Pseudotsugamenziesii) in the Coast Range Mountain foothills. His thesis included plot locations, summaries of vegetation characteristics and point estimates of bird densities for each plot. To our knowledge, the Anderson data represent the oldest structured survey of Pacific Northwest winter forest bird communities with density estimates. Given the paucity of similar data, we re-surveyed his plots after aligning methods with his and adding modern components  to facilitate comparisons of changes in abundances. We preserved more extensive metadata than were preserved from Anderson's surveys, including georeferenced point count survey locations to facilitate more precisely repeatable future re-surveys.Long-term changes in bird populations during winter are poorly evaluated in many parts of the world. We re-surveyed forest bird communities during winter, 2019-2021, in seven large plots originally surveyed from 1968 through to 1970, near Corvallis, Oregon, USA by Stanley Anderson, a graduate student at Oregon State University in the 1960s. Anderson counted birds and measured forest plant communities within the forests dominated by Douglas-fir (Original surveys of winter bird populations in the Pacific Northwest, USA, based on georeferenced locations within seven large plots originally surveyed, 1968-1970. In addition to raw count data of all bird species detected, we include information from distance sampling and time-interval sampling methods. To our knowledge, this is one of the only structured surveys of winter forest bird populations in the Coast Range Mountains, USA. Long-term data that utilise standardised and structured methodologies are ideal for quantifying change in bird populations. With increasing interest in quantifying the drivers of biodiversity responses to climate change and other forms of environmental change, the perspective that comparisons with older datasets provide elevates the importance of historic surveys. In addition, preserving metadata from current surveys so that future re-surveys can evaluate temporal change in statistically rigorous ways also elevates the value of current biodiversity surveys. Unfortunately, long-term survey data do not exist for most biogeographic regions. An alternative method to assess change across time is to compare historic collections of short-term data with modern re-surveys of the same sites. Such re-surveys can provide snapshots of potential changes in bird abundance, occupancy, and community composition . Many hiThis paper shares data from re-surveys in 2019-2022 of seven large plots in the Oregon Coast Range Mountains originally surveyed in 1968-1970. Anderson  surveyed bird abundances and inventoried species, providing point estimates of abundance for all species he encountered within his plots. He used multiple methods, but reported that his point counts formed the primary basis for his abundance estimates. Although his raw data were not preserved, his thesis, uniquely for that era, presented the abundance estimates for each plot and species. To compare with his results, we conducted approximately 320 stationary point counts which resulted in 4613 avian records. Given the rarity of structured winter bird surveys from this region, we share the raw data and metadata to facilitate future re-surveys and provide data for additional regional comparisons of winter bird communities. Our workflow for aligning the two datasets are described in Clements (2022). Comparisons with the original survey data show declines of four of the six most regularly detected forest bird species, one of the six species remaining relatively stable in numbers and one increasing strongly . SpeciesAvian surveysAnderson  noted the location of his plots, which were quarter sections (65 ha) within the Township and Range divisions of the Public Land Survey System. The exact locations of Anderson's bird surveys within quarter sections were unclear but most quarter sections were within more extensively forested areas, as determined from aerial photos. Therefore, we conducted our surveys within the same quarter sections and assumed, because we were surveying the same habitats, that our survey results would be reliably comparable to Anderson’s. In two cases (plots 6 and 7), access was reduced because of COVID-19-related regulations so some counts we conducted were outside of the quarter-sections, but within 250 m of plot boundaries and in the same habitats. We categorised our survey locations as “In” plots or “Out” of plots so we could assess potential differences in bird estimates.Anderson counted birds using four different methods , but stated that the sample count method produced the most accurate density estimates, which were consistent with his estimates from information combining all his techniques . The samTo maximise our chances of reliably comparing our results with Anderson’s, we also used stationary (point) counts that were spaced approximately 100 m apart along roads and trails and were 10 minutes in duration. Plots were visited four times total, during which we conducted 10 to 15 counts per visit. All counts were conducted from dawn to noon on days with minimal rain or wind. Each plot was surveyed twice each year in January through to March, usually once by each skilled observer (except for plots 3 and 4 which were surveyed twice by the same observer in 2021). The total effort amounted to 35 to 55 total counts in each plot. Numbers of points varied amongst years and plots and are preserved in the data archived here. We made no attempt to repeat surveys at the same locations across years. All survey locatons are identified by their latitude and longitude (see below). During each count we recorded each bird that was detected, the distance to it (checked with laser rangefinder), the cardinal direction of its initial detection and detection type . If a bird was heard within approximately 20 m, but was not seen, an effort was made to visually locate that individual. Thus, we repeated Anderson’s methods as closely as possible. At the same time, we used modern counting methods with unlimited radius count areas and time-interval tracking of individual birds where each count was divided into ten 1-minute intervals . We also measured the latitude and longitude of each point location with hand-held GPS units, typically Garmin etrex 10 (accurate to within 10 m).Counts were conducted as back-to-back 5-min surveys totalling 10 min each. Within each 5-min count, each bird was given a detection-no detection (dnd) history, based on whether or not it was detected in each of the five one-minute intervals. A 1 indicates the bird was detected (irrespective of detection cue type) within that interval. A 0 indicates it was not detected. Thus, dnd histories are strings of five 0 s and 1 s with one entry of a 1 or 0 per interval. An exception was for 1455 records where the dnd histories included just two total intervals for a 10-min count. In the latter case, if a bird was detected during the first 5 min of the 10 min count, it was noted as 1 in the min1 interval and, if it was not detected, it was noted as 0 in the min1 interval. Likewise, for the second 5-min of a 10-min count, if a bird was detected, it was noted as 1 in the min2 interval and 0 if it was not.Habitat surveysAnderson recorded trees per acre, average canopy cover, height class distribution, shrub density and qualitative descriptions of vegetation in each plot. Anderson used a variety of methods to produce his results. We used methods described by We surveyed avian communities and vegetation structure at seven of Anderson's original 10 plots in the eastern foothills of the Coast Mountain Range outside of Corvallis, Oregon, USA. These plots are described below using their quarter-section and the degrees-decimal latitude and longitude coordinates of each corner Tables .Data range: 2019-6-01 – 2022-2-28; 2022-3-01 – 2022-4-30.Bird surveys were conducted between December 2019 and February 2022. Vegetation surveys were conducted between March and April 2022.Creative Commons Public Domain Waiver (CC-Zero)Survey of winter bird communities and vegetation in the Oregon Coast Rangehttps://doi.org/10.5061/dryad.rxwdbrvc82Clements_Robinson_winterbirdsurveys_OregonCoastRange.csv.csvhttps://doi.org/10.5061/dryad.rxwdbrvc8Clements_Robinson_vegetationsurveys_OregonCoastRange.csv.csvhttps://doi.org/10.5061/dryad.rxwdbrvc8


What were the characteristics and outcomes of patients admitted to COVID-19 treatment centers during Zambia’s first 4 COVID-19 waves?In this cohort study of 3876 patients, across-wave analyses of data, including identification of risk factors for in-hospital mortality in Zambia, were in line with data reported elsewhere. Within-wave analyses revealed a pattern in which it appeared that admission of higher-risk patients was prioritized during periods of surging demand for health services during COVID-19 waves.These findings suggest that increased efforts to expand health system capacity and improve health system resiliency in Zambia and other countries with resource-limited health systems are warranted. This cohort study examines patient characteristics and risk factors for in-hospital death among patients admitted to COVID-19 treatment centers in Zambia during the COVID-19 epidemic. Few epidemiologic studies related to COVID-19 have emerged from countries in Africa, where demographic characteristics, epidemiology, and health system capacity differ from other parts of the world.To describe the characteristics and outcomes of patients admitted to COVID-19 treatment centers, assess risk factors for in-hospital death, and explore how treatment center admissions were affected by COVID-19 waves in Zambia.This retrospective cohort study assessed patients admitted to COVID-19 treatment centers in 5 Zambian cities between March 1, 2020, and February 28, 2022.Risk factors for in-hospital mortality, including patient age and severity of COVID-19, at treatment center admission.Patient information was collected, including inpatient disposition (discharged or died). Differences across and within COVID-19 waves were assessed. Mixed-effects logistic regression models were used to assess associations between risk factors and in-hospital mortality as well as between characteristics of admitted patients and timing of admission.P < .001). Factors associated with in-hospital mortality included older age  and HIV infection . Within waves, patients who were admitted during weeks 5 to 9 had significantly higher odds of being 60 years or older  or having severe COVID-19 at admission  than those admitted during the first 4 weeks.A total of 3876 patients were admitted during 4 COVID-19 waves . Compared with the first 3 waves (pooled), the proportion of patients who were 60 years or older admitted during wave 4, when the Omicron variant was circulating, was significantly lower (250 of 1009 [24.8%] vs 1116 of 2837 [39.3%]; The characteristics of admitted patients during the Omicron wave and risk factors for in-hospital mortality in Zambia reflect data reported elsewhere. Within-wave analyses revealed a pattern in which it appeared that admission of higher-risk patients was prioritized during periods when there were surges in demand for health services during COVID-19 waves. These findings support the need to expand health system capacity and improve health system resiliency in Zambia and other countries with resource-limited health systems. The timing and public health impacts of waves are influenced by a variety of factors, including natural and vaccine-induced immunity,11 Some variant strains have also demonstrated increased (or decreased) virulence.9 When a new variant emerges or a new wave begins, collection and analysis of epidemiologic data provide useful information for researchers interested in understanding how new mutations affect disease pathogenesis and for public health officials assessing how the variant will affect (or has affected) health systems. Reports describing such analyses are available,15 but few are from countries in sub-Saharan Africa, where demographic characteristics, epidemiology, and health system capacity differ from other parts of the world.Many of the differences between waves can be attributed to the ability of SARS-CoV-2 to mutate and produce characteristics that favor increased transmissibility and immune evasion.Understanding how epidemiologic data collected in Zambia changed across and during COVID-19 waves also has potential to help inform future public health responses. The influence of health system capacity is particularly important to elucidate, because limited health system capacity directly affects downstream epidemiologic indicators during periods of surging demand . In this report, we examine the characteristics of patients admitted to COVID-19 treatment centers during the COVID-19 epidemic in Zambia, assess risk factors for in-hospital death, and explore how characteristics of admitted patients were affected by periods of surging demand.16 The study protocol was approved by the University of Zambia Biomedical Research Ethics Committee. The study protocol was determined to be research according to the Centers for Disease Control and Prevention (CDC) human research protection procedures, but the CDC investigators did not interact with the study participants or have access to identifiable data or specimens for research purposes. Patients provided verbal consent for care and inclusion in the study. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.We conducted a retrospective cohort study of patients admitted to COVID-19 treatment centers in Zambia from March 1, 2020, through February 28, 2022. Beginning in March 2020, patients diagnosed with probable or confirmed COVID-19 in Zambia were admitted to Ministry of Health COVID-19 treatment centers that had specifically designated isolation and treatment units staffed by clinicians and nurses trained in COVID-19 clinical management. The study was conceived during the early weeks of the COVID-19 epidemic in Zambia and eventually included COVID-19 treatment centers in 5 of Zambia’s largest cities: Lusaka (4 treatment centers), Ndola, Kitwe, Kabwe, and Livingstone.17 Patients at participating treatment centers had demographic and clinical data collected at admission and during hospitalization until they were discharged or died. Data were collected using a standardized case record form adapted from the World Health Organization18 and entered into REDCap (Research Electronic Data Capture) electronic data capture tools by trained staff at a later date.20 Severe COVID-19 was defined as having an oxygen saturation level below 90%, having a respiratory rate greater than 30 breaths/min, or needing oxygen therapy.21 HIV status was self-reported, and patients with unknown status or who were deemed eligible and consented for HIV testing were tested at admission. Comorbidities were self-reported and included cardiac disease, hypertension, diabetes, other pulmonary disease, active tuberculosis, previous tuberculosis, asthma, kidney disease, liver disease, neurologic disorder, asplenia, malignant neoplasm, and current smoking. The number of comorbidities (not including HIV infection) was summed for each patient.Per national guidelines, admission to COVID-19 treatment centers was recommended for all patients with severe respiratory illness or those with both mild respiratory illness and risk factors for severe disease.22 Because of fluctuating testing volumes and incomplete data on national hospitalizations, epidemic waves were defined based on visual inspection of an epidemic curve generated using publicly available data . Data on relative variant genome frequency by region from the Global Initiative on Sharing Avian Influenza Data (GISAID) were used to define the SARS-CoV-2 variant that was circulating during each wave.24National data on daily SARS-CoV-2–positive tests and test positivity rates are collected by the Zambia National Public Health Institute and reported publicly.ble data .23 The s25Zambia began offering COVID-19 vaccination before the start of wave 3 in April 2021. The ChAdOx1-S COVID-19 vaccine (AstraZeneca) was the first vaccine type available in Zambia, followed by Ad26.COV2.S (Janssen), mRNA-127 (Moderna), BNT162b2 (Pfizer-BioNTech), and BBIBP-CorV (Sinopharm). An additional vaccine dose after completing a primary vaccine series  became available in January 2022. Estimates for the proportion of Zambia’s population that were vaccinated against COVID-19  were less than 1% at the start of wave 3 and 4.5% at the start of wave 4.27 with combined data from waves 1, 2, and 3.Data analyses were conducted across waves and within waves. Data from patients admitted during nonwave periods were excluded (approximately 9% of study patients). Across-wave analyses focused on descriptive statistics and comparing wave 4, which was dominated by SARS-CoV-2 variant of concern Omicron (B.1.1.529) (a variant widely reported to be less likely to cause severe COVID-19),Mixed-effects logistic regression models were used to assess associations between risk factors for poor COVID-19 outcomes and in-hospital mortality in both unadjusted and adjusted models. Risk factors included wave of admission, sex, age, number of self-reported comorbidities, HIV status, COVID-19 severity at admission, and COVID-19 treatment center. Mixed-effects models were used to account for the hierarchical structure of the study data (COVID-19 treatment center included as a random-effects term).Within-wave analyses were used to investigate whether surges in demand for health services altered the demographic profiles and clinical characteristics of patients admitted to COVID-19 treatment centers. Data from admitted patients were aggregated into 4-week segments within wave periods, with the first segment of each wave beginning on the respective wave’s start date. We assumed, based on anecdotal evidence, that patients admitted during the first 4-week segment of a wave were admitted during a period of baseline health system demand, whereas patients admitted during subsequent 4-week segments were admitted during periods of surging health system demand. Age and COVID-19 disease status for patients admitted during periods of baseline demand (first 4 weeks) were compared with subsequent 4-week segments. Associations between age of 60 years or older and severe COVID-19 at admission and being admitted during a period of baseline demand vs surging demand were assessed using mixed-effects logistic regression models.Data were analyzed using R software, version 4.02 . An α = .05 was used with 2-tailed tests to assess statistical significance.A total of 312 937 positive SARS-CoV-2 test results were reported in Zambia by the Zambia National Public Health Institute between March 18, 2020, and March 1, 2022. During that same period, 4208 patients were included in the COVID-19 Clinical Outcomes Study, and 3876 (92.1%) were admitted during wave periods .Four waves were identified during the study period . Among pIn the fully adjusted model, admission during wave 2 had significantly lower odds of in-hospital death (compared with wave 4) , and admission during wave 3 had higher odds of in-hospital death (compared with wave 4) , although the latter association was not significant . The fulInspection of trends across all 4 waves indicated that increased proportions of patients at high risk for poor COVID-19 outcomes  were admitted during segments that fell in the middle or, in some cases, at the end of waves vs at the beginning . Indeed,Demographic profiles, clinical characteristics, and outcomes of patients admitted to COVID-19 treatment centers varied across 4 distinct COVID-19 waves in Zambia. Within waves, we observed a consistent pattern in which the proportion of patients at high risk for poor COVID-19 outcomes increased as waves progressed. This finding was likely indicative of admission prioritization necessitated by a combination of surges in demand for health services and Zambia’s limited health system capacity. These findings are among the first to explore this aspect of the COVID-19 epidemic and support the urgent need to expand health system capacity and improve health system resiliency in Zambia and in other countries with resource-limited health systems.27 patients admitted to COVID-19 treatment centers during wave 4, when the SARS-CoV-2 Omicron variant was circulating in Zambia, were younger, less likely to have comorbidities, and less likely to have severe COVID-19 at admission. These findings appear to be consistent with observational data that show reduced risk of severe disease with Omicron infection,27 although the inclusion of patients with incidental COVID-19 infections likely also affected the findings.26 It remains unclear whether the reduced risk of severe disease with Omicron infection reflects partial immunity from vaccination and/or prior infection or whether it is associated with reduced pathogenicity of the Omicron variant.29As reported elsewhere,31 Our finding that HIV infection was significantly associated with in-hospital death is particularly relevant for Zambia, which has an HIV prevalence of 11.1% among persons aged 15 to 49 years,32 as well as other countries in the sub-Saharan region of Africa, which has the world’s highest burden of persons living with HIV.33 Factors that drive this association could be related to poorly controlled HIV infection,16 resulting in immunosuppression and opportunistic infections, as well as residual confounding related to higher risk for noncommunicable diseases in persons living with HIV.35Beyond wave of admission, other risk factors associated with in-hospital death at COVID-19 treatment centers in Zambia were consistent with previously reported data.37Within-wave data indicating that the proportion of high-risk patient admissions increased during waves suggest that there was prioritization of admissions during periods of surging demand for health services. The apparent prioritization, which was likely necessitated by limited capacity at Zambia’s COVID-19 treatment centers, likely had a direct effect on lower-risk patients seeking COVID-19 treatment center admission. Those lower-risk patients may have been admitted during periods of baseline demand but, because of prioritization, were likely not admitted during periods of surging demand. Although the health outcomes of these patients are unknown, without the care provided in COVID-19 treatment centers, it is possible that they experienced high levels of morbidity and even mortality. Indeed, reports noted that increases in community deaths and all-cause mortality aligned with COVID-19 waves in Zambia.38 but could benefit from additional resources and wider adoption. Additional practices can also be implemented to improve health system resiliency as it relates to COVID-19. These practices include continuing to implement infection prevention and control practices, using home-based care in lieu of unnecessary hospitalizations, establishing and expanding access for targeted antivirals, and continuing to expand SARS-CoV-2 vaccination. Although the study described in this article was conducted in Zambia, it is likely that resource-limited health systems in other countries experienced challenges when faced with surging demand for health services during COVID-19 waves. Understanding those challenges and the country-specific measures needed to address them will be critical as countries work to develop higher-capacity and more resilient health systems.Addressing health system capacity limitations and implementing practices to improve health system resiliency are critical as Zambia prepares for upcoming COVID-19 waves and future epidemics. Expansion of health system infrastructure is needed, including additional health facilities, hospital beds, ventilators, and oxygen production facilities. Added investments in human resources are similarly needed for the country to develop a larger and better-trained workforce capable of responding to the needs presented by epidemics such as COVID-19. Use of virtual platforms to conduct trainings for health workers is one approach that is already in use in Zambia39 which might limit the generalizability of these findings beyond hospitalized patients. Fourth, even though we controlled for available covariates, confounding may still be present in our model that assessed risk factors for in-hospital mortality. Fifth, it is possible that the findings from our within-wave analyses were influenced by the use of 4-week segments or reflect the timing of COVID-19 transmission dynamics  rather than prioritization of treatment center admission toward high-risk patients. Sixth, as with many studies conducted during emergency responses, data completeness was a limitation because staff who were responsible for data collection were also responding to other urgent demands at the COVID-19 treatment centers.The findings of this study are subject to at least 6 limitations. First, we used GISAID to correlate SARS-CoV-2 variants with specific waves rather than sequencing specimens from patients in the study. Second, reliable data were not available on national COVID-19–related hospitalization numbers or COVID-19 hospitalizations by facility, which limits our understanding of the reach and generalizability of this study and prevents us from reporting changes in hospitalization rates across waves. Third, this study was performed in hospitalized patients and thus subject to collider bias,Although the changes in epidemiologic data across waves described in this cohort study largely reflect those reported elsewhere, within-wave analyses revealed a pattern in which surges in demand for health services during COVID-19 waves in Zambia aligned with a prioritization of treatment center admissions toward higher-risk patients. This pattern, which was likely driven by the limited capacity of Zambia’s health system, underscores the need to expand health system capacity and strengthen health system resiliency in Zambia and other countries to prepare for future epidemics.


Inflammation drives cardiovascular disease (CVD) in individuals with underlying chronic inflammatory diseases, including People with HIV (PWH), independently of dyslipidemia. Adjunctive treatments that lower inflammation may be useful to lower CVD risk in such populations. There is very little data on the efficacy of Chinese herbal medicine (CHM) in reducing inflammation in PWH to address its potential in reducing this CVD risk factor, therefore we evaluated its impact on inflammatory biomarkers relevant to CVD risk in the general population. Six English and Chinese databases were searched for studies investigating CHM’s effects on inflammatory biomarkers relevant to CVD from respective inceptions to February 2022. A systematic review and meta-analysis of randomized controlled trials (RCTs) were conducted and the most-frequently prescribed herbs were identified. Thirty-eight RCTs involving 4,047 participants were included. Greater than or equal to 50% of included studies had a low risk of bias in five domains  and 97% had a high risk of performance bias. CHM provided significant additive effects on attenuating relevant inflammatory indices including hs-CRP , IL-6  and TNF-α levels , but no significant effects on hs-CRP were found between CHM and placebo when co-treating with Western drugs . No severe adverse events were reported in CHM groups. The two most prevalent herbs present in formulae demonstrating reduction of at least one inflammatory biomarker were Dan shen  and Huang qi . CHM, in combination with standard anti-inflammatory medications, may depress inflammation and reduce the risk of inflammatory conditions such as CVD. Rigorously-conducted trials and adequate reporting are needed to provide more robust evidence supporting the use of CHM to reduce CVD risk in people with underlying chronic inflammation such as PWH. People with HIV (PWH) have an approximately twofold increased risk of cardiovascular disease (CVD) which is independent of traditional risk factors and is a major cause of morbidity and mortality in this population . CVD is Inflammation activates monocytes and vascular endothelial cells, promoting monocyte migration into coronary arteries and initiating atherosclerotic plaque formation and progression . There iChinese herbal medicine (CHM) as a major component of traditional Chinese medicine, is a fully institutionalized part of Chinese health care and is widely used with western medicine . CHM refAs there are insufficient studies to date on the effect of CHM in reducing inflammatory biomarkers relevant to CVD in PWH, we conducted a meta-analysis of studies addressing this research question in the general population with CVD to inform future studies on PWH and provide data which may be of relevance to the potential use of CHM in other inflammatory conditions where CVD risk may be driven by factors apart from traditional risk factors.This systematic review was conducted following the requirements of the Cochrane Handbook . It is rSix databases  were searched for peer-reviewed full-text randomized controlled trials (RCTs) published regardless of language of publication from their respective inceptions to February 2022 by using CHM, traditional Chinese medicine, CVD, inflammation/anti-inflammatory and their MeSH terms and synonyms as keywords. The results were exported into, and managed by, EndNote. The reference lists of relevant trials and reviews were screened for additional studies. The list of keywords and sample search strategies are provided in Articles retrieved for evaluation were assessed by two independent reviewers. One reviewer screened the title and abstract and two independent reviewers screened the full-text of potential records for eligibility. Any disagreement was discussed between the two reviewers. If an agreement could not be reached, discrepancies were discussed and resolved with a third party.All searched articles were screened and evaluated according to the following inclusion and exclusion criteria: articles were considered for inclusion if they (1) were RCTs, (2) recruited individuals with cardiovascular conditions , (3) contained CHM in any oral forms (such as decoction and capsule) as treatment, (4) included placebo or no treatment as a control intervention and (5) assessed inflammatory biomarkers as one of the outcome measures. Trials evaluating the additive effects of CHM  were also included. Eligible co-interventions considered were western medicine (WM) drugs (for CVD and/or inflammation), lifestyle changes , and routine treatment (for CVD and/or inflammation). Routine treatment refers to three situations: (1) where only the categories of the drugs were provided  but the publication did not specify drug name and/or dosage; (2) routine care such as oxygen supplementation were included in the intervention; or (3) where the authors claimed without specifying that the control group received routine/standard treatment.Studies were excluded if they were not RCTs or recruited participants with atrial fibrillation, cerebrovascular conditions, secondary CVD , post-surgical conditions, underlying disease and complications , or acute/emergency conditions . Studies including participants aged <18 years, or who were pregnant, were also excluded. Studies employing ineligible CHM treatment , provided insufficient information on formula name/ingredients, or used inconsistent formulae in the CHM group  were excluded. Trials were also excluded if they assessed the combination of CHM and other Chinese medicine treatment , employed an ineligible control group , failed to clearly identify the co-interventions, or assessed comparisons which could not identify the clinical effects of CHM .All extracted data were entered into a predesigned data collection form which included details about article bibliography profile , participants , interventions (number of included comparisons and duration) and outcome measures. Information about control interventions and herbal ingredients, forms and administration of treatment intervention were entered into a separate table. In the present study, outcome measures evaluated were the inflammatory biomarkers high sensitivity c-reactive protein (hs-CRP), interleukin (IL)-1, IL-6, IL-8, IL-10, monocyte chemotactic protein-1 (MCP-1), and tumor necrosis factor (TNF)-α and endothelial activation markers soluble intercellular adhesion molecule-1 (sICAM-1) and soluble vascular cell adhesion molecule-1 (sVCAM-1). The most commonly studied herbs in the included studies with positive results were summarized descriptively. Where herbal ingredients were not provided in a paper, the formula names were checked in Zhong Yi Fang Ji Da Ci Dian  for detaThe quality of included RCTs was assessed against the risk of bias  according to the risk of bias tool from the Cochrane Handbook  independI2. To minimize potential heterogeneity, random effects were applied where I2 was >50%   , 43 (higrnation) , 53, 54 rnation) , 45–52. One study  used a pNone of the included RCTs provided sufficient information on the blinding of outcome assessors. However, as all the included outcome measures in this review are laboratory results, the outcome measurements were not likely to be influenced by lack of blinding. Thus, they have low risk of detection bias.High attrition bias was noticed in two studies: one study  performeTrial protocol was not identified in any of the included studies. After comparing the described outcomes in the “Materials and methods” section to those reported in the “Results” section, five studies failed to report findings for all the outcome measures described in “Materials and methods” section , 47, 54 p-value > 0.05 for assessed parameters     , 46–50, 0.00001) , 51–53 a 0.0002) , 52, 53  0.0002) –3. TableSeven other inflammatory biomarkers were assessed in five separate studies.One study  comparedOne study  assessedOne study  assessedOne study  assessedTwo studies assessed IL-10 levels, reporting different results: one study  reportedSimilar to the meta-analysis, subgroup analyses of covariates  identified significant differences. Subgroup analysis of co-interventions showed that CHM significantly reduced hs-CRP when comparing different co-interventions, conditions and trial duration, and IL-6 and TNF-α when comparing different trial duration . Detaile2 = 8.84, p < 0.01). Sixteen of the included studies assessed safety and reported no significant difference between the groups, with no vital adverse events in the treatment group  and IL-6 , but not in TNF-α .Visual inspection of funnel plots revealed potential asymmetry for hs-CRP, IL-6 and TNF-α , howevern = 16), Huang qi  and Gan cao , followed by Dang gui , Chuan xiong , Bai zhu , Niu xi  and Tian ma .Among the 37 studies included for meta-analyses, 35 studies reported lowering effects of CHM for at least one inflammatory biomarker. One study employed a single herb  as CHM intervention . The ingTo the best of our knowledge, this is the first meta-analysis that has sought to provide an assessment of the clinical effects of CHM, alone and in combination with other treatments, on inflammatory biomarkers in individuals with underlying CVD. Findings from 38 included studies  revealed that CHM may significantly decrease plasma levels of inflammatory biomarkers relevant to CVD risk  as well as the monocyte chemokine MCP-1, and markers of vascular endothelial cell activation, sICAM-1 and sVCAM-1. In contrast, the analysis did not reveal any clinical effects of CHM in combination with a standard intervention on the levels of the anti-inflammatory cytokine IL-10, based on data from two included studies. Categorical subgroup analysis identified substantial between-group differences for covariates, suggesting that the hs-CRP attenuating effect did not alter among different covariates , whereas the anti-inflammatory effects related to IL-6 and TNF-α may vary depending on co-interventions and conditions. Most included formulae for meta-analyses were identified to have lowering effects on at least one inflammatory biomarker, except those from two studies , 49. DanCardiovascular disease is a leading cause of death in PWH  and raten = 70) by a university-affiliated-hospital-based RCT conducted in Guangxi, China. Two formulas were provided to participants in the CHM group (n = 140) based on their Chinese medicine differential diagnosis: Shen Ling Fu Zheng Capsules for “Qi and Blood deficiency syndrome,” Qing Du Capsules for “damp-heat syndrome.” CHM significantly reduced serum IL-6 levels compared to placebo after an 18-month intervention . Similar results were seen in our meta-regression of IL-6 levels. Our finding also bring attention to the possible categorical difference triggered by co-intervention regimes and types of CVD. The present systematic review is, to our knowledge, the first meta-analysis that sought to provide an assessment on clinical effects of CHM (with or without co-interventions) on inflammatory biomarkers among cardiovascular condition populations.Inflammation plays a crucial role in the pathophysiology of CVD, yet it is not known whether CHM can improve inflammatory outcomes and markers of innate immune activation relevant to CVD. We initially intended to evaluate this in PWH, since there is considerable evidence that residual inflammation and innate immune activation contributes to CVD in these individuals independently of traditional risk factors such as hypercholesterolemia. However, preliminary surveys revealed that there was an absence of studies on the anti-inflammatory effects of CHM in PWH to enable a systematic review or meta-analyses to be performed to address this question. Most current meta-analyses either focused on CHM for the treatment of AIDS  or invesThere is comparatively little data on whether reduction of inflammatory biomarkers following therapeutic interventions correlates with improved outcomes with respect to risk and progression of CVD, especially in PWH. However, given the association between higher levels of inflammatory biomarkers and the risk of CVD it is reasonable to assume that treatments that reduce inflammation will be beneficial in this regard. However, it is important that direct evidence for this is obtained by examining how reduction in inflammation impacts more robust measures of coronary artery disease, including surrogate measures of atherosclerosis such as carotid artery intima-media thickness and pulse wave velocity, direct measures of atherosclerotic disease such as imaging of plaques, and cardiovascular events.Salvia miltiorrhiza Bge.  Bge. var. mongholicus (Bge.) Hsiao and Astragalus membranaceus (Fisch.) Bge. , Huang qi  and Gan cao (Glycyrrhizae Radix et Rhizoma) were the most frequently used herbs in the included formulas with anti-inflammatory effects. Dan shen is the root of iza Bge. . The extiza Bge. . Huang qh.) Bge. . One of h.) Bge. . Gan caolabra L. . Both thlabra L. ; β-glycylabra L. , can redlabra L. . In the H9 cells . LithospH9 cells . Gan caoH9 cells . GlycyrrH9 cells ; glycocoH9 cells , 75. TheI2 is a measure of the extent of heterogeneity (I2 of the three main outcomes were 96% (hs-CPR), 95% (IL-6) and 92% (TNF-α), which indicated considerable heterogeneity. Subgroup analysis was applied to further explore heterogeneity. After subgroup analysis of the included studies based on the co-interventions and conditions, several had an I2 below 50% : TNF-α of the routine treatment subgroup (43%), hs-CRP of the WM plus lifestyle subgroup (0%), IL-6 of the WM plus routine treatment subgroup (0%), hs-CRP and IL-6 of the coronary artery disease subgroup (0 and 0%), IL-6 of the atherosclerosis subgroup (0%). Subgroup analysis of trial duration did not make a noticeable difference to I2. The statistical heterogeneity showed variation in intervention effects in these included studies. This review aimed to take a broader perspective in the intervention effects of CHM on inflammatory biomarkers in CVD. Therefore, clinical variation may possibly lead to the high heterogeneity: (1) CVD included diverse diseases. Due to the limited number of included studies, the categorization of condition subgroup analysis was not very specific and the inflammatory drivers of these diseases may be different; (2) similar to that observed with condition, even though subgroup analysis of the co-interventions reduced the I2 to a certain level, the diversity of the control groups could contribute to the high heterogeneity; (3) distinctiveness of the CHM interventions should also be considered. The herbal ingredients of each formula are different. These adhesion interventions may not have the same intervention effects in the same way in every included studies.ogeneity . The I2 The quality of methodology plays an essential role in the strength of evidence: pre-registered protocols, rigorous study design (including application of suitable placebo controls) and adherence to adequate reporting will improve the credibility of findings. None of the included studies performed protocol registration before trial conduct. A weakness of the data set used in the present meta-analysis is that only one study  introducNoting the limitation of quality and quantity of the results, this meta-analysis demonstrates that a combination of CHM and other therapies administered in CVD studies may attenuate inflammatory biomarker levels, especially hs-CRP, IL-6, TNF-α, MCP-1, sICAM-1 and sVCAM-1. Dan shen, Huang qi and Gan cao were the most frequently used herbs in the included formulas with anti-inflammatory effects, which are worth exploring further in the context of CVD in settings of chronic inflammatory conditions including HIV infection. Rigorously-conducted trials and adequate reporting following published standards are needed to provide more robust findings.The original contributions presented in this study are included in the article/AJ, AZ, and ML conceptualized the review. ML performed the search. ML and IZ performed manuscript screening, data mining, and data analysis. AZ discussed disagreement. ML took the lead in writing the manuscript and wrote the original draft. JT, AH, AZ, and AJ provided editing and feedback on each version of the manuscript. All authors approved the final version of the manuscript accepted for publication.


Little information is available on the costs of respiratory syncytial virus (RSV) in Vietnam or other low- and middle-income countries. Our study estimated the costs of LRTIs associated with RSV infection among children in southern Vietnam.We conducted a prospective cohort study evaluating household and societal costs associated with LRTIs stratified by RSV status and severity among children under 2 years old who sought care at a major pediatric referral hospital in southern Vietnam. Enrollment periods were September 2019–December 2019, October 2020–June 2021 and October 2021–December 2021. RSV status was confirmed by a validated RT-PCR assay. RSV rapid detection antigen (RDA) test performance was also evaluated. Data on resource utilization, direct medical and non-medical costs, and indirect costs were collected from billing records and supplemented by patient-level questionnaires. All costs are reported in 2022 US dollars.536 children were enrolled in the study, with a median age of 7 months (interquartile range [IQR] 3–12). This included 210 (39.2%) children from the outpatient department, 318 children (59.3%) from the inpatient respiratory department (RD), and 8 children (1.5%) from the intensive care unit (ICU). Nearly 20% (105/536) were RSV positive: 3.9 percent (21/536) from the outpatient department, 15.7% (84/536) from the RD, and none from the ICU. The median total cost associated with LRTI per patient was US$52 (IQR 32–86) for outpatients and US$184 (IQR 109–287) for RD inpatients. For RSV-associated LRTIs, the median total cost per infection episode per patient was US$52 (IQR 32–85) for outpatients and US$165 (IQR 95–249) for RD inpatients. Total out-of-pocket costs of one non-ICU admission of RSV-associated LRTI ranged from 32%-70% of the monthly minimum wage per person (US$160) in Ho Chi Minh City. The sensitivity and the specificity of RSV RDA test were 88.2% (95% CI 63.6–98.5%) and 100% (95% CI 93.3–100%), respectively.These are the first data reporting the substantial economic burden of RSV-associated illness in young children in Vietnam. This study informs policymakers in planning health care resources and highlights the urgency of RSV disease prevention.The online version contains supplementary material available at 10.1186/s12879-023-08024-2. Acute lower respiratory tract infections (LRTIs) are a major public health problem for children in Vietnam and in all low- and middle-income countries (LMICs). Respiratory syncytial virus (RSV) is the leading cause of LRTIs in pediatric populations. Epidemiological studies have consistently shown high rates of severe RSV infection in children between 1 and 5 months of age, with 99 percent of deaths occurring in LMICs where 90% of children under 5 years old live , 3. A poThe RSV season in South Vietnam typically occurs between May and November, during the rainy season –4. ChildThere is currently no licensed RSV vaccine. Passive prophylaxis for RSV is available through palivizumab , a neutralizing anti-RSV fusion protein monoclonal antibody (mAb), but due to its high cost, palivizumab is not available or has minimal access in Vietnam and other LMICs .Among the 40 recent RSV vaccine and mAb candidates, the long-acting mAb Nirsevimab™  is the most advanced and has shown encouraging results in Phase 2b and 3 trials . NirseviA robust estimate of RSV-related health burden and cost will help evaluate the potential impact and cost-effectiveness of future RSV prevention and inform national procurement and prioritization decisions. To our knowledge, no information is available on the costs of RSV in Vietnam and very little is available for other LMICs in Southeast Asia . AlthougWe present here estimates of direct medical and non-medical costs and indirect costs of LRTIs associated with RSV infections among children visiting for consultations at outpatient departments or hospitalized at CH1. In this study, we also evaluated the performance of the RSV RDA test Immuno AG1 in a subset of patients.A prospective cohort study was conducted at the outpatient and inpatient departments of CH1, a tertiary pediatric care facility previously involved in studies assessing LRTIs and RSV in HCMC, Vietnam .In general, patients with LRTIs, defined as cough or difficulty breathing , initialWe evaluated the costs associated with LRTIs, stratified by RSV status and severity, among children under 2 years of age seeking care at CH1.Mild: patients admitted only to the outpatient department2) at admission ≥ 93 percentModerate: patients admitted to the respiratory department and who had saturation oxygen (SaO2) < 93 percentSevere: patients admitted to the respiratory department and who had saturation oxygen at admission  recommendations  as belowChildren were recruited to our study prospectively. Only patients who met all inclusion criteria and did not have any exclusion criteria were enrolled. Inclusion criteria were having cough and/or difficulty breathing, a clinical diagnosis of LRTI based on WHO criteria , and onsWe used the formula below, based on WHO guidelines for estimating treatment costs for diarrheal disease , to calcIn the formula, CV is the coefficient of variation and N is the annual case load. Annual case loads for both outpatient and inpatient calculations were based on historical data from CH1 regarding annual LRTI admissions  swab was collected from each study participant, within 24 h of study enrollment, to determine RSV infection status using a validated real-time RT-PCR (reverse transcription polymerase chain reaction) test . NPs forA random sample of 30 percent of participants admitted to the respiratory ward department also had an additional NP swab . This additional NP swab was tested immediately on-site via a commercial RSV RDA test .Although the physicians who treated patients were not aware of RSV RT-PCR results, they received results from the RDA test in real-time. All laboratory technicians who conducted the RT-PCR testing were blinded about the RSV RDA results.Costs were estimated from the societal and household perspectives. Three components of cost and resource utilization data were collected for the study:(1) Health care utilization and cost data from facility-level billing data.(2) A day-of-visit parent questionnaire to assess household-level costs incurred prior to arriving at the facility. For outpatient participants, any costs due to seeking care incurred on the day of the visit were also collected.(3) A post-discharge parent questionnaire to identify additional costs incurred by the household for the same illness 1 week after discharge from the facility.The billing data were used to identify resource utilization as well as direct medical and overhead costs associated with care received in the facility. Data parameters collected include types of services received during the visit , consultation fees, medications, laboratory tests, imaging , and other procedures . The proportion of costs covered by the household and national health insurance system (NHIS) was also reflected on facility-level billing data. Consumption of health care was the direct medical cost that covered the total costs of disease episode.The total costs per case from the societal perspective including direct medical costs (from both billing and questionnaire data), non-medical costs, and indirect costs incurred prior to admission, during the visit and after discharge.The household-level costs included patient-level out-of-pocket, non-medical costs , and indirect costs  during the disease course. These costs were obtained through standardized questionnaires conducted with parents (pre- and post-discharge), as described above.Hence, we reported:We also collected data on total costs, stratified by time of spending, using a specific questionnaire for each spending period. Pre-hospital costs include direct medical costs, non-medical costs, and indirect costs estimated from household surveys for any health care services sought before the day of outpatient visit or inpatient/ICU admission. Costs incurred during the hospital stay include direct medical costs from billing data plus non-medical and medical out-of-pocket spending  for the day of outpatient visit or the episode of inpatient stay, as well as indirect costs incurred during the disease episode. Similarly, follow-up visit costs include direct medical costs from billing data for follow-up visits at the hospital, as well as out-of-pocket spending and indirect costs for any care sought after the day of outpatient visit or discharge until completing a follow-up visit.The NHIS covers a certain proportion of the direct medical cost, depending on the type of medicines and procedures received during the hospitalization or the visit. The NHIS coverage amount was reflected on the billing data collected for the study. The remaining cost was from the out-of-pocket expenditures by the patient’s family .Demographic characteristics of children and their families were also collected through questionnaires for both outpatients and inpatients.Figure Costs were estimated by the following categories: cost per episode, cost by component , and cost by RSV status and severity .In calculating indirect costs, opportunity costs of lost income due to missed work were based on the average salary reported by participants in the study, and opportunity costs of lost leisure time were based on the minimum Vietnamese income based on government data, for the study period . ContinuWe also evaluated sensitivity, specificity, and positive and negative predictive value of the RSV antigen rapid test  and calculated 95 percent CIs. Statistical comparisons between the RSV antigen rapid test and the validated real-time RT-PCR were performed using McNemar’s test .All statistical analysis was implemented using Stata version 16.0 (StataCorp LP). All statistical tests were conducted at the two-tailed 5 percent significance level.We enrolled 537 children in our study. One case was discarded from the final analysis as more than 50 percent of the relevant data were missing, including the billing cost and the questionnaire data; thus, the full analysis was conducted on 536 participants. This included 210 children (39.2 percent) from the outpatient department; 318 children (59.3 percent) from the respiratory department; and 8 children (1.5 percent) from the ICU. Demographic and clinical characteristics of patients are shown in Table The median total cost from the societal perspective per LRTI episode (regardless of RSV status) per patient was US$52 (IQR 32–86) for outpatients and US$184 (IQR 110–287) for inpatients (not including ICU patients). As the costs of ICU cases were substantial compared to the costs of other inpatients, we estimated the total costs of ICU cases separately. The total cost per ICU admission was an 11-times the total cost per non-ICU admission  for outpatients and US$165 (IQR 95–249) for inpatients  for outpatients and US$190 (IQR 123–292) for inpatients. The median total cost per disease episode for RSV-negative cases was significantly higher than the total cost of RSV-positive cases  for inpatients. Specifically, the direct medical cost, in-hospital cost, and follow-up cost for non-RSV LRTI cases were significantly higher than those for RSV-LRTI cases (Table Approximately 1 percent (3/210) and 86.8 percent (276/318) of outpatients and inpatients were covered by the NHIS, respectively.The median total household cost per LRTI period per patient was US$51 IQR 32–85) for outpatients and US$112 (IQR 58–184) for inpatients (not including ICU patients). The total household cost per ICU admission was a 5-times the total household cost per non-ICU admission (Table 2–85 for Most families of outpatients (87.1 percent), non-ICU inpatients (86.8 percent), and ICU patients (100 percent) reported having to use their savings to cover disease treatment and lost income Table .For RSV-associated LRTI cases, the median total household cost per infection per patient was US$52 IQR 32–85) for outpatients and US$117 (IQR 65–183) for inpatients . Among inpatients, significant differences in the antibiotic, laboratory cost, imaging and procedure costs were observed between the RSV and non-RSV groups  Table . Non-RSVIn total, we had 70 cases tested by both RSV RDA and RSV real-time RT-PCR. The number of RSV detections by RSV RDA test (Immuno AG1) test was not significantly lower than by real-time RT-PCR (15/70 [21.4 percent] versus 17/70 [24.3 percent), McNemar test p-value = 0.5). Table Based on the results shown in Table Our study showed the costs associated with LRTIs are substantial and somewhat higher among children with RSV-negative status and increased severity of disease. For outpatient visits, a patient’s family usually paid out-of-pocket for examination and treatment. Inpatients less than 5 years of age are covered by the NHIS at 80 percent of the costs of listed medications and interventions if they follow their catchment area for hospitalization, are admitted due to an emergency, or are formally referred to a tertiary public hospital. Despite this coverage, the total out-of-pocket cost for outpatients and non-ICU inpatients for one LRTI event were still significant, representing about 32 to 70 percent of the monthly minimum wage per person ($160)  in HCMC.During the first year of life, incidences of acute lower respiratory infections (ALRI) leading to outpatient visit and hospitalization were previously reported to be 461 and 81 per 1000 children, respectively . Among cBecause the clinicians in our study were not blinded for the RSV RDA result, this could have influenced their decisions on the treatment and management for those cases. We compared the demographics, clinical characteristics, societal costs, out-of-pocket costs and the consumption of health care system costs between patients having their sample processed with the RDA test and those who did not have the RDA test  pneumoniae [S. pneumoniae carriage has been previously reported to significantly increase the S. pneumoniae load and be associated with radiologically confirmed pneumonia [In our study, other pathogens (non-RSV status) and severity were the two factors significantly associated with the total costs Table , specifieumoniae . RSV co-neumonia , which lneumonia .Of note, it has been shown that the incidences of severe RSV cases and associated deaths have been disproportionally reported in LMICs such as Vietnam . The higThere are some limitations in our study that should be acknowledged. First, our study was conducted during the COVID-19 pandemic, limiting the patient enrollment and interrupting data collection. The usual RSV season also changed during COVID-19 and became hard to predict. It was not clear if RSV infections during COVID-19 pandemic are less severe than pre-pandemic infections. We did not see differences in severity but cannot make conclusions as our sample size was small. Although we met our lower-bound target for the number of children to be enrolled (based on an expected 30 percent RSV-positivity rate), the overall number of RSV cases in our study was lower than anticipated (19 percent) and, thus, we did not reach the estimated sample sizes for RSV-positive cases, limiting the certainty of these cost estimates. Second, we did not have RSV cases among ICU patients. We were able to characterize non-RSV ICU costs, but we cannot draw concrete costs for very severe RSV cases. Hence, this study does not allow us to describe the whole spectrum of costs associated with RSV disease. Third, we did not record information about number of eligible patients across the facility due to high hospital volumes and, therefore, are not able to characterize the difference between eligible LRTI patients and those enrolled in our study. This limits our ability to evaluate potential selection bias and generalizability of the study. Fourth, our costs relied on billing data, which often reflect charges to the patient or payer and may or may not reflect the true costs of resource utilization. Finally, we did not have the information about the gestational age of patients in our study, we cannot estimate what the impacts of the preterm births on RSV cost. Compared to term infants, preterm infants have a 2- to threefold higher hospitalisation rate , 32 and This study provides, for the first time, the complete costs (direct and indirect) associated with LRTIs in Vietnam in general and with RSV-LRTIs specifically. These costs are substantial, leading to significant economic burden to families and potentially to the health care system and country as the NHIS covers about 80 percent of the direct medical cost. These cost estimates provide valuable data for economic analysis of LRTI management and preventive strategies generally and for LRTIs associated with RSV.Additional file 1. Additional Tables.


This is a review of full-length articles strictly concerning subacute thyroiditis (SAT) in relation to the SARS-CoV-2 virus infection (SVI) and COVID-19 vaccine (COV) that were published between the 1st of March 2020 and the 21st of March 2022 in PubMed-indexed journals. A total of 161 cases were reported as follows: 81 cases of SAT–SVI , 80 respective cases of SAT–COV . To our knowledge, this represents the largest cohort of reported cases until the present time. SAT–SVI was detected in adults aged between 18 and 85 years, mostly in middle-aged females. SAT–COVID-19 timing classifies SAT as viral  and post-viral . The clinical spectrum has two patterns: either that accompanying a severe COVID-19 infection with multi-organ spreading (most frequent with lung involvement) or as an asymptomatic infection, with SAT being the single manifestation or the first presentation. Either way, SAT may remain unrecognized. Some data suggest that more intense neck pain, more frequent fever, and more frequent hypothyroidism at 3 months are identified when compared with non-SAT–SVI, but other authors have identified similar presentations and outcomes. Post-COVID-19 fatigue may be due to residual post-SAT hypothyroidism. The practical importance of SAT–SVI derives from the fact that thyroid hormone anomalies aggravate the general status of severe infections . If misdiagnosed, SAT results in unnecessary treatment with anti-thyroid drugs or even antibiotics for fever of unknown cause. Once recognized, SAT does not seem to require a particular approach when compared with non-COVID-19 cases, including the need for glucocorticoid therapy and the rate of permanent hypothyroidism. A complete resolution of thyroid hormone anomalies and inflammation is expected, except for cases with persistent hypothyroidism. SAT–COV follows within a few hours to a few weeks, with an average of 2 weeks (no particular pattern is related to the first or second vaccine dose). Pathogenesis includes molecular mimicry and immunoinflammatory anomalies, and some have suggested that this is part of ASIA syndrome (autoimmune/inflammatory syndrome induced by adjuvants). An alternative hypothesis to vaccine-related increased autoimmunity is vaccine-induced hyperviscosity; however, this is supported by incomplete evidence. From what we know so far concerning the risk factors, a prior episode of non-SVI–SAT is not associated with a higher risk of SAT–COV, nor is a previous history of coronavirus infection by itself. Post-vaccine SAT usually has a less severe presentation and a good outcome. Generally, the female sex is prone to developing any type of SAT. HLA susceptibility is probably related to both new types of SATs. The current low level of statistical evidence is expected to change in the future. Practitioners should be aware of SAT–COV, which does not restrict immunization protocols in any case. Subacute thyroiditis (SAT), also known as De Quervain or granulomatous thyroiditis, is caused by a viral infection, especially (but not exclusively) of the upper respiratory tract or by post-viral inflammation. The female/male ratio is 5/1 and the incidence is 12.1/100,000 people/year. It follows a tri-phasic clinical presentation: first—hyperthyroidism, second—transitory hypothyroidism that is remitted in 90–95% of cases, meaning the third phase—normal thyroid function—follows (except for the 5–10% of cases with permanent hypothyroidism requiring lifelong levothyroxine substitution) [Our purpose is to present practical information on the COVID-19 infection and vaccination-related SAT as a new entity among cases of De Quervain thyroiditis.This is a narrative review of the English-language literature. We included in our COVID-19-related analysis only full-length papers that were published between the 1st of March 2020 and the 21st of March 2022. Studies were identified through a PubMed-based search using the following keywords: “COVID-19 infection”, ”coronavirus”, or “COVID-19 vaccination/vaccine” in different combinations with “thyroiditis”, “thyroid”, and “thyroid disease” (see Tables 1 and 2). The inclusion criteria were clinically relevant data strictly concerning SAT in relation to either the coronavirus infection or immunization against the virus. Exclusion criteria were other types of thyroid involvement, such as destructive thyroiditis, Hashimoto’s thyroiditis, Basedow–Graves disease, etc.The elements of statistical evidence for COVID-19-related SAT included 2 retrospective studies, 5 case series, and 29 case reports ; 80 respective cases confirmed with SAT following COVID-19 vaccination, which included 1 longitudinal study, 14 case series, 17 case reports; and also 1 prospective study that included 12 patients, with 6 patients in each category.SAT might occur simultaneously with COVID-19 infection  or it might occur within days or weeks after infection ; the term “post-viral thyroiditis” seems more appropriate for this form ,3. KnowiThe clinical picture may be associated with the typical signs and symptoms underlying hyperthyroidism and thyroid inflammation and/or various COVID-19 infection presentations  ,39,40,41Some authors suggest the presence of more intense neck pain, a higher incidence of fever, and more frequent hypothyroidism at 3 months, which is opposite to other forms of SAT not associated with COVID infection . HoweverThe real incidence of SAT among subjects infected with SARS-CoV-2 virus is currently unknown. The severity of COVID-19 infection that is complicated with SAT varies from severe presentations requiring hospitalization and admission to intensive care units to completely asymptomatic cases. There is one report of an 85-year-old male with postmortem diagnosis of SAT . As expeBased on the published data, we considered two types of SAT-associated clinical spectra. One pattern involves patients heavily affected by the COVID-19 infection and the other involves patients that develop a mildly symptomatic (or completely asymptomatic) form. Both of them may be under-diagnosed concerning SAT, for different reasons. It has been suggested that in cases with severe COVID-19 disease, SAT is difficult to recognize since multiple other manifestations of infection are present and mask thyroid inflammation, which otherwise should be listed among the extra-pulmonary spreading of the coronavirus infection ,8. Some Expert opinions indicate a high rate of suboptimal diagnosis of SAT since the condition is more frequent than we know. Routine COVID-19 tests are useful in each new case of SAT in the COVID-19 pandemic. Post-viral forms are more clearly identified and adequately recognized in the absence of other viral infections ,34,35,36Nowadays, COVID-19 infection correlates with a new entity of SAT, which is traditionally known to be associated with various viral infections  ,13,14. TThe pathogenesis of thyroid involvement includes virus-induced direct damage of follicular cells followed by fibrosis (mostly found in destructive thyroiditis) due to the coronavirus targeting angiotensin converting enzyme 2, which is abundant in the thyroid; immune anomalies; either increased or suppressed immune response, especially during severe infections; cytokine imbalances ; thyroid inflammation may also be caused by the host immune response to coronavirus , not only by direct invasion ,15,16,17Inflammation assays are positive in the majority of SAT cases. Based on the current data, there is no clear correlation between the clinical presentation of SAT during/after COVID-19 infection and inflammatory markers, nor was a highly suggestive marker of potential thyroid involvement identified in a patient developing coronavirus infection . IL-6 seems to be correlated with the severity of thyrotoxicosis. Thyroid hormone imbalance  might display the pitfall of FT3 levels .The traditional evaluation based on thyroid ultrasound is also very practical and it should be routinely performed in each case of SAT considering the limits of the pandemic-related digital medicine era ,21,22,23It is important to be aware of this particular complication of the COVID-19 infection and to differentiate it from other acute complications such as pneumonia or thromboembolism as some complications require investigations where iodine contrast is used for computer tomography. Such investigations might aggravate thyroid hormone anomalies, such as, for instance, in cases with primary hyperthyroidism . Also, on = 6) diagnosed with vaccine-induced thyroiditis; the clinical presentation, laboratory assessments and therapies were similar among patients with COVID-19 versus non-COVID-19 etiologies, while SAT after immunization had statistically significantly fewer severe symptoms [We identified a single, prospective study from 2022 which included 64 patients, with 18.8% of them diagnosed with coronavirus-related SAT (3 out of 12 patients had thyroid involvement as the first sign of COVID-19 infection) and 9.3% of all . The current level of statistical evidence is expected to change in the next period of time.SAT as a result of an immune response caused by COVID-19 vaccination represents a new topic in the medical literature due to the novelty of the vaccine ,77,78,79The thyroid’s involvement appears after vaccination, within a period ranging between a few hours and a few weeks (no particular pattern related to the first or second vaccine dose has been identified so far) . A diffeThe pathogenesis of vaccine-induced thyroiditis includes anomalies of immuneoinflammatory response, as similarly described in viral and post-viral forms. Immune system over-activation and molecular mimicry between the thyroid (such as thyroid peroxidase peptide sequences) and vaccine components  represent the most important mechanism. Another hypothesis includes ASIA syndrome (autoimmune/auto inflammatory syndrome induced by adjuvants), suggesting that the vaccination acts as a trigger of autoimmune thyroid response, but this is yet to be determined ,77,78,79In vaccine-induced thyroiditis, neck pain and general changes due to inflammation may require general glucocorticoid therapy but only in selected cases and not as a general rule; hyperthyroidism–related tachycardia may be controlled with beta-blockers such as propranolol .Despite these reports, vaccination against COVID-19 is encouraged since SAT is extremely rare and is typically associated with good clinical evolution and good long-term outcomes due to the condition being self-limiting ,48.From what we know so far concerning the risk factors, a prior episode of SAT with non-COVID 19 etiology is not associated with a higher risk of vaccine-related thyroiditis, nor is a previous history of coronavirus infection itself. Similarly, an SAT episode after the first dose of the vaccine does not restrict immunization protocols.In general, the female sex is more prone to developing any type of SAT or autoimmune condition. Among the few reported cases, a series showed two sisters developing SAT symptoms after immunization. A specific genetic background is difficult to consider at this point (except for HLA susceptibility in non-coronavirus SAT) ,50. MoreWe also mention a reported case of (what most likely was) acute thyroiditis in association with bilateral optic neuritis after vaccination  ,53.We conclude that vaccine-related SAT is typically less symptomatic than other types and variably follows either the first or second dose within an average of 2 weeks. The diagnosis should be established in the absence of other well-known etiologies. The pathogenic mechanisms are less understood; we do not know any individual, new risk factors. Complete remission is found in all cases without specific requirements other than what we already know for SAT.Practitioners should be aware of post-vaccine SAT, which does not represent at all a restriction for vaccination as it appears as an exceptional event based on our current level of evidence ,77,78,79SAT is part of the large, heterogeneous endocrine picture described in relation to the COVID-19 infection. From an endocrinological point of view, a high risk of severe COVID-19 infection is related to the presence of diabetes mellitus, high blood pressure, obesity, Cushing’s syndrome, sleep apnea , coagulation anomalies, glucocorticoid therapy for various conditions, etc. ,81. HypoSuspecting or confirming SAT in patients infected with coronavirus represents a small piece of an otherwise very complex puzzle of the thyroid’s involvement under these specific circumstances . GeneralIt is already known that any kind of infection, including coronavirus infection, may trigger hyperthyroidism that can manifest in various ways, including severe forms like thyroid storm, and needs to be differentiated from SAT-related transitory hyperthyroidism ,92. The Some authors have suggested that the COVID-19 infection might trigger the autoimmune mechanisms underlying Hashimoto’s thyroiditis  ,97,98. AThe rate of hypothyroidism in coronavirus-positive patients varies from study to study by up to one-fifth, and the underlying mechanisms are mostly represented by low-T3 syndrome as well as hypophysitis-related hypothyroidism, destructive thyroiditis, primary autoimmune hypothyroidism, residual thyroid insufficiency after an episode of SAT, etc. . The thyA highly debated topic in the scientific community relates to the potential events involving COVID-19 immunization, including endocrine elements. It has been suggested that vaccination might aggravate eye disease or primary hyperthyroidism in patients with a history of Basedow–Graves disease. However, the level of statistical evidence remains poor up to this moment, and currently, the most probable pathogenic link is post-vaccine increased immunogenicity (including autoimmunity exacerbation and molecular mimicry), probably in susceptible individuals, although this has yet to be determined ,118,119.Generally, we consider that vaccination against COVID-19 is not associated with particular risks in patients with prior diagnoses of different endocrine conditions. Some diseases like diabetes mellitus and adrenal insufficiency have a particular indication for vaccine prioritization due to the higher risk of infections and more severe SARS-CoV-2 evolution ,124. WheWhether symptoms of SAT after a certain vaccine dose should delay the following vaccine administration is debatable and, for the moment, we not do have any particular concerns regarding protocols for immunization . There is only one longitudinal study, published in 2022, which included 15 cases with vaccine-associated thyroiditis of the subacute type, occurring a median of 11.5 days after immunization and a median remission time of 11.5 weeks; seven out of nine individuals were re-vaccinated and did not experience a relapse of subacute thyroiditis, while two out of nine individuals suffered an aggravation of SAT due to the second dose of the vaccine inoculation . AccordiOverall, we recognize that the number of reported cases considering this new entity is low when compared with the millions of people that have had the coronavirus infection and received the vaccine, but many other unexpected novel clinical entities have been reported amid the pandemic at first with a low level of statistical evidence since the COVID-19 pandemic is an extraordinary, never-before-seen medical experience. This is why the lack of adequate recognition and diagnosis represents one more reason to spread the information and increase awareness in order to reduce disease-related burden if adequately recognized (including unnecessary investigations or medications) and to apply optimum health care for improved quality of life in affected patients. To our knowledge, this is the largest number of patients with either COVID-19-related or vaccine-associated SAT to date see .Based on the studies we have analyzed so far, 161 cases of COVID-19 infection and vaccine-related SAT have been reported. SAT in adults infected with coronavirus is a rare event that can be detected during or after infection. Early recognition improves clinical evolution and outcomes, despite the fact that the condition is self-limiting. Even though it is uncommon and still under the umbrella of low statistical evidence, practitioners should be aware of post-vaccine SAT, which does not restrict protocols for further vaccination.


Whether a genetic predisposition to psychiatric disorders is associated with coronavirus disease 2019 (COVID-19) is unknown.Our analytic sample consisted of 287,123 white British participants in UK Biobank who were alive on 31 January 2020. We performed a genome-wide association study (GWAS) analysis for each psychiatric disorder  in a randomly selected half of the study population (“base dataset”). For the other half (“target dataset”), the polygenic risk score (PRS) was calculated as a proxy of individuals’ genetic predisposition to a given psychiatric phenotype using discovered genetic variants from the base dataset. Ascertainment of COVID-19 was based on the Public Health England dataset, inpatient hospital data, or death registers in UK Biobank. COVID-19 cases from hospitalization records or death records were considered “severe cases.” The association between the PRS for psychiatric disorders and COVID-19 risk was examined using logistic regression. We also repeated PRS analyses based on publicly available GWAS summary statistics.A total of 143,562 participants  were used for PRS analyses. A higher genetic predisposition to psychiatric disorders was associated with an increased risk of any COVID-19 and severe COVID-19. The adjusted odds ratio (OR) for any COVID-19 was 1.07  and 1.06 (95% CI 1.01–1.11) among individuals with a high genetic risk (above the upper tertile of the PRS) for substance misuse and depression, respectively, compared with individuals with a low genetic risk (below the lower tertile). Slightly higher ORs were noted for severe COVID-19, and similar result patterns were obtained in analyses based on publicly available GWAS summary statistics.Our findings suggest a potential role of genetic factors in the observed phenotypic association between psychiatric disorders and COVID-19. Our data underscore the need for increased medical surveillance for this vulnerable population during the COVID-19 pandemic.The online version contains supplementary material available at 10.1186/s12916-022-02520-z. With over 518 million infected people and over 6 million related deaths, the coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, has led to an unprecedented crisis worldwide [Previous studies have reported an increased risk of infections (including life-threatening infections) among individuals with psychiatric disorders , 4. LikeBased on the results of genome-wide association studies (GWAS), a polygenic risk score (PRS), or the sum of all risk alleles weighted by the effect size of each variant, can be generated. The PRS represents an individual’s overall genetic risk for a given disease, such as a psychiatric disorder. It can further be used to predict the risk of developing a second disease outcome and thereby illustrating the genetic association between a disease pair , 12.As a continuation of our previous research, which demonstrated a robust association between pre-pandemic psychiatric disorders and COVID-19 risk , here, wOur study was based on data from the large-scale prospective cohort of UK Biobank, which enrolled 502,507 individuals aged between 40 and 69 years across the UK during 2006–2010 . Genotyping data were obtained from 488,377 blood samples collected at baseline for each participant. They were assayed using UK BiLEVE  and UK Biobank Axiom Array . After qPhenotypic data such as sex and birth year were collected upon recruitment using a questionnaire. Health-related outcomes were obtained through periodically linked data from multiple national datasets, including death registries and inpatient hospital data from across England, Scotland, and Wales . After tUK Biobank collected all data after written informed consent had been obtained from each participant. The study protocol had full ethical approval (16/NW/0274) from the National Research Ethics Service of the UK National Health Service. The present study was also approved (2020.661) by the biomedical research ethics committee of West China Hospital .To align with our previous analyses of phenotypic association , we usedn = 346,502). Standard quality control of GWAS was carried out. Briefly, we restricted our analyses to autosomal biallelic SNPs and removed variants with a call rate <98%, minor allele frequency <0.01, or deviation from Hardy–Weinberg equilibrium (p < 10−6). Then, related individuals, up to the third degree  [We conducted genetic analyses of white British participants in UK Biobank who were registered in England at recruitment, alive, and trackable on 31 January 2020 and had available genetic data (> 0.044) , were ren = 10,868) to avoid the influence of phenotypic associations  on the identification of the genetic background for the exposure trait . Specifically, the associations between these phenotypes (as binary variables) and each eligible SNP were tested using logistic regression models adjusted for the covariates sex, birth year, genotyping array, and the top-10 PCs. Second, given that publicly available GWAS of the five specific psychiatric disorders have larger discovery samples, in the dataset that included all eligible and unrelated participants , we also generated PRSs for these psychiatric disorders based on summary statistics from publicly available GWAS [We calculated the PRS for each studied psychiatric disorder based on two types of GWAS summary statistics. First, due to human heterogeneity (and therefore possible limited portability of the PRS between populations ) and to ble GWAS –21.Fig. The PRS for each exposure trait was calculated as the weighted sum of the risk alleles based on the summary statistics  of the GWAS results mentioned above using the least absolute shrinkage and selection operator (LASSO) approach, which allowed heavy shrinkage in the effect estimates of SNPs via regularization methods . The hg1p < 0.05). The variance explained by the PRS was assessed as the difference in variance from the full model, including the PRS compared with the basic model, and represented as Nagelkerke’s R2.First, we validated the predictability of the generated PRSs  on the corresponding category of psychiatric disorder in UK Biobank data using logistic regression models adjusted for sex, birth year , genotyping array, and significant PCs for population heterogeneity  with 95% confidence intervals (CIs) derived from logistic regression models adjusted for the covariates mentioned above. In addition to treating the standardized PRS as a continuous variable, we also divided participants into “low,” “moderate,” and “high” genetic risk groups based on the tertile distribution of the PRS and then compared the risk of COVID-19 outcomes using the group of low genetic risk  as the reference. The degree of pleiotropy or genetic correlation between psychiatric disorders and COVID-19 was additionally estimated using cross-trait LD score regression based on publicly available GWAS summary statistics of both traits –21, 24.p-value below a certain threshold . Then, through clumping, only SNPs that were largely independent of each other were retained so that their effects could be summed. We computed the PRS under 10 p-value thresholds .In sensitivity analyses, all the analyses stated above were repeated after the application of the standard clumping + thresholding (C+T) approach, instead of the LASSO approach used in the main analyses, for PRS calculation. This method handles the shrinkage-of-effect size estimates through selectively including SNPs with a GWAS association p < 0.05 (two-sided) was considered significant.PLINK (version 1.9) and R software (version 4.0) were used for GWAS and PRS calculations  . LD scorIn total, 287,123 participants , as well as between depression and COVID-19   between substance misuse and COVID-19 , we observed largely identical estimates as those in the main analyses  were associated with a risk of COVID-19, including severe COVID-19, among a population aged >50 years who are highly vulnerable to such an infectious disease. To our knowledge, this is the first study, to date, that has examined the relationship of the genetic basis of psychiatric disorders with COVID-19 using individual-level genotyping data. Our findings aid the interpretation of the reported phenotypic association of pre-pandemic psychiatric disorders with COVID-19 risk by demonstrating a role of an underlying genetic mechanism in this link. The genetically driven susceptibility to COVID-19  among individuals prone to psychiatric disorders underscores the need for heightened clinical awareness and medical care for this vulnerable population during the COVID-19 pandemic. Further studies are, however, warranted to verify our findings and to explore functional mechanisms.The hypothesis that a pre-existing psychiatric disorder may influence susceptibility to infectious diseases has long been discussed. Many previous studies have consistently reported an increased risk of infection, including pneumonia , sepsis Accumulating evidence, including our previous work , has revThe merits of the present study include the application of the PRS to assess the association between the genetic risk of psychiatric disorders and COVID-19 in a large population. This strategy enabled the identification of a dose–response relationship between the PRS for different psychiatric traits and COVID-19 . These findings corroborate the previously observed phenotypic associations between psychiatric disorders and COVID-19 and shed light on the underlying mechanisms of such associations. In addition, because population heterogeneity may undermine the prediction accuracy of a PRS due to not only the different ancestries but also differences in the characteristics of populations , we inip-value thresholds were applied, possibly resulting in “noise” from redundant loci, and its impact on the observed associations is unclear. Third, UK Biobank is not a representative sample of the entire UK population, as it recruited only 5.5% of the target population, and the participants were predominately white (94%), were more likely living in less socioeconomically deprived areas than non-participants, and had fewer self-reported health conditions than the general population [Our study had four main limitations. First, the genetic association between stress-related disorders and COVID-19 remains inclusive as different results were obtained using PRSs based on GWAS from UK Biobank and those generated from GWAS of the population-based Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH) study . Howeverpulation . Also, tIn the UK Biobank population, we found genetic predispositions to several psychiatric disorders were associated with an increased risk of COVID-19, which may partially explain the observed phenotypic associations between psychiatric disorders and COVID-19. Notably, the gene-driven susceptibility to COVID-19 among individuals prone to psychiatric disorders underscores the need for extra awareness and medical care for this vulnerable population. Further studies are needed to identify particular genetic variants with the aim of understanding the underlying mechanisms, optimizing risk stratification, and providing molecular targets for COVID-19 prevention.Additional file 1: Table S1. International Classification of Disease (ICD) codes, ninth (ICD-9) and tenth (ICD-10) revisions for diagnoses used in this study. Table S2. The association between polygenic risk scores  for varied psychiatric disorders and its corresponding psychiatric disorders. Table S3. Associations between polygenic risk scores  for substance misuse and the risk of substance misuse at different p value thresholds. Table S4. Associations between polygenic risk scores  for depression and the risk of depression at different p value thresholds. Table S5. Associations between polygenic risk scores  for anxiety and the risk of anxiety at different p value thresholds. Table S6. Associations between polygenic risk scores  for psychotic disorder and the risk of psychotic disorder at different p value thresholds. Table S7. Associations between polygenic risk scores  for stress-related disorder and the risk of stress-related disorder at different p value thresholds. Table S8. The association between polygenic risk scores  for psychiatric disorders and COVID-19. Table S9. The association between polygenic risk scores  for psychiatric disorders under different p value thresholds and COVID-19. Figure S1. Flowchart of the entire GWAS quality control process. Figure S2. Manhattan of the SNP-Based GWAS of psychiatric disorders, using UK Biobank base data (SNP=single-nucleotide polymorphism). Figure S3. The association between categorized polygenic risk scores  for psychiatric disorders and COVID-19 riska.