Tuesday, January 05, 2021

 

Public Health Messaging in an Era of Social Media

JAMA. Published online January 4, 2021. doi:10.1001/jama.2020.24514

Public health organizations have always used messaging to educate the public in an attempt to control the spread of epidemic diseases. Early efforts that relied on word-of-mouth communication and poster campaigns transitioned to radio and television as those technologies emerged, yet these forms of communication likely have become less effective in a crowded, noisy, and confrontational online environment. Over the past decade, emerging digital platforms have become sophisticated, targeted, and responsive in reaching and influencing the public.

Widespread physical distancing during the coronavirus disease 2019 (COVID-19) pandemic has limited informal social interactions and exposure to signage is considerably lessened as many people limit travel, order products online, and work from home. An effective messaging strategy will require meeting people where they are and through the information networks and devices they use for day-to-day interactions. As of December 27, 2020, more than 80 million people have contracted COVID-19 and 1.7 million have died. The need for effective public health messaging about community spread, prevention measures, and vaccines is more important than ever.

Digital platforms are powerful yet underused tools for engaging the public and should be considered essential for public health preparedness, response, and recovery. This Viewpoint explores the following 4 strategies to advance public health messaging during this and future public health emergencies: deploying countermeasures for misinformation, surveillance of digital data to inform messaging, partnering with trusted messengers, and promoting equity through messaging.

Misinformation Is a Public Health Crisis

Misinformation is a serious threat to public health, especially during pandemics. Misinformation has likely accelerated the spread of COVID-19 by fragmenting and influencing the response to prevention strategies like wearing a mask and physical distancing. Misinformation has emerged about nearly every aspect of the pandemic, including the origins of the virus (eg, it was manufactured in a laboratory), treatments (eg, bleach, alcohol), and vaccine safety (eg, vaccines include embedded microchips).

Misinformation is very difficult to correct because it is massive in volume, contagious, and can appear to come from trusted social networks. Because misinformation is not labeled, distinguishing it from credible information can be increasingly difficult, particularly when highly politicized and opposing views are categorized as fake news. Furthermore, misinformation is being distributed from multiple sources, including government leaders in the US and reportedly from highly organized groups in Russia and other countries.

Recent research is informing the development of approaches to counteract misinformation. Prior studies support a “find and replace” approach that includes posting timely corrections about what is false and why and frequently reiterating accurate information.1 The vaccine hesitancy literature also suggests the importance of understanding the origin of rumors and false information.2 This approach can help to understand the concerns and ideologies that can inform development of thoughtful and responsive countermeasures. Because each social media platform has different features and users, the strategies that public health organizations use to address misinformation should also be distinct and responsive to the nuances of each site. A study of 8 million posts about COVID-19 on 5 social media platforms (Twitter, Instagram, YouTube, Reddit, and Gab) demonstrated variability in both the content of accurate and inaccurate information and ways in which it spread across each site.3 Sites varied in the volume of COVID-19–related messages about topics like cures, therapies, and protection advice; for example, Reddit limited the influence of questionable sources, whereas Gab increased the diffusion of questionable sources.

Public health organizations should also work with technology companies that have platforms with the infrastructure to label misinformation and limit its spread. In response to COVID-19, the World Health Organization partnered with several entities (eg, Facebook, Twitter) to address false information and promote health updates.

In the midst of this pandemic, there is an immediate need to evaluate the effectiveness of these and other countermeasures against misinformation. It will be difficult to make progress toward an unmeasured and understudied problem. Identifying misinformation as a public health crisis has the potential to bring the full weight of public health organizations and other entities toward addressing this problem.

Surveillance of Digital Data to Inform Public Health Messaging

Data from social media can provide insights about the response of the public to preventive health measures. For example, information from social media, digital platforms (eg, OpenTable, Google), and remote sensors is already being used to track the movement of populations and understand where individuals are adhering to guidelines about physical distancing and where they are not. A study of 580 million tweets posted during the early months (ie, January to May 2020) of the pandemic showed that the geographic information associated with tweets can be used as a proxy for human mobility.4 Public health organizations can use this real-time data to inform messaging content and where to target hyperlocal messages about public health measures (eg, shelter in place).

In addition to serving as a proxy for behavioral responses, social media data can also provide information about public opinion and sentiment in response to public health interventions. Traditional approaches for assessing public attitudes and well-being rely on phone or mail surveys. Delays in the availability of these types of data limit their use for timely surveillance in evolving crises. During the early phases of the COVID-19 pandemic, the Penn Medicine Center for Digital Health and the World Well-Being Project launched a public-facing platform that uses machine learning approaches to synthesize data from Twitter about COVID-19 in real time.5 Based on data from this platform and others and to enhance public awareness, the Washington State Department of Health posts weekly online behavioral health situation reports containing regional estimates of sentiment, loneliness, and anxiety.6 This approach could be further harnessed to support situational awareness and enable more directed health messaging to address well-being and population-level mental health needs in response to COVID-19.

Align With Trusted Messengers

Individuals often rely on information that is passed along from people they know and respect. Community organizers and political campaign strategists invest in identifying individuals embedded in communities to help spread important information to their friends and neighbors. Similarly, public health organizations should partner with community influencers (eg, community health workers, religious leaders) who can help with propagating trustworthy messages and dismantling false ones through online channels. In the case of vaccine campaigns, trusted messengers are often engaged to disseminate information about vaccine safety.2

Public health organizations can also learn from social movements such as #BlackLivesMatter and #MeToo in elevating the ideas and experiences of young and often marginalized groups to shape a national dialogue that leads to change. Frontline leaders for these movements are often trusted messengers that public health organizations can collaborate with when trying to access vulnerable communities. Messages crafted and disseminated by these influencers will likely reach more people and be more persuasive than those created solely by organizations. Furthermore, messages that speak to multiple facets of what people are experiencing (eg, COVID-19 and racism, anxiety, unemployment, caregiving, homeschooling, or isolation) and leverage narratives and storytelling are likely to be effective.

Equity and Public Health Messaging

To date, Black, Latino, and Native American communities are disproportionately affected by COVID-19 and its economic consequences. Conversations about equity are at the forefront of national consciousness as a result of the intersecting events of the COVID-19 pandemic and the killing of George Floyd and other Black individuals. Racism is a centuries-old construct that will not be dismantled with a series of hashtags and Instagram stories. However, social media is an important tool that public health organizations can use to address racism and equity with the same focus as the messages being deployed about COVID-19.

Inequalities contribute to how different groups access, process, and share information. Research from the H1N1 pandemic highlighted how differences in exposure to information and differences in modes of delivery could influence the response to the outbreak.7 Structural barriers should also be addressed. Disparities in access to broadband and Wi-Fi limit exposure and access to digital content for some populations. This has been of particular salience for online public school education during this pandemic and supports the need for equity in access to technology infrastructure as well as a diversity of communication approaches.

Attentiveness to Risks and a Need for Adaptability

There are notable risks (eg, violations of privacy, data security, perpetuation of bias) with engaging on social media that must be carefully considered for deploying health messaging online. Yet, not expanding traditional approaches and engaging with the full complement of available digital strategies represents a missed opportunity. Just as communication approaches have evolved over time to respond to each emerging public health emergency, there is now urgency for harnessing new approaches to effectively engage the public.

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Article Information

Corresponding Author: Raina M. Merchant, MD, MSHP, University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA 19104 (raina.merchant@pennmedicine.upenn.edu).

Published Online: January 4, 2021. doi:10.1001/jama.2020.24514

Conflict of Interest Disclosures: Dr Merchant is funded by grant R01HL141844 from the National Heart, Lung, and Blood Institute, grant R01HG009655 from the National Human Genome Research Institute, and grant R21 1DA050761 from the National Institute on Drug Abuse. Dr South is funded by award 76233 from the Robert Wood Johnson Foundation Harold Amos Medical Faculty Development Program. Dr Lurie is an employee of the Coalition for Epidemic Preparedness Innovation.

Additional Information: Dr Lurie served as Assistant Secretary for Preparedness and Response in the US Department of Health and Human Services from 2009 to 2017.

References
1.
Vraga  EK, Bode  L.  Correction as a solution for health misinformation on social media.   Am J Public Health. 2020;110(S3):S278-S280. doi:10.2105/AJPH.2020.305916PubMedGoogle ScholarCrossref
2.
Larson  H.  Stuck: How Vaccine Rumors Start—And Why They Don’t Go Away. Oxford University Press; 2020.
3.
Cinelli  M, Quattrociocchi  W, Galeazzi  A,  et al.  The COVID-19 social media infodemic.   Sci Rep. 2020;10(1):16598. doi:10.1038/s41598-020-73510-5PubMedGoogle ScholarCrossref
4.
Huang  X, Li  Z, Jiang  Y, Li  X, Porter  D.  Twitter reveals human mobility dynamics during the COVID-19 pandemic.   PLoS One. 2020;15(11):e0241957. doi:10.1371/journal.pone.0241957PubMedGoogle Scholar
5.
Guntuku  SC, Sherman  G, Stokes  DC,  et al.  Tracking mental health and symptom mentions on Twitter during COVID-19.   J Gen Intern Med. 2020;35(9):2798-2800. doi:10.1007/s11606-020-05988-8PubMedGoogle ScholarCrossref
6.
Washington State Department of Health. Behavioral health resources and recommendations: weekly situation reports. Accessed November 18, 2020. https://www.doh.wa.gov/Emergencies/COVID19/HealthcareProviders/BehavioralHealthResources
7.
Lin  L, Savoia  E, Agboola  F, Viswanath  K.  What have we learned about communication inequalities during the H1N1 pandemic: a systematic review 
           of the literature.   BMC Public Health. 2014;14:484. doi:10.1186/1471-2458-14-484


Public Mobility and Social Media Attention in Response to COVID-19 in Sweden and Denmark

JAMA Netw Open. 2021;4(1):e2033478. doi:10.1001/jamanetworkopen.2020.33478
Introduction

To reduce the spread of coronavirus disease 2019 (COVID-19), countries have implemented different societal interventions, and evaluation of the effect on public response is needed.1 Sweden and Denmark are comparable countries in terms of health care and sociodemographic characteristics; however, Denmark was one of the first countries to enforce lockdown and subsequent gradual reopening, whereas Sweden has had few restrictions, largely limited to public recommendations. We assessed public mobility and social media attention associated with COVID-19 spread and societal interventions from February 15 to June 14, 2020, in Denmark and Sweden.

Methods

For this ecological study, public mobility was measured by Google mobility reports, providing daily percentage change in the number of visitors to public spaces compared with baseline (January 3 to February 6, 2020).2 We focused on mobility across retail and recreational spaces based on previous findings.3 Daily volume of tweets including COVID-19–related hashtags and keywords (eTable in the Supplement) were collected via Sprout Social to study public attention to the disease, capturing 732 634 tweets in Sweden and 324 730 in Denmark. Because this study used summary statistics rather than individual-level data, an institutional ethical review permit was not required according to the rules at Karolinska Institutet, Sweden. This study followed the part of the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline that are relevant to ecological studies.

The exposures that we focused on were disease spread (ie, daily new COVID-19 cases)4 and key societal interventions and announcements (ie, addresses by the prime minister or head of state) targeting public mobility. Interrupted time series analyses were conducted to evaluate the association between societal interventions and mobility, including both a level (β1) and a slope change (β2) that can be interpreted as the modeled percentage change in mobility from before to after the intervention.5 Statistical analyses were performed using R, version 3.6.3 (R Project for Statistical Computing). Details on measures and interrupted time series analyses are provided in the eMethods in the Supplement.

Results

The percentage of mobility change, the number of COVID-19–related tweets, and the number of confirmed cases of COVID-19 are plotted with key societal interventions in the Figure. Daily volumes of COVID-19–related tweets followed a similar pattern in Denmark and Sweden, with volumes increasing exponentially during the beginning of disease spread and peaking between March 13 and 17 (from 746 on February 14 to 16 851 on March 13 in Sweden and from 25 to 8398 in Denmark). During this period, the World Health Organization declared a COVID-19 pandemic, Denmark announced a lockdown, and Sweden banned public gatherings or more than 500 people and recommended remote higher education and work. Twitter volumes then decreased with time in both countries. Public mobility to retail and recreation decreased in both countries during the study period, with a greater reduction in Denmark during lockdown. The maximum mean (SD) mobility reduction (apart from public holidays) in Denmark was 38% (2.6%) (from March 23 to 29) and 24% (2.1%) in Sweden (from March 30 to April 5). By the last week of the study period (June 8 to 14), mobility had returned to baseline levels in both countries, with a mean (SD) change of 2.14% (4.4%) in Denmark and −0.14% (5.1%) in Sweden (Figure).

The interrupted time series analyses showed that the Danish lockdown was associated with a 20% immediate decrease in mobility (β1, –20.19; 95% CI, –27.79 to –12.59) (Table). The ban of gatherings of more than 10 people coincided with the Queen’s address to the nation and was associated with a 12% decrease in mobility (β1, –12.20; 95% CI, –19.74 to –4.65). Phase 1 and 2 of the Danish reopening were associated with immediate reductions in mobility but increased mobility in the subsequent days. In Sweden, only the ban on public gatherings of more than 500 people was associated with a significant decrease in mobility (β1,–11.04; 95% CI, –14.66 to –7.42). Neither the prime minister’s nor the King’s address to the nation showed a statistically significant association with mobility. In both countries, higher numbers of daily confirmed COVID-19 cases were associated with reduced mobility (Denmark: β1,–2.66 [95% CI, –4.51 to –0.82]; Sweden: β1, –0.62 [95% CI, –0.84 to –0.40]).

Discussion

In this cross-country comparison, stricter interventions were associated with larger reductions in mobility. Bans on public gatherings showed stronger associations with mobility in both countries compared with national announcements by country leaders and public recommendations, which were the primary intervention types in Sweden. We also found a delay in reaching maximum reduction in mobility after restrictions and for normalization of mobility after relaxation of restrictions, in line with prior research.6 Social media attention to COVID-19 on Twitter was greater for early interventions and disease spread than for later interventions and disease spread, with nearly identical patterns of decreasing COVID-19–related Twitter volumes across Denmark and Sweden despite marked difference in number of cases and the type of societal interventions. Better understanding of how interventions are associated with public mobility and attention may guide policy in relation to the resurgence of COVID-19 currently observed in many countries. Our findings are limited to short-term effects of specific interventions and do not capture interactions between these nor other important factors likely to be associated with public mobility and attention (eg, global media, guidelines from international health care organizations, and treatment and vaccine developments). Any conclusions are limited by the representativeness of Twitter and Goggle mobility data.

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Article Information

Accepted for Publication: November 22, 2020.

Published: January 4, 2021. doi:10.1001/jamanetworkopen.2020.33478

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

Corresponding Author: Isabell Brikell, PhD, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12 A, 171 77 Stockholm, Sweden (isabell.brikell@ki.se).

Author Contributions: Drs Brikell and Zhang contributed equally to this work. Drs Brikell and Zhang had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Brikell, Chang.

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

Drafting of the manuscript: Brikell.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Zhang, Chang.

Obtained funding: Chang.

Administrative, technical, or material support: Zhang, Chang.

Supervision: Brikell, Dalsgaard, Chang.

Conflict of Interest Disclosures: Dr Brikell reported receiving grants from the Swedish Brain Foundation. Dr Dalsgaard reported receiving grants from The Lundbeck Foundation, the Novo Nordisk Foundation, the European Commission, Helsefonden, and the European Union’s Horizon 2020 research and innovation programme. Dr Chang reported receiving grants from the Swedish Research Council and the Swedish Research Council for Health, Working Life and Welfare. No other disclosures were reported.

References
1.
Flaxman  S, Mishra  S, Gandy  A,  et al; Imperial College COVID-19 Response Team.  Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe.   Nature. 2020;584(7820):257-261. doi:10.1038/s41586-020-2405-7PubMedGoogle ScholarCrossref
2.
Google LLC. Google COVID-19 community mobility reports. Accessed June 14, 2020. https://www.google.com/covid19/mobility/
3.
Drake  TM, Docherty  AB, Weiser  TG, Yule  S, Sheikh  A, Harrison  EM.  The effects of physical distancing on population mobility during the COVID-19 pandemic in the UK.   Lancet Digit Health. 2020;2(8):e385-e387. doi:10.1016/S2589-7500(20)30134-5PubMedGoogle ScholarCrossref
4.
Dong  E, Du  H, Gardner  L.  An interactive web-based dashboard to track COVID-19 in real time.   Lancet Infect Dis. 2020;20(5):533-534. doi:10.1016/S1473-3099(20)30120-1PubMedGoogle ScholarCrossref
5.
Bernal  JL, Cummins  S, Gasparrini  A.  Interrupted time series regression for the evaluation of public health interventions: a tutorial.   Int J Epidemiol. 2017;46(1):348-355.PubMedGoogle Scholar
6.
Li  Y, Campbell  H, Kulkarni  D,  et al. The temporal association of introducing and lifting non-pharmaceutical interventions with the time-varying reproduction (R) number of SARS-CoV-2: a modelling study across 131 countries.  The Lancet Infect Dis. Published online October 20, 2020. doi:10.1016/S1473-3099(20)30785-4

Largest, most diverse ever study of prostate cancer genetics brings disparities into focus

Eighty-six new risk factors identified in research co-led by USC Norris Comprehensive Cancer Center

KECK SCHOOL OF MEDICINE OF USC

Research News

Some racial and ethnic groups suffer relatively more often, and fare worse, from common ailments compared to others. Prostate cancer is one disease where such health disparities occur: risk for the disease is about 75 percent higher, and prostate cancer is more than twice as deadly, in Blacks compared to whites. Yet whites are often overrepresented as research participants, making these differences difficult to understand and, ultimately, address.

With this problem in mind, scientists at the USC Center for Genetic Epidemiology and the Institute for Cancer Research, London, led a study that brings together data from the majority of genomic prostate cancer studies globally. Including more than 200,000 men of European, African, Asian and Hispanic ancestry from around the world, the study is the largest, most diverse genetic analysis ever conducted for prostate cancer -- and possibly for any other cancer.

The paper appears today in Nature Genetics.

The study's authors identified 86 new genetic variations that increase risk for prostate cancer, not previously discovered, bringing the total number of risk loci for prostate cancer to 269. Applying a model for assessing prostate cancer risk based on the interplay of these genetic factors, the researchers showed that men of African ancestry inherit about twice the prostate cancer risk on average compared to men of European ancestry, while men of Asian ancestry inherit about three-quarter the risk of their white counterparts -- evidence that genetics play some part in the differences in how often cancer occurs in different racial groups.

This research is also a step toward applying precision medicine to early detection.

"Our long-term objective is to develop a genetic risk score that can be used to determine a man's risk of developing prostate cancer," said corresponding author Christopher Haiman, ScD, professor of preventive medicine at the Keck School of Medicine of USC and director of the USC Center for Genetic Epidemiology. "Men at higher risk may benefit from earlier and more frequent screening, so the disease can be identified when it's more treatable."

Study tackles health disparities

Praise for the study's potential in increasing health equity came from Jonathan W. Simons, MD, president and chief executive officer of the Prostate Cancer Foundation. The foundation funds Haiman's other work leading the RESPOND initiative exploring the disease among African American men.

"PCF believes that Dr. Haiman's research findings will lead to more effective prostate cancer precision screening strategies for men of West African ancestry," Simons said. "PCF is certain that identification of these very high-risk individuals will make a positive impact on this significant health care disparity."

Haiman and his colleagues used genomic datasets from countries including the U.S., the UK, Sweden, Japan, and Ghana to compare 107,247 men with prostate cancer to a control group comprising 127,006 men. By examining a spectrum of races and ethnicities, the study's authors aim to make the genetic risk score more useful for more people.

"We not only found new markers of risk, but also demonstrated that, by combining genetic information across populations, we were able to identify a risk profile that can be applied across populations," said Haiman. "This emphasizes the value of adding multiple racial and ethnic populations into genetic studies."

Risk score could contribute to better screening

Today's screening guidelines for prostate cancer suggest that those 55 and older with average risk can choose to take the prostate-specific antigen (PSA) test in consultation with their physicians. High PSA levels are associated with prostate cancer, but the PSA test tends to detect slow-growing tumors. With widespread use, it too often leads to unnecessary treatment.

The PSA test's value as a screening tool would grow if it were deployed selectively to monitor people found to be at high risk for prostate cancer -- which is where the genetic risk score could come into play. Those at particularly high risk might even begin screening before age 55.

In order to translate the current research findings into better early detection, a large-scale clinical trial would be needed.

"Most important, unlike previous screening trials, this one would need to be more representative of the diversity we see in the world," Haiman said. "No population should get left behind."

###

About this study

The paper has more than 230 co-authors, representing scores of organizations. Co-first authors of the study are David Conti, PhD, professor of preventive medicine, and Burcu Darst, PhD, a postdoctoral scholar and research associate, both of the Keck School of Medicine. Co-senior authors are Haiman, on behalf of the Elucidating Loci Involved in Prostate Cancer Susceptibility (ELLIPSE) consortium, and the ICR's Rosalind Eeles, FMedSci, PhD, FRCP, FRCR, and Zsofia Kote-Jarai, PhD, on behalf of the Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium.

This study was supported by National Institutes of Health (U19CA148537, R01CA194393, K99CA246063), with further support from Cancer Research UK, Prostate Cancer UK, and the Movember Foundation.

Changes in Abortion in Texas Following an Executive Order Ban During the Coronavirus Pandemic

Kari White, PhD, MPH1; Bhavik Kumar, MD, MPH2; Vinita Goyal, MD, MPH3; et alRobin Wallace, MD, MAS4; Sarah C. M. Roberts, DrPH5; Daniel Grossman, MD5

Author Affiliations Article Information


Research Letter
January 4, 2021

JAMA. Published online January 4, 2021. doi:10.1001/jama.2020.24096

In response to the coronavirus pandemic, Texas Governor Greg Abbott issued an executive order on March 22, 2020, postponing surgeries and procedures that were not medically necessary.1 Texas officials interpreted this to prohibit most abortions until the order expired on April 21, 2020, contrary to medical associations’ recommendations.2

The objective of this analysis was to assess changes in abortions following the executive order. We also hypothesized that abortions performed at 12 weeks’ gestational age (GA) or more would increase after the order expired.3

Methods

The University of Texas at Austin and University of California, San Francisco, institutional review boards approved this study and waived informed consent. Since January 2017, monthly data were requested from Texas abortion facilities on the number of medication abortions, procedural abortions at less than 12 weeks’ GA, and procedural abortions at 12 weeks’ GA or more. Of 24 Texas facilities, 18 reported data for 2019 and 2020, including 4 that opened in 2019. These facilities provide 93% of abortions in Texas, according to comparisons with state vital statistics data.4

Monthly data were also collected on the number of Texas residents obtaining abortions at 30 of the 37 open facilities in Arkansas, Colorado, Kansas, Louisiana, Oklahoma, and New Mexico from February 2020 through May 2020 and compared with 2017 data collected previously from these states (Supplement).

Negative binomial regression models were used to estimate the percent change in the number of in-state abortions that occurred in February, March, April (the month most affected by the order), and May 2020 for all abortions, medication abortions, procedures less than 12 weeks’ GA, and procedures 12 weeks’ GA or more relative to the same month in 2019, and all out-of-state abortions among Texas residents relative to 2017, separately. A second set of negative binomial models were used to estimate whether monthly in-state abortions occurring in February, March, April, and May 2020 differed from the overall linear trend in Texas since January 2019, after adjustment for the number of abortion facilities and abortion seasonality. Stata version 15 was used for analyses (StataCorp). A 95% CI not including the null defined statistical significance.
Results

Texas facilities provided 18 268 abortions from February through May 2019 and 16 349 abortions during these months in 2020 (Table 1). Overall, 4608 abortions were provided in April 2019 and 2856 in April 2020, a 38.0% (95% CI, −40.8% to −35.1%) decrease.

Texas residents receiving care at out-of-state facilities increased from 157 in February 2020 to 947 in April 2020; monthly totals ranged from 107 to 165 in 2017.

The number of medication abortions increased from 1808 in April 2019 to 2297 in April 2020, accounting for 39% and 80% of all abortions, respectively (Table 2). After adjustment for time trends and number of facilities, there was a 17.4% (95% CI, −7.1% to 48.4%) difference in the number of medication abortions in April 2020 relative to that expected had the linear trend from January 2019 continued. Compared with April 2019, there were fewer procedural abortions less than 12 weeks’ GA (2318 vs 317) and at 12 weeks’ GA or more (482 vs 242) in April 2020. After the executive order was lifted in May 2020, 815 procedural abortions at 12 weeks’ GA or more were provided vs 507 in May 2019, an 82.6% (95% CI, 46.7% to 127.4%) increase over that expected based on linear trends.




Discussion

These data show that abortions declined in Texas during the executive order. Stay-at-home orders, facilities’ coronavirus precautions, and patients’ reluctance to seek in-person care may also have contributed to the decline. Other Texas patients traveled out of state or requested medications online.5 Abortions at 12 weeks’ GA or more increased after the order expired, which likely reflects delays in care among those who waited for an appointment and facilities’ limited capacity to meet backlogged patient need. Although abortions later in pregnancy are very safe, they are associated with a higher risk of complications and may require additional visits compared with those provided earlier in pregnancy.6

Study limitations include lack of data from some Texas and out-of-state facilities, which may affect these estimates. Monthly facility data do not allow assessment of changes associated with the exact timing of the order.

Section Editor: Jody W. Zylke, MD


Table 1. Number of Abortions Provided in Texas and to Texas Residents at Out-of-State Facilities and Percent Change in Abortions, February-May 2019 and February-May 2020a
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Table 2. Distribution of Abortion Type and Percent Change in Number of Abortions in Texas, February-May 2019 and February-May 2020
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Scientists discover how mother-of-pearl self-assembles into a perfect structure

Opposites attract

TECHNISCHE UNIVERSITÄT DRESDEN

Research News

IMAGE

IMAGE: A CROSS-SECTION THROUGH A SHELL SHOWING THE PERIODICALLY LAYERED NACRE ON TOP OF A PRISMATIC SHELL STRUCTURE. view more 

CREDIT: © IGOR ZLOTNIKOV

Mollusks build shells to protect their soft tissues from predators. Nacre, also known as the mother of pearl, has an intricate, highly regular structure that makes it an incredibly strong material. Depending on the species, nacres can reach tens of centimeters in length. No matter the size, each nacre is built from materials deposited by a multitude of single cells at multiple different locations at the same time. How exactly this highly periodic and uniform structure emerges from the initial disorder was unknown until now.

Nacre formation starts uncoordinated with the cells depositing the material simultaneously at different locations. Not surprisingly, the early nacre structure is not very regular. At this point, it is full of defects. "In the very beginning, the layered mineral-organic tissue is full of structural faults that propagate through a number of layers like a helix. In fact, they look like a spiral staircase, having either right-handed or left-handed orientation," says Dr. Igor Zlotnikov, research group leader at the B CUBE - Center for Molecular Bioengineering at TU Dresden. "The role of these defects in forming such a periodic tissue has never been established. On the other hand, the mature nacre is defect-free, with a regular, uniform structure. How could perfection emerge from such disorder?"

The researchers from the Zlotnikov group collaborated with the European Synchrotron Radiation Facility (ESRF) in Grenoble to take a very detailed look at the internal structure of the early and mature nacre. Using synchrotron-based holographic X-ray nano-tomography the researchers could capture the growth of nacre over time. "Nacre is an extremely fine structure, having organic features below 50 nm in size. Beamline ID16A at the ESRF provided us with an unprecedented capability to visualize nacre in three-dimensions," explains Dr. Zlotnikov. "The combination of electron dense and highly periodical inorganic platelets with delicate and slender organic interfaces makes nacre a challenging structure to image. Cryogenic imaging helped us to obtain the resolving power we needed," explains Dr. Pacureanu from the X-ray Nanoprobe group at the ESRF.

The analysis of data was quite a challenge. The researchers developed a segmentation algorithm using neural networks and trained it to separate different layers of nacre. In this way, they were able to follow what happens to the structural defects as nacre grows.

The behavior of structural defects in a growing nacre was surprising. Defects of opposite screw direction were attracted to each other from vast distances. The right-handed and left-handed defects moved through the structure, until they met, and cancelled each other out. These events led to a tissue-wide synchronization. Over time, it allowed the structure to develop into a perfectly regular and defect-free.

Periodic structures similar to nacre are produced by many different animal species. The researchers think that the newly discovered mechanism could drive not only the formation of nacre but also other biogenic structures.


CAPTION

The end-station at the holographic X-ray nano-tomography beamline (ID16A) at ESRF.

CREDIT

© Igor Zlotnikov


###

Dr. Igor Zlotnikov is a leader of a multidisciplinary group at the B CUBE, TU Dresden. The group studies the interplay between physics of materials and cellular control. The Zlotnikov group implements state-of-the-art techniques from a large spectrum of fields in life and physical sciences to address the fundamental question of how the nature uses thermodynamic principles to generate complex structures. The group is funded by the Federal Ministry of Education and Research (BMBF; grant 03Z22EN11).

Publication:

Maksim Beliaev, Dana Zoellner, Alexandra Pacureanu, Paul Zasklansky, and Igor Zlotnikov: Dynamics of Topological Defects and Structural Synchronization in a Forming Periodic Tissue. Nature Physics (January 2021)
doi: 10.1038/s41567-020-01069-z

B CUBE - Center for Molecular Bioengineering was founded as a Center for Innovation Competence within the initiative "Unternehmen Region" of the German Federal Ministry of Education and Research. It is part of the Center for Molecular and Cellular Bioengineering (CMCB). B CUBE research focuses on the investigation of living structures on a molecular level, translating the ensuing knowledge into innovative methods, materials and technologies.

Web: http://www.tu-dresden.de/bcube