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      Development of a spatial sampling protocol using GIS to measure health disparities in Bobo-Dioulasso, Burkina Faso, a medium-sized African city

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          Abstract

          Background

          Many cities in developing countries experience an unplanned and rapid growth. Several studies have shown that the irregular urbanization and equipment of cities produce different health risks and uneven exposure to specific diseases. Consequently, health surveys within cities should be carried out at the micro-local scale and sampling methods should try to capture this urban diversity.

          Methods

          This article describes the methodology used to develop a multi-stage sampling protocol to select a population for a demographic survey that investigates health disparities in the medium-sized city of Bobo-Dioulasso, Burkina Faso. It is based on the characterization of Bobo-Dioulasso city typology by taking into account the city heterogeneity, as determined by analysis of the built environment and of the distribution of urban infrastructures, such as healthcare structures or even water fountains, by photo-interpretation of aerial photographs and satellite images. Principal component analysis and hierarchical ascendant classification were then used to generate the city typology.

          Results

          Five groups of spaces with specific profiles were identified according to a set of variables which could be considered as proxy indicators of health status. Within these five groups, four sub-spaces were randomly selected for the study. We were then able to survey 1045 households in all the selected sub-spaces. The pertinence of this approach is discussed regarding to classical sampling as random walk method for example.

          Conclusion

          This urban space typology allowed to select a population living in areas representative of the uneven urbanization process, and to characterize its health status in regards to several indicators (nutritional status, communicable and non-communicable diseases, and anaemia). Although this method should be validated and compared with more established methods, it appears as an alternative in developing countries where geographic and population data are scarce.

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          Most cited references25

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          Urbanization, urbanicity, and health.

          D Vlahov (2002)
          A majority of the world's population will live in urban areas by 2007. The most rapidly urbanizing cities are in less-wealthy nations, and the pace of growth varies among regions. There are few data linking features of cities to the health of populations. We suggest a framework to guide inquiry into features of the urban environment that affect health and well-being. We consider two key dimensions: urbanization and urbanicity. Urbanization refers to change in size, density, and heterogeneity of cities. Urbanicity refers to the impact of living in urban areas at a given time. A review of the published literature suggests that most of the important factors that affect health can be considered within three broad themes: the social environment, the physical environment, and access to health and social services. The development of urban health as a discipline will need to draw on the strengths of diverse academic areas of study (e.g., ecology, epidemiology, sociology). Cross-national research may provide insights about the key features of cities and how urbanization influences population health.
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            Demographic, epidemiological, and health transitions: are they relevant to population health patterns in Africa?

            Background Studies of trends in population changes and epidemiological profiles in the developing world have overwhelmingly relied upon the concepts of demographic, epidemiological, and health transitions, even though their usefulness in describing and understanding population and health trends in developing countries has been repeatedly called into question. The issue is particularly relevant for the study of population health patterns in Africa and sub-Saharan Africa, as the history and experience there differs substantially from that of Western Europe and North America, for which these concepts were originally developed. Objective The aim of this study is two-fold: to review and clarify any distinction between the concepts of demographic transition, epidemiological transition and health transition and to identify summary indicators of population health to test how well these concepts apply in Africa. Results Notwithstanding the characteristically diverse African context, Africa is a continent of uncertainties and emergencies where discontinuities and interruptions of health, disease, and mortality trends reflect the enduring fragility and instability of countries and the vulnerabilities of individuals and populations in the continent. Africa as a whole remains the furthest behind the world's regions in terms of health improvements and longevity, as do its sub-Saharan African regions and societies specifically. This study documents: 1) theoretically and empirically the similarities and differences between the demographic transition, epidemiological transition, and health transition; 2) simple summary indicators that can be used to evaluate their descriptive and predictive features; 3) marked disparities in the onset and pace of variations and divergent trends in health, disease, and mortality patterns as well as fertility and life expectancy trajectories among African countries and regions over the past 60 years; 4) the rapid decline in infant mortality and gains in life expectancy from the 1950s through the 1990s in a context of preponderant communicable diseases in all African countries; 5) the salient role of adult mortality, mostly ascribed to HIV/AIDS and co-morbidities, since the 1990s in reversing trends in mortality decline, its interruption of life expectancy improvements, and its reversal of gender differences in life expectancies disadvantaging women in several countries with the highest prevalence of HIV/AIDS; 6) the huge impact of wars in reversing the trends in under-five mortality decline in sub-Saharan countries in the 1990s and beyond. These assessments of these transition frameworks and these phenomena were not well documented to date for all five regions and 57 countries of Africa. Conclusion Prevailing frameworks of demographic, epidemiological, and health transitions as descriptive and predictive models are incomplete or irrelevant for charting the population and health experiences and prospects of national populations in the African context.
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              Don't spin the pen: two alternative methods for second-stage sampling in urban cluster surveys

              In two-stage cluster surveys, the traditional method used in second-stage sampling (in which the first household in a cluster is selected) is time-consuming and may result in biased estimates of the indicator of interest. Firstly, a random direction from the center of the cluster is selected, usually by spinning a pen. The houses along that direction are then counted out to the boundary of the cluster, and one is then selected at random to be the first household surveyed. This process favors households towards the center of the cluster, but it could easily be improved. During a recent meningitis vaccination coverage survey in Maradi, Niger, we compared this method of first household selection to two alternatives in urban zones: 1) using a superimposed grid on the map of the cluster area and randomly selecting an intersection; and 2) drawing the perimeter of the cluster area using a Global Positioning System (GPS) and randomly selecting one point within the perimeter. Although we only compared a limited number of clusters using each method, we found the sampling grid method to be the fastest and easiest for field survey teams, although it does require a map of the area. Selecting a random GPS point was also found to be a good method, once adequate training can be provided. Spinning the pen and counting households to the boundary was the most complicated and time-consuming. The two methods tested here represent simpler, quicker and potentially more robust alternatives to spinning the pen for cluster surveys in urban areas. However, in rural areas, these alternatives would favor initial household selection from lower density (or even potentially empty) areas. Bearing in mind these limitations, as well as available resources and feasibility, investigators should choose the most appropriate method for their particular survey context.
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                Author and article information

                Contributors
                daouda.kassie@cirad.fr
                annaroudot@gmail.com
                nadine.dessay@ird.fr
                jlp@unistra.fr
                salem.gerard@gmail.com
                florence.fournet@ird.fr
                Journal
                Int J Health Geogr
                Int J Health Geogr
                International Journal of Health Geographics
                BioMed Central (London )
                1476-072X
                18 April 2017
                18 April 2017
                2017
                : 16
                : 14
                Affiliations
                [1 ]ISNI 0000 0001 2156 4014, GRID grid.7902.c, , Université Paris Ouest Nanterre La Défense, ; 200 Avenue de la République, 92000 Nanterre, France
                [2 ]ISNI 0000 0001 2153 9871, GRID grid.8183.2, , CIRAD, ASTRE, CIRAD TA C-22/E, ; Campus International de Baillarguet, 34398 Montpellier Cedex 5, France
                [3 ]ISNI 0000000122879528, GRID grid.4399.7, ESPACE DEV, , Institut de Recherche pour le Développement, ; Maison de la Télédetection, 500 rue Jean-François Breton, 34093 Montpellier Cedex 5, France
                [4 ]ISNI 0000 0001 2157 9291, GRID grid.11843.3f, , Université De Strasbourg, ; 4 Rue Blaise Pascal, 67081 Strasbourg, France
                [5 ]ISNI 0000000122879528, GRID grid.4399.7, CEPED, , Institut de Recherche pour le Développement, ; 19 Rue Jacob, 75006 Paris, France
                [6 ]ISNI 0000000122879528, GRID grid.4399.7, MIVEGEC, , Institut de Recherche pour le Développement, ; 911, Avenue Agropolis, BP 64501, 34394 Montpellier Cedex 5, France
                [7 ]ISNI 0000 0004 0564 0509, GRID grid.457337.1, , Institut de Recherche en Sciences de la Santé, ; 01 BP 545, Bobo-Dioulasso, Burkina Faso
                Author information
                http://orcid.org/0000-0003-0220-8131
                Article
                87
                10.1186/s12942-017-0087-7
                5395894
                28420404
                5a77c9bf-c85a-408d-b1f0-e52fe2ad4e68
                © The Author(s) 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 8 November 2016
                : 6 April 2017
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001665, Agence Nationale de la Recherche;
                Award ID: ANR12-INEG-0007
                Award Recipient :
                Funded by: CNRS (PEPS)
                Award ID: PEPS107-IE
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2017

                Public health
                health disparities,spatial sampling,typology,medium-sized city,bobo-dioulasso
                Public health
                health disparities, spatial sampling, typology, medium-sized city, bobo-dioulasso

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