Neighbourhood-level housing quality indices for health assessment in Dakar, Senegal

Submitted: 23 June 2020
Accepted: 24 October 2020
Published: 5 May 2021
Abstract Views: 2085
PDF: 762
HTML: 144
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Authors

In sub-Saharan African cities, the dearth of accurate and detailed data is a major problem in the study of health and socioeconomic changes driven by rapid urbanization. Data on both health determinants and health outcomes are often lacking or are of poor quality. Proxies associated with socioeconomic differences are needed to compensate the lack of data. One of the most straightforward proxies is housing quality, which is a multidimensional concept including characteristics of both the built and natural environments. In this work, we combined the 2013 census data with remotely sensed land cover and land use data at a very high resolution in order to develop an integrated housing quality-based typology of the neighbourhoods in Dakar, Senegal. Principal component analysis and hierarchical classification were used to derive neighbourhood housing quality indices and four neighbourhood profiles. Paired tests revealed significant variations in the censusderived mortality rates between profile 1, associated with the lowest housing quality, and the three other profiles. These findings demonstrate the importance of housing quality as an important health risk factor. From a public health perspective, it should be a useful contribution for geographically targeted planning health policies, at the neighbourhood spatial level, which is the most appropriate administrative level for interventions.

Dimensions

Altmetric

PlumX Metrics

Downloads

Download data is not yet available.

Citations

Adjei PO-W, Kyei PO, 2013. Linkages between income, housing quality and disease occurrence in rural Ghana. J Hous Built Environ 28:35-49. DOI: https://doi.org/10.1007/s10901-012-9277-6
ANSD (Agence Nationale de la Statistique et de la Démographie), 2013. Rapport définitif RGPHAE-2013, 418 pp. Available from: http://www.ansd.sn/ressources/RGPHAE-2013/resultats-definitifs.htm
Arias E, De Vos S, 1996. Using housing items to indicate socioeconomic status: Latin America. Soc Indic Res 38:53-80. DOI: https://doi.org/10.1007/BF00293786
Ayubi E, Safiri S, 2018. Geographic variation in morbidity and mortality of cerebrovascular diseases in Korea during 2011-2015: bias due to spatial autocorrelation and modifiable areal unit problem. J Stroke Cerebrovasc Dis Off J Natl Stroke Assoc 27:1123. DOI: https://doi.org/10.1016/j.jstrokecerebrovasdis.2017.11.039
Bawah AA, Zuberi Z, 2004. Socioeconomic status and child mortality: an illustration using housing and household characteristics from African census data. Afr Popul Stud 19:9-29.
Bhatta B, Saraswati S, Bandyopadhyay D, 2010. Urban sprawl measurement from remote sensing data. Appl Geogr 30:731-40. DOI: https://doi.org/10.1016/j.apgeog.2010.02.002
Boadi K, Kuitunen M, Raheem K, Hanninen K, 2005. Urbanisation without development: environmental and health implications in African cities. Environ Dev Sustain 7:465-500. DOI: https://doi.org/10.1007/s10668-004-5410-3
Borderon M, Oliveau S, 2017. Vulnérabilités sociales et changement d’échelle. Espace Popul Sociétés Space Popul Soc 2016/3. Available from: http://journals.openedition.org/eps/7012 DOI: https://doi.org/10.4000/eps.7012
Borderon M, Oliveau S, Machault V, Vignolles C, Lacaux J-P, N’Donky A, 2014. Qualifier les espaces urbains à Dakar, Sénégal. Cybergeo Eur J Geogr. Available from: http://journals.openedition.org/cybergeo/26250 DOI: https://doi.org/10.4000/cybergeo.26250
Comrey AL, Lee HB, 2016. A first course in factor analysis, 2nd edn. Psychology Press & Routledge (Taylor & Francis), London, UK.
Dlodlo RA, Fujiwara PI, Hwalima ZE, Mungofa S, Harries AD, 2011. Adult mortality in the cities of Bulawayo and Harare, Zimbabwe: 1979-2008. J Int AIDS Soc 14:S2-S2. DOI: https://doi.org/10.1186/1758-2652-14-S1-S2
Dos Santos S, Rautu I, Diop M, Abdou Illou MM, Ndonky A, Le Hesran J-Y, Lalou R, 2015. The influence of environmental factors on childhood fever during the rainy season in an African city: a multilevel approach in Dakar, Senegal. Popul Environ 36:429-51. DOI: https://doi.org/10.1007/s11111-014-0224-1
Dye C, 2008. Health and urban living. Science 319:766-9. DOI: https://doi.org/10.1126/science.1150198
Georganos S, Gadiaga AN, Linard C, Grippa T, Vanhuysse S, Mboga N, Wolff E, Dujardin S, Lennert M, 2019. Modelling the wealth index of demographic and health surveys within cities using very high-resolution remotely sensed information. Remote Sens 11:2543. DOI: https://doi.org/10.3390/rs11212543
Gething PW, Tatem AJ, Bird T, Burgert C, 2015. Creating spatial interpolation surfaces with DHS data. DHS Spatial Analysis Reports No. 11. Rockville, Maryland, USA: ICF International. Available from: https://dhsprogram.com/pubs/pdf/SAR11/SAR11.pdf
Grippa T, Georganos S, 2018. Dakar land use map at street block level (Zenodo). Available from: https://zenodo.org/record/1291389#.YIfSILUzY2w
Grippa T, Lennert M, Beaumont B, Vanhuysse S, Stephenne N, Wolff E, 2017a. An open-source semi-automated processing chain for urban object-based classification. Remote Sens 9:358. DOI: https://doi.org/10.3390/rs9040358
Grippa T, Georganos S, Vanhuysse SG, Lennert M, Wolff E, 2017b. A local segmentation parameter optimization approach for mapping heterogeneous urban environments using VHR imagery. In: W. Heldens, N. Chrysoulakis, T. Erbertseder, and Y. Zhang (Eds.), Remote sensing technologies and applications in urban environments II. SPIE. Warsaw, Poland, p. 20. DOI: https://doi.org/10.1117/12.2278422
Grippa T, Georganos S, Zarougui S, Bognounou P, Diboulo E, Forget Y, Lennert M, Vanhuysse S, Mboga N, Wolff E, 2018. Mapping urban land use at street block level using open street map, remote sensing data, and spatial metrics. ISPRS Int J Geo-Inf 7:246. DOI: https://doi.org/10.3390/ijgi7070246
Gulyani S, Bassett EM, Talukdar D, 2014. A tale of two cities: a multi-dimensional portrait of poverty and living conditions in the slums of Dakar and Nairobi. Habitat Int 43:98-107. DOI: https://doi.org/10.1016/j.habitatint.2014.01.001
Günther I, Harttgen K, 2012. Deadly cities? spatial inequalities in mortality in sub-Saharan Africa. Popul Dev Rev 38:469-86. DOI: https://doi.org/10.1111/j.1728-4457.2012.00512.x
Herrin WE, Amaral MM, Balihuta AM, 2013. The relationships between housing quality and occupant health in Uganda. Soc Sci Med 81:115-22. DOI: https://doi.org/10.1016/j.socscimed.2012.12.017
Jankowska MM, Benza M, Weeks JR, 2013. Estimating spatial inequalities of urban child mortality. Demogr Res 28:33-62. DOI: https://doi.org/10.4054/DemRes.2013.28.2
Kessides C, 2007. The urban transition in Sub-Saharan Africa: challenges and opportunities. Environ Plan C Gov Policy 25:466-85. DOI: https://doi.org/10.1068/c3p
Kofie RY, Attua EM, Nabila JS, 2008. Poverty and socio-economic consequences of Buruli ulcer (Mycobacterium ulcerans) in the Ga West District of Ghana. Nor Geogr Tidsskr - Nor J Geogr 62:210-21. DOI: https://doi.org/10.1080/00291950802335855
Korah PI, Matthews T, Tomerini D. 2019. Characterising spatial and temporal patterns of urban evolution in Sub-Saharan Africa: the case of Accra, Ghana. Land Use Policy 87:104049. DOI: https://doi.org/10.1016/j.landusepol.2019.104049
Lanrewaju F, 2012. Urbanization, housing quality and environmental degeneration in Nigeria. J Geogr Reg Plan 5:422-9. DOI: https://doi.org/10.5897/JGRP12.060
Lê S, Josse J, Husson F, 2008. FactoMineR: an R package for multivariate analysis. J Stat Softw 25. DOI: https://doi.org/10.18637/jss.v025.i01
Li G, Weng Q, 2007. Measuring the quality of life in city of Indianapolis by integration of remote sensing and census data. Int J Remote Sens 28:249-67. DOI: https://doi.org/10.1080/01431160600735624
Linard C, Tatem AJ, Gilbert M, 2013. Modelling spatial patterns of urban growth in Africa. Appl Geogr 44:23-32. DOI: https://doi.org/10.1016/j.apgeog.2013.07.009
Lo CP, Faber BJ, 1997. Integration of landsat thematic mapper and census data for quality of life assessment. Remote Sens Environ 62:143-57. DOI: https://doi.org/10.1016/S0034-4257(97)00088-6
Masquelier B, Pison G, Rakotonirina J, Rasoanomenjanahary A, 2019. Estimating cause-specific mortality in Madagascar: an evaluation of death notification data from the capital city. Popul Health Metr 17:8. DOI: https://doi.org/10.1186/s12963-019-0190-z
Ndiaye I, 2015. Étalement urbain et différenciation sociospatiale à Dakar (Sénégal). Cah Géographie Qué 59:47-69. DOI: https://doi.org/10.7202/1034348ar
Owens A, 2012. Neighborhoods on the rise: a typology of neighborhoods experiencing socioeconomic ascent. City Community 11:345-69. DOI: https://doi.org/10.1111/j.1540-6040.2012.01412.x
Quentin W, Abosede O, Aka J, Akweongo P, Dinard K, Ezeh A, Hamed R, Kayembe PK, Mitike G, Mtei G, 2014. Inequalities in child mortality in ten major African cities. BMC Med 12:95. DOI: https://doi.org/10.1186/1741-7015-12-95
Rahman A, Kumar Y, Fazal S, Bhaskaran S, 2011. Urbanization and quality of urban environment using remote sensing and GIS techniques in East Delhi-India. J Geogr Inf Syst 03:62. DOI: https://doi.org/10.4236/jgis.2011.31005
Salem G, 1998. La santé dans la ville: géographie d’un petit espace dense. Karthala Editions, Pikine, Sénégal.
Satterthwaite D, Sverdlik A, Brown D, 2019. Revealing and responding to multiple health risks in informal settlements in Sub-Saharan African cities. J Urban Health 96:112-22. DOI: https://doi.org/10.1007/s11524-018-0264-4
Suglia SF, Duarte CS, Sandel MT, 2011. Housing quality, housing instability, and maternal mental health. J Urban Health 88:1105-116. DOI: https://doi.org/10.1007/s11524-011-9587-0
Tapiador FJ, Avelar S, Tavares-Corrêa C, Zah R, 2011. Deriving fine-scale socioeconomic information of urban areas using very high-resolution satellite imagery. Int J Remote Sens 32:6437-56. DOI: https://doi.org/10.1080/01431161.2010.512928
Tatem AJ, Noor AM, Hagen C, von Gregorio AD, Hay SI, 2007. High resolution population maps for low income nations: combining land cover and census in East Africa. PLoS One 2:e1298. DOI: https://doi.org/10.1371/journal.pone.0001298
Thomson CN, Hardin P, 2000. Remote sensing/GIS integration to identify potential low-income housing sites. Cities 17:97-109. DOI: https://doi.org/10.1016/S0264-2751(00)00005-6
Tusting LS, Bisanzio D, Alabaster G, Cameron E, Cibulskis R, Davies M, Flaxman S, Gibson HS, Knudsen J, Mbogo C, Okumu FO, von Seidlein L, Weiss DJ, Lindsay SW, Gething PW, Bhatt S, 2019. Mapping changes in housing in sub-Saharan Africa from 2000 to 2015. Nature 568:391-4. DOI: https://doi.org/10.1038/s41586-019-1050-5
UN-Habitat, 2008. Sénégal: Profil urbain de Dakar; 36 pp. Available from: https://unhabitat.org/books/senegal-profilurbain-de-dakar-french/
Vaid U, Evans GW, 2017. Housing quality and health: an evaluation of slum rehabilitation in India. Environ Behav 49:771-90. DOI: https://doi.org/10.1177/0013916516667975
Wagstaff A, 2002. Poverty and health sector inequalities. Bull World Health Organ 80:97-105.
Yaya S, Hudani A, Udenigwe O, Shah V, Ekholuenetale M, Bishwajit G, 2018. Improving water, sanitation and hygiene practices, and housing quality to prevent diarrhea among under-five children in Nigeria. Trop Med Infect Dis 3:41. DOI: https://doi.org/10.3390/tropicalmed3020041
Yaya S, Uthman OA, Okonofua F, Bishwajit G, 2019. Decomposing the rural-urban gap in the factors of underfive mortality in sub-Saharan Africa? Evidence from 35 countries. BMC Public Health 19:616. DOI: https://doi.org/10.1186/s12889-019-6940-9

How to Cite

Gadiaga, A. N., De Longueville, F., Georganos, S., Grippa, T., Dujardin, S., Diène, A. N., Masquelier, B., Diallo, M., & Linard, C. (2021). Neighbourhood-level housing quality indices for health assessment in Dakar, Senegal. Geospatial Health, 16(1). https://doi.org/10.4081/gh.2021.910