Feasibility of satellite image and GIS sampling for population representative surveys: a case study from rural Guatemala.

BACKGROUND: Population-representative household survey methods require up-to-date sampling frames and sample designs that minimize time and cost of fieldwork especially in low- and middle-income countries. Traditional methods such as multi-stage cluster sampling, random-walk, or spatial sampling can be cumbersome, costly or inaccurate, leading to well-known biases. However, a new tool, Epicentre's Geo-Sampler program, allows simple random sampling of structures, which can eliminate some of these biases. We describe the study design process, experiences and lessons learned using Geo-Sampler for selection of a population representative sample for a kidney disease survey in two sites in Guatemala. RESULTS: We successfully used Epicentre's Geo-sampler tool to sample 650 structures in two semi-urban Guatemalan communities. Overall, 82% of sampled structures were residential and could be approached for recruitment. Sample selection could be conducted by one person after 30 min of training. The process from sample selection to creating field maps took approximately 40 h. CONCLUSION: In combination with our design protocols, the Epicentre Geo-Sampler tool provided a feasible, rapid and lower-cost alternative to select a representative population sample for a prevalence survey in our semi-urban Guatemalan setting. The tool may work less well in settings with heavy arboreal cover or densely populated urban settings with multiple living units per structure. Similarly, while the method is an efficient step forward for including non-traditional living arrangements (people residing permanently or temporarily in businesses, religious institutions or other structures), it does not account for some of the most marginalized and vulnerable people in a population-the unhoused, street dwellers or people living in vehicles.

Near real-time forecasting for cholera decision making in Haiti after Hurricane Matthew.

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.

High-resolution spatial analysis of cholera patients reported in Artibonite department, Haiti in 2010-2011.

BACKGROUND: Cholera is caused by Vibrio cholerae, and is transmitted through fecal-oral contact. Infection occurs after the ingestion of the bacteria and is usually asymptomatic. In a minority of cases, it causes acute diarrhea and vomiting, which can lead to potentially fatal severe dehydration, especially in the absence of appropriate medical care. Immunity occurs after infection and typically lasts 6-36 months. Cholera is responsible for outbreaks in many African and Asian developing countries, and caused localised and episodic epidemics in South America until the early 1990s. Haiti, despite its low socioeconomic status and poor sanitation, had never reported cholera before the recent outbreak that started in October 2010, with over 720,000 cases and over 8700 deaths (Case fatality rate: 1.2%) through 8 december 2014. So far, this outbreak has seen 3 epidemic peaks, and it is expected that cholera will remain in Haiti for some time. METHODOLOGY/FINDINGS: To trace the path of the early epidemic and to identify hot spots and potential transmission hubs during peaks, we examined the spatial distribution of cholera patients during the first two peaks in Artibonite, the second-most populous department of Haiti. We extracted the geographic origin of 84,000 patients treated in local health facilities between October 2010 and December 2011 and mapped these addresses to 63 rural communal sections and 9 urban cities. Spatial and cluster analysis showed that during the first peak, cholera spread along the Artibonite River and the main roads, and sub-communal attack rates ranged from 0.1% to 10.7%. During the second peak, remote mountain areas were most affected, although sometimes to very different degrees even in closely neighboring locations. Sub-communal attack rates during the second peak ranged from 0.2% to 13.7%. The relative risks at the sub-communal level during the second phase showed an inverse pattern compared to the first phase. CONCLUSION/SIGNIFICANCE: These findings demonstrate the value of high-resolution mapping for pinpointing locations most affected by cholera, and in the future could help prioritize the places in need of interventions such as improvement of sanitation and vaccination. The findings also describe spatio-temporal transmission patterns of the epidemic in a cholera-naïve country such as Haiti. By identifying transmission hubs, it is possible to target prevention strategies that, over time, could reduce transmission of the disease and eventually eliminate cholera in Haiti.

Mortality Rates during Cholera Epidemic, Haiti, 2010-2011.

The 2010 cholera epidemic in Haiti was one of the largest cholera epidemics ever recorded. To estimate the magnitude of the death toll during the first wave of the epidemic, we retrospectively conducted surveys at 4 sites in the northern part of Haiti. Overall, 70,903 participants were included; at all sites, the crude mortality rates (19.1-35.4 deaths/1,000 person-years) were higher than the expected baseline mortality rate for Haiti (9 deaths/1,000 person-years). This finding represents an excess of 3,406 deaths (2.9-fold increase) for the 4.4% of the Haiti population covered by these surveys, suggesting a substantially higher cholera mortality rate than previously reported.

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.