Friday, September 10, 2021


Estimates of COVID-19 Cases and Deaths Among Nursing Home Residents Not Reported in Federal Data

 

Original Investigation 
Public Health
September 9, 2021
JAMA Netw Open. 2021;4(9):e2122885. doi:10.1001/jamanetworkopen.2021.22885


Key Points

Question  How many COVID-19 cases and deaths at nursing homes were missed in the federal National Healthcare Safety Network (NHSN) reporting system owing to the delayed start in required reporting?

Findings  In this cross-sectional study of 15 307 US nursing homes, approximately 44% of COVID-19 cases and 40% of COVID-19 deaths that occurred before the start of reporting were not reported in the first NHSN submission in sample states, suggesting there were more than 68 000 unreported cases and 16 000 unreported deaths nationally.

Meaning  These findings suggest that federal NHSN data understate total COVID-19 cases and deaths in nursing homes and that using these data without accounting for this issue may result in misleading conclusions about the determinants of nursing home outbreaks.

Abstract

Importance  Federal data underestimate the impact of COVID-19 on US nursing homes because federal reporting guidelines did not require facilities to report case and death data until the week ending May 24, 2020.

Objective  To assess the magnitude of unreported cases and deaths in the National Healthcare Safety Network (NHSN) and provide national estimates of cases and deaths adjusted for nonreporting.

Design, Setting, and Participants  This is a cross-sectional study comparing COVID-19 cases and deaths reported by US nursing homes to the NHSN with those reported to state departments of health in late May 2020. The sample includes nursing homes from 20 states, with 4598 facilities in 12 states that required facilities to report cases and 7401 facilities in 19 states that required facilities to report deaths. Estimates of nonreporting were extrapolated to infer the national (15 397 facilities) unreported cases and deaths in both May and December 2020. Data were analyzed from December 2020 to May 2021.

Exposures  Nursing home ownership (for-profit or not-for-profit), chain affiliation, size, Centers for Medicare & Medicaid Services star rating, and state.

Main Outcomes and Measures  The main outcome was the difference between the COVID-19 cases and deaths reported by each facility to their state department of health vs those reported to the NHSN.

Results  Among 15 415 US nursing homes, including 4599 with state case data and 7405 with state death data, a mean (SE) of 43.7% (1.4%) of COVID-19 cases and 40.0% (1.1%) of COVID-19 deaths prior to May 24 were not reported in the first NHSN submission in sample states, suggesting that 68 613 cases and 16 623 deaths were omitted nationwide, representing 11.6% of COVID-19 cases and 14.0% of COVID-19 deaths among nursing home residents in 2020.

Conclusions and Relevance  These findings suggest that federal NHSN data understated total cases and deaths in nursing homes. Failure to account for this issue may lead to misleading conclusions about the role of different facility characteristics and state or federal policies in explaining COVID outbreaks.

Introduction

Although nursing homes have been centers for outbreaks and excess mortality from the COVID-19 pandemic, the federal government did not require nursing homes to report cases and deaths from COVID-19 until May 24, 2020, more than 3 months after the first reported nursing home outbreak at Life Care Center of Kirkland, Washington.1,2 In addition, in the first submission to the Centers for Disease Control and Prevention (CDC) National Healthcare Safety Network (NHSN), facilities were given the option—but were not required—to retrospectively report cases and deaths from earlier in the pandemic.3 For example, the Life Care Center of Kirkland reported zero cumulative COVID-19 cases in the first NHSN submission, despite a March 2020 CDC investigation identifying 81 COVID-19 cases and 23 COVID-19 deaths among residents.4,5 It is not known how many facilities chose to report retrospective data to the NHSN and what factors may have influenced their decisions (eg, data availability, reporting burden, reputation). As a result, although these data are widely known to undercount total cases and deaths in nursing homes, the degree of nonreporting, and thus the true impact of COVID-19 on nursing homes, remains unknown.6,7

In light of the federal data limitations, significant efforts have been made to provide alternative estimates of COVID-19 cases and deaths in nursing homes.8,9 However, these alternative estimates generally rely on a patchwork of state and local sources and have their own limitations. Data are not available for all states and include significant numbers of non–nursing home residences (eg, assisted living) in some states, and only nursing homes in others.

To our knowledge, no previous study has used the available data sources in combination with the federal data to estimate national nursing home COVID-19 cases and deaths. This study aims to fill that gap. We have 2 objectives: to compare data from state and federal sources in 20 states with state health department data to estimate the probability that a COVID-19 case or death that occurred prior to the beginning of NHSN reporting was reported to the NHSN, and to apply an extrapolation method to produce adjusted national estimates of cumulative COVID-19 cases and deaths at 2 time points, the date of the first NHSN submission (May 24, 2020), and the date of the last submission of 2020 (December 27, 2020).

Methods

This cross-sectional study was determined not to be human participants research by the University of California, Los Angeles, institutional review board; therefore, it was exempt from further review and informed consent. This study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cross-sectional studies.

Data

This cross-sectional study used data from all US nursing homes in late May 2020. Data sources included NHSN COVID-19 Nursing Home Data set,3 state health department data, Center for Medicare & Medicaid Services Nursing Home Compare10 and Provider of Services file,11 Brown University’s Long Term Care: Facts on Care in the United States,12 and The New York Times COVID-19 Database.13

The NHSN COVID-19 Nursing Home Data set contains weekly facility-level data on new and cumulative COVID-19 cases and deaths. In the first submission on May 24, 2020, new and cumulative cases and deaths were identical, and may or may not have included retrospective cases and deaths. In all following submissions, new cases and deaths represented cases and deaths from the week ending with the submission date, while cumulative cases and deaths date back to May 24, 2020, or earlier (if the facility reported retrospectively).

To supplement the NHSN data, we collected facility-level data from 20 state health departments that required reporting of COVID-19 cases or deaths dating back to the beginning of the pandemic. We collected cumulative resident case data from 12 states and cumulative death data from 19 states, as reported between May 21 and May 29, 2020, to compare with the May 24, 2020, NHSN submissions. It is important to note that states varied substantially in the data they reported (eAppendix 1 in the Supplement). Briefly, states varied in what facility types were included (just nursing homes or other congregate care settings), what geographic information was provided, and in the completeness of their case and death data (ie, some states omitted non–laboratory-confirmed data, data from facilities below a certain case threshold, or data from transferred residents).

We constructed an algorithm to match these data using the facility name and available geographic information to national provider identifiers in the Center for Medicare & Medicaid Services Nursing Home Compare database using fuzzy-matching and geocoding techniques. This allowed us to separate nursing homes from non–nursing homes in the state health department data and also allowed us to match case and death data to facility characteristics.

We used data on overall star ratings, ownership (for-profit, nonprofit, or public), and number of beds from the March 2020 Nursing Home Compare file, chain affiliation from the 2020 Provider of Services file, and share of residents whose primary source of payment was Medicaid and share of residents who were non-White from the 2017 Long Term Care: Facts on Care in the United States dataset. Race/ethnicity was self-identified, and non-White residents were all residents who responded American Indian or Alaskan Native, Asian or Pacific Islander, non-Hispanic Black, or Hispanic. Daily data on total US population cases were obtained from The New York Times COVID-19 Database.

Variables

Our main outcome was whether a resident COVID-19 case or death prior to May 24 was not reported to the NHSN in the May 24 data submission. First, we defined the adjusted total number of cases and deaths as of May 24, 2020 for each facility as the larger of what the facility reported to NHSN on May 24, 2020, and to their state health department on the nearest date for which we have data (within 5 days for all states). This measure assumes that facilities were unlikely to overreport cases or deaths and is likely conservative, since it does not include cases and deaths not reported to either source. The difference between the reported and adjusted estimates are cases and deaths that were reported to state authorities but not to the NHSN. Using this difference as the numerator, and the adjusted estimate of cases and deaths as the denominator, we calculated the percentage of cases and deaths prior to May 24, 2020, that were not reported to the NHSN.

We also examined the associations between reporting and nursing home characteristics, including ownership (for-profit, and not-for-profit), chain affiliation, size according to number of beds (<100, 100-150, 150-200, and >200), and overall star rating.

Statistical Analysis

First, we described the composition of nursing homes overall and of facilities in the samples with state case data and with state death data. We used t tests of the difference in means (assuming unequal variances) for these descriptive variables between facilities included and not included in each analysis sample.

We examined variation in the percentage of cases and deaths as of May 24 that were not reported to the NHSN across facility characteristics, as well as by state. We performed linear regression of an indicator variable for nonreporting at the case or death level, where the independent variables are categorical variables for facility ownership, chain affiliation, size, star rating, and state. Then, we calculated estimated means from a model that included each of these facility characteristics separately (unadjusted sample means), as well as from a model that included all of the facility characteristics simultaneously (adjusted sample means). The overall unadjusted sample mean and SE were calculated from a model that only included a constant term.

We extrapolated our findings from the 20 sample states to the remaining states without state health department data (nonsample states) to estimate total national cases and deaths as of May 24. To do this, we used the (adjusted) linear regression estimates to estimate each nonsample state facility’s probability of nonreporting and then divided the facility’s NHSN report by this probability. Because it is not possible to estimate state fixed effects for the nonsample states, we used the case- or death-weighted mean of the sample state fixed effects from each regression (eAppendix 2 in the Supplement). The underlying assumption for the extrapolation was that facilities in sample states were equally likely to not report a case or death as facilities in nonsample states, conditional on our control variables. Insofar as this assumption was violated, it is likely that our national estimate of unreported cases is too low, because facilities in states that required early reporting would likely be most able to provide retrospective reports.

We also assessed the continued influence of unreported cases and deaths on estimates of the toll of the COVID-19 pandemic later in the year. To do this, we assumed that new cases and deaths reported to the NHSN after May 24 were accurate. To compute the count of cases and deaths at year-end, we added the NHSN estimate of cases and deaths on December 27 (the last submission of the year) to our measure of the unreported cases and deaths.

Finally, we applied an additional imputation method to obtain estimates of weekly cases and deaths prior to May 24 (rather than simply the cumulative estimate on May 24). Specifically, for each week prior to May 24, we calculate the share of pre–May 24 cases and deaths in the total population (not just nursing homes) that occurred in that week using The New York Times COVID-19 database. We assumed this share was the same as the share of pre–May 24 nursing home cases and deaths that occurred in that week and used these shares to distribute the pre–May 24 cases and deaths across weeks. For example, if The New York Times database indicated that 5% of pre–May 24 general population deaths occurred in the week ending May 10, we would assign 5% of our estimate of pre–May 24 nursing home deaths to that week. This is equivalent to assuming that the share of population cases and deaths occurring in nursing homes is constant prior to May 24.

Our primary analysis did not account for differences in state reporting requirements. To investigate how these differences might affect our estimates, we collected additional state health department data from later dates. We used these data to calculate the ratio of state estimates of post–May 24 cases and deaths to the corresponding federal estimate. If states had the exact same reporting requirements as the NHSN, we would expect these estimates to align exactly, ie, the ratio should be exactly 1. On the other hand, if state requirements were significantly more or less restrictive than the NHSN data, we would expect to see ratios significantly different from 1.

Analysis was conducted using Stata statistical software version 16.1 (StataCorp). P values were 2-sided, and statistical significance was set at P = .05. Data were analyzed from December 2020 to May 2021.

Results

The Table provides summary statistics on the full sample of 15 415 nursing homes and 2 analysis samples: 4599 facilities in 12 states with state case data, and 7405 facilities in 19 states with state death data. We found several statistically significant differences between facilities in our analysis samples and the remaining facilities. Facilities in both analysis samples had significantly more cases and deaths (using the NHSN data) than their counterparts in nonsample states by the date of the first NHSN report (mean [SD] cases per facility, 8.1 [19.9] vs 2.4 [9.2]; P < .001; mean [SD] deaths per facility, 2.2 [5.9] vs 1.4 [8.1]; P < .001). The analysis samples also included more facilities in the Northeast and West and fewer in the Midwest, more for-profit facilities, and more facilities with 150 beds or more (Table). The star rating distributions of sample and nonsample facilities were similar.

As presented in Figure 1, a mean (SE) of 43.7% (1.4%) of cases and 40.0% (1.1%) of deaths that occurred prior to May 24 were not reported to the NHSN in the analysis samples. Figure 1 also presents unadjusted and adjusted means from a linear regression of the share of cases and deaths that were not reported on facility ownership type, chain affiliation, size, and overall star rating. The adjusted means for the included covariates were between 40% and 50% for cases and between 35% and 45% for deaths. We found no statistically significant differences along these characteristics.

Figure 2, A and B, summarize the percentage of cases and deaths that were unreported as of May 24 by state. We found more variation by state than by facility characteristic: in most of our sample states, between 40% and 60% of cases as of May 24 were unreported, and between 30% and 50% of deaths as of May 24 were unreported. However, some of this variation may be attributable to differences in state reporting requirements. Importantly for our extrapolation assumption, we did not find much systematic regional correlation in this measure as of May 24.

Figure 2, C and D, show the impact of these unreported cases and deaths with year-end totals using data from December 27 (the last NHSN submission in 2020). The percentages of cases and deaths that were unreported were reduced by year-end (the overall mean in sample states was 13.9% of cases and 18.7% of deaths), reflecting the continued toll of the pandemic on nursing homes after the beginning of reliable reporting. There was also clear regional correlation in these year-end percentages, with states in the Northeast having the highest percentages, meaning that the delay in required reporting had the greatest impact on year-end totals in these states.

Using the raw NHSN data would imply that similar numbers of nursing home residents died in New York and California in 2020 (5776 in New York and 5633 in California, equating to 5.0 deaths per 100 beds in New York and 4.8 deaths per 100 beds in California). However, after accounting for unreported deaths, we estimate that nursing homes in New York experienced 9276 deaths (8.1 deaths per 100 beds), compared with 6487 in California (5.5 deaths per 100 beds). In addition to the aggregate estimates, our facility-level corrections are available online.14

Figure 3 shows the result of extrapolating the probability of nonreporting of pre–May 24 cases and deaths to nonsample states to produce national estimates of unreported cases and deaths. There were 90 264 cases and 25 355 deaths reported nationwide in the first NHSN submission on May 24. By using our adjusted regression to estimate the share of cases and deaths that were not reported at each nonsample state facility, we estimate that 68 613 cases and 16 623 deaths were omitted in the first NHSN submission owing to the lack of required retrospective reporting, implying that a mean of 43.2% of cases and 39.6% of deaths were omitted nationally. By adding these undercount estimates to the December 27 totals (the last NHSN submission of 2020), we estimate that the year-end total nursing home case count was 592 629, and the death count was 118 335. Unreported cases and deaths accounted for 11.6% and 14.0% of these totals, respectively.

Finally, Figure 4 shows these estimates in the context of the evolution of the pandemic by imputing the time pattern of cases and deaths before May 24 using case and death data for the general population. The delay in required reporting means that the NHSN data miss a significant period of the pandemic, in which cases and deaths were increasing more rapidly than any other point in 2020 except during the wave in the final months of the year.

eAppendix 3 in the Supplement shows the result of comparing state and federal data collected later in the pandemic. Ratios that are greater than 1 indicate that state data report higher cases and deaths compared with federal data, whereas ratios less than 1 indicate state data report lower cases and deaths compared with federal data. We found that for several states (ie, California, Colorado, Georgia, Kentucky, and Pennsylvania), the state and federal data for cases and deaths were in agreement after May 24, with ratios between 0.88 and 1.09. In other states, the state data had higher reported cases and deaths than the federal data (ie, Connecticut, Florida, Massachusetts, New Jersey, and Rhode Island), with ratios ranging from 1.20 to 1.57, and, in a few states, the state data are lower than the federal data (ie, New Hampshire, Tennessee, New York), with ratios ranging from 0.61 to 0.82.

Discussion

This cross-sectional study used data from 20 state health departments to evaluate and supplement federal data on COVID-19 cases and deaths in nursing homes. We estimate that 44.7% of COVID-19 cases and 40.0% of COVID-19 deaths occurring prior to May 24 were not reported in the first NHSN submission. These unreported cases and deaths had a significant influence on our estimates of total cases and deaths attributable to COVID-19 in nursing homes, accounting for 11.6% of cases and 14.0% of deaths in the year-end totals.

We did not find differences in nonreporting by facility characteristics (ie, region, ownership, chain affiliation, or star rating) as of May 24. This implies that facilities of all types omitted previous cases and deaths in the first NHSN submission. This may demonstrate a widespread inability of nursing homes to reliably collect data early in the pandemic or that pressures to report fewer cases and deaths were common to all facilities.

Accounting for this delay is important when comparing the toll of the pandemic across places. Consistent with the fact that states in the Northeast were hit hardest in the early months of the pandemic but generally experienced lower case and death rates in later months, we found that unreported cases and deaths represented a significantly larger share of year-end totals in the Northeast than in the South and West, where most cases and deaths occurred later.

Limitations

This study has some limitations. Some limitations of our estimates are the use of extrapolation from sample states to nonsample states, potentially differing reporting requirements across states, and the fact that our analysis does not include cases and deaths that were not reported to state or federal authorities. We also did not analyze reporting of staff cases and deaths. Regarding extrapolation, although facilities in sample states and nonsample states differed significantly on several important characteristics (eg, region, ownership, size), we do not find that these characteristics were associated with the likelihood of nonreporting; thus, we believe our extrapolation is reasonable. Regarding state reporting requirements, the fact that our estimates were similar for both cases and deaths is reassuring. We also used later state reports to assess the degree to which these differences may have affected our estimates. We found that some states may have defined cases and deaths more broadly than the NHSN, and others may have used more conservative definitions. For example, New York’s health department excluded resident deaths that took place outside of the facility, such as when a patient died after being discharged to a hospital.15 These findings have implications for the interpretation of our estimates: in states with broader reporting requirements, our undercount estimate may be overstated, while in states with more restrictive definitions, our undercount estimate may be understated.

Conclusions

The findings of this cross-sectional study suggest that federal NHSN data understated total COVID-19 cases and deaths in nursing homes. To date, both academic and policymakers’ analyses of facility-level determinants of infections and mortality have likely been limited owing to the reliance on federal estimates.16-18 In particular, use of the unadjusted federal data may help explain why some reports found an association between lower-rated nursing homes and COVID-19 outbreaks (a conclusion that guided early enforcement actions against nursing homes), while others did not.19-22 Our data, which we have made publicly available,14 also offer the ability to credibly study the associations of facility responses and state and federal policy in the early months of the pandemic with slowing the spread in nursing homes, which is not possible with the federal data owing to missing data.

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Article Information

Accepted for Publication: June 24, 2021.

Published: September 9, 2021. doi:10.1001/jamanetworkopen.2021.22885

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Shen K et al. JAMA Network Open.

Corresponding Author: Karen Shen, PhD, Department of Economics, Harvard University, 1805 Cambridge St, Cambridge, MA 02138 (karenshen@g.harvard.edu).

Author Contributions: Dr Shen had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: All authors.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Shen, Loomer, Gandhi.

Critical revision of the manuscript for important intellectual content: Abrams, Grabowski.

Supervision: Gandhi.

Conflict of Interest Disclosures: Dr Loomer reported receiving personal fees from the American Health Care Association outside the submitted work. Dr Grabowski reported receiving personal fees from naviHealth, Medicare Payment Advisory Commission, RTI International, Abt Associates, Analysis Group, and Compass Lexecon and grants from the National Institutes on Aging (NIA), Agency for Healthcare Research and Quality, the Arnold Foundation, and the Warren Alpert Foundation outside the submitted work. Dr Gandhi reported receiving grants from the NIA through the National Bureau of Economic Research, National Institute for Health Care Management, University of California, Los Angeles (UCLA), Ziman Center for Real Estate, UCLA Fink Center for Finance & Investment, UCLA Price Center for Entrepreneurship, the UCLA Morrison Center for Marketing and Data Analytics, Harvard Institute for Quantitative Social Sciences, and Harvard University Lab for Economic Applications Policy outside the submitted work. No other disclosures were reported.

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Tribal institute releases report examining effects of climate change on Indigenous peoples, lands and culture


Reports and Proceedings

NORTHERN ARIZONA UNIVERSITY

Researchers from the Institute of Tribal Environmental Professionals this week launched the State of Tribes and Climate Change (STACC) report, which examines the disproportionate effect climate change has on Indigenous lands and people and the added strain tribes experience as they respond to damaging climate events, which are increasing in frequency and severity.

The STACC report builds on the Intergovernmental Panel on Climate Change report, released Aug. 9. ITEP, which is a tribal institute at Northern Arizona University, brought together with more than 90 authors representing diverse entities and perspectives to create this report, which includes not only research into the effects of climate change but also the voices of Indigenous people who are on the front lines in experiencing and responding to climate disasters.

This report is the first of its kind to be produced in the United States.

“We wanted to change the discourse in this arena and ensure the tribes had their voices and recommendations amplified,” said Ann Marie Chischilly, ITEP director and interim vice president of the Office of Native American Initiatives at NAU. “It’s my hope that the federal government and its partners will take this report and support tribes more directly.”

ITEP’s Tribes & Climate Change Program spearheaded this report by convening the author team including 34 tribal authors who provided personal narratives. Nikki Cooley, the climate program’s co-manager, testified in front of the U.S. House Select Committee on the Climate Crisis in July and shared key findings and recommendations from the STACC report. Those findings include the need for greater meaningful participation of Native American and Alaska Native researchers and communities in future climate assessments as well as looking more closely at the on-the-ground work tribes are doing every day.

“Tribes are investing efforts in adaptation planning and projects to keep their communities, ecosystems and people healthy,” she said. “In doing so, they are implementing the most cutting-edge work on climate. Tribal nations are actively creating climate vulnerability assessments, adaptation plans, and hazard mitigation plans. Protecting traditional knowledges is an important part of these processes. Locally relevant and regionally specific information is needed to understand local climate impacts and develop solutions that incorporate local, traditional and western knowledge for holistic solutions.”

In making decisions about protecting land, air and water, governments should work with tribal nations and support their sovereignty and self-determination in protecting these resources. Such collaboration also will inform governments about Indigenous peoples’ worldviews, which center relationality, responsibility and reciprocity. Collaborations need to address the sacred and communal nature of resources.

“I stress the importance of acknowledging the unique challenges Native American and Alaska Native villages face when it comes to the climate crisis,” Cooley said. “Many of our people live in rural and underserved areas where they have little to no access to water, food and emergency services. The lack of infrastructure on most tribal communities is increasing the stress on the people, natural environment and the costs of maintaining.”

The STACC report also will be cited in an Indigenous peoples section in the North America chapter of the sixth IPCC Assessment Report, Climate Change 2022: Impacts, Adaptation, and Vulnerability that will be published in February. ITEP TCCP co-managers Cooley and Karen Cozzetto and former ITEP staff Dara Marks-Marino were part of the authorship team contributing to the Indigenous Peoples section. It also will be cited in the Indigenous Peoples and Tribes section of the upcoming fifth National Climate Assessment. 

Other partners include the Bureau of Indian Affairs Tribal Climate Resilience Program.

Read the report.

UNH receives $1.8 million grant to study road resilience to sea level rise


Grant and Award Announcement

UNIVERSITY OF NEW HAMPSHIRE

Flooding and road resilience 

IMAGE: CAR ATTEMPTS TO PASS THROUGH A FREQUENTLY FLOODED ROAD IN HAMPTON, N.H. DURING ANOTHER BOUT OF FLOODING CAUSED BY A KING TIDE. view more 

CREDIT: TIM BRIGGS / UNH/NHSG

DURHAM, N.H.— After a summer of high heat, steady sea level rise and devastating hurricanes, coastal roads have continued to take a severe beating resulting in endless wear and tear. Because these roadways have become increasingly vulnerable, the National Oceanic and Atmospheric Administration (NOAA) has awarded a $1.8 million grant to researchers at the University of New Hampshire to study how and why coastal hazards like excessive flooding are causing roads to crack and crumble and find ways to protect them.

“We’re trying to better understand the causal links of not only the extreme events but also the gradual changes in sea level rise that can increase the rate of damage to pavement and trigger failures that require major road reconstruction,” said Jo Sias, professor of civil and environmental engineering. “We’re looking at storm surges and wave action but also factors like the amount of time the pavement is under water.”

The focus of the project is to understand the combined hazards of overtopping and subsurface moisture – flooding from above and below the road. UNH researchers – and their partners at the University of South Alabama and the Rockingham Planning Commission - will develop a number of hydrodynamic models that can analyze fluids in motion. They will use new data collected in the field as well as historical information to create high-resolution models to study groundwater and pavement as well as perform an adaptation impact assessment to develop a toolkit to help assess the vulnerability of roadways to flooding hazards. Researchers say that while engineers have investigated these impacts independently, an approach is needed that combines the different effects to better evaluate options when it comes to pavement alternatives. This information will be valuable to state and town officials to assess the impact of sea level rise on the longevity of coastal roadways and help implement practical alternatives for communities to protect the infrastructure.

“It’s important to prioritize and share this information so we can create important decision-making tools, identify institutional barriers and develop policies needed to update state transportation agency coastal resilience practices,” said Sias. “Improving coastal roads to withstand the increasing water hazards is important not only for transportation and the people who live there but also for the overall economy and ecosystems in the area.”  

Taking into account factors like climate change and shifting weather patterns, the study will focus on two geographically and geologically diverse coastal regions – the northeast coast of New Hampshire and the southeast coast of Alabama. The research team will work with key end-users to determine adaptation approaches and new management policies and practices for transportation agencies that positively impact surrounding communities.

The grant is part of NOAA’s Effects of Sea Level Rise Program (ESLR) which is based in the National Centers for Coastal Ocean Science (NCCOS). The ESLR provides a suite of science products to inform coastal managers of local coastal vulnerability and solutions to mitigate flood risk.

NCCOS delivers ecosystem science solutions for NOAA’s National Ocean Service and its partners, bringing research, scientific information and tools to help balance the nation’s ecological, social and economic goals.

The University of New Hampshire inspires innovation and transforms lives in our state, nation and world. More than 16,000 students from all 50 states and 71 countries engage with an award-winning faculty in top-ranked programs in business, engineering, law, health and human services, liberal arts and the sciences across more than 200 programs of study. As one of the nation’s highest-performing research universities, UNH partners with NASA, NOAA, NSF and NIH, and receives more than $110 million in competitive external funding every year to further explore and define the frontiers of land, sea and space. 

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PHOTOS FOR DOWNLOAD

https://www.unh.edu/unhtoday/sites/default/files/media/flooding_hampton_car_briggs.jpeg

Caption: Car attempts to pass through a frequently flooded road in Hampton, N.H. during another bout of flooding caused by a king tide.

Photo Credit: Tim Briggs / UNH

 

https://www.unh.edu/unhtoday/sites/default/files/media/flooding_aerial_hampton_briggs.jpeg

Caption: Drone shot of section of Hampton, N.H. that experiences excessive flooding which continually submerges coastal roadways.

Photo Credit: Tim Briggs / UNH

 

https://www.unh.edu/unhtoday/sites/default/files/media/flooding_manchester_st_hampton_briggs.jpeg

Caption: Increased sea level rise leaves roads in a section of Hampton, N.H. underwater during a king tide.

Photo Credit: Tim Briggs / UNH

 

https://www.unh.edu/unhtoday/sites/default/files/media/flooding_street_sign_briggs.jpeg

Caption: Street sign and mailbox along a road in Hampton, N.H. rise out of flood waters during a king tide.

Photo Credit: Tim Briggs / UNH

Landmark global report: 1 in 3 tree species face extinction


First report to document the conservation status of the world’s tree species


Reports and Proceedings

THE MORTON ARBORETUM

Quercus brandegeei, a threatened oak endemic to Mexico. 

IMAGE: QUERCUS BRANDEGEEI, A THREATENED OAK ENDEMIC TO MEXICO. CREDIT: THE MORTON ARBORETUM. view more 

CREDIT: THE MORTON ARBORETUM

LISLE, Ill. (September 9, 2021)—One in three trees worldwide are facing extinction, with human use among the greatest threats, according to the first State of the World’s Trees report published September 1 by Botanic Gardens Conservation International (BGCI).

According to the report, 30% (17,500) of the world’s 60,000 tree species are currently at risk of extinction. That is twice the number of threatened mammals, birds, amphibians and reptiles combined. Of those, more than 440 species of trees are on the brink of extinction, meaning fewer than 50 individuals of those species remain in the wild.

The report marks a major milestone in the Global Tree Assessment (GTA) initiative, and is the result of five years of research among more than 60 institutions to identify major gaps in tree conservation efforts. The Morton Arboretum, BGCI-US, NatureServe and the United States Botanic Garden (USBG) collaborated to provide a major contribution to the global report by delivering the first-ever comprehensive assessments of all 841 native continental U.S. tree species. Nearly all U.S. tree assessments have been completed, revealing that an estimated 1 in 10 U.S. tree species are threatened with extinction. The full report on the conservation status of all U.S. trees will be available in early 2022.

“This assessment makes clear that the world’s trees are in danger,” said Gerard T. Donnelly, Ph.D., president and CEO of The Morton Arboretum. “As keystone species in forest ecosystems, trees support many other plants and living things that are also vanishing from the planet. Saving a tree species means saving much more than the trees themselves,” he added.

The greatest threats facing trees, according to the report, are habitat loss from agriculture and grazing, followed by over-exploitation from logging and harvesting. Climate change and extreme weather are emerging threats to tree species globally, and many trees risk losing areas of suitable habitat, it concludes. This affects species in both temperate and tropical habitats, with cloud forest tree species of Central America being at particular risk. 

At least 180 tree species are directly threatened by sea level rise and severe weather events. These threats are most severe to island species, including magnolias in the Caribbean. 

The report finds hope for the future, however, as conservation efforts led by the botanical community worldwide are growing. At least 64% of all tree species can be found in at least one protected area, and about 30% are in botanical garden collections and seed banks. However, tree conservation researchers stress that further action is needed.

“We can’t lose sight of the impact that botanical institutions can have through globally coordinated efforts,” said Murphy Westwood, Ph.D., the Arboretum’s vice president of science and conservation. “Conservationists have known that many tree species are threatened, but we now have a comprehensive roadmap for action with properly outlined priorities and a valuable tool for discussions with policymakers,” she added.

The Arboretum has led or contributed to more than 1,000 tree assessments since 2015. This includes several landmark publications such as The Red List of Oaks 2020, the first comprehensive assessment of the world’s oak species, which found that nearly one-third of all oaks are at risk of extinction. Previous reports also include The Red List of US Oaks in 2017 and The Red List of Fraxinus—the first comprehensive global assessment of ash trees, which first identified five North American ash species as critically endangered due to the emerald ash borer pest.

The collaborators plan to catalyze action among policymakers and conservation experts internationally. To aid action, BGCI is launching a new GlobalTree Portal, an online database tracking conservation efforts for trees at species, country and global levels. 

Both the report and portal show for the first time which trees need the most protection, where action is needed most urgently and, most importantly, where the gaps in conservation efforts exist so that resources and expertise can be deployed most effectively.

However, Westwood noted that it’s not just experts and politicians who can have an impact. “Saving trees is a global effort that requires local action,” she stressed. “Individuals and community organizations can support the planting of native and threatened tree species through their local arboretum or botanical garden, or by simply planting the right tree in the right place, and giving it the right care so that it can thrive for many years to come.” 

The full State of the World’s Trees report is available on The Morton Arboretum website

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About The Morton Arboretum

The Morton Arboretum is a global leader and collaborator in tree science and conservation. Its Center for Tree Science brings together researchers to develop scientific knowledge and technical expertise to address the key challenges facing trees. The Global Tree Conservation Program works with the botanical garden community to prevent the extinction of priority threatened tree species in biodiversity hotspots around the world. The Arboretum founded the Chicago Region Trees Initiative, a coalition that includes 200 organizations working to improve the health of the regional forest; and ArbNet, an international community of arboreta working to advance the purposes of tree-focused public gardens, and the only accreditation program specific to arboreta. 

About Botanic Gardens Conservation International

Botanic Gardens Conservation International (BGCI) is the world’s largest plant conservation network, comprising more than 650 botanical institutions in over 100 countries. Established in 1987, BGCI is a registered charity with offices in the UK, US, Singapore, China and Kenya. BGCI leverages the expertise at botanic gardens worldwide for tree conservation with the Global Tree Assessment, BGCI’s Tree Conservation Fund and the Global Trees Campaign.

 


How serotonin curbs cocaine addiction


By identifying the role of serotonin during cocaine use, UNIGE scientists explain why only one in five persons becomes addicted to this drug.

Peer-Reviewed Publication

UNIVERSITÉ DE GENÈVE

How serotonin curbs cocaine addiction 

IMAGE: COCAINE ADDICTION AFFECTS ONE IN FIVE PEOPLE. view more 

CREDIT: © UNIGE, CHRISTIAN LÃœSCHER

Contrary to common thinking, cocaine triggers an addiction only in 20% of the consumers. But what happens in their brains when they lose control of their consumption? Thanks to a recent experimental method, neuroscientists at the University of Geneva (UNIGE), Switzerland, have revealed a brain mechanism specific to cocaine, which has the particularity of triggering a massive increase in serotonin in addition to the increase in dopamine common to all drugs. Indeed, serotonin acts as an intrinsic brake on the overexcitement of the reward system elicited by dopamine, the neurotransmitter that causes addiction. These results are published in the journal Science.

Addiction is defined as the compulsive search for a substance despite the negative consequences, whereas dependence is characterised as the occurrence of a withdrawal symptom — the physical effects of which vary greatly from one substance to another — when consumption is stopped abruptly. It thus affects everyone, whereas addiction affects only a minority of users, even after prolonged exposure. For example, it is estimated that 20% of cocaine users and 30% of opiate users are addicted. “The same principle applies to all potentially addictive products”, says Christian Lüscher, a professor in the Department of Basic Neurosciences at the UNIGE Faculty of Medicine, who led the research. “Here in Switzerland, for instance, almost all adults consume alcohol from time to time, which is a strong stimulator of the reward system. However, only a small proportion of us will become alcoholics.”


Addiction triples without serotonin

To assess how cocaine addiction arises in the brain, the research team developed a series of experiments. “Most of the time, scientific experiments aim to reproduce a systematic mechanism. Here, the difficulty lies in observing a random phenomenon, which is triggered only once in five times”, explains Yue Li, a researcher in Christian Lüscher’s laboratory and first author of the study.

The scientists first taught a large group of mice to self-administer cocaine voluntarily, and then added a constraint: each time they self-administered cocaine, the mice received a slightly unpleasant stimulus (electric shock or air jet). Two groups then emerged: 80% of the mice stopped their consumption, while 20% continued, despite the unpleasantness. “This compulsive behaviour is precisely what defines addiction, which affects 20% of individuals, in mice as well as in humans”, emphasises Vincent Pascoli, a scientific collaborator in the Geneva group and co-author of this study.

The experiment was repeated with mice in which cocaine was no longer linked to the serotonin transporter, so that only dopamine increased when the substance was taken. 60% of the animals then developed an addiction. The same was found in other animals with a reward system stimulation protocol that did not affect serotonin. “If serotonin is administered to the latter group, the rate of addiction falls to 20%”, says Christian Lüscher. “Cocaine therefore has a kind of natural brake that is effective four times out of five.”


A delicate synaptic balance

When cocaine is consumed, two forces are at work in the brain: dopamine on the one hand, whose sudden increase leads to compulsion, and serotonin on the other, which acts as a brake on compulsion. Addiction therefore occurs when an imbalance is created between these two neuroregulators and dopamine overtakes serotonin.

“Actually, dopamine triggers a phenomenon of synaptic plasticity, through the strengthening of connections between synapses in the cortex and those in the dorsal striatum. This intense stimulation of the reward system then triggers compulsion. Serotonin has the opposite effect by inhibiting the reinforcement induced by dopamine to keep the reward system under control”, explains Christian Lüscher.


What about other drugs?

Apart from the increase in dopamine, each substance has its own specificity and effect on the brain. If the addictive effect of cocaine is naturally reduced by serotonin, what about other drugs? The Geneva neuroscientists will now look at opiates — which are more addictive than cocaine — and ketamine, which is much less so. The aim is to understand in detail how the brain reacts to these drugs and why some people are much more vulnerable to their harmful effects than others.

 

Young female black bears in Asheville, North Carolina, are big, have cubs early


Peer-Reviewed Publication

NORTH CAROLINA STATE UNIVERSITY

Black bears (Ursus americanus) reproduced at a younger age in urban areas and were nearly twice the size of bears in national forests shortly after their first birthday, researchers from North Carolina State University and the N.C. Wildlife Resources Commission found in a new study.

Published in the Journal of Mammalogy, the study of the reproduction and size of wild black bears living in and around the city of Asheville, North Carolina, has important implications for managing urban bear populations. Also, the results raise questions about the foraging activities and diet of urban bears, and whether food from people or an abundance of natural food could be providing the bears with a reproductive advantage.

“Some of the bears in Asheville are reproducing at a young age, and they are big,” said the study’s lead author Nick Gould, postdoctoral research scholar at NC State. “It definitely leads us, as researchers, to ask additional questions: What’s driving this kind of weight gain in young bears this early in life? Are they eating natural foods, bird seed and ornamental fruit, or feeding on residential garbage?”

For this study, researchers collaborated with the residents of Asheville to capture black bears on private property between April 2014 and September 2018. The bears were temporarily sedated and then released on-site where they were captured. Researchers collected data on the bears’ weight, age, general condition, sex, and other information. The research team used GPS-equipped radio collars, which were designed to fall off naturally or to be released remotely, to track the female bears to their den sites to monitor reproductive activity.

They collected data on a total of 36 female bears around 1 year of age in Asheville. As a point of comparison, the researchers used data from three previous studies of bears living in rural areas in national forests in North Carolina and Virginia. They compared the data for urban female yearling bears to data for 95 female yearlings in rural forested areas.

Researchers determined that the 36 female bears in Asheville weighed an average of nearly 100 pounds at 1 to 1 and a half years of age. In contrast, the sample of 95 female bears living in the three national forests weighed an average of 50 pounds at a similar age.

Of the 12 female bears they were able to track back to their dens by their second birthdays in Asheville, seven produced a total of 11 cubs. In comparison, none of the three studies of bears in rural forested areas found that bears produced cubs by their second birthday.

“We didn’t expect 2-year-old females to be giving birth,” Gould said. “Based on what we know about black bears, we thought we’d see litters from bears 3 years of age and older. These results open up new areas of research to learn more about wildlife living among people in developed areas.”

The researchers analyzed and compared the availability of an important natural food source for bears – acorns and other nuts – and didn’t find differences that could help explain the larger size of these young female bears in the city.

However, researchers didn’t examine the availability of other important natural food sources like berries and didn’t investigate whether bears were eating bird seed or food and garbage left out by people. That is the current focus of ongoing research in the Asheville area led by study co-author Chris DePerno, professor of fisheries, wildlife and conservation biology at NC State, with Gould and graduate student Jennifer Strules.  

“Interestingly, natural food production, in the form of nuts and other ‘hard mast’ food sources, did not influence cub production for urban bears,” Gould said. “We are left to conclude that either natural foods, in the form of soft mast like berries, or anthropogenic food sources in the form of garbage, bird seed, ornamental fruit trees or intentional feeding by people, is influencing the weight gain and early reproduction.”

Reproduction is one piece of the equation needed to better understand black bears inhabiting urban areas. Researchers said that while they appear to be reproducing earlier in some cities, urban bears’ mortality may also be high, as they are more likely to be involved in collisions with vehicles.

“If mortality is high enough to exceed reproduction, then that population is likely going to be a sink,” Gould said. “If bears are attracted to Asheville, and they establish residency because of the supplemental food sources it offers, they’re also going to be exposed to collisions with vehicles, legal harvest, and other anthropogenic threats, and therefore mortality may outpace reproduction, suggesting the population might be functioning as a sink.”

This study is part the North Carolina Urban/Suburban Bear Study, initiated by NC State and the N.C. Wildlife Resources Commission to understand urban black bears’ survival and causes of mortality, movements, reproduction and other factors when they live around cities. The goal is to help wildlife managers develop better policies for bears and other wildlife near cities.

“The entire objective is to help the Wildlife Commission better manage bears,” DePerno said. “The urban-rural interface is larger with population growth and development, and that puts greater pressure on wildlife populations.

“We have a situation in western North Carolina where we have people in a wonderful area with a lot of bears,” he added. “We want to understand: Is this a source or sink population? Are the bears moving into huntable areas? Are they considered more as residential or transitory? We are trying to understand what these bears are doing and their entire life history, including what is killing them, and what are they eating.”

Researchers also want to help educate the public.

“We want to provide good information about black bears based on the science, so we can help guide people in urban areas with bears, in Asheville or otherwise, to live responsibly with them,” Gould said.

The study, “Growth and reproduction by young urban and black rural bears,” was published online July 10, 2021, in the Journal of Mammalogy. In addition to Gould and DePerno, the other co-authors were Roger Powell and Colleen Olfenbuttel. The project was funded by the Pittman-Robertson Federal Aid to Wildlife Restoration Grant and is a joint research project between the N.C. Wildlife Resources Commission and the Fisheries, Wildlife, and Conservation Biology Program at North Carolina State University

-oleniacz-

Note to editors: The abstract follows.

“Growth and reproduction by young urban and rural black bears”

Authors: Nicholas P. Gould, Roger Powell, Colleen Olfenbuttel and Christopher S. DePerno

DOI: 10.1093/jmammal/gyab066

Published online in the Journal of Mammalogy on July 10, 2021

Abstract: Human-dominated landscapes contain fragmented natural land cover interspersed throughout an urban matrix. Animals that occupy human-dominated landscapes often grow and reproduce differently than conspecifics. Female American black bears (Ursus americanus) produce litters for the first time usually at age 4 years; 2-yearolds rarely give birth. We visited winter bear dens and trapped bears in spring and summer to compare the reproductive output and weight of female black bears within the city limits of Asheville, North Carolina, and three forested rural sites in North Carolina and Virginia representative of the undeveloped habitat of Asheville. Urban yearling females weighed nearly double (45.0 kg ± 8.1 [± SD]; n = 36) that of yearling females from the three rural study sites (23.2 ± 8.5 [Pisgah], 23.6 ± 8.3 [Virginia SW], and 23.9 ± 9.7 [Virginia NW]; n = 95). Across all sites, hard mast production during the autumn, when females were cubs, did not affect their weights as yearlings. Seven of 12 (58%) 2-year-old urban bears produced 11 cubs (mean litter size = 1.6 ± 0.8), but no 2-year-old rural females produced cubs. Production of hard mast in the autumn, when females were yearlings, did not influence cub production by 2-year-old female bears at the urban site. We hypothesize that reproduction by 2-year-old bears is linked to the availability of anthropogenic food sources associated with urban environments. To inform population level management decisions, managers and researchers should quantify urban food sources and the effects on black bear life history. If high fecundity allows urban populations to sustain relatively high mortality rates, then urban bear populations may be source populations for surrounding, rural areas. Alternately, if reproduction in urban populations cannot match high time-specific or age-specific urban mortality rates, then urban populations may be sinks for the surrounding areas.

Meat and Dairy Industry 'Fanning the Flames' of Climate and Biodiversity Crises: Report

Bolstering the case for urgent policy change, the sector's top 20 companies collectively produce more planet-heating emissions than some fossil fuel giants and European countries.



A new report highlights how giants of the meat and dairy industry contribue to the climate and biodiversity crises. 
(Photo: Tim Geers/Flickr/cc)

JESSICA CORBETT
September 7, 2021

A report released Tuesday by European campaigners highlights how the global industrial animal farming sector, backed by billions from major financial institutions, is fueling the intertwined climate and biodiversity crises—and what policymakers can do to better protect people and the planet.

"The food and farming sector in industrialized countries, which accounts for about one-third of global greenhouse gas emissions, is far from doing its fair share to reduce them."
—Meat Atlas 2021

Meat Atlas 2021 (pdf)—published by Friends of the Earth Europe, its German arm Bund für Umwelt und Naturschutz, and the Berlin-based Heinrich-Böll-Stiftung—says the food sector is responsible for 21% to 37% of planet-heating emissions, over half of which comes from industrial animal farming.

Along with featuring "facts and figures about the animals we eat," the report explains that scientists have been "stressing for over a decade that a climate- and biodiversity-friendly diet contains less than half the amount of meat consumed in industrialized countries today."

"However, an ambitious and dedicated political shift in agriculture and food policy to tackle the climate crisis seems far away," the report continues. "The food and farming sector in industrialized countries, which accounts for about one-third of global greenhouse gas emissions, is far from doing its fair share to reduce them."

Leaders at the three groups behind the atlas argue in its introduction that "contrary to what politicians might claim, laws and regulations can steer our consumption decisions in favor of sustainability and health. There are numerous instruments for this: fiscal, informational, and legal."

"European and national food strategies should contain such instruments, as well as those which support sustainable livestock breeding and a transition of the industry towards more locally embedded models in order to create fair and sustainable food environments," the trio writes. "They should also reinforce environmental and social laws as well as animal welfare legislation in order to shift the focus of current industrial meat production to quality instead of quantity."

The atlas uses several data points to make the case that industrial farming is wreaking havoc on the planet—including findings from 2018 that "just five meat-and-milk giants, JBS, Tyson, Cargill, Dairy Farmers of America, and Fonterra, produce more combined emissions per year than major oil players like Exxon, Shell, or BP. Taken together, 20 livestock firms are responsible for more greenhouse gas emissions than Germany, Britain, or France."


Although some animal farming industry giants are privately owned, the atlas acknowledges, "others are at least partially listed on the stock exchanges" and "financial firms are major investors, underwriters, and lenders to the sector."

More than 2,500 investment banks, private banks, and pension funds poured $478 billion into meat and dairy firms from 2015 to 2020, the report says, emphasizing that BlackRock, Capital Group, Vanguard, and the Norwegian government pension fund are among the top investors.

"While many financiers have made commitments to environmental policies and targets," the atlas explains, "the impacts of industrial-scale agriculture are yet to be regulated across financial and legal platforms."

Meat Atlas 2021 also explores various other aspects of the industry including consolidation, trade policies, pandemic risk, land conflicts, water use, pesticides, and microbial resistance. According to the report, key takeaways include:
More than one billion people around the world earn their living by keeping livestock. Traditional and nature-friendly animal husbandry is coming under pressure from industrialized agriculture.
Almost two-thirds of the world's 600 million poor livestock keepers are women. They face disadvantages because they have limited access to land, services, and farm ownership.
Conflicts over land are on the rise, in part because of industrial meat production. More and more people are being killed for defending the right to land.
The use of antibiotics in animal husbandry is resulting in more and more microbial resistance. This threatens the effectiveness of antibiotics, one of the most important types of treatment in human medicine.

The leading producers of fodder crops are among the largest users of pesticides—which contaminate groundwater and harm biodiversity.

"Industrial meat farming is fanning the flames of climate crisis and biodiversity collapse while threatening the health of farmers, workers, and consumers—the evidence is resounding," said Stanka Becheva, food and agriculture campaigner at Friends of the Earth Europe, in a statement Tuesday.

Though Becheva took aim at the European Union's policymakers in particular, her message about what changes are needed has broader applicability.

"The E.U. needs to curb this insatiable industry, but right now its leaders are just eating out of Big Agribusiness' hand," she said. "Europe must act to clamp down on deforestation and human rights violations in supply chains, facilitate the switch to more plant-based diets, and redirect billions of euros of subsidies and finance to small sustainable farmers."

Surveys suggest such moves would be popular, Heinrich-Böll-Stiftung president Barbara Unmüßig pointed out Tuesday.

"As the polls in this Meat Atlas 2021 show, the younger generations in Germany—but also in other countries—share this critical assessment: They no longer accept the meat industry's business model," Unmüßig said.

"More than 70% of German young adults are willing to pay more for meat if the production conditions change fundamentally," she explained. "But the most decisive result: a huge majority of over 80% see politics in the duty to finally set binding conditions for a climate-friendly agriculture, better animal husbandry and a climate-friendly diet."



Meat production "is expected to increase by another 40 million tonnes a year by 2029," which "would take the total output to around 366 million tonnes a year, unless policy changes intervene," according to the atlas. "Although 80% of the growth is likely to take place in the Global South, the biggest producers will remain China, Brazil, the USA, and the members of the European Union."

Noting that "the economic interests of the meat industry, which is worth billions, and the refusal of politicians to reform strategically and coherently are keeping us on a tortuous path overstretching the ecological limits of the planet," Unmüßig warned that "the way things are, we will need to reduce meat production by half."

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