Meningitis and climate: from science to practice
© Pérez García-Pando et al.; licensee Springer. 2014
Received: 1 October 2013
Accepted: 16 April 2014
Published: 17 June 2014
Meningococcal meningitis is a climate sensitive infectious disease. The regional extent of the Meningitis Belt in Africa, where the majority of epidemics occur, was originally defined by Lapeysonnie in the 1960s. A combination of climatic and environmental conditions and biological and social factors have been associated to the spatial and temporal patterns of epidemics observed since the disease first emerged in West Africa over a century ago. However, there is still a lack of knowledge and data that would allow disentangling the relative effects of the diverse risk factors upon epidemics. The Meningitis Environmental Risk Information Technologies Initiative (MERIT), a collaborative research-to-practice consortium, seeks to inform national and regional prevention and control strategies across the African Meningitis Belt through the provision of new data and tools that better determine risk factors. In particular MERIT seeks to consolidate a body of knowledge that provides evidence of the contribution of climatic and environmental factors to seasonal and year-to-year variations in meningococcal meningitis incidence at both district and national scales. Here we review recent research and practice seeking to provide useful information for the epidemic response strategy of National Ministries of Health in the Meningitis Belt of Africa. In particular the research and derived tools described in this paper have focused at “getting science into policy and practice” by engaging with practitioner communities under the umbrella of MERIT to ensure the relevance of their work to operational decision-making. We limit our focus to that of reactive vaccination for meningococcal meningitis. Important but external to our discussion is the development and implementation of the new conjugate vaccine, which specifically targets meningococcus A.
KeywordsMeningococcal meningitis Meningitis belt Climate Mineral dust Relative humidity Epidemics Reactive vaccination Forecasting Risk factors Africa Statistical models
Meningococcal meningitis is an infection of the thin lining that surrounds the brain and spinal cord. While there are many causes of meningitis, the epidemic form of the disease is caused by bacteria Neisseria Meningitidis. Meningitis is one of the most feared diseases in Africa because of its rapid onset and high rates of long-term disability and fatality. Epidemics pose a serious threat to populations and place a severe burden on public health systems and on socio-economic development.
Getting science into policy and practice is a challenge, particularly in public health where evidence-based policies are expected. For diseases such as meningitis that are climate sensitive, there is the perception that climate information may be relevant to improving control measures. However, delivering apparently simple changes into an epidemic response strategy requires applied cross-disciplinary science focusing on practical solutions to problems that evolve with time.
The MERIT (Meningitis Environmental Research Information Technologies) initiative was launched in 2007 as a multi-sectoral partnership led by the World Health Organization (WHO) to provide a platform for public health specialists, epidemiologists, immunologists, microbiologists, demographers and climate and environment specialists to work together to provide innovative solutions for the control of meningococcal meningitis epidemics in the African Sahel. The history of MERIT, its membership and its processes are described in detail elsewhere (Thomson et al. 2013). The International Research Institute for Climate and Society (IRI) has played a key role in MERIT since its inception, helping to frame the initiative and Steering Committee, while IRI staff, students, adjuncts and partners followed key lines of inquiry. In 2012 the MERIT initiative was endorsed by an external panel of experts (Thomson et al. 2013).
For over a century, epidemics of meningococcal meningitis have occurred in Africa. Until recently, the main control approach has been reactive vaccination after an outbreak has reached a defined threshold. Delays in vaccine procurement and delivery limit the success of this approach, which is also unable to prevent the occurrence of new epidemics (Roberts 2008). Meningococcal conjugate vaccines, which can prevent meningococcal carriage and thus interrupt transmission, may be effective at preventing epidemics and are currently being ‘rolled out’ across the region (LaForce et al. 2007). Despite the rapid implementation of a new conjugate vaccine (which targets serogroup A only), a recent review indicated that there is still a need for further work to identify the host, organism and environmental factors that contribute to the geographic location, seasonality and inter-annual variability of meningococcal disease and to predict and control epidemics in Africa (Greenwood 2013).
improve the impact of reactive mass vaccination campaigns, prepare for the following epidemic season, and refine the response strategy for epidemics. After the introduction of the new conjugate vaccine, this refers to epidemics due to serogroups other than A;
preventive vaccination campaigns; guide the introduction of the conjugate A vaccine and estimate its impact, and
forecasting the location of future epidemics on 5 to 10-year time horizons in order to assess possible vaccine needs.
Broad spatial and seasonal patterns of meningitis in Africa have been related to climate and other risk factors since the eco-epidemiology of the disease was first described (Lapeyssonnie 1963). Yet, the introduction of climate information and other risk factors as part of a control strategy is relatively new (World Health Organization 2004).
This paper reviews the advances and challenges in science and practice to develop Early Warning Systems (EWSs) for meningococcal meningitis in Africa. Specifically, the paper overviews the main characteristics of epidemic meningitis in Africa, reviews the current understanding of the role of climate upon the spatial and temporal variability of the disease, discusses the conceptual and mathematical models that combine risk factors to explain the spatiotemporal epidemic patterns, and presents tools and statistical models recently developed with potential operational application in the context of epidemic response. Important but external to our discussion is the development and introduction of the new conjugate vaccine that specifically targets meningococcus A.
Meningitis epidemics in sub-Saharan Africa
The Meningitis Belt
Spatial and temporal dynamics of meningitis
From the 1980s, epidemics occurred throughout the Meningitis Belt in Benin, Burkina Faso, Chad, The Gambia, Ghana, Mali, Niger, Nigeria, Senegal and Togo. Severe epidemics occurred in Ethiopia, Sudan and Chad in 1987–1990, with more than 30,000 cases reported in Sudan in 1988 (World Health Organization 1998). In 1995–1997 a strong epidemic wave (Figure 2) affected Niger (more than 25,000 cases in 1995 and more than 16,000 cases in 1996), Northern Nigeria (more than 100,000 cases in 1996), Burkina Faso (more than 40,000 cases in 1996 and more than 20,000 in 1997) and Mali (more than 7,000 cases in 1996, more than 10,000 in 1997).
Meningitis incidence is seasonally dependent, with cases increasing at the beginning of the dry season from November/December, typically peaking in February-March-April and declining rapidly with the onset of rains in May (Figure 3). In Niger, incidence peaked at week 14 of the year on average and epidemics lasted on average 10 weeks (Djingarey et al. 2008). Incidence displays strong spatial and interannual variability at regional (Figure 2), national (Figure 3) and district levels (Paireau et al. 2012; Tall et al. 2012).
Risk factors for epidemics are not fully understood. The heterogeneous spatial and temporal distribution of epidemics (Figures 2 and 3) suggests that a complex interaction involving host, organism and environment is necessary for an epidemic to occur (Greenwood 1987; Moore 1992). A significant proportion of the global population carries the bacteria asymptomatically in the nose and throat, and never develops the invasive disease. The bacteria are transmitted from one person to another through respiratory droplets or throat secretions. Under certain circumstances, the bacteria become pathogenic, invading the naso-pharageal epithelial cells (colonization) and entering the blood stream (invasion). The case fatality rate generally ranges between 10% and 15% in the belt (Trotter & Greenwood 2007). The risk of acquiring meningococcal disease decreases with age. Disease or carriage protects against disease caused by the same serogroup (Trotter & Greenwood 2007). As carriage does not necessarily induce invasive disease (Wenzel et al. 1973), humoral immunity (Griffiss et al. 1987) and herd (i.e. population) immunity to a prevalent strain (Greenwood 1987) may be among the most important factors in the prevention of epidemics. Clone virulence or introduction of novel clones of a serogroup in a susceptible population, though population movements, contributes to trigger epidemics (Moore et al. 1989). Over the past 40 years ST-1 complex/subgroup I/II, ST-4 complex/subgroup IV, and ST-5 complex/subgroup III (shifting to ST-7 since the mid 90’s) have successively caused the majority of serogroup A epidemics in the Meningitis Belt (Harrison et al. 2009). However, the introduction of novel clones during periods of susceptibility is insufficient to trigger epidemics. A clear evidence of this insufficiency is that even during major epidemic waves, meningitis is suppressed during the rainy season. During the dry season, coincident respiratory infections and climate conditions including high levels of mineral dust are thought to increase invasive disease and transmission (Moore 1992; Mueller & Gessner 2010). Other risk factors may be genetic susceptibility (Davila et al. 2010), poverty and household crowding (Moore 1992), and exposure to smoke from cooking fires (Hodgson et al. 2001).
The role of climate and dust
Environmental and climate conditions have long been highlighted as driving factors of epidemics (Waddy 1952; Lapeyssonnie 1963; Greenwood et al. 1984; Cheesbrough et al. 1995). Epidemics and seasonal upsurges in endemic disease occur during the dry season and subside at the onset of the rains (Lapeyssonnie 1963; Molesworth et al. 2002; Sultan et al. 2005). The broad spatial pattern and seasonality of meningitis suggests that certain environmental factors, such as low absolute humidity (Cheesbrough et al. 1995; Molesworth et al. 2003) and relative humidity (Dukić et al. 2012), temperature (Dukić et al. 2012) and dusty atmospheric conditions (Thomson et al. 2006; Agier et al. 2013a; Pérez García-Pando et al. 2014) play an important role. Identifying the specific climate factor that drives epidemics is challenging because many environmental variables have a prominent seasonal cycle that covaries with disease incidence. In addition, although the interactions between mucosal epithelial cells and Neisseria Meningitidis are well known (van Deuren et al. 2000), the effects of climate and dust on the pathogenesis and transmission of the bacteria have not been studied in vivo (Palmgren 2009).
The abrupt shift in the seasonal cycle of the total number of cases in Mali at the sixth week of the year has been related to the dry season wind maximum (Sultan et al. 2005). In Niger, the average seasonal peak in meningitis is preceded by a week or two by a peak in dust optical depth (Martiny & Chiapello 2013). One important difficulty in uncovering drivers of infectious diseases is to identify the appropriate scale of analysis (Pascual & Dobson 2004) since local determinants of epidemics may obscure the impact of larger-scale environmental conditions. The relationship between disease and climate apparent at large spatial scales may not be appropriate to resolve local variability, which is crucial for public health interventions in the Meningitis Belt. Within multiple districts in Niger, Agier et al. (2013a) found similar time-lags between the occurrence of dust outbreaks and meningitis, which suggests that dust information may be useful in epidemiological and forecasting models. Dukić et al. (2012) applied Generalized Additive Models treating time-varying confounding processes (e.g. seasonal population migration, new strains) as a background function that varies in magnitude by year, and asked how much of the intraseasonal variability each year could be related to specific variables that vary more quickly in time. The analysis of 11 years of laboratory-confirmed meningitis cases from Navrongo, Ghana, showed that accounting for local weather improved estimates of monthly cases by up to 40%. In particular, the current maximum monthly temperature and the previous month’s relative humidity and CO emissions due to fires showed the most value in anticipating meningitis cases. We note that the southern Sahel is strongly affected by biomass burning aerosols in addition to dust outbreaks during the dry season. This is consistent with the results of a survey of Navrongo-area residents, which indicted that meningitis is associated with hot and high aerosol conditions (Hayden et al. 2013).
The seasonal cycle box plot of weekly incidence for 38 districts in Niger (Figure 4) features median values below 5 weekly cases per 100,000 population and strong outliers reaching up to 50 weekly cases per 100,000 population. An ongoing debate is whether variations in climatic variables can at least in part explain year-to-year variations in seasonal incidence (amplitude) or if this influence is just related to the timing of onset, peak and end of meningitis. However, at least in some regions, the timing of onset and peak has been associated with the amplitude of epidemics (de Chabalier et al. 2000). Several studies have related anomalies in amplitude to anomalies in climate. In semi-arid, northern Benin, 14 to 34.5% of the temporal variability of the disease over 28 years was related to low absolute humidity associated with variations of the Harmattan. Rainfall anomalies in January and dust anomalies in October appeared to be the most consistent predictors of anomalies in seasonal incidence at district level in West Africa (Thomson et al. 2006), and about 25% of the year-to-year disease variance at national scale in Niger could be explained by variations in the December-averaged meridional wind (Yaka et al. 2008). Early cases, population density, wind and dust information in the early season could explain a 41% of the year-to-year spatiotemporal variability of the disease at district level in Niger (Pérez García-Pando et al. 2014).
Conceptual, mathematical and statistical models
Meningitis dynamics may be better understood and predicted with mathematical modeling. Conceptual models combining relevant factors have been proposed to guide the development of mathematical models, understand the disease and improve practical interventions (Moore 1992; Mueller & Gessner 2010). Loss of herd immunity to specific strains may have contributed to the initiation of large-scale epidemic cycles observed in the Meningitis Belt (Figure 2). The subsequent development of herd immunity due to widespread carriage of the epidemic strain during the cycle may have limited transmission ending the epidemic wave (Moore 1992). In this conceptual model, transmission is considered seasonally independent since studies haven’t found a systematic variation in the carriage prevalence of meningococcal serogroups by season (Trotter & Greenwood 2007; Harrison et al. 2009). Epidemics during the dry season were then explained by a combination of climatic conditions and widespread respiratory infections decreasing mucosal protection and thus promoting invasion rather than carriage in a low herd immunity setting.
The three main hypotheses to explain the effects of climate and environment upon seasonal incidence are an increased risk of invasive disease after infection, an increased risk of transmission (including adhesion and colonization) and a combination thereof. A more recent and refined conceptual model (Mueller & Gessner 2010) describes the heterogeneous spatiotemporal dynamics of meningitis (Figure 3) distinguishing among endemic incidence during the rainy season, ubiquitous hyperendemicity during the dry season, occasional localized epidemics, and large-scale epidemic waves spanning communities or years. In this framework, the transition from endemic to ubiquitous hyperendemic conditions would be caused by an increased risk of invasion of a virulent strain due to damage of the pharyngeal mucosa by dry and dusty climate. The transition from hyperendemicity to localized epidemics would involve increased pharyngeal colonization and/or transmission through coughing and sneezing possibly caused by viral respiratory infection epidemics or other local-scale co-factors, a hypothesis that is compatible with significant increases in carriage prevalence of the specific virulent strain during epidemics (Mueller et al. 2008) and the heterogeneous and sporadic occurrence of meningococcal epidemics (Tall et al. 2012). A regional epidemic wave would result from a wider geographic spread of epidemic co-factors such as a viral epidemic or the introduction of a new virulent strain.
In both models dry and dusty conditions are considered to affect invasion rather than carriage and transmission. Yet, the contribution of climate to carriage and transmission by, for example, enhancing seasonal viral epidemics (Fuhrmann 2010) cannot be ruled out. Besides damaging the pharyngeal mucosa, some other controversial hypotheses about the role of dust include the activation of the meningococcus through the high iron content of dust particles and the impact of high dust levels on human behavior, including crowding and reduced ventilation (Thomson et al. 2009). Results from a deterministic compartmental model showed that the irregular timing of meningitis epidemics could be caused by the interaction of temporary immunity conferred by carriage together with seasonal changes in transmission rather than seasonal changes in invasion (Irving et al. 2011). Results from a parsimonious statistical model fitted to data in Nigeria supported the hypothesis of a generalized regional increase in the rate of invasive disease and suggested that the end of an epidemic was driven more by the reduction in susceptible individuals and a decrease in transmission rather than by a decline in the rate of invasive disease (Jandarov et al. 2012).
Potential tools and models to improve epidemic response
Response to meningitis epidemics is based on weekly incidence thresholds at the district level (World Health Organization 2000): the alert threshold, defined as 5 cases per 100,000 inhabitants per week for populations greater than 30,000 inhabitants and 2 cases in one week for populations of less than 30,000 inhabitants, is used to launch an investigation at the start of an epidemic, check epidemic preparedness, start a vaccination campaign if there is an epidemic in a neighbouring district and prioritize areas for vaccination campaigns in the course of an epidemic. The epidemic threshold, which is used to step up mass vaccination, is defined as 10 cases (or 15 cases depending on context) per 100,000 inhabitants for populations greater than 30,000 inhabitants and 5 cases in one week for populations of less than 30,000 inhabitants. This strategy depends on timely surveillance, and rapid response, which are difficult to achieve in less-developed countries. The strategy can therefore benefit from improved surveillance and forecasting tools.
Sub-district level surveillance
Data on confirmed cases at a sub-district spatial scale in Niger and Burkina Faso have highlighted significant intra-district heterogeneity and interannual variability (Paireau et al. 2012; Tall et al. 2012). Besides improving future mathematical modeling, surveillance and redefinition of weekly epidemic thresholds at the sub-district Health Centre level could improve reactive vaccination response. In Burkina Faso, if an epidemic threshold of 5 cases per 100,000 in one week was used, epidemics could have been spotted more precisely and in one quarter of instances at least one week earlier than with the current district level strategy (Tall et al. 2012).
Despite progress in surveillance and research, the incomplete understanding of meningitis epidemic patterns and the lack of quality data at the required temporal and spatial scales limit the use of mathematical modeling to forecast epidemics. EWSs for infectious diseases aim to identify whether an epidemic will occur and to predict the magnitude of incidence (World Health Organization 2004). The latter may be unattainable at precision, regardless of the quality of information, due to small-scale stochastic effects (Drake 2005). However, from a policy perspective, the exact magnitude of incidence is of less interest in comparison to whether or not the incidence crosses an action-triggering threshold.
Statistical models based on incidence
Given the complex nature of meningitis epidemics that seem to elude description by deterministic models, statistical models have been developed to forecast the probability of exceeding WHO’s incidence-based decision thresholds. The motivation was to provide an estimate of the likelihood that the thresholds would be exceeded in the near future. Due to uncertainties in the factors that drive epidemics at the weekly time scale, these models were developed using an empirical approach i.e. no assumptions as to what drives changes in incidence were made, with the original forecasts based primarily on previously observed incidence but without considering climate information or other factors.
Beresniak et al. (2012) evaluated the risk that an epidemic would occur in a particular district after epidemics had been reported in other districts. Through a Bayesian network, the probabilities of districts on alert or epidemic situation influencing other districts were estimated for Niger. When considering districts reaching epidemic alert or epidemic thresholds potentially influencing other districts in 2004, 2005 and 2006, the proportion of the outbreaks observed that were successfully predicted for the period 2006–2008 was 63% when the epidemic threshold was used and 91% when the alert threshold was used.
Model evaluation summaries for weekly-scale statistical models (Agier et al. 2013b ; Stanton et al. 2013 ) with respect to how well they predict whether or not the incidence the following week will exceed the epidemic threshold (Criterion 1), and how well they forecast an epidemic prior to its onset during a 12 month period (Criterion 2)
There is a lack of spatially and temporally resolved data on carriage, herd immunity, previous vaccination type and coverage, serogroup type, clonal virulence and coincident respiratory infections that are thought to explain the epidemic pattern. Although gridded climate and dust data generated by models and/or observations contain uncertainties, their availability at the required spatial and temporal scales is an advantage for potential meningitis forecasting to the extent that they influence the evolution of epidemics.
An exploratory analysis was undertaken on extending empirical statistical models at weekly and district scales (Stanton et al. 2013) in Niger using weather variables including temperature, specific and relative humidity, wind and dust (Stanton et al. 2011) derived from long-term integrations with a regional dust model (Pérez et al. 2011) constrained by the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis (Kalnay et al. 1996). Lagged weekly and four-weekly averaged weather variables were considered for inclusion both in a linear model with a fixed seasonal trend, and a dynamic linear model with a flexible seasonal trend. More specifically, in order to disentangle the relationship between climate variables and meningitis incidence induced through a common seasonal and/or linear trend from a potential causal relationship, the climate variables included in the models were in the form of residuals obtained by subtracting both a linear trend plus a harmonic trend of order 3 from each of the climate variables under consideration. Although there was evidence of a weak relationship between residuals of weather and meningitis, particularly when taking average conditions over the preceding 4-week period, the strength of the relationship was insignificant in comparison to the strong seasonal trend already introduced by the incidence data. In addition, the inclusion of weather in these specific statistical models decreased the models’ performance in terms of being able to predict when the epidemic threshold would be exceeded.
Practice and future
Model predictions contain uncertainty, which translates into complexity for decision-makers. Another challenge is the introduction of new tools to a diverse operational community in an environment that evolves rapidly as new interventions, technologies and policy options become available. As indicated above, the reactive vaccination strategy is currently being superseded by a preventive vaccination strategy focused on the delivery of the meningitis A conjugate vaccine. Statistical prediction models need to be amended to account for the introduction of the conjugate vaccine and their value needs to be tested and validated in close collaboration with decision-makers.
In order to initiate the process whereby environmental information and predictive models might be used operationally in meningitis epidemic response the MERIT community undertook a pilot outbreak prediction exercise conducted during the 2011–2012 meningitis season in Africa. During this exercise public health professionals from several WHO and Ministry of Health (MoH) offices engaged with modelers, statisticians and climate/weather and environmental scientists to explore outputs from predictive models alongside the ‘real time’ evolving meningitis situation in four countries: Benin, Chad, Nigeria and Togo. The flow of information was continuous and led to the regular collection and quality control of both epidemiological and climate-related information. The observed and forecasted relative humidity and dust events in the region were also considered in order to analyze the environmental conditions favorable for epidemics. This exercise, which was facilitated by weekly teleconference calls and the sharing of data was also an opportunity to assess the effect of exchange of information between partners at the international and country levels. Learning from the 2011–2012 exercise, the 6th MERIT Technical Meeting, held in Accra, Ghana in November 2012 agreed to a similar exercise to be conducted in 2013 meningitis season (MERIT 2012), the results and conclusions of which will be published in the near future by the relevant participants.
Establishing EWSs for climate related disasters is a particular focus of the ‘Global Framework for Climate Services’ (Hewitt et al. 2012) and communities concerned with disaster risk reduction and climate change adaptation (Thomson 2013). Developing such EWSs for epidemic diseases (a specific type of disaster) requires the full participation of the health community and an understanding of their norms and practices. A priori a strong relationship between climate and environmental drivers must be shown to exist for such information to be considered useful to decision-makers. Research to date suggests that the geographic location, seasonality and year-to-year variability of meningococcal meningitis are associated in part with climatic and environmental factors. Although the determination of specific seasonal climate drivers including dust are difficult to identify, current models that incorporate environmental data, previous incidence, and/or other risk factors have shown sufficient skills to be further amended and tested for operational use. Also models are expected to improve as fine-scale surveillance and other sources of data on risk factors become available in the Meningitis Belt. The cooperation among diverse research and practice communities within MERIT provides a model for facilitating a better management of climate-sensitive diseases and provides guidance on how climate services for health might develop over time.
The authors thank Yang Liu for technical support and preparation of some figures. CPGP acknowledges the Earth Institute at Columbia University (EI), NOAA and DoE for their support. This work was also supported by the EI Cross-Cutting Initiative project: ”Atmospheric aerosol impacts on health in sub-Saharan Africa”, and NASA ROSES Earth sciences applications feasibility studies, and a generous grant from the Google.org Foundation. SH is a staff member of the World Health Organization. The author alone is responsible for the views expressed in this publication and they do not necessarily represent the decisions, policy or views of the World Health Organization.
Responsible editor: Venkata S Murthy Gudlavalleti
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