Analysing Spatio-Temporal Clustering of Meningococcal Meningitis Outbreaks in Niger Reveals Opportunities for Improved Disease Control

University of California San Diego School of Medicine, United States of America
PLoS Neglected Tropical Diseases (Impact Factor: 4.45). 03/2012; 6(3):e1577. DOI: 10.1371/journal.pntd.0001577
Source: PubMed


Meningococcal meningitis is a major health problem in the "African Meningitis Belt" where recurrent epidemics occur during the hot, dry season. In Niger, a central country belonging to the Meningitis Belt, reported meningitis cases varied between 1,000 and 13,000 from 2003 to 2009, with a case-fatality rate of 5-15%.
In order to gain insight in the epidemiology of meningococcal meningitis in Niger and to improve control strategies, the emergence of the epidemics and their diffusion patterns at a fine spatial scale have been investigated. A statistical analysis of the spatio-temporal distribution of confirmed meningococcal meningitis cases was performed between 2002 and 2009, based on health centre catchment areas (HCCAs) as spatial units. Anselin's local Moran's I test for spatial autocorrelation and Kulldorff's spatial scan statistic were used to identify spatial and spatio-temporal clusters of cases. Spatial clusters were detected every year and most frequently occurred within nine southern districts. Clusters most often encompassed few HCCAs within a district, without expanding to the entire district. Besides, strong intra-district heterogeneity and inter-annual variability in the spatio-temporal epidemic patterns were observed. To further investigate the benefit of using a finer spatial scale for surveillance and disease control, we compared timeliness of epidemic detection at the HCCA level versus district level and showed that a decision based on threshold estimated at the HCCA level may lead to earlier detection of outbreaks.
Our findings provide an evidence-based approach to improve control of meningitis in sub-Saharan Africa. First, they can assist public health authorities in Niger to better adjust allocation of resources (antibiotics, rapid diagnostic tests and medical staff). Then, this spatio-temporal analysis showed that surveillance at a finer spatial scale (HCCA) would be more efficient for public health response: outbreaks would be detected earlier and reactive vaccination would be better targeted.

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Available from: Jean-Marc Collard,
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    • "However, our model (if amended to account for the introduction of conjugate A vaccine) could lead to an early-season alert that climate and other conditions are potentially conducive to an epidemic, which could initiate an early response strategy including increased surveillance, ensuring that stocks of vaccines are in-country, that protocols and procedures are in place, and that district health teams and members of the public likely to be affected are forewarned and prepared. If the presence of the pathogen or an increase in incidence is subsequently confirmed based on surveillance systems at district or finer levels (Paireau et al. 2012; Tall et al. 2012), early warnings could be followed by additional actions as needed. "
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    ABSTRACT: Background: Epidemics of meningococcal meningitis are concentrated in sub-Saharan Africa during the dry season, a period when the region is affected by the Harmattan, a dry and dusty northeasterly trade wind blowing from the Sahara into the Gulf of Guinea. Objectives: We examined the potential of climate-based statistical forecasting models to predict seasonal incidence of meningitis in Niger at both the national and district levels. Data and methods: We used time series of meningitis incidence from 1986 through 2006 for 38 districts in Niger. We tested models based on data that would be readily available in an operational framework, such as climate and dust, population, and the incidence of early cases before the onset of the meningitis season in January–May. Incidence was used as a proxy for immunological state, susceptibility, and carriage in the population. We compared a range of negative binomial generalized linear models fitted to the meningitis data. Results: At the national level, a model using early incidence in December and averaged November–December zonal wind provided the best fit (pseudo-R2 = 0.57), with zonal wind having the greatest impact. A model with surface dust concentration as a predictive variable performed indistinguishably well. At the district level, the best spatiotemporal model included zonal wind, dust concentration, early incidence in December, and population density (pseudo-R2 = 0.41). Conclusions: We showed that wind and dust information and incidence in the early dry season predict part of the year-to-year variability of the seasonal incidence of meningitis at both national and district levels in Niger. Models of this form could provide an early-season alert that wind, dust, and other conditions are potentially conducive to an epidemic.
    Environmental Health Perspectives 07/2014; 122(7):679–686. DOI:10.1289/ehp.1306640 · 7.98 Impact Factor
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    • "Nm W strains of ST-11 have emerged again in Niger in 2010, causing a small epidemic (72.2% of all meningococci) [28]. As shown in Figure 5, the onset of epidemics due to Nm W strains started in the HCCA of three districts, two located at the borders with Nigeria and one located at the border with Burkina Faso, suggesting that Nm W strains were already present in the population and widespread in Niger, making diffusion from a single point unlikely [29]. In 2011, all Nm W strains tested were ST-11. "
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    ABSTRACT: The epidemiology of bacterial meningitis in the African 'meningitis belt' changes periodically. In order to design an effective vaccination strategy, we have examined the epidemiological and microbiological patterns of bacterial meningitis, and especially that of meningococcal meningitis, in Niger during the period 2008-2011. During this period a mass vaccination campaign with the newly developed meningococcal A conjugate vaccine (MenAfriVac(R)) was undertaken. Cerebrospinal fluid samples were collected from health facilities throughout Niger and analysed by culture, seroagglutination and/or speciation polymerase chain reaction, followed by genogrouping PCR for of Neisseria meningitidis infections. A sample of strains were analysed by multi-locus sequence typing. N. meningitidis serogroup A cases were prevalent in 2008 and 2009 [98.6% and 97.5% of all N. meningitidis cases respectively]. The prevalence of serogroup A declined in 2010 [26.4%], with the emergence of serogroup W Sequence Type (ST) 11 [72.2% of cases], and the serogroup A meningococcus finally disappeared in 2011. The geographical distribution of cases N. meningitidis serogroups A and W within Niger is described. The substantial decline of serogroup A cases that has been observed from 2010 onwards in Niger seems to be due to several factors including a major polysaccharide A/C vaccination campaign in 2009, the introduction of MenAfriVac(R) in 10 districts at risk in December 2010, the natural dynamics of meningococcal infection and the persistence of serogroup A sequence-type 7 for about 10 years. The emergence of serogroup W strains suggests that there may be a need for serogroup W containing vaccines in Niger in the coming years.
    BMC Infectious Diseases 12/2013; 13(1):576. DOI:10.1186/1471-2334-13-576 · 2.61 Impact Factor
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    • "The methodologies utilized here are useable in other locations where detailed data may be lacking. Mapped outputs from this study can be further integrated with other types of data such as mortality rates of births and treatable diseases such as polio, malaria and meningitis (see [96]), to identify critical areas where improvements to health services would be beneficial. The approach is also applicable for crisis management to identify the best placement of temporary/mobile facilities in regions where disease may be present during seasonal outbreaks (i.e. "
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    ABSTRACT: Background Ease of access to health care is of great importance in any country but particularly in countries such as Niger where restricted access can put people at risk of mortality from diseases such as measles, meningitis, polio, pneumonia and malaria. This paper analyzes the physical access of populations to health facilities within Niger with an emphasis on the effect of seasonal conditions and the implications of these conditions in terms of availability of adequate health services, provision of drugs and vaccinations. The majority of the transport within Niger is pedestrian, thus the paper emphasizes access by those walking to facilities for care. Further analysis compared the change in accessibility for vehicular travel since public health workers do travel by vehicle when carrying out vaccination campaigns and related proactive health care activities. Results The majority of the roads in Niger are non-paved (90%). Six districts, mainly in the region of Tahoua lack medical facilities. Patient to health facility ratios were best in Agadez with 7000 people served per health facility. During the dry season 39% of the population was within 1-hours walk to a health center, with the percentage decreasing to 24% during the wet season. Further analyses revealed that vaccination rates were strongly correlated with distance. Children living in clusters within 1-hour of a health center had 1.88 times higher odds of complete vaccination by age 1-year compared to children living in clusters further from a health center (p < 0.05). Three key geographic areas were highlighted where access to health centers took greater than 4 h walk during the wet and dry season. Access for more than 730,000 people can be improved in these areas with the addition of 17 health facilities to the current total of 504 during the dry season (260,000 during the wet season). Conclusions This study highlights critical areas in Niger where health services/facilities are lacking. A second finding is that population served by health facilities will be severely overestimated if assessments are solely conducted during the dry season. Mapped outputs can be used for future decision making processes and analysis.
    International Journal of Health Geographics 06/2012; 11(1):24. DOI:10.1186/1476-072X-11-24 · 2.62 Impact Factor
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