Suchergebnisse

Pikine, a 12 km de Dakar, a ete installe a partir de 1952, pour permettre le developpement du Grand-Dakar. A l'aide des cartes, l'auteur illustre le developpement qui a eu lieu. On constate deux developpements paralleles: celui des quartiers reguliers et celui des quartiers irreguliers. A partir de 1970, l'evolution des quartiers irreguliers est nettement plus rapide, a partir de 1986, la population est plus importante dans les quartiers irreguliers que dans les reguliers. L'auteur tente de definir le processus d'urbanisation illegale a partir des constructions des bornes-fontaines, lequelles construites par l'Etat devraient se trouver dans les zones regulieres et met, par la, en evidence les bases d'un certain clientelisme. Puis il tente de trouver une explication a la perte de controle urbain de l'Etat, d'en degager les consequences sociales, economiques et culturelles pour les deux quartiers et d'analyser les mecanismes par lesquels les quartiers irreguliers disposent d'equipements collectifs. Enfin, il presente l'organisation politique et sociale des quartiers irreguliers et les relations entre cette organisation et les partis politiques. (DÜI-Mfg)

Urban crisis and social control in Pikine. Public fountains and clientelism.
The mention of norms andor management replaces talks about outer factors or malfunctions to explain the senegalese urban crisis. This prevents an analysis of the social and political practice under attack. Through the example of Pikine, the Author shows that the bad urban management comes from the social and territorial strategies of the State, revealing one of the functional links between State and society. A detailed analysis of the urban political organization at the level of the Socialist Party and at the level of the municipality points out the networks and the mecanisms used by the State to maintain control over towns that were bound to troubles and marginalization. It is only now, when the State is not any longer able to play its supporting role and when the territorial control mecanisms set up in Pikine by the PS are out of action that a real crisis situation occurs.

Die rasche Verstädterung stellt die Gesundheitsversorgung in Afrika angesichts knapper Kassen vor nahezu unlösbare Aufgaben. In den Städten treten neue Krankheiten auf, alte, schon überwunden geglaubte, tauchen wieder auf. Die Ernährungsgewohnheiten verändern sich, auch dies hat epidemiologische Konsequenzen. Neben der modernen Medizin, zu der wegen ihrer hohen Kosten nicht alle Schichten Zugang haben, erlebt die traditionelle Medizin einen Aufschwung. (DÜI-Sbd)

International audience ; Background: Geographical objectives and probabilistic methods are difficult to reconcile in a unique health survey. Probabilistic methods focus on individuals to provide estimates of a variable's prevalence with a certain precision, while geographical approaches emphasise the selection of specific areas to study interactions between spatial characteristics and health outcomes. A sample selected from a small number of specific areas creates statistical challenges: the observations are not independent at the local level, and this results in poor statistical validity at the global level. Therefore, it is difficult to construct a sample that is appropriate for both geographical and probability methods.Methods: We used a two-stage selection procedure with a first non-random stage of selection of clusters. Instead of randomly selecting clusters, we deliberately chose a group of clusters, which as a whole would contain all the variation in health measures in the population. As there was no health information available before the survey, we selected a priori determinants that can influence the spatial homogeneity of the health characteristics. This method yields a distribution of variables in the sample that closely resembles that in the overall population, something that cannot be guaranteed with randomly-selected clusters, especially if the number of selected clusters is small. In this way, we were able to survey specific areas while minimising design effects and maximising statistical precision.Application: We applied this strategy in a health survey carried out in Vientiane, Lao People's Democratic Republic. We selected well-known health determinants with unequal spatial distribution within the city: nationality and literacy. We deliberately selected a combination of clusters whose distribution of nationality and literacy is similar to the distribution in the general population.Conclusion: This paper describes the conceptual reasoning behind the construction of the survey sample and shows that it can be advantageous to choose clusters using reasoned hypotheses, based on both probability and geographical approaches, in contrast to a conventional, random cluster selection strategy.

International audience ; Background: Geographical objectives and probabilistic methods are difficult to reconcile in a unique health survey. Probabilistic methods focus on individuals to provide estimates of a variable's prevalence with a certain precision, while geographical approaches emphasise the selection of specific areas to study interactions between spatial characteristics and health outcomes. A sample selected from a small number of specific areas creates statistical challenges: the observations are not independent at the local level, and this results in poor statistical validity at the global level. Therefore, it is difficult to construct a sample that is appropriate for both geographical and probability methods.Methods: We used a two-stage selection procedure with a first non-random stage of selection of clusters. Instead of randomly selecting clusters, we deliberately chose a group of clusters, which as a whole would contain all the variation in health measures in the population. As there was no health information available before the survey, we selected a priori determinants that can influence the spatial homogeneity of the health characteristics. This method yields a distribution of variables in the sample that closely resembles that in the overall population, something that cannot be guaranteed with randomly-selected clusters, especially if the number of selected clusters is small. In this way, we were able to survey specific areas while minimising design effects and maximising statistical precision.Application: We applied this strategy in a health survey carried out in Vientiane, Lao People's Democratic Republic. We selected well-known health determinants with unequal spatial distribution within the city: nationality and literacy. We deliberately selected a combination of clusters whose distribution of nationality and literacy is similar to the distribution in the general population.Conclusion: This paper describes the conceptual reasoning behind the construction of the survey sample and shows that it can be advantageous to choose clusters using reasoned hypotheses, based on both probability and geographical approaches, in contrast to a conventional, random cluster selection strategy.

International audience ; Background: Geographical objectives and probabilistic methods are difficult to reconcile in a unique health survey. Probabilistic methods focus on individuals to provide estimates of a variable's prevalence with a certain precision, while geographical approaches emphasise the selection of specific areas to study interactions between spatial characteristics and health outcomes. A sample selected from a small number of specific areas creates statistical challenges: the observations are not independent at the local level, and this results in poor statistical validity at the global level. Therefore, it is difficult to construct a sample that is appropriate for both geographical and probability methods.Methods: We used a two-stage selection procedure with a first non-random stage of selection of clusters. Instead of randomly selecting clusters, we deliberately chose a group of clusters, which as a whole would contain all the variation in health measures in the population. As there was no health information available before the survey, we selected a priori determinants that can influence the spatial homogeneity of the health characteristics. This method yields a distribution of variables in the sample that closely resembles that in the overall population, something that cannot be guaranteed with randomly-selected clusters, especially if the number of selected clusters is small. In this way, we were able to survey specific areas while minimising design effects and maximising statistical precision.Application: We applied this strategy in a health survey carried out in Vientiane, Lao People's Democratic Republic. We selected well-known health determinants with unequal spatial distribution within the city: nationality and literacy. We deliberately selected a combination of clusters whose distribution of nationality and literacy is similar to the distribution in the general population.Conclusion: This paper describes the conceptual reasoning behind the construction of the survey sample and shows ...

International audience ; Background: Geographical objectives and probabilistic methods are difficult to reconcile in a unique health survey. Probabilistic methods focus on individuals to provide estimates of a variable's prevalence with a certain precision, while geographical approaches emphasise the selection of specific areas to study interactions between spatial characteristics and health outcomes. A sample selected from a small number of specific areas creates statistical challenges: the observations are not independent at the local level, and this results in poor statistical validity at the global level. Therefore, it is difficult to construct a sample that is appropriate for both geographical and probability methods.Methods: We used a two-stage selection procedure with a first non-random stage of selection of clusters. Instead of randomly selecting clusters, we deliberately chose a group of clusters, which as a whole would contain all the variation in health measures in the population. As there was no health information available before the survey, we selected a priori determinants that can influence the spatial homogeneity of the health characteristics. This method yields a distribution of variables in the sample that closely resembles that in the overall population, something that cannot be guaranteed with randomly-selected clusters, especially if the number of selected clusters is small. In this way, we were able to survey specific areas while minimising design effects and maximising statistical precision.Application: We applied this strategy in a health survey carried out in Vientiane, Lao People's Democratic Republic. We selected well-known health determinants with unequal spatial distribution within the city: nationality and literacy. We deliberately selected a combination of clusters whose distribution of nationality and literacy is similar to the distribution in the general population.Conclusion: This paper describes the conceptual reasoning behind the construction of the survey sample and shows ...

International audience ; Background: Geographical objectives and probabilistic methods are difficult to reconcile in a unique health survey. Probabilistic methods focus on individuals to provide estimates of a variable's prevalence with a certain precision, while geographical approaches emphasise the selection of specific areas to study interactions between spatial characteristics and health outcomes. A sample selected from a small number of specific areas creates statistical challenges: the observations are not independent at the local level, and this results in poor statistical validity at the global level. Therefore, it is difficult to construct a sample that is appropriate for both geographical and probability methods.Methods: We used a two-stage selection procedure with a first non-random stage of selection of clusters. Instead of randomly selecting clusters, we deliberately chose a group of clusters, which as a whole would contain all the variation in health measures in the population. As there was no health information available before the survey, we selected a priori determinants that can influence the spatial homogeneity of the health characteristics. This method yields a distribution of variables in the sample that closely resembles that in the overall population, something that cannot be guaranteed with randomly-selected clusters, especially if the number of selected clusters is small. In this way, we were able to survey specific areas while minimising design effects and maximising statistical precision.Application: We applied this strategy in a health survey carried out in Vientiane, Lao People's Democratic Republic. We selected well-known health determinants with unequal spatial distribution within the city: nationality and literacy. We deliberately selected a combination of clusters whose distribution of nationality and literacy is similar to the distribution in the general population.Conclusion: This paper describes the conceptual reasoning behind the construction of the survey sample and shows that it can be advantageous to choose clusters using reasoned hypotheses, based on both probability and geographical approaches, in contrast to a conventional, random cluster selection strategy.

International audience ; BACKGROUND:The Integrated Disease Surveillance and Response (IDSR) strategy implemented by the World Health Organization (WHO) in Africa has produced a large amount of data on participating countries, and in particular on the Democratic Republic of Congo (DRC). These data are increasingly considered as unevaluable and, therefore, as requiring a rigorous process of validation before they can be used for research or public health purposes. The aim of this study was to propose a method to assess the level of adequacy of IDSR morbidity data in reflecting actual morbidity.METHODS:A systematic search of English- and French-language articles was performed in Scopus, Medline, Science Direct, Springer Link, Cochrane, Cairn, Persée, and Erudit databases. Other types of documents were identified through manual searches. Selected articles focused on the determinants of the discrepancies (differences) between reported morbidity and actual morbidity. An adequacy score was constructed using some of the identified determinants. This score was applied to the 15 weekly reported diseases monitored by IDSR surveillance in the DRC. A classification was established using the Jenks method and a sensitivity analysis was performed. Twenty-three classes of determinants were identified in 35 IDSR technical guides and reports of outbreak investigations and in 71 out of 2254 researched articles. For each of the 15 weekly reported diseases, the SIA was composed of 12 items grouped in 6 dimensions.RESULTS:The SIA classified the 15 weekly reported diseases into 3 categories or types: high score or good adequacy (value > = 14), moderate score or fair adequacy (value > = 8 and < 14), and low score or low or non-adequacy (value < 8). Regardless of the criteria used in the sensitivity analysis, there was no notable variation in SIA values or categories for any of the 15 weekly reported diseases.CONCLUSION:In a context of sparse health information in low- and middle-income countries, this study developed a score to help classify IDSR morbidity data as usable, usable after adjustment, or unusable. This score can serve to prioritize, optimize, and interpret data analyses for epidemiological research or public health purposes.

International audience ; BACKGROUND:The Integrated Disease Surveillance and Response (IDSR) strategy implemented by the World Health Organization (WHO) in Africa has produced a large amount of data on participating countries, and in particular on the Democratic Republic of Congo (DRC). These data are increasingly considered as unevaluable and, therefore, as requiring a rigorous process of validation before they can be used for research or public health purposes. The aim of this study was to propose a method to assess the level of adequacy of IDSR morbidity data in reflecting actual morbidity.METHODS:A systematic search of English- and French-language articles was performed in Scopus, Medline, Science Direct, Springer Link, Cochrane, Cairn, Persée, and Erudit databases. Other types of documents were identified through manual searches. Selected articles focused on the determinants of the discrepancies (differences) between reported morbidity and actual morbidity. An adequacy score was constructed using some of the identified determinants. This score was applied to the 15 weekly reported diseases monitored by IDSR surveillance in the DRC. A classification was established using the Jenks method and a sensitivity analysis was performed. Twenty-three classes of determinants were identified in 35 IDSR technical guides and reports of outbreak investigations and in 71 out of 2254 researched articles. For each of the 15 weekly reported diseases, the SIA was composed of 12 items grouped in 6 dimensions.RESULTS:The SIA classified the 15 weekly reported diseases into 3 categories or types: high score or good adequacy (value > = 14), moderate score or fair adequacy (value > = 8 and < 14), and low score or low or non-adequacy (value < 8). Regardless of the criteria used in the sensitivity analysis, there was no notable variation in SIA values or categories for any of the 15 weekly reported diseases.CONCLUSION:In a context of sparse health information in low- and middle-income countries, this study developed a score to help classify IDSR morbidity data as usable, usable after adjustment, or unusable. This score can serve to prioritize, optimize, and interpret data analyses for epidemiological research or public health purposes.