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Understanding the Cholera Epidemic, Haiti

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Understanding the Cholera Epidemic, Haiti Understanding the Cholera Epidemic, Haiti Renaud Piarroux, Robert Barrais, Benoît Faucher, Rachel Haus, Martine Piarroux, Jean Gaudart, Roc Magloire, and Didier Raoult After onset of a cholera epidemic in Haiti in mid- for importing cholera, along with accusations of illegal October 2010, a team of researchers from France and dumping of waste tank contents (3). A cholera outbreak Haiti implemented fi eld investigations and built a database was indeed reported in Nepal’s capital city of Kathmandu of daily cases to facilitate identifi cation of communes on September 23, 2010, shortly before troops left for Haiti most affected. Several models were used to identify (4,5). Two hypotheses then emerged to explain cholera in spatiotemporal clusters, assess relative risk associated Haiti. with the epidemic’s spread, and investigate causes of its rapid expansion in Artibonite Department. Spatiotemporal Some researchers posited the transmission of an analyses highlighted 5 signifi cant clusters (p<0.001): 1 environmental strain to humans (6). Reasoning by near Mirebalais (October 16–19) next to a United Nations analogy with cholera epidemiology in South Asia, they camp with defi cient sanitation, 1 along the Artibonite River hypothesized that weather conditions, i.e., the La Niña (October 20–28), and 3 caused by the centrifugal epidemic phenomenon, might have promoted the growth of V. spread during November. The regression model indicated cholerae in its environmental reservoir (6). The second that cholera more severely affected communes in the coastal hypothesis suggested importation of the disease from a plain (risk ratio 4.91) along the Artibonite River downstream cholera-endemic country. The sequencing of 2 isolates of Mirebalais (risk ratio 4.60). Our fi ndings strongly suggest of V. cholerae supported this second hypothesis by that contamination of the Artibonite and 1 of its tributaries establishing an exogenous origin, probably from southern downstream from a military camp triggered the epidemic. Asia or eastern Africa (7). Responding to a request from Haitian authorities to the French Embassy for the support n October 21, 2010, the Haitian Ministry of Public of epidemiologists, we conducted a joint French–Haitian OHealth and Population (MSPP) reported a cholera investigation during November 7–November 27, 2010, to epidemic caused by Vibrio cholerae O1, serotype Ogawa, clarify the source of the epidemic and its unusual dynamic. biotype El Tor (1). This epidemic was surprising as no cholera outbreak had been reported in Haiti for more Morbidity and Mortality Survey than a century (1,2). Numerous media rapidly related As soon as the epidemic was recognized, a nationwide the epidemic to the deadly earthquake that Haiti had monitoring program was implemented to register all experienced 9 months earlier. However, simultaneously, a ambulatory patients, hospital admissions, and deaths (1). rumor held recently incoming Nepalese soldiers responsible Each day, all government and nongovernmental health facilities in Haiti reported cases to the Direction of Health Author affi liations: Université de la Méditerranée, Marseilles, in each department, which colligated data before sending France (R. Piarroux, B. Faucher, J. Gaudart, D. Raoult); Ministère them to MSPP. For this study, the departments were asked de la Santé Publique et de la Population, Port-au-Prince, Haiti (R. to provide more precise data corresponding to the 140 Barrais, R. Magloire); Service de Santé des Armées, Paris, France Haitian communes from October 16 through November (R. Haus); and Martine Piarroux Université de Franche-Comté, 30. Probable cholera cases were defi ned as profuse, Besançon, France (M. Piarroux) acute watery diarrhea in persons. In each department, DOI: 10.3201/eid1707.110059 bacteriologic confi rmation was obtained only for the fi rst Emerging Infectious Diseases • www.cdc.gov/eid • Vol. 17, No. 7, July 2011 1161 SYNOPSIS cases. Children <5 years of age were included because the over-dispersion of the data (variance was greater than age was not always reported. Community deaths were mean), we used a quasi-Poisson model (variance = c × additionally reported by local authorities. Comparison with mean, where c is an estimated constant) (14). The use of epidemiologic surveys performed by other actors (Doctors a Poisson model would not have been relevant because without Borders, medical brigades from Cuba) enabled the main assumption for Poisson models is that variance confi rmation of the consistency of the database. Cholera equals mean. The GAM was allowed to model the count of incidence was calculated by using population numbers from cases in each commune, analyzing 1 continuous variable Haitian authorities and mapped together with environmental (distance to Mirebalais) and 3 binary variables (location settings by using ArcGIS (ESRI, Redlands, CA, USA). downstream of Meille River, presence of camps of IDP, Maps of gridded population density (8), communes, rivers, and commune partially or totally located in coastal plain). roads, altitude, internally displaced persons (IDP) camps, The models were adjusted on the population and the and health facilities were obtained from Haitian authorities spatial distribution of communes. Conditions of use were and the United Nations Stabilization Mission in Haiti checked by using classical graphic means. The goodness- (MINUSTAH) website (http://minustah.org). of-fi t was also assessed by the percentage of explained deviance. Field Surveys In the communes bordering the Artibonite River, The fi rst team of epidemiologists from Haiti went to namely Mirebalais, St-Marc, Dessalines, Petite-Rivière- Mirebalais during October 19–24. Then, from November de-l’Artibonite, Grande Saline, Verrettes, Desdunes, 7–27, epidemiology teams from France and Haiti visited and L’Estère, during October 16–31, we searched for the most affected areas, namely Mirebalais, St-Marc, synchronizations between communal epidemiologic Gonaïves, Cap Haïtien, St-Michel-de-l’Attalaye, Petite- curves by calculating and testing Spearman correlation Rivière-de-l’Artibonite, Ennery, Plaisance, and Port-au- coeffi cients. Statistical analyses were performed by using Prince. These visits included interviews with health actors R version 2.10.1 software (www.r-project.org/foundation), and civilian authorities and investigation of environmental particularly with the mgcv package (GAM modeling) (11). risks among inhabitants and patients from cholera treatment We compared p values to the probability threshold α = 0.05. centers. Initiation Statistics On October 18, the Cuban medical brigades reported To investigate for space–time case clustering, we an increase of acute watery diarrhea (61 cases treated in analyzed the daily case numbers in each Haitian commune Mirebalais during the preceding week) to MSPP. On October from October 16 through November 30 using SaTScan 18, the situation worsened, with 28 new admissions and software (Kulldorf, Cambridge, UK). To detect clusters, 2 deaths. MSPP immediately sent a Haitian investigation this software systematically moves a circular scanning team, which found that the epidemic began October 14. The window of increasing diameter over the studied region fi rst hospitalized patients were members of a family living and compares observed case numbers inside the window in Meille (also spelled Méyè), a small village 2 km south to the numbers that would be expected under the null of Mirebalais (Figure 1). On October 19, the investigators hypothesis (random distribution of cases). The maximum identifi ed 10 other cases in the 16 houses near the index allowed cluster size corresponded to 50% of the Haitian family’s house. Five of the 6 samples collected in Meille population. The statistical signifi cance for each cluster from these outpatients, who became sick during October was obtained through Monte Carlo hypothesis testing, i.e., 14–19, yielded V. cholerae O1, serotype Ogawa, biotype results of the likelihood function were compared with 999 El Tor. Environmental and water source samples proved random replications of the dataset generated under the null negative. hypothesis (9,10). Meille village hosted a MINUSTAH camp, which On the basis of these results, we further analyzed was set up just above a stream fl owing into the Artibonite risk factors for spread in Ouest, Centre, and Artibonite River. Newly incoming Nepalese soldiers arrived there Departments during October 20–28 using a regression on October 9, 12, and 16. The Haitian epidemiologists model with adjustment on spatial variability. The initial observed sanitary defi ciencies, including a pipe discharging focus, Mirebalais, was precluded to better estimate the sewage from the camp into the river. Villagers used water relationship between the epidemic spread and the distance from this stream for cooking and drinking. to the epidemic source. Because data on cholera cases On October 21, the epidemic was also investigated in were non-normally distributed and thus violated basic several wards of Mirebalais. Inhabitants of Mirebalais drew assumptions for linear regression, we used a generalized water from the rivers because the water supply network was additive model (GAM) (11–13). Furthermore, because of being repaired. Notably, prisoners drank water from the 1162 Emerging Infectious Diseases • www.cdc.gov/eid • Vol. 17, No. 7, July 2011 Cholera Epidemic, Haiti Figure 1. Location of health centers reporting cholera cases in communes along the Artibonite River on October 20, 2010, Haiti.
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