Extending Data for Urban Health Decision-Making: a Menu of New and Potential Neighborhood-Level Health Determinants Datasets in LMICs

Uncovering the determinants of health in deprived urban neighborhoods in Accra, Ghana: a qualitative and participatory reconnaissance study

Background

Delivering primary care services within the context of rapid urbanization and a changing disease burden is a major challenge in sub-Saharan Africa. Rural models of primary care, including the "Community-based Health Planning and Services" (CHPS) programme in Ghana, have shown improved health outcomes. However, adapting these to the urban context has proved problematic. Differences in the determinants of health found in these settings may help to explain the challenges of delivering CHPS in poor urban neighborhoods in Accra. To inform the redesign of CHPS for the urban context, we aimed to understand the determinants driving health and engagement with health services in three informal settlements in Accra.

Methods

This study formed a reconnaissance phase for a subsequent participatory action research study. We used qualitative and participatory methods to explore the influence of wider and proximal determinants on health and the use and perceptions of CHPS. Three transect walks with community leaders across the study settings informed interview guides and the recruitment of suitable participants for key informant and focus group interviews. Using a Framework Approach, we analysed transcripts and reports from these activities and developed themes and sub-themes in participants' experiences accessing healthcare.

Results

Our findings highlight the importance of wider and proximal determinants of health including physical environment, gender and other social stratifiers including age, ethnicity, religion and disability, on health, health seeking behavior and personal behaviors such as substance misuse, tobacco use and alcohol. Utilization of CHPS was low and seen primarily as a service for maternal and child health. Private providers, ranging from informal drug stores to private clinics, were used most commonly. Community leaders and groups were active, but engagement was limited by opportunity costs for members.

Conclusion

Traditional service delivery packages need to be adapted to include non-communicable diseases driven by risk behaviors such as tobacco, unhealthy diet, alcohol and substance abuse. Assets such as volunteerism and nurses embedded within communities are challenging to attain in complex urban settings, yet other assets exist including occupational associations and a range of informal and private providers that could support delivery of preventive and promotive health care with equitable reach.

Geospatial methodology for determining the regional prevalence of hospital-reported childhood intussusception in patients from India

Both developed and developing countries carry a large burden of pediatric intussusception. Sentinel site surveillance-based studies have highlighted the difference in the regional incidence of intussusception. The objectives of this manuscript were to geospatially map the locations of hospital-confirmed childhood intussusception cases reported from sentinel hospitals, identify clustering and dispersion, and reveal the potential causes of the underlying pattern. Geospatial analysis revealed positive clustering patterns, i.e., a Moran's I of 0.071 at a statistically significant (p value < 0.0010) Z score of 16.14 for the intussusception cases across India (cases mapped n = 2221), with 14 hotspots in two states (Kerala = 6 and Tamil Nadu = 8) at the 95% CI. Granular analysis indicated that 67% of the reported cases resided < 50 km from the sentinel hospitals, and the average travel distance to the sentinel hospital from the patient residence was calculated as 47 km (CI 95% min 1 km-max 378 km). Easy access and facility referral preferences were identified as the main causes of the existing clustering pattern of the disease. We recommend designing community-based surveillance studies to improve the understanding of the prevalence and regional epidemiological burden of the disease.

An Iterative, Participatory Approach to Developing a Neighborhood-Level Indicator System of Health and Wellbeing

Despite increased awareness of the essential role of neighborhood characteristics for residents' health and wellbeing, the development of neighborhood-level indicator systems has received relatively little attention to date. To address this gap, we describe the participatory development process of a small-area indicator system that includes information on local health needs in a pilot neighborhood in the German city of Mannheim. To identify relevant indicators, we partnered with representatives of the city's public health department and used an iterative approach that included multiple Plan-Do-Check-Act cycles with ongoing feedback from local key stakeholders. The described process resulted in a web-based indicator system with a total of 86 indicators. Additionally, 123 indicators were perceived as relevant by stakeholders but could not be included due to data unavailability. Overall, stakeholders evaluated the participatory approach as useful. Even though the onset of the COVID-19 pandemic and the lack of some data elements hindered instrument development, close collaboration with public health partners facilitated the process. To identify and target sub-national health inequalities, we encourage local public health stakeholders to develop meaningful and useful neighborhood-level indicator systems, building on our experiences from the applied development process and considering identified barriers and facilitators.

Earth Observations and Statistics: Unlocking Sociodemographic Knowledge through the Power of Satellite Images

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.

Assessing Inequalities in Wellbeing at a Neighbourhood Scale in Low-Middle-Income-Country Secondary Cities and Their Implications for Long-Term Livability

To ensure future sustainability, cities need to consider concepts of livability and resident wellbeing alongside environmental, economic and infrastructure development equity. The current rapid urbanization experienced in many regions is leading to sustainability challenges, but also offers the opportunity to deliver infrastructure supporting the social aspects of cities and the services that underpin them alongside economic growth. Unfortunately, evidence of what is needed to deliver urban wellbeing is largely absent from the global south. This paper contributes to filling this knowledge gap through a novel interdisciplinary mixed methods study undertaken in two rapidly changing cities (one Thai and one Kenyan) using qualitative surveys, subjective wellbeing and stress measurements, and spatial analysis of urban infrastructure distribution. We find the absence of basic infrastructure (including waste removal, water availability and quality) unsurprisingly causes significant stress for city residents. However, once these services are in place, smaller variations (inequalities) in social (crime, tenure) and environmental (noise, air quality) conditions begin to play a greater role in determining differences in subjective wellbeing across a city. Our results indicate that spending time in urban greenspaces can mitigate the stressful impacts of city living even for residents of informal neighborhoods. Our data also highlights the importance of places that enable social interactions supporting wellbeing-whether green or built. These results demonstrate the need for diversity and equity in the provision of public realm spaces to ensure social and spatial justice. These findings strengthen the need to promote long term livability in LMIC urban planning alongside economic growth, environmental sustainability, and resilience.

Geospatial evaluation of trade-offs between equity in physical access to healthcare and health systems efficiency

Introduction

Decisions regarding the geographical placement of healthcare services require consideration of trade-offs between equity and efficiency, but few empirical assessments are available. We applied a novel geospatial framework to study these trade-offs in four African countries.

Methods

Geolocation data on population density (a surrogate for efficiency), health centres and cancer referral centres in Kenya, Malawi, Tanzania and Rwanda were obtained from online databases. Travel time to the closest facility (a surrogate for equity) was estimated with 1 km resolution using the Access Mod 5 least cost distance algorithm. We studied associations between district-level average population density and travel time to closest facility for each country using Pearson’s correlation, and spatial autocorrelation using the Global Moran’s I statistic. Geographical clusters of districts with inefficient resource allocation were identified using the bivariate local indicator of spatial autocorrelation.

Results

Population density was inversely associated with travel time for all countries and levels of the health system (Pearson’s correlation range, health centres: −0.89 to −0.71; cancer referral centres: −0.92 to −0.43), favouring efficiency. For health centres, negative spatial autocorrelation (geographical clustering of dissimilar values of population density and travel time) was weaker in Rwanda (−0.310) and Tanzania (−0.292), countries with explicit policies supporting equitable access to rural healthcare, relative to Kenya (−0.579) and Malawi (−0.543). Stronger spatial autocorrelation was observed for cancer referral centres (Rwanda: −0.341; Tanzania: −0.259; Kenya: −0.595; Malawi: −0.666). Significant geographical clusters of sparsely populated districts with long travel times to care were identified across countries.

Conclusion

Negative spatial correlations suggested that the geographical distribution of health services favoured efficiency over equity, but spatial autocorrelation measures revealed more equitable geographical distribution of facilities in certain countries. These findings suggest that even when prioritising efficiency, thoughtful decisions regarding geographical allocation could increase equitable physical access to services.

Building a Methodological Foundation for Impactful Urban Planetary Health Science

Anthropogenic environmental change will heavily impact cities, yet associated health risks will depend significantly on decisions made by urban leaders across a wide range of non-health sectors, including transport, energy, housing, basic urban services, and others. A subset of planetary health researchers focus on understanding the urban health impacts of global environmental change, and how these vary globally and within cities. Such researchers increasingly adopt collaborative transdisciplinary approaches to engage policy-makers, private citizens, and other actors in identifying and evaluating potential policy solutions that will reduce environmental impacts in ways that simultaneously promote health, equity, and/or local economies-in other words, maximising 'co-benefits'. This report presents observations from a participatory workshop focused on challenges and opportunities for urban planetary health research. The workshop, held at the 16th International Conference on Urban Health (ICUH) in Xiamen, China, in November 2019, brought together 49 participants and covered topics related to collaboration, data, and research impact. It featured research projects funded by the Wellcome Trust's Our Planet Our Health (OPOH) programme. This report aims to concisely summarise and disseminate participants' collective contributions to current methodological practice in urban planetary health research.

Assessing Urban Health Inequities through a Multidimensional and Participatory Framework: Evidence from the EURO-HEALTHY Project

Urban health inequities often reflect and follow the geographic patterns of inequality in the social, economic and environmental conditions within a city-the so-called determinants of health. Evidence of patterns within these conditions can support decision-making by identifying where action is urgent and which policies and interventions are needed to mitigate negative impacts and enhance positive impacts. Within the scope of the EU-funded project EURO-HEALTHY (Shaping EUROpean policies to promote HEALTH equitY), the City of Lisbon was selected as a case study to apply a multidimensional and participatory assessment approach of urban health whose purpose was to inform the evaluation of policies and interventions with potential to address local health gaps. In this paper, we present the set of indicators identified as drivers of urban health inequities within the City of Lisbon, exploring the added value of using a spatial indicator framework together with a participation process to orient a place-based assessment and to inform policies aimed at reducing health inequities. Two workshops with a panel of local stakeholders from health and social care services, municipal departments (e.g. urban planning, environment, social rights and education) and non-governmental and community-based organizations were organized. The aim was to engage local stakeholders to identify locally critical situations and select indicators of health determinants from a spatial equity perspective. To support the analysis, a matrix of 46 indicators of health determinants, with data disaggregated at the city neighbourhood scale, was constructed and was complemented with maps. The panel identified critical situations for urban health equity in 28 indicators across eight intervention axes: economic conditions, social protection and security; education; demographic change; lifestyles and behaviours; physical environment; built environment; road safety and healthcare resources and performance. The geographical distribution of identified critical situations showed that all 24 city neighbourhoods presented one or more problems. A group of neighbourhoods systematically perform worse in most indicators from different intervention axes, requiring not only priority action but mainly a multi- and intersectoral policy response. The indicator matrices and maps have provided a snapshot of urban inequities across different intervention axes, making a compelling argument for boosting intersectoral work across municipal departments and local stakeholders in the City of Lisbon. This study, by integrating local evidence in combination with social elements, pinpoints the importance of a place-based approach for assessing urban health equity.

Multiple Global Population Datasets: Differences and Spatial Distribution Characteristics

Spatial data of regional populations are indispensable in studying the impact of human activities on resource utilization and the ecological environment. Because the differences between datasets and their spatial distribution are still unclear, this has become a puzzle in data selection and application. This study is based on four mainstream spatialized population datasets: the History Database of the Global Environment version 3.2.000 (HYDE), Gridded Population of the World version 4 (GPWv4), Global Human Settlement Layer (GHSL), and WorldPop. In view of possible influences of geographical factors, this study analyzes the differences in accuracy of population estimation by computing relative errors and population spatial distribution consistency in different regions by comparing datasets pixel by pixel. The results demonstrate the following: (1) Source data, spatialization methods, and case area features affect the precision of datasets. As the main data source is statistical data and the spatialization method maintains the population in the administrative region, the populations of GPWv4 and GHSL are closest to the statistical data value. (2) The application of remote sensing, mobile communication, and other geospatial data makes the datasets more accurate in the United Kingdom, with rich information, and the absolute value of relative errors is less than 4%. In the Tibet Autonomous Region of China, where data are hard to obtain, the four datasets have larger relative errors. However, the area where the four datasets are completely consistent is as high as 84.73% in Tibet, while in the UK it is only 66.76%. (3) The areas where the spatial patterns of the four datasets are completely consistent are mainly distributed in areas with low population density, or with developed urbanization and concentrated population distribution. Areas where the datasets have poor consistency are mainly distributed in medium population density areas with high urbanization levels. Therefore, in such areas, a more careful assessment should be made during the data application process, and more emphasis should be placed on improving data accuracy when using spatialization methods.

Need for an Integrated Deprived Area “Slum” Mapping System (IDEAMAPS) in Low- and Middle-Income Countries (LMICs)

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.

The Role of Earth Observation in an Integrated Deprived Area Mapping “System” for Low-to-Middle Income Countries

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.

Understanding the rise of cardiometabolic diseases in low- and middle-income countries

Increases in the prevalence of noncommunicable diseases (NCDs), particularly cardiometabolic diseases such as cardiovascular disease, stroke and diabetes, and their major risk factors have not been uniform across settings: for example, cardiovascular disease mortality has declined over recent decades in high-income countries but increased in low- and middle-income countries (LMICs). The factors contributing to this rise are varied and are influenced by environmental, social, political and commercial determinants of health, among other factors. This Review focuses on understanding the rise of cardiometabolic diseases in LMICs, with particular emphasis on obesity and its drivers, together with broader environmental and macro determinants of health, as well as LMIC-based responses to counteract cardiometabolic diseases.