Evaluating Geospatial Sampling Frames with a Novel Field Census for a Malaria Household Survey in Artibonite, Haiti.

The Ministry of Public Health and Population in Haiti is committed to malaria elimination. In 2017, we used novel methods to conduct a census, monitor progress, and return to sampled households (HH) before a cross-sectional survey in La Chapelle and Verrettes communes in Artibonite department ("the 2017 Artibonite HH census"). Geospatial PDFs with digitized structures and basemaps were loaded onto tablets. Enumerators captured GPS coordinates and details of each HH and points of interest. The census used 1 km2 enumeration areas (EAs) to draw a representative sample. Three remote sampling frames were compared with the 2017 Artibonite HH census. First, 2003 census EAs with 2012 population estimates from the Haitian Institute of Statistics and Informatics were standardized to the study EAs. The second sampling frame used the 2016 LandScanTM population estimates and study EAs. The third sampling frame used structures ≥3 m2 manually digitized using Maxar satellite images. In each study EA, 70% of structures were estimated to be inhabited with 4.5 persons/HH. The census identified 33,060 inhabited HHs with an estimated population of 121,593 and 6,126 points of interest. Using daily coverage maps and including digitized structures were novel methods that improved the census quality. Manual digitization was closest to the census sampling frame results with 30,514 digitized structures in the study area. The LandScanTM method performed better in urban areas; however, it produced the highest number of HHs to sample. If a census is not possible, when feasible, remotely digitizing structures and estimating occupancy may provide a close estimate.

Spatial Clustering and Risk Factors for Malaria Infections and Marker of Recent Exposure to Plasmodium falciparum from a Household Survey in Artibonite, Haiti.

Targeting malaria interventions in elimination settings where transmission is heterogeneous is essential to ensure the efficient use of resources. Identifying the most important risk factors among persons experiencing a range of exposure can facilitate such targeting. A cross-sectional household survey was conducted in Artibonite, Haiti, to identify and characterize spatial clustering of malaria infections. Household members (N = 21,813) from 6,962 households were surveyed and tested for malaria. An infection was defined as testing positive for Plasmodium falciparum by either a conventional or novel highly sensitive rapid diagnostic test. Seropositivity to the early transcribed membrane protein 5 antigen 1 represented recent exposure to P. falciparum. Clusters were identified using SaTScan. Associations among individual, household, and environmental risk factors for malaria, recent exposure, and living in spatial clusters of these outcomes were evaluated. Malaria infection was detected in 161 individuals (median age: 15 years). Weighted malaria prevalence was low (0.56%; 95% CI: 0.45-0.70%). Serological evidence of recent exposure was detected in 1,134 individuals. Bed net use, household wealth, and elevation were protective, whereas being febrile, over age 5 years, and living in either households with rudimentary wall material or farther from the road increased the odds of malaria. Two predominant overlapping spatial clusters of infection and recent exposure were identified. Individual, household, and environmental risk factors are associated with the odds of individual risk and recent exposure in Artibonite; spatial clusters are primarily associated with household-level risk factors. Findings from serology testing can further strengthen the targeting of interventions.

Quantifying the dynamics of migration after Hurricane Maria in Puerto Rico.

Population displacement may occur after natural disasters, permanently altering the demographic composition of the affected regions. Measuring this displacement is vital for both optimal postdisaster resource allocation and calculation of measures of public health interest such as mortality estimates. Here, we analyzed data generated by mobile phones and social media to estimate the weekly island-wide population at risk and within-island geographic heterogeneity of migration in Puerto Rico after Hurricane Maria. We compared these two data sources with population estimates derived from air travel records and census data. We observed a loss of population across all data sources throughout the study period; however, the magnitude and dynamics differ by the data source. Census data predict a population loss of just over 129,000 from July 2017 to July 2018, a 4% decrease; air travel data predict a population loss of 168,295 for the same period, a 5% decrease; mobile phone-based estimates predict a loss of 235,375 from July 2017 to May 2018, an 8% decrease; and social media-based estimates predict a loss of 476,779 from August 2017 to August 2018, a 17% decrease. On average, municipalities with a smaller population size lost a bigger proportion of their population. Moreover, we infer that these municipalities experienced greater infrastructure damage as measured by the proportion of unknown locations stemming from these regions. Finally, our analysis measures a general shift of population from rural to urban centers within the island. Passively collected data provide a promising supplement to current at-risk population estimation procedures; however, each data source has its own biases and limitations.

Mortality in Puerto Rico after Hurricane Maria.

BACKGROUND: Quantifying the effect of natural disasters on society is critical for recovery of public health services and infrastructure. The death toll can be difficult to assess in the aftermath of a major disaster. In September 2017, Hurricane Maria caused massive infrastructural damage to Puerto Rico, but its effect on mortality remains contentious. The official death count is 64. METHODS: Using a representative, stratified sample, we surveyed 3299 randomly chosen households across Puerto Rico to produce an independent estimate of all-cause mortality after the hurricane. Respondents were asked about displacement, infrastructure loss, and causes of death. We calculated excess deaths by comparing our estimated post-hurricane mortality rate with official rates for the same period in 2016. RESULTS: From the survey data, we estimated a mortality rate of 14.3 deaths (95% confidence interval [CI], 9.8 to 18.9) per 1000 persons from September 20 through December 31, 2017. This rate yielded a total of 4645 excess deaths during this period (95% CI, 793 to 8498), equivalent to a 62% increase in the mortality rate as compared with the same period in 2016. However, this number is likely to be an underestimate because of survivor bias. The mortality rate remained high through the end of December 2017, and one third of the deaths were attributed to delayed or interrupted health care. Hurricane-related migration was substantial. CONCLUSIONS: This household-based survey suggests that the number of excess deaths related to Hurricane Maria in Puerto Rico is more than 70 times the official estimate. (Funded by the Harvard T.H. Chan School of Public Health and others.).