scholarly journals Temporal Trends and Potential Drivers of Stranded Marine Debris on Beaches Within Two US National Marine Sanctuaries Using Citizen Science Data

2020 ◽  
Vol 8 ◽  
Author(s):  
Amy V. Uhrin ◽  
Sherry Lippiatt ◽  
Carlie E. Herring ◽  
Kyle Dettloff ◽  
Kate Bimrose ◽  
...  

Marine debris is a threat to our ocean that can be more effectively addressed through monitoring and assessment of items stranded on shorelines. This study engaged citizen scientists to conduct shoreline marine debris surveys according to a published NOAA protocol within the Greater Farallones and Olympic Coast National Marine Sanctuaries on the west coast of the United States. Here, we use the results of these multi-year monitoring data to estimate marine debris abundance and temporal trends, and identify drivers of debris loads. Changes in debris counts and composition are shown to reflect seasonal patterns of coastal upwelling and downwelling, but longer temporal trends in overall debris loads depend on the sampling window. Identifying drivers of stranded debris is challenging given the observational nature of the data. A linear increase in total expected debris counts was observed when up to five participants are conducting a survey, suggesting a need to standardize the number of participants and their search pattern for debris in shoreline monitoring efforts. Lastly, we discuss the application of shoreline marine debris data to evaluate the impact of management decisions and identify new targets for mitigation.

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248702
Author(s):  
Brian Neelon ◽  
Fedelis Mutiso ◽  
Noel T. Mueller ◽  
John L. Pearce ◽  
Sara E. Benjamin-Neelon

Background Socially vulnerable communities may be at higher risk for COVID-19 outbreaks in the US. However, no prior studies examined temporal trends and differential effects of social vulnerability on COVID-19 incidence and death rates. Therefore, we examined temporal trends among counties with high and low social vulnerability to quantify disparities in trends over time. Methods We conducted a longitudinal analysis examining COVID-19 incidence and death rates from March 15 to December 31, 2020, for each US county using data from USAFacts. We classified counties using the Social Vulnerability Index (SVI), a percentile-based measure from the Centers for Disease Control and Prevention, with higher values indicating more vulnerability. Using a Bayesian hierarchical negative binomial model, we estimated daily risk ratios (RRs) comparing counties in the first (lower) and fourth (upper) SVI quartiles, adjusting for rurality, percentage in poor or fair health, percentage female, percentage of smokers, county average daily fine particulate matter (PM2.5), percentage of primary care physicians per 100,000 residents, daily temperature and precipitation, and proportion tested for COVID-19. Results At the outset of the pandemic, the most vulnerable counties had, on average, fewer cases per 100,000 than least vulnerable SVI quartile. However, on March 28, we observed a crossover effect in which the most vulnerable counties experienced higher COVID-19 incidence rates compared to the least vulnerable counties (RR = 1.05, 95% PI: 0.98, 1.12). Vulnerable counties had higher death rates starting on May 21 (RR = 1.08, 95% PI: 1.00,1.16). However, by October, this trend reversed and the most vulnerable counties had lower death rates compared to least vulnerable counties. Conclusions The impact of COVID-19 is not static but can migrate from less vulnerable counties to more vulnerable counties and back again over time.


2018 ◽  
Vol 28 (6) ◽  
pp. 844-853 ◽  
Author(s):  
Sarah Cohen ◽  
Harel Gilutz ◽  
Ariane J. Marelli ◽  
Laurence Iserin ◽  
Arriel Benis ◽  
...  

AbstractThe need for population-based studies of adults with CHD has motivated the growing use of secondary analyses of administrative health data in a variety of jurisdictions worldwide. We aimed at systematically reviewing all studies using administrative health data sources for adult CHD research from 2006 to 2016. Using PubMed and Embase (1 January, 2006 to 1 January, 2016), we identified 2217 abstracts, from which 59 studies were included in this review. These comprised 12 different data sources from six countries. Of these, 55% originated in the United States of America, 28% in Canada, and 17% in Europe and Asia. No study was published before 2007, after which the number of publications grew exponentially. In all, 41% of the studies were cross-sectional and 25% were retrospective cohort studies with a wide variation in the availability of patient-level compared with hospitalisation-level episodes of care; 58% of studies from eight different data sources linked administrative data at a patient level; and 37% of studies reported validation procedures. Assessing resource utilisation and temporal trends of relevant epidemiological and outcome end points were the most reported objectives. The median impact factor of publication journals was 4.04, with an interquartile range of 3.15, 7.44. Although not designed for research purposes, administrative health databases have become powerful data sources for studying adult CHD populations because of their large sample sizes, comprehensive records, and long observation periods, providing a useful tool to further develop quality of care improvement programmes. Data linkage with electronic records will become important in obtaining more granular life-long adult CHD data. The health services nature of the data optimises the impact on policy and public health.


2020 ◽  
Vol 133 (3) ◽  
pp. 865-874 ◽  
Author(s):  
Arman Jahangiri ◽  
Patrick M. Flanigan ◽  
Maxine Arnush ◽  
Ankush Chandra ◽  
Jonathan W. Rick ◽  
...  

OBJECTIVENeurosurgeons play an important role in advancing medicine through research, the funding of which is historically linked to the National Institutes of Health (NIH). The authors defined variables associated with neurosurgical NIH funding, prevalence of funded topics by neurosurgical subspecialty, and temporal trends in NIH neurosurgical funding.METHODSThe authors conducted a retrospective review of NIH-funded American Association of Neurological Surgeons members using NIH RePORTER (http://report.nih.gov/) for the years 1991–2015.RESULTSThe authors followed 6515 neurosurgeons from 1991 to 2015, including 6107 (94%) non–MD-PhD physicians and 408 (6%) MD-PhDs. NIH grants were awarded to 393 (6%) neurosurgeons, with 23.2% of all first-time grants awarded to the top 5 funded institutions. The average total funded grant-years per funded neurosurgeon was 12.5 (range 1–85 grant-years). A higher percentage of MD-PhDs were NIH funded than MDs (22% [n = 91] vs 5% [n = 297], p < 0.0001). The most common grants awarded were R01 (128, 33%), K08 (69, 18%), F32 (60, 15%), M01 (50, 13%), and R21 (39, 10%). F32 and K08 recipients were 9-fold (18% vs 2%, p < 0.001) and 19-fold (38% vs 2%, p < 0.001) more likely to procure an R01 and procured R01 funding earlier in their careers (F32: 7 vs 12 years after residency, p = 0.03; K08: 9 vs 12 years, p = 0.01). Each year, the number of neurosurgeons with active grants linearly increased by 2.2 (R2 = 0.81, p < 0.001), whereas the number of total active grants run by neurosurgeons increased at nearly twice the rate (4.0 grants/year) (R2 = 0.91, p < 0.001). Of NIH-funded neurosurgical grants, 33 (9%) transitioned to funded clinical trial(s). Funded neurosurgical subspecialties included neuro-oncology (33%), functional/epilepsy (32%), cerebrovascular (17%), trauma (10%), and spine (6%). Finally, the authors modeled trends in the number of active training grants and found a linear increase in active R01s (R2 = 0.95, p < 0.001); however, both F32 (R2 = 0.36, p = 0.01) and K08 (R2 = 0.67, p < 0.001) funding had a significant parabolic rise and fall centered around 2003.CONCLUSIONSThe authors observed an upward trend in R01s awarded to neurosurgeons during the last quarter century. However, their findings of decreased K08 and F32 training grant funding to neurosurgeons and the impact of these training grants on the ultimate success and time to success for neurosurgeons seeking R01 funding suggests that this upward trend in R01 funding for neurosurgeons will be difficult to maintain. The authors’ work underscores the importance of continued selection and mentorship of neurosurgeons capable of impacting patient care through research, including the MD-PhDs, who are noted to be more represented among NIH-funded neurosurgeons.


2016 ◽  
Vol 21 (1) ◽  
pp. 59-81 ◽  
Author(s):  
Richard Stansfield ◽  
Kirk R. Williams ◽  
Karen F. Parker

Although research has established economic disadvantage as one of the strongest, most robust predictors of urban violence, the conditions under which this relation holds need further elaboration. This study examines the disadvantage–violence link across age-specific transitional periods from adolescence to adulthood and provides theoretical arguments for why the strength of this relation should decline with age. Using 90 of the largest cities in the United States, the present study analyzes the impact of economic disadvantage and other urban conditions (residential instability, family disruption, and population heterogeneity) on age-specific homicide counts from 1984 to 2006. The analytical strategy incorporates temporal trends by using negative binomial fixed-effects regression models. The results reveal a consistent decline from adolescence to adulthood in the strength of the estimated effects of economic disadvantage, residential instability, and family disruption on homicide trends. The findings are discussed in terms of the implications for future research and public policy.


Author(s):  
A. Akinbobola ◽  
T Fafure

This study seeks to assess the land use land cover (LULC) and spatial-temporal trends of six outdoor thermal comfort indices in four Local Government Areas (LGAs) of Ogun state, Southwestern, Nigeria. Data used for this study are air temperature, relative humidity, cloud cover and wind speed which span from 1982 to 2018. These data were obtained from ERA-INTERIM archive. The 1986, 2000 and 2018 used for the analysis of the LULC were from the satellite imagery hosted by the United States Geological Survey (USGS). Landsat Thematic Mapper, Landsat 7 and Landsat 8 Operational Land Imager data of 1986, 2000 and 2018 to assess the changes that have taken place between these periods. Thermal comfort indices such as Effective Temperature (ET), Temperature Humidity Index (THI), Mean radiant temperature (MRT) and Relative Strain Index (RSI) were used. Rayman model was used for the computation of the three thermal comfort indices (MRT, PET, PMV). The results show decrease in vegetation, forest, and an increase in percentage of built-up areas between 1986–2000, and 2000–2018. A rapid increase in built-up areas in the three (Abeokuta South, Ifo, Shagamu,) of the four LGAs, while one (Ijebu East) has a slow increase in the built-up areas. The trend in the thermal comfort indices also shows that thermal discomfort had been on increase for the past 37 years and it was observed that the level of comfort has deteriorated more in the last decade compared to the previous decade especially in the built-up areas. This work suggests a framework for evaluating the relationship between the quantitative and qualitative parameters linking the microclimatic environment with subjective thermal assessment. This will contribute to the development of thermal comfort standards for outdoor urban settings. Also, the study will help urban planners in their decision making, and in heat forecast.


2020 ◽  
Author(s):  
Brian Neelon ◽  
Fedelis Mutiso ◽  
Noel T Mueller ◽  
John L Pearce ◽  
Sara E Benjamin-Neelon

Background: Emerging evidence suggests that socially vulnerable communities are at higher risk for coronavirus disease 2019 (COVID-19) outbreaks in the United States. However, no prior studies have examined temporal trends and differential effects of social vulnerability on COVID-19 incidence and death rates. The purpose of this study was to examine temporal trends among counties with high and low social vulnerability and to quantify disparities in these trends over time. We hypothesized that highly vulnerable counties would have higher incidence and death rates compared to less vulnerable counties and that this disparity would widen as the pandemic progressed. Methods: We conducted a retrospective longitudinal analysis examining COVID-19 incidence and death rates from March 1 to August 31, 2020 for each county in the US. We obtained daily COVID-19 incident case and death data from USAFacts and the Johns Hopkins Center for Systems Science and Engineering. We classified counties using the Social Vulnerability Index (SVI), a percentile-based measure from the Centers for Disease Control and Prevention in which higher scores represent more vulnerability. Using a Bayesian hierarchical negative binomial model, we estimated daily risk ratios (RRs) comparing counties in the first (lower) and fourth (upper) SVI quartiles. We adjusted for percentage of the county designated as rural, percentage in poor or fair health, percentage of adult smokers, county average daily fine particulate matter (PM2.5), percentage of primary care physicians per 100,000 residents, and the proportion tested for COVID-19 in the state. Results: In unadjusted analyses, we found that for most of March 2020, counties in the upper SVI quartile had significantly fewer cases per 100,000 than lower SVI quartile counties. However, on March 30, we observed a crossover effect in which the RR became significantly greater than 1.00 (RR = 1.10, 95% PI: 1.03, 1.18), indicating that the most vulnerable counties had, on average, higher COVID-19 incidence rates compared to least vulnerable counties. Upper SVI quartile counties had higher death rates on average starting on March 30 (RR = 1.17, 95% PI: 1.01,1.36). The death rate RR achieved a maximum value on July 29 (RR = 3.22, 95% PI: 2.91, 3.58), indicating that most vulnerable counties had, on average, 3.22 times more deaths per million than the least vulnerable counties. However, by late August, the lower quartile started to catch up to the upper quartile. In adjusted models, the RRs were attenuated for both incidence cases and deaths, indicating that the adjustment variables partially explained the associations. We also found positive associations between COVID-19 cases and deaths and percentage of the county designated as rural, percentage of resident in fair or poor health, and average daily PM2.5. Conclusions: Results indicate that the impact of COVID-19 is not static but can migrate from less vulnerable counties to more vulnerable counties over time. This highlights the importance of protecting vulnerable populations as the pandemic unfolds.


2014 ◽  
Vol 121 (6) ◽  
pp. 1158-1165 ◽  
Author(s):  
Ayumi Maeda ◽  
Brian T. Bateman ◽  
Caitlin R. Clancy ◽  
Andreea A. Creanga ◽  
Lisa R. Leffert

Abstract Background: The authors investigated nationwide trends in opioid abuse or dependence during pregnancy and assessed the impact on maternal and obstetrical outcomes in the United States. Methods: Hospitalizations for delivery were extracted from the Nationwide Inpatient Sample from 1998 to 2011. Temporal trends were assessed and logistic regression was used to examine the associations between maternal opioid abuse or dependence and obstetrical outcomes adjusting for relevant confounders. Results: The prevalence of opioid abuse or dependence during pregnancy increased from 0.17% (1998) to 0.39% (2011) for an increase of 127%. Deliveries associated with maternal opioid abuse or dependence compared with those without opioid abuse or dependence were associated with an increased odds of maternal death during hospitalization (adjusted odds ratio [aOR], 4.6; 95% CI, 1.8 to 12.1, crude incidence 0.03 vs. 0.006%), cardiac arrest (aOR, 3.6; 95% CI, 1.4 to 9.1; 0.04 vs. 0.01%), intrauterine growth restriction (aOR, 2.7; 95% CI, 2.4 to 2.9; 6.8 vs. 2.1%), placental abruption (aOR, 2.4; 95% CI, 2.1 to 2.6; 3.8 vs. 1.1%), length of stay more than 7 days (aOR, 2.2; 95% CI, 2.0 to 2.5; 3.0 vs. 1.2%), preterm labor (aOR, 2.1; 95% CI, 2.0 to 2.3; 17.3 vs. 7.4%), oligohydramnios (aOR, 1.7; 95% CI, 1.6 to 1.9; 4.5 vs. 2.8%), transfusion (aOR, 1.7; 95% CI, 1.5 to 1.9; 2.0 vs. 1.0%), stillbirth (aOR, 1.5; 95% CI, 1.3 to 1.8; 1.2 vs. 0.6%), premature rupture of membranes (aOR, 1.4; 95% CI, 1.3 to 1.6; 5.7 vs. 3.8%), and cesarean delivery (aOR, 1.2; 95% CI, 1.1 to 1.3; 36.3 vs. 33.1%). Conclusions: Opioid abuse or dependence during pregnancy is associated with considerable obstetrical morbidity and mortality, and its prevalence is dramatically increasing in the United States. Identifying preventive strategies and therapeutic interventions in pregnant women who abuse drugs are important priorities for clinicians and scientists.


2019 ◽  
Vol 11 (3) ◽  
pp. 623
Author(s):  
Rongrong Li ◽  
Xuefeng Wang

China's solar energy industry is developing rapidly and China's solar energy research is experiencing a high speed of development alongside it. Is China's solar energy research growth quantity-driven (paper-driven) or quality-driven (citation-driven)? Answering this question is important for China's solar research field and industrial sector, and has implications for China’s other renewable research programs. Applying statistical methods, the citation analysis method, and web of science data, this study investigated China’s solar energy research between 2007 and 2015 from two perspectives: quantity (numbers of papers) and quality (number of paper citations). The results show that the number of Science Citation Index Expanded(SCI-E)papers on solar energy in China has grown rapidly, surpassing the United States to become the world leader in 2015. However, the growth rate in scientific production was consistently higher than the growth rate of the number of times cited. When considering the average number of times a paper was cited among the top ten countries researching solar energy, China was in last place from 2007 to 2015. Further, the impact and effectiveness of China’s papers were below the world average from 2010 to 2015, and experienced a sharp decreasing trend. These results suggest that China's solar energy research is a quantitatively driven model, with a mismatch between quantity and quality. New policies should be introduced to encourage high-quality research and achieve a balance between quantity and quality.


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