While recent literature has shown the efficacy of the SARS-CoV-2 vaccine in preventing infection, it's impact on need for emergency care/hospitalization in breakthrough infections remain unclear, particularly in regions with a high rate of variant viral strains. We aimed to determine if vaccination reduces hospital visits in breakthrough COVID-19.This observational cohort analysis compared unvaccinated (UV), partially vaccinated (PV), and fully vaccinated (FV) adult patients with SARS-CoV-2 infection requiring emergency care(EC)/hospitalization within an eight-hospital system in Michigan. Demographic and clinical variables were obtained from the electronic record. Vaccination data was obtained from the Michigan Care Improvement Registry and Centers for Disease Control vaccine tracker. Primary endpoint was rate of emergency care/hospitalization encounters among patients diagnosed with COVID-19. Secondary outcome was severe disease-composite outcome .Between December 15,2020 and April 30,2021, 11,834 EC encounters were included:10,880 (91.9%) UV, 825 (7%) PV, 129 (1.1%) FV. Average age was 53.0 ± 18.2 and 52.8% were female. Accounting for the SARS-CoV-2 vaccination population groups in Michigan, the ED encounters/hospitalizations rate relevant to COVID-19 was 96% lower in FV versus UV  in negative binomial regression. COVID-19 EC visits rate peaked at 22.61, 12.88, and 1.29 visits per 100000 for the UV, PV, and FV groups, respectively. In the propensity-score matching weights analysis, FV had a lower risk of composite disease compared to UV but statistically insignificant .The need for emergency care/hospitalization due to breakthrough COVID-19 is an exceedingly rare event in fully vaccinated patients. As vaccination has increased regionally, EC visits amongst fully vaccinated individuals have remained low and occur much less frequently than unvaccinated individuals. If hospital-based treatment is required, elderly patients with significant comorbidities are at high-risk for severe outcomes regardless of vaccination status. We searched PubMed on April 5, 2021 for evidence surrounding breakthrough COVID-19 in immunized patients leading to hospital or emergency care encounters using the search terms (SARS-CoV-2 OR novel coronavirus OR COVID-19) AND (vaccination OR immunization) AND  with no language or time restrictions. Vaccine efficacy has been established by pharmaceutical trials and has triggered the availability of vaccines across the globe. However, effectiveness of vaccines at preventing hospital based treatment for COVID-19 in fully immunized patients in a real world population is poorly understood with no prior publications. Further, it is unclear if vaccines protect patients from severe outcomes associated with COVID-19.This study is one of the first large, real world investigations addressing the likelihood of fully vaccinated patients requiring hospital-based care in breakthrough COVID-19. We found that ED encounters/hospitalizations related to COVID-19 were 96% lower in fully vaccinated patients compared to unvaccinated patients . However, if hospital based care is required for fully vaccinated patients, the risk of severe outcomes is similar to unvaccinated patients.The need for emergency care/hospitalization due to breakthrough COVID-19 is an exceedingly rare event in fully vaccinated patients. As vaccination has increased regionally, EC visits amongst fully vaccinated individuals have remained low and occur much less frequently than unvaccinated individuals. If hospital-based treatment is required, elderly patients with significant comorbidities are at high-risk for severe outcomes regardless of vaccination status.Alt-text: Unlabelled box1The COVID-19 pandemic has continued to cause significant morbidity and mortality around the globe with over 146 million cases and 3 million deaths as of April 25, 2021. [8]In-vitro studies have shown several variants and mutations to be more transmissible and less sensitive to natural or vaccine-induced antibodies compared to the wild-SARS CoV-2 virus. th and April 12th 2021. in vitro studies have noted a loss in neutralizing activity by vaccine-induced antibodies when the E484K mutation was introduced to the B.1.1.7 variant. [15]In the latest surge of COVID-19, the state of Michigan has been more severely impacted than the rest of the United States. Vaccination efforts in the State of Michigan have been ongoing since December. 22.1This was a multicenter observational cohort study through electronic health record  analysis to assess vaccine effectiveness on need for emergency care/hospitalizations and severe outcomes in patients with breakthrough COVID-19 comparing fully vaccinated (FV), partially vaccinated (PV), and unvaccinated (UV) patients.The study was conducted at Beaumont Health, an eight-hospital acute care regional health system caring for 2.2 million people across the communities within the Metro Detroit area. The hospitals range from a large tertiary care academic center to intermediate-sized and smaller community hospitals.Consecutive adult patients greater than 18 years of age presenting to the emergency department with confirmed COVID-19 as a primary diagnosis were eligible for inclusion. Patients with prior laboratory confirmed SARS-CoV-2 infection, pediatric patients, or those still hospitalized after the designated follow-up date of May 15, 2021 were excluded. The study was approved by the Institutional Review Board at Beaumont Health and registered on clinicaltrials.gov (Identifier: NCT04912700). Written informed consent requirement was waived due to the retrospective nature of this study. Data were analyzed and interpreted by the authors.2.2Patients were categorized as either UV, PV, or FV. UV individuals were defined as having positive laboratory SARS-CoV-2 testing with no record of immunization against SARS-CoV-2 or first-dose vaccination after symptom onset. PV individuals were defined as having positive laboratory SARS-CoV-2 testing and symptom onset after a single dose of either mRNA  vaccine, or < 14 days after the second dose of either mRNA vaccine  or < 14 days after the administration of the single dose of viral vector vaccine (Johnson & Johnson). FV individuals were defined as having positive laboratory testing for SARS-CoV-2 and symptom onset >14 days since administered of second dose of either mRNA vaccine, or >14 days since administration of viral vector vaccine (Johnson & Johnson).SARS-CoV-2 infection was defined as primary diagnosis of COVID-19 by ICD-10-CM codes in the EHR and either laboratory confirmed positive result (rapid antigen testing or reverse-transcriptase-polymerase-chain-reaction (RT-PCR) by nasopharyngeal swab) or reference to confirmed laboratory diagnosis in the emergency encounter provider note.2.3EHR data was used to confirm SARS-CoV-2 infection and categorize vaccinated patients. For patients with primary diagnosis of COVID-19 without laboratory confirmed SARS-CoV-2 within the institutional EHR, emergency care provider records were manually reviewed by two independent physicians to confirm outside laboratory diagnosis of infection. Physicians also reviewed provider notes for all PV and FV patients to determine onset of symptoms. The date of symptom onset was used to accurately categorize UV, PV, and FV groups.Demographic, clinical, and outcomes data were obtained from the EHR. Demographics included age, race, and gender. Clinical data included comorbidities, body mass index (BMI), and number of previous ED visits within the past 6 months. ICD-10-CM codes for comorbidities were used to calculate the Elixhauser comorbidity weighted scores as described by the Agency of Healthcare Research and Quality (AHRQ). Hospital admission was based on the clinical judgment of the treating emergency medicine provider and length of stay was calculated for all admitted patients. Discharge disposition post-hospitalization was based on patients’ clinical condition. Patients were either discharged to home, skilled nursing home, rehabilitation facility, hospice, or expired in the hospital.Vaccination data was made available by the state of Michigan via the Michigan Care Improvement Registry (MCIR) and therefore captured patients who had been vaccinated outside of the Beaumont Health system. [14]2.4The primary outcome was rate of emergency care/hospitalization encounters with a diagnosis of COVID-19 among UV, PV, and FV groups. Rate of encounters was defined as number of newly presenting hospital-based COVID-19 within the health system divided by the state population within each respective vaccination group expressed as a rate per 100000 visits. Weekly rates of COVID-19 ED encounters were presented for each vaccination group.Secondary outcomes included severe disease represented as a composite outcome , hospital length of stay, renal replacement therapy, ECMO supplemental oxygen , and noninvasive ventilation.2.5Bivariate analyses were stratified by vaccinated status  using means ± standard deviations and medians with interquartile ranges (IQRs) for continuous variables and frequencies with percentages for categorical variables. Kruskal-Wallis (exact) test (continuous variables) and Chi-squared or Fisher's exact test  were used to compare differences among three categories of vaccination status.To investigate the effect of vaccination status on the occurrence of COVID-19 emergency care/hospitalization encounters, a negative binomial regression analysis with the log-link, accounting for any potential overdispersion, was used based on weekly rates to the State SARS-CoV-2 vaccination population groups. To characterize variation of the trend in weekly rates across the study period, joinpoint regression analysis  through each category of vaccination status on the log-scale with up to 2 joinpoints were used through grid search of joinpoints by Monte Carlo permutation tests. [18]Cox proportional hazards regression models were used to examine the association between vaccination status and severity of illness, a composite outcome of ICU admission, mechanical ventilation, or in-hospital death. An initial multivariable Cox regression model was built, controlling for demographic characteristics and clinical variables including the Elixhauser weighted score and occurrence of ED visits prior to 6 months. We applied the test for proportionality assumption based on the Schoenfeld residuals. Stratified Cox regression was applied to adjust the potential nonproportional hazards if necessary. In addition, to eliminate the bias of patient characteristics on exposure of vaccination status, we used propensity-score methods to reduce the effect of confounding. The individual propensities for exposure of vaccination status were estimated from a multivariable multinomial logistic regression model that included the same covariates as the multivariable Cox regression. In settings with rare outcomes and unequal exposure distributions of vaccination status, we applied matching weight procedure for assessing the effect of vaccination status on severity of illness. Ethical approval: This study was approved by the Institutional Review Board at Beaumont HealthRole of the funding source: None3Between December 15, 2020 and April 30, 2021 there were a total of 169000 ED encounters within our hospital system. We identified 11895 of these encounters that met our inclusion criteria. After further exclusion of 61 encounters, that remained admitted after our designated follow up date, we were able to analyze 10880 unvaccinated, 825 partially vaccinated, and 129 fully vaccinated ED encounters. .Figure 1Complete demographic and comorbidity data for the cohort is displayed in β: 0.04, 95% CI 0.03 to 0.06, p < 0.001). To characterize variation of the trend across study period, the weekly rate of COVID-19 ED visits to the State SARS-CoV-2 vaccination population groups was depicted for each category of vaccination status  were excluded from the analysis. Fortunately, only 61 (0.5%) patients were still hospitalized. While some of the potentially more severe cases with longer hospital durations were excluded from the analysis, only three cases were excluded due to this reason in the fully vaccinated group, the main population of interest. As the information was time sensitive, we decided it was appropriate to move forward with analysis before waiting for all patient encounters to be complete. Additionally, we assumed vaccination rate of study patients was similar to the published data regarding vaccination status from the state population. It is possible that there were slight variations that were not captured with this methodology. We also assumed our study population had similar rates of variant disease as reported by the state. Test samples were periodically sent to and audited by the state laboratory and internal hospital quality data confirmed our assumptions on rates of variants. Finally, while vaccination data from the state registry was generally robust, in seven cases the data from the MCIR was insensible with some patients receiving a combination of vaccination types or more than two doses. These cases were excluded from the analysis. Additionally, we could not make any vaccination specific conclusions. As the number of fully vaccinated patients was small in our cohort, further sub-analysis by vaccination type was not possible.In summary, emergency visits and hospitalization in fully vaccinated patients with breakthrough COVID-19 are extremely rare events even in a region with high incidence of variants. When hospitalization occurs, immunized patients are older with many comorbidities. In this high-risk population, risk for severe disease was similar in unvaccinated and vaccinated patients. Future studies are needed to reassess vaccination effectiveness broadly and by type of vaccine as mutations and variants evolve.AB and SJ designed the study, had full access to all data in the study, and take responsibility for the integrity and accuracy of the data analysis. AB, SJ, MHG, SN, GM, LQ and NWC contributed to study subject enrollment, data collection, data analysis. NWC conducted the formal statistical analysis. All authors contributed to the writing and editing of the manuscript. All authors contributed to data acquisition, data analysis, or data interpretation, and all reviewed and approved the final version of the manuscript. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.None.None.None.The data that support the findings of this study are available via a data access agreement. Please contact the corresponding author (AB) for this request.


Approximately 10% of coronavirus disease 2019 (COVID-19) patients will experience long COVID. There is no study of long COVID in mild COVID-19 patients in South Africa. This study aimed, firstly, to describe the prevalence of long COVID in mild COVID-19 patients in Cape Town, and, secondly, to document the impact of COVID-19 on patients’ well-being, work, and their access to long COVID treatment.In this retrospective cross-sectional study, a random sample of adults diagnosed with mild COVID-19 were called two months post-diagnosis. The participants telephonically completed a standardised survey describing their long COVID symptoms, missed workdays, and health-seeking behaviour. Medical records were reviewed for comorbidities, original COVID-19 symptoms, and treatment.It was found that 60% of patients with mild COVID-19 had ≥ 1 long COVID symptom, while 35% had ≥ 3 ongoing symptoms for two months. Dyspnoea and fatigue were the most common symptoms. The findings revealed that 52% of employed patients missed work and 25% of patients self-reported non-recovery from their COVID-19. Moreover, 24% of patients consulted a clinician for long COVID, but only 7% of patients received long COVID care in the public sector. Of the 17% of patients requiring additional help for long COVID, 56% were interested in assistance by text message or telephonic consultation.Over a half of mild COVID-19 patients experienced at least one long COVID symptom for two months and nearly 20% needed additional medical treatment. Very few patients utilised the public sector for long COVID treatment. There is a great need for long COVID treatment in public healthcare services and patients are receptive to remote care. Psychological and cognitive problems are also common, with multiple studies highlighting an increase in anxiety, depression, post-traumatic stress disorder, memory impairment, and sleep disturbances.13 In South Africa, the most common symptoms reported 1-month post-discharge were fatigue (69.8%), shortness of breath (32.0%), headache (17.5%), weakness in arms or legs (17.5%), and poor concentration or confusion (16.0%).8Globally, long COVID is associated with a range of fluctuating symptoms. Common physical symptoms include cough, fatigue, dyspnoea, myalgia, headaches, anosmia, sore throat, and chest pain.14 – community clinicians in the public sector have reported that only a relatively few patients were returning to primary healthcare facilities for treatment of long COVID. This situation is in contrast to the literature and anecdotal reports of higher rates of long COVID in the private sector. It is unlikely that there is a biological difference between South African public sector patients post-COVID-19 and their counterparts in the global north. Several explanations might account for the perceived differences in healthcare utilisation in the South African public sector. However, aside from the NICD study of severe COVID-19 patients post-hospitalisation, there is no data on the prevalence of long COVID in non-hospitalised patients in South Africa.8 Leading experts on infectious diseases recommended further research, especially in the South African context with its high burden of comorbid infectious and non-communicable diseases, which may complicate the manifestations of long COVID.1Although Cape Town in South Africa was severely affected by the COVID-19 epidemic – with over 300 000 cases in the Cape metro by September 2021The primary objective of this study is to describe the prevalence of long COVID symptoms in patients with confirmed non-critical SARS-CoV-2 infection in Cape Town, South Africa two months after diagnosis. The second objective is to document the impact of long COVID on patients’ self-perceived well-being and the impact of long COVID on their employment. The third objective is to assess whether public sector patients received medical care for long COVID. If the patients did not receive care, this study aims to find out some of the barriers to the treatment for long COVID in the public sector.15 Contact tracers called each confirmed SARS-C0V-2 positive patient to identify the household contacts for quarantine; they kept logbooks for recording the contact information of the patients.Retreat Community Health Centre (CHC) is a 24-h healthcare facility that provides both acute and chronic outpatient primary healthcare. Patients requiring admission are referred to the district hospital. Retreat CHC drains patients from low-income communities and squatter camps in Cape Town’s Southern peninsula and part of the Cape Flats. In total, 653 adults were diagnosed with COVID-19 by polymerase chain reaction (PCR) test at Retreat CHC between 15 December 2020 and 31 January 2021, during the second wave of COVID-19 in South Africa which was dominated by the Beta variant. During that time, public sector PCR testing was limited to all adults ≥ 45 years old, patients of any age with high-risk comorbidities, healthcare workers, and others who work in congregate settings.4 All adults with confirmed SARS-CoV-2 infection diagnosed at Retreat CHC in the second wave of COVID-19 were eligible to participate. However, contact tracers only called a random sample of the patients they originally notified by telephone between 15 December 2020 and 31 January 2021, as recorded in case logbooks. The contact tracers called 352/653 eligible patients between 15 February 2021 and 31 March 2021. Three attempts were made to contact each patient. Of those patients who were called, 175 patients were reached and they were invited to participate in the study. One patient refused to take part in the study. The remaining 174 patients consented to participate in the study.For this retrospective cross-sectional study, contact tracers phoned a random sample of adults ≥ 18 years old diagnosed with SARS-CoV-2 infection using PCR test at Retreat CHC 2 months post-diagnosis. Two months were selected to compare the results to similar studies in the literature that also followed up patients after 60 days.16 For patients complaining of fatigue, the WHO performance status classification scale was used to grade the participants’ level of activity post-COVID-19.17 Work status and the number of workdays missed post-isolation were documented. The patients were asked if they consulted a clinician for persistent COVID-19 symptoms beyond the isolation period. If they did not, they were asked if it was because they felt well or because of any barrier to access healthcare – they believed the CHC would turn away non-emergencies, the wait was too long, or an alternative reason. Finally, the patients were asked if they would like to receive medical help for ongoing symptoms and what type of help they preferred – a traditional clinic visit, a text message interaction, a telephone call, or a community group visit.The contact tracers telephonically completed a standardised questionnaire with each participant. The patients were asked to rate their general health – if they felt better, worse, or the same as before COVID-19. They were asked about the persistence and severity of common long COVID symptoms including dyspnoea, fever, fatigue, headache, chest pain, gastrointestinal complaints, myalgia, arthralgia, anosmia, ageusia, palpitations, and skin changes. The Modified Medical Research Council (MMRC) Dyspnoea scale was used to rate dyspnoea.Following the survey, we attempted to review the medical folders for all the patients who were surveyed. We managed to locate 157/174 folders. Data extracted from the individual patient medical folders included patient comorbidities, acute COVID-19 symptoms and care, and the number of medical visits at the CHC for persistent COVID-19 symptoms post isolation.p < 0.05). Quantitative data were analysed using STATA .Descriptive statistics were generated for patient demographics, comorbidities, employment status, acute COVID-19 symptoms, number of medical consultations for persistent symptoms post isolation, number of missed workdays, self-reported non-recovery, long COVID symptoms, and the desire for additional medical consultation for long COVID. Multivariate Logistic Regression was used to analyse if individual categorical patient characteristics – gender, age, number of COVID symptoms, initial severity of COVID – were predictive of long COVID symptoms. Odds ratios (ORs) were generated with 95% confidence intervals (CI)  and the Western Cape Department of Health Provincial Research Committee (reference WC_202102_017).We surveyed 174/653 (27%) of PCR-confirmed SARS-CoV-2 infections diagnosed in the second wave of COVID-19 at Retreat CHC 2 months post-diagnosis. There was a mean of 62.3 (6.9) days between diagnosis and survey. Participant baseline demographics and medical comorbidities are described in Nearly all participants – 154/157 (98%) – had mild COVID-19 with outpatient management. The mean oxygenation saturation level at the time of diagnosis was 97% (2.3), with only eight patients having saturations below 93% at the initial time of consultation. See Two months post diagnosis, 105/174 (60%) participants had at least one long COVID symptom, with 62/174 (35%) reporting three or more ongoing symptoms . The mosOf the patients surveyed, 25.6% self-reported non-recovery from COVID-19 (‘feeling worse than before COVID-19’). Nearly 20%  stated that they wanted additional medical help for ongoing COVID-19 symptoms at two months post diagnosis. Of those who wanted help, 11 (37%) preferred assistance via a text message communication, six (20%) wanted a telephonic consultation, 13 (43%) desired a traditional clinic visit, and one (3%) wanted a community support group.p < 0.001).On multivariable analysis, neither sex, age, baseline severity of disease, or number of initial COVID-19 symptoms were predictive of self-reported dyspnoea, fatigue, ≥ 3 long COVID symptoms, or self-reported non-recovery. Self-reported non-recovery – or generally ‘feeling worse’ than prior to the SARS-CoV-2 infection – was highly predictive of having ≥ 3 long COVID symptoms in comparison to those participants who felt the same or better than prior to COVID-19 , dyspnoea (20%), ageusia (20%), anosmia (18%), headaches (16%), and body aches (15%), all consistent with the spectrum of long COVID reported in both the literature and NICD study. The NICD patients had a higher rate of fatigue (70%), dyspnoea (32%), and headaches (18%), but that is expected with their more severe disease. These parallels suggest that South Africans with mild COVID-19 disease are at risk for a similar array of long COVID symptoms as their more severe counterparts, even if at a lower rate.18 – it is possible that many of the patients surveyed have undiagnosed lung disease and experienced fatigue and dyspnoea before and after their COVID-19 infection.1 Further study is needed to find out what proportion of these long COVID symptoms are directly related to the SARS-CoV-2 infection versus pre-existing morbidity.Although 60% of the patients who surveyed had ≥ 1 long COVID symptom and 35% had ≥ 3 symptoms at two months, only 25% of patients reported a feeling generally worse than prior to their COVID-19 infection. This mismatch between persistent COVID-19 symptoms and self-reported non-recovery suggests that, despite being surveyed on new or persistent symptoms, many patients may have experienced long COVID-like symptoms prior to infection. Given the high prevalence of poverty and chronic obstructive pulmonary disease (COPD) in Cape Town – over 20% of the population was estimated to have COPD19 However, given that many patients cannot afford private healthcare, the public sector must make long COVID treatment and rehabilitation services more accessible at the primary healthcare level, even during ongoing COVID-19 waves.Nonetheless, given the scale of mild COVID-19 infections in South Africa, even if 25% self-report non-recovery from their illness at two months post infection, that represents a significant number of people struggling with ongoing long COVID symptoms. In our study, 52% of employed patients missed work because of ongoing COVID-19 symptoms. About 24% of patients had additional clinical consultations for persistent symptoms outside of the isolation period. However, 71% of those visits were with private general practitioners. Only 7% of patients had follow-up consultations in the public sector, thus supporting our hypothesis that relatively few patients are utilising public primary healthcare facilities for ongoing long COVID treatment. During COVID-19 surges many primary healthcare services were de-escalated, or reduced, and rehabilitation professionals often redeployed to contact tracing teams.Most patients who surveyed were very appreciative of the follow-up. One patient commented ‘I can’t believe the Department of Health care’s how I am doing’, a comment that suggests the public sector must be more patient-centred and welcoming to improve utilisation for non-urgent concerns. Nearly 20% of our patients wanted additional long COVID treatment, but over a half preferred remote consultations. Even if the public healthcare system does not have the capacity to offer in-person treatment and rehabilitation services for all its long COVID patients, our study suggests that many patients would be open to remote medical services that might be more feasible for the public sector to scale up for many long COVID patients.Because of our small sample size, we were unable to identify baseline characteristics associated with developing long COVID. Larger studies are needed to identify which patients with mild COVID-19 disease are more likely to struggle with ongoing symptoms. However, we did find that patients with self-reported non-recovery were more likely to have ≥ 3 long COVID symptoms. This suggests that asking patients if they subjectively feel better might be a sufficient outpatient screen for identifying primary healthcare patients in need of long COVID treatment.This study had several limitations. Firstly, because of the clinic population, we surveyed a racially homogenous sample. Although we have no reason to believe that South Africans of different racial backgrounds would have different disease symptoms, they might have different health-seeking behaviours or access to care. Secondly, because of testing restrictions in the public sector, our study population was older than the general population, which could bias results given that younger patients are more likely to have mild or asymptomatic disease. Thirdly, we did not survey the psychological impact of COVID-19 in the primary healthcare setting, information that is also needed to shape the primary healthcare response to long COVID. Fourthly, we chose to survey patients at two months post diagnosis to compare to prior studies. Further study is needed to identify the proportion of patients experiencing long COVID at one month, three months and beyond to understand the full extent of morbidity.Fifthly, because of study resource constraints, we surveyed a relatively small sample of patients. Additional studies are needed on a population level to confirm if our results are generalisable to the broader South African population. Nonetheless, given that no other data exists documenting the extent of long COVID in mild COVID-19 patients in South Africa, we believe that this study provides a valuable start to documenting the prevalence of long COVID in COVID-19 patients who were not hospitalised.We found a high prevalence of persistent long COVID symptoms at two months in patients with mild COVID-19 illness, but poor utilisation of the public sector for treatment. The public sector must provide primary healthcare treatment for long COVID patients who otherwise might suffer ongoing morbidity because of their symptoms.


Flares of juvenile idiopathic arthritis (JIA) have been described in the context of various infections. Flares of rheumatic diseases in adults have been described following infection with SARS-CoV-2 in several cohorts. So far, the effect of infection with SARS-CoV-2 on the course of JIA is unknown.The database of the German Center for Pediatric and Adolescent Rheumatology was searched for patients with confirmed infection with SARS-CoV-2 and subsequent disease flare, admitted from July 2020 until June 2021. cJADAS-27, ESR and C-reactive protein, as well as uveitis activity, medication at the time of flare and treatment of flare was extracted. Patient cases were described individually.n = 2) or inactive disease on medication (n = 3) were identified, with flare symptoms up to four weeks after infection with SARS-CoV-2.Out of 988 patients admitted, five patients with remission off medication (Flares can occur after infection with SARS-CoV-2 in patients with JIA in remission or inactive disease on medication. Treating physicians need to be aware of this fact, especially when counseling patients with rheumatic diseases about the respective dangers of COVID-19 and vaccination against SARS-CoV-2. Juvenile idiopathic arthritis (JIA) is the most common chronic rheumatic disease in children, with a reported prevalence that varies between 16 and 150 per 100,000 [The course of JIA may have multiple periods of inactive disease and flares of inflammatory activity .The COVID-19 pandemic, caused by SARS-CoV-2 infection, has seen at least 190 million confirmed cases and over 4 million deaths worldwide recorded until July 2021. In most people the infection will cause a mild-to-moderate flu-like illness characterized by fever, cough, and loss of taste and smell, among other symptoms; some patients require hospitalization and ventilatory support and are at acute risk of dying . In adulChildren are among the least affected by the disease, with COVID-19 less frequent and less aggressive in this age group . Rare caWe report five cases of patients with JIA in remission or long-term inactive disease on medication who developed flares of their disease in close temporal correlation with a confirmed prior diagnosis of SARS-CoV-2 infection.The database of the German Center for Pediatric and Adolescent Rheumatology in Garmisch-Partenkirchen was searched for patients with serologically confirmed coronavirus infection and subsequent flares of their rheumatic disease. A flare was defined as new signs of arthritis or uveitis, i.e. an increase in the total joint count or JADAS or increase in anterior chamber cells or flare requiring a modification in therapy, following a period of inactive disease.Inclusion criteria were: 1) a diagnosis of juvenile idiopathic arthritis according to ILAR criteria , 2) flarIn 988 cases with JIA admitted from July 2020 until June 2021 to the German Center for Pediatric and Adolescent Rheumatology, serology for COVID-19 was determined in 178 cases. Of these, 13 samples were positive. Six patients had no signs of arthritis flare but a recent history of other symptoms compatible with infection with SARS-CoV-2. Two had signs of arthritis flare after discontinuation of medication following the diagnosis of COVID-19. The remaining five patients with flare after infection with SARS-CoV-2, despite no change in medication, are presented here , but negative for IgM antibodies and PCR and antigen testing.Case 2 is a 17-year-old adolescent male who had been diagnosed with rheumatoid-factor negative polyarthritis at the age of 11 years and six months. He was treated with intraarticular steroid injections, methotrexate and in the course of his disease, etanercept, adalimumab and tocilizumab. He was switched to baricitinib 6 months prior to admission and experienced a complete response, with confirmed inactivity by the treating physician. However, he presented with arthritis in both ankle and subtalar joints three weeks after COVID-19, with inapparent clinical symptoms diagnosed by antigen testing after his brother had developed a symptomatic infection with SARS-CoV-2.Case 3 is a nine-year-old girl who had been diagnosed with rheumatoid-factor negative arthritis at the age of ten months. She was initially treated with methotrexate, intraarticular steroid injections, and etanercept was added at age three years. She had occasional mild flares, the last one at age seven years where dosage was adjusted.She presented on etanercept (approx. 0.7 mg/kg) with arthritis in both knees and the left ankle, both ongoing for three months. This had developed shortly after COVID-19 of the whole family (confirmed by antigen testing at that time) where she had fever and mild diarrhea.Case 4 is an 18-year-old adolescent male who had been diagnosed with enthesitis associated arthritis at age 11 years. He was treated with methotrexate, hydroxychloroquine and low-dose prednisolone until medication was stopped at the age of 17 years. He continued well without medication until he developed an infection with SARS-CoV-2, with positive antigen test, with signs of infection in the upper respiratory tract, vertigo and loss of taste. He developed increasing joint pains shortly thereafter and presented three months later with confirmed arthritis in the right knee, both wrists, ankles and several finger joints.Case 5 is a 15-year-old adolescent female who had been diagnosed with extended oligoarthritis starting at age three. She was treated with multiple intraarticular steroid joint injections and methotrexate, but was started on etanercept at age nine due to ongoing arthritis. At age ten she developed uveitis of the left eye, when she was switched first to adalimumab, then infliximab, and then due to antibodies to both, to tocilizumab. Due to ongoing arthritis she was switched to golimumab at age 13 which, together with methotrexate, led to lasting remission. She presented approximately 18 months later, four weeks after COVID-19, confirmed by antigen test, with arthritis in both wrists, hips, knees, ankles and the right temporomandibular joint.Here we describe five cases of patients with JIA, in clinical remission or inactive on medication, who developed a flare of their disease shortly after a documented infection with SARS-CoV-2. Time between infection and flare can only be roughly estimated, as patients were asymptomatic in two cases and arthritis was in most cases assessed by a trained rheumatologist at a later date. In general, the time span between infection and start of joint symptoms appears to be between one and four weeks. Patients were on various medications, or off medication completely, but all were inactive for at least six months prior to the viral infection with SARS-CoV-2.M. pneumoniae and C. jejuni [Frequency and clinical symptoms of acute respiratory infections in patients with chronic disease have been poorly studied. Respiratory infections in children with cystic fibrosis was not more frequent, but associated with higher morbidity compared to healthy controls . Infecti. jejuni . Flares . jejuni .Even without prior infections, flares are frequent occurrences in JIA patients in inactive disease, with 42.5% of patients developing a flare within one year of achieving inactive disease . A recenPatients infected with SARS-CoV-2 have shown to exhibit a variety of elevated cytokine levels, most prominently IL-6 , 19. TheAn early survey from Italy in adults showed the benefit of continuing rheumatic medications to stabilize the disease and prevent flares . The AmeThis is a retrospective case series, with no definitive proof of pathophysiological correlation between documented infection and observed flare. Further studies in a larger cohort are necessary to determine the risk of flare in the JIA population and possible ways to prevent this, beyond the obvious benefits of vaccination.In conclusion, we describe flares after infection with SARS-CoV-2 in patients with JIA in remission or inactive disease on medication. Paediatric rheumatologists need to be aware of this fact in their daily practice, and the possibility of flares despite adequate medication might add emphasis to the recommendation to vaccinate against COVID-19. However, more studies are necessary to establish the true risk of flares in JIA from COVID-19, including the risk of vaccination against SARS-CoV-2.


The relationships of the coronavirus disease 2019 (COVID-19) vaccination with reactogenicity and the humoral immune response are important to study. The current study aimed to assess the reactogenicity and immunogenicity of the Pfizer and AstraZeneca COVID-19 vaccines among adults in Madinah, Saudi Arabia.st and June 30th, 2021. Data of height and weight were collected to assess the weight status of percipients. An evaluation of seropositivity for anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies was assessed using enzyme-linked immunosorbent assay (ELISA).A cross-sectional study, including 365 randomly selected adult Pfizer or AstraZeneca vaccine recipients who received a homologous prime-boost vaccination between February 1p < 0.05). Spike-specific IgG antibodies were detected in 98.9% of participants after the receipt of two vaccine doses, including 99.5% of Pfizer vaccine recipients and 98.3% of AstraZeneca vaccine recipients. Significantly, higher proportions of participants in the <35-year age group developed a humoral immune response after the first vaccine dose compared with the participants in other age groups.Among the participants, 69% (n = 250) reported at least one vaccine-related symptom. Pain at the injection site was the most frequently reported vaccine-related symptom. The mean total score for vaccine-related symptoms was significantly higher among participants who received the AstraZeneca vaccine, women, and participants with no previous COVID-19 infection (Participants who received the Pfizer COVID-19 vaccine reported fewer vaccine-related complications compared with those who received the AstraZeneca COVID-19 vaccine, but no serious side effects were reported in response to either vaccine. Health status and age were factors that may influence COVID-19 vaccine effectiveness for the generation of antibodies against the SARS-CoV-2 spike protein. Coronavirus disease 2019 (COVID-19) has rapidly become a leading cause of death and both short- and long-term morbidity among individuals older than 45 years .https://www.cdc.gov/coronavirus/2019-ncov/vaccines/faq.html, accessed on 11 October 2021), were omitted from vaccine-related clinical trials. Although previous infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus is thought to provokes a natural immunity that is durable for at least 6 months . The AstraZeneca and Pfizer/BioNTech vaccines have demonstrated an immunogenic nature accompanied by significant effectiveness for preventing COVID-19 disease .This cross-sectional study recruited adult participants aged 18 years or older who received both doses of either the Pfizer or AstraZeneca COVID-19 vaccine and visited the vaccine center at Taibah University between February 1 and June 30, 2021. In accordance with recommendations, the time between the first and second Pfizer vaccinations was 3 weeks, whereas 12 weeks separated the administration of the AstraZeneca doses. The study was advertised at the vaccine center, and banners were placed in the waiting rooms. Initially, data were collected from 952 individuals who agreed to participate The online survey collected sociodemographic data, including age, sex, nationality, education level, and career. Data concerning blood type and medical history (previous infection with COVID-19) were also self-reported. According to medical history and self-reported health status, the responses were grouped into 3 categories: 1) healthy; 2) immune-related diseases ; and 3) chronic diseases . All self-reported data were collected using an online survey, and items were validated/completed during the in-person visit.via the online survey. Height and weight were used to calculate the body mass index (BMI) to determine the weight status of participants aged >18 years, using the World Health Organization cutoffs .Anthropometric measurements (height and weight) were collected Blood samples (5 ml) in EDTA tubes were drawn 10–14 days after receiving the first and second doses of either vaccine.The enzyme-linked immunosorbent assay (ELISA) was implemented according to a previously published method . BrieflyParticipants were asked about the symptoms they experienced after receiving the vaccine. Symptoms stated were headache, fever, pain injection site, and nausea. Participants were able to select multiple responses. A score of one was awarded to each participants who selected the symptom of a concern and then the total symptom score was calculated for each participants (maximum 4 points out of 4).Data for categorical variables are presented as the frequency and proportion. Fisher’s exact test was used to compare proportions across different groups, and post-hoc testing was performed to further investigate significant associations. Bonferroni adjustment to alpha was used for post-hoc tests to correct for multiple testing. Data for continuous variables are presented as the mean ± standard deviation. The Shapiro–Wilk test was used to assess the distribution normality of the total symptom score. The Mann–Whitney U test and Kruskal–Wallis test were used to compare the means of vaccine-related symptoms between groups . Logistic regression analysis was performed to investigate predictors of change in anti-S protein IgG antibody status after the first dose and after the second dose. All models were adjusted for participants’ age and sex. Variables were coded as follows: age ; sex ; health status ; weight status for participants >18 years ; blood type ; previous SARS-CoV-2 infection ; vaccine type ; change in results . SPSS was used to analyze all data presented in this study . All tests were two-tailed, and a 95% confidence level was used to assess the significance of the findings.A total of 365 participants (38.3%) completed the study Figure 1p < 0.05). The associations between vaccine-related symptoms and vaccine type are shown in We investigated the reactogenicity profiles after receiving the first dose of either the AstraZeneca or Pfizer vaccine, and 69.0% percent (n = 250) of participants reported experiencing at least 1 vaccine-related symptom. Pain at the injection site was the most frequently reported vaccine-related symptom , followed by fever  and headache ; nausea was the least commonly reported symptom  and was only reported among those who received the AstraZeneca vaccine. Significantly higher proportions of participants who experienced headache, fever, pain at the injection site, and nausea received the AstraZeneca vaccine than received the Pfizer vaccine . Additionally, the mean total score for vaccine-related symptoms was significantly higher among women than among men . The mean total score of vaccine-related symptoms was also significantly higher among participants with no previous infection than among participants with reported any previous SARS-CoV-2 infection . The mean total scores of vaccine-related symptoms were similar across the different age groups (p = 0.914).The total score of vaccine-related symptoms was calculated, and the mean total score was significantly higher among participants who received the AstraZeneca vaccine compared with those who received the Pfizer vaccine  after receiving the first dose of either vaccine, and most participants developed an anti-S IgG antibody response after receiving the second dose . We also found that 16% (n = 58) of participants did not develop an anti-S IgG antibody until after the second dose was received.p = 0.030). A significant inverse relationship between age and anti-S IgG antibody status was found in this study (p = 0.032). We also performed post hoc testing, which showed that a significantly higher proportion of participants in the <35-year age group developed a humoral immune response after receiving the first dose of vaccines compared with the other age groups (p = 0.007). No association was found between age and anti-S IgG antibody status after receiving the second dose. Additionally, no associations were identified between other sociodemographic characteristics and anti-S IgG antibody status after receiving either vaccine dose.Interestingly, we found that participants who developed an anti-S IgG antibody response after the first dose were significantly younger than those who did not develop any humoral immune response . Additionally, post hoc testing showed that a significantly higher proportion of healthy participants developed humoral immune responses after the first dose compared with participants with immune-related diseases and those with chronic diseases . The data showed that the proportions of participants stratified by anti-S IgG antibody status were similar across all weight groups after the first dose, whereas after receiving the second dose, a significantly lower proportion of underweight participants developed a humoral immune response compared with participants classified as healthy weight, overweight, or obese (p = 0.001). Associations between participants’ health characteristics and anti-S IgG antibody status are presented in Associations between anti-S IgG antibody status and health characteristics were observed after performing ELISA. We found that chronic disease was significantly associated with anti-S IgG antibody status after both the first and second doses .Logistic regression analysis showed that no health characteristics were able to significantly predict the seropositivity in anti-S IgG antibody after either the first dose or the second dose. Only the presence of chronic diseases predicted higher odds of a seropositivity in anti-S IgG antibody status compared with healthy participants with no chronic diseases .COVID-19 vaccines have been used with huge success in several countries globally; however, greater than half of the worldwide population remains unvaccinated  developed a humoral immune response after receiving the second dose, indicating that 16% of the total population developed an immune response only after the second dose. Therefore, a second dose was necessary to achieve a higher seroconversion percentage. A recent study showed that approximately 20% of hospitalized patients received at least one vaccine dose, although several patients had not received a second dose .Remarkably, our study showed that younger participants in the <35-year age group experienced a significantly increased seropositivity rate (92.4%) and generated anti-S IgG antibodies after the first dose compared with participants in other age groups . The second COVID-19 vaccine dose is necessary to increase antibody levels for protection from the infection. Our results are in agreement with the results of several studies that have reported an age-dependent immune response to COVID-19 vaccination, which concluded that anti-S antibody levels were significantly lower among older vaccine recipients , 23.Our study showed that the presence of chronic diseases  was associated with lower seropositivity rates. Healthy participants were expected to develop a humoral immune response regardless of the number of doses received compared with participants with immune-related diseases or chronic diseases. However, a higher proportion of vaccine recipients with immune-related diseases developed a humoral immune response than vaccine recipients with chronic diseases. SARS-CoV-2 vaccination in healthy people has been reported to produce immune protection against COVID-19 in both immune system arms, humoral and cellular immune responses .Among participants who developed humoral immune responses, the proportion of underweight participants was significantly lower than those of other weight groups . In fact, the prevalence of overweight and obesity in Saudi Arabia is very high. In 2019, the prevalence of overweight and obesity among Saudi adults in Jeddah City was 35.1% and 34.8%, respectively . In addiUnderweight individuals are commonly malnourished and more likely to present with inadequate levels of multiple nutrients that play key roles in the function of the immune system. Population-level malnutrition has been associated with increased proportions of fatal COVID-19 in regions with higher undernutrition burdens . Thus, iOur study is the first to compare the reactogenicity and immunogenicity of the most commonly used vaccines, Pfizer and AstraZeneca, in Saudi Arabia and several other settings. However, this study is limited by its cross-sectional design, preventing the long-term effects of vaccination from being evaluated. Furthermore, data concerning ethnicity were lacking; thus, we were unable to control for this variable in our analysis. However, data concerning ethnicity were not collected, as previous research in Saudi Arabia showed no association between ethnicity and immunogenicity (unpublished data). Information concerning health conditions, previous COVID-19 infection, and side effects following vaccination were self-reported, which could introduce temporal gaps in the reporting. In addition, we did not assess the associations of cellular immune responses with suitable reactogenicity profiles. We emphasize that our investigation in the current work is observational rather than serving as a formal evaluation.Findings of this study suggest that different levels of reactogenicity and immunogenicity may be observed depending on the vaccine type, age, chronic disease status, and weight status. Participants who received the AstraZeneca vaccine reported more vaccine-related symptoms than those who received the Pfizer vaccine; however, both vaccines were well-tolerated and effective. These data could assist governments and policy-makers in the establishment of regulations concerning the provision of paid leave to promote vaccination, in addition to guiding individuals in scheduling their commitments post-vaccination. Future research should explore the long-term health outcomes related to commonly used vaccines at the national and international levels. Additionally, future studies should investigate the impacts of nutritional status on the efficacy of the COVID-19 vaccinations.The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the College of Applied Medical Sciences, Taibah University (2021/96/117/MLT). The patients/participants provided their written informed consent to participate in this study.WMa and WMu contributed to conception and design of the study. WMa and WMu organized the database. WMu performed the statistical analysis. WMa wrote the first draft of the manuscript. WMu wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.The authors extend their appreciation to, Taibah University, represented by the Deanship of Scientific Research, for funding this project# RC-442/4.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Hearing impairment is a frequent human sensory impairment. It was estimated that over 50% of those aged >75 years experience hearing impairment in the United States. Several hearing impairment–related factors are detectable through screening; thus, further deterioration can be avoided. Early identification of hearing impairment is the key to effective management. However, hearing screening resources are scarce or inaccessible, underlining the importance of developing user-friendly mobile health care systems for universal hearing screening. Mobile health  applications (apps) act as platforms for personalized hearing screening to evaluate an individual’s risk of developing hearing impairment. We aimed to evaluate and compare the accuracy of smartphone-based air conduction and bone conduction audiometry self-tests with that of standard air conduction and bone conduction pure-tone audiometry tests. Moreover, we evaluated the use of smartphone-based air conduction and bone conduction audiometry self-tests in conductive hearing loss diagnosis. We recruited 103 patients (206 ears) from an otology clinic. All patients were aged ≥20 years. Patients who were diagnosed with active otorrhea was excluded. Moderate hearing impairment was defined as hearing loss with mean hearing thresholds >40 dB. All patients underwent four hearing tests performed by a board-certified audiologist: a smartphone-based air conduction audiometry self-test, smartphone-based bone conduction audiometry self-test, standard air-conduction pure-tone audiometry, and standard bone conduction pure-tone audiometry. We compared and analyzed the results of the smartphone-based air conduction and bone conduction audiometry self-tests with those of the standard air conduction and bone conduction pure-tone audiometry tests. The sensitivity of the smartphone-based air conduction audiometry self-test was 0.80  and its specificity was 0.84 (95% CI = 0.76–0.90), respectively. The sensitivity of the smartphone-based bone conduction audiometry self-test was 0.64 (95% CI = 0.53–0.75) and its specificity was 0.71 (95% CI = 0.62–0.78). Among all the ears, 24 were diagnosed with conductive hearing loss. The smartphone-based audiometry self-tests correctly diagnosed conductive hearing loss in 17 of those ears. The personalized smartphone-based audiometry self-tests correctly diagnosed hearing loss with high sensitivity and high specificity, and they can be a reliable screening test to rule out moderate hearing impairment among the population. It provided patients with moderate hearing impairment with personalized strategies for symptomatic control and facilitated individual case management for medical practitioners. Hearing impairment is among the most prevalent sensory impairments in human beings. As documented in various studies, the worldwide prevalence of hearing loss is approximately between 4.0% and 18.1% ,3,4,5. DNevertheless, numerous countries experience practical challenges in preventing and rehabilitating hearing impairment. The first obstacle is the limited medical resources failing to meet patient demands. The diagnostic benchmark for hearing impairment is the standard audiogram. Nevertheless, financial or geographic restrictions can impede prompt audiogram testing . AdvanceAccordingly, we attempted to develop a cost-effective hearing examination for evaluating an individual’s hearing condition. Smartphone-based hearing tests have been developed as screening and assessment tools to recognize patients with hearing deficits. Hence, our aim was to assess the efficacy of smartphone-based audiometry tests in screening patients with moderate hearing impairment and to validate these tests against standard pure-tone audiometry. We also evaluated and compared the accuracy of smartphone-based air conduction and bone conduction audiometry self-tests with their corresponding standard pure-tone audiometry tests. Secondarily, we aimed to evaluate the effectiveness of smartphone-based air conduction and bone conduction audiometry self-tests for diagnosing conductive hearing loss.In total, 103 participants  who were aged older than 20 years were recruited to the otology outpatient department of Kaohsiung Veterans General Hospital and Shuang Ho Hospital. Patients with existing otorrhea and cognitive impairment were excluded from recruitment. For both ears of each participant, four hearing examinations were performed randomly: a smartphone-based air conduction audiometry self-test, smartphone-based bone conduction audiometry self-test, standard air-conduction pure-tone audiometry, and standard bone conduction pure-tone audiometry. These tests were performed by a board-certificated audiologist in randomized order and in a soundproof room with an average A-weighted ambient noise level of 35 dB during each visit. Informed consent was obtained from all patients.The four hearing tests for all participants were conducted by a board-certificated audiologist in a soundproof room randomly. The four tests were arranged in random order and there was no time lag between tests. We did not calibrate because we aimed to stick to the reality in which calibration may not be readily available even if the patients use the same smartphone and headphone. Mean hearing thresholds were defined at 500, 1000, 2000, and 4000 Hz. For hearing loss with mean hearing thresholds >40 dB, moderate hearing impairment was impressed. We applied the 2 × 2 tables to summarize the statistics in our study. The results were collected for further evaluation and statistical analysis.Audiometry self-tests were completed using an iPhone . An application named uHear , through which patients can test their air conduction pure-tone hearing sensitivity, was downloaded to the phone through the iTunes App Store. uHear employs a “10-dB down and 5-dB up’’ principle ,19, and This self-test was also completed with an iPhone . The uHear application  was downloaded from the iTunes App Store. The procedure of this smartphone-based bone conduction audiometry self-test was the same as that of the air conduction variant. The results of the hearing sensitivity test were visualized in a typical audiogram format after test completion. Furthermore, the smartphone-based bone conduction audiometry self-test was conducted in a soundproof room whose average ambient noise level was less than 35 dB HL. AfterShokz Sportz 2 bone conduction headphones  were used for all participants. This smartphone-based self-test was completed by each participant in a soundproof room. Finally, we put the results of this smartphone-based bone conduction audiometry self-test and those of standard bone conduction pure-tone audiometry tests into comparison.A Grason-Stadler GSI-61 clinical audiometer  was used to perform the standard pure-tone audiometry test. Telephonics TDH-50P audiometric headphones  were used to perform the test. All clinical audiometers and accessory devices met 1996 American National Standards Institute S3.6 guidelines . The tesThe Grason-Stadler GSI-61 clinical audiometer as well as a B-71 bone conductor  were used to perform the standard bone conduction pure-tone audiometry test. All clinical audiometers and accessories met the 1996 American National Standards Institute S3.6 guidelines . The tesIn total, 103 patients  were examined in our study. The mean age of the patients was 55.23 ± 6.14 years. Of these patients, 69 were men and 36 were women. Detailed patient demographics are summarized in As indicated in The results of the smartphone-based bone conduction audiometry self-test were compared with that of the standard bone conduction pure-tone audiometry test, as presented in Conductive hearing loss is defined as an air–bone gap of at least 20 dB compared with a normal bone conduction hearing threshold. Among the 206 ears, 24 were diagnosed with conductive hearing loss by using the standard air conduction and bone conduction pure-tone audiometry tests. The smartphone-based air conduction and bone conduction audiometry self-tests correctly diagnosed 17 of those 24 ears with conductive hearing loss.Masalski et al. reported a 15.6% worldwide prevalence of hearing impairment on the basis of 116,733 hearing tests performed by Android users on mobile devices ,18,21. PIn our study, we compared results from a smartphone-based air conduction audiometry self-test and those from a standard air conduction pure-tone audiometry test. Among the 88 ears determined as having moderate hearing impairment (PTA > 40 dB HL) by the standard air conduction pure-tone audiometry test, 71 recorded a PTA of >40 dB HL in the smartphone-based air conduction audiometry self-test, indicating an 80% sensitivity. Moreover, among the 118 ears without moderate hearing impairment (PTA ≤ 40 dB HL) documented in the standard air conduction pure-tone audiometry test, 99 registered a PTA of ≤40 dB in the smartphone-based air conduction audiometry self-test, demonstrating 84% specificity. These findings coincide with those of previous studies. Li et al. observed that the sensitivity and specificity of smartphone-based self-tests performed by older (>65 years) adults were 92% and 76%, respectively . Sara etOur pioneering study also correlated the accuracy of the smartphone-based bone conduction audiometry self-test and that of the standard bone conduction pure-tone audiometry test. Among the 76 ears with moderate hearing impairment (PTA > 40 dB HL) documented in the standard bone conduction pure-tone audiometry test, 49 registered a PTA of >40 dB HL in the smartphone-based bone conduction audiometry self-test, indicating a sensitivity of 64%. Furthermore, among the 130 ears deemed not to have moderate hearing impairment (PTA ≤ 40 dB HL) in the standard bone conduction pure-tone audiometry test, 92 recorded a PTA of ≤40 dB in the smartphone-based bone conduction audiometry self-test, suggesting a 71% specificity. Previous studies have mostly focused on smartphone-based air conduction audiometry self-test accuracy. By contrast, our study is the first to use AfterShokz Sportz 2 bone conduction headphones to perform experiments on the accuracy of smartphone-based bone conduction audiometry. Moreover, by comparing the results of standard air conduction pure-tone audiometry with those of standard bone conduction pure-tone audiometry, we observed that standard air conduction pure-tone audiometry possessed higher sensitivity as well as higher specificity. This difference was due to the sound leakage engendered by the AfterShokz Sportz 2 bone conduction headphones used in the smartphone-based bone conduction audiometry test. To resolve this problem, we thoroughly compared the AfterShokz Sportz 2 bone conduction headphones with the Sennheiser hd201 headphones, discovering that the Sennheiser hd201 headphones fit snugly in the ear canals. This may explain the higher sensitivity and specificity observed in the smartphone-based air conduction audiometry self-test.Conductive hearing loss is defined as an air–bone gap of at least 20 dB compared with a normal bone conduction hearing threshold. Among the 206 ears, 24 were diagnosed with conductive hearing loss in the standard air conduction pure-tone audiometry test and standard bone conduction pure-tone audiometry test. Our results indicate that smartphone-based air conduction and bone conduction audiometry self-tests correctly diagnosed 17 of these 24 ears with conductive hearing loss.The reason why this study is important and novel is that we aimed to develop an accurate method to detect hearing impairment with the use of commonly available portable mobile devices, so that early diagnosis and prompt treatment of hearing impairment can be achieved.This study has several limitations. First, the standard air conduction and bone conduction pure-tone audiometry protocol requires audiologists to apply masking, which aims to acoustically separate the two ears. However, when conducting the smartphone-based air conduction and bone conduction audiometry self-tests, we did not apply masking noise. During the hearing tests, the contralateral ear may hear a tone presented in the tested ear. This phenomenon is called crossover, and it occurs when an air-conducted signal is intense enough to cause the skull to vibrate. Sound is transmitted through bone conduction, allowing the patient to hear the tone in the nontested ear. In future studies, changes should be introduced to the application to prevent improper reaction to the masking noise.Second, we used AfterShokz headphones in our study because it is one of the most common bone conduction earphones available in Taiwan. We could hardly find mastoid bone-conduction earphones. Despite the fact that the AfterShokz headphone has its bone conduction through the temporomandibular joint, we chose it as the experimental device because of its widespread popularity in the real world. We did not calibrate because we aimed to stick to the reality in which calibration may not be readily available even if the patients use the same smartphone and headphone. We performed the experiment in a soundproof booth in order to minimize the uncontrollable sound effect of the surrounding environment. We aimed to investigate the efficacy of the smartphone-based air audiometry self-test under the circumstance with standardized controllable factors. Testing in more realistic scenarios will be a direction of further research.We chose mobile phones instead of laptops because of their higher coverage in many developing countries. Thanks to the production of domestic mobile phone models, mobile phones have become more affordable for people’s daily communication. The overall cost of our study was 330 US dollars, consisting of 200 US dollars for the mobile phone, 50 US dollars for the air conduction earphone, and 80 US dollars for the bone conduction earphone.Besides, in order to minimize possible errors, all participants underwent the experiment under an audiologist’s guidance. It is agreed that if patients were asked to carry out the experimental procedures by themselves, many of them may fail to perform the test, which in turn causes larger procedural variation and affects the participant inclusion of our study. We included the audiologist’s instruction because many people in Taiwan are not familiar with English and it would be difficult for them to carry out the audiological tests on themselves. In order to deal with these unanswered questions, we hope to carry out further investigation in the future. We wish to perform further studies without the guidance of an audiologist.Both smartphone-based air conduction and bone conduction audiometry self-tests demonstrated high sensitivity and high specificity in diagnosing moderate hearing impairment. The mobile health application was a user-friendly personalized mHealth application, which aids precise evaluation and early intervention of moderate hearing impairment. Compared with a standard pure-tone audiometry test, the smartphone-based air conduction and bone conduction audiometry self-tests proved capable of detecting conductive hearing loss with reliable sensitivity and specificity. In conclusion, it is a reliable hearing screening tool but can only be achieved if hardware is correctly calibrated or at least standardized and the examination is carried out in a quiet environment. We will refine the mobile health application based on users’ experience and the collected dataWe thank all participants and their families for supporting the study.


The coronavirus pandemic has disproportionally impacted racial and ethnic minority communities in the United States. Patterns of these disparities may be changing over time as outbreaks occur in different communities. Utilizing electronic health record data from the US Department of Veterans Affairs (VA), we estimated odds ratios, stratified by time period and region, for testing positive among 1,313,402 individuals tested for SARS-CoV-2 between February 12, 2020 and August 16, 2021 at VA medical facilities. We adjusted for personal characteristics  and a wide range of clinical characteristics that have been evaluated in prior SARS-CoV-2 reports and could potentially explain racial/ethnic disparities in SARS-CoV-2. Our study found racial and ethnic disparities for testing positive were most pronounced at the beginning of the pandemic and decreased over time. A key finding was that the disparity among Hispanic individuals attenuated but remained elevated, while disparities among Asian individuals reversed by March 1, 2021. The variation in racial and ethnic disparities in SARS-CoV-2 positivity by time and region, independent of underlying health status and other demographic characteristics in a nationwide cohort, provides important insight for strategies to prevent further outbreaks. Evidence has highlighted the vast disparities in SARS-CoV-2 infection and subsequent COVID-19 among persons who were Black, Hispanic or Latino, Native Hawaiian/Pacific Islander, American Indian/Alaska Native, or Asian12. Recently, additional analyses have suggested that racial and ethnic disparities may be changing over time as outbreaks spread from racially and ethnically diverse metropolitan centers to more rural and less diverse areas14. In this report, we updated our previous analyses5 to evaluate changes in disparities for testing positive with SARS-CoV-2 over the first 18 months of the pandemic and by geographic region in the largest integrated healthcare system in the United States.The coronavirus pandemic has disproportionally impacted racial and ethnic minority communities in the United States5. In brief, SARS-CoV-2 tests were identified using a text searching algorithm of laboratory results at VA for terms consistent with SARS-CoV-2 or COVID-19. For individuals with multiple tests, we selected the first positive test. For those without a positive test during the study period, we selected the first negative test. Analysis of test samples was performed in VA, state public health, and commercial laboratories using FDA Emergency Use Authorization-approved SARS-CoV-2 assays. Nearly all the tests utilized nasopharyngeal swabs. We did not include antibody tests in this analysis.Utilizing national electronic health record data from the US Department of Veterans Affairs (VA), we conducted a retrospective cohort analysis of all Veterans tested for SARS-CoV-2 in VA medical facilities between February 12, 2020 and August 16, 2021. Methods have been previously described in detail16. Clinical characteristics included baseline comorbidities , substance use , medication history . Baseline for personal and clinical characteristics was defined as the date of specimen collection for the selected SARS-CoV-2 test. Models were additionally conditioned on VA site of care.We calculated the crude prevalence of testing positive for SARS-CoV-2 by time and race/ethnicity and calculated confidence intervals using the normal approximation. We used multivariable logistic regression to estimate odds ratios (OR) and 95% confidence intervals (CI) for testing positive for SARS-CoV-2 for Black, Hispanic or Latino (Hispanic), Asian, American Indian/Alaska Native, Native Hawaiian/Pacific Islander, and people of mixed race (Mixed), relative to White individuals. Race and ethnicity were self-reported. Individuals who reported Hispanic ethnicity were included in the Hispanic group regardless of any other self-reported race. A small proportion (4.0%) of individuals tested for SARS-CoV-2 with missing race/ethnicity information were excluded from analysis. All models were adjusted for other personal characteristics  and a wide range of clinical characteristics that have been evaluated in prior SARS-CoV-2 reports and could potentially explain racial/ethnic disparities in SARS-CoV-2 positivityModels were a priori stratified into five waves based on the temporal distribution of SARS-CoV-2 cases nationally: February 12–May 31, 2020 (wave 1); June 1–September 30, 2020 (wave 2); October 1, 2020–February 28, 2021 (wave 3); March 1, 2021–June 30, 2021 (wave 4); and July 1, 2021–August 16, 2021 (wave 5). Due to the large size of the third national wave, we split this period into two waves containing roughly equal numbers of SARS-CoV-2 cases ; and December 12, 2020–February 28, 2021 (wave 3b).To evaluate regional differences by time, models were further stratified by US Census region . Odds ratios in strata by time and region with less than five cases were not reported due to potential privacy concerns and instability of statistical estimates. Data analysis was performed using SAS version 9.4 .This study was approved by the institutional review boards of VA Connecticut Healthcare System (VA AJ0013) and Yale University (1506016006). It has been granted a waiver of informed consent and is Health Insurance Portability and Accountability Act compliant.Views expressed are those of the authors and the contents of this article do not represent the views of the US Department of Veterans Affairs or the United States Government.Of 1,313,402 individuals tested for SARS-CoV-2 in the VA between February 2020 and August 2021, there were 144,597 (11.0%) who tested positive for SARS-CoV-2 , Hispanic , American Indian/Alaska Native , or Native Hawaiian/Pacific Islander  had elevated odds of testing positive, with no evidence of disparities among people of Mixed race  compared to White individuals over the entire study period. Asian individuals had lower odds of testing positive compared to White individuals  over the entire study period. However, there was substantial variation over time. Disparities for testing positive decreased for all racial and ethnic minorities over the study period  and the South  compared with the Northeast 1.58, 1.41–1.76) and the West   to taking sick leave that would help reduce the spread of SARS-CoV-218.Our findings on disparities for testing positive among Black and Hispanic individuals in the first months of the pandemic have been demonstrated previously17. An additional hypothesis is that SARS-CoV-2 cases among Asian individuals might be underreported to VA as Asian groups may experience differential barriers to care and have a lower likelihood of utilizing VA services, including getting tested at VA, than White individuals19.Another novel finding was the identification of increased odds of testing SARS-CoV-2 positive among Asian individuals in the first wave of the pandemic, which was obscured in the time-pooled model. We also found that Asian individuals had a lower odds of testing positive than White individuals between March and August 2021. One hypothesis for the reduced odds of testing positive among Asian groups is the higher rate of vaccination among Asian individuals compared with White individuals which may account for their reduced odds of testing positive for SARS-CoV-2 in this analysis20. Of note, access to free or subsidized care at VA has reduced the impact of negative social determinants of health on SARS-CoV-2 outcomes as prior reports found no racial or ethnic disparities in mortality among patients who tested positive for SARS-CoV-2 in the VA4.Our findings of racial and ethnic disparities for testing positive for SARS-CoV-2 provide important insight to help tailor strategies to contain and prevent further outbreaks in the United States. Early in the pandemic, tailored interventions to groups with higher risks may have been most effective. Now that the epidemic has generalized from large metropolitan centers with very high incidence to a more consistent rate of incidence across the country, racial and ethnic groups may be affected more equally suggesting that widescale prevention interventions for all persons may be most effective. However, the persistent disparities among Hispanic groups suggest that focused assessment and data-informed interventions in partnership with affected communities remains important in the effort to curtail infection hotspots, such as those due to a SARS-CoV-2 resurgence from the Delta and Omicron variants. SARS-CoV-2 is impacting all communities and is now much less concentrated in specific vulnerable groups compared to early in the pandemic. This does not imply that the overall cumulative burden of COVID-19 may be equal, as marginalized populations such as persons of color experienced substantial excess rates earlier in the epidemic and may experience excess extended effects or more severe outcomes from infection5. In brief, we only examined tests administered in VA; therefore, our results may not be representative of all Veterans tested for SARS-CoV-2. Second, although this population was primarily male, it included over 140,000 women. Third, as is the case with most electronic health record data sources, we did not have the necessary information to account for all social determinants of health  in our analysis, which are critical to understanding and preventing health inequities, particularly in infectious disease outbreaks. Fourth, SARS-CoV-2 test availability varied by local case load, facility supply, and local policy, particularly in the first wave when non-Hispanic Black and Hispanic patients were more likely to receive a SARS-CoV-2 test than non-Hispanic White patients12. However, after the first wave, testing was similar among all race and ethnic groups and test availability was unlikely to influence our results. Finally, while misclassified or missing data on race and ethnicity are often a concern, in this cohort, race and ethnicity were self-reported and there was a very small percentage with missing data. Focused research is needed to evaluate the association between social determinants of health and disparities seen during the COVID-19 pandemic as they may operate as confounders or may be on the causal pathway.The VA electronic health record database offers the single largest nationwide data resource available in the United States with the required information on system-wide testing and detailed medical histories to examine racial and ethnic disparities. Our analysis identified time and regional variation in racial and ethnic disparities in SARS-CoV-2 positivity over the first 18 months of the pandemic independent of underlying health status and other key factors in a large, nationwide cohort. However, our analysis should be interpreted with some limitations, including those we previously described in detail


Blood pressure targets for oldest-old people have been long debated due to the concern that more stringent targets are associated with increased mortality. We aimed to investigate the association between changes of late-life systolic blood pressure (SBP), mean SBP and SBP variability (SBPV), and all-cause mortality in oldest-old.Based on the community-based Chinese Longitudinal Healthy Longevity Survey with follow-up conducted in the 3-year interval, we assembled a retrospective cohort of 6639 participants ≥ 80 years with available blood pressure measurements at baseline and second wave. The primary exposures were mean SBP and SBPV  measured between baseline and second wave. The primary outcome was all-cause mortality assessed from the second wave.During 21443.1 person-years of follow-up, 4622 death was recorded. U-shaped associations of mortality with mean SBP and SBPV were identified; the value of 137 mmHg and 4.0 %/year conferred the minimum mortality risk, respectively. The associations of a larger SBPV with an increased mortality risk were observed for both rises and large falls in SBP. The hazard ratio was 1.11  with large falls in SBPV and 1.08  with large rises in SBPV.U-shaped associations between late-life SBP and SBPV and all-cause mortality were found. Our study suggests that a stable SBP level in the middle range is related to lower mortality risk in the oldest-old.The online version contains supplementary material available at 10.1186/s12877-021-02492-4. Hypertension is the leading determinant of major cardiovascular disease events and mortality, affecting over half of elderly people worldwide . Its manBesides, blood pressure variability was identified as a potential risk factor for adverse outcomes, such as arterial remodeling , macro- Using data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS), we aimed to investigate the association between both mean SBP and SBPV and all-cause mortality in Chinese oldest-old.This study is embedded in the CLHLS, which is a national cohort focusing on older Chinese people and is the largest cohort of centenarians in the world. A detailed study design of CLHLS has been published elsewhere . By usine-Fig. 1.The baseline survey of the current study was conducted in 2005, with follow-up waves conducted in 2008, 2011, 2014, and 2018. A further extension of the cohort was initiated in the 2008 and 2011 waves following the same study protocol. An overview of the study population is shown in e-Fig. 2, the primary exposures were mean SBP and SBPV, measured between baseline and second wave. The primary outcome was all-cause mortality, identified from the second wave.As shown in Mean SBP was assessed by calculating the updated arithmetic mean of SBP in the consecutive two waves from 2005 onwards ((Second wave + Baseline)/2). Within-individual SBPV between two sequential waves was defined as the difference in SBP between two waves divided by the mean ((Second wave-Baseline)/mean). To account for slightly different visit intervals, this measurement was further scaled to the average variation per year, assuming a constant rate of variation between the two waves .Information on covariates was collected at baseline using a structured questionnaire, including sociodemographic characteristics (body mass index (BMI), educational level, economic income (high vs. medium/low)), lifestyle habits and medical history. Smoking status was defined as current, past, or never smoker. Alcohol consumption (current vs. former/never) was assessed based on the question “Do you currently drink alcohol?”. Visual status was defined as “good” or “poor” according to whether participants could identify the break in the image of a circle held before them. Cognitive function was measured by the Chinese version of Mini-Mental State Examination (MMSE) and we defined cognitive impairment based on both MMSE score and education level: <18 for those without formal education, < 21 for those with 1–6 years of education, < 25 for those with more than 6 years of education . Restric2); (2) participants having trouble standing up from a chair without the assistance of arms; and (3) a positive response to the question “how many times suffering from serious illness in the past two years”. We categorized frailty status into: frail (two or three components), pre-frail (one component), and robust (no component).Frailty was assessed by the adjusted osteoporotic fracture index , 18, whiPrimary analyses. Our analysis focused on the association between changes in SBP (mean SBP and SBPV), assessed over two sequential study waves, and all-cause mortality, among oldest-old. Person-time accumulated from the second wave (first assessment of mean SBP and SBPV) until the date of death, date of loss to follow-up, or the end date of follow-up (the latest follow-up visits for the CLHLS conducted in 2018), whichever came first.We first investigated the associations between continuous mean SBP and SBPV and all-cause mortality using Cox proportional hazards models with penalized splines, which examine the potential non-linear or irregular shape of the hazard functions. Other covariates, such as BMI, could also exert a non-linear effect here. Following the suggested procedure , we obtaAll Cox models were adjusted for baseline covariates, including age, sex, BMI, educational background, economic income, smoking status, alcohol consumption, visual status, cognitive impairment, restriction in activities of daily living, comorbidity, and cohort. The proportional hazard assumption was assessed by visual inspection of the scaled Schoenfeld residuals plot. Single imputation with the expectation-maximization method was used to deal with missing covariates (percentage of missing covariates: 10.4 %).Additional analyses. To identify potential effect modification, we stratified the analyses by self-reported doctor-diagnosed hypertension and frailty status at baseline. Interaction was formally tested on a multiplicative scale by adding a product term to the model.Sensitivity analyses. Given the previously reported terminal decline in SBP at the end-of-life [-of-life , we checOf the 6639 participants included , 4018 (61 %) were women, and the mean (SD) age was 90.7 (7.0) years. Table and Fig. The results of the Cox proportional hazards model with penalized splines suggested a U-shaped association between mean SBP and all-cause mortality Fig. . Over 2.2Although no significant interaction was found during additional analyses, a stronger association between mean SBP and mortality was noted among those who had self-reported hypertension or with pre-frailty status Fig. . Among p). The HR was 1.11  with large falls in SBPV and 1.08  with large rises in SBPV.Cox proportional hazards models with penalized splines shown a U-shaped association between SBPV and all-cause mortality; the lowest mortality risk was found among participants with SBPV of 4.0 %/year Fig. . The ass2We found no significant interaction term during additional analyses. After further stratifying participants by hypertension or frailty status, similar trends were found in each subgroup, while the magnitude of association with SBPV was somewhat larger among participants with self-reported hypertension Fig. .e-Fig. 3). These association estimates appeared to have wider CIs after different lag periods were considered but still suggested U-shaped. Results based on data from complete case also did not affect the main finding.Findings were consistent in the sensitivity analyses (see Fig. Based on a large-scale community-based cohort, our results indicated U-shaped associations between late-life SBP and SBPV and risk of all-cause mortality, with mean SBP of 137 mmHg, SBPV of 4.0 %/year, related to the lowest mortality risk. The associations of a larger SBP variation with an increased mortality risk were observed for both rises and large falls in SBP. Consistent with the 2019 Chinese guideline which recommends that the threshold for antihypertensive therapy is SBP ≥ 150 mmHg for patients older than 80 years, our findings further added this by suggesting that keeping a stable blood pressure level is also important for hypertension management among the oldest-old.The observed association between lower SBP and increased risk of all-cause mortality among oldest-old is in line with previous concern regarding the intensity of antihypertensive treatment in elderly population . AlthougA U-shaped or J-shaped relationship between targeted SBP and risk of morbidity and mortality has long been suggested . This hyVariability in blood pressure has been recognized as a potential risk factor, whereas no standard formula available for its calculation. Multiple measures, such as standard deviation, coefficient of variation, and root successive variance, hamper the understanding of blood pressure variability . In our Blood pressure variability, especially long-term, is associated with cardiovascular and mortality outcomes and shows additional prognostic value independent of mean blood pressure . AlthougOur study has some unique and useful features. The most important feature is that based on the large-scale population-based cohort, we thoroughly investigated associations of changes of late-life SBP with risk of mortality among oldest-old, which filled in a certain knowledge gap about control of late-life blood pressure in older population . On the Our findings suggest that SBP variability might be an important factor in understanding mortality risk in oldest-old, affirming the need to develop better strategies for blood pressure management in this population.Additional file 1.