Dataset Information

Increase in Reported Cholera Cases in Haiti Following Hurricane Matthew: An Interrupted Time Series Model.

ABSTRACT: Matthew, a category 4 hurricane, struck Haiti on October 4, 2016, causing widespread flooding and damage to buildings and crops, and resulted in many deaths. The damage caused by Matthew raised concerns of increased cholera transmission particularly in Sud and Grand'Anse departments, regions which were hit most heavily by the storm. To evaluate the change in reported cholera cases following Hurricane Matthew on reported cholera cases, we used interrupted time series regression models of daily reported cholera cases, controlling for the impact of both rainfall, following a 4-week lag, and seasonality, from 2013 through 2016. Our results indicate a significant increase in reported cholera cases after Matthew, suggesting that the storm resulted in an immediate surge in suspect cases, and a decline in reported cholera cases in the 46-day post-storm period, after controlling for rainfall and seasonality. Regression models stratified by the department indicate that the impact of the hurricane was regional, with larger surges in the two most highly storm-affected departments: Sud and Grand'Anse. These models were able to provide input to the Ministry of Health in Haiti on the national and regional impact of Hurricane Matthew and, with further development, could provide the flexibility of use in other emergency situations. This article highlights the need for continued cholera prevention and control efforts, particularly in the wake of natural disasters such as hurricanes, and the continued need for intensive cholera surveillance nationally.

SUBMITTER: Hulland E

PROVIDER: S-EPMC6367609 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Increase in Reported Cholera Cases in Haiti Following Hurricane Matthew: An Interrupted Time Series Model.

Hulland Erin E Subaiya Saleena S Pierre Katilla K Barthelemy Nickolson N Pierre Jean Samuel JS Dismer Amber A Juin Stanley S Fitter David D Brunkard Joan J

The American journal of tropical medicine and hygiene 20190201 2

Matthew, a category 4 hurricane, struck Haiti on October 4, 2016, causing widespread flooding and damage to buildings and crops, and resulted in many deaths. The damage caused by Matthew raised concerns of increased cholera transmission particularly in Sud and Grand'Anse departments, regions which were hit most heavily by the storm. To evaluate the change in reported cholera cases following Hurricane Matthew on reported cholera cases, we used interrupted time series regression models of daily re ...[more]

PMID: 30594260

Similar Datasets

Project description:Computational models of cholera transmission can provide objective insights into the course of an ongoing epidemic and aid decision making on allocation of health care resources. However, models are typically designed, calibrated and interpreted post-hoc. Here, we report the efforts of a team from academia, field research and humanitarian organizations to model in near real-time the Haitian cholera outbreak after Hurricane Matthew in October 2016, to assess risk and to quantitatively estimate the efficacy of a then ongoing vaccination campaign. A rainfall-driven, spatially-explicit meta-community model of cholera transmission was coupled to a data assimilation scheme for computing short-term projections of the epidemic in near real-time. The model was used to forecast cholera incidence for the months after the passage of the hurricane (October-December 2016) and to predict the impact of a planned oral cholera vaccination campaign. Our first projection, from October 29 to December 31, predicted the highest incidence in the departments of Grande Anse and Sud, accounting for about 45% of the total cases in Haiti. The projection included a second peak in cholera incidence in early December largely driven by heavy rainfall forecasts, confirming the urgency for rapid intervention. A second projection (from November 12 to December 31) used updated rainfall forecasts to estimate that 835 cases would be averted by vaccinations in Grande Anse (90% Prediction Interval [PI] 476-1284) and 995 in Sud (90% PI 508-2043). The experience gained by this modeling effort shows that state-of-the-art computational modeling and data-assimilation methods can produce informative near real-time projections of cholera incidence. Collaboration among modelers and field epidemiologists is indispensable to gain fast access to field data and to translate model results into operational recommendations for emergency management during an outbreak. Future efforts should thus draw together multi-disciplinary teams to ensure model outputs are appropriately based, interpreted and communicated.

Project description:BackgroundOn October 4, 2016, Hurricane Matthew struck southwest Haiti as a category 4 storm. The goal of this study was to evaluate the impact of the hurricane on tuberculosis (TB) services and patient outcomes in the three severely affected departments-Sud, Grand'Anse, and Nippes-of southwest Haiti.MethodsWe developed a standard questionnaire to assess a convenience sample of health facilities in the affected areas, a patient tracking form, and a line list for tracking all patients with drug-susceptible TB registered in care six months before the hurricane. We analyzed data from the national TB electronic surveillance system to determine outcomes for all patients receiving anti-TB treatment in the affected areas. We used logistic regression analysis to determine factors associated with treatment success.ResultsOf the 66 health facilities in the three affected departments, we assessed 31, accounting for 536 (45.7%) of 1,174 TB patients registered in care when Hurricane Matthew made landfall in Haiti. Three (9.7%) health facilities sustained moderate to severe damage, whereas 18 (58.1%) were closed for <1 week, and five (16.1%) for ≥1 week. Four weeks after the hurricane, 398 (73.1%) of the 536 patients in the assessed facilities were located. Treatment success in the affected departments one year after the hurricane was 81.4%. Receiving care outside the municipality of residence (adjusted odds ratio [aOR]: 0.46, 95% confidence interval [CI]: 0.27-0.80) and HIV positivity (aOR: 0.31, 95% CI: 0.19-0.51) or unknown HIV status (aOR: 0.49, 95% CI: 0.33-0.74) were associated with significantly lower rates of treatment success.ConclusionsDespite major challenges, a high percentage of patients receiving anti-TB treatment before the hurricane were located and successfully treated in southwest Haiti. The lessons learned and results presented here may help inform policies and guidelines in similar settings for effective TB control after a natural disaster.

Project description:BackgroundIn Haiti where there are high rates of maternal and neonatal mortality, efforts to reduce mortality and improve maternal newborn child health (MNCH) must be tracked and monitored to measure their success. At a rural Haitian hospital, local surveillance efforts allowed for the capture of MNCH indicators. In March 2018, a new stand-alone maternity unit was opened, with increased staff, personnel, and physical space. We aimed to determine if the new maternity unit brought about improvements in maternal and neonatal outcomes.MethodsWe conducted an interrupted time series analysis using data collected between July 2016 and October 2019 including 20 months before the opening of the maternity unit and 20 months after. We examined maternal-neonatal outcomes such as physiological (vaginal) births, caesarean birth, postpartum hemorrhage (PPH), maternal deaths, stillbirths and undesirable outcomes (eclampsia, PPH, perineal laceration, postpartum infection, maternal death or stillbirth).ResultsImmediately after the opening of the new maternity, the number of physiological births decreased by 7.0% (β = - 0.070; 95% CI: - 0.110 to - 0.029; p = 0.001) and there was an increase of 6.7% in caesarean births (β = 0.067; 95% CI: 0.026 to 0.107; p = 0.002). For all undesirable outcomes, preintervention there was an increasing trend of 1.8% (β = 0.018; 95% CI: 0.013 to 0.024; p < 0.001), an immediate 14.4% decrease after the intervention (β = - 0.144; 95% CI: - 0.255 to - 0.033; p = 0.012), and a decreasing trend of 1.8% through the postintervention period (β = - 0.018; 95% CI: - 0.026 to - 0.009; p < 0.001). No other significant level or trend changes were noted.ConclusionsThe new maternity unit led to an upward trend in caesarean births yet an overall reduction in all undesirable maternal and neonatal outcomes. The new maternity unit at this rural Haitian hospital positively impacted and improved maternal and neonatal outcomes.

Dataset Information

Increase in Reported Cholera Cases in Haiti Following Hurricane Matthew: An Interrupted Time Series Model.

Publications

Increase in Reported Cholera Cases in Haiti Following Hurricane Matthew: An Interrupted Time Series Model.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets