Mueller etal 2017

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SCIENtIFIC REPORTs | 7: 11570 | DOI:10.1038/s41598-017-11889-4
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The association between
respiratory tract infection incidence
and localised meningitis epidemics:
an analysis of high-resolution
surveillance data from Burkina Faso
Judith E. Mueller1,2, Maxime Woringer
3, Souleymane Porgho4, Yoann Madec2, Haoua Tall5,
Nadège Martiny6 & Brice W. Bicaba4
Meningococcal meningitis epidemics in the African meningitis belt consist of localised meningitis
epidemics (LME) that reach attack proportions of 1% within a few weeks. A meningococcal serogroup
A conjugate vaccine was introduced in meningitis belt countries from 2010 on, but LME due to
other serogroups continue to occur. The mechanisms underlying LME are poorly understood, but
an association with respiratory pathogens has been hypothesised. We analysed national routine
surveillance data in high spatial resolution (health centre level) from 13 districts in Burkina Faso, 2004–
2014. We dened LME as a weekly incidence rate of suspected meningitis ≥75 per 100,000 during ≥2
weeks; and high incidence episodes of respiratory tract infections (RTI) as the 5th quintile of monthly
incidences. We included 10,334 health centre month observations during the meningitis season
(January-May), including 85 with LME, and 1891 (1820) high-incidence episodes of upper (lower) RTI.
In mixed eects logistic regression accounting for spatial structure, and controlling for dust conditions,
relative air humidity and month, the occurrence of LME was strongly associated with high incidence
episodes of upper (odds ratio 23.9, 95%-condence interval 3.1–185.3), but not lower RTI. In the African
meningitis belt, meningitis epidemics may be triggered by outbreaks of upper RTI.
e meningitis belt in sub-Saharan Africa has the highest incidence rate of meningococcal meningitis world-
wide. is acute infection of the meninges occurs here with pronounced seasonality (seasonal hyperendemicity),
sporadic localised epidemics and epidemic waves which emerge every 7–10 years1. e meningitis belt is char-
acterized by six months of dry season and low annual precipitation amounts of 300–1100 mm2 during one single
rainy season. During a localised meningitis epidemic (LME), the attack proportion can reach around 1% within
a few weeks ese LME usually do not concern all health centres of a district, but only a small subset3, 4. During
epidemic waves, the frequency of LME is increased during 1–3 consecutive years. In any instance, case fatality is
about 10% despite timely treatment and 20% of survivors have long-term psychomotor sequelae5.
e mechanisms behind the meningitis belt epidemiology are only partially understood. While the seasonal
hyperendemicity is most likely related to the climatic conditions (low relative air humidity and dust-loaded
wind)6, 7, the occurrence of epidemic waves could in part be explained by meningococcal strain evolutions
and population immunity8–10. However, the factors leading to LME remain unknown. A hypothetical explan-
atory model proposed that a viral respiratory infection epidemic in a given population may act as an epidemic
co-factor1, by rapidly increasing meningococcal transmission and acquisition in the nasopharynx. is would
lead to a proportional increase in disease, on the background of high risk of invasive disease given carriage dur-
ing the dry season11. If this hypothesis was correct, routine surveillance data of meningitis-belt countries would
1EHESP French School of Public Health, Sorbonne Paris Cité, Paris, France. 2Institut Pasteur, Paris, France. 3École
Normale Supérieure, Paris, France. 4Direction de la lutte contre la maladie, Ministry of Health, Ouagadougou,
Burkina Faso. 5Agence de Médecine Préventive, Ouagadougou, Burkina Faso. 6UMR6282 BIOGEOSCIENCES,
University of Burgundy, Dijon, France. Correspondence and requests for materials should be addressed to J.E.M.
(email: judith.mueller@ehesp.fr)
Received: 3 March 2017
Accepted: 30 August 2017
Published: xx xx xxxx
OPEN

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show an association between episodes of high incidence rates of acute respiratory infection and the occurrence
of localised meningitis epidemics during the dry season. To explore this hypothesis, we conducted an ecological
study based on routinely notied respiratory infections and bacterial meningitis from Burkina Faso.
Burkina Faso, which lies in the meningitis belt, introduced a meningococcal serogroup A conjugate vaccine
by mass campaign into the 1- to 29-year-old population during 201012. is vaccine introduction appears to have
eliminated serogroup A meningitis epidemics, while epidemics due to other serogroups (W, X, C) continue to
occur9, 13. Burkina Faso also introduced Haemophilus inuenzae type b and pneumococcal conjugate vaccine into
the Expanded Program on Immunization in 2006 and 2013 respectively. ese pathogens are, not involved in
meningitis epidemics, although pneumococci substantially contribute to the seasonality of bacterial meningitis
and cause outbreaks. Meningitis epidemics remain a major burden to the population and understanding the
pathophysiological mechanism behind the phenomenon is needed to design appropriate and sustainable preven-
tion strategies.
Methods
Data compilation. We used routine surveillance data at health centre resolution from 13 health districts
in Burkina Faso, including weekly case counts of suspected acute bacterial meningitis3 and monthly case counts
of clinically suspected upper (URTI) and lower (LRTI) respiratory tract infections. According to the national
surveillance guidelines, URTI cases included otitis, severe sore throat and rhinopharyngitis; LRTI cases included
pneumonia, bronchitis and bronchopneumonia. Two health regions (Hauts-Bassins and Nord) and two addi-
tional health districts (Boulsa in Centre-Nord region and Dédougou in Boucle du Mouhoun region), were
selected for pre-existing research collaboration and history of occurrence of epidemic events. We contacted their
district statistical oces to retrieve health centre level data for the period 2004 through 2014. For one of ve
districts in the Nord region, no data on respiratory infections could be retrieved for any year (Yako). Overall, 13
districts provided thus data (Fig.1). Due to logistical constraints, 2004–2005 data were collected for three districts
(Houndé, Orodara, Séguenéga) only, and 2013–2014 data for ve districts (Boulsa, Dédougou, Houndé, Orodara,
Séguenega), only. ese districts are situated across the four regions. Ninety-ve health centres in the Nord region
were excluded as population size was unknown. Another 13 district years were excluded due to missing data on
respiratory infections. e data used in this project had previously been collected by health authorities for the
purpose of routine surveillance and disease control. e data consists of aggregated case counts without any
individual-level information. As standard clinical procedures were not changed by routine surveillance and indi-
vidual data were not recorded, no informed consent had been collected from patients. e present project of data
analysis received authorisation from the Department of Disease Control (Direction de la lutte contre la maladie,
DLM) at the Ministry of Health, Burkina Faso.
To validate the resulting database, we aggregated the observations into district weeks and compared them
to the number of weekly cases per district routinely reported by the Ministry of Health to the World Health
Figure 1. Flow chart of data compilation, exclusion and inclusion. *number of health centres increasing over
years, thus a substantial amount of HCM is registered as missing. HCM, health centre months. LME, localised
meningitis epidemic. URTI/LRTI, upper/lower respiratory tract infections.

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Organization. Based on over 4,000 district weeks, 62% of the health centre weeks showed a mismatch of ≤2 cases
and 93% of ≤5 cases (median error: 0).
Denition of outcome and exposure variables. e incidence threshold recommended by WHO for
epidemic response (10 per 100,000) is applied at the district level. By contrast, to identify individual localised
epidemic events, we dened LME based on a threshold of weekly incidence rate at the health centre level, as pre-
viously reported4. e best performance in identifying epidemic annual incidences of ≥0.8% in individual health
centre was obtained with a weekly threshold of ≥75 cases per 100,000 inhabitants during at least two consecutive
weeks with at least 5 cases per week (sensitivity 98.5%, specicity 98.0%). Because URTI and LRTI data were
available at monthly intervals only, months were categorized according to the identication of at least one week
of LME. For a sensitivity analysis, we dened LME as a monthly meningitis incidence of 125 cases per 100,000.
We assigned a predominant serogroup to each LME based on district level laboratory reports by the Ministry of
Health. Because LE were identied only during January-May, we restricted the analyses to this period.
To avoid bias due to certain surveillance practices or population size estimation error, we excluded 722 health
centre months which reported incidences of URTI or LRTI above the 95th percentile for >12 consecutive months.
We categorised URTI and LRTI monthly incidences by quintiles across all health centre months, and considered
episodes of URTI and LRTI above the 5th quintile (≥2.47 per 1000 inhabitants for URTI and ≥13.47 for LRTI) as
high incidence episodes. Primary analyses took into account URTI, LRTI and LE observations during the same
month. We created a secondary exposure variable to evaluate the association between LE during a given month
and the level of URTI and LRTI during the month prior. We further dierentiated months with rst occurrence
of LRTI or URTI incidences in the 5th quintile, from those where high incidences were already observed during
the previous month. Finally, we conducted separate analyses grouping years with high serogroup A incidence
(206–2008) and those with predominance of serogroups W and X (2010–2014). For these additional analyses,
lower quintiles were collapsed to obtain sucient statistical power.
Statistical analyses. As the outcome was epidemic/not epidemic and the data contained a spatial structure
with monthly observations clustered within health centres, which lie within districts, we used a mixed-eects
model for binary distribution specifying nested random eects for health centre and for districts with unstruc-
tured covariance (Stata command melogit). In the main analysis, a xed eect for calendar year was introduced.
In an alternative analysis, we included a crossed eects component with calendar year as a third random eect;
this model required substantially greater computational eort, but yielded similar odds ratios (<10% dierence).
All analyses were conducted in Stata/IC14.1 (StataCorp. 2015).
Confounding variables. Potential confounders of the association between URTI or LRTI episodes and LME
could be factors that increase transmission or disease susceptibility for meningococcal and respiratory disease
pathogens locally and for a limited duration. We therefore included dust conditions, relative air humidity and
calendar month into the multivariate models. For dust conditions, we used the MODIS deep-blue aerosol optical
thickness (AOT) product at a daily time-step, which has been shown to be a good proxy for the dust load on the
ground surface in Burkina Faso during the dry season14. e deep blue AOT, initially available at a 10 km-spatial
resolution, were subsampled on a 1 km × 1 km grid, and pixels lying within the health centre coverage region
were averaged. Values were further averaged on a monthly scale. We categorised monthly AOT values in quartiles
across health centre months from January through to May. Data on monthly means of relative air humidity for the
main city of each health region were extracted from the website tutiempo.net (http://tutiempo.net). No data were
available for the Centre-Nord region (Boulsa district). We used three categories of relative air humidity <20%,
20–39.9% and ≥40%.
Data availability statement. e data that support the ndings of this study are available from Direction
de lutte contre la maladie (DLM), Ministry of Health of Burkina Faso, but restrictions apply to the availability of
these data, which were used under license for the current study, and so are not publicly available. Data related to
the present work are however available from the authors upon reasonable request and with permission of DLM,
Ministry of Health of Burkina Faso. For this purpose, please contact the corresponding author JEM.
Results
Among the 512 health centres included, which corresponded to 67,584 health centre months theoretically tar-
geted, meningitis or population size data were not be retrieved for 33,477 health centre months (Fig.1). Reasons
included partial collection for most ancient and most recent years, missing population size (in particular for
private clinics or regional reference hospitals) and missing meningitis data (in particular for health centre months
prior to the creation of a new health centre). Meningitis incidence data were thus available from 427 health cen-
tres (34,107 health centre months), during which 15,420 suspected meningitis cases were reported. e typical
incidence peak at the end of March was found in all districts, but declined over the observed time period (Fig.2,
and supplementary material S1 and S2a). Particularly high incidences were observed during the epidemic wave
from 2006 to 2008. We identied 72 LME, corresponding to 114 health centre months. Until 2008, all identied
LME were related to serogroup A. Aer 2011, following serogroup A conjugate vaccine introduction, only sero-
groups W and X were found.
Overall, 401,110 URTI cases and 1,681,212 LRTI cases were reported during the observation period,
with monthly incidence rates showing two seasonal peaks in October and February (supplementary material
FigureS2b,c). Half (44% and 46%, respectively) of URTI and LRTI cases were reported from January through to
May, even though incidences did not vary substantially across months during this period (Fig.3). About half of
the high URTI incidence episodes lasted for four months or longer. AOT showed the typical maximum during

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SCIENtIFIC REPORTs | 7: 11570 | DOI:10.1038/s41598-017-11889-4
the month of March15 and relative air humidity increased from January towards the end of the dry season in May
(Fig.4).
For the period between January and May, we excluded health centre months with continuous peak URTI
or LRTI incidences. We retained 10,334 health centre months for which data on meningitis and URTI or LRTI
incidence were available (Fig.1). ese included 10,311 health centre months for URTI and 10,319 health centre
months for LRTI; and 51 LME events, corresponding to 85 health centre months with LME (Table1).
An LME was present during 0.8% of the health centre months considered, and this proportion increased from
0.2 to 1.8% across URTI quintiles (Table2). e corresponding crude odds ratios (95% condence interval (CI))
showed an increased risk of LME with increasing URTI incidence quintiles, with an OR of 10.10 (3.25–31.34)
in the 5th quintile (Table3) and a signicant eect from the 3rd quintile onwards. Aer multiple adjustments,
odds ratios were higher, however with wider condence intervals. e crude odds ratio (95% CI) tended to be
substantially but not signicantly greater when a high incidence episode had been observed the month prior
(12.23 (3.86–38.75)) compared to rst-time episodes (6.07 (1.61–22.86)) (supplementary material TableS1). A
weaker association was observed with a one month lag, linking LME to high URTI incidence episodes during the
previous month (2.44, 1.07–5.59) (supplementary material TableS1). Strong associations were observed in anal-
yses including the period between 2006 and 2008, where epidemic waves were due to serogroup A (5th vs 1st−4th
quintile, crude OR 3.26, 95% condence interval 1.62–6.57) or for the period from 2010 onwards, when only
serogroups W and X were found (14.22, 0.91–223.18) (supplementary material TableS1). In the sensitivity analy-
sis using a less specic but more sensitive LME denition of 125 monthly cases per 100,000, a similar increase of
odds ratios across URTI quintiles was observed, with an OR of 4.89 (2.55, 9.38) for the h quintile.
Figure 2. Monthly incidence of suspected meningitis at the health center level during the meningitis season
(December–May) in 13 districts of Burkina Faso, 2004–2014. e lines represent median, 75th and 95th
percentile. Median incidences are 0 and thus not visible.
Figure 3. Monthly incidence of upper (URI) and lower (LRI) respiratory infections during December–May in
13 districts of Burkina Faso, 2004–2014. e lines represent median, 25th and 75th percentile.

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An LME was present in 0.3 to 1.4% of health centre months across quintiles of LRTI (Table2), however
without any trend. e odds ratios (95% CI) varied from 0.41 (0.16–1.06) to 1.89 (0.97–3.69), without any trend
(Table4). A strong and signicant association was observed from the fourth LRTI incidence quintile upwards
only when adjusting for calendar year (5th quintile, 6.68 (1.94–23.07)) (Table4). However, in analyses separating
periods 2006–2008 and 2010–2014, the 5th quintile was signicantly associated with LME occurrence, compared
to other quintiles (supplementary material TableS1). e association between LME and the 5th LRTI quintile was
Figure 4. Average monthly aerosol optical index (AOT) and relative air humidity (RH) during January–May in
13 (model including RH: 12) districts of Burkina Faso, 2004–2014. e lines represent median, 25th and 75th
percentile.
Year District
Number
of HC
with LME
episodes
Number of weeks during which the
LME denition was met in the HC Predominant
meningococcal
serogroup
during LME*median (range)
2005 Orodara 1 1 (1) ND
2006
Dafra 1 4 A
Dandé 22 (1–3) A
Houndé 8 3 (1–5) A
Karangasso 38 (3–12) A
Lena 65 (2–8) A
Orodara 1 2 A
Seguenega 6 2.5 (1–4) A
Titao 33 (1–6) A
2007
Boulsa 25 (2–8) A
Dandé 1 4 A
Houndé 1 2 A
Orodara 3 4.5 (2–6) A
Seguenega 2 2.5 (2–3) A
Titao 22.5 (2–3) A
2008
Boulsa 24.5 (4–5) A
Orodara 2 3.5 (2–5) A
Seguenega 1 1 A
Seguenega 2 1.5 (1–2) X
Titao 1 3 A
2014 Houndé 1 3 W
Table 1. Characteristics of localised meningitis epidemics (LME) identied in 13 health districts, Burkina Faso,
2004–2014. LE were dened as ≥75 cases per 100,000 inhabitants during at least two out of four consecutive
weeks and with ≥5 cases per week. Table shows only LME where data on respiratory infection incidence was
available. HC, health centres. ND, not dened. *according Ministry of Health, Epidemiological surveillance
department Burkina Faso.

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weaker when the high incidence episode existed already during the month prior compared to rst-time events
(supplementary material TableS1).
Discussion
In this analysis of health centre surveillance data from Burkina Faso spanning a decade, we found that high
incidence episodes of URTI were strongly associated with the occurrence of localised meningitis epidemics. e
association with LRTI was inconsistent and only seen aer adjusting for calendar year.
ese ndings strengthen the hypothesis that acute respiratory tract infections act as co-factors for the occur-
rence of localised meningitis epidemics in the meningitis belt1, 16. e trend of a gradually increasing risk of LME
URTI LRTI
Quintiles of incidence rate
(per 1000)
No LME
month
(N = 10,226)
LME
month
(N = 85)
No LME
month
(N = 10,234)
LME
month
(N = 85)
1st quintile <0.39 2213 (98.8) 4 (0.2) <2.98 2382 (99.2) 20 (0.8)
2nd quintile 0.39-<0.78 2079 (99.6) 8 (0.4) 2.98-<5.37 2171 (99.4) 14 (0.6)
3rd quintile 0.78-<1.33 2047 (99.9) 22 (1.1) 5.37-<8.22 1999 (98.7) 6 (0.3)
4th quintile 1.33-<2.47 1996 (99.2) 16 (0.8) 8.28-<13.47 1862 (98.6) 26 (1.4)
5th quintile ≥2.47 1891(98.2) 35 (1.8) ≥13.47 1820 (99.0) 19 (1.0)
Table 2. Health centre months with and without localised meningitis epidemics (LME) by dierent categories
of monthly incidence of upper and lower respiratory tract infection (URTI, LRTI). Burkina Faso, 2004–2014. N
(%).
Crude Adjusted for AOT, RH and
month Adjusted for AOT, RH,
month and year
URTI monthly incidence rate (per 1000)
<0.39 1 1 1
0.39–<0.78 2.25 (0.66–7.65) 5.21 (0.61–44.04) 3.68 (0.39–34.69)
0.78–<1.33 6.85 (2.26–20.72) 15.58 (2.01–120.67) 20.11 (2.28–177.58)
1.33–<2.47 5.15 (1.62–16.30) 8.18 (1.02–65.57) 16.10 (1.73–149.83)
≥2.47 10.10 (3.25–31.34) 23.94 (3.09–185.30) 54.55 (6.00–495.63)
Monthly mean AOT (quartiles)
<0.40 1 1
0.40–<0.51 0.89 (0.33–2.36) 4.36 (1.04–18.25)
0.51–<0.66 1.57 (0.62–3.98) 2.11 (0.42–10.69)
≥0.66 1.64 (0.63–4.29) 6.10 (1.00–37.09)
Monthly mean RH
≥40% 1 1
20–39.9% 0.68 (0.26–1.74) 1.23 (0.38–4.00)
<20% 2.13 (0.61–7.47) 0.51 (0.12–2.21)
Month
January 1 1
February 3.88 (1.04–14.54) 3.06 (0.71–13.14)
March 11.58 (2.99–44.83) 4.62 (0.75–28.69)
April 15.65 (3.23–75.78) 3.17 (0.39–25.79)
May 1.80 (0.24–13.35) 0.22 (0.02–2.33)
Ye ar *
2005 1.97 (0.09–43.39)
2006 171.98 (16.52–1790.36)
2007 17.76 (1.56–201.83)
2008 2.94 (0.25–35.06)
2010 2.49 (0.20–30.48)
2014 1
Table 3. Association between high incidence episodes of upper respiratory tract infections (URTI) and
occurrence of localised meningitis epidemic, Burkina Faso, 2004–2014. All models are mixed-eect logistic
regression accounting for spatial data structure. e model adjusting for relative air humidity (RH) includes
only 12 districts. Odds ratio and 95% condence intervals AOT, aerosol optical thickness. *Years 2004, 2009,
2011–13 predicts failure perfectly, dropped from model.

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with increasing URTI incidence or duration, the persistence of the association with a time lag and the strength of
the observed association may be considered as arguments for a causal relation.
At the individual level, (viral) inammation of the nasopharygeal mucosa facilitates bacterial adhesion and
colonization, including that of meningococci17, 18, which can result in increased transmission in the community.
is eect is supported by associations between meningococcal carriage and clinical pharyngeal inammation
that had previously been described during meningitis epidemics19, 20, in contrast to absence of this association
outside epidemics, as reported by a large multi-country carriage study21. e biological eect could be empha-
sized by behavioural changes induced by respiratory infections, such as sneezing and coughing, which can further
facilitate meningococcal transmission. At the population level, such accelerated transmission is supported by the
over10 times increase in meningococcal carriage prevalence during epidemic situations, as observed by most
studies during meningitis epidemics11, 20. According to the hypothetical model cited above1, the surge in transmis-
sion causes the epidemic increase in meningitis incidence, mediated by a high case-carrier ratio during the dry
season11. e high risk of invasive disease given carriage, in turn could be related to the damage of the mucosal
barrier function during periods of low humidity and high dust load.
Viral especially inuenza infections lead to immune depression, which facilitates the development of invasive
bacterial disease, including meningococcal disease22. Studies during meningitis epidemics in the meningitis belt or
outbreaks in Europe have reported strong associations between inuenza infection or u-like symptoms and men-
ingitis15, 23, 24. While we believe that accelerated bacterial transmission should be the main mechanism behind the
hypothesised link between RTI and meningitis epidemcis, this eect could further accelerate the emergence of an
LME. In our analysis, an association between LRTI and LME was seen only aer adjustment for calendar year. is
adjustment considerably increased odds ratios for both URTI and LRTI, suggesting negative confounding. Given
the limited number of LME included overall, caution is required when interpreting the year-adjusted results. e
dierence in odds ratio between URTI and LRTI may mean that inammation of the nasopharynx is required to
obtain the epidemiogenic eect in question; or that the incriminated pathogen rarely causes LRTI. However, given
the limited quality of the available RTI data, we cannot conclude on the absence of an eect from LRTI.
Crude Adjusted for AOT,
RH, and month Adjusted for AOT,
RH, month and year
LRTI monthly incidence rate (per 1000)
<2.98 1 1 1
2.98–<5.37 0.93 (0.45–1.94) 0.78 (0.35–1.77) 1.76 (0.61–5.07)
5.37–<8.22 0.41 (0.16–1.06) 0.25 (0.08–0.77) 0.66 (0.17–2.66)
8.28–<13.47 1.89 (0.97–3.69) 1.05 (0.48–2.28) 4.00 (1.35–11.89)
≥13.47 1.05 (0.59–2.21) 0.53 (0.22–1.26) 6.68 (1.94–23.07)
Monthly mean AOT
<0.40 1 1
0.40–<0.51 0.87 (0.33–2.32) 4.37 (1.06–18.09)
0.51–<0.66 1.42 (0.56–3.58) 1.93 (0.39–9.56)
≥0.66 1.68 (0.65–4.39) 5.85 (0.99–34.40)
Monthly mean of RH
≥40% 1 1
20–<40% 0.61 (0.24–1.54) 0.93 (0.28–3.05)
<20% 1.94 (0.58–6.53) 0.52 (0.12–2.30)
Month
January 1 1
February 4.16 (1.11–15.53) 3.01 (0.71–12.65)
March 11.37 (2.94–43.89) 5.13 (0.84–31.18)
April 14.23 (3.01–67.16) 4.18 (0.53–33.12)
May 1.43 (0.20–10.44) 0.29 (0.03–3.00)
Year*
2005 2.10 (0.10–45.98)
2006 173.33 (16.18–
1856.81)
2007 17.53 (1.50–204.51)
2008 3.34 (0.28–40.48)
2010 1.88 (0.15–22.91)
2014 1
Table 4. Association between high incidence episodes of lower respiratory tract infections (LRTI) and
occurrence of localised meningitis epidemics, Burkina Faso, 2004–2014. All models are mixed-eect logistic
regression accounting for spatial data structure. e model adjusting for relative air humidity (RH) includes
only 12 districts. Odds ratio and 95% condence intervals AOT, aerosol optical thickness. *Years 2004, 2009,
2011–13 predicts failure perfectly, dropped from model.

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e limited number of LME did not allow serogroup-specic analysis, but a strong association between URTI
and LME was seen during epidemic waves due to serogroup A and epidemics due to serogroups W or X. e
recent epidemic wave due to a new meningococcal serogroup C strain in Nigeria and Niger9 was constituted of
localised epidemics that were spread across the epidemic regions without any systematic pattern25. In the light of
the present ndings, the epidemic emergence could be interpreted as a coincidence between the introduction of
a new meningococcal strain and the occurrence of respiratory pathogen outbreaks.
URTI usually show mild clinical symptoms and do not lead to medical consultation, especially in the low
resource settings of Burkina Faso. In consequence, our URTI incidences based on consultations are probably
largely underestimated. Furthermore, URTI and LRTI reporting may have been neglected during LME due to the
strain on the health care system. Both would have led to an underestimated association between URTI or LRTI
and LME.
No laboratory information was available for any of the reported respiratory infections. One case-control study
during a meningitis epidemic in Chad reported higher prevalence of mycoplasmal and viral infection among
cases compared to controls16. Investigating the nature of the respiratory pathogen (a specic pathogen or a generic
eect of various pathogens) will require clinical studies during LME. In theory, meningococcal colonisation itself
could cause URTI symptoms; however, if so, they would be mild and not motivate seeking medical care.
Our study has several limitations. First, we may not have adjusted for all relevant confounding factors.
Although the ecological level is appropriate in analysing risk factors for epidemic occurrence, no conclusion on
a causal link can be made from this type of study, as several behavioural and biological aspects, which are only
available from individual level studies (eg, crowded living conditions, smoking, complement deciencies), may
interplay26. Although we had collected information on social events, cultural context and vaccination campaigns
with polysaccharide meningococcal vaccines, the exhaustiveness of the data did not allow inclusion in the present
analysis.
e routine nature of the data limits the validity of our results. Misclassication between URTI and LRTI is
likely, as clinical symptoms overlap. e reporting practice may have evolved over the observed period, in par-
ticular for LRTI, targeted for pandemic u preparedness during that decade. Also, reporting practice probably
varied between health regions and districts. Although these eects should have been taken into account by the
mixed eects logistic regression models, reporting artefacts may occur. Finally, our analysis assumes that changes
in RTI reporting incidence are parallel to changes in the true RTI incidence in the general population, which may
not be the case, given social and behavioural variations or seasonality of other diseases.
We have selected aerosol load and relative air humidity to represent climatic inuences that could explain the
observed association, and we acknowledge that other factors, such as temperature or wind speed could be consid-
ered in future analyses investigating the hypothesis.
Our study shows high incidence rates of acute respiratory infections in Burkina Faso, with seasonal peaks.
A study in Benin found that an increase of consultations for LRTI occurs during the cold dry season27. Further
evaluation of the specic burden of respiratory diseases in the meningitis belt and their association with climatic
factors is needed.
In conclusion, this study adds evidence to the hypothesis that URTI outbreaks during the dry season function
as co-factors for localised meningitis epidemics. Depending on the aetiology, this may open opportunities for
the development of serogroup-unspecic preventive interventions at the population level, including vaccination
against the involved pathogen. Given the continuing diculties in preventing and controlling epidemics of all
meningococcal serogroups, any additional preventive intervention may be benecial. Beyond this, the described
association represent a striking example of the interactions between climate, bacteria and viruses that threaten
human health.
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Acknowledgements
We acknowledge the work of the Burkina Faso health centre nurses who reported cases in parallel to clinical
care over one decade. We thank regional and district level health ocers and statisticians for their collaboration
in the data compilation. Compilation of the health centre level data was supported by the World Health
Organization. is work was conducted with support from the TELEDM project located at Centre de Recherches
de Climatologie (CRC)/BIOGEOSCIENCES (University of Burgundy) and funded by Centre national d’études
spatiales (CNES). It was conducted in the frame of a Masters internship of the ENS, École normale Supérieure,
Paris (MW).
Author Contributions
J.E.M. and H.T. designed the work. J.E.M., M.W., S.P., H.T., N.M. implemented data compilation and preparation.
J.E.M. and Y.M. conducted data analyses. All authors contributed to interpretation of the results and have
reviewed the manuscript.
Additional Information
Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-017-11889-4.
Competing Interests: e authors declare that they have no competing interests.
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