Klaudia Porten

SurvNet Electronic Surveillance System for Infectious Disease Outbreaks, Germany

Electronic Surveillance System for Infectious Disease Outbreaks, Germany This system has managed detailed information on 30,578 disease outbreaks.

Poliomyelitis outbreak, Pointe-Noire, Republic of the Congo, September 2010-February 2011.

On November 4, 2010, the Republic of the Congo declared a poliomyelitis outbreak. A cross-sectional survey in Pointe-Noire showed poor sanitary conditions and low vaccination coverage (55.5%), particularly among young adults. Supplementary vaccination should focus on older age groups in countries with evidence of immunity gaps.

Mortality Rates during Cholera Epidemic, Haiti, 2010–2011

Actual rates were higher than rates calculated from healthcare facility reports.

Use of Capture-Recapture to Estimate Underreporting of Ebola Virus Disease, Montserrado County, Liberia.

To the Editor: Underreporting of cases during a large outbreak of disease is not without precedent (1–5). Health systems in West Africa were ill-prepared for the arrival of Ebola virus disease (Ebola) (6). The Ebola outbreak in Liberia was declared on March 31, 2014, and peaked in September 2014. However, by mid-June, the outbreak had reached Montserrado County, where the capital, Monrovia, is located. In response, the Liberia Ministry of Health and Social Welfare (MOHSW) created a National Ebola Hotline: upon receipt of a call, a MOHSW case investigation team was dispatched to the site of the possible case. Additionally, persons could seek care at an Ebola Treatment Unit (ETU) or be referred to an ETU by another health care facility. During June 1–August 14, 2014, MOHSW, Medecins Sans Frontieres, and the US nongovernment organization Samaritan’s Purse managed 3 ETUs in Montserrado County, including 2 in Monrovia operated by Eternal Love Winning Africa (ELWA). In August 2014, to assess the extent of underreporting in the midst of the Ebola outbreak, we analyzed 2 sources of data collected during June 1–August 14. The first comprised data collected by MOHSW case investigation teams. These data were collected on MOHSW case forms and entered into a database emulating these forms using Epi Info version 7 software (Centers for Disease Control and Prevention, Atlanta, GA, USA). The second data source (designed on Excel 2003; Microsoft, Redmond, WA, USA) comprised data on all patients admitted to the 2 ELWA ETUs (ELWA1 and ELWA2). We used a capture–recapture (CRC) approach. CRC can evaluate the completeness of reporting and thereby be used to correct for underreporting (7). CRC methods use data from overlapping databases to estimate the number of unreported cases and thus more closely derive the true number of Ebola cases. Both databases were populated and managed separately, although the included Ebola cases are assumed to reflect the same patient population in Montserrado County. These 2 databases enabled us to use CRC to estimate the true number of Ebola cases in Montserrado County. To be included in either database, a case must have been classified as suspected, probable, or confirmed Ebola. The case definitions, following the official MOHSW definition for Ebola, were identical in both databases. Eventually, after laboratory confirmation, cases could be reclassified as “not a case” and thus be excluded from the analysis. To estimate the total number of Ebola cases during the study period, we used Chapman’s 2-sample CRC population estimate (7); we calculated the 95% CI as proposed by Wittes et al. (8). We performed a sensitivity analysis measuring impact of error in matching cases during record linkage. A total of 227 Ebola cases were recorded in the MOHSW database and 99 Ebola cases in the Montserrado County ETUs database (Table). Of these, 25 were found in both databases, 202 in the MOHSW database only, and 74 in the Montserrado County ETU database only. We estimated that the cumulative number of Ebola cases for Montserrado County during the study period was 876 (95% CI 608–1,143). A sensitivity analysis performed with ±5 cases showed that, with 5 additional cases in common between databases, the cumulative number of cases would decrease to 734 (95% CI 537–931); with 5 additional discordant cases, the estimate would increase to 1,085 (95% CI 700–1,469). Our analysis shows that the number of cases in Montserrado Country was at least 3-fold higher than that reported during the study period. Our study had several limitations. According to the doctor in charge of data collection up to August 4, some forms (<10) completed at the beginning of June 2014 might have been misplaced. Additionally, some patients who entered the ETU were not recorded in the registry book (˂5). CRC assumes a closed population. In Montserrado County, persons can move freely. In both databases, we included only cases that occurred in or were reported in Montserrado County. CRC assumes that links between the 2 sources based on identifying case information are error free. The sensitivity analysis suggested that even if up to 5 case matches were not detected, our conclusion was relatively robust. CRC assumes homogeneity in the likelihood of being captured and recaptured and that data sources are independent. In our analysis, homogeneity is unlikely. For example, the MOHSW database was more likely to capture cases in persons more likely to seek care; the ETU database was more likely to detect cases in persons referred by health workers. Similar behaviors might have resulted in positive dependency in each data source. Both heterogeneity and positive dependency with data sources leads to underestimation. Despite these limitations, we estimated more Ebola cases than were reported through official channels during the beginning of the outbreak in Montserrado County. Routine studies similar to ours can rapidly provide public health officials managing the outbreak response with estimates of underreporting and enable timely mobilization of appropriate resources. However, we believe that further exploration of this technique to better understand the possible difference of capture preference of each source may help improve the technique and benefit future outbreaks.

Outcomes For Street Children and Youth Under Multidisciplinary Care in a Drop-In Centre in Tegucigalpa, Honduras

Background: There is little evidence to describe the feasibility and outcomes of services for the care of street children and youth in low-income countries. Aims: To describe the outcomes of a multidisciplinary case management approach delivered in a drop-in centre for street children and youth. Methods: A longitudinal study of street children and youth followed in an urban drop-in centre. Four hundred (400) street children and youth received a multidisciplinary case management therapeutic package based on the community reinforcement approach. The main outcomes were changes in psychological distress, substance abuse and social situation scores. Results: The median follow-up time for the cohort was 18 months. There were reductions in the levels of psychological distress (p = 0.0001) and substance abuse (p ≤ 0.0001) in the cohort as well as an improvement in the social situation of street children and youth (p = 0.0001). There was a main effect of gender (p < 0.001) and a significant interaction of gender over time (p < 0.001) on improvements in levels of psychological distress. Survival analysis showed that the probability of remaining on substances at 12 months was 0.76 (95% CI: 0.69–0.81) and 0.51 (95% CI: 0.42–0.59) at 24 months. At 12 months, fewer female patients remained using substances compared to male (p < 0.01). Conclusion: To be most effective, programmes and strategies for children and youth in street situations in developing countries should target both their health and social needs.

Geographic Distribution and Mortality Risk Factors during the Cholera Outbreak in a Rural Region of Haiti, 2010-2011

Background In 2010 and 2011, Haiti was heavily affected by a large cholera outbreak that spread throughout the country. Although national health structure-based cholera surveillance was rapidly initiated, a substantial number of community cases might have been missed, particularly in remote areas. We conducted a community-based survey in a large rural, mountainous area across four districts of the Nord department including areas with good versus poor accessibility by road, and rapid versus delayed response to the outbreak to document the true cholera burden and assess geographic distribution and risk factors for cholera mortality. Methodology/Principal Findings A two-stage, household-based cluster survey was conducted in 138 clusters of 23 households in four districts of the Nord Department from April 22nd to May 13th 2011. A total of 3,187 households and 16,900 individuals were included in the survey, of whom 2,034 (12.0%) reported at least one episode of watery diarrhea since the beginning of the outbreak. The two more remote districts, Borgne and Pilate were most affected with attack rates up to 16.2%, and case fatality rates up to 15.2% as compared to the two more accessible districts. Care seeking was also less frequent in the more remote areas with as low as 61.6% of reported patients seeking care. Living in remote areas was found as a risk factor for mortality together with older age, greater severity of illness and not seeking care. Conclusions/Significance These results highlight important geographical disparities and demonstrate that the epidemic caused the highest burden both in terms of cases and deaths in the most remote areas, where up to 5% of the population may have died during the first months of the epidemic. Adapted strategies are needed to rapidly provide treatment as well as prevention measures in remote communities.

Emerging Filoviral Disease in Uganda: Proposed Explanations and Research Directions

Outbreaks of Ebola and Marburg virus diseases have recently increased in frequency in Uganda. This increase is probably caused by a combination of improved surveillance and laboratory capacity, increased contact between humans and the natural reservoir of the viruses, and fluctuations in viral load and prevalence within this reservoir. The roles of these proposed explanations must be investigated in order to guide appropriate responses to the changing epidemiological profile. Other African settings in which multiple filoviral outbreaks have occurred could also benefit from such information.

Cholera epidemic in Yemen, 2016–18: an analysis of surveillance data

Summary Background In war-torn Yemen, reports of confirmed cholera started in late September, 2016. The disease continues to plague Yemen today in what has become the largest documented cholera epidemic of modern times. We aimed to describe the key epidemiological features of this epidemic, including the drivers of cholera transmission during the outbreak. Methods The Yemen Health Authorities set up a national cholera surveillance system to collect information on suspected cholera cases presenting at health facilities. Individual variables included symptom onset date, age, severity of dehydration, and rapid diagnostic test result. Suspected cholera cases were confirmed by culture, and a subset of samples had additional phenotypic and genotypic analysis. We first conducted descriptive analyses at national and governorate levels. We divided the epidemic into three time periods: the first wave (Sept 28, 2016, to April 23, 2017), the increasing phase of the second wave (April 24, 2017, to July 2, 2017), and the decreasing phase of the second wave (July 3, 2017, to March 12, 2018). We reconstructed the changes in cholera transmission over time by estimating the instantaneous reproduction number, Rt. Finally, we estimated the association between rainfall and the daily cholera incidence during the increasing phase of the second epidemic wave by fitting a spatiotemporal regression model. Findings From Sept 28, 2016, to March 12, 2018, 1 103 683 suspected cholera cases (attack rate 3·69%) and 2385 deaths (case fatality risk 0·22%) were reported countrywide. The epidemic consisted of two distinct waves with a surge in transmission in May, 2017, corresponding to a median Rt of more than 2 in 13 of 23 governorates. Microbiological analyses suggested that the same Vibrio cholerae O1 Ogawa strain circulated in both waves. We found a positive, non-linear, association between weekly rainfall and suspected cholera incidence in the following 10 days; the relative risk of cholera after a weekly rainfall of 25 mm was 1·42 (95% CI 1·31–1·55) compared with a week without rain. Interpretation Our analysis suggests that the small first cholera epidemic wave seeded cholera across Yemen during the dry season. When the rains returned in April, 2017, they triggered widespread cholera transmission that led to the large second wave. These results suggest that cholera could resurge during the ongoing 2018 rainy season if transmission remains active. Therefore, health authorities and partners should immediately enhance current control efforts to mitigate the risk of a new cholera epidemic wave in Yemen. Funding Health Authorities of Yemen, WHO, and Médecins Sans Frontières.

Impact and Lessons Learned from Mass Drug Administrations of Malaria Chemoprevention during the Ebola Outbreak in Monrovia, Liberia, 2014

Background In October 2014, during the Ebola outbreak in Liberia healthcare services were limited while malaria transmission continued. Médecins Sans Frontières (MSF) implemented a mass drug administration (MDA) of malaria chemoprevention (CP) in Monrovia to reduce malaria-associated morbidity. In order to inform future interventions, we described the scale of the MDA, evaluated its acceptance and estimated the effectiveness. Methods MSF carried out two rounds of MDA with artesunate/amodiaquine (ASAQ) targeting four neighbourhoods of Monrovia (October to December 2014). We systematically selected households in the distribution area and administered standardized questionnaires. We calculated incidence ratios (IR) of side effects using poisson regression and compared self-reported fever risk differences (RD) pre- and post-MDA using a z-test. Findings In total, 1,259,699 courses of ASAQ-CP were distributed. All households surveyed (n = 222; 1233 household members) attended the MDA in round 1 (r1) and 96% in round 2 (r2) (212/222 households; 1,154 household members). 52% (643/1233) initiated ASAQ-CP in r1 and 22% (256/1154) in r2. Of those not initiating ASAQ-CP, 29% (172/590) saved it for later in r1, 47% (423/898) in r2. Experiencing side effects in r1 was not associated with ASAQ-CP initiation in r2 (IR 1.0, 95%CI 0.49–2.1). The incidence of self-reported fever decreased from 4.2% (52/1229) in the month prior to r1 to 1.5% (18/1229) after r1 (p<0.001) and decrease was larger among household members completing ASAQ-CP (RD = 4.9%) compared to those not initiating ASAQ-CP (RD = 0.6%) in r1 (p<0.001). Conclusions The reduction in self-reported fever cases following the intervention suggests that MDAs may be effective in reducing cases of fever during Ebola outbreaks. Despite high coverage, initiation of ASAQ-CP was low. Combining MDAs with longer term interventions to prevent malaria and to improve access to healthcare may reduce both the incidence of malaria and the proportion of respondents saving their treatment for future malaria episodes.

Descriptive epidemiology of typhoid fever during an epidemic in Harare, Zimbabwe, 2012

Background Typhoid fever remains a significant public health problem in developing countries. In October 2011, a typhoid fever epidemic was declared in Harare, Zimbabwe - the fourth enteric infection epidemic since 2008. To orient control activities, we described the epidemiology and spatiotemporal clustering of the epidemic in Dzivaresekwa and Kuwadzana, the two most affected suburbs of Harare. Methods A typhoid fever case-patient register was analysed to describe the epidemic. To explore clustering, we constructed a dataset comprising GPS coordinates of case-patient residences and randomly sampled residential locations (spatial controls). The scale and significance of clustering was explored with Ripley K functions. Cluster locations were determined by a random labelling technique and confirmed using Kulldorffs spatial scan statistic. Principal Findings We analysed data from 2570 confirmed and suspected case-patients, and found significant spatiotemporal clustering of typhoid fever in two non-overlapping areas, which appeared to be linked to environmental sources. Peak relative risk was more than six times greater than in areas lying outside the cluster ranges. Clusters were identified in similar geographical ranges by both random labelling and Kulldorffs spatial scan statistic. The spatial scale at which typhoid fever clustered was highly localised, with significant clustering at distances up to 4.5 km and peak levels at approximately 3.5 km. The epicentre of infection transmission shifted from one cluster to the other during the course of the epidemic. Conclusions This study demonstrated highly localised clustering of typhoid fever during an epidemic in an urban African setting, and highlights the importance of spatiotemporal analysis for making timely decisions about targetting prevention and control activities and reinforcing treatment during epidemics. This approach should be integrated into existing surveillance systems to facilitate early detection of epidemics and identify their spatial range.