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People living in slums and other deprived areas in low- and middle-income country (LMIC) cities are under-represented in censuses, and subsequently in "top-down" gridded population estimates. Modelled gridded population data are a unique source of disaggregated population information to calculate local development indicators such as the Sustainable Development Goals (SDGs). This study evaluates if, and how, WorldPop-Global (WPG) -Unconstrained and -Constrained “top-down” datasets might be improved in a simulated realistic LMIC urban population by incorporating slum profile population counts into model training. We found that the WPG-Unconstrained model with or without slum training data grossly underestimated population in urban deprived areas while grossly overestimating population in rural areas. SDG 11.1.1, the percent of population living in slums, for example, was estimated to be 20% or less compared to a "true" value of 29.5%. The WPG-Constrained model, which included building auxiliary datasets, far more accurately estimated the population in all grid cells (including rural areas), and the inclusion of slum training data further improved estimates such that SDG 11.1.1 was estimated at 27.1% and 27.0%, respectively. Inclusion of building metrics and slum training data in “top-down” gridded population models can substantially improve grid cell-level accuracy in both urban and rural areas.

The majority of urban inhabitants in low- and middle-income country (LMIC) cities live in deprived urban areas. However, policy efforts and the monitoring of global goals and agendas such as the United Nation's Sustainable Development Goals (SDGs) and UN-Habitat New Urban Agenda are hindered by the unavailability of statistical and spatial data at metropolitan, city and sub-city scales. Deprivation is a complex and multidimensional concept, and presently, there is a strong focus within the existing literature on household-level (including individual) deprivation and less on area-level deprivation and this is problematic because deprivation at the area and household-level are known to interrelate and result in multiple challenges for individuals and communities. Within this scoping review, we build on existing literature that focuses on household- or area-level deprivation to arrive at a combined understanding of how urban deprivation is defined in relation to LMIC cities. The scoping review of existing literature was used in conjunction with local stakeholder workshops to produce a framework titled “Domains of Deprivation Framework”. The Domains of Deprivation Framework conceptualizes urban deprivation at three different scales, including at the household scale, within the area scale and at the area connect scale. It includes nine domains, (1) Socio-Economic Status and (2) Housing Domains (Household scale); (3) Social Hazards & Assets, (4) Physical Hazards & Assets, (5) Unplanned Urbanization and (6) Contamination (Within Area scale); and (7) Infrastructure, (8) Facilities & Services and (9) City Governance (Area Connect scale). The Domains of Deprivation Framework is designed to support diverse urban, health, poverty, and development initiatives globally to characterize and address deprivation in LMIC cities from a holistic perspective, combining traditional data sources (e.g., surveys or census data) with new data sources (e.g., Earth Observation data).

Introduction Chronic kidney disease (CKD) is an emerging public health priority in Central America. However, data on the prevalence of CKD in Guatemala, Central America’s most populous country, are limited, especially for rural communities. Methods We conducted a population-representative survey of 2 rural agricultural municipalities in Guatemala. We collected anthropometric data, blood pressure, serum and urine creatinine, glycosylated hemoglobin, and urine albumin. Sociodemographic, health, and exposure data were self-reported. Results We enrolled 807 individuals (63% of all eligible, 35% male, mean age 39.5 years). An estimated 4.0% (95% confidence interval [CI] 2.4–6.6) had CKD, defined as an estimated glomerular filtration rate (eGFR) less than 60 ml/min per 1.73 m2. Most individuals with an eGFR below 60 ml/min per 1.73 m2 had diabetes or hypertension. In multivariable analysis, the important factors associated with risk for an eGFR less than 60 ml/min per 1.73 m2 included a history of diabetes or hypertension (adjusted odds ratio [aOR] 11.21; 95% CI 3.28–38.24), underweight (body mass index [BMI], Graphical abstract

The continuous urbanisation in most Low-to-Middle-Income-Country (LMIC) cities is accompanied by rapid socio-economic changes in urban and peri-urban areas. Urban transformation processes, such as gentrification as well as the increase in poor urban neighbourhoods (e.g., slums) produce new urban patterns. The intersection of very rapid socio-economic and demographic dynamics are often insufficiently understood, and relevant data for understanding them are commonly unavailable, dated, or too coarse (resolution). Traditional survey-based methods (e.g., census) are carried out at low temporal granularity and do not allow for frequent updates of large urban areas. Researchers and policymakers typically work with very dated data, which do not reflect on-the-ground realities and data aggregation hide socio-economic disparities. Therefore, the potential of Earth Observations (EO) needs to be unlocked. EO data have the ability to provide information at detailed spatial and temporal scales so as to support monitoring transformations. In this paper, we showcase how recent innovations in EO and Artificial Intelligence (AI) can provide relevant, rapid information about socio-economic conditions, and in particular on poor urban neighbourhoods, when large scale and/or multi-temporal data are required, e.g., to support Sustainable Development Goals (SDG) monitoring. We provide solutions to key challenges, including the provision of multi-scale data, the reduction in data costs, and the mapping of socio-economic conditions. These innovations fill data gaps for the production of statistical information, addressing the problems of access to field-based data under COVID-19.

A continuously growing number of urban inhabitants in Low- and Middle-income Country (LMIC) cities live in deprived areas. Such areas are under-serviced and characterized by poor living and environmental conditions, where the unplanned morphology interacts with physical hazards. While such areas proliferate, climate change has increasingly severe impacts on them. Deprived areas are often located in high-risk zones, e.g., flood zones. However, the absence of global databases on such areas is an obstacle to locate and prioritize hotspots of deprived communities exposed to climate change. Consequently, quantifying the numbers of exposed inhabitants is not possible, though it is required in support of the Sustainable Development Goals (SDGs) (e.g., 11, 13) and local adaptation strategies. Earth observation (EO) that allows producing such data fall short of providing city-level information due to computational constraints and unsolved methodological challenges related to scalability and transferability. This paper presents a framework to combine EO data with data on hazards (e.g., storms, floods) that impact urban areas and, in particular, deprived communities. First results in pilot cities show that deprived communities are systematically more exposed to physical hazards as compared to formal built-up areas. These problems are expected to intensify in the context of climate change, as most hazards will increase in their severity and frequency.

The number of people living in deprived urban areas within low and middle income countries (LMICs) is large and predicted to continue to grow. Mapping these areas over time and space in a consistent manner is important for monitoring the Sustainable Development Goals (SDGs) and degree of deprivation between cities. This work describes the development of a mapping ecosystem to do this. The first steps are to define urban extents and collect the data needed for developing deprived area models. The goal of the mapping ecosystem is to produce maps of ranges in deprived areas that are comparable between cities and can be used by local governments and researchers to map within city variations in deprivation. By developing and describing this ecosystem, we hope to replicate this in other cities and countries so that this type of work can be expanded.

Disaggregated population counts are needed to calculate health, economic, and development indicators in Low- and Middle-Income Countries (LMICs), especially in settings of rapid urbanisation. Censuses are often outdated and inaccurate in LMIC settings, and rarely disaggregated at fine geographic scale. Modelled gridded population datasets derived from census data have become widely used by development researchers and practitioners. These datasets are evaluated for accuracy at the spatial scale of the input data which is often much courser (e.g. administrative units) than the neighbourhood or cell-level scale of many applications. We simulate a realistic "true" 2016 population in Khomas, Namibia, a majority urban region, and introduce realistic levels of outdatedness (over 15 years) and inaccuracy in slum, non-slum, and rural areas. We aggregate these simulated realistic populations by census and administrative boundaries (to mimic census data), and generate 32 gridded population datasets that are typical of a LMIC setting using WorldPop-Global-Unconstrained gridded population approach. We evaluate the cell-level accuracy of these simulated datasets using the original "true" population as a reference. In our simulation, we found large cell-level errors, particularly in slum cells, driven by the use of average population densities in large areal units to determine cell-level population densities. Age, accuracy, and aggregation of the input data also played a role in these errors. We suggest incorporating finer-scale training data into gridded population models generally, and WorldPop-Global-Unconstrained in particular (e.g., from routine household surveys or slum community population counts), and use of new building footprint datasets as a covariate to improve cell-level accuracy. It is important to measure accuracy of gridded population datasets at spatial scales more consistent with how the data are being applied, especially if they are to be used for monitoring key development indicators at neighbourhood scales with relevance to small dense deprived areas within larger administrative units.

Disaggregated population counts are needed to calculate health, economic, and development indicators in Low- and Middle-Income Countries (LMICs), especially in settings of rapid urbanisation. Censuses are often outdated and inaccurate in LMIC settings, and rarely disaggregated at fine geographic scale. Modelled gridded population datasets derived from census data have become widely used by development researchers and practitioners; however, none of these datasets have been evaluated for accuracy of population estimates at the grid cell-level. This is because the finest-scale population figures generally available to data producers are those input into gridded population models and disaggregated to smaller grid cells (e.g., 100x100m). We simulate a realistic "true" 2016 population in Khomas, Namibia, a majority urban region, and introduce realistic levels of outdatedness (over 15 years) and inaccuracy in slum, non-slum, and rural areas. We then aggregate these simulated realistic populations by census and administrative boundaries (to mimic census data), and generate 32 gridded population datasets that are typical of a LMIC setting using WorldPop-Global's gridded population approach. We evaluate the cell-level accuracy of these simulated WorldPop-Global datasets, using the original "true" population as a reference. In our simulation, we found large cell-level errors, particularly in urban cells, driven by WorldPop-Global's use of average population densities in large areal units to determine cell-level population densities. Age, accuracy, and aggregation of the input data did play a primary role in these errors. We suggest incorporating finer-scale training data into gridded population models generally, and WorldPop-Global in particular (e.g., from simulated populations, routine household surveys, or slum community profiles), and use of new building footprint datasets as a covariate to improve cell-level accuracy of gridded population data. It is important to measure cell-level accuracy of all gridded population datasets, especially if they are to be used for monitoring key development indicators.

The methods used in low- and middle-income countries’ (LMICs) household surveys have not changed in four decades; however, LMIC societies have changed substantially and now face unprecedented rates of urbanization and urbanization of poverty. This mismatch may result in unintentional exclusion of vulnerable and mobile urban populations. We compare three survey method innovations with standard survey methods in Kathmandu, Dhaka, and Hanoi and summarize feasibility of our innovative methods in terms of time, cost, skill requirements, and experiences. We used descriptive statistics and regression techniques to compare respondent characteristics in samples drawn with innovative versus standard survey designs and household definitions, adjusting for sample probability weights and clustering. Feasibility of innovative methods was evaluated using a thematic framework analysis of focus group discussions with survey field staff, and via survey planner budgets. We found that a common household definition excluded single adults (46.9%) and migrant-headed households (6.7%), as well as non-married (8.5%), unemployed (10.5%), disabled (9.3%), and studying adults (14.3%). Further, standard two-stage sampling resulted in fewer single adult and non-family households than an innovative area-microcensus design; however, two-stage sampling resulted in more tent and shack dwellers. Our survey innovations provided good value for money, and field staff experiences were neutral or positive. Staff recommended streamlining field tools and pairing technical and survey content experts during fieldwork. This evidence of exclusion of vulnerable and mobile urban populations in LMIC household surveys is deeply concerning and underscores the need to modernize survey methods and practices. Electronic supplementary material The online version of this article (10.1007/s11524-020-00485-z) contains supplementary material, which is available to authorized users.

Introduction In low- and middle-income countries (LMICs), household survey data are a main source of information for planning, evaluation, and decision-making. Standard surveys are based on censuses, however, for many LMICs it has been more than 10 years since their last census and they face high urban growth rates. Over the last decade, survey designers have begun to use modelled gridded population estimates as sample frames. We summarize the state of the emerging field of gridded population survey sampling, focussing on LMICs. Methods We performed a systematic scoping review in Scopus of specific gridded population datasets and "population" or "household" "survey" reports, and solicited additional published and unpublished sources from colleagues. Results We identified 43 national and sub-national gridded population-based household surveys implemented across 29 LMICs. Gridded population surveys used automated and manual approaches to derive clusters from WorldPop and LandScan gridded population estimates. After sampling, some survey teams interviewed all households in each cluster or segment, and others sampled households from larger clusters. Tools to select gridded population survey clusters include the GridSample R package, Geo-sampling tool, and GridSample.org. In the field, gridded population surveys generally relied on geographically accurate maps based on satellite imagery or OpenStreetMap, and a tablet or GPS technology for navigation. Conclusions For gridded population survey sampling to be adopted more widely, several strategic questions need answering regarding cell-level accuracy and uncertainty of gridded population estimates, the methods used to group/split cells into sample frame units, design effects of new sample designs, and feasibility of tools and methods to implement surveys across diverse settings.

Background Population-representative household survey methods require up-to-date sampling frames and sample designs that minimize time and cost of fieldwork especially in low- and middle-income countries. Traditional methods such as multi-stage cluster sampling, random-walk, or spatial sampling can be cumbersome, costly or inaccurate, leading to well-known biases. However, a new tool, Epicentre’s Geo-Sampler program, allows simple random sampling of structures, which can eliminate some of these biases. We describe the study design process, experiences and lessons learned using Geo-Sampler for selection of a population representative sample for a kidney disease survey in two sites in Guatemala. Results We successfully used Epicentre’s Geo-sampler tool to sample 650 structures in two semi-urban Guatemalan communities. Overall, 82% of sampled structures were residential and could be approached for recruitment. Sample selection could be conducted by one person after 30 min of training. The process from sample selection to creating field maps took approximately 40 h. Conclusion In combination with our design protocols, the Epicentre Geo-Sampler tool provided a feasible, rapid and lower-cost alternative to select a representative population sample for a prevalence survey in our semi-urban Guatemalan setting. The tool may work less well in settings with heavy arboreal cover or densely populated urban settings with multiple living units per structure. Similarly, while the method is an efficient step forward for including non-traditional living arrangements (people residing permanently or temporarily in businesses, religious institutions or other structures), it does not account for some of the most marginalized and vulnerable people in a population–the unhoused, street dwellers or people living in vehicles.

Objective: In low- and middle-income countries (LMICs), household survey data are a main source of information for planning, evaluation, and decision-making. Standard surveys are based on censuses, however, for many LMICs it has been more than ten years since their last census and they face high urban growth rates. Over the last decade, survey designers have begun to use modelled gridded population estimates as sample frames. We summarize the state of the emerging field of gridded population survey sampling, focussing on LMICs. Methods: We performed a systematic review and identified 23 national and sub-national gridded population-based household surveys implemented across 18 LMICs. Findings: Gridded population surveys used automated and manual approaches to derive clusters from WorldPop and LandScan gridded population estimates. After sampling, many surveys interviewed all households in each cluster or segment, though some sampled households from larger clusters. Tools to select gridded population survey clusters include the GridSample R package, Geo-sampling tool, and GridSample.org. In the field, gridded population surveys generally relied on geographically accurate maps based on satellite imagery or OpenStreetMap, and a tablet or GPS technology for navigation. Conclusions: For gridded population survey sampling to be adopted more widely, several strategic questions need answering regarding cell-level accuracy and uncertainty of gridded population estimates, the methods used to group/split cells into sample frame units, design effects of new sample designs, and feasibility of tools and methods to implement surveys across diverse settings.

Ninety percent of the people added to the planet over the next 30 years will live in African and Asian cities, and a large portion of these populations will reside in deprived neighborhoods defined by slum conditions, informal settlement, or inadequate housing. The four current approaches to neighborhood deprivation mapping are largely silo-ed, and each fall short of producing accurate, timely, comparable maps that reflect local contexts. The first approach, classifying “slum households” in census and survey data and aggregating to administrative areas, reflects household-level rather than neighborhood-level deprivation. The second approach, field-based mapping, can produce the most accurate and context-relevant maps for a given neighborhood, however it requires substantial resources, preventing up-scaling. The third and fourth approaches, human interpretation and machine classification of satellite, aerial, or drone imagery, both overemphasize informal settlements, and fail to represent key social characteristics of deprived areas such as lack of tenure, exposure to pollution, and lack of basic public services. The latter, machine classification of imagery, can be automated and extended to incorporate new and multiple sources of data. This diverse collection of authors represent experts from these four approaches to neighborhood deprivation mapping. We summarize common areas of understanding, and present a set of requirements to produce maps of deprived urban areas that can be used by local-to-international stakeholders for advocacy, planning, and decision-making.

Background: The methods used in low- and middle-income country (LMIC) household surveys have not changed in four decades; however, LMIC societies have changed substantially. This mismatch may result in unintentional exclusion of vulnerable and mobile urban populations. We compare three survey method innovations with standard survey methods in Kathmandu, Dhaka, and Hanoi, and summarize feasibility of our innovative methods in terms of time, cost, skill requirements, and experiences. Methods: We used descriptive statistics and regression techniques to compare respondent characteristics in samples drawn with innovative versus standard survey designs and household definitions, adjusting for sample probability weights and clustering. Feasibility of innovative methods was evaluated using a thematic framework analysis of focus group discussions with survey field staff, and via survey planner budgets. Findings: We found that a common household definition excluded single adult (46.9%) and migrant headed households (6.7%), as well as non-married (8.5%), unemployed (10.5%), disabled (9.3%), and studying (14.3%) adults. Further, standard two-stage sampling resulted in fewer single adult and non-family households than an innovative one-stage design; however, two-stage sampling resulted in more tent and shack dwellers. Our survey innovations provided good value for money and field staff experiences were neutral or positive. Staff recommended streamlining field tools and pairing technical and survey content experts during fieldwork. Interpretation: This evidence of unintentional exclusion of vulnerable and mobile urban populations in LMIC household surveys is deeply concerning, and underscores the need to modernize survey methods and practices. Funding: UK Medical Research Council and UK Economic and Social Research Council. Declaration of Interest: All authors declare no conflicts of interest. Ethical Approval: Ethics approvals were obtained from the University of Leeds (ref:MREC16-137), University of Southampton (ref:26819), Nepal Health Research Council (ref:1761), Bangladesh Medical Research Council (ref:BMRC/NREC/RP/2016-2019/317), and Hanoi University of Public Health (ref:324/2017/YTCC-HD3).

Urbanization in the global South has been accompanied by the proliferation of vast informal and marginalized urban areas that lack access to essential services and infrastructure. UN-Habitat estimates that close to a billion people currently live in these deprived and informal urban settlements, generally grouped under the term of urban slums. Two major knowledge gaps undermine the efforts to monitor progress towards the corresponding sustainable development goal (i.e. SDG 11-Sustainable Cities and Communities). First, the data available for cities worldwide is patchy and insufficient to differentiate between the diversity of urban areas with respect to their access to essential services and their specific infrastructure needs. Second, existing approaches used to map deprived areas (i.e. aggregated household data, Earth observation (EO), and community-driven data collection) are mostly siloed, and, individually, they often lack transferability and scalability and fail to include the opinions of different interest groups. In particular, EO-based-deprived area mapping approaches are mostly top-down, with very little attention given to ground information and interaction with urban communities and stakeholders. Existing top-down methods should be complemented with bottom-up approaches to produce routinely updated, accurate, and timely deprived area maps. In this review, we first assess the strengths and limitations of existing deprived area mapping methods. We then propose an Integrated Deprived Area Mapping System (IDeAMapS) framework that leverages the strengths of EO-and community-based approaches. The proposed framework offers a way forward to map deprived areas globally, routinely, and with maximum accuracy to support SDG 11 monitoring and the needs of different interest groups., SCOPUS: re.j, info:eu-repo/semantics/published

Urbanisation brings with it rapid socio-economic change with volatile livelihoods and unstable ownership of assets. Yet, current measures of wealth are based predominantly on static livelihoods found in rural areas. We sought to assess the extent to which seven common measures of wealth appropriately capture vulnerability to poverty in urban areas. We then sought to develop a measure that captures the characteristics of one urban area in Nepal. We collected and analysed data from 1,180 households collected during a survey conducted between November 2017 and January 2018 and designed to be representative of the Kathmandu valley. A separate survey of a sub set of households was conducted using participatory qualitative methods in slum and non-slum neighbourhoods. A series of currently used indices of deprivation were calculated from questionnaire data. We used bivariate statistical methods to examine the association between each index and identify characteristics of poor and non-poor. Qualitative data was used to identify characteristics of poverty from the perspective of urban poor communities which were used to construct an Urban Poverty Index that combined asset and consumption focused context specific measures of poverty that could be proxied by easily measured indicators as assessed through multivariate modelling. We found a strong but not perfect association between each measure of poverty. There was disagreement when comparing the consumption and deprivation index on the classification of 19% of the sample. Choice of short-term monetary and longer-term capital approaches accounted for much of the difference. Those who reported migrating due to economic necessity were most likely to be categorised as poor. A combined index was developed to capture these dimension of poverty and understand urban vulnerability. A second version of the index was constructed that can be computed using a smaller range of variables to identify those in poverty. Current measures may hide important aspects of urban poverty. Those who migrate out of economic necessity are particularly vulnerable. A composite index of socioeconomic status helps to capture the complex nature of economic vulnerability.

Ninety percent of the people added to the planet over the next 30 years will live in African and Asian cities, and a large portion of these populations will reside in deprived neighborhoods defined by slum conditions, informal settlement, or inadequate housing. The four current approaches to neighborhood deprivation mapping are largely silo-ed, and each fall short of producing accurate, timely, comparable maps that reflect local contexts. The first approach, classifying “slum households” in census and survey data and aggregating to administrative areas, reflects household-level rather than neighborhood-level deprivation. The second approach, field-based mapping, can produce the most accurate and context-relevant maps for a given neighborhood, however it requires substantial resources, preventing up-scaling. The third and fourth approaches, human interpretation and machine classification of satellite, aerial, or drone imagery, both overemphasize informal settlements, and fail to represent key social characteristics of deprived areas such as lack of tenure, exposure to pollution, and lack of basic public services. The latter, machine classification of imagery, can be automated and extended to incorporate new and multiple sources of data. This diverse collection of authors represent experts from these four approaches to neighborhood deprivation mapping. We summarize common areas of understanding, and present a set of requirements to produce maps of deprived urban areas that can be used by local-to-international stakeholders for advocacy, planning, and decision-making.

Objective Public health interventions are often implemented at large scale and their evaluation seems to be difficult because they are usually multiple and their pathways to effect are complex and subject to modification by contextual factors. We assessed whether controlling for rainfall related variables altered estimates of the efficacy of a health program in rural Rwanda and have a quantifiable effect on an intervention evaluation outcomes. Methods We conducted a retrospective quasi-experimental study using previously collected cross-sectional data from the 2005 and 2010 Rwanda Demographic and Health Surveys (DHS), 2010 DHS oversampled data, monthly rainfall data collected from meteorological stations over the same period, and modeled output of long-term rainfall averages, soil moisture, and rain water runoff. Difference in difference models were used. Results Rainfall factors confounded the PIH intervention impact evaluation. When we adjusted our estimates of program effect by controlling for a variety of rainfall variables, several effectiveness estimates changed by 10% or more. The analyses that did not adjust for rainfall-related variables underestimated the intervention effect on the prevalence of ARI by 14.3%, fever by 52.4% and stunting by 10.2%. Conversely, the unadjusted analysis overestimated the intervention's effect on diarrhea by 56.5% and wasting by 80%. Conclusion Rainfall-related patterns have a quantifiable effect on program evaluation results and highlighted the importance and complexity of controlling for contextual factors in quasi-experimental design evaluations. This article is protected by copyright. All rights reserved.

Routine and accurate data on deprivation are needed for urban planning and decision support at various scales (i.e. from community to international). However, analyzing information requirements of diverse users on urban deprivation, we found that data are often not available or inaccessible. To bridge this data gap, Earth Observation (EO) data can support access to frequently updated spatial information. However, a user-centered approach is urgently required for the production of EO-based mapping products. Combining an online survey and several forms of user interactions, we defined five system specifications (derived from user requirements) for designing an open-access spatial information system for deprived urban areas. First, gridded maps represent the optimal spatial granularity to deal with high uncertainties of boundaries of deprived areas and to protect privacy. Second, a high temporal granularity of 1–2 years is important to respond to the high spatial dynamics of urban areas. Third, detailed local-scale information should be part of a city-to-global information system. Fourth, both aspects, community assets and risks, need to be part of an information system, and such data need to be combined with local community-based information. Fifth, in particular, civil society and government users should have fair access to data that bridges the digital barriers. A data ecosystem on urban deprivation meeting these requirements will be able to support community-level action for improving living conditions in deprived areas, local science-based policymaking, and tracking progress towards global targets such as the SDGs., info:eu-repo/semantics/published

IntroductionAs rapid urbanisation transforms the sociodemographic structures within cities, standard survey methods, which have remained unchanged for many years, under-represent the urban poorest. This leads to an overly positive picture of urban health, distorting appropriate allocation of resources between rural and urban and within urban areas. Here, we present a protocol for our study which (i) tests novel methods to improve representation of urban populations in household surveys and measure mental health and injuries, (ii) explores urban poverty and compares measures of poverty and ‘slumness’ and (iii) works with city authorities to understand, and potentially improve, utilisation of data on urban health for planning more equitable services.Methods and analysisWe will conduct household surveys in Kathmandu, Hanoi and Dhaka to test novel methods: (i) gridded population sampling; (ii) enumeration using open-access online maps and (iii) one-stage versus two-stage cluster sampling. We will test reliability of an observational tool to categorise neighbourhoods as slum areas. Within the survey, we will assess the appropriateness of a short set of questions to measure depression and injuries. Questionnaire data will also be used to compare asset-based, consumption-based and income-based measures of poverty. Participatory methods will identify perceptions of wealth in two communities in each city. The analysis will combine quantitative and qualitative findings to recommend appropriate measures of poverty in urban areas. We will conduct qualitative interviews and establish communities of practice with government staff in each city on use of data for planning. Framework approach will be used to analyse qualitative data allowing comparison across city settings.Ethics and disseminationEthical approvals have been granted by ethics committees from the UK, Nepal, Bangladesh and Vietnam. Findings will be disseminated through conference papers, peer-reviewed open access articles and workshops with policy-makers and survey experts in Kathmandu, Hanoi and Dhaka.

Rapid and uncontrolled urbanisation across low and middle-income countries is leading to ever expanding numbers of urban poor, defined here as slum dwellers and the homeless. It is estimated that 828 million people are currently living in slum conditions. If governments, donors and NGOs are to respond to these growing inequities they need data that adequately represents the needs of the urban poorest as well as others across the socio-economic spectrum. We report on the findings of a special session held at the International Conference on Urban Health, Dhaka 2015. We present an overview of the need for data on urban health for planning and allocating resources to address urban inequities. Such data needs to provide information on differences between urban and rural areas nationally, between and within urban communities. We discuss the limitations of data most commonly available to national and municipality level government, donor and NGO staff. In particular we assess, with reference to the WHO’s Urban HEART tool, the challenges in the design of household surveys in understanding urban health inequities. We then present two novel approaches aimed at improving the information on the health of the urban poorest. The first uses gridded population sampling techniques within the design and implementation of household surveys and the second adapts Urban HEART into a participatory approach which enables slum residents to assess indicators whilst simultaneously planning the response. We argue that if progress is to be made towards inclusive, safe, resilient and sustainable cities, as articulated in Sustainable Development Goal 11, then understanding urban health inequities is a vital pre-requisite to an effective response by governments, donors, NGOs and communities.

Ninety percent of the people added to the planet over the next 30 years will live in African and Asian cities, and a large portion of these populations will reside in deprived neighborhoods defined by slum conditions, informal settlement, or inadequate housing. The four current approaches to neighborhood deprivation mapping are largely siloed, and each fall short of producing accurate, timely, and comparable maps that reflect local contexts. The first approach, classifying "slum households" in census and survey data, reflects household-level rather than neighborhood-level deprivation. The second approach, field-based mapping, can produce the most accurate and context-relevant maps for a given neighborhood, however it requires substantial resources, preventing up-scaling. The third and fourth approaches, human (visual) interpretation and machine classification of air or spaceborne imagery, both overemphasize informal settlements, and fail to represent key social characteristics of deprived areas such as lack of tenure, exposure to pollution, and lack of public services. We summarize common areas of understanding, and present a set of requirements and a framework to produce routine, accurate maps of deprived urban areas that can be used by local-to-international stakeholders for advocacy, planning, and decision-making across Low-and Middle-Income Countries (LMICs). We suggest that machine learning models be extended to incorporate social area-level covariates and regular contributions of up-to-date and context-relevant field-based classification of deprived urban areas., SCOPUS: ar.j, info:eu-repo/semantics/published

International audience

IntroductionAlthough Rwanda’s health system underwent major reforms and improvements after the 1994 Genocide, the health system and population health in the southeast lagged behind other areas. In 2005, Partners In Health and the Rwandan Ministry of Health began a health system strengthening intervention in this region. We evaluate potential impacts of the intervention on maternal and child health indicators.MethodsCombining results from the 2005 and 2010 Demographic and Health Surveys with those from a supplemental 2010 survey, we compared changes in health system output indicators and population health outcomes between 2005 and 2010 as reported by women living in the intervention area with those reported by the pooled population of women from all other rural areas of the country, controlling for potential confounding by economic and demographic variables.ResultsOverall health system coverage improved similarly in the comparison groups between 2005 and 2010, with an indicator of composite coverage of child health interventions increasing from 57.9% to 75.0% in the intervention area and from 58.7% to 73.8% in the other rural areas. Under-five mortality declined by an annual rate of 12.8% in the intervention area, from 229.8 to 83.2 deaths per 1000 live births, and by 8.9% in other rural areas, from 157.7 to 75.8 deaths per 1000 live births. Improvements were most marked among the poorest households.ConclusionWe observed dramatic improvements in population health outcomes including under-five mortality between 2005 and 2010 in rural Rwanda generally and in the intervention area specifically.

Background Sustained investments in Rwanda’s health system have led to historic reductions in under five (U5) mortality. Although Rwanda achieved an estimated 68% decrease in the national under U5 mortality rate between 2002 and 2012, according to the national census, 5.8% of children still do not reach their fifth birthday, requiring the next wave of child mortality prevention strategies. Methods This is a cross-sectional study of 9002 births to 6328 women age 15–49 in the 2010 Rwanda Demographic and Health Survey. We tested bivariate associations between 29 covariates and U5 mortality, retaining covariates with an odds ratio p

IntroductionThe Sustainable Development Goals framed an unprecedented commitment to achieve global convergence in child and maternal mortality rates through 2030. To meet those targets, essential health services must be scaled via integration with strengthened health systems. This is especially urgent in Madagascar, the country with the lowest level of financing for health in the world. Here, we present an interim evaluation of the first 2 years of a district-level health system strengthening (HSS) initiative in rural Madagascar, using estimates of intervention coverage and mortality rates from a district-wide longitudinal cohort.MethodsWe carried out a district representative household survey at baseline of the HSS intervention in over 1500 households in Ifanadiana district. The first follow-up was after the first 2 years of the initiative. For each survey, we estimated maternal, newborn and child health (MNCH) coverage, healthcare inequalities and child mortality rates both in the initial intervention catchment area and in the rest of the district. We evaluated changes between the two areas through difference-in-differences analyses. We estimated annual changes in health centre per capita utilisation from 2013 to 2016.ResultsThe intervention was associated with 19.1% and 36.4% decreases in under-five and neonatal mortality, respectively, although these were not statistically significant. The composite coverage index (a summary measure of MNCH coverage) increased by 30.1%, with a notable 63% increase in deliveries in health facilities. Improvements in coverage were substantially larger in the HSS catchment area and led to an overall reduction in healthcare inequalities. Health centre utilisation rates in the catchment tripled for most types of care during the study period.ConclusionAt the earliest stages of an HSS intervention, the rapid improvements observed for Ifanadiana add to preliminary evidence supporting the untapped and poorly understood potential of integrated HSS interventions on population health.

IntroductionAlthough Rwanda’s health system underwent major reforms and improvements after the 1994 Genocide, the health system and population health in the southeast lagged behind other areas. In 2005 Partners In Health and the Rwandan Ministry of Health began a health system strengthening intervention in this region.MethodsCombining results from the 2005 and 2010 Demographic and Health Surveys with those from a supplemental 2010 survey, we compared changes in health system output indicators and population health outcomes between 2005 and 2010 as reported by 21,338 women living in the intervention area and similar rural areas, controlling for potential confounding by economic and demographic variables.ResultsOverall health system coverage improved similarly in both regions between 2005 and 2010, with an indicator of composite coverage of child health interventions increasing from 57.9% to 75.0% in the intervention area and from 58.7% to 73.8% in other rural areas. Despite experiencing poorer health outcomes in 2005, the intervention area caught up to or exceeded other rural areas on 23 of 25 indicators. Most notably, under-five mortality declined by an annual rate of 12.8% in the intervention area, from 229.8 to 83.2 deaths per 1000 live births, and by 8.9% in other rural areas, from 157.7 to 75.8 deaths per 1000 live births. Improvements were most dramatic among the poorest households.ConclusionWe observed dramatic improvements in population health outcomes including under-five mortality between 2005 and 2010 in rural Rwanda generally, and in the intervention area specifically.SUMMARY BOXWhat is already known about this topic?Much of the evidence that health system strengthening in rural Africa has improved health outcomes comes from studies of targeted regional interventions such as performance based financing or community health worker programs, rather than integrated interventions that encompass multiple components including infrastructure and supply chain investments, health management information system, workforce training and incentives at all levels, community health workers, and free services for poor patients.In addition to these experimental or quasi-experimental studies, a series of case studies have documented individual nations, pathways to achieving millennium development goal 4 target, the reduction of under-five mortality by two thirds between 1990 and 2015.These reports suggest that improvements in coverage of reproductive, maternal and child health indicators explain some, but not all, of the decline in child mortality and that these successes occurred in the context of national gains in health, nutrition and food security, sanitation, poverty reduction, and access to clean water.What are the new findings?Coverage of most maternal and child health care interventions improved at a similar pace in our rural intervention area and other rural areas.Despite experiencing poorer health outcomes in 2005, our rural intervention area caught up to or exceeded other rural areas on 23 of 25 population health indicators by 2010.Infant and under-5 mortality declined in our rural intervention area even more precipitously than in other rural areas of Rwanda between 2005 and 2010.How might this influence practice?The process of strengthening national health systems often involves trade-offs between a focus on first testing individual programs that distributed widely, as is often practiced by pilot programs with multilateral institutions, or implementing multiple simultaneous programs locally. Our results show that integrated health system strengthening interventions can be locally adapted to enable the rapid expansion of health care coverage as well as dramatic improvements in population health outcomes.Integrated multi-level interventions can also help narrow the health care coverage and outcome gap between richer and poorer members of a society.National governments can leverage nongovernmental partners to achieve the health related sustainable development goals through joint implementation of national health policy.

ObjectiveDespite documented associations between stunting and cognitive development, few population-level studies have measured both indicators in individual children or assessed stunting’s associations with other developmental domains.DesignMeta-analysis using publicly available data from fifteen Multiple Indicator Cluster Surveys (MICS-4) to assess the association between stunting and development, controlling for maternal education, family wealth, books in the home, developmentally supportive parenting and sex of the child, stratified by country prevalence of breast-feeding (‘low BF’SettingFifteen low- and middle-income countries.SubjectsPublically available data from 58 513 children aged 36–59 months.ResultsSevere stunting (height-for-age Z-score Z-score ConclusionsStunting is associated with many but not all developmental domains across a diversity of countries and cultures. However, associations varied by country breast-feeding prevalence and developmental domain.

BACKGROUND:To guide efficient investment of limited health resources in sub-Saharan Africa, local researchers need to be involved in, and guide, health system and policy research. While extensive survey and census data are available to health researchers and program officers in resource-limited countries, local involvement and leadership in research is limited due to inadequate experience, lack of dedicated research time and weak interagency connections, among other challenges. Many research-strengthening initiatives host prolonged fellowships out-of-country, yet their approaches have not been evaluated for effectiveness in involvement and development of local leadership in research.METHODS:We developed, implemented and evaluated a multi-month, deliverable-driven, survey analysis training based in Rwanda to strengthen skills of five local research leaders, 15 statisticians, and a PhD candidate. Research leaders applied with a specific research question relevant to country challenges and committed to leading an analysis to publication. Statisticians with prerequisite statistical training and experience with a statistical software applied to participate in class-based trainings and complete an assigned analysis. Both statisticians and research leaders were provided ongoing in-country mentoring for analysis and manuscript writing.RESULTS:Participants reported a high level of skill, knowledge and collaborator development from class-based trainings and out-of-class mentorship that were sustained 1 year later. Five of six manuscripts were authored by multi-institution teams and submitted to international peer-reviewed scientific journals, and three-quarters of the participants mentored others in survey data analysis or conducted an additional survey analysis in the year following the training.CONCLUSIONS:Our model was effective in utilizing existing survey data and strengthening skills among full-time working professionals without disrupting ongoing work commitments and using few resources. Critical to our success were a transparent, robust application process and time limited training supplemented by ongoing, in-country mentoring toward manuscript deliverables that were led by Rwanda's health research leaders.

BACKGROUND:Diarrhea among children under 5 years of age has long been a major public health concern. Previous studies have suggested an association between rainfall and diarrhea. Here, we examined the association between Rwandan rainfall patterns and childhood diarrhea and the impact of household sanitation variables on this relationship.METHODS:We derived a series of rain-related variables in Rwanda based on daily rainfall measurements and hydrological models built from daily precipitation measurements collected between 2009 and 2011. Using these data and the 2010 Rwanda Demographic and Health Survey database, we measured the association between total monthly rainfall, monthly rainfall intensity, runoff water and anomalous rainfall and the occurrence of diarrhea in children under 5 years of age.RESULTS:Among the 8601 children under 5 years of age included in the survey, 13.2 % reported having diarrhea within the 2 weeks prior to the survey. We found that higher levels of runoff were protective against diarrhea compared to low levels among children who lived in households with unimproved toilet facilities (OR?=?0.54, 95 % CI: [0.34, 0.87] for moderate runoff and OR?=?0.50, 95 % CI: [0.29, 0.86] for high runoff) but had no impact among children in household with improved toilets.CONCLUSION:Our finding that children in households with unimproved toilets were less likely to report diarrhea during periods of high runoff highlights the vulnerabilities of those living without adequate sanitation to the negative health impacts of environmental events.

Background The Ebola virus disease (EVD) epidemic has threatened access to basic health services through facility closures, resource diversion, and decreased demand due to community fear and distrust. While modeling studies have attempted to estimate the impact of these disruptions, no studies have yet utilized population-based survey data. Methods and Findings We conducted a two-stage, cluster-sample household survey in Rivercess County, Liberia, in March–April 2015, which included a maternal and reproductive health module. We constructed a retrospective cohort of births beginning 4 y before the first day of survey administration (beginning March 24, 2011). We then fit logistic regression models to estimate associations between our primary outcome, facility-based delivery (FBD), and time period, defined as the pre-EVD period (March 24, 2011–June 14, 2014) or EVD period (June 15, 2014–April 13, 2015). We fit both univariable and multivariable models, adjusted for known predictors of facility delivery, accounting for clustering using linearized standard errors. To strengthen causal inference, we also conducted stratified analyses to assess changes in FBD by whether respondents believed that health facility attendance was an EVD risk factor. A total of 1,298 women from 941 households completed the survey. Median age at the time of survey was 29 y, and over 80% had a primary education or less. There were 686 births reported in the pre-EVD period and 212 in the EVD period. The unadjusted odds ratio of facility-based delivery in the EVD period was 0.66 (95% confidence interval [CI] 0.48–0.90, p-value = 0.010). Adjustment for potential confounders did not change the observed association, either in the principal model (adjusted odds ratio [AOR] = 0.70, 95%CI 0.50–0.98, p = 0.037) or a fully adjusted model (AOR = 0.69, 95%CI 0.50–0.97, p = 0.033). The association was robust in sensitivity analyses. The reduction in FBD during the EVD period was observed among those reporting a belief that health facilities are or may be a source of Ebola transmission (AOR = 0.59, 95%CI 0.36–0.97, p = 0.038), but not those without such a belief (AOR = 0.90, 95%CI 0.59–1.37, p = 0.612). Limitations include the possibility of FBD secular trends coincident with the EVD period, recall errors, and social desirability bias. Conclusions We detected a 30% decreased odds of FBD after the start of EVD in a rural Liberian county with relatively few cases. Because health facilities never closed in Rivercess County, this estimate may under-approximate the effect seen in the most heavily affected areas. These are the first population-based survey data to show collateral disruptions to facility-based delivery caused by the West African EVD epidemic, and they reinforce the need to consider the full spectrum of implications caused by public health emergencies., John Kraemer and colleagues estimate changes in facility-based delivery during the Ebola virus disease epidemic in rural Liberia using cross-sectional data from a population-based household survey., Author Summary Why was this study done? The 2014–2015 West African Ebola virus disease (EVD) epidemic killed approximately 11,300 people directly, but its effects are magnified by collateral health consequences. Mathematical models and facility-based data provide evidence of substantial health service interruptions, but little population-based survey data has been published from the three principally affected countries. Maternal health services, which are dependent on functioning health systems, are likely to be particularly susceptible to external shocks, such as the EVD epidemic. What did the researchers do and find? We used a cluster-sample survey to produce a representative sample of births in Rivercess County, Liberia, a part of the country with relatively limited Ebola transmission. Controlling for potential confounders, we compared the odds of facility-based delivery among 686 births in the period before the EVD epidemic with 212 births during the epidemic. We identified a 30% reduction in the odds of facility-based delivery during the EVD epidemic. The odds of facility-based delivery were 41% lower among women who reported a belief that Ebola was or may be transmitted in health facilities, but not significantly lower among women who reported believing that Ebola was not transmitted in health facilities. What do these findings mean? This study provides further evidence that the 2014–2015 West African EVD epidemic caused serious collateral harm to health services. This study also underscores the need to maintain focus on long-term health system reconstruction after the EVD epidemic.

BACKGROUND:Postnatal care (PNC) in the first seven days is important for preventing morbidity and mortality in mothers and new-borns. Sub-Saharan African countries, which account for 62 % of maternal deaths globally, have made major efforts to increase PNC utilisation, but utilisation rates remains low even in countries like Rwanda where PNC services are universally available for free. This study identifies key socio-economic and demographic factors associated with PNC utilisation in Rwanda to inform improved PNC policies and programs.METHODS:This is a secondary analysis of the 2010 Demographic and Health Survey, a national multi-stage, cross-sectional survey. In bivariate analysis, we used chi-square tests to identify demographic and socio-economic factors associated with PNC utilisation at ??=?0.1. Pearson's R statistic (r?>?0.5) was used to identify collinear covariates, and to choose which covariate was more strongly associated with PNC utilisation. Manual backward stepwise logistic regression was performed on the remaining covariates to identify key factors associated with PNC utilisation at ??=?0.05. All analyses were performed in Stata 13 adjusting for sampling weights, clustering, and stratification.RESULTS:Of the 2,748 women with a live birth in the last two years who answered question about PNC utilisation, 353 (12.8 %) returned for PNC services within seven days after birth. Three factors were positively associated with PNC use: delivering at a health facility (OR: 2.97; 95 % CI: 2.28-3.87), being married but not involved with one's own health care decision-making (OR: 1.69; 95 % CI: 1.17, 2.44) compared to being married and involved; and being in the second (OR: 1.46; 95 % CI: 1.01-2.09) or richest wealth quintile (OR: 2.04; 95 % CI: 1.27-3.29) compared to the poorest. Mother's older age at delivery was negatively associated with PNC use (20-29 - OR: 0.51, 95 % CI: 0.29-0.87; 30-39 - OR: 0.47, 95 % CI: 0.27-0.83; 40-49 - OR: 0.32, 95 % CI: 0.16-0.64).CONCLUSIONS:Low PNC utilisation in Rwanda appears to be a universal problem though older age and poverty are further barriers to PNC utilisation. A recent change in the provision of BCG vaccination to new-borns might promote widespread PNC utilisation. We further recommend targeted campaigns to older mothers and poorest mothers, focusing on perceptions of health system quality, cultural beliefs, and pregnancy risks.

BACKGROUND:In low and middle-income countries, acute lower respiratory illness is responsible for roughly 1 in every 5 child deaths. Rwanda has made major health system improvements including its community health worker systems, and it is one of the few countries in Africa to meet the 2015 Millennium Development Goals, although prevalence of acute lower respiratory infections (4 %) is similar to other countries in sub-Saharan Africa. This study aims to assess social, economic, and environmental factors associated with acute lower respiratory infections among children under five to inform potential further improvements in the health system.METHODS:This is a cross-sectional study using data collected from women interviewed in the 2010 DHS about 8,484 surviving children under five. Based on a literature review, we defined 19 health, social, economic, and environmental potential risk factors, tested bivariate associations with acute lower respiratory infections, and advanced variables significant at the 0.1 confidence level to logistic regression modelling. We used manual backward stepwise regression to arrive at a final model. All analyses were performed in Stata v13 and adjusted for complex sample design.RESULTS:The following factors were independently associated with acute lower respiratory infections: child's age, anemia level, and receipt of Vitamin A; household toilet type and residence, and season of interview. In multivariate regression, being in the bottom ten percent of households (OR: 1.27, 95 % CI: 0.85-1.87) or being interviewed during the rainy season (OR: 1.61, 95 % CI: 1.24-2.09) was positively associated with acute lower respiratory infections, while urban residence (OR: 0.58, 95 % CI: 0.38-0.88) and being age 24-59 months versus 0-11 months (OR: 0.53, 95 % CI: 0.40-0.69) was negatively associated with acute lower respiratory infections.CONCLUSION:Potential areas for intervention including community campaigns about acute lower respiratory infections symptoms and treatment, and continued poverty reduction through rural electrification and modern stove distribution which may reduce use of dirty cooking fuel, improve living conditions, and reduce barriers to health care.