coverage (Prevalence Ratio=0.85, 95% CI=0.76–0.95) and modern housing (PR=0.58, 95% CI=0.49–0.69). Community-level protective factors included increased median wealth (PR=0.87, 95% CI=0.83–0.92). Education, wealth, and modern housing showed protective associations in cities but not in rural areas.

Conclusions

The DRC continues to suffer from a high burden of malaria; interventions that target high-risk groups and sustained investment in malaria control are sorely needed. Areas of high prevalence should be prioritised for interventions to target the largest reservoirs for further transmission.

Lockdowns result in changes in human mobility which may impact the epidemiologic dynamics of SARS-CoV-2

In response to the SARS-CoV-2 pandemic, unprecedented travel restrictions and stay-at-home orders were enacted around the world. Ultimately, the public’s response to announcements of lockdowns—defined as restrictions on both local movement or long distance travel—will determine how effective these kinds of interventions are. Here, we evaluate the effects of lockdowns on human mobility and simulate how these changes may affect epidemic spread by analyzing aggregated mobility data from mobile phones. We show that in 2020 following lockdown announcements but prior to their implementation, both local and long distance movement increased in multiple locations, and urban-to-rural migration was observed around the world. To examine how these behavioral responses to lockdown policies may contribute to epidemic spread, we developed a simple agent-based spatial model. Our model shows that this increased movement has the potential to increase seeding of the epidemic in less urban areas, which could undermine the goal of the lockdown in preventing disease spread. Lockdowns play a key role in reducing contacts and controlling outbreaks, but appropriate messaging surrounding their announcement and careful evaluation of changes in mobility are needed to mitigate the possible unintended consequences.

Measuring Human and Economic Activity From Satellite Imagery to Support City-Scale Decision-Making During COVID-19 Pandemic

The COVID-19 outbreak forced governments worldwide to impose lockdowns and quarantines to prevent virus transmission. As a consequence, there are disruptions in human and economic activities all over the globe. The recovery process is also expected to be rough. Economic activities impact social behaviors, which leave signatures in satellite images that can be automatically detected and classified. Satellite imagery can support the decision-making of analysts and policymakers by providing a different kind of visibility into the unfolding economic changes. In this article, we use a deep learning approach that combines strategic location sampling and an ensemble of lightweight convolutional neural networks (CNNs) to recognize specific elements in satellite images that could be used to compute economic indicators based on it, automatically. This CNN ensemble framework ranked third place in the US Department of Defense xView challenge, the most advanced benchmark for object detection in satellite images. We show the potential of our framework for temporal analysis using the US IARPA Function Map of the World (fMoW) dataset. We also show results on real examples of different sites before and after the COVID-19 outbreak to illustrate different measurable indicators. Our code and annotated high-resolution aerial scenes before and after the outbreak are available on GitHub.1.https://github.com/maups/covid19-satellite-analysis.

The exploration of the dispersal of British military families in England following the Strategic Defence and Security Review 2010

Strictly relying on publicly available data, this study depicts and quantifies the spatial pattern of England’s military families with dependent children. England’s Service Pupil Premium for the financial years between 2011 and 2019 is used as a proxy variable to estimate the density of service children at the parliamentary constituency level. Methodologically, the approach allows an assessment of spatial movements of a population or a cohort. The results inform policy makers by providing evidence-based findings about the location of England’s military families and how the distribution has changed between 2011 and 2019. The results show empirical evidence supporting the hypothesis that, at a macro scale, beyond commuting distance, England’s military families are becoming increasingly dispersed. We argue that the findings unveil spatial dynamics that have practical issues of housing, employment, and education regarding military families.

Understanding the drivers of mortality in African savannah elephants

Populations of African savannah elephants (Loxodonta africana) have been declining due to poaching, human-elephant conflict, and habitat loss. Understanding the causes of these declines could aid in stabilizing elephant populations. We used data from the Great Elephant Census, a 19-country aerial survey of savannah elephants conducted in 2014 and 2015, to examine effects of a suite of variables on elephant mortality. Independent variables included spatially explicit measures of natural processes and human presence as well as country-level socioeconomic measures. Our dependent variable was the carcass ratio, the ratio of dead elephants to live plus dead elephants, which is an index of recent elephant mortality. Carcass ratios are inversely proportional to population growth rates of elephants over the 4 yr prior to a survey. At the scale of survey strata (n = 275, median area = 1,222 km² ), we found strong negative associations for carcass ratios with vegetation greenness at the time of the survey, overseas development aid to the country, and distance to the nearest international border. At the scale of ecosystems (n = 42, median area = 12,085 km² ), carcass ratios increased with drought frequency and decreased with human density and overseas development aid to the country. Both stratum- and ecosystem-scale models explained well under one-half of the variance in carcass ratios. The differences in results between scales suggest that the drivers of mortality may be scale-specific and that the corresponding solutions may vary by scale as well.

Predicting the environmental suitability and population at risk of podoconiosis in Africa

Podoconiosis is a type of tropical lymphedema that causes massive swelling of the lower limbs. The disease is associated with both economic insecurity, due to long-term morbidity-related loss of productivity, and intense social stigma. The geographical distribution and burden of podoconiosis in Africa are uncertain. We applied statistical modelling to the most comprehensive database compiled to date to predict the environmental suitability of podoconiosis in the African continent. By combining climate and environmental data and overlaying population figures, we predicted the environmental suitability and human population at risk of podoconiosis in Africa. Environmental suitability for podoconiosis was predicted in 29 African countries. In the year 2020, the total population in areas suitable for podoconiosis is estimated at 114.5 million people, (95% uncertainty interval: 109.4-123.9) with 16.9 million in areas suitable for both lymphatic filariasis and podoconiosis. Of the total 5,712 implementation units (typically second administrative-level units, such as districts) defined by the World Health Organization in Africa, 1,655 (29.0%) were found to be environmentally suitable for podoconiosis. The majority of implementation units with high environmental suitability are located in Angola (80, 4.8%), Cameroon (170, 10.3%), the DRC (244, 14.7%), Ethiopia (495, 29.9%), Kenya (217, 13.1%), Uganda (116, 7.0%) and Tanzania (112, 6.8%). Of the 1,655 environmentally suitable implementation units, 960 (58.0%) require more detailed community-level mapping. Our estimates provide key evidence of the population at risk and geographical extent of podoconiosis in Africa, which will help decision-makers to better plan more integrated intervention programmes.

Global Nighttime Light Change from 1992 to 2017: Brighter and More Uniform

Nighttime light images record the brightness of the Earth surface, indicating the scope and intensity of human activities. However, there are few studies on the long-term changes in global nighttime lights. In this paper, the authors constructed a long time series (1992~2017) nighttime light dataset combining the Defense Meteorological Satellites Program/Operational Linescan System (DMSP-OLS) and the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) data sources and observed the following: (1) Global nighttime lights have become brighter. The global nighttime brightness in 2017 was 2.2 times that of 1992. Approximately 40.3% of the lighted area was significantly brightened, and an area of 1.3 × 107 km2 transitioned from an unlighted area to a lighted area. (2) Approximately 85.7% of the nighttime light increase occurred in the low-brightness zone (LBZ). Therefore, global brightness has become more uniform than before. (3) China, India, and the United States have led the global lighting trend. The increase in Chinese nighttime lights is the largest, with an average annual growth of 6.48%, followed by the light growth in India, while the United States has the largest brightened area. (4) The changes in nighttime lights in developing countries (e.g., China and India) are closely and positively related to their electricity consumption, industrial added value and gross domestic product (GDP). The shift of the LBZ center from Asia to Africa indicates the intercontinental transition of poverty.

Environmental temperature and growth faltering in African children: a cross-sectional study

BACKGROUND

Child growth faltering persists in sub-Saharan Africa despite the scale-up of nutrition, water, and sanitation interventions over the past 2 decades. High temperatures have been hypothesised to contribute to child growth faltering via an adaptive response to heat, reduced appetite, and the energetic cost of thermoregulation. We did a cross-sectional study to assess whether child growth faltering is related to environmental temperature in sub-Saharan Africa.

METHODS

Data were extracted from 52 Demographic and Heath Surveys, dating from 2003 to 2016, that recorded anthropometric data in children aged 0-5 years, and were linked with remotely sensed monthly mean daytime land surface temperature for 2000-16. The odds of stunting (low height-for-age), wasting (low weight-for-height), and underweight (low weight-for-age) relative to monthly mean daytime land surface temperature were determined using multivariable logistic regression.

FINDINGS

The study population comprised 656 107 children resident in 373 012 households. Monthly mean daytime land surface temperature above 35°C was associated with increases in the odds of wasting (odds ratio 1·27, 95% CI 1·16-1·38; p<0·0001), underweight (1·09, 1·02-1·16; p=0·0073), and concurrent stunting with wasting (1·23, 1·07-1·41; p=0·0037), but a reduction in stunting (0·90, 0·85-0·96; p=0·00047) compared with a monthly mean daytime land surface temperature of less than 30°C.

INTERPRETATION

Children living in hotter parts of sub-Saharan Africa are more likely to be wasted, underweight, and concurrently stunted and wasted, but less likely to be stunted, than in cooler areas. Studies are needed to further investigate the relationship between temperature and child growth, including whether there is a direct effect not mediated by food security, regional wealth, and other environmental variables. Rising temperature, linked to anthropogenic climate change, might increase child growth faltering in sub-Saharan Africa.

FUNDING

UK Medical Research Council and UK Global Challenges Research Fund.

Measuring the spatial heterogeneity on the reduction of vaginal fistula burden in Ethiopia between 2005 and 2016

Vaginal fistula is a shattering maternal complication characterized by an anomalous opening between the bladder and/or rectum and vagina resulting in continuous leakage of urine or stool. Although prevalent in Ethiopia, its magnitude and distribution is not well studied. We used statistical mapping models using 2005 and 2016 Ethiopia Demographic Health Surveys data combined with a suite of potential risk factors to estimate the burden of vaginal fistula among women of childbearing age. The estimated number of women of childbearing age with lifetime and untreated vaginal fistula in 2016 were 72,533 (95% CI 38,235-124,103) and 31,961 (95% CI 11,596-70,309) respectively. These figures show reduction from the 2005 estimates: 98,098 (95% CI 49,819-170,737) lifetime and 59,114 (95% CI 26,580-118,158) untreated cases of vaginal fistula. The number of districts having more than 200 untreated cases declined drastically from 54 in 2005 to 6 in 2016. Our results show a significant subnational variation in the burden of vaginal fistula. Overall, between 2005 and 2016 there was substantial reduction in the prevalence of vaginal fistula in Ethiopia. Our results help guide local level tracking, planning, spatial targeting of resources and implementation of interventions against vaginal fistula.

How Data-Poor Countries Remain Data Poor: Underestimation of Human Settlements in Burkina Faso as Observed from Nighttime Light Data

The traditional ways of measuring global sustainable development and economic development schemes and their progress suffer from a number of serious shortcomings. Remote sensing and specifically nighttime light has become a popular supplement to official statistics by providing an objective measure of human settlement that can be used as a proxy for population and economic development measures. With the increased availability and use of the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and data in social science, it has played an important role in data collection, including measuring human development and economic growth. Numerous studies are using nighttime light data to analyze dynamic regions such as expansions of urban areas and rapid industrialization often highlight the problem of saturation in urban centers with high light intensity. However, the quality of nighttime light data and its appropriateness for analyzing areas and regions with low and fluctuating levels of light have rarely been questioned or studied. This study examines the accuracy of DMSP-OLS and VIIRS-DNB by analyzing 147 communities in Burkina Faso to provide insights about problems related to the study of areas with a low intensity of nighttime light during the studied period from 1992 to 2012. It found that up to 57% of the communities studied were undetectable throughout the period, and only 9% of communities studied had a 100% detection rate. Unsurprisingly, the result provides evidence that detection rates in both datasets are particularly low (3%) for settlements with 0–9999 inhabitants, as well as for larger settlements with population of 10,000–24,999 (28%). Cross-checking with VIIRS-DNB for the year 2012 shows similar results. These findings suggest that careful consideration must be given to the use of nighttime light data in research and global comparisons to monitor the progress of the United Nation’s Sustainable Development Goals, especially when including developing countries with areas containing low electrification rates and low population density.

New Perspectives for Mapping Global Population Distribution Using World Settlement Footprint Products

In the production of gridded population maps, remotely sensed, human settlement datasets rank among the most important geographical factors to estimate population densities and distributions at regional and global scales. Within this context, the German Aerospace Centre (DLR) has developed a new suite of global layers, which accurately describe the built-up environment and its characteristics at high spatial resolution: (i) the World Settlement Footprint 2015 layer (WSF-2015), a binary settlement mask; and (ii) the experimental World Settlement Footprint Density 2015 layer (WSF-2015-Density), representing the percentage of impervious surface. This research systematically compares the effectiveness of both layers for producing population distribution maps through a dasymetric mapping approach in nine low-, middle-, and highly urbanised countries. Results indicate that the WSF-2015-Density layer can produce population distribution maps with higher qualitative and quantitative accuracies in comparison to the already established binary approach, especially in those countries where a good percentage of building structures have been identified within the rural areas. Moreover, our results suggest that population distribution accuracies could substantially improve through the dynamic preselection of the input layers and the correct parameterisation of the Settlement Size Complexity (SSC) index.

The changing landscape of Plasmodium falciparum drug resistance in the Democratic Republic of Congo

BACKGROUND

Drug resistant malaria is a growing concern in the Democratic Republic of the Congo (DRC), where previous studies indicate that parasites resistant to sulfadoxine/pyrimethamine or chloroquine are spatially clustered. This study explores longitudinal changes in spatial patterns to understand how resistant malaria may be spreading within the DRC, using samples from nation-wide population-representative surveys.

METHODS

We selected 552 children with PCR-detectable Plasmodium falciparum infection and identified known variants in the pfdhps and pfcrt genes associated with resistance. We compared the proportion of mutant parasites in 2013 to those previously reported from adults in 2007, and identified risk factors for carrying a resistant allele using multivariate mixed-effects modeling. Finally, we fit a spatial-temporal model to the observed data, providing smooth allele frequency estimates over space and time.

RESULTS

The proportion of co-occurring pfdhps K540E/A581G mutations increased by 16% between 2007 and 2013. The spatial-temporal model suggests that the spatial range of the pfdhps double mutants expanded over time, while the prevalence and range of pfcrt mutations remained steady.

CONCLUSIONS

This study uses population-representative samples to describe the changing landscape of SP resistance within the DRC, and the persistence of chloroquine resistance. Vigilant molecular surveillance is critical for controlling the spread of resistance.

Mapping changes in housing in sub-Saharan Africa from 2000 to 2015

Access to adequate housing is a fundamental human right, essential to human security, nutrition and health, and a core objective of the United Nations Sustainable Development Goals1,2. Globally, the housing need is most acute in Africa, where the population will more than double by 2050. However, existing data on housing quality across Africa are limited primarily to urban areas and are mostly recorded at the national level. Here we quantify changes in housing in sub-Saharan Africa from 2000 to 2015 by combining national survey data within a geostatistical framework. We show a marked transformation of housing in urban and rural sub-Saharan Africa between 2000 and 2015, with the prevalence of improved housing (with improved water and sanitation, sufficient living area and durable construction) doubling from 11% (95% confidence interval, 10-12%) to 23% (21-25%). However, 53 (50-57) million urban Africans (47% (44-50%) of the urban population analysed) were living in unimproved housing in 2015. We provide high-resolution, standardized estimates of housing conditions across sub-Saharan Africa. Our maps provide a baseline for measuring change and a mechanism to guide interventions during the era of the Sustainable Development Goals.

Characterizing local-scale heterogeneity of malaria risk: a case study in Bunkpurugu-Yunyoo district in northern Ghana

BACKGROUND

Bayesian methods have been used to generate country-level and global maps of malaria prevalence. With increasing availability of detailed malaria surveillance data, these methodologies can also be used to identify fine-scale heterogeneity of malaria parasitaemia for operational prevention and control of malaria.

METHODS

In this article, a Bayesian geostatistical model was applied to six malaria parasitaemia surveys conducted during rainy and dry seasons between November 2010 and 2013 to characterize the micro-scale spatial heterogeneity of malaria risk in northern Ghana.

RESULTS

The geostatistical model showed substantial spatial heterogeneity, with malaria parasite prevalence varying between 19 and 90%, and revealing a northeast to southwest gradient of predicted risk. The spatial distribution of prevalence was heavily influenced by two modest urban centres, with a substantially lower prevalence in urban centres compared to rural areas. Although strong seasonal variations were observed, spatial malaria prevalence patterns did not change substantially from year to year. Furthermore, independent surveillance data suggested that the model had a relatively good predictive performance when extrapolated to a neighbouring district.

CONCLUSIONS

This high variability in malaria prevalence is striking, given that this small area (approximately 30 km × 40 km) was purportedly homogeneous based on country-level spatial analysis, suggesting that fine-scale parasitaemia data might be critical to guide district-level programmatic efforts to prevent and control malaria. Extrapolations results suggest that fine-scale parasitaemia data can be useful for spatial predictions in neighbouring unsampled districts and does not have to be collected every year to aid district-level operations, helping to alleviate concerns regarding the cost of fine-scale data collection.

Estimation of Poverty Using Random Forest Regression with Multi-Source Data: A Case Study in Bangladesh

Spatially explicit and reliable data on poverty is critical for both policy makers and researchers. However, such data remain scarce particularly in developing countries. Current research is limited in using environmental data from different sources in isolation to estimate poverty despite the fact that poverty is a complex phenomenon which cannot be quantified either theoretically or practically by one single data type. This study proposes a random forest regression (RFR) model to estimate poverty at 10 km × 10 km spatial resolution by combining features extracted from multiple data sources, including the National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (DNB) nighttime light (NTL) data, Google satellite imagery, land cover map, road map and division headquarter location data. The household wealth index (WI) drawn from the Demographic and Health Surveys (DHS) program was used to reflect poverty level. We trained the RFR model using data in Bangladesh and applied the model to both Bangladesh and Nepal to evaluate the model’s accuracy. The results show that the R2 between the actual and estimated WI in Bangladesh is 0.70, indicating a good predictive power of our model in WI estimation. The R2 between actual and estimated WI of 0.61 in Nepal also indicates a good generalization ability of the model. Furthermore, a negative correlation is observed between the district average WI and the poverty head count ratio (HCR) in Bangladesh with the Pearson Correlation Coefficient of -0.6. Using Gini importance, we identify that proximity to urban areas is the most important variable to explain poverty which contribute to 37.9% of the explanatory power. Compared to the study that used NTL and Google satellite imagery in isolation to estimate poverty, our method increases the accuracy of estimation. Given that the data we use are globally and publicly available, the methodology reported in this study would also be applicable in other countries or regions to estimate the extent of poverty.

Mapping the potential distributions of etiological agent, vectors, and reservoirs of Japanese Encephalitis in Asia and Australia

Japanese encephalitis virus (JEV) is a substantial cause of viral encephalitis, morbidity, and mortality in South-East Asia and the Western Pacific. World Health Organization recognized Japanese Encephalitis (JE) as a public health priority in demands to initiate active vaccination programs. Recently, the geographic distribution of JEV has apparently expanded into other areas in the Pacific islands and northern Australia; however, major gaps exist in knowledge in regard to its current distribution. Here, we mapped the potential distribution of mosquito vectors of JEV (Culex tritaeniorhynchus, Cx. pseudovishnui, Cx. vishnui, Cx. fuscocephala, Cx. gelidus), and reservoirs (Egretta garzetta, E. intermedia, Nycticorax nycticorax) based on ecological niche modeling approach. Ecological niche models predicted all species to occur across Central, South and South East Asia; however, Cx. tritaeniorhynchus, E. garzetta, E. intermedia, and N. nycticorax had broader potential distributions extending west to parts of the Arabian Peninsula. All predictions were robust and significantly better than random (P < 0.001). We also tested the JEV prediction based on 4335 additional independent human case records collected by the Chinese Information System for Disease Control and Prevention (CISDCP); 4075 cases were successfully predicted by the model (P < 0.001). Finally, we tested the ecological niche similarity among JEV, vector, and reservoir species and could not reject any of the null hypotheses of niche similarity in all combination pairs.

Environmental suitability for lymphatic filariasis in Nigeria

BACKGROUND

Lymphatic filariasis (LF) is a mosquito-borne parasitic disease and a major cause of disability worldwide. It is one of the neglected tropical diseases identified by the World Health Organization for elimination as a public health problem by 2020. Maps displaying disease distribution are helpful tools to identify high-risk areas and target scarce control resources.

METHODS

We used pre-intervention site-level occurrence data from 1192 survey sites collected during extensive mapping surveys by the Nigeria Ministry of Health. Using an ensemble of machine learning modelling algorithms (generalised boosted models and random forest), we mapped the ecological niche of LF at a spatial resolution of 1 km2. By overlaying gridded estimates of population density, we estimated the human population living in LF risk areas on a 100 × 100 m scale.

RESULTS

Our maps demonstrate that there is a heterogeneous distribution of LF risk areas across Nigeria, with large portions of northern Nigeria having more environmentally suitable conditions for the occurrence of LF. Here we estimated that approximately 110 million individuals live in areas at risk of LF transmission.

CONCLUSIONS

Machine learning and ensemble modelling are powerful tools to map disease risk and are known to yield more accurate predictive models with less uncertainty than single models. The resulting map provides a geographical framework to target control efforts and assess its potential impacts.

Nighttime lights as a proxy for human development at the local level

Nighttime lights, calculated from weather satellite recordings, are increasingly used by social scientists as a proxy for economic activity or economic development in subnational regions of developing countries where disaggregated data from statistical offices are not available. However, so far, our understanding of what nighttime lights capture in these countries is limited. We use geo-referenced Demographic and Health Surveys (DHS) from 29 African countries to construct indicators of household wealth, education and health for DHS cluster locations as well as for grid cells of roughly 50 × 50 km. We show that nighttime lights are positively associated with these location-specific indicators of human development, and that the variation in nighttime lights can explain a substantial share in the variation in these indicators. We conclude that nighttime lights are a good proxy for human development at the local level.

Cross-border movement, economic development and malaria elimination in the Kingdom of Saudi Arabia

Malaria at international borders presents particular challenges with regards to elimination. International borders share common malaria ecologies, yet neighboring countries are often at different stages of the control-to-elimination pathway. Herein, we present a case study on malaria, and its control, at the border between Saudi Arabia and Yemen. Malaria program activity reports, case data, and ancillary information have been assembled from national health information systems, archives, and other related sources. Information was analyzed as a semi-quantitative time series, between 2000 and 2017, to provide a plausibility framework to understand the possible contributions of factors related to control activities, conflict, economic development, migration, and climate. The malaria recession in the Yemeni border regions of Saudi Arabia is a likely consequence of multiple, coincidental factors, including scaled elimination activities, cross-border vector control, periods of low rainfall, and economic development. The temporal alignment of many of these factors suggests that economic development may have changed the receptivity to the extent that it mitigated against surges in vulnerability posed by imported malaria from its endemic neighbor Yemen. In many border areas of the world, malaria is likely to be sustained through a complex congruence of factors, including poverty, conflict, and migration.

Predicted distribution and burden of podoconiosis in Cameroon

INTRODUCTION

Understanding the number of cases of podoconiosis, its geographical distribution and the population at risk are crucial to estimating the burden of this disease in endemic countries. We assessed each of these using nationwide data on podoconiosis prevalence in Cameroon.

METHODS

We analysed data arising from two cross-sectional surveys in Cameroon. The dataset was combined with a suite of environmental and climate data and analysed within a robust statistical framework, which included machine learning-based approaches and geostatistical modelling. The environmental limits, spatial variation of predicted prevalence, population at risk and number of cases of podoconiosis were each estimated.

RESULTS

A total of 214 729 records of individuals screened for podoconiosis were gathered from 748 communities in all 10 regions of Cameroon. Of these screened individuals, 882 (0.41%; 95% CI 0.38 to 0.44) were living with podoconiosis. High environmental suitability for podoconiosis was predicted in three regions of Cameroon (Adamawa, North West and North). The national population living in areas environmentally suitable for podoconiosis was estimated at 5.2 (95% CI 4.7 to 5.8) million, which corresponds to 22.3% of Cameroon's population in 2015. Countrywide, in 2015, the number of adults estimated to be suffering from podoconiosis was 41 556 (95% CI, 1170 to 240 993). Four regions (Central, Littoral, North and North West) contributed 61.2% of the cases.

CONCLUSION

In Cameroon, podoconiosis is more widely distributed geographically than was initially expected. The number of cases and the population at risk are considerable. Expanding morbidity management and follow-up of cases is of utmost necessity. Promotion of footwear use and regular foot hygiene should be at the forefront of any intervention plan.

Spatio-temporal mapping of Madagascar's Malaria Indicator Survey results to assess Plasmodium falciparum endemicity trends between 2011 and 2016

BACKGROUND

Reliable measures of disease burden over time are necessary to evaluate the impact of interventions and assess sub-national trends in the distribution of infection. Three Malaria Indicator Surveys (MISs) have been conducted in Madagascar since 2011. They provide a valuable resource to assess changes in burden that is complementary to the country's routine case reporting system.

METHODS

A Bayesian geostatistical spatio-temporal model was developed in an integrated nested Laplace approximation framework to map the prevalence of Plasmodium falciparum malaria infection among children from 6 to 59 months in age across Madagascar for 2011, 2013 and 2016 based on the MIS datasets. The model was informed by a suite of environmental and socio-demographic covariates known to influence infection prevalence. Spatio-temporal trends were quantified across the country.

RESULTS

Despite a relatively small decrease between 2013 and 2016, the prevalence of malaria infection has increased substantially in all areas of Madagascar since 2011. In 2011, almost half (42.3%) of the country's population lived in areas of very low malaria risk (<1% parasite prevalence), but by 2016, this had dropped to only 26.7% of the population. Meanwhile, the population in high transmission areas (prevalence >20%) increased from only 2.2% in 2011 to 9.2% in 2016. A comparison of the model-based estimates with the raw MIS results indicates there was an underestimation of the situation in 2016, since the raw figures likely associated with survey timings were delayed until after the peak transmission season.

CONCLUSIONS

Malaria remains an important health problem in Madagascar. The monthly and annual prevalence maps developed here provide a way to evaluate the magnitude of change over time, taking into account variability in survey input data. These methods can contribute to monitoring sub-national trends of malaria prevalence in Madagascar as the country aims for geographically progressive elimination.

Estimating the number of cases of podoconiosis in Ethiopia using geostatistical methods

BACKGROUND

In 2011, the World Health Organization recognized podoconiosis as one of the neglected tropical diseases. Nonetheless, the number of people with podoconiosis and the geographical distribution of the disease is poorly understood. Based on a nationwide mapping survey and geostatistical modelling, we predict the prevalence of podoconiosis and estimate the number of cases across Ethiopia.

METHODS

We used nationwide data collected in Ethiopia between 2008 and 2013. Data were available for 141,238 individuals from 1,442 villages in 775 districts from all nine regional states and two city administrations. We developed a geostatistical model of podoconiosis prevalence among adults (individuals aged 15 years or above), by combining environmental factors. The number of people with podoconiosis was then estimated using a gridded map of adult population density for 2015.

RESULTS

Podoconiosis is endemic in 345 districts in Ethiopia: 144 in Oromia, 128 in Southern Nations, Nationalities and People's [SNNP], 64 in Amhara, 4 in Benishangul Gumuz, 4 in Tigray and 1 in Somali Regional State. Nationally, our estimates suggest that 1,537,963 adults (95% confidence intervals, 290,923-4,577,031 adults) were living with podoconiosis in 2015. Three regions (SNNP, Oromia and Amhara) contributed 99% of the cases. The highest proportion of individuals with podoconiosis resided in the SNNP (39%), while 32% and 29% of people with podoconiosis resided in Oromia and Amhara Regional States, respectively. Tigray and Benishangul Gumuz Regional States bore lower burdens, and in the remaining regions, podoconiosis was almost non-existent.  Discussion: The estimates of podoconiosis cases presented here based upon the combination of currently available epidemiological data and a robust modelling approach clearly show that podoconiosis is highly endemic in Ethiopia. Given the presence of low cost prevention, and morbidity management and disability prevention services, it is our collective responsibility to scale-up interventions rapidly.

Mapping poverty using mobile phone and satellite data

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.

Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing

Quantifying and monitoring the spatial and temporal dynamics of the global land cover is critical for better understanding many of the Earth's land surface processes. However, the lack of regularly updated, continental-scale, and high spatial resolution (30 m) land cover data limit our ability to better understand the spatial extent and the temporal dynamics of land surface changes. Despite the free availability of high spatial resolution Landsat satellite data, continental-scale land cover mapping using high resolution Landsat satellite data was not feasible until now due to the need for high-performance computing to store, process, and analyze this large volume of high resolution satellite data. In this study, we present an approach to quantify continental land cover and impervious surface changes over a long period of time (15 years) using high resolution Landsat satellite observations and Google Earth Engine cloud computing platform. The approach applied here to overcome the computational challenges of handling big earth observation data by using cloud computing can help scientists and practitioners who lack high-performance computational resources.

Is increasing urbanicity associated with changes in breastfeeding duration in rural India? An analysis of cross-sectional household data from the Andhra Pradesh children and parents study

OBJECTIVE

To investigate whether village-level urbanicity and lower level socioeconomic factors are associated with breastfeeding practices in transitioning rural communities in India.

SETTING

29 villages in Ranga Reddy district, southern India between 2011 and 2014.

PARTICIPANTS

7848 children under 6 years identified via a cross-sectional household survey conducted as part of the Andhra Pradesh Children and Parents Study.

OUTCOME MEASURES

Two key indicators of optimal breastfeeding: termination of exclusive breastfeeding before 6 months and discontinuation of breastfeeding by 24 months. Village urbanicity was classified as low, medium or high according to satellite assessed night-light intensity.

RESULTS

Breastfeeding initiation was almost universal, and approximately two in three children were exclusively breastfed to 6 months and a similar proportion breastfed to 24 months. Using multilevel logistic regression, increasing urbanicity was associated with breastfeeding discontinuation before 24 months (medium urbanicity OR 1.45, 95% CI 0.71 to 2.96; high urbanicity OR 2.96, 95% CI 1.45 to 6.05) but not with early (<6 months) termination of exclusive breastfeeding. Increased maternal education was independently associated with both measures of suboptimal breastfeeding, and higher household socioeconomic position was associated with early termination of exclusive breastfeeding.

CONCLUSION

In this transitional Indian rural community, early stage urbanicity was associated with a shorter duration of breastfeeding. Closer surveillance of changes in breastfeeding practices alongside appropriate intervention strategies are recommended for emerging economies.

Estimating the number of cases of podoconiosis in Ethiopia using geostatistical methods

BACKGROUND

In 2011, the World Health Organization recognized podoconiosis as one of the neglected tropical diseases. Nonetheless, the number of people with podoconiosis and the geographical distribution of the disease is poorly understood. Based on a nationwide mapping survey and geostatistical modelling, we predict the prevalence of podoconiosis and estimate the number of cases across Ethiopia.

METHODS

We used nationwide data collected in Ethiopia between 2008 and 2013. Data were available for 141,238 individuals from 1,442 villages in 775 districts from all nine regional states and two city administrations. We developed a geostatistical model of podoconiosis prevalence among adults (individuals aged 15 years or above), by combining environmental factors. The number of people with podoconiosis was then estimated using a gridded map of adult population density for 2015.

RESULTS

Podoconiosis is endemic in 345 districts in Ethiopia: 144 in Oromia, 128 in Southern Nations, Nationalities and People's [SNNP], 64 in Amhara, 4 in Benishangul Gumuz, 4 in Tigray and 1 in Somali Regional State. Nationally, our estimates suggest that 1,537,963 adults (95% confidence intervals, 290,923-4,577,031 adults) were living with podoconiosis in 2015. Three regions (SNNP, Oromia and Amhara) contributed 99% of the cases. The highest proportion of individuals with podoconiosis resided in the SNNP (39%), while 32% and 29% of people with podoconiosis resided in Oromia and Amhara Regional States, respectively. Tigray and Benishangul Gumuz Regional States bore lower burdens, and in the remaining regions, podoconiosis was almost non-existent.  Discussion: The estimates of podoconiosis cases presented here based upon the combination of currently available epidemiological data and a robust modelling approach clearly show that podoconiosis is highly endemic in Ethiopia. Given the presence of low cost prevention, and morbidity management and disability prevention services, it is our collective responsibility to scale-up interventions rapidly.

Multiscale assessment of progress of electrification in Indonesia based on brightness level derived from nighttime satellite imagery

Availability of electricity can be used as an indicator to proximate parameters related to human well-being. Overall, the electrification process in Indonesia has been accelerating in the past two decades. Unfortunately, monitoring the country's progress on its effort to provide wider access to electricity poses challenges due to inconsistency of data provided by each national bureau, and limited availability of information. This study attempts to provide a reliable measure by employing nighttime satellite imagery to observe and to map the progress of electrification within a duration of 20 years, from 1993 to 2013. Brightness of 67,021 settlement-size points in 1993, 2003, and 2013 was assessed using data from DMSP/OLS instruments to study the electrification progress in the three service regions (Sumatera, Java-Bali, and East Indonesia) of the country's public electricity company, PLN. Observation of all service areas shows that the increase in brightness, which correspond with higher electricity development and consumption, has positive correlation with both population density (R² = 0.70) and urban change (R² = 0.79). Moreover, urban change has a stronger correlation with brightness, which is probably due to the high energy consumption in urban area per capita. This study also found that the brightness in Java-Bali region is very dominant, while the brightness in other areas has been lagging during the period of analysis. The slow development of electricity infrastructure, particularly in major parts of East Indonesia region, affects the low economic growth in some areas and formed vicious cycle.

Tuning the white light spectrum of light emitting diode lamps to reduce attraction of nocturnal arthropods

Artificial lighting allows humans to be active at night, but has many unintended consequences, including interference with ecological processes, disruption of circadian rhythms and increased exposure to insect vectors of diseases. Although ultraviolet and blue light are usually most attractive to arthropods, degree of attraction varies among orders. With a focus on future indoor lighting applications, we manipulated the spectrum of white lamps to investigate the influence of spectral composition on number of arthropods attracted. We compared numbers of arthropods captured at three customizable light-emitting diode (LED) lamps (3510, 2704 and 2728 K), two commercial LED lamps (2700 K), two commercial compact fluorescent lamps (CFLs; 2700 K) and a control. We configured the three custom LEDs to minimize invertebrate attraction based on published attraction curves for honeybees and moths. Lamps were placed with pan traps at an urban and two rural study sites in Los Angeles, California. For all invertebrate orders combined, our custom LED configurations were less attractive than the commercial LED lamps or CFLs of similar colour temperatures. Thus, adjusting spectral composition of white light to minimize attracting nocturnal arthropods is feasible; not all lights with the same colour temperature are equally attractive to arthropods.

Metrics in Urban Health: Current Developments and Future Prospects

The research community has shown increasing interest in developing and using metrics to determine the relationships between urban living and health. In particular, we have seen a recent exponential increase in efforts aiming to investigate and apply metrics for urban health, especially the health impacts of the social and built environments as well as air pollution. A greater recognition of the need to investigate the impacts and trends of health inequities is also evident through more recent literature. Data availability and accuracy have improved through new affordable technologies for mapping, geographic information systems (GIS), and remote sensing. However, less research has been conducted in low- and middle-income countries where quality data are not always available, and capacity for analyzing available data may be limited. For this increased interest in research and development of metrics to be meaningful, the best available evidence must be accessible to decision makers to improve health impacts through urban policies.

Remotely measuring populations during a crisis by overlaying two data sources

BACKGROUND

Societal instability and crises can cause rapid, large-scale movements. These movements are poorly understood and difficult to measure but strongly impact health. Data on these movements are important for planning response efforts. We retrospectively analyzed movement patterns surrounding a 2010 humanitarian crisis caused by internal political conflict in Côte d'Ivoire using two different methods.

METHODS

We used two remote measures, nighttime lights satellite imagery and anonymized mobile phone call detail records, to assess average population sizes as well as dynamic population changes. These data sources detect movements across different spatial and temporal scales.

RESULTS

The two data sources showed strong agreement in average measures of population sizes. Because the spatiotemporal resolution of the data sources differed, we were able to obtain measurements on long- and short-term dynamic elements of populations at different points throughout the crisis.

CONCLUSIONS

Using complementary, remote data sources to measure movement shows promise for future use in humanitarian crises. We conclude with challenges of remotely measuring movement and provide suggestions for future research and methodological developments.

Re-examining environmental correlates of Plasmodium falciparum malaria endemicity: a data-intensive variable selection approach

Background

Malaria risk maps play an increasingly important role in disease control planning, implementation, and evaluation. The construction of these maps using modern geospatial techniques relies on covariate grids: continuous surfaces quantifying environmental factors that partially explain spatial heterogeneity in malaria endemicity. Although crucial, past variable selection processes for this purpose have often been subjective and ad-hoc, with many covariates used in modeling with little quantitative justification.

Methods

This research consists of an extensive covariate construction and selection process for predicting Plasmodium falciparum parasite rates (PfPR) in Africa for years 2000-2012. First, a literature review was conducted to establish a comprehensive list of covariates used for malaria mapping. Second, a library of covariate data was assembled to reflect this list, a process that included the construction of multiple, temporally dynamic datasets. Third, the resulting set of covariates was leveraged to create more than 50 million possible covariate terms via factorial combinations of different spatial and temporal aggregations, transformations, and pairwise interactions. Fourth, the expanded set of covariates was reduced via successive selection criteria to yield a robust covariate subset that was assessed using an out-of-sample validation approach.

Results

The final covariate subset included predominately dynamic covariates and it substantially out-performed earlier sets used by the Malaria Atlas Project (MAP) for creating global malaria risk maps, with the pseudo-R² value for the out-of-sample validation increasing from 0.43 to 0.52. Dynamic covariates improved the model, with 17 of the 20 new covariates consisting of monthly or annual products, but the selected covariates were typically interaction terms that included both dynamic and synoptic datasets. Thus the interplay between normal (i.e., long-term averages) and immediate conditions may be key for characterizing environmental controls on parasite rate.

Conclusions

This analysis represents the first effort to systematically audit covariate utility for malaria mapping and then derive an objective, empirically based set of environmental covariates for modeling PfPR. The new covariates produce more reliable representations of malaria risk patterns and how they are changing through time, and these covariates will be used to characterize spatially and temporally varying environmental conditions affecting PfPR within a geostatistical-modeling framework, thus building upon previous research by MAP that produced global malaria maps for 2007 and 2010.

Electronic supplementary material

The online version of this article (doi:10.1186/s12936-015-0574-x) contains supplementary material, which is available to authorized users.

Explaining seasonal fluctuations of measles in Niger using nighttime lights imagery

Measles epidemics in West Africa cause a significant proportion of vaccine-preventable childhood mortality. Epidemics are strongly seasonal, but the drivers of these fluctuations are poorly understood, which limits the predictability of outbreaks and the dynamic response to immunization. We show that measles seasonality can be explained by spatiotemporal changes in population density, which we measure by quantifying anthropogenic light from satellite imagery. We find that measles transmission and population density are highly correlated for three cities in Niger. With dynamic epidemic models, we demonstrate that measures of population density are essential for predicting epidemic progression at the city level and improving intervention strategies. In addition to epidemiological applications, the ability to measure fine-scale changes in population density has implications for public health, crisis management, and economic development.

Roundtable on Urban Living Environment Research (RULER)

For 18 months in 2009-2010, the Rockefeller Foundation provided support to establish the Roundtable on Urban Living Environment Research (RULER). Composed of leading experts in population health measurement from a variety of disciplines, sectors, and continents, RULER met for the purpose of reviewing existing methods of measurement for urban health in the context of recent reports from UN agencies on health inequities in urban settings. The audience for this report was identified as international, national, and local governing bodies; civil society; and donor agencies. The goal of the report was to identify gaps in measurement that must be filled in order to assess and evaluate population health in urban settings, especially in informal settlements (or slums) in low- and middle-income countries. Care must be taken to integrate recommendations with existing platforms (e.g., Health Metrics Network, the Institute for Health Metrics and Evaluation) that could incorporate, mature, and sustain efforts to address these gaps and promote effective data for healthy urban management. RULER noted that these existing platforms focus primarily on health outcomes and systems, mainly at the national level. Although substantial reviews of health outcomes and health service measures had been conducted elsewhere, such reviews covered these in an aggregate and perhaps misleading way. For example, some spatial aspects of health inequities, such as those pointed to in the 2008 report from the WHO's Commission on the Social Determinants of Health, received limited attention. If RULER were to focus on health inequities in the urban environment, access to disaggregated data was a priority. RULER observed that some urban health metrics were already available, if not always appreciated and utilized in ongoing efforts (e.g., census data with granular data on households, water, and sanitation but with little attention paid to the spatial dimensions of these data). Other less obvious elements had not exploited the gains realized in spatial measurement technology and techniques (e.g., defining geographic and social urban informal settlement boundaries, classification of population-based amenities and hazards, and innovative spatial measurement of local governance for health). In summary, the RULER team identified three major areas for enhancing measurement to motivate action for urban health-namely, disaggregation of geographic areas for intra-urban risk assessment and action, measures for both social environment and governance, and measures for a better understanding of the implications of the physical (e.g., climate) and built environment for health. The challenge of addressing these elements in resource-poor settings was acknowledged, as was the intensely political nature of urban health metrics. The RULER team went further to identify existing global health metrics structures that could serve as platforms for more granular metrics specific for urban settings.