 | Toussaint Rouamba, Seydou Nakanabo-Diallo, Karim Derra, Eli Rouamba, Adama Kazienga, Yasuko Inoue, Ernest K Ouédraogo, Moussa Waongo, Sokhna Dieng, Abdoulaye Guindo, Boukary Ouédraogo, Kankoé Lévi Sallah, Seydou Barro, Pascal Yaka, Fati Kirakoya-Samadoulougou, Halidou Tinto, Jean Gaudart Socioeconomic and environmental factors associated with malaria hotspots in the Nanoro demographic surveillance area, Burkina Faso (Journal Article) In: BMC Public Health, vol. 19, no. 1, pp. 249, 2019, ISSN: 1471-2458. @article{Rouamba2019-el,
title = {Socioeconomic and environmental factors associated with malaria hotspots in the Nanoro demographic surveillance area, Burkina Faso},
author = {Toussaint Rouamba and Seydou Nakanabo-Diallo and Karim Derra and Eli Rouamba and Adama Kazienga and Yasuko Inoue and Ernest K Ou\'{e}draogo and Moussa Waongo and Sokhna Dieng and Abdoulaye Guindo and Boukary Ou\'{e}draogo and Kanko\'{e} L\'{e}vi Sallah and Seydou Barro and Pascal Yaka and Fati Kirakoya-Samadoulougou and Halidou Tinto and Jean Gaudart},
doi = {10.1186/s12889-019-6565-z},
issn = {1471-2458},
year = {2019},
date = {2019-02-01},
urldate = {2019-02-01},
journal = {BMC Public Health},
volume = {19},
number = {1},
pages = {249},
publisher = {Springer Science and Business Media LLC},
abstract = {BACKGROUND: With limited resources and spatio-temporal
heterogeneity of malaria in developing countries, it is still
difficult to assess the real impact of socioeconomic and
environmental factors in order to set up targeted campaigns
against malaria at an accurate scale. Our goal was to detect
malaria hotspots in rural area and assess the extent to which
household socioeconomic status and meteorological recordings may
explain the occurrence and evolution of these hotspots. METHODS:
Data on malaria cases from 2010 to 2014 and on socioeconomic and
meteorological factors were acquired from four health facilities
within the Nanoro demographic surveillance area. Statistical
cross correlation was used to quantify the temporal association
between weekly malaria incidence and meteorological factors.
Local spatial autocorrelation analysis was performed and
restricted to each transmission period using Kulldorff's
elliptic spatial scan statistic. Univariate and multivariable
analysis were used to assess the principal socioeconomic and
meteorological determinants of malaria hotspots using a
Generalized Estimating Equation (GEE) approach. RESULTS:
Rainfall and temperature were positively and significantly
associated with malaria incidence, with a lag time of 9 and 14
weeks, respectively. Spatial analysis showed a spatial
autocorrelation of malaria incidence and significant hotspots
which was relatively stable throughout the study period.
Furthermore, low socioeconomic status households were strongly associated with malaria hotspots (aOR = 1.21, 95% confidence
interval: 1.03-1.40). CONCLUSION: These fine-scale findings
highlight a relatively stable spatio-temporal pattern of malaria
risk and indicate that social and environmental factors play an
important role in malaria incidence. Integrating data on these
factors into existing malaria struggle tools would help in the
development of sustainable bottleneck strategies adapted to the
local context for malaria control.},
keywords = {Bottleneck strategies; Hotspots; Lag time; Malaria; Meteorological factors; Socioeconomic status; Spatial epidemiology; Spatio-temporal analysis},
pubstate = {published},
tppubtype = {article}
}
BACKGROUND: With limited resources and spatio-temporal
heterogeneity of malaria in developing countries, it is still
difficult to assess the real impact of socioeconomic and
environmental factors in order to set up targeted campaigns
against malaria at an accurate scale. Our goal was to detect
malaria hotspots in rural area and assess the extent to which
household socioeconomic status and meteorological recordings may
explain the occurrence and evolution of these hotspots. METHODS:
Data on malaria cases from 2010 to 2014 and on socioeconomic and
meteorological factors were acquired from four health facilities
within the Nanoro demographic surveillance area. Statistical
cross correlation was used to quantify the temporal association
between weekly malaria incidence and meteorological factors.
Local spatial autocorrelation analysis was performed and
restricted to each transmission period using Kulldorff’s
elliptic spatial scan statistic. Univariate and multivariable
analysis were used to assess the principal socioeconomic and
meteorological determinants of malaria hotspots using a
Generalized Estimating Equation (GEE) approach. RESULTS:
Rainfall and temperature were positively and significantly
associated with malaria incidence, with a lag time of 9 and 14
weeks, respectively. Spatial analysis showed a spatial
autocorrelation of malaria incidence and significant hotspots
which was relatively stable throughout the study period.
Furthermore, low socioeconomic status households were strongly associated with malaria hotspots (aOR = 1.21, 95% confidence
interval: 1.03-1.40). CONCLUSION: These fine-scale findings
highlight a relatively stable spatio-temporal pattern of malaria
risk and indicate that social and environmental factors play an
important role in malaria incidence. Integrating data on these
factors into existing malaria struggle tools would help in the
development of sustainable bottleneck strategies adapted to the
local context for malaria control. |
