Search Results (6)

Effective flood planning requires spatially complete hazard information, yet regulatory flood products can contain unresolved hazard classifications and provide limited uncertainty information. In addition, existing models for flood mapping often rely on random sampling that neglects spatial dependence among neighboring areas, struggle to model zero-inflated, bounded area shares (i.e., shares with many zero values and a bounded 0–1 range), and offer limited distribution-level explainability. To address these limitations, this study developed a calibrated hurdle conditional diffusion framework to estimate the Federal Emergency Management Agency (FEMA) Special Flood Hazard Area (SFHA) share and the probability that coverage exceeds 10% for Louisiana census block groups. Within this framework, a hurdle component separates zero from positive coverage, a conditional diffusion model estimates bounded positive-share distributions, spatial blocking evaluates geographic transfer, and probabilistic calibration supports exceedance probabilities and prediction intervals. Terrain, land-cover, wetland, hydrographic, climate, and soil predictors were evaluated across 3776 block groups with resolved FEMA information. The conditional regression component achieved a mean absolute error of 0.172 for the primary continuous SFHA-share prediction and strong parish-level agreement (Pearson r = 0.834). For the secondary screening task of identifying census block groups with ≥10% SFHA share, the calibrated hurdle diffusion model yielded an area under the receiver operating characteristic curve of 0.852. Statewide predictions were generated for all 4294 census block groups, including 518 unresolved units with a mean predicted SFHA share of 33.3%. Distributional Reliability Explanation Attribution (DREA) identified flooded soils, elevation, topographic wetness, wetlands, and water proximity as reliable predictors of distributional displacement, uncertainty, exceedance probability, and probabilistic skill. Overall, the framework supports leakage-safe, uncertainty-aware screening while complementing authoritative FEMA flood maps. Full article

Limited inclusion of ethnically diverse and socioeconomically disadvantaged populations in pain research undermines external validity, generalizability, and equity. This comprehensive review synthesized evidence on effective strategies to recruit and retain these populations in pain studies. We searched Medline, Web of Science, Embase, Scopus, and CENTRAL (12 April 2025) and included studies in which ethnically diverse and socioeconomically disadvantaged participants comprised ≥75% of the sample. Quantitative data were pooled using random-effects meta-analyses of proportions, with prespecified subgroup analyses, and qualitative findings were integrated through thematic synthesis. Certainty of evidence was evaluated using GRADE. Eighteen studies (n = 4611; 11 experimental, 5 observational, 2 qualitative), primarily from the United States and involving chronic pain, met inclusion criteria. Overall enrollment among ethnically diverse and socioeconomically disadvantaged groups was 37% (95% CI 18–58%), with significantly higher enrollment in observational studies (84%, 95% CI 78–90%) than in experimental trials (22%, 95% CI 10–36%; p < 0.001). Overall retention was 77% (95% CI 64–88%) and did not differ significantly by study design. Statewide disease-clinic networks, purposive community-leader engagement, and snowball sampling produced the highest enrollment, whereas medical-record screening yielded the lowest enrollment but the highest retention. Compensation and reminder strategies were similarly effective for retention. Thematic synthesis highlighted trust, culturally and linguistically tailored communication, hybrid and flexible visit schedules, transportation assistance, and clinician engagement as key facilitators. GRADE certainty was low for enrollment and moderate for retention. Community-engaged recruitment strategies, clinician referrals, culturally tailored materials, hybrid procedures, and modest incentives can substantially improve participation of ethnically diverse and socioeconomically disadvantaged populations in pain research. Standardized CONSORT-style reporting of recruitment/retention flowcharts according to strategy and ethnicity/socioeconomic status is essential to refine evidence-based strategies in the future. Full article

Southern U.S. forests are essential for carbon storage and timber production but are increasingly impacted by natural disturbances, highlighting the need to understand their dynamics and recovery. Canopy cover is a key indicator of forest health and resilience. Advances in remote sensing, such as NASA’s GEDI spaceborne LiDAR, enable more precise mapping of canopy cover. Although GEDI provides accurate data, its limited spatial coverage restricts large-scale assessments. To address this, we combined GEDI with Synthetic Aperture Radar (SAR), and optical imagery (Sentinel-1 GRD and Landsat–Sentinel Harmonized (HLS)) data to create a comprehensive canopy cover map for Florida. Using a random forest algorithm, our model achieved an R² of 0.69, RMSD of 0.17, and MD of 0.001, based on out-of-bag samples for internal validation. Geographic coordinates and the red spectral channel emerged as the most influential predictors. External validation with airborne laser scanning (ALS) data across three sites yielded an R² of 0.70, RMSD of 0.29, and MD of −0.22, confirming the model’s accuracy and robustness in unseen areas. Statewide analysis showed lower canopy cover in southern versus northern Florida, with wetland forests exhibiting higher cover than upland sites. This study demonstrates the potential of integrating multiple remote sensing datasets to produce accurate vegetation maps, supporting forest management and sustainability efforts in Florida. Full article

Mapping of fire extent and severity across broad landscapes and timeframes using remote sensing approaches is valuable to inform ecological research, biodiversity conservation and fire management. Compiling imagery from various satellite sensors can assist in long-term fire history mapping; however, inherent sensor differences need to be considered. The New South Wales Fire Extent and Severity Mapping (FESM) program uses imagery from Sentinel and Landsat satellites, along with supervised classification algorithms, to produce state-wide fire maps over recent decades. In this study, we compared FESM outputs from Sentinel 2 and Landsat 8 sensors, which have different spatial and spectral resolutions. We undertook independent accuracy assessments of both Sentinel 2 and Landsat 8 sensor algorithms using high-resolution aerial imagery from eight training fires. We also compared the FESM outputs from both sensors across 27 case study fires. We compared the mapped areas of fire severity classes between outputs and assessed the classification agreement at random sampling points. Our independent accuracy assessment demonstrated very similar levels of accuracy for both sensor algorithms. We also found that there was substantial agreement between the outputs from the two sensors. Agreement on the extent of burnt versus unburnt areas was very high, and the severity classification of burnt areas was typically either in agreement between the sensors or in disagreement by only one severity class (e.g., low and moderate severity or high and extreme severity). Differences between outputs are likely partly due to differences in sensor resolution (10 m and 30 m pixel sizes for Sentinel 2 and Landsat 8, respectively) and may be influenced by landscape complexity, such as terrain roughness and foliage cover. Overall, this study supports the combined use of both sensors in remote sensing applications for fire extent and severity mapping. Full article

We compared a tailored and a targeted intervention designed to increase genetic testing, clinical breast exam (CBE), and mammography in young breast cancer survivors (YBCS) (diagnosed <45 years old) and their blood relatives. A two-arm cluster randomized trial recruited a random sample of YBCS from the Michigan cancer registry and up to two of their blood relatives. Participants were stratified according to race and randomly assigned as family units to the tailored (n = 637) or the targeted (n = 595) intervention. Approximately 40% of participants were Black. Based on intention-to-treat analyses, YBCS in the tailored arm reported higher self-efficacy for genetic services (p = 0.0205) at 8-months follow-up. Genetic testing increased approximately 5% for YBCS in the tailored and the targeted arm (p ≤ 0.001; p < 0.001) and for Black and White/Other YBCS (p < 0.001; p < 0.001). CBEs and mammograms increased significantly in both arms, 5% for YBCS and 10% for relatives and were similar for Blacks and White/Others. YBCS and relatives needing less support from providers reported significantly higher self-efficacy and intention for genetic testing and surveillance. Black participants reported significantly higher satisfaction and acceptability. Effects of these two low-resource interventions were comparable to previous studies. Materials are suitable for Black women at risk for hereditary breast/ovarian cancer (HBOC). Full article

This paper measures tobacco polices in statewide representative samples of secondary and mixed schools in Victoria, Australia and Washington, US (N = 3,466 students from 285 schools) and tests their association with student smoking. Results from confounder-adjusted random effects (multi-level) regression models revealed that the odds of student perception of peer smoking on school grounds are decreased in schools that have strict enforcement of policy (odds ratio (OR) = 0.45; 95% CI: 0.25 to 0.82; p = 0.009). There was no clear evidence in this study that a comprehensive smoking ban, harsh penalties, remedial penalties, harm minimization policy or abstinence policy impact on any of the smoking outcomes. Full article