Mapathons versus automated feature extraction: a comparative analysis for strengthening immunization microplanning.

BACKGROUND: Social instability and logistical factors like the displacement of vulnerable populations, the difficulty of accessing these populations, and the lack of geographic information for hard-to-reach areas continue to serve as barriers to global essential immunizations (EI). Microplanning, a population-based, healthcare intervention planning method has begun to leverage geographic information system (GIS) technology and geospatial methods to improve the remote identification and mapping of vulnerable populations to ensure inclusion in outreach and immunization services, when feasible. We compare two methods of accomplishing a remote inventory of building locations to assess their accuracy and similarity to currently employed microplan line-lists in the study area. METHODS: The outputs of a crowd-sourced digitization effort, or mapathon, were compared to those of a machine-learning algorithm for digitization, referred to as automatic feature extraction (AFE). The following accuracy assessments were employed to determine the performance of each feature generation method: (1) an agreement analysis of the two methods assessed the occurrence of matches across the two outputs, where agreements were labeled as "befriended" and disagreements as "lonely"; (2) true and false positive percentages of each method were calculated in comparison to satellite imagery; (3) counts of features generated from both the mapathon and AFE were statistically compared to the number of features listed in the microplan line-list for the study area; and (4) population estimates for both feature generation method were determined for every structure identified assuming a total of three households per compound, with each household averaging two adults and 5 children. RESULTS: The mapathon and AFE outputs detected 92,713 and 53,150 features, respectively. A higher proportion (30%) of AFE features were befriended compared with befriended mapathon points (28%). The AFE had a higher true positive rate (90.5%) of identifying structures than the mapathon (84.5%). The difference in the average number of features identified per area between the microplan and mapathon points was larger (t = 3.56) than the microplan and AFE (t = - 2.09) (alpha = 0.05). CONCLUSIONS: Our findings indicate AFE outputs had higher agreement (i.e., befriended), slightly higher likelihood of correctly identifying a structure, and were more similar to the local microplan line-lists than the mapathon outputs. These findings suggest AFE may be more accurate for identifying structures in high-resolution satellite imagery than mapathons. However, they both had their advantages and the ideal method would utilize both methods in tandem.

Understanding geographic variations of indoor radon potential for comprehensive cancer control planning.

PURPOSE: Lung cancer is the leading cause of U.S. cancer deaths and radon is the second leading risk factor for lung cancer. By better understanding geologic variations of radon production in states, comprehensive cancer control efforts could be improved. The study purpose was to assess states with the greatest potential for elevated radon and the likelihood of radon-related actions in National Comprehensive Cancer Control Program (NCCCP) awardee cancer plans. METHODS: Two state-level variables were derived to approximate potential for elevated radon using the Environmental Protection Agency county map and the 2015 U.S. Census. The association between radon potential and inclusion of radon activity within cancer plans was evaluated using logistic regression. RESULTS: Fifty-one percent of cancer plans recognized an association between radon and cancer risk, and included measurable radon activities. Most states with high radon potential included radon activity in cancer plans. Both measures of radon potential were significantly associated with NCCCP cancer plans including radon activity. CONCLUSIONS: Geospatial analyses help to prioritize radon-related lung cancer activities. In areas with high potential for radon exposure, increasing knowledge about potential for radon exposure may result in increased radon testing, mitigation, or other radon reducing strategies, and ultimately reduction of lung cancer deaths.

Finding inhabited settlements and tracking vaccination progress: the application of satellite imagery analysis to guide the immunization response to confirmation of previously-undetected, ongoing endemic wild poliovirus transmission in Borno State, Nigeria.

BACKGROUND: Four wild polio-virus cases were reported in Borno State, Nigeria 2016, 1 year after Nigeria had been removed from the list of polio endemic countries by the World Health Organization. Resulting from Nigeria's decade long conflict with Boko Haram, health officials had been unable to access as much as 60% of the settlements in Borno, hindering vaccination and surveillance efforts. This lack of accessibility made it difficult for the government to assess the current population distribution within Borno. This study aimed to use high resolution, visible band satellite imagery to assess the habitation of inaccessible villages in Borno State. METHODS: Using high resolution (31-50 cm) imagery from DigitalGlobe, analysts evaluated the habitation status of settlements in Borno State identified by Nigeria's Vaccination Tracking System. The analysts looked at imagery of each settlement and, using vegetation (overgrowth vs. cleared) as a proxy for human habitation, classified settlements into three categories: inhabited, partially abandoned, and abandoned. Analysts also classified the intact percentage of each settlement starting at 0% (totally destroyed since last assessment) and increasing in 25% intervals through 100% (completely intact but not expanded) up to 200+% (more than doubled in size) by looking for destroyed buildings. These assessments were then used to adjust previously established population estimates for each settlement. These new population distributions were compared to vaccination efforts to determine the number of children under 5 unreached by vaccination teams. RESULTS: Of the 11,927 settlements assessed 3203 were assessed as abandoned (1892 of those completely destroyed), 662 as partially abandoned, and 8062 as fully inhabited as of December of 2017. Comparing the derived population estimates from the new assessments to previous assessment and the activities of vaccination teams shows that an estimated 180,155 of the 337,411 under five children who were unreached in 2016 were reached in 2017 (70.5% through vaccination efforts in previously inaccessible areas, 29.5% through displacement to accessible areas). CONCLUSIONS: This study's methodology provides important planning and situation awareness information to health workers in Borno, Nigeria, and may serve as a model for future data gathering efforts in inaccessible regions.

Radon Testing Status in Schools by Radon Zone and School Location and Demographic Characteristics: United States, 2014.

Radon is a naturally occurring, radioactive, colorless, odorless gas, and the second leading cause of lung cancer. The 1990-1991 National School Radon Survey estimated that more than 70,000 schoolrooms nationwide had "high short-term radon levels." Using data from a nationally representative survey of schools in the United States (N = 568; response rate = 69%), we examined the location and demographic characteristics of U.S. schools that had ever been tested for radon and whether having been tested varied by radon zone, which predicts average indoor radon levels in U.S. counties. Overall, 46.0% (95% confidence interval [39.8%, 52.4%]) of schools reported that they had ever been tested for radon. Testing significantly varied by region, percentage of minority students, and radon zone. These findings highlight the need for improved awareness of radon testing in schools, as testing is the only way to identify when remediation is needed.

Transforming geographic scale: a comparison of combined population and areal weighting to other interpolation methods.

BACKGROUND: Transforming spatial data from one scale to another is a challenge in geographic analysis. As part of a larger, primary study to determine a possible association between travel barriers to pediatric cancer facilities and adolescent cancer mortality across the United States, we examined methods to estimate mortality within zones at varying distances from these facilities: (1) geographic centroid assignment, (2) population-weighted centroid assignment, (3) simple areal weighting, (4) combined population and areal weighting, and (5) geostatistical areal interpolation. For the primary study, we used county mortality counts from the National Center for Health Statistics (NCHS) and population data by census tract for the United States to estimate zone mortality. In this paper, to evaluate the five mortality estimation methods, we employed address-level mortality data from the state of Georgia in conjunction with census data. Our objective here is to identify the simplest method that returns accurate mortality estimates. RESULTS: The distribution of Georgia county adolescent cancer mortality counts mirrors the Poisson distribution of the NCHS counts for the U.S. Likewise, zone value patterns, along with the error measures of hierarchy and fit, are similar for the state and the nation. Therefore, Georgia data are suitable for methods testing. The mean absolute value arithmetic differences between the observed counts for Georgia and the five methods were 5.50, 5.00, 4.17, 2.74, and 3.43, respectively. Comparing the methods through paired t-tests of absolute value arithmetic differences showed no statistical difference among the methods. However, we found a strong positive correlation (r = 0.63) between estimated Georgia mortality rates and combined weighting rates at zone level. Most importantly, Bland-Altman plots indicated acceptable agreement between paired arithmetic differences of Georgia rates and combined population and areal weighting rates. CONCLUSIONS: This research contributes to the literature on areal interpolation, demonstrating that combined population and areal weighting, compared to other tested methods, returns the most accurate estimates of mortality in transforming small counts by county to aggregated counts for large, non-standard study zones. This conceptually simple cartographic method should be of interest to public health practitioners and researchers limited to analysis of data for relatively large enumeration units.

Using search-constrained inverse distance weight modeling for near real-time riverine flood modeling: Harris County, Texas, USA before, during, and after Hurricane Harvey.

Flooding poses a serious public health hazard throughout the world. Flood modeling is an important tool for emergency preparedness and response, but some common methods require a high degree of expertise or may be unworkable due to poor data quality or data availability issues. The conceptually simple method of inverse distance weight modeling offers an alternative. Using stream gauges as inputs, this study interpolated stream elevation via inverse distance weight modeling under 15 different model input parameter scenarios for Harris County, Texas, USA, from August 25th to September 15th, 2017 (before, during, and after Hurricane Harvey inundated the county). A digital elevation model was used to identify areas where modeled stream elevation exceeded ground elevation, indicating flooding. Imagery and observed high water marks were used to validate the models' outputs. There was a high degree of agreement (between 79 and 88%) between imagery and model outputs of parameterizations visually validated. Quantitative validations based on high water marks were also positive, with a Nash-Sutcliffe efficiency of in excess of .6 for all parameterizations relative to a Nash-Sutcliffe efficiency of the benchmark of 0.56. Inverse distance weight modeling offers a simple, accurate method for first-order estimations of riverine flooding in near real-time using readily available data, and outputs are robust to some alterations to input parameters.

The use of gamma-survey measurements to better understand radon potential in urban areas.

Accounting for as much as 14% of all lung cancers worldwide, cumulative radon progeny exposure is the leading cause of lung cancer among never-smokers both internationally and in the United States. To understand the risk of radon progeny exposure, studies have mapped radon potential using aircraft-based measurements of gamma emissions. However, these efforts are hampered in urban areas where the built environment obstructs aerial data collection. To address part of this limitation, this study aimed to evaluate the effectiveness of using in situ gamma readings (taken with a scintillation probe attached to a ratemeter) to assess radon potential in an urban environment: DeKalb County, part of the Atlanta metropolitan area, Georgia, USA. After taking gamma measurements at 402 survey sites, empirical Bayesian kriging was used to create a continuous surface of predicted gamma readings for the county. We paired these predicted gamma readings with indoor radon concentration data from 1351 residential locations. Statistical tests showed the interpolated gamma values were significantly but weakly positively related with indoor radon concentrations, though this relationship is decreasingly informative at finer geographic scales. Geology, gamma readings, and indoor radon were interrelated, with granitic gneiss generally having the highest gamma readings and highest radon concentrations and ultramafic rock having the lowest of each. Our findings indicate the highest geogenic radon potential may exists in the relatively undeveloped southeastern part of the county. It is possible that in situ gamma, in concert with other variables, could offer an alternative to aerial radioactivity measurements when determining radon potential, though future work will be needed to address this project's limitations.