ReviewBMJ global health2019
Because space matters: conceptual framework to help distinguish slum from non-slum urban areas.
Review in BMJ global health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 4 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Prevalence of mental disorders among young people living in urban slums of low- and middle- income countries: A systematic review and Meta-analysis.Social psychiatry and psychiatric epidemiology · 2026Pooled it
- Pooled it
- Global prevalence and trends in hypertension and type 2 diabetes mellitus among slum residents: a systematic review and meta-analysis.BMJ open · 2022Pooled it
- Non-genetic risk and protective factors and biomarkers for neurological disorders: a meta-umbrella systematic review of umbrella reviews.BMC medicine · 2021Pooled it
- Public parks utilization and citizen satisfaction in Bangkok Metropolitan: An integrated theoretical model for tropical urban health.PloS one · 2026Article
- Exposed yet unmapped: evidence of differential flood exposure in deprived urban areas using citizen science.npj urban sustainability · 2026Article
- Topography and environmental deficiencies are associated with chikungunya virus exposure in urban informal settlements in Salvador, Brazil.PLoS neglected tropical diseases · 2025Article
- Intimate partner violence and quality of life of young women in urban slum and non-slum communities, Ibadan, Nigeria.BMC public health · 2025Article
- Differences in walking access to healthcare facilities between formal and informal areas in 19 sub-Saharan African cities.Communications medicine · 2025Article
- Improving complex health systems and lived environments for maternal and perinatal well-being in urban sub-Saharan Africa: the UrbanBirth Collective.Journal of global health · 2025Article
- Alcohol marketing as a commercial determinant of health: daily diary insights from young women in Kampala.Health promotion international · 2025Article
- Trends and Inequalities in Maternal and Newborn Health Services for Unplanned Settlements of Lusaka City, Zambia.Journal of urban health : bulletin of the New York Academy of Medicine · 2024Article
- Association Between Household Deprivation and Living in Informal Settlements and Incidence of Diarrhea in Children Under 5 in Eleven Latin American Cities.Journal of urban health : bulletin of the New York Academy of Medicine · 2024Article
- Identifying deprived "slum" neighbourhoods in the Greater Accra Metropolitan Area of Ghana using census and remote sensing data.World development · 2023Article
- Intimate partner violence among young women in Ibadan, Nigeria: are there slum and non-slum differences?BMC women's health · 2023Article
- Developing an intervention to improve the quality of childcare centers in resource-poor urban settings: a mixed methods study in Nairobi, Kenya.Frontiers in public health · 2023Article
- A Qualitative Assessment of Place and Mental Health: Perspectives of Young Women Ages 18-24 Living in the Urban Slums of Kampala, Uganda.International journal of environmental research and public health · 2022Article
- Article
- Comparative study of slum and non-slum populations to inform urban expansion of Universal Health Coverage.Journal of family medicine and primary care · 2022Article
- A community engagement approach for an integrated early childhood development intervention: a case study of an urban informal settlement with Kenyans and embedded refugees.BMC public health · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite an estimated one billion people around the world living in slums, most surveys of health and well-being do not distinguish between slum and non-slum urban residents. Identifying people who live in slums is important for research purposes and also to enable policymakers, programme managers, donors and non-governmental organisations to better target investments and services to areas of greatest deprivation. However, there is no consensus on what a slum is let alone how slums can be distinguished from non-slum urban precincts. Nor has attention been given to a more fine-grained classification of urban spaces that might go beyond a simple slum/non-slum dichotomy. The purpose of this paper is to provide a conceptual framework to help tackle the related issues of slum definition and classification of the urban landscape. We discuss:The concept of space as an epidemiological variable that results in 'neighbourhood effects'.The problems of slum area definition when there is no 'gold standard'.A long-list of variables from which a selection must be made in defining or classifying urban slum spaces.Methods to combine any set of identified variables in an operational slum area definition.Two basic approaches to spatial slum area definitions-top-down (starting with a predefined area which is then classified according to features present in that area) and bottom-up (defining the areal unit based on its features).Different requirements of a slum area definition according to its intended use.Implications for research and future development.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.