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PICO 1 Summary Report

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Meningitis Outbreak Response intervention thresholds in sub-Saharan Africa Report for the WHO Meningitis Guideline Revision May 2014 Prepared by Dr Caroline Trotter ([email protected])

Recommendation question: Following the introduction of MenAfriVac, what criteria

should be used to determine when to start mass vaccination in outbreaks of

meningococcal meningitis?

Current position: The WHO currently recommends for areas of population greater than 30,000: an

alert threshold of 5 cases per 100,000 inhabitants per week; and an epidemic threshold of 10 per

100,000 in 1 week when epidemic risk is high, or 15 per 100,000 per week otherwise1. For small

populations, thresholds are defined by absolute numbers of cases. In most instances, the

operational epidemic threshold is 10 per 100,000, with the higher threshold of 15 per 100,000 being

rarely used.

Background and aims

In the African meningitis belt, meningitis epidemics are detected by using weekly incidence

thresholds. The current thresholds were established on the recommendation of a consensus

meeting on detection of meningitis epidemics in Africa, held in Paris on 20 June 2000. Data to inform

this consensus was primarily from Neisseria meningitidis group A (NmA) epidemics2-4. As the large-

scale use of the NmA conjugate vaccine, MenAfriVac®, is expected to substantially reduce the

burden of disease in the meningitis belt, and the epidemiology of disease due to other groups may

be different to NmA, it is timely to review the current thresholds.

The aim of this paper is to address the PICO question outlined above (PICO 1). Since there have been

no outbreaks of group A disease in populations immunised with MenAfriVac® and no group C or Y

outbreaks have been documented in the meningitis belt in recent years, these analyses concentrate

on N. meningitidis group W (NmW) outbreaks.

PICO Question 1

In outbreaks of meningococcal meningitis due to vaccine preventable serogroups, how many

cases and deaths are potentially averted when mass vaccination is implemented at different

thresholds?

Population: total population in a defined district or subdistrict affected by a C, W or Y meningitis

outbreak (or A after introduction of MenAfriVac)

Intervention: reactive vaccination campaigns with an appropriate vaccine launched when a given

attack rate (or other agreed criteria) is reached

Comparator: reactive vaccination campaigns with an appropriate vaccine launched when the

current epidemic threshold is reached

Outcome: cases, deaths


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Methods

Data sources

Several sources of data were used to construct an NmW dataset, as summarised in table 1. There

was considerable overlap in the data sources used for PICO 1 and PICO 3. All data were at district

level; there were no available data at the sub-district level.

Table 1: Data sources for PICO 1 analysis

Data Source Description

Suspected case data WHO IST Ougadougou (Clement Lingani)

Weekly case counts by district from 2005 onwards, covering most countries in the meningitis belt though not all countries for all years.

ICG vaccine requestsa

WHO Geneva (Katya Fernandez)

Documented requests for vaccine to implement reactive immunisation campaigns 2006-2013

Laboratory line lists WHO Geneva (Laurence Cibrelus)

Line lists of laboratory reports collated from various countries and sources

Additional data from Burkina Faso 2002, 2003

WHO Geneva (Katya Fernandez)

Weekly case counts by district and laboratory data

Additional data from Burkina Faso 2010, 2012, 2013

CDC (Ryan Novak) Laboratory confirmed meningitis cases (line listing)

Additional data from Gambia 2012

MRC Gambia (Jahangir Hossain)

Weekly case counts (suspected and confirmed) from epidemic regions with associated laboratory data

Imperial database WHO Geneva (Katya Fernandez)

Total cases by district and year with additional laboratory data for Burkina Faso, Chad, Niger and Mali used to analyse NmA vs NmW outbreak size

Data from these different sources were incorporated into one database. Suspected case data was

reorganised so that one line represented one district year with different columns showing cases by

week. Laboratory line lists of individual cases were manipulated to provide totals by district and

year; these were then matched to the weekly suspected case data by district and year. Additional

information from other sources (table 1) was then added to this database.

District years with both weekly counts of suspected cases and some evidence of NmW disease were

included in the NmW dataset. Evidence of NmW was usually in the form of laboratory confirmation;

initially any districts with 2 or more laboratory confirmed NmW cases in a year were included. The

proportion of confirmed cases that were NmW compared to all N. meningitidis confirmed cases was

then examined, and district years with >50% NmW were retained. Some additional district years

were included on the basis of an ICG request for NmW containing vaccine for reactive vaccination.

Then, any district years with 20 or fewer suspected cases in total were excluded (33 district years).

Since surveillance is most active during the ‘meningitis season’, data from weeks 1-26 was used.

a The ICG is a partnership between WHO, UNICEF, Médecins Sans Frontières (MSF), and the International Federation of the Red Cross (IFRC) established to provide globally coordinated emergency response for epidemic meningitis through the management of emergency vaccine stockpiles.


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Reactive vaccination response time

Data from ICG between 2006 and 2013 was used to determine the range, mean and median time

taken from a request for vaccine and implementation of a reactive vaccination campaign.

Estimating cases occurring after different weekly incidence thresholds

Thresholds of 7, 5 and 3 per 100,000 (below the current epidemic threshold of 10 per 100,000) were

considered.

The week that a given threshold was crossed (wt) was identified, and the cases that occurred in

subsequent weeks were summed, up to week 26 (w26). Since the seasonal incidence of meningitis is

high, ‘hyperendemic’ seasonal activity may need to be distinguished from epidemic activity. Mueller

& Gessner report that in Burkina Faso during January through May 2008, 96% and 79%, respectively,

of the 63 districts reported a weekly incidence rate above 1 or 2 per 100,000 during at least 4

weeks5. In addition, the suspected case data may contain cases of meningitis caused by other

pathogens. Therefore, in the main analyses, cases that occurred after weekly incidence declined to a

‘normal’ seasonal incidence of <2 per 100,000 (noted as wn) were excluded.

Estimating vaccine preventable cases

Because it would not be feasible to instantly implement a reactive vaccination campaign, a time lag

(based on the observed reactive vaccination response time) was included, so that cases were only

assumed to be prevented by vaccination following this interval (wt+lag, e.g. wt+6). The number of

vaccine preventable cases was estimated by multiplying the total number of cases that occurred

between wt+lag and wn by the effective vaccine coverage (VEC). The effective vaccine coverage is a

composite variable that summarises both vaccine effectiveness and uptake. E.g. vaccine uptake of

95% multiplied by vaccine effectiveness of 90% gives a VEC of 86%; values of 75% and 90% were used

in this analysis. Some previous reactive campaigns with polysaccharide vaccine have restricted the

vaccine to 2 to 29 year olds because of lower immunogenicity in young children and low disease risk

in older adults. Although there is variation by outbreak, approximately 16% of NmW cases in recent

outbreaks have occurred in children less than 2 years of age 6 (see also PICO 3). The effect of

excluding <2 year old children from the vaccine campaign was also considered, by assuming that

16% of cases occurred in this age group. The exclusion of <2 year olds in this way in the model could

in practice be as a result of either not targeting this age group for vaccination or low immunogenicity

in the youngest children.

Estimating deaths prevented at different weekly incidence thresholds

The number of deaths is not presented but can be estimated by applying the average case fatality

experienced in NmW outbreaks (11.6%)(PICO 3 Report).

Definition of an NmW epidemic

The studies used to inform the existing thresholds defined an epidemic to be a cumulative incidence

of 100 cases per 100,000 population (lower cumulative incidences of 70, 80 and 90 per 100,000 were

considered in sensitivity analyses). There is evidence that NmW epidemics are, on average, less

intense than NmA epidemics (Griffin et al, paper in preparation). A range of cumulative incidences

are used to define an epidemic here, from a minimum seasonal incidence of 20 per 100,000 to a

maximum of 100 per 100,000 (with 40, 60 and 80 per 100,000 also considered).


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Threshold performance

The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of

different weekly thresholds for detecting an epidemic were calculated. The definition of an epidemic

season was varied between 20 and 100 per 100,000, as discussed above.

Post MenAfriVac® dataset

To investigate the properties of the thresholds further, the number of events (i.e. district years

where a specific threshold was reached) that occurred in a representative dataset was estimated.

Weekly suspected case data from countries that had completed MenAfriVac® campaigns was used

for this purpose. This ‘post MenAfriVac® dataset’, included district years from Mali, Niger, Burkina

Faso in both 2012 and 2013 and from Chad in 2013 only.

Results

Description of NmW dataset

The final dataset constructed for this analysis comprised 136 district years with both weekly

suspected case data and some evidence of NmW disease. There are a total of 20,777 suspected

cases, with 2318 confirmed NmW cases (11.1% confirmed overall). Burkina Faso accounted for 82

(60%) of these district years, with Mali and Niger contributing 14 and 17 district years respectively

and 7 other countries (Benin, Chad, Cote d’Ivoire, Gambia, Ghana, Guinea, Nigeria) contributing

between 2 and 7 district years each. The districts included in the NmW dataset are shown in figure 1.

District population sizes ranged from 59,330 to 884,859, with a median size of 263,110. There were

no districts with a population <30,000 in this dataset.

Figure 1: Map of districts in the meningitis belt with confirmed W disease between 2002 and 2013

included in this analysis. Note that some districts may be appear in the dataset for more than 1 year.


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Of the 136 district years in the NmW dataset, 99 reached a cumulative seasonal incidence of 20 per

100,000, 68 district years reached 40 per 100,000, 55 district years were ≥60 per 100,000 and 36

were ≥80 per 100,000. Only 22 district-years reached the previously used epidemic definition of 100

per 100,000 and 15 of these occurred in Burkina Faso.

The total seasonal incidence ranged between 3 and 506 per 100,000 in the 136 district years. In the

99 district years exceeding a seasonal incidence of 20 per 100,000, the peak weekly incidence ranged

from 2.5 to 104 per 100,000 overall, with a median peak incidence of 6.2 per 100,000. Among these

districts, the peak was observed between week 2 and week 17 (median week 13).

Reactive vaccination response time

There were 153 vaccine requests logged with ICG between 2006 and 2013. The mean response time

from vaccine being requested to reactive immunisation being implemented was 26 days. The

minimum response time (excluding those instances where vaccine stocks were already held in-

country) was 10 days.

A vaccination campaign takes 1-2 weeks to complete and a further week is required for vaccinated

individuals to mount a protective immune response. The average lag time is therefore likely to be in

the region of 6 weeks. We also considered an optimistic 4 week lag and an unrealistic 2 week lag for

illustration purposes.

The mean time from threshold to peak weekly incidence is shown in table 2. It is clear that a lower

threshold buys more time in which to respond before the peak is reached.

Table 2: Time from threshold to peak incidence

Threshold (weekly incidence per 100,000)

Number of district years reaching threshold

Mean interval from threshold to peak incidence in weeks (days)

10 49 1.44 (10.1)

7 66 2.59 (18.1)

5 77 3.25 (22.8)

3 98 5.64 (39.5)

Potentially preventable cases at different weekly incidence thresholds

The number of cases occurring in the weeks after the threshold was reached, up to week 26 are

shown in table 3. A more conservative count is also shown which excludes cases that occur after the

incidence has returned to a normal seasonal incidence of 2 per 100,000 per week. The addition of a

6 week time lag, which seems the most likely based on ICG data, decreases the number of

potentially preventable cases substantially. If a 4 week or even a 2 week time lag could be achieved,

substantially more cases (approximately 2 and 3 times as many for a lag of 4 and 2 weeks

respectively) are potentially preventable.


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Table 3: Suspected cases occurring after weekly incidence threshold reached


∑Cases week wt* to w26

∑Cases week wt to wn**

∑Cases week wt+2 to w26



∑Cases week wt+6 to wn

10 9731 9025 6951 4219 1756 1127

7 12258 11181 9170 5732 2557 1635

5 13756 12470 10895 7367 3796 2727

3 16186 14566 13891 10786 7328 5955

*wt= week at which the threshold is reached

** wn= week at which incidence returns to ‘normal’ seasonal baseline of 2 per 100,000 per week

More detail is given on the number of cases occurring from 6 weeks after the threshold was reached

(wt+6) until return to normal seasonal activity (wn) in table 4, together with the average number of

cases per district and the range.

Table 4: Number of cases occurring 6 weeks after the threshold was reached until return to ‘normal

seasonal activity’ of 2 per 100,000 per week.



Cases occurring in weeks wt+6 to wn

Mean cases per district (range)

Median cases per district (IQR)

10 49 1127 23* (0-434) 0 (0, 14)

7 66 1635 25 (0-434) 6 (0, 18)

5 77 2727 35 (0-783) 10 (0, 31)

3 98 5955 61 (2-1769) 14 (0, 67) * The current threshold is 10 per 100,000. The number of cases occurring per event after this threshold was reached is

shown for information, but vaccination was instigated at this point in many of the districts which will have curtailed the

epidemic, which may make this threshold seem less favourable.

A greater number of cases (and cases per event) are prevented as the thresholds are lowered. The

proportion of districts where more than 20 cases are potentially preventable increases from 24% to

35% and then to 45% as the threshold is lowered (from 7 to 5 to 3 per 100,000 respectively).

However, as the threshold is lowered, successively more individuals would have been targeted for

reactive immunisation, i.e. an additional 4.0 million with a threshold of 7, an additional 7.0 million

with a threshold of 5 per 100,000 and an additional 13.8 million with the lowest threshold of 3 per

100,000 (assuming the whole district population is targeted).

To investigate the robustness of these results, the distribution of the cases averted by outbreak was

examined. A large number (1769) of the additional cases prevented by the lowest threshold of 3 per

100,000 per week were due to one district year (figure 2); Pissy in Burkina Faso 2002 where there

was a large NmW epidemic. However, the proportion of cases occurring in this district compared to

the total over all districts was similar for thresholds of 3, 5 and 7 (27%), so the relative merits of the

thresholds are unchanged if this district is excluded.


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Figure 2: Distribution of cases occurring after a given threshold until return to normal seasonal

incidence by district year

Of the district years reaching a threshold of 7 per 100,000 per week, 74% went on to pass a

threshold of 10 per 100,000 per week; this was 63% for a threshold of 5 per 100,000 per week and

50% for the lowest threshold of 3 per 100,000 per week. To investigate any residual effects of

vaccination triggered by the current threshold of 10 per 100,000, the districts known to have been

vaccinated with an NmW-containing vaccine were excluded (table 5). The mean cases per district,

i.e. those that were potentially preventable, were higher than in table 4, but the relative advantage

of the lowest threshold remained.

Table 5: Number of cases occurring 6 weeks after the threshold was reached until return to ‘normal

seasonal activity’ of 2 per 100,000 per week, excluding vaccinated districts where reactive campaigns

with an NmW containing vaccine was implemented.



Cases occurring in weeks wt+6 to wn

Mean cases per district (range)

Median cases per district (IQR)

10 32 1050 33 (0-434) 11 (2,48)

7 49 1387 28 (0-434) 14 (6, 40)

5 60 2267 38 (0-783) 19 (8, 47)

3 81 5215 64 (2-1769) 42 (10, 112)


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Vaccine-preventable cases at different thresholds and vaccine assumptions

The estimated number of cases that could be prevented by reactive vaccination at each threshold

under varying assumptions of effective vaccine coverage is shown in table 6. The exclusion of

children under 2 years of age substantially reduces the number of cases that could be prevented. In

all of the scenarios considered, the average number of cases prevented per event is fewer than 60,

with 11 out of 16 scenarios preventing fewer than 30 cases per event.

Table 6: Cases prevented by reactive vaccination with different thresholds under different

assumptions of effective vaccine coverage (VEC), assuming a 6 week lag


Cases occurring in weeks wt+6 to wn (number of events)

Cases prevented VEC=75% (per event)


Cases prevented VEC=75%, <2y/o excluded (per event)


10 1127 (49) 845 (17) 1014 (21) 710 (14) 852 (17)

7 1635 (66) 1226 (19) 1472 (22) 1030 (16) 1236 (19)

5 2727 (77) 2045 (27) 2454 (32) 1718 (22) 2062 (27)

3 5955 (98) 4466 (46) 5360 (55) 3752 (39) 4502 (46)

Improving reactive vaccination response

The gains in the number of cases that could be prevented if the time between threshold and

effective vaccination were 4 weeks rather than 6 weeks are shown in table 7. As expected, many

more cases are prevented with a shorter lag. The number of cases prevented per event is higher

(better) under the current threshold of 10 per 100,000 per week if a 4 week lag is assumed than the

lowest threshold of 3 per 100,000 per week with a 6 week lag.

Table 7: Cases prevented by reactive vaccination with different thresholds under different

assumptions of effective vaccine coverage (VEC), assuming a 4 week lag


Cases occurring in weeks wt+4 to wn (number of events)





10 3549 (49) 2662 (54) 3194 (65) 2236 (46) 2683(55)

7 4696 (66) 3522 (53) 4226 (64) 2958 (45) 3550 (54)

5 6181 (77) 4636 (60) 5563 (72) 3894 (51) 4673 (60)

3 9312 (98) 6984 (71) 8381 (86) 5867 (60) 7040 (72)

Threshold performance

The performance of the weekly thresholds compared to different definitions of an epidemic is

shown in table 8. The appropriateness of the threshold is associated with the definition of an

‘epidemic’; i.e. lower thresholds are more appropriate when a lower cumulative incidence is used to

define an epidemic. The ‘best’ threshold for each definition of an epidemic is highlighted. The


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analysis was repeated for Burkina Faso only and for all others excluding Burkina Faso, although this

did not markedly change the results (not shown).

Table 8: Performance of different weekly thresholds compared to a cumulative seasonal incidence of

20, 40, 60, 80 or 100 per 100,000 population – full NmW dataset

Seasonal incidence per 100,000

Weekly threshold per 100,000

Sensitivity % (95% CI)

Specificity % (95% CI)

PPV % (95% CI)

NPV % (95% CI)

20 10 49.5 (41.1, 57.9) 100 (100,100) 100 (100,100) 42.5 (34.2, 50.8)

7 66.7 (58.7, 74.6) 100 (100,100) 100 (100, 100) 52.9 (44.5, 61.3)

5 76.8 (69.7, 83.9) 97.3 (94.6, 100) 98.7 (96.8, 100) 61.0 (52.8, 69.2)

3 96.0 (92.6, 99.3) 91.9 (87.3, 96.5) 96.9 (94.0, 99.8) 89.5 (84.3, 94.6)

40 10 70.6 (62.9, 78.3) 98.5 (96.5, 100) 98.0 (95.6, 100) 77.0 (69.9, 84.0)

7 94.1 (90.1, 98.1) 97.1 (94.2, 99.9) 97.0 (94.1, 99.9) 94.3 (90.4, 98.2)

5 100 (100, 100) 86.7 (81.1, 92.4) 88.3 (82.9, 93.7) 100 (100, 100)

3 100 (100, 100) 55.9 (47.5, 64.2) 69.4 (61.6, 77.1) 100 (100, 100)

60 10 81.8 (75.3, 88.3) 95.1 (91.4, 98.7) 91.8 (87.2, 96.4) 88.5 (83.2, 93.9)

7 100 (100,100) 86.7 (80.7, 92.2) 83.3 (77.1, 89.6) 100 (100,100)

5 100 (100,100) 72.8 (65.4, 80.3) 71.4 (63.8, 79.0) 100 (100,100)

3 100 (100,100) 46.9 (38.5, 55.3) 56.1 (47.8, 64.5) 100 (100, 100)

80 10 91.7 (87.0, 96.3) 84.0 (77.8, 90.1) 67.4 (59.5, 75.2) 96.6 (93.5, 99.6)

7 100 (100,100) 70.0 (62.3, 77.7) 54.6 (46.8, 62.9) 100 (100,100)

5 100 (100,100) 59.0 (50.7, 67.3) 46.8 (38.4, 55.1) 100 (100,100)

3 100 (100,100) 38.0 (29.8, 46.2) 36.7 (28.6, 44.8) 100 (100,100)

100 10 100 (100,100) 76.3 (69.2, 83.5) 44.9 (36.5, 53.2) 100 (100, 100)

7 100 (100,100) 61.4 (53.2, 69.6) 33.3 (25.4, 41.3) 100 (100,100)

5 100 (100,100) 51.8 (43.4, 60.1) 28.6 (21.0, 36.2) 100 (100, 100)

3 100 (100,100) 33.3 (25.4, 41.3) 22.4 (15.4, 29.5) 101 (100,100)

Number of events occurring at different thresholds post-MenAfriVac®

The post-MenAfriVac® dataset comprised 395 district years from 2012 and 2013. The median

cumulative seasonal incidence among all districts was 1.7 per 100,000 ranging from 0 to 111 per

100,000. There were fewer than 10 cases per year reported in 237 of the 395 district years. Sixty-two

districts reached a cumulative seasonal incidence of 20 per 100,000. Three districts reached a

cumulative seasonal incidence in excess of 100 per 100,000; these 3 districts exceeded a weekly

incidence threshold of 10 per 100,000.

The number of districts reaching different weekly thresholds is shown in table 9.


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Table 9: Number of districts in post MenAfriVac® dataset that reach different weekly thresholds and

the median seasonal incidence in districts reaching that threshold

Weekly incidence threshold Number of districts Median seasonal incidence in districts reaching threshold

10 per 100,000 15/ 395 77 per 100,000

7 per 100,000 24 /395 70 per 100,000

5 per 100,000 35 /395 64 per 100,000

3 per 100,000 63 /395 33 per 100,000

Based on these figures, in the post-MenAfriVac® era, 2, 3, 5 or 9 ‘events’ would occur at thresholds

of 10, 7, 5 or 3 per 100,000 per week respectively in a typical year in a high incidence country. This

does not take into account the laboratory confirmation of causative pathogens that would also be

required to determine an appropriate response.

Discussion

A range of analyses are presented here to inform the evaluation of the current operational

thresholds for detecting meningitis epidemics in the post-MenAfriVac® era. Weekly incidence

thresholds of 7, 5 and 3 per 100,000 were considered and compared to the current threshold of 10

per 100,000. Substantially more cases were potentially preventable when a threshold of 3 per

100,000 was used, largely because this resulted in the greatest time between the threshold being

reached and the peak of the epidemic, allowing for a more effective response. Decreasing the lag

time from 6 to 4 weeks (i.e. the time between a district reaching the current action threshold of 10

per 100,000 per week and vaccine protection) was at least as effective as decreasing the action

threshold from 10 per 100,000 per week to 3 per 100,000 per week, in terms of the number of cases

prevented per event. Although this may be challenging to achieve, the resources required are likely

to be considerably less than the costs of additional vaccines required under a lower action threshold.

The current threshold of 10 per 100,000 was effective at detecting large outbreaks. Lowering the

threshold will result in many more ‘events’ being detected and action being taken to deal with much

lower cumulative seasonal incidences.

The performance of the thresholds, in terms of sensitivity, specificity, PPV and NPV was assessed

relative to a definition of an epidemic based on seasonal cumulative incidence. Using the previous

epidemic definition of 100 per 100,000, the current action threshold of 10 per 100,000 performs

well. If a lower seasonal cumulative incidence is used, then lower weekly incidence thresholds

perform better. The definition of an epidemic is therefore a critical question and is based on a rather

subjective assessment.

There are important limitations to the analyses presented here. The data are from a variety of

sources and although a wide net was cast in search of relevant data there is no assessment of data

completeness or data quality. Under-reporting remains a substantial problem notwithstanding

ongoing initiatives to improve surveillance in the region. In particular, the paucity of laboratory

confirmed cases is problematic. A pragmatic approach was taken here, using as much of the

available data as possible. The definition of a NmW ‘outbreak’ for inclusion in the dataset used here

was therefore broad, with the inclusion criteria of at least 2 laboratory confirmed NmW and at least

50% of all Nm being NmW. Nevertheless it is likely that we have excluded some relevant district

years (e.g. from Burkina Faso in 2002). Another difficulty, again related to the lack of linked

laboratory confirmation, is that it is challenging to assess the contribution of pneumococcal

meningitis to the suspected case counts. The epidemiology of pneumococcal meningitis displays


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several of the same features as meningococcal meningitis (including seasonality) but is also changing

as more and more countries in Africa are introducing pneumococcal conjugate vaccines. Clearly,

information on the causative pathogen is crucial in determining the appropriate response.

Continuation of efforts to strengthen laboratory capacity is therefore important. A further limitation

is that there is no consideration here of populations smaller than district level, e.g. sub-district or

health centre level, although previous work has shown that this could be an effective way of

identifying localised outbreaks7. In addition, there was no data on special populations such as

displaced people living in refugee camps.

These analyses inform the discussion on the most appropriate epidemic thresholds in the post-

MenAfriVac® era. It is important to further consider the feasibility of responding to more events if a

lower threshold is adopted and the information requirements in addition to weekly suspected case

data, particularly on laboratory confirmed cases.

Conclusions

The current threshold of 10 per 100,000 per week is sensitive and specific for detecting large

NmW outbreaks (with a cumulative seasonal incidence of at least 80 per 100,000).

Lower weekly incidence thresholds perform better in detecting smaller outbreaks.

Assuming a 6 week interval between the action threshold being reached and effective

vaccination, the most cases in total and per event could be prevented using the lowest

threshold of 3 per 100,000 per week.

Adopting a lower threshold than currently used would considerably increase the number of

events requiring action and the number of vaccine doses required.

Improving the lag time between the action threshold and effective vaccination from 6 weeks

to 4 weeks is at least as effective as lowering the threshold and would not increase the

number of events requiring action.

The quality of this evidence is low.


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Evidence profile: NmW cases potentially averted by reactive vaccination

Quality assessment Summary of findings: Mean NmW cases prevented by vaccination per event (range)*

Design Limitations Inconsistency Indirectness Imprecision Publication bias

Threshold 10 (N events= 49)




Quality Importance

Modelling study

Serious limitations (low proportion of cases laboratory confirmed)

No serious inconsistency

Serious indirectness (modelling of observational data)

*Serious imprecision (wide range)

Not relevant

6 week lag 17 (0-325)

19 (0-325)

27 (0-587)

46 (0-1327)

VERY LOW

CRITICAL

As above

4 week lag 54 (0-960)

53 (0-960)

60 (0-1171)

71 (0-1512)

VERY LOW

CRITICAL

*The mean and the full range are given here. Although the wide range suggests serious uncertainty in the estimates of effect, this rather reflects the heterogeneity in the

epidemiology of epidemic meningitis in the African meningitis belt.


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References

1. WHO. Detecting meningococcal meningitis epidemics in highly endemic African countries. WHO recommendation. Wkly Epidemiol Rec 2000; 75: 306–09.

2. Kaninda AV, Belanger F, Lewis R, Batchassi E, Aplogan A, Yakoua Y, Paquet C. Effectiveness of incidence thresholds for detection and control of meningococcal meningitis epidemics in northern Togo. Int J Epidemiol. 2000;29(5):933-40.

3. Lewis R, Nathan N, Diarra L, Belanger F, Paquet C. Timely detection of meningococcal meningitis epidemics in Africa. Lancet. 2001;358(9278):287-93.

4. Leake JA, Kone ML, Yada AA, et al. Early detection and response to meningococcal disease epidemics in sub-Saharan Africa: appraisal of the WHO strategy. Bull World Health Organ. 2002;80(5):342-9.

5. Mueller JE, Gessner BD. A hypothetical explanatory model for meningococcal meningitis in the African meningitis belt. International journal of infectious diseases : Int J Infect 2010; 14(7): e553-9

6. Collard JM, Issaka B, Zaneidou M, et al. Epidemiological changes in meningococcal meningitis in

Niger from 2008 to 2011 and the impact of vaccination. BMC infectious diseases 2013; 13: 576.

7. Tall H, Hugonnet S, Donnen P, et al. Definition and characterization of localised meningitis

epidemics in Burkina Faso: a longitudinal retrospective study. BMC infectious diseases 2012; 12:

2.

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