Les difficultés d'accès aux soins de santé des indigents vivant dans des ménages non pauvres

Kadio, Kadidiatou; Ridde, Valéry; Samb, Oumar Mallé

doi:10.3917/spub.137.0089

Cited by 12 publications

(7 citation statements)

References 12 publications

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“…A number of studies have found an association between spatial inequalities in access to care and spatial heterogeneity of malaria incidence [42,43]. Yet, contrary to what has been reported elsewhere [44], we failed to found a correlation between the number of malaria cases and the Euclidean distance between villages and their corresponding HC.…”

Section: Discussioncontrasting

confidence: 99%

Spatio-temporal analysis and prediction of malaria cases using remote sensing meteorological data in Diébougou health district, Burkina Faso, 2016-2017

Bationo

Gaudart

Sokhna

et al. 2021

Preprint

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Background: Malaria control and prevention programs are more efficient and cost-effective when they target hotspots or select the best periods of year to implement interventions. This study aimed to identify the spatial distribution of malaria hotspots at the village level in Diébougou health district, Burkina Faso, and to model the temporal dynamics of malaria cases as a function of meteorological conditions and of the distance between villages and health centers (HCs). Methods: Case data for 27 villages were collected in 13 HCs using continuous passive case detection. Meteorological data were obtained through remote sensing. Two synthetic meteorological indicators (SMIs) were created to summarize meteorological variables. Spatial hotspots were detected using the Kulldorf scanning method. A General Additive Model was used to determine the time lag between cases and SMIs and to evaluate the effect of SMIs and distance to HC on the temporal evolution of malaria cases. The multivariate model was fitted with data from the epidemic year to predict the number of cases in the following outbreak. Results: Overall, the incidence rate in the area was 429.13 cases per 1,000 person-year with important spatial and temporal heterogeneities. Four spatial hotspots, involving 7 of the 27 villages, were detected, for an incidence rate of 854.02 cases per 1,000 person-year. The hotspot with the highest risk (relative risk = 4.06) consisted of a single village, with an incidence rate of 1,750.75 cases per 1,000 person-years. The multivariate analysis found greater variability in incidence between HCs than between villages linked to the same HC. The epidemic year was characterized by a major peak during the second part of the rainy season and a secondary peak during the dry-hot season. The time lag that generated the better predictions of cases was 9 weeks for SMI1 (positively correlated with precipitation variables and associated with the first peak of cases) and 16 weeks for SMI2 (positively correlated with temperature variables and associated with the secondary peak of cases). Euclidian distance to HC was not found to be a predictor of malaria cases recorded in HC. The prediction followed the overall pattern of the time series of reported cases and predicted the onset of the following outbreak with a precision of less than 3 weeks. Conclusions: Our spatio-temporal analysis of malaria cases in Diebougou health district, Burkina Faso, provides a powerful prospective method for identifying and predicting high-risk areas and high-transmission periods that could be targeted in future malaria control and prevention campaigns. Keywords Geo-epidemiology, Spatial Clusters, temporal dynamics, nonlinear relationship, prediction.

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Section: Discussioncontrasting

confidence: 99%

Spatio-temporal analysis and prediction of malaria cases using remote sensing meteorological data in Diébougou health district, Burkina Faso, 2016-2017

Bationo

Gaudart

Sokhna

et al. 2021

Preprint

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“…A number of studies have found an association between spatial inequalities in access to care and spatial heterogeneity of malaria incidence 34 , 35 . Yet, contrary to what has been reported elsewhere 36 , we failed to found a correlation between the number of malaria cases and the Euclidean distance between villages and their corresponding HC.…”

Section: Discussionmentioning

confidence: 99%

Spatio-temporal analysis and prediction of malaria cases using remote sensing meteorological data in Diébougou health district, Burkina Faso, 2016–2017

Bationo

Gaudart

Dieng

et al. 2021

Sci Rep

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Malaria control and prevention programs are more efficient and cost-effective when they target hotspots or select the best periods of year to implement interventions. This study aimed to identify the spatial distribution of malaria hotspots at the village level in Diébougou health district, Burkina Faso, and to model the temporal dynamics of malaria cases as a function of meteorological conditions and of the distance between villages and health centres (HCs). Case data for 27 villages were collected in 13 HCs. Meteorological data were obtained through remote sensing. Two synthetic meteorological indicators (SMIs) were created to summarize meteorological variables. Spatial hotspots were detected using the Kulldorf scanning method. A General Additive Model was used to determine the time lag between cases and SMIs and to evaluate the effect of SMIs and distance to HC on the temporal evolution of malaria cases. The multivariate model was fitted with data from the epidemic year to predict the number of cases in the following outbreak. Overall, the incidence rate in the area was 429.13 cases per 1000 person-year with important spatial and temporal heterogeneities. Four spatial hotspots, involving 7 of the 27 villages, were detected, for an incidence rate of 854.02 cases per 1000 person-year. The hotspot with the highest risk (relative risk = 4.06) consisted of a single village, with an incidence rate of 1750.75 cases per 1000 person-years. The multivariate analysis found greater variability in incidence between HCs than between villages linked to the same HC. The time lag that generated the better predictions of cases was 9 weeks for SMI1 (positively correlated with precipitation variables) and 16 weeks for SMI2 (positively correlated with temperature variables. The prediction followed the overall pattern of the time series of reported cases and predicted the onset of the following outbreak with a precision of less than 3 weeks. This analysis of malaria cases in Diébougou health district, Burkina Faso, provides a powerful prospective method for identifying and predicting high-risk areas and high-transmission periods that could be targeted in future malaria control and prevention campaigns.

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“…Given difficult living conditions and rapid social changes, it appears elders are becoming more numerous and more dependent on their children, who unfortunately are often unable to take care of their aging parents [ 42 ]. Other studies in Burkina Faso have identified elders as a population incurring several risks, such as not having their basic needs fulfilled [ 43 ], being vulnerable and needing particular help and support [ 13 , 22 ], experiencing psychological distress and depressive symptoms [ 43 ], and being marginalized due to their increasing psychological distress and diminished functioning, which can be related to shame for family members [ 44 ].…”

Section: Discussionmentioning

confidence: 99%

“…Nanama and Frongillo [ 34 ] found, in a rural setting in Burkina Faso, that food insecurity was an important vulnerability factor associated with anxious, depressive, and somatic symptoms. Duthé and colleagues also found that poor health status predicted the risk of developing depressive symptoms [ 35 ], and a qualitative study conducted with indigents and their relatives showed that poor health status was an indicator of extreme vulnerability, even among the least poor households identified as worst-off in rural settings [ 22 ].…”

Section: Discussionmentioning

confidence: 99%

“…Poor mental health and the presence of psychopathologies were unanimously recognized as being important indicators of vulnerability for indigence [ 20 ]. Despite this community insight, no previous empirical studies in West Africa have investigated mental health indicators among indigents [ 21 ], even though they are a group with specific health needs and less access to health care than the general population [ 18 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

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An exploratory study assessing psychological distress of indigents in Burkina Faso: a step forward in understanding mental health needs in West Africa

Pigeon-Gagné

Hassan

Yaogo³

et al. 2017

Int J Equity Health

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BackgroundPoverty is known as an important determinant of health, but empirical data are still missing on the relationships between poverty, other adverse living conditions, and psychological distress, particularly in low-income countries.This study aimed to assess mental health needs and psychological distress among the poorest in rural settings in Burkina Faso where food security and access to water, electricity, schooling, and healthcare are limited.MethodsWe randomly selected 2000 individuals previously identified as indigents by a community-targeting process. Interviewers visited participants (n = 1652) in their homes and completed a questionnaire on mental health variables that included presence and intensity of anxious, depressive, psychotic, and aggressive symptoms, as well as level of psychological distress. Descriptive statistics, Spearman correlations, and logistic regressions were performed.ResultsIn all, 40.2% of the sample reported 10 or more anxious/depressive symptoms in the past 30 days, and 25.5% reported having experienced at least one psychotic symptom over their lifetime, 65.6% of whom had had those symptoms for many years. The number of anxious and depressive symptoms was significantly associated with the level of psychological distress (r = 0.423, p < .001). Predictors of distress level included: poor health condition (F(1) = 23.743, p <. 001), being a woman (F(1) = 43.926, p < .001), not having any income (F(1) = 16.185, p < .001), having begged for food in the past 30 days (F(1) = 12.387, p < .001), being illiterate, and being older (F(1) = 21.487, p < .001). Approximately one third of respondents reporting anxious/depressive or psychotic symptoms (28.2 and 30.0%, respectively) had not talked about their symptoms to anyone in their social network.ConclusionsThese results suggest alarmingly high levels of psychological distress and reported symptoms among the poorest in rural settings in Burkina Faso, which can be explained by their difficult living conditions. However, these results must be interpreted from a transcultural perspective to avoid decontextualized misinterpretations. Ethnographic works are needed to document the larger context within which these distress results can be analyzed.

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Les difficultés d'accès aux soins de santé des indigents vivant dans des ménages non pauvres

Cited by 12 publications

References 12 publications

Spatio-temporal analysis and prediction of malaria cases using remote sensing meteorological data in Diébougou health district, Burkina Faso, 2016-2017

Spatio-temporal analysis and prediction of malaria cases using remote sensing meteorological data in Diébougou health district, Burkina Faso, 2016-2017

Spatio-temporal analysis and prediction of malaria cases using remote sensing meteorological data in Diébougou health district, Burkina Faso, 2016–2017

An exploratory study assessing psychological distress of indigents in Burkina Faso: a step forward in understanding mental health needs in West Africa

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