Detection of infectious disease outbreaks in twenty-two fragile states, 2000-2010: a systematic review

Fragile states are home to a sixth of the world's population, and their populations are particularly vulnerable to infectious disease outbreaks. Timely surveillance and control are essential to minimise the impact of these outbreaks, but little evidence is published about the effectiveness of existing surveillance systems. We did a systematic review of the circumstances (mode) of detection of outbreaks occurring in 22 fragile states in the decade 2000-2010 (i.e. all states consistently meeting fragility criteria during the timeframe of the review), as well as time lags from onset to detection of these outbreaks, and from detection to further events in their timeline. The aim of this review was to enhance the evidence base for implementing infectious disease surveillance in these complex, resource-constrained settings, and to assess the relative importance of different routes whereby outbreak detection occurs. We identified 61 reports concerning 38 outbreaks. Twenty of these were detected by existing surveillance systems, but 10 detections occurred following formal notifications by participating health facilities rather than data analysis. A further 15 outbreaks were detected by informal notifications, including rumours. There were long delays from onset to detection (median 29 days) and from detection to further events (investigation, confirmation, declaration, control). Existing surveillance systems yielded the shortest detection delays when linked to reduced barriers to health care and frequent analysis and reporting of incidence data. Epidemic surveillance and control appear to be insufficiently timely in fragile states, and need to be strengthened. Greater reliance on formal and informal notifications is warranted. Outbreak reports should be more standardised and enable monitoring of surveillance systems' effectiveness.


Introduction
The World Bank describes a fragile state as a country 'facing particularly severe development challenges such as weak institutional capacity, poor governance, political instability, and frequently ongoing violence or the legacy effects of past severe conflict' [1].
In 2009, 29 countries were considered fragile, comprising a sixth of the world's population [2,3]. Fragile states generally feature poor health indicators, high malnutrition prevalence, scarcity of skilled health workers and worsening rates of extreme poverty [4][5][6]. Their populations are also highly vulnerable to infectious disease outbreaks, a reflection of inadequate government services and armed conflict-related phenomena such as forced displacement [7]. It has been suggested that most major epidemics worldwide occur in complex emergency and/or natural disaster settings [8].
Detection and early containment of outbreaks in these settings is also challenging, as highlighted by the Global Polio Eradication Initiative's recent setbacks in several fragile states, where genetic analysis has demonstrated previously undetected poliovirus transmission of one year or more duration [9]. Given the intensity of polio surveillance compared to other epidemic detection systems, it is plausible that many other disease outbreaks are detected late or not at all in these same settings.
The importance of epidemic surveillance is recognised, but there is a scarcity of evidence on optimal ways to detect outbreaks in the unique situations of fragile states, where routine health information systems are weak, diagnostic tools limited and resources for structured surveillance, such as training, sample transport and data transmission, very constrained. It has been suggested, at least for early warning systems in humanitarian emergencies, that emphasis should be placed on detecting alerts from health facilities or other informal sources (e.g. community informants and the media), rather than on analysis of weekly or other surveillance data, which often feature low completeness and timeliness, or high background noise due to non-specific case definitions [10]. So as to contribute to the evidence basis, we carried out a review of how outbreaks have been detected in 22 states that consistently met definitions of fragility over the past decade, and of the timeliness of alert and response processes.

Methods
A systematic review of the published literature was performed to identify reports describing infectious disease outbreaks which began after 31 st December 1999, within a predefined list of fragile states. The list of fragile states was created using the World Bank's quantitative definition, taking into account both the eligibility of a country to receive an interest-free International Development Association loan and a nation's Country Policy and Institutional Assessment score [11]. Countries which met this definition for at least ten out of eleven years from the year 2000 to 2010 (see Additional File 1) were included in this study [2,[12][13][14][15]. The final list of fragile states included in the review comprised Afghanistan, Angola, Burundi, the Central African Republic, Chad, Comoros, the Democratic Republic of the Congo, Guinea, Guinea-Bissau, Haiti, Liberia, Myanmar, the Republic of the Congo, Sao Tome et Principe, Sierra Leone, the Solomon Islands, Somalia, Sudan, Tajikistan, Timor-Leste, Togo and Zimbabwe.
Between 28 th July 2010 and 23 rd August 2010, a combined OVID SP search of the MEDLINE, EMBASE and Global Health databases was done. OVID SP is a search engine that taps into various literature databases relevant for global health. MEDLINE is a database of life sciences and biomedical journals. EMBASE is similar to MEDLINE but focuses on drug therapeutic studies. Global Health focuses on public health and medical science and includes conference abstracts, thesis reports, electronic information and other hard to find material. The basic search concepts were '(fragile state of interest) AND (epidemic-prone event) AND (detection)'. Each concept was expanded and variations of terms, including contemporary and historic, French and Spanish were included (see Additional File 1). Limitations applied were 'from 2000 to present' and 'humans'.
Outbreak descriptions were excluded from the search if they primarily involved foreign military forces, or if the disease of interest was HIV or poliomyelitis, due to the specific nature of surveillance for these two diseases.
Reports were included in the review if the circumstances of initial detection of the outbreak were reported; and/or if the time from onset to detection of the outbreak (determined using the definition in Table  1) could be calculated. Whenever this information was not clear based on the published report, we emailed the corresponding author once so as to solicit the missing information. We excluded the report if authors did not reply or could not provide the information requested.
For each eligible outbreak, the mode of detection was categorised into (i) data analysis if an existing surveillance system detected the outbreak by noticing a temporal increase in aggregate incidence, either above a pre-determined threshold or at levels considered unusual compared to the baseline; (ii) formal notification if the initial alert was raised by health workers as part of an ongoing surveillance system; and (iii) informal notification if the alert was raised through mechanisms other than an existing surveillance system, either by health workers or other community members. Both authors made this classification independently and came to a consensus decision on any discrepant choices.
Whenever available, we also calculated time lags from detection or onset to further events in the outbreak timeline, as per the definitions in Table 1.

Search strategy results
Of the 2634 abstracts produced by the search strategy, 58 reports describing 38 separate outbreaks were found eligible (Figure 1 Of the 58 reports included in the review, 20 were primarily authored by the World Health Organization; 14 Table 1 Definitions used for dates of interest in the outbreak timeline

Event Definition
Onset For diseases of which one case constitutes a potential outbreak, the date of onset of symptoms of the primary case. For diseases that are normally endemic but are considered epidemic when an unusual increase in burden is observed, the date on which the outbreak threshold was crossed, according to the authors. If investigation revealed previous undetected outbreaks of the same health event, this was also noted.

Detection
The date a report of a possible outbreak was sent to the highest appropriate level of authority. This could be the date of initial detection, if no authorities were required to be notified.

Confirmation
The date on which the aetiologic agent of the outbreak was confirmed.

Investigation
The date an investigation team arrived to the outbreak-affected community.

Declaration
The date the outbreak was officially declared as such by health authorities of the country concerned.

Control
The first day of a reactive vaccination campaign (we only computed the date of this event for diseases for which vaccination was the main control intervention available, since the date of implementation of other control interventions, such as water and sanitation, is difficult to define).

Mode of detection of outbreaks
Among the 35 outbreaks for which mode of detection information was available, 20 (57.1%) were detected through existing surveillance systems, with 10 detected by data analysis (Table 2) and 10 by formal notification (Table 3). Fifteen outbreaks (42.9%) were initially detected through informal notifications (Table 4). For three further outbreaks (yellow fever virus in Guinea, Bounouma subprefecture, August 2008 [16]; Salmonella typhi in central Myanmar, September 2000 [17]; Borrelia spp. relapsing fever in Southern Sudan, 2000 [18]), the mode of detection was unclear, but time to detection was available: these are included in the timeliness findings (see below). Reports suggested that data analysis proved successful when there was frequent reporting and analysis of data, and with the provision of a free and dependable supply of medication (outbreaks 2, 8). Poor reporting practices delayed detection (outbreak 9). In two instances failures were compensated for by informal notifications after substantial delays (outbreaks 25,35).
For both data analysis and formal notifications, limited access to and distrust of health services delayed detection (outbreaks 6,7,8,19). In two instances, warnings provided by a geographic information system and detection of an outbreak amongst local wildlife led to enhanced surveillance and eventually detection (outbreaks 16,33).
Informal notifications originated from a local non-governmental organisation (NGO) (outbreak 27), a research Plasmodium falciparum (7) MSF reported an alert after quadrupling of cases. Historical comparisons were hampered by changes in diagnostic strategies and reduced health care utilisation rates due to flooding. Weekly reporting and analysis, and a free and steady supply of anti-malarials may have favoured early detection.

Timeliness of detection and other events
Overall, the median lag time from onset to detection was 29 days (range 7-80) in 16 outbreaks for which this information was available. In two cases, investigation also unveiled previously undetected and undiagnosed outbreaks due to the same agent. Outbreaks detected through informal notifications appeared to feature the longest detection delays (Figure 2).
From the date of detection, further median (range) delays were 7 days (0-30) to investigation, 23 (5-42) to confirmation, 30  to declaration and 55  to start of control (reactive vaccination). Numbers were small and no obvious pattern emerged according to the aetiologic agent's route of transmission (Figure 3), but long delays were obvious for some vector-borne disease outbreaks.
Considering the time since reported onset of the outbreak, delays were longer: 42 days (8-87) to investigation in nine outbreaks for which both time to detection and time from detection to investigation were available; 53 (14-71, five outbreaks) to confirmation; 56 (36-61, three outbreaks) to declaration; and 80 (78-86, three outbreaks) to control.
Early warning alert and response network (EWARN) systems set up in southern Sudan and Darfur in 2000 Yellow fever virus (30) A sentinel surveillance system of hospitals and clinics was in place. Jaundice cases were reported promptly by state health officers through the central surveillance system, but yellow fever was not initially considered and the outbreak was initially attributed to dengue. Laboratory investigation was not initially pursued. Confirmation and the start of control occurred more than a month after notification.  Yersinia pestis (28) An informal alert of an epidemic, initially thought to be of haemorrhagic fever, was notified by local health providers in a camp for diamond miners. Red Cross volunteers informed local health authorities of a rumour of four suspicious deaths. A week later, a regional investigation team notified an alert of viral haemorrhagic fever to the central level. Impassable roads delayed the response team's arrival by 4 days. The response team was blamed for people dying and for bringing the disease. There was fear of isolation centres and athome isolation kits were experimented with. and 2004 respectively, were involved in six Sudanese outbreaks. These outbreaks generally featured the shortest times from onset through to confirmation (outbreaks 6,9,17,18,20,38). Cooperation by communities was greatly hampered by fear and distrust of control teams and biomedical interventions during investigations of Ebola virus and Marburg virus outbreaks (outbreaks 16,23,31). Other obstacles to investigation included poor road conditions and insecurity (outbreaks 31, 26, 33). On two occasions, misconceptions by authorities and subsequently late investigations significantly delayed confirmation of causative agents (outbreaks 2, 19).

Discussion and conclusions
This review suggests that over the last decade surveillance systems have played a considerable role in early outbreak detection in the 22 fragile states included in the review. However, on the whole data analysis seemed to lead to a minority of outbreak detections, with both formal and informal notifications of alerts playing a more prominent, though less timely role. Certain elements of the system played an important role in sensitivity and timeliness, including reduced barriers to health facility utilisation and frequent data analysis. Combining knowledge of the seasonal outbreak risks particular to each area with predictive tools such as geographic information systems could be used to improve the effectiveness of such systems. More importantly, surveillance systems in fragile states should enhance the detection of alerts outside routine data analysis, by focussing more efforts on building both formal and informal networks of informants, particularly where acute emergency conditions or remoteness prevent sophisticated data collection and analysis.
Our review suggested that timeliness of detection, investigation and response is poor for most outbreaks occurring in fragile states, with up to five months elapsing until the start of meaningful control. These delays negate most of the advantages of surveillance and make containment extremely difficult.
Our review is limited by our search strategy, which did not capture outbreaks described in the grey literature. Furthermore, findings may not apply to other states that met fragility criteria for only some of the years within the review's timeframe. Publication bias is likely to influence our findings, but its direction is Table 4 Details on outbreaks detected through informal notifications (n = 15) (Continued) 35 Zimbabwe, Aug 2008 [71][72][73] Vibrio cholerae Due to collapsing health services, surveillance system completeness was estimated at 30%. The initial recognition of the epidemic was an increased number of cases of 'watery diarrhoea' being noted by Municipal Health Clinics. The ability of the Public Health Laboratory to confirm cholera was greatly limited by shortages of manpower and resources resulting from economic crisis. A second wave of the epidemic from Oct 2008 spread to all provinces and neighbouring countries. The Zimbabwean government declared an epidemic in Dec 2008.
* Investigation revealed previously undetected or undiagnosed outbreaks; () indicates that dates were estimated.

Figure 2
Delay in days from onset to detection in 15 outbreaks, by mode of detection.
Bruckner and Checchi Conflict and Health 2011, 5:13 http://www.conflictandhealth.com/content/5/1/13 difficult to gauge: while large outbreaks that were intensively investigated and controlled are more likely to be the subject of publications, small outbreaks that were detected early and contained are probably underreported. We noted that the vast majority of reports included were authored by institutions based outside the affected countries, with only one report coming from the national ministry of health. This suggests a need to strengthen capacity by fragile states to communicate outbreak surveillance findings, so as to promote ownership of surveillance and outbreak control, and raise the profile of outbreaks and epidemic-prone diseases that international counterparts would not otherwise respond to. During data abstraction, the considerable heterogeneity of formats and variables included in outbreak reports was apparent. We recommend that a more standardised format be introduced for papers reporting outbreaks, particularly affecting vulnerable populations; and that key meta-data such as the dates of salient events in the outbreak timeline and the circumstances of detection always be reported, so as to enable ongoing global monitoring of the effectiveness of surveillance systems and outbreak control interventions.