Meningitis Outbreak Response intervention thresholds in sub-saharan Africa

Meningitis Outbreak Response intervention thresholds in sub-saharan Africa Report for the WHO Meningitis Guideline Revision May 2014 Prepared by Dr Caroline Trotter (clt56@cam.ac.uk) 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 otherwise 1. 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 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 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) epidemics 2-4. As the largescale 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. 1

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 requests a WHO Geneva (Katya Fernandez) Documented requests for vaccine to implement reactive immunisation campaigns 2006-2013 Laboratory line lists WHO Geneva Line lists of laboratory reports collated from various Additional data from Burkina Faso 2002, 2003 Additional data from Burkina Faso 2010, 2012, 2013 Additional data from Gambia 2012 Imperial database (Laurence Cibrelus) WHO Geneva (Katya Fernandez) CDC (Ryan Novak) MRC Gambia (Jahangir Hossain) WHO Geneva (Katya Fernandez) countries and sources Weekly case counts by district and laboratory data Laboratory confirmed meningitis cases (line listing) Weekly case counts (suspected and confirmed) from epidemic regions with associated laboratory data 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. 2

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 weeks 5. 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 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 (V EC ). 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 V EC 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 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). 3

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. 4

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 incountry) 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. 5

Table 3: Suspected cases occurring after weekly incidence threshold reached Threshold (weekly incidence per 100,000) week wt* to w26 week wt to wn** week wt+2 to w26 week wt+4 to w26 week wt+6 to w26 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 week wt+6 to wn 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. Threshold (weekly incidence per 100,000) Number of district years reaching threshold 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 are 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 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. 6

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. Threshold (weekly incidence per 100,000) Number of district years reaching threshold 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) 7

Vaccine-preventable cases at different thresholds and vaccine assumptions The estimated number of cases that could be 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. In all of the scenarios considered, the average number of cases per event is fewer than 60, with 11 out of 16 scenarios preventing fewer than 30 cases per event. Table 6: by reactive vaccination with different thresholds under different assumptions of effective vaccine coverage (V EC ), assuming a 6 week lag Threshold (weekly incidence per 100,000) occurring in weeks wt+6 to wn (number of events) V EC =75% (per V EC =90% (per V EC =75%, <2y/o excluded (per V EC =90%, <2y/o excluded (per 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 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 with a shorter lag. The number of cases 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: by reactive vaccination with different thresholds under different assumptions of effective vaccine coverage (V EC ), assuming a 4 week lag Threshold (weekly incidence per 100,000) occurring in weeks wt+4 to wn (number of events) V EC =75% (per V EC =90% (per V EC =75%, <2y/o excluded (per V EC =90%, <2y/o excluded (per 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 8

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. 9

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 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 10

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 outbreaks 7. 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 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. 11

Evidence profile: NmW cases potentially averted by reactive vaccination Quality assessment Design Limitations Inconsistency Indirectness Imprecision Publication bias Modelling study Serious limitations (low proportion of cases laboratory confirmed) No serious inconsistency Serious indirectness (modelling of observational data) As above *Serious imprecision (wide range) Not relevant PICO 1 Summary Report Summary of findings: Mean NmW cases by vaccination per event (range)* Threshold Threshold Threshold Threshold Quality Importance 10 7 5 3 (N events= (N events= (N events= (N events= 49) 66) 77) 98) 6 week lag 17 (0-325) 4 week lag 54 (0-960) 19 (0-325) 53 (0-960) 27 (0-587) 60 (0-1171) 46 (0-1327) 71 (0-1512) VERY LOW VERY LOW CRITICAL 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. 12

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