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Learning how to conduct accurate, discipline-specific academic research can feel daunting at first. But, with a solid understanding of the reasoning behind why we use academic citations coupled with knowledge of the basics, you’ll learn how to cite sources with accuracy and confidence.
When it comes to academic research, citing sources correctly is arguably as important as the research itself. "Your instructors are expecting your work to adhere to these professional standards," said Amanda Girard , research support manager of Shapiro Library at Southern New Hampshire University (SNHU).
With Shapiro Library for the past three years, Girard manages the library’s research support services, which includes SNHU’s 24/7 library chat and email support. She holds an undergraduate degree in professional writing and a graduate degree in library and information science. She said that accurate citations show that you have done your research on a topic and are knowledgeable about current ideas from those actively working in the field.
In other words, when you cite sources according to the academic style of your discipline, you’re giving credit where credit is due.
Citing sources properly ensures you’re following high academic and professional standards for integrity and ethics.
“When you cite a source, you can ethically use others’ research. If you are not adequately citing the information you claim in your work, it would be considered plagiarism ,” said Shannon Geary '16 , peer tutor at SNHU.
Geary has an undergraduate degree in communication from SNHU and has served on the academic support team for close to 2 years. Her job includes helping students learn how to conduct research and write academically.
“In academic writing, it is crucial to state where you are receiving your information from,” she said. “Citing your sources ensures that you are following academic integrity standards.”
According to Geary and Girard, several key reasons for citing sources are:
Ultimately, citing sources is a formalized way for you to share ideas as part of a bigger conversation among others in your field. It’s a way to build off of and reference one another’s ideas, Girard said.
Any time you use an original quote or paraphrase someone else’s ideas, you need to cite that material, according to Geary.
“The only time we do not need to cite is when presenting an original thought or general knowledge,” she said.
While the specific format for citing sources can vary based on the style used, several key elements are always included, according to Girard. Those are:
By giving credit to the authors, researchers and experts you cite, you’re building credibility. You’re showing that your argument is built on solid research.
“Proper citation not only builds a writer's authority but also ensures the reliability of the work,” Geary said. “Properly formatted citations are a roadmap for instructors and other readers to verify the information we present in our work.”
Certain disciplines adhere to specific citation standards because different disciplines prioritize certain information and research styles . The most common citation styles used in academic research, according to Geary, are:
The benefit of using the same format as other researchers within a discipline is that the framework of presenting ideas allows you to “speak the same language,” according to Girard.
Are you writing a paper that needs to use APA citation, but don’t know what that means? No worries. You’ve come to the right place.
Are you writing a paper for which you need to know how to use MLA formatting, but don’t know what that means? No worries. You’ve come to the right place.
Keeping track of your research as you go is one of the best ways to ensure you’re citing appropriately and correctly based on the style that your academic discipline uses.
“Through careful citation, authors ensure their audience can distinguish between borrowed material and original thoughts, safeguarding their academic reputation and following academic honesty policies,” Geary said.
Some tips that she and Girard shared to ensure you’re citing sources correctly include:
How to cite a reference in academic writing.
A citation consists of two pieces: an in-text citation that is typically short and a longer list of references or works cited (depending on the style used) at the end of the paper.
“In-text citations immediately acknowledge the use of external source information and its exact location,” Geary said. While each style uses a slightly different format for in-text citations that reference the research, you may expect to need the page number, author’s name and possibly date of publication in parentheses at the end of a sentence or passage, according to Geary.
A longer entry listing the complete details of the resource you referenced should also be included on the references or works cited page at the end of the paper. The full citation is provided with complete details of the source, such as author, title, publication date and more, Geary said.
The two-part aspect of citations is because of readability. “You can imagine how putting the full citation would break up the flow of a paper,” Girard said. “So, a shortened version is used (in the text).”
“For example, if an in-text citation reads (Jones, 2024), the reader immediately knows that the ideas presented are coming from Jones’s work, and they can explore the comprehensive citation on the final page,” she said.
The in-text citation and full citation together provide a transparent trail of the author's process of engaging with research.
“Their combined use also facilitates further research by following a standardized style (APA, MLA, Chicago), guaranteeing that other scholars can easily connect and build upon their work in the future,” Geary said.
Developing and demonstrating your research skills, enhancing your work’s credibility and engaging ethically with the intellectual contributions of others are at the core of the citation process no matter which style you use.
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A former higher education administrator, Dr. Marie Morganelli is a career educator and writer. She has taught and tutored composition, literature, and writing at all levels from middle school through graduate school. With two graduate degrees in English language and literature, her focus — whether teaching or writing — is in helping to raise the voices of others through the power of storytelling. Connect with her on LinkedIn .
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A symptom was included if at least 5% of infected or uninfected participants reported experiencing that symptom. Adjusted odds ratios and risk differences were estimated from models that included infection status as the exposure and the presence of each prolonged symptom as the outcome, with adjustment for sex assigned at birth and race and ethnicity (see eMethods in Supplement 3 ).
A, Least absolute shrinkage and selection operator (LASSO) was used to fit a logistic regression model to identify which symptoms could be used to identify individuals likely to have PASC. Estimated log odds ratios were divided by 0.10 and rounded up to the nearest 0.5 to calculate symptom scores. An individual’s PASC research index is calculated by summing the scores for each prolonged symptom a participant reported (ie, the participant experienced the symptom for 4 weeks since the beginning of the pandemic and is currently experiencing it at the time of the survey). B, The optimal index threshold for PASC was selected using bootstrapping to estimate standard error bars. An approximation of the “elbow” method was used to identify the cutoff where the number of uninfected participants misclassified as PASC-probable stabilized (eMethods in Supplement 3 ). The threshold (index of at least 5.5) can be used to identify school-age children with PASC for research purposes. Using this threshold, the percentage of infected PASC-probable school-age children with each symptom was as follows: headache, 55%; trouble with memory or focusing, 45%; trouble sleeping, 44%; stomach pain, 43%; nausea or vomiting, 34%; back or neck pain, 30%; itchy skin or skin rash, 29%; fear about specific things, 26%; feeling lightheaded or dizzy, 26%; and refusing to go to school, 23%. C, Participant responses to 3 questions from the Patient-Reported Outcomes Measurement Information System (PROMIS) Global 10 survey, stratified into 7 groups: participants with a zero PASC research index and no prolonged symptoms, zero PASC research index but at least 1 prolonged symptom, and participants with nonzero PASC index, divided into quintiles. The dark vertical line indicates the index threshold for PASC. Each cell is shaded according to the frequency of each response within each column, ranging from 0% to 100%.
A, Least absolute shrinkage and selection operator (LASSO) was used to fit a logistic regression model to identify which symptoms could be used to identify individuals likely to have PASC. Estimated log odds ratios were divided by 0.10 and rounded up to the nearest 0.5 to calculate symptom scores. An individual’s PASC research index is calculated by summing the scores for each prolonged symptom a participant reported (ie, the participant experienced the symptom for 4 weeks since the beginning of the pandemic and is currently experiencing it at the time of the survey). B, The optimal index threshold for PASC was selected using 95% CIs to estimate error bars. An approximation of the “elbow” method was used to identify the cutoff where the number of uninfected participants misclassified as PASC-probable stabilized (eMethods in Supplement 3 ). The threshold (index of at least 5) can be used to identify adolescents with PASC for research purposes. Using this threshold, the percentage of infected PASC-probable adolescents with each symptom was as follows: daytime tiredness/sleepiness or low energy, 80%; body, muscle, or joint pain, 61%; headache, 56%; trouble with memory or focusing, 47%; tired after walking, 42%; back or neck pain, 40%; feeling lightheaded or dizzy, 39%; and change or loss in smell or taste, 34%. C, Participant responses to 3 questions from the Patient-Reported Outcomes Measurement Information System (PROMIS) Global 10 survey, stratified into 7 groups: participants with a zero PASC research index and no prolonged symptoms, zero PASC research index but at least 1 prolonged symptoms, and participants with nonzero PASC index, divided into quintiles. The dark vertical line indicates the index threshold for PASC (to the left is PASC-unspecified, to the right is PASC-probable). Each cell is shaded according to the frequency of each response within each column, ranging from 0% to 100%.
Symptoms, sorted from most to least common in the study population overall, are in the center column. Left columns correspond to school-age children in 3 groups: uninfected, infected and not meeting the PASC research index threshold (infected, PASC-unspecified), and infected and meeting the PASC research index threshold (infected, PASC-probable). The columns on the right correspond to adolescents with columns in the reverse order. Note that school-age children were not asked about panic attacks. Frequency of each prolonged symptom is indicated by shading, from 0% to 100%.
A and B, Subgroups formed using consensus clustering to group participants with similar symptom profiles (based on prolonged symptoms contributing to the PASC research index only). Four clusters were identified in PASC-probable school-age children and 3 clusters among adolescents. C and D, Frequencies of each prolonged symptom are shown for each cluster, where shading indicates frequency from 0%-100%. Symptoms that contribute to the PASC research index are above the dark horizontal line, and those below do not contribute to the PASC research index, sorted in decreasing frequency among all PASC-probable participants. Symptoms present in <5% of participants in every cluster were omitted. The full set of symptoms is in eFigure 6 in Supplement 3 .
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Gross RS , Thaweethai T , Kleinman LC, et al. Characterizing Long COVID in Children and Adolescents. JAMA. Published online August 21, 2024. doi:10.1001/jama.2024.12747
© 2024
Question What prolonged symptoms experienced by youth are most associated with SARS-CoV-2 infection?
Findings Among 5367 participants in the RECOVER-Pediatrics cohort study, 14 symptoms in both school-age children (6-11 years) and adolescents (12-17 years) were more common in those with vs without SARS-CoV-2 infection history, with 4 additional symptoms in school-age children only and 3 in adolescents only. Empirically derived indices for PASC research and associated clustering patterns were developed.
Meaning This study developed research indices for characterizing pediatric PASC. Symptom patterns were similar but distinguishable between school-age children and adolescents, highlighting the importance of characterizing PASC separately in different age groups.
Importance Most research to understand postacute sequelae of SARS-CoV-2 infection (PASC), or long COVID, has focused on adults, with less known about this complex condition in children. Research is needed to characterize pediatric PASC to enable studies of underlying mechanisms that will guide future treatment.
Objective To identify the most common prolonged symptoms experienced by children (aged 6 to 17 years) after SARS-CoV-2 infection, how these symptoms differ by age (school-age [6-11 years] vs adolescents [12-17 years]), how they cluster into distinct phenotypes, and what symptoms in combination could be used as an empirically derived index to assist researchers to study the likely presence of PASC.
Design, Setting, and Participants Multicenter longitudinal observational cohort study with participants recruited from more than 60 US health care and community settings between March 2022 and December 2023, including school-age children and adolescents with and without SARS-CoV-2 infection history.
Exposure SARS-CoV-2 infection.
Main Outcomes and Measures PASC and 89 prolonged symptoms across 9 symptom domains.
Results A total of 898 school-age children (751 with previous SARS-CoV-2 infection [referred to as infected ] and 147 without [referred to as uninfected ]; mean age, 8.6 years; 49% female; 11% were Black or African American, 34% were Hispanic, Latino, or Spanish, and 60% were White) and 4469 adolescents (3109 infected and 1360 uninfected; mean age, 14.8 years; 48% female; 13% were Black or African American, 21% were Hispanic, Latino, or Spanish, and 73% were White) were included. Median time between first infection and symptom survey was 506 days for school-age children and 556 days for adolescents. In models adjusted for sex and race and ethnicity, 14 symptoms in both school-age children and adolescents were more common in those with SARS-CoV-2 infection history compared with those without infection history, with 4 additional symptoms in school-age children only and 3 in adolescents only. These symptoms affected almost every organ system. Combinations of symptoms most associated with infection history were identified to form a PASC research index for each age group; these indices correlated with poorer overall health and quality of life. The index emphasizes neurocognitive, pain, and gastrointestinal symptoms in school-age children but change or loss in smell or taste, pain, and fatigue/malaise–related symptoms in adolescents. Clustering analyses identified 4 PASC symptom phenotypes in school-age children and 3 in adolescents.
Conclusions and Relevance This study developed research indices for characterizing PASC in children and adolescents. Symptom patterns were similar but distinguishable between the 2 groups, highlighting the importance of characterizing PASC separately for these age ranges.
Long COVID, or postacute sequelae of SARS-CoV-2 infection (PASC), has been broadly defined as symptoms, signs, and conditions that develop, persist, or relapse over time after SARS-CoV-2 infection. 1 , 2 These symptoms can last weeks, months, or years after the acute infection resolves and can have debilitating effects. Some experts believe that worldwide, an estimated 65 million people are living with PASC, 3 with impacts on population-level health anticipated to last for decades. Most research characterizing PASC has focused on adults, 4 leading to misperception that pediatric PASC is rare or presents similarly to PASC in adults. 5 , 6 This may lead clinicians to miss symptoms or misdiagnose children. Consistent with the life course framework in which developmental stage influences health outcomes, 7 PASC may present in both similar and different ways compared with adults.
Studies of pediatric PASC have documented a wide range of symptoms involving every organ system. 8 - 11 Most pediatric research has focused on individual symptoms and either pooled data from different ages or focused on adolescents. Little is known about differences in PASC symptoms between school-age children (6-11 years) and adolescents (12-17 years). 12 , 13 The absence of a consistent analytic approach to objectively identify children with PASC hinders the research needed to identify underlying mechanisms of disease and treatment targets. The National Institutes of Health–funded Researching COVID to Enhance Recovery ( RECOVER ) Initiative aims to fill these gaps by bringing together researchers, clinicians, communities, and families in a comprehensive study of PASC in children. 14 The aims of this analysis of the RECOVER-Pediatrics cohort were to identify (1) common prolonged symptoms experienced by children (6 to 17 years old) after SARS-CoV-2 infection, (2) how these symptoms differ by age (school-age vs adolescents), (3) how symptoms cluster into phenotypes, and (4) what symptoms in combination could be used as an empirically derived index to help researchers consistently assess the likely presence of PASC. These indices, like the one previously developed for the RECOVER-Adult cohort (18 years or older), 15 were intended to be used to identify factors that distinguish children who likely have developed PASC from those who may not have and to help evaluate risk factors for developing PASC, elucidate its pathophysiology, and enable follow-up to analyze recovery and relapse.
The RECOVER Pediatric Observational Cohort Study (RECOVER-Pediatrics) 14 is a combined retrospective and prospective longitudinal study including 4 cohorts. Data presented are from 2 cohorts: the de novo RECOVER cohort, including participants from birth through 25 years with and without SARS-CoV-2 infection history newly recruited from health care and community settings, and the extant National Institutes of Health–funded Adolescent Brain Cognitive Development cohort, 16 - 18 the largest long-term US study of brain development in adolescence. The protocol and statistical analysis plan for RECOVER-Pediatrics were previously described 19 (see Supplements 1 and 2 ). Data were obtained from more than 60 sites (eTable 1 in Supplement 3 ). The study received institutional review board approval from NYU Grossman School of Medicine (de novo cohort) or UC San Diego Human Research Protections Program (Adolescent Brain Cognitive Development cohort), with other institutions relying on these single institutional review boards. Caregiver-child pairs provided informed consent and age-appropriate assent. Strengthening and Reporting of Observational Studies in Epidemiology (STROBE) guidelines were followed.
The analytic sample included individuals aged 6 to 17 years enrolled between March 16, 2022, and December 16, 2023, with and without known SARS-CoV-2 infection history ( infected and uninfected , respectively). Child age was recorded at symptom survey completion.
For these analyses, the infected group included participants who completed their survey about prolonged symptoms at least 90 days after their first infection, reported by their caregivers (eMethods in Supplement 3 ). SARS-CoV-2 antibodies were not required. The uninfected group was defined by caregiver report and required confirmation of negative nucleocapsid antibodies at enrollment. Those thought to be uninfected but found to be antibody-positive (Ab+) within 30 days of survey completion were analyzed separately to understand asymptomatic infection. 20 Throughout, uninfected refers strictly to uninfected participants who were confirmed to be nucleocapsid antibody–negative.
Infected participants with an unknown date for their first infection, participants with history of multisystem inflammatory syndrome in children (because this is a well-characterized entity), 21 - 25 uninfected participants without antibody testing, and participants with missing symptom surveys (defined as <50% of questions completed) were excluded.
Caregivers completed a comprehensive symptom survey remotely (interviewer-administered if needed) assessing 89 prolonged symptoms across 9 domains, using health literacy–informed principles and plain-language descriptions (eTable 2 in Supplement 3 ). 19 , 26 Some symptoms describing a similar phenotype were combined into composites, resulting in 75 symptoms (eMethods in Supplement 3 ): general (12 symptoms), eyes/ears/nose/throat (15 symptoms), heart/lungs (10 symptoms), gastrointestinal (6 symptoms), dermatologic (5 symptoms), musculoskeletal (3 symptoms), neurologic (6 symptoms), behavioral/psychological (14 symptoms), and menstrual (4 symptoms). The same symptoms were assessed in both age groups (except panic attacks, which were assessed in adolescents only). Menstrual symptoms were assessed in those assigned female or intersex at birth and who started menstruating (reported only among adolescents).
The primary outcome was a prolonged symptom lasting for more than 4 weeks that started or became worse since the beginning of the pandemic and was present at the time of survey completion (at least 90 days after infection). If a symptom lasted for more than 4 weeks but was absent at survey completion, it was not counted as a prolonged symptom.
Patient-Reported Outcomes Measurement Information System (PROMIS) Global Health Scales were assessed, measuring caregiver perception of the child’s overall health, physical health, and quality of life. 27
The main exposure variable was SARS-CoV-2 infection. Other variables included sex, race and ethnicity, geographic origin, time since SARS-CoV-2 infection, calendar time of enrollment, and SARS-CoV-2 vaccination status (eMethods in Supplement 3 ). Like other variables, race and ethnicity were collected via caregiver report based on prespecified categories and measured to enhance understanding of racial and ethnic differences in PASC. Caregiver variables included relationship to child and educational attainment.
Statistical analyses were modeled after those published for RECOVER-Adult and were age-stratified. 15 The analysis calculated the proportion of participants who reported each prolonged symptom and who reported experiencing at least 1 prolonged symptom among infected and uninfected participants separately (eTable 3 in Supplement 3 ). For symptoms present in at least 5% of infected participants (candidate symptoms), the risk difference, odds ratio, and relative risk for infected vs uninfected participants were estimated using linear, logistic, and Poisson regression, respectively, adjusting for sex and race and ethnicity (eMethods in Supplement 3 ). Second, to identify combinations of symptoms that could be used for research, a penalized logistic regression approach (least absolute shrinkage and selection operator [LASSO]) 28 was used to identify what candidate symptoms (predictors) were best at differentiating participants with or without an infection history (outcome). 15 Because all sexes were combined for this analysis, menstrual symptoms were excluded. Based on the model fit, each symptom was assigned a score corresponding to the estimated log odds ratio, where a higher symptom score indicated a stronger association with infection. A total index was calculated for each participant by summing the individual scores for each symptom reported. An optimal index threshold for identifying PASC was selected based on the proportion of uninfected participants who were likely misclassified as having PASC (eMethods in Supplement 3 ). Participants meeting the index threshold were categorized as PASC-probable and others were categorized as PASC-unspecified . PASC rates were reported among infected and uninfected participants separately. Among infected participants, these rates were also reported by whether they were infected by December 1, 2021 (when the Omicron variant became the dominant US strain).
Third, the analysis examined correlations between PASC indices and caregiver-reported overall child health, quality of life, and physical health and symptoms selected by LASSO. Further, the frequency of all symptoms was reported in infected PASC-probable, infected PASC-unspecified, and uninfected participants separately. Fourth, symptom patterns were investigated among infected participants categorized as PASC-probable. Correlations between symptoms contributing to the PASC index among infected PASC-probable participants were calculated. K-means consensus clustering was performed based on symptoms contributing to the PASC index to identify distinct PASC symptom profiles. 29 The number of different systems affected among infected PASC-probable participants was then summarized by counting the systems in which at least 1 prolonged symptom was reported. Fifth, we summarized the characteristics and symptomatology of uninfected participants found to be Ab+.
This study included 751 infected and 147 uninfected school-age children and 3109 infected and 1369 uninfected adolescents (see cohort identification details in eFigure 1 in Supplement 3 ). The Table and eTable 4 in Supplement 3 contain demographic and infection history characteristics, respectively. eTable 5 in Supplement 3 contains demographic characteristics for the adolescent cohort, stratified by recruiting cohort (Adolescent Brain Cognitive Development vs de novo).
Overall, 45% of infected (338/751) and 33% of uninfected (48/147) school-age children and 39% of infected (1219/3109) and 27% of uninfected (372/1369) adolescents reported having at least 1 prolonged symptom. Twenty-six symptoms in infected school-age children and 18 symptoms in infected adolescents were prolonged in at least 5% of participants ( Figure 1 ). The lower 95% confidence bound of the adjusted odds ratio exceeded 0 for 14 symptoms in both school-age children and adolescents, with 4 additional symptoms in school-age children only and 3 in adolescents only ( Figure 1 ). The frequency of each symptom among infected participants did not differ after stratification into quintiles based on time between first infection and symptom survey date (eFigure 2 in Supplement 3 ).
The LASSO analysis identified 10 symptoms in school-age children and 8 in adolescents that were most associated with infection history ( Figures 2 A and 3 A). Optimal index thresholds of 5.5 in school-age children and 5.0 in adolescents were identified ( Figures 2 B and 3 B). Overall, 152 infected (20%) and 6 uninfected (4%) school-age children and 445 infected (14%) and 44 uninfected (3%) adolescents met or exceeded this index threshold (eTable 6 in Supplement 3 ). This percentage was higher for participants infected before vs after the emergence of Omicron (21% vs 14% for school-age children; 17% vs 7% for adolescents). Correlations between symptoms that contributed to the index are shown in eFigure 3 in Supplement 3 . Correlations between these symptoms and those that did not contribute to the index are shown in eTable 7 in Supplement 3 . Some uninfected participants may have met the index threshold due to misclassification or due to having other symptoms.
In both age groups, higher PASC research indices were correlated with worse PROMIS scores ( Figures 2 C and 3 C). The number of systems affected among infected PASC-probable participants (eFigure 4 in Supplement 3 ) indicated substantial multisystem burden.
Figure 4 shows the percentage of participants in each age group experiencing each symptom after stratification into 3 subgroups: infected PASC-probable, infected PASC-unspecified, and uninfected. The most common prolonged symptom among PASC-probable school-age children that also contributed to the PASC research index ( Figures 2 B and 4 ) was headache (57%), followed by trouble with memory/focusing and trouble sleeping (44%) and stomach pain (43%). Among symptoms that did not contribute to the index, body/muscle/joint pain (51%), daytime tiredness/sleepiness or low energy (49%), and feeling anxious (47%) were the most common ( Figure 4 ). The distribution of symptoms was similar between PASC-unspecified and uninfected school-age children.
Among PASC-probable adolescents, the most common prolonged symptoms contributing to the index ( Figures 3 B and 4 ) were daytime tiredness/sleepiness or low energy (80%), body/muscle/joint pain (60%), headaches (55%), and trouble with memory/focusing (47%). Among symptoms that did not contribute to the index, trouble sleeping (47%), feeling anxious (47%), and feeling sad/depressed (38%) were the most common ( Figure 4 ). The distribution of symptoms was similar between PASC-unspecified and uninfected adolescent participants.
Among school-age children, 4 symptom clusters were identified ( Figure 5 ). Cluster 1 had high rates of many symptoms and the highest symptom burden. Cluster 2 was characterized by high rates of headache (95%), body/muscle/joint pain (60%), and daytime tiredness/sleepiness or low energy (52%). Cluster 3 was characterized by higher rates of trouble sleeping (64%) and trouble with memory/focusing (62%). Cluster 4 was characterized predominantly by stomach pain (100%) and nausea/vomiting (61%). Among adolescents, 3 clusters were identified ( Figure 5 ). Cluster 1 had high rates of many symptoms, similar to the first school-age cluster. Cluster 2 was characterized by high rates of daytime tiredness/sleepiness or low energy (89%) and body/muscle/joint pain (87%). Cluster 3 was characterized by having change/loss in smell or taste (100%), with relatively low rates of all other symptoms. The clusters with the most symptoms in both school-age children and adolescents (cluster 1) had the highest mean number of systems affected (eTable 8 in Supplement 3 ) and were correlated with poorer overall health and quality of life (eFigure 5 in Supplement 3 ).
Overall, 64 school-age children and 781 adolescents enrolled as uninfected but were Ab+ (ie, asymptomatically infected; eFigure 1 and eTable 9 in Supplement 3 ). Among school-age children, 6 (9%) met the index threshold whereas 18 (28%) reported experiencing at least 1 prolonged symptom. Among adolescents, 29 (4%) met the index threshold and 175 (22%) reported at least 1 prolonged symptom.
Symptom frequencies for all groups (infected, uninfected, and uninfected Ab+), including estimated risk ratios and odds ratios, are shown in eTable 10 in Supplement 3 .
In this large-scale study, children with probable PASC experienced prolonged symptoms in almost every organ system, with the majority having multisystem involvement. A clear pattern of symptom differences was identified between school-age children and adolescents, underscoring the importance of characterizing PASC separately in these 2 age groups.
This study developed an empirically derived index that can be used to help researchers identify children likely to have PASC, which was associated with overall health, physical health, and quality of life. This PASC research index, distinct for each age group, used combinations of 10 symptoms in school-age children and 8 symptoms in adolescents to indicate the likelihood of PASC. Although many other symptoms were more common in infected compared with uninfected participants, symptoms selected for the index were those that were most associated with infection history. Because these other symptoms were highly associated with the symptoms selected for the index (eTable 7 in Supplement 3 ), it was rare for participants not meeting the index threshold to have these other symptoms ( Figure 4 ). In this cohort, 20% of infected school-age children exceeded the PASC symptom threshold, while 14% of adolescents exceeded the threshold. PASC symptoms clustered into 4 distinct clusters in school-age children and 3 in adolescents.
The PASC research index presents a framework for future studies and can be used as a continuous or binary outcome variable (based on derived thresholds) to determine risk factors for developing PASC and the trajectory of PASC and its resolution (or relapse). Although this provisional index may be used for research, it is not intended for clinical practice, and 1 symptom may be sufficient to indicate PASC in any given child.
This study makes a substantial contribution to the understanding of pediatric PASC. Most research to understand PASC symptoms has focused on adults, potentially due to the misperception that children were not severely affected by COVID-19, leaving childhood symptoms less understood. Most prior pediatric studies have relied on electronic health records. 30 , 31 The current study had the advantage of comprehensively assessing caregiver-reported symptoms across every organ system, examining them in combination, and comparing them directly to an uninfected seronegative control group. The symptoms identified as being related to PASC were associated with infection, not only symptoms that became more common during the pandemic.
This study identified separate PASC research indices for school-age children and adolescents based on symptoms most likely to differentiate between those with and without an infection history. Higher indices were correlated with worse functional outcomes, and those with indices meeting the PASC threshold reported many prolonged symptoms, not just those selected by LASSO. 28 The strongest differentiators of infection history in adults (RECOVER-Adult study) 15 and adolescents overlapped considerably. There was less overlap between adults and school-age children. These findings underscore the need for separate assessments in different age groups. This may be one reason that younger children with PASC are being undercounted in studies and/or undiagnosed clinically, although undercounting may also be due to younger children being less able to recognize and report symptoms. The pathophysiology behind these age-related differences warrants future study, given substantial changes in growth, development, immunological factors, and pubertal hormones that occur across the life course. 11
Among infected participants, there was a wide range of time elapsed between infection and survey completion (median [IQR] time was 501 [297-801] days for school-age children and 518 [333-810] days for adolescents). However, symptom frequency did not change meaningfully when comparing different times between infection and survey completion, underscoring the usefulness of the PASC index for any child in the postacute phase of SARS-CoV-2 infection.
Four symptom clusters in school-age children and 3 in adolescents were identified. In both age groups, there was a single cluster with high symptom burden (as in adults) and a cluster predominated by fatigue and pain symptoms. Other clusters differed by age. School-age children had a cluster with neuropsychological and sleep impacts and another with gastrointestinal predominance. Adolescents had a cluster that was primarily loss of taste and smell, 32 similar to that found in adults, which was not noted in the school-age clusters. Clusters predominated by respiratory symptoms were not identified, possibly related to community recruitment or few participants with severe acute illness. Future research should evaluate whether these pediatric clusters are associated with different pathophysiology from adults, 33 - 35 which will be critical for identifying the treatment targets needed for clinical trials. 36 - 40
This study has limitations. First, the research index is not intended for use in clinical practice to diagnose PASC. Rather it must be considered with clinical judgement because children may have PASC without meeting the index threshold. There are many prolonged symptoms that differ between those previously infected and uninfected with SARS-CoV-2 that are not part of this index. It remains unknown how many children with other diagnoses would have similar prolonged symptoms. This index may evolve over time with changing variants and population immunity. Although children with higher PASC indices report worse quality of life, the cross-sectional analyses preclude causal inference. If a symptom lasted more than 4 weeks but was absent at survey completion, it was not included as a prolonged symptom because this index was not meant to describe incidence. However, it can be used for longitudinal follow-up of recovery and relapse, which would not be possible if resolved symptoms were used in the calculations.
Second, the population prevalence of pediatric PASC cannot be determined with the current design because participants with more prolonged symptoms may have been more inclined to enroll. To mitigate differences that may have resulted from having an extant adolescent cohort, community outreach within the school-age group was encouraged.
Third, some participants in the infected and uninfected groups could have been misclassified. Infected participants were not required to have evidence of SARS-CoV-2 infection; this study relied on caregiver-reported COVID-19 infection history, given variable access to testing. Uninfected children were confirmed to not have SARS-CoV-2 antibodies, but it is possible that some may have been unknowingly infected without developing antibodies or their immunity waned. 41 Uninfected participants may have another postviral syndrome or other conditions that may have symptoms and even pathophysiology that overlaps with PASC. 42 Despite this uncertainty, important differences between infected and uninfected groups were detected.
Fourth, given that symptoms were caregiver-reported, recall bias is possible. In addition, caregiver perceptions of their adolescents’ symptoms may differ from those of the adolescents themselves. However, to enable valid comparisons across age groups, data collection methods were standardized. Future analyses will combine caregiver-reported surveys with objective measures collected during the in-person longitudinal study phase. 19
Fifth, this empirically derived index is a framework that identified commonalities for research purposes. Iterative adaptation of how PASC is assessed may occur as more RECOVER data are collected and as children are followed up. Future analyses will examine PASC symptoms in early childhood (birth to 5 years) and the effects of SARS-CoV-2 on worsening underlying conditions and increasing new conditions, 43 - 45 such as diabetes, 46 autoimmune diseases, 47 neurocognitive disorders, and postinfectious syndromes. 11
In this large-scale study, symptoms that characterized pediatric PASC differed by age group, and several distinct phenotypic PASC presentations were described. The research indices developed here will help researchers identify children and adolescents with high likelihood of PASC. Although these indices will require further research and validation, this work provides an important step toward a clinically useful tool for diagnosis with the ultimate goal of supporting optimal care for youth with PASC.
Accepted for Publication: June 4, 2024.
Published Online: August 21, 2024. doi:10.1001/jama.2024.12747
Corresponding Author: Rachel S. Gross, MD, MS, NYU Grossman School of Medicine, 462 First Ave, New York, NY 10016 ( [email protected] ).
RECOVER-Pediatrics Group Authors: Venkataraman Balaraman, MD; Amanda Bogie, MD; Hulya Bukulmez, MD; Allen J. Dozor, MD; Daniel Eckrich, MS; Amy J. Elliott, PhD; Danielle N. Evans, DHSc, MHA; Jonathan S. Farkas, MD; E. Vincent S. Faustino, MD, MHS; Laura Fischer, MPH; Sunanda Gaur, MD; Ashraf S. Harahsheh, MD; Uzma N. Hasan, MD; Daniel S. Hsia, MD; Gredia Huerta-Montañez, MD; Kathy D. Hummel, MSN; Matt P. Kadish, MD; David C. Kaelber, MD, MPH; Sankaran Krishnan, MD, MPH; Jessica S. Kosut, MD; Jerry Larrabee, MD; Peter Paul C. Lim, MD; Ian C. Michelow, MD; Carlos R. Oliveira, MD, PhD; Hengameh Raissy, PharmD; Zaira Rosario-Pabon, MS; Judith L. Ross, MD; Alice I. Sato, MD, PhD; Michelle D. Stevenson, MD, MS; Maria M. Talavera-Barber, DO; Ronald J. Teufel, MD, MSCR; Kathryn E. Weakley, MD, MSc; Emily Zimmerman, PhD, CCC-SLP; Marie-Abele C. Bind, PhD; James Chan, MA; Zoe Guan, PhD; Richard E. Morse, BA; Harrison T. Reeder, PhD; Natascha Akshoomoff, PhD; Judy L. Aschner, MD; Rakesh Bhattacharjee, MD; Lesley A. Cottrell, PhD; Kelly Cowan, MD; Viren A. D'Sa, MD; Alexander G. Fiks, MD, MSCE; Maria L. Gennaro, MD; Katherine Irby, MD; Manaswitha Khare, MD; Jeremy Landeo Guttierrez, MD, MPH; Russell J. McCulloh, MD, MS; Shalu Narang, MD; Manette Ness-Cochinwala, MD; Sheila Nolan, MD; Paul Palumbo, MD; Julie Ryu, MD; Juan C. Salazar, MD, MPH; Rangaraj Selvarangan, PhD; Cheryl R. Stein, PhD; Alan Werzberger, MD; William T. Zempsky, MD, MPH; Robin Aupperle, PhD; Fiona C. Baker, PhD; Marie T. Banich, PhD; Deanna M. Barch, PhD; Arielle Baskin-Sommers, PhD; James M. Bjork, PhD; Susan Y. Bookheimer, PhD; Sandra A. Brown, PhD; BJ Casey, PhD; Linda Chang, MD; Duncan B. Clark, MD, PhD; Anders M. Dale, PhD; Mirella Dapretto, PhD; Thomas M. Ernst, PhD; Damien A. Fair, PA-C, PhD; Sarah W. Feldstein Ewing, PhD; John J. Foxe, PhD; Edward G. Freedman, PhD; Naomi P. Friedman, PhD; Hugh Garavan, PhD; Dylan G. Gee, PhD; Raul Gonzalez, PhD; Kevin M. Gray, MD; Mary M. Heitzeg, PhD; Megan M. Herting, PhD; Joanna Jacobus, PhD; Angela R. Laird, PhD; Christine L. Larson, PhD; Krista M. Lisdahl, PhD; Monica Luciana, PhD; Beatriz Luna, PhD; Pamela A.F. Madden, PhD; Erin C. McGlade, PhD; Eva M. Müller-Oehring, PhD; Bonnie J. Nagel, PhD; Michael C. Neale, PhD; Martin P. Paulus, PhD; Alexandra S. Potter, PhD; Perry F. Renshaw, MD, PhD; Elizabeth R. Sowell, PhD; Lindsay M. Squeglia, PhD; Susan Tapert, PhD; Lucina Q. Uddin, PhD; Sylia Wilson, PhD; Deborah A. Yurgelun-Todd, PhD.
Affiliations of RECOVER-Pediatrics Group Authors: Department of Biostatistics, Massachusetts General Hospital, Boston (Chan, Guan, Morse, Reeder); Division of Respiratory Medicine, Department of Pediatrics, UC San Diego School of Medicine, Rady Children’s Hospital, San Diego, California (Bhattacharjee, Guttierrez, Ryu); Division of Biostatistics, Department of Medicine, Massachusetts General Hospital, Boston (Bind); Department of Pediatrics, Kapi'olani Medical Center for Women and Children, University of Hawaii, John A. Burns School of Medicine, Honolulu, Hawaii (Balaraman, Kosut); Department of Pediatrics, Oklahoma University Health Science Center, Oklahoma City (Bogie); Division of Pediatric Rheumatology, Department of Pediatrics, MetroHealth System, Cleveland, Ohio (Bukulmez); Division of Pediatric Pulmonology, Allergy, and Sleep Medicine, Department of Pediatrics, Boston Children's Health Physicians, New York Medical College, Valhalla (Dozor, Krishnan); Department of Biomedical Research Informatics Center, Nemours Children's Hospital Delaware, Wilmington (Eckrich); Avera Research Institute, Sioux Falls, South Dakota (Elliott); Division of Research, Department of Research Administration, Arkansas Children's Hospital, Little Rock (Evans, Hummel); Department of Pediatrics, NYU Grossman School of Medicine, New York City Health and Hospitals Bellevue, New York (Farkas); Department of Pediatrics, Yale University School of Medicine, New Haven, Connecticut (Faustino); Pediatric Research Office, University of Nebraska Medical Center, Omaha (Fischer); Division of Allergy, Immunology, and Infectious Diseases, Department of Pediatrics, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey (Gaur); Division of Cardiology, Department of Pediatrics, Children's National Hospital, The George Washington University School of Medicine & Health Sciences, Washington, DC (Harahsheh); Division of Infectious Diseases, Department of Pediatrics, Cooperman Barnabas Medical Center, Livingston, New Jersey (Hasan); Department of Clinical Trials Unit, Pennington Biomedical Research Center, Baton Rouge, Louisiana (Hsia); Division of Puerto Rico Testsite for Exploring Contamination Threats, Northeastern University, Boston, Massachusetts (Huerta-Montañez); Division of General Pediatrics, Department of Pediatrics, University of New Mexico School of Medicine, Albuquerque (Kadish, Larrabee); Departments of Pediatrics, Internal Medicine, and Population and Quantitative Health Sciences, MetroHealth System, Cleveland, Ohio (Kaelber); Division of Infectious Diseases, Department of Pediatrics, University of South Dakota Sanford School of Medicine, Avera Research Institute, Sioux Falls (Lim); Division of Infectious Diseases, Department of Pediatrics, Connecticut Children's Medical Center, University of Connecticut School of Medicine, Hartford (Michelow, Salazar); Division of Infectious Diseases, Department of Pediatrics, Yale University School of Medicine, New Haven, Connecticut (Oliveira); Division of Pulmonary, Department of Pediatrics, University of New Mexico School of Medicine, Albuquerque (Raissy); Division of Puerto Rico Testsite for Exploring Contamination Threats, Department of Civil and Environmental Engineering, Northeastern University, Boston, Massachusetts (Rosario-Pabon); Division of Pediatrics Administration, Department of Pediatrics, Thomas Jefferson University, Nemours Children's Hospital Delaware, Philadelphia, Pennsylvania (Ross); Division of Infectious Disease, Department of Pediatrics, University of Nebraska Medical Center, Omaha (Sato); Division of Norton Children's Emergency Medicine, Department of Pediatrics, University of Louisville School of Medicine, Louisville, Kentucky (Stevenson); Department of Pediatrics, University of South Dakota Sanford School of Medicine, Avera Research Institute, Sioux Falls (Talavera-Barber); Department of Pediatrics, Medical University of South Carolina, Charleston (Teufel); Division of Norton Children's Infectious Diseases, Department of Pediatrics, University of Louisville School of Medicine, Louisville, Kentucky (Weakley); Division of Puerto Rico Testsite for Exploring Contamination Threats, Department of Communication Sciences & Disorders, Northeastern University, Boston, Massachusetts (Zimmerman); Department of Psychiatry, UC San Diego School of Medicine, Rady Children's Hospital, San Diego, California (Akshoomoff, Jacobus, Tapert); Center for Discovery and Innovation, Department of Pediatrics, Hackensack Meridian School of Medicine, Nutley, New Jersey (Aschner); Department of Pediatrics, West Virginia University, Morgantown (Cottrell); Division of Pediatric Pulmonology, Department of Pediatrics, University of Vermont, Burlington (Cowan); Department of Developmental Pediatrics, Rhode Island Hospital, Providence (D'Sa); Division of General Pediatrics, Department of Pediatrics, Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia (Fiks); Public Health Research Institute, Departments of Medicine, Rutgers Robert Wood Johnson Medical School, Newark, New Jersey (Gennaro); Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock (Irby); Division of Hospital Medicine, Department of Pediatrics, UC San Diego School of Medicine, Rady Children's Hospital, San Diego, California (Khare); Division of Pediatric Hospital Medicine, Department of Pediatrics, University of Nebraska Medical Center, Omaha (McCulloh); Cooperman Barnabas Medical Center, Livingston, New Jersey (Narang); Nicklaus Children's Hospital, Division of Population Health, Quality, and Implementation Sciences (PopQuIS), Department of Pediatrics, Rutgers Robert Wood Johnson Medical School, Miami, Florida (Ness-Cochinwala); Division of Infectious Diseases, Department of Pediatrics, Boston Children's Health Physicians, New York Medical College, Valhalla (Nolan); Divisions of Infectious Disease and International Health, Departments of Pediatrics and Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire (Palumbo); Department of Pediatrics, Children's Mercy Hospital and Clinics, Kansas City, Missouri (Selvarangan); Department of Child and Adolescent Psychiatry, NYU Grossman School of Medicine, New York (Stein); Department of Pediatrics, Columbia University Vagelos College of Physicians and Surgeons, Best Healthcare Inc, Monroe, New York (Werzberger); Department of Pediatrics, Connecticut Children's Medical Center, University of Connecticut School of Medicine, Hartford (Zempsky); Laureate Institute for Brain Research, Tulsa, Oklahoma (Aupperle, Paulus); Center for Health Sciences, SRI International, Menlo Park, California (Baker, Müller-Oehring); Institute of Cognitive Science and Department of Psychology and Neuroscience, University of Colorado Boulder (Banich); Departments of Psychological & Brain Sciences, Psychiatry, and Radiology, Washington University in St Louis, St Louis, Missouri (Barch); Department of Psychology, Yale University, New Haven, Connecticut (Baskin-Sommers, Gee); Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond (Bjork); Department of Psychiatry and Biobehavioral Sciences, University of Southern California, Children's Hospital Los Angeles (Bookheimer, Dapretto, Uddin); Department of Psychology and Psychiatry, UC San Diego School of Medicine, Rady Children's Hospital, San Diego, California (Brown); Department of Psychology, Barnard College - Columbia University, New York, New York (Casey); Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Baltimore (Chang, Ernst); Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania (Clark, Luna); Departments of Neurosciences, Radiology, and Psychiatry, UC San Diego School of Medicine, Rady Children's Hospital, San Diego, California (Dale); Institute of Child Development, Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Oregon Health & Science University, Minneapolis (Fair); Department of Psychology, University of Rhode Island, Kingston (Feldstein Ewing); Deptartment of Neuroscience, Del Monte Institute for Neuroscience, University of Rochester School of Medicine, Rochester, New York (Foxe, Freedman); Institute for Behavioral Genetics and Department of Psychology and Neuroscience, University of Colorado Boulder (Friedman); Department of Psychiatry, University of Vermont, Burlington (Garavan); Department of Psychology, Florida International University, Miami (Gonzalez); Division of Addiction Sciences, Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston (Gray, Squeglia); Department of Psychiatry, University of Michigan, Ann Arbor (Heitzeg); Department of Population and Public Health Sciences, University of Southern California, Children's Hospital Los Angeles (Herting); Department of Physics, Florida International University, Miami (Laird); Department of Psychology, University of Wisconsin-Milwaukee (Larson, Lisdahl); Department of Psychology, University of Minnesota, Minneapolis (Luciana); Department of Psychiatry, Washington University in St Louis, St Louis, Missouri (Madden); Departments of Psychiatry and Veteran Affairs, MIRECC, University of Utah School of Medicine, Salt Lake City (McGlade, Renshaw, Yurgelun-Todd); Department of Psychiatry, Oregon Health & Science University, Portland (Nagel); Virginia Institute for Psychiatric & Behavioral Genetics, Virginia Commonwealth University, Richmond (Neale); Division of Clinical Neuroscience Research Unit, Department of Psychiatry, University of Vermont, Burlington (Potter); Department of Pediatrics, University of Southern California, Children's Hospital Los Angeles (Sowell); Institute of Child Development, University of Minnesota, Minneapolis (Wilson).
Author Contributions: Drs Thaweethai and Foulkes 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. Drs Gross and Thaweethai contributed equally as co–first authors and Drs Foulkes and Stockwell contributed equally as co–senior authors.
Concept and design: Gross, Thaweethai, Kleinman, Snowden, Milner, Tantisira, Rhee, Jernigan, Kinser, Salisbury, Warburton, Mohandas, Flaherman, Metz, Karlson, Chibnik, Pant, Gallagher, Gennaro, Lamendola-Essel, Katz, Yin, Dreyer, Carmilani, Coombs, Fitzgerald, Taylor, Evans, Huerta-Montanez, Kaelber, Oliveira, Raissy, Reeder, Baker, Brown, Dale, D'Sa, Fair, Lisdahl, Luna, McGlade, Renshaw, Foulkes, Selvarangan, Stockwell, Yurgelun-Todd.
Acquisition, analysis, or interpretation of data: All authors.
Drafting of the manuscript: Gross, Thaweethai, Snowden, Kinser, Warburton, Mohandas, Krishnamoorthy, Gallagher, Katz, Carmilani, Coombs, Fitzgerald, Taylor, Eckrich, Raissy, Ross, Sato, Feldstein Ewing, Paulus, Stockwell, Squeglia.
Critical review of the manuscript for important intellectual content: All authors.
Statistical analysis: Fischer, Thaweethai, Chibnik, Pant, Krishnamoorthy, Letts, Sato, Reeder, Teufel, Neale, Bind, Chan, Foulkes.
Obtained funding: Gross, Thaweethai, Kleinman, Rosenzweig, Tantisira, Rhee, Jernigan, Kinser, Salisbury, Warburton, Katz, Elliott, Raissy, Aschner, Baker, Barch, Baskin-Sommers, Bjork, Bookheimer, Casey, Chang, Clark, Dale, Dapretto, Ernst, Fair, Feldstein Ewing, Foxe, Friedman, Gee, Gonzalez, Gray, Herting, Jacobus, Laird, Lisdahl, Luciana, Muller-Oehring, Nagel, Neale, Paulus, Renshaw, Salazar, Selvarangan, Stockwell, Tapert, Wilson.
Administrative, technical, or material support: Fischer, Gross, Hasan, Hsia, Kadish, Kleinman, Kosut, Snowden, Milner, Rhee, Jernigan, Warburton, Wood, Truong, Flaherman, Karlson, Gallagher, Lamendola-Essel, Hasson, Katz, Taylor, Teufel, Eckrich, Evans, Farkas, Faustino, Huerta-Montanez, Jacobus, Kaelber, Krishnan, Raissy, Morse, Cottrell, Fiks, Landeo Guttierrez, Ness-Cochinwala, Ryu, Stein, Baskin-Sommers, Aupperle, Brown, Bukulmez, Chan, Chang, Clark, Dale, Dozor, Ernst, Foxe, Freedman, Garavan, Gee, Laird, Lisdahl, McGlade, Paulus, Renshaw, Salazar, Stevenson, Squeglia, Uddin, Werzberger.
Supervision: Gross, Hsia, Thaweethai, Kleinman, Snowden, Rhee, Jernigan, Kinser, Warburton, Mohandas, Wood, Chibnik, Lamendola-Essel, Katz, Kaelber, Krishnan, Oliveira, Cottrell, Ness-Cochinwala, Akshoomoff, Aschner, Banich, Baskin-Sommers, Brown, Bukulmez, Chan, Chang, Clark, Dozor, Gee, Heitzeg, Herting, Hummel, Larrabee, Lisdahl, McGlade, Nolan, Renshaw, Fiks, Foulkes, Jacobus, Larson, Luciana, Salazar, Sowell, Stockwell, Teufel, Werzberger, Yurgelun-Todd.
Other - discussions of findings: Gallagher.
Other - communication of scientific findings: Fitzgerald.
Other - Contributed experience and knowledge from the patient/caregiver, and Infection Associated Chronic Condition community, perspective: Letts.
Conflict of Interest Disclosures: Dr Kleinman reported receiving grants from New York University via subcontract of NIH during the conduct of the study; owning shares in Amgen, Regeneron, Sanofi, and GLAXF; and being a member of the board of Dartnet Institute and member of the board of health of Borough of Metuchen, Quality Matters, Inc . Dr Snowden reported serving on a Pfizer COVID-19 advisory board, which ended in November 2023. Dr Milner reported serving on a scientific advisory board for Blueprint Medicine and receiving grants from Pharming. Dr Jernigan reported receiving grants from University of California San Diego OTA during the conduct of the study. Dr Salisbury reported receiving grants from NIH and HRSA and donated funds from Anthem outside the submitted work. Dr Newburger reported receiving grants from Pfizer for an observational study on COVID-19 associated myocarditis, serving on a data and safety monitoring committee for BMS, and serving on an independent events adjudication committee for pediatric apixiban study outside the submitted work. Dr Truong reported being co–principal investigator on a Pfizer-funded study to assess long-term sequalae of vaccine-associated myocarditis. Dr Metz reported being a site principal investigator for Pfizer studies of SARS-CoV-2 vaccination in pregnancy, RSV vaccination in pregnancy, and Paxlovid in pregnancy. Dr Dreyer reported receiving grants from NYU Grossman School of Medicine during the conduct of the study. Dr Aschner reported being a stockholder in Gilead Sciences. Dr Bhattacharjee reported serving on an advisory board for Jazz Pharmaceuticals. Dr Werzberger reported receiving funding from Merck for a hepatitis A vaccine immunology study. Dr Zempsky reported affiliations with OmmioHealth, Lundbeck Pharmacueticals, and Editas. Dr Banich reported receiving grants from University of Colorado Boulder during the conduct of the study. Dr Barch reported receiving grants from NIMH and NIDA during the conduct of the study. Dr Bhattacharjee reported consulting for Jazz Pharmaceuticals and Avadel Pharmaceuticals outside the submitted work. Dr Dale reported being a founder of and holding equity in CorTechs Labs, Inc; serving on a scientific advisory board for CorTechs Labs, Inc, Human Longevity, Inc, and the Mohn Medical Imaging and Visualization Centre; and receiving funding through a research agreement with General Electric Healthcare (GEHC). Dr Fair reported being a patent holder for the Framewise Integrated Real-Time Motion Monitoring (FIRMM) software and a cofounder of Turing Medical, Inc. Dr Fiks reported receiving personal fees from Rutgers and salary support from AAP during the conduct of the study; receiving support from American Medical Association for travel and honorarium from Atlantic Health Systems and Boston Medical Center, PCORI, and Emory University; and having a patent for decision support software known as Care Assistant pending. Dr Foulkes reported receiving grants from NIH/NHLBI during the conduct of the study. Dr Gray reported receiving grants from Aelis Farma and personal fees from Indivior and Jazz Pharmaceuticals outside the submitted work. Dr McCulloh reported receiving grants from University of Arkansas for Medical Sciences sub-awardee for the NIH RECOVER grant during the conduct of the study and grants from Merck Foundation for vaccine communication research through the Merck Investigator Studies Program outside the submitted work. Dr McGlade reported receiving salary support from Department of Veteran Affairs outside the submitted work. Dr Neale reported receiving grants from NIH/NIDA during the conduct of the study. Dr Palumbo reported being a member of a data and safety monitoring committee for Gilead and Janssen outside the submitted work. Dr Paulus reported receiving grants from National Institute on Drug Abuse during the conduct of the study, receiving royalties from an article on methamphetamine in UpToDate, and having compensated consulting agreement with Boehringer Ingelheim International GmbH. Dr Ross reported receiving grants from Nemours Children’s Health-DE NIH RECOVER STUDY during the conduct of the study. Dr Stockwell reported receiving grants from CDC to Trustees of Columbia related to SARS-CoV-2 infection and vaccination research and service agreement paid to trustees of Columbia for being associate director of pediatric research in office settings from American Academy of Pediatrics outside the submitted work. Dr Teufel reported receiving grants from HRSA and Duke outside the submitted work. No other disclosures were reported.
Funding/Support: This research was funded by NIH agreements OT2HL161841, OT2HL161847, and OT2HL156812, with additional support from grant R01 HL162373.
Role of the Funder/Sponsor: The NIH had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Group Information: The RECOVER-Pediatrics Consortium appear listed in Supplement 4 .
Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the RECOVER Program or the NIH.
Data Sharing Statement: See Supplement 5 .
Additional Contributions: We would like to thank the National Community Engagement Group, all patient, caregiver, and community representatives, and all the participants enrolled in the RECOVER Initiative.
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A new study by The University of Texas at Austin School of Nursing researchers, including assistant professors Heather Cuevas, PhD, APRN, ACNS, FCNS , and Beth Heitkemper, PhD, RN , along with alumna Jeeyeon Kim, PhD, explores subjective cognitive dysfunction in nondementia-related chronic illnesses.
Their work, titled " Subjective Cognitive Dysfunction in Chronic Illness: A Systematic Review and Meta-Synthesis ," was published in the August 2024 edition of the Western Journal of Nursing Research.
The study systematically reviewed 25 qualitative studies and developed a model to explain how individuals with chronic illnesses experience and adapt to cognitive dysfunction. The findings are organized into four key themes: symptoms, health care, self-perception and relationships. The research highlights the impact of cognitive dysfunction on health care interactions and personal life and suggests that further research is needed to understand its role in chronic illness.
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Using our prior research on prison wages and medical copays, researchers found that higher copays obstruct access to necessary healthcare behind bars, even as prison populations face increasing rates of physical and mental health conditions..
by Emily Widra , August 29, 2024
In most states, people incarcerated in prisons must pay medical copays 1 and fees for physician visits, medications, dental treatment, and other health services. While these copays may be as little as two or five dollars, they still represent massive barriers to healthcare. This is because incarcerated people are disproportionately poor to start with, and those who work typically earn less than a dollar an hour and many don’t work at all. A new report published in JAMA Internal Medicine builds on our analyses of prison copay and wage policies across all state prison systems and the findings are clear: medical copays in prisons are associated with worse access to healthcare behind bars. These unaffordable fees are particularly devastating because they deter necessary care among an incarcerated population that faces many medical conditions — often at higher rates than national averages — and routinely faces inadequate health services behind bars.
In their recent publication, Dr. Lupez and her fellow researchers analyzed nationally representative data from state and federal prison populations published in the Bureau of Justice Statistics’ Survey of Prison Inmates, 2016 . 2 While we previously published our own analysis of the same dataset in 2021, this new research goes further by analyzing changes from the 2004 data and mapping our copays and wages data onto health data from people in prison. The researchers compared the 2004 and 2016 iterations of the Survey and found that, overall, people in prison are facing more chronic physical and mental health conditions than they were in 2004.
Additionally, Dr. Lupez and her colleagues measured the effect of prison medical copays on access to specific healthcare services (including pregnancy-related care), access to clinicians for people with chronic physical conditions, and the continuation of medications for mental health. For each state, 3 they used our 2017 copay and wage data to categorize each survey participant into one of three categories: no copays, copay amounts less than or equal to one week’s prison wage, and copay amounts greater than one week’s prison wage. 4 Their results provide further evidence that medical copays limit access to care among the most vulnerable people in the system.
We already know that prison healthcare regularly falls short of the constitutional duty to care for those in custody. While most people in state prison report having seen a healthcare provider at least once since admission, nearly 1 in 5 have gone without a single health-related visit since entering state prison. Accordingly, the authors first examined general access to healthcare among three groups of people in state and federal prisons: people who were pregnant when admitted to prison, people with chronic physical conditions, and people with mental health conditions. They found that even the most basic care — like obstetric exams for pregnant people or any visit with a healthcare provider for people with chronic conditions — is not provided to surprisingly large portions of the affected population.
Treatment for chronic physical conditions. More than 1 in 10 people (14%) with at least one chronic condition in state and federal prisons had not been seen by a clinician since they were incarcerated. Within their first year of imprisonment, more than a fifth of people with chronic conditions (22%) had not yet been seen by a healthcare provider. Chronic diseases — by definition — require ongoing medical attention, and for the 62% people in state and federal prisons 5 who have them, the lack of consistent, adequate medical treatment can have disastrous and fatal consequences .
Mental healthcare . Among the almost 400,000 people in state and federal prisons with chronic mental health conditions, 6 one third (33%) had not received any clinical mental health treatment since entering prison. Again, those who were within their first year of incarceration were even more likely to report no treatment: 39% had not yet received any mental health treatment compared to 29% of people incarcerated for more than one year. Similarly, more than 41% of people experiencing severe psychological distress 7 in state and federal prisons had not received mental health treatment. 8 And more than one third (34%) of people who had been taking prescription medication for a mental health condition at the time of their offense had not received their medication since entering prison. 9
Pregnancy-related healthcare. Standard prenatal healthcare for pregnant people involves monthly doctor’s appointments at minimum, as well as screenings , tests , vaccinations , and patient education usually conducted by a perinatal specialist. But a shocking proportion of pregnant people in state prisons did not receive so much as an obstetric examination (9%), see any outside providers or specialists (26%), or any pregnancy-education from a healthcare provider (50%) after entering prison.
When healthcare needs come up against an arduous and expensive sick call process , people are forced to jump through arbitrary hoops just to see a doctor — or delay or forgo medical care altogether — as their health deteriorates. The researchers found that prison systems with more expensive medical copays (relative to prison wages) limit access to necessary healthcare for incarcerated pregnant people and those with chronic conditions more than prisons with no copays or copays equivalent to or less than one week’s prison wage.
Healthcare in prisons is subpar for almost any medical condition, and chronic physical conditions are often more prevalent behind bars than in the general population. The researchers found that medical copays clearly impact access to healthcare for the more than 500,000 people incarcerated in state and federal prisons who have conditions like heart disease, asthma, kidney disease, and hepatitis C. This is particularly alarming considering many of these conditions require regular medical management or can even be cured.
People with chronic conditions in state prisons where copays exceed a week’s wage are less likely to have seen a healthcare clinician while incarcerated than those in prisons that charge no copays or lower copay amounts. Among people with chronic physical conditions who have been incarcerated for more than one year, 12% of incarcerated people who face more unaffordable copays have not seen a clinician. Meanwhile, in prisons with relatively lower copays or no copays, less than 8% of people with chronic physical conditions have not yet seen a clinician after being incarcerated for more than one year.
Similarly, pregnant people in state prisons do not receive standard prenatal care and medical copays make this situation worse. Pregnant people in state prisons without medical copays or with lower copays relative to their wages were more likely to have received an obstetrical examination and clinical pregnancy education than those in prisons with copay amounts more than a week’s prison wage. 10
Copay waivers and exemptions. Researchers also identified prison policies granting copay exemptions for some healthcare services for some people: at least 25 state departments of corrections and the federal prison system have copay waivers for chronic conditions, while 13 states have waivers for pregnancy-related care. However, as correctional health expert Dr. Homer Venters explains: “many chronic care problems aren’t detected when a person arrives [at the jail or prison], so to get treatment… requires the sick call process… Many systems have a practice of requiring two or three nursing sick call encounters before a person sees a doctor.” 11 In other words, someone who meets the exemption criteria likely will still need to pay copays for the initial two or three nursing sick call visits before clinicians identify them as someone who should be exempt from copays.
It’s worth noting that the researchers repeated their analysis of how copays impact access to healthcare services among people without any reported chronic conditions in state prisons. If the chronic care waivers were working as intended, they reasoned, people who do not have chronic conditions would experience even lower rates of healthcare access compared to people with chronic conditions who have their copays waived. Instead, they found people who do not have chronic conditions appear to face similar rates of healthcare access as those who do, suggesting that these waivers are not routinely and consistently applied in a way that actually promotes healthcare access for the most vulnerable people in prison.
In addition to finding that higher copays restrict healthcare access, the researchers delved into demographic and health-related changes among the prison population between 2004 and 2016.
Demographics. People in prison in 2016 were more likely to be older; identify as Hispanic, multiracial, or some race other than white or Black; 12 and to have been incarcerated multiple times compared to those incarcerated in 2004. These are groups of people that already face significant barriers to healthcare and often have poor health outcomes, even outside of prison. Older adults are more likely to have more medical conditions , which are already more prevalent behind bars. Outside of prison, Hispanic adults report less access to regular medical care and higher rates of uninsurance , while Black, Hispanic, and American Indian or Alaska Native people are less likely to receive necessary mental healthcare and have faced larger declines in life expectancy than their white counterparts. Ultimately, multiple periods of incarceration can negatively affect health status , and experiencing years of limited resources , inaccessibility , and understaffing in prison healthcare creates a situation in which each year spent in prison takes two years off of an individual’s life expectancy.
Chronic physical conditions. As we discussed in our 2021 report, Chronic Punishment: The unmet health needs of people in state prisons , chronic physical conditions and infectious diseases are more prevalent in prisons than among the nation at large. This newest analysis from Dr. Lupez and her colleagues reveals that these chronic conditions are more common in state and federal prisons than they were in 2004. For example, the percentage of the prison population facing at least one chronic condition increased from 56% in 2004 to 62% in 2016, and the percentage facing three or more chronic conditions increased from 12% to 15%. In other words, a larger portion of the prison population is facing chronic illness, and in many cases multiple chronic illnesses.
Mental health conditions. The researchers also found that mental health conditions were more common in 2016 than in 2004. The Bureau of Justice Statistics’ Survey offers data on the proportion of the state and federal prison population who have ever had depression, anxiety, psychotic disorders, manic disorders, posttraumatic stress disorder (PTSD), and personality disorders. The prevalence of every one of these conditions increased in the prison population between 2004 and 2016: in 2004, nearly a quarter of the prison population reported one mental health condition and by 2016, this had increased to more than 40% of people in prison. 13 Not only are mental health conditions more common, but more people are facing chronic mental health conditions in particular: the proportion of people in prison with chronic mental health conditions practically doubled from 2004 to 2016 (14% to 27%).
This study ultimately underscores the urgency of ending medical copays and healthcare fees in prisons and jails, and provides evidence that unaffordable copays put necessary medical care out of reach for far too many incarcerated people. Generally speaking, this study shows that incarcerated people are sicker than ever, the healthcare options available to them are grossly inadequate, and people are not getting the constitutionally-guaranteed care that they need — regardless of whether prisons charge copays or not.
As a final note, we are gratified whenever our work is repurposed by other researchers like this — it’s why we publish our detailed appendix tables and other data collections, many of which can be found in our Data Toolbox . If you are a researcher using our data, we encourage you to reach out with any questions and to let us know how you are using our work.
Unlike non-incarcerated people, people in prison do not have a choice about their medical coverage, nor how “cost sharing” applies to them. There is no “insurance” system that covers them, so the term “copay” is a misnomer for the fee they are charged to request a medical appointment or to obtain a prescription. As the organization Voice of the Experienced argues, the use of this term legitimizes these unaffordable fees, which deter people from seeking needed medical care. They suggest more descriptive terms such as “medical request fees” or “sick call fees.” ↩
The most recent iteration of the Survey was administered in 2016 and the data were published in 2021 . While the data reflect the prison population in 2016, this study is still the most recent source for the information used in this study. ↩
The data from the Survey of Prison Inmates is not broken down by the state in which individual respondents are incarcerated, but the researchers used the respondent’s state of residence before incarceration as a proxy for state of incarceration. They excluded states from the sample that did not have a known prison minimum wage or copay amount, including Delaware, Maine, Nevada, and Washington. Because three states — California , Virginia , and Illinois — eliminated copays after the 2016 administration of the Survey , they are still included in this sample, although they no longer charge medical copays. ↩
For the purposes of calculating an average week’s wage in prison, the authors used our estimate of a 31.75 hour work week (an average workday of 6.35 hours). Not everyone in prison works , but for those that do, incarcerated workers in regular, non-industry prison jobs (i.e., jobs that are directed by the Department of Corrections and support the prison facility) had an average minimum daily wage of 86 cents in our 2017 analysis. Incarcerated people assigned to work for state-owned businesses (i.e., “industry” jobs that produce goods and provide services that are sold to government agencies) earned between 33 cents and $1.41 per hour on average — roughly twice as much as people assigned to regular prison jobs. For state-specific information on prison wages, an explanation of different types of prison work assignments, and more detail on the methodology behind these calculations, see our 2017 publication, How much do incarcerated people earn in each state? ↩
This study utilized a definition of chronic conditions that included ever having hypertension, heart disease, diabetes, stroke, asthma, cirrhosis, HIV, or cancer or currently having kidney disease, hepatitis B, hepatitis C, or joint disease. The Bureau of Justice Statistics, in their report on health of people in prison based on the 2016 Survey , estimated that 504,000 people in state prisons and 57,700 people in federal prisons currently had at least one chronic physical condition (including cancer, high blood pressure, hypertension, stroke-related problems, diabetes, high blood sugar, heart-related problems, kidney-related problems, arthritis, asthma, or liver cirrhosis). The estimated number of incarcerated people who have ever had a chronic physical condition was upwards of 715,000. ↩
The researchers categorized psychotic disorders, bipolar/manic disorders, and personality disorders as “chronic” mental health conditions. ↩
Severe psychological distress was calculated using the Kessler Psychological Distress Scale (K6) that was included in the 2016 Bureau of Justice Statistics’ Survey . The K6 scale is composed of six survey questions regarding one’s mental state in the past 30 days and a composite score of 13 or higher reflects “severe psychological distress.” ↩
Research has found a robust association between psychological distress and mortality , and higher levels of psychological distress are associated with suicide , which continues to be a growing cause of death in prisons and jails . ↩
It is widely understood that inconsistent use of prescription medications (often called “medication non-adherence” in medical research) for mental health conditions is associated with poorer outcomes , including diminished treatment efficacy, worsened symptoms, and reduced responsiveness to future treatments. ↩
The statistical analysis of the effects of copays on access to pregnancy-related healthcare were not statistically significant, likely due — in part — to a small sample size (only 178 pregnant people were in prisons without co-pays). ↩
This quotation is published in the study’s appendix. Dr. Homer Venters previously served as chief medical officer of the New York City jail system and currently serves as a Federal Court Monitor of health services in jail and prison settings. ↩
Because of the small sample size, the researchers combined the Survey of Prison Inmates’ non-Hispanic racial categories of American Indian or Alaska Native, Asian, Native Hawaiian or Other Pacific Islander, and “other” race (i.e., a different racial identity that does not fit in the racial categories the Bureau of Justice Statistics uses). ↩
This increase is not due to a change in definitions between 2004 and 2016, as the authors used the same list of mental health conditions in the 2004 and 2016 iterations of the Bureau of Justice Statistics’ Survey of Prison Inmates : depression, anxiety, psychotic disorders, manic disorders, post-traumatic stress disorder (PTSD), and personality disorder. In 2016, the Bureau of Justice Statistics added an additional mental health measure — severe psychological distress — but since this was not used in the 2004 iteration, it is not included in the change in the number of people reporting mental health conditions. ↩
Emily Widra is a Senior Research Analyst at the Prison Policy Initiative. ( Other articles | Full bio | Contact )
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The straightforward and effective method was developed at the Flow Chemistry group at the Van ‘t Hoff Institute for Molecular Sciences led by Prof. Timothy Noël, in cooperation with researchers in Italy, Spain and the UK, both from academia and industry. Applying the principles of flow chemistry, where reactions take place in closed systems of small tubes, makes for safe and controlled chemistry. It also offers greater versatility and flexibility over more common procedures using traditional chemical glassware.
Many pharmaceutical compounds (such as anti-depressants) as well as agrochemical compounds (such as pesticides) benefit from the presence of a trifluoromethyl (-CF 3 ) group. It enhances hydrophobicity and increases metabolic stability, thus improving efficacy and lowering the required dose or concentration.
To introduce the fluorine atoms in these molecules, their synthesis often requires bespoke fluorinated reagents. Many of these are among the family of PFAS compounds and thus will face future legislation. The synthesis protocol now presented in the Science paper provides a viable alternative since it only requires caesium fluoride salt as the fluorine source. Such PFAS-free synthesis of fluorinated agents can provide an environmentally more friendly option for the synthesis of pharmaceutical compounds, which motivated scientists from AstraZeneca to participate in the research.
In addition, the new synthesis protocol enables coupling of the CF 3 group through a sulphur (S), nitrogen (N) or oxygen (O) atom. Such fluorinated motifs confer unique features to drug molecules and agrochemicals, impacting their lipophilicity, oxidation resistance, and acid-base properties.
The Science paper presents a versatile microfluidic flow module for generating reactive N–, S– and O–CF 3 anions. These are prepared in a packed bed flow reactor containing the caesium fluoride salt. Appropriate (S, O or N containing) precursors are then led through this reactor.
There, they are fluorinated with high efficiency due to the high surface area of the salt in the packed bed as well and the improved mixing of the organic intermediates. Importantly, this approach also offers enhanced safety as all formed intermediates are contained within the microfluidic system.
Another important feature of the flow system described in Science is the integration of the anion generating module with a downstream reaction module. There, the N–, S– or O–CF 3 anions react with appropriate substrates to achieve pharmaceutical and agrochemical active ingredients as the desired end products.
In combination, the anion generator module and the downstream reactor provide a streamlined platform for the derivatization of molecules bearing N–, S– and O–CF 3 motifs. This innovative approach is poised to impact the development of new pharmaceutical drugs by enhancing their properties while improving safety and sustainability in their production processes. In their Science paper, the researchers report the combination of various anions with a range of substrates, resulting in multiple fluorinated products with relevance to pharmaceutical and agrochemical syntheses. In many cases the research team was able to report very satisfactory yields. Moreover, the operational parameters (e.g. reaction times) offer a good prospect for actual implementation in an academic as well as an industrial context.
Mauro Spennacchio, Miguel Bernús, Jelena Stanić, Daniele Mazzarella, Marco Colella, James J. Douglas, Omar Boutureira, Timothy Noël: A unified flow strategy for the preparation and use of trifluoromethyl-heteroatom anions. Science, 385, 6712, p991-996 DOI: 10.1126/science.adq2954
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The U.S. has witnessed a resurgence of labor activism, with teachers at the forefront. We examine how teacher strikes affect compensation, working conditions, and productivity with an original dataset of 772 teacher strikes generating 48 million student days idle between 2007 and 2023. Using an event study framework, we find that, on average, strikes increase compensation by 8% and lower pupil-teacher ratios by 0.5 students, driven by new state revenues. We find little evidence of sizable impacts on student achievement up to five years post-strike, though strikes lasting 10 or more days decrease math achievement in the short-term.
Correspondence regarding the manuscript can be sent to Melissa Arnold Lyon: Rockefeller College of Public Affairs and Policy, University at Albany, 135 Western Avenue, Albany, NY 12222 ([email protected]). We are very grateful for research assistance from Zoe Beckman, Eunice Chong, Summer Dai, Stephanie Tu, Sarah Newberger, Hyesang Noh, Adam Shephardson, and Natalie Truong. This research also benefited immensely from the helpful feedback of Jesse Bruhn, Jake Rosenfeld, Sarah Anzia, and the seminar participants at Michigan State University, the 2023 APSA Comparative Labor Politics Workshop, and the 2023 APSA and 2022 AEFP Annual Meetings. This research was supported by a NAEd/Spencer Postdoctoral Fellowship awarded to Lyon. This research was completed before Matthew Kraft joined the Council of Economic Advisers as a senior economist in July of 2024. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
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A symptom was included if at least 5% of infected or uninfected participants reported experiencing that symptom. Adjusted odds ratios and risk differences were estimated from models that included infection status as the exposure and the presence of each prolonged symptom as the outcome, with adjustment for sex assigned at birth and race and ethnicity (see eMethods in Supplement 3).
The University of Texas at Austin School of Nursing assistant professors Heather Cuevas, PhD, APRN, ACNS, FCNS, and Beth Heitkemper, PhD, RN, along with alumna Jeeyeon Kim, PhD, co-authored a research paper published in the August 2024 edition of the Western Journal of Nursing Research.
The long-term effects of COVID-19 are still being studied, and the incidence rate of LC may change over time. In the UK, studies have explored LC symptoms and risk factors in non-hospitalised individuals using primary care records 4 and consolidated evidence on persistent symptoms and their associations in broader populations. 5 Additionally, there has been significant interest in Patient ...
Here, we examine a great Neolithic engineering feat: the Menga dolmen, Iberia's largest megalithic monument. As listed by UNESCO, the Antequera megalithic site includes two natural formations, La Peña de los Enamorados and El Torcal karstic massif, and four major megalithic monuments: Menga, Viera, El Romeral, and the one recently discovered at Piedras Blancas, at the foot of La Peña de ...
They suggest more descriptive terms such as "medical request fees" or "sick call fees." ↩. The most recent iteration of the Survey was administered in 2016 and the data were published in 2021. While the data reflect the prison population in 2016, this study is still the most recent source for the information used in this study. ↩
Chemists at the University of Amsterdam have developed a method to furnish a range of molecules with a trifluoromethyl group attached to a sulphur, nitrogen or oxygen atom. Their procedure, which has just been published in Science, avoids the use of PFAS reagents. It thus provides an environmentally friendly synthesis route for pharmaceutical and agrochemical compounds that rely on the ...
The U.S. has witnessed a resurgence of labor activism, with teachers at the forefront. We examine how teacher strikes affect compensation, working conditions, and productivity with an original dataset of 772 teacher strikes generating 48 million student days idle between 2007 and 2023. Using an ...