Since the beginning of the COVID-19 pandemic, SARS-CoV-2 has caused a global burden for health care systems due to high morbidity and mortality rates, leading to caseloads that episodically surpass hospital resources. Due to different disease manifestations, the triage of patients at high risk for a poor outcome continues to be a major challenge for clinicians. The AIFELL score was developed as a simple decision instrument for emergency rooms to distinguish COVID-19 patients in severe disease stages from less severe COVID-19 and non-COVID-19 cases. In the present study, we aimed to evaluate the AIFELL score as a prediction tool for clinical deterioration and disease severity in hospitalized COVID-19 patients. During the second wave of the COVID-19 pandemic in Switzerland, we analyzed consecutively hospitalized patients at the Triemli Hospital Zurich from the end of November 2020 until mid-February 2021. Statistical analyses were performed for group comparisons and to evaluate significance. AIFELL scores of patients developing severe COVID-19 stages IIb and III during hospitalization were significantly higher upon admission compared to those patients not surpassing stages I and IIa. Group comparisons indicated significantly different AIFELL scores between each stage. In conclusion, the AIFELL score at admission was useful to predict the disease severity and progression in hospitalized COVID-19 patients. Two years after coronavirus disease 2019 (COVID-19) became a worldwide pandemic, the causative agent named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is still a considerable burden for the health care systems of many countries, leading to restrictions of personal movement and business, as well as travel, and sometimes even leading to lockdowns. Despite available vaccines and associated government-funded vaccination campaigns, the spread of SARS-CoV-2 has not been stopped yet and has resulted in recurrent waves of increasing infection rates, which have put a considerable strain on hospitals and, in particular, their intensive care units (ICU). Different mutations of the SARS-CoV-2 spike protein occurred, leading to the emergence of new strains, including the Alpha, Beta, Gamma, Delta and Omicron variants , higher Vaccination campaigns, though initially successful in countries such as Israel  or GreatCurrently, the main concern is severe cases causing the need for intensive care treatment. Although different testing approaches with sufficient capabilities, including quantitative polymerase chain reaction (qPCR) and rapid antigen tests, are currently widely available in industrialized countries, it is still a challenge to identify patients at high risk for deterioration or severe disease course in emergency rooms, hospital wards or general practice offices in order to give them the necessary attention. Clinical presentations of COVID-19, especially among outpatients, can vary greatly  and, ideThe AIFELL score was developed and used as a simple frontline triage tool for patients with suspected COVID-19 during the first wave of the pandemic to identify symptomatic patients infected with SARS-CoV-2 from patients presenting with unspecific general or respiratory symptoms in the ER or general practice setting. It includes the patient history components of altered smell or taste and fever, radiologically documented lung infiltrates and the laboratory parameters of C-reactive protein (CRP), elevated lactate dehydrogenase (LDH) and lymphocytopenia (absolute count) .During subsequent increases in infection rates after the first wave, other clinical applications of the simple score were taken into consideration. Since patients in more severe COVID-19 stages II and III  could beThe score was retrospectively applied to consecutively hospitalized SARS-CoV-2-positive patients from 22 November 2020 until 17 February 2021 that were treated at the City Hospital Zurich Triemli  during the second wave of the COVID-19 pandemic in Switzerland. The Department of Internal Medicine of the Triemli Hospital agreed to participate as an external partner to apply the AIFELL score in a different hospital location than where it was initially developed. Patients were either admitted through the emergency rooms (ER) from an outpatient setting or directly to the medical ward or the ICU from other hospitals in the area around Zurich (Switzerland). SARS-CoV-2 swabs for qPCR analyses were either performed in the ER of the Triemli Hospital or by the hospitals and outpatient clinics who referred the patients for further treatment. Inclusion criteria were a positive SARS-CoV-2 swab result analyzed using qPCR at admission, a blood sample available upon admission, including at least two of the three considered laboratory parameters , chest imaging (X-ray or computed tomography of the thorax) and documented body temperature. Declaration of consent for the use of health-related data and samples for research purposes was signed by every patient. Exclusion criteria were patients with fewer than two laboratory parameters available and missing chest imaging or documentation of body temperature. In addition, patients were excluded if COVID-19 was not the main reason for hospitalization in order to prevent bias regarding laboratory values. AIFELL scores were calculated for each patient at hospital admission, as described in our previous publication . Due to t-test, a multiple linear regression analysis and an ANOVA with multiple measurements were calculated for group comparisons and to determine statistical significance. A multiple linear regression was performed with the AIFELL score at hospital admission to evaluate its predictive capability regarding the maximum COVID-19 stage developed during hospitalization. Next, the course of the illness was analyzed by a two-factorial ANOVA for repeated measurements with within-factor time (pre- and post-measurement) and between-factor group (AIFELL score). In order to account for the influence of sphericity, a Greenhouse–Geisser/Bonferroni correction for repeated testing was implemented. For data collection and statistical analyses, Microsoft Excel 2016 and SPSS 24 were used. Results are given as mean values ± standard deviation (SD) unless indicated otherwise.For baseline characteristics, a test for normal distribution and outliers, as well as a z-standardization, were performed on the original raw data. Student’s n = 154, 64% male) was 69 years ± 15.7 (range from 23 to 95 years). Baseline demographic, clinical and laboratory characteristics are shown in The mean age of the included consecutively hospitalized COVID-19 patients (t-tests showed highly significant results (p < 0.01).The average AIFELL score at hospital admission, regardless of the disease stage, was 3.7 ± 0.96. A stage-based comparison of AIFELL scores revealed significant differences between each stage . A groupn = 125) had significantly higher AIFELL scores at admission (3.98 ± 0.75) compared to patients who maximally developed COVID-19 stages I and IIa , as shown in AIFELL scores ≥3 points at admission were associated with development to more severe disease stages (II and III) during the hospital stay, needing a more intensive therapeutic approach . Patients needing oxygen supplementation due to hypoxia during hospitalization .In those patients who deteriorated during hospitalization (n = 28) from stage IIa/IIb to III or stage IIa to IIb, the average AIFELL score at admission was 3.96 ± 0.83. The average AIFELL score of patients who did not deteriorate during hospitalization (n = 126) was 3.68 ± 0.99, regardless of their disease stage. The mean AIFELL score at admission of patients with mild COVID-19 (stage I), who also did not deteriorate, was 2.08 ± 0.86, and is therefore significantly lower than the score obtained of patients reaching severity stages II and III  = 21.459,  = 35.2% .p = 0.005). Furthermore, there was a significant group effect  = 19.867, p ≤ 0.001), so the AIFELL score predicted different courses of symptom development  over time during hospitalization  = 8.101, lization . TherefoIn the presented study, the AIFELL score was used as a prediction instrument for disease severity and progression to evaluate its performance in the hospital setting in patients with recently diagnosed COVID-19. Our results indicate that an increased AIFELL score at admission is able to predict the ultimate disease severity or disease progression in patients hospitalized due to COVID-19.Several scoring systems for COVID-19 with different purposes  and components have been developed during the pandemic ,22,23,24www.aifell.net was created to provide a comprehensive overview of all components, and features a basic online calculator, as well as a printable calculation form. Due to specified limits and the objective nature of the components, there is less room for inter-rater variability.The AIFELL score, in contrast, is a simple tool consisting of only basic, low-cost laboratory parameters, patient history and imaging results. It was initially developed based on data from a mixed, representative cohort of COVID-19 patients and studied in consecutive ER patients with suspected COVID-19. It is easy to calculate and does not necessarily require any additional tools, making it practical for resource-limited settings, such as smaller hospitals or general practitioner offices. Nevertheless, the website Based on the results of this study, we suggest the calculation of the AIFELL score at hospital admission for every patient with respiratory or other symptoms possibly associated with COVID-19, so it may serve as a triage tool and, in the case of a positive SARS-CoV-2 swab, it may additionally serve as a predictor for disease progression. Another reasonable time point for the calculation of the AIFELL score is when disease deterioration has occurred to estimate the probable outcome. Since we demonstrated that the AIFELL score at admission is significantly higher in patients developing more severe COVID-19 stages during hospitalization, it provides useful information for clinical decision making, serving as a red flag for disease deterioration. From a clinical perspective, there is a relevant difference between the COVID-19 stages IIa and IIb, or even III, because hypoxia requires immediate treatment to avoid life-threatening complications. Furthermore, in severe COVID-19, proinflammatory cascades are activated, which eventually may lead to endothelial dysfunction and hypercoagulability, resulting in life-threatening complications, such as myocardial infarction  or acuteComponents of the AIFELL score that are associated with severe stages of COVID-19 in the scientific literature are consolidative infiltrates as a sign of pulmonary involvement , fever  and elevDysosmia and dysgeusia tend to be associated with a mild or moderate disease course , but plaOther inflammation biomarkers, such as the neutrophil count, the neutrophil-to-lymphocyte ratio (NLR) and monocyte-to-lymphocyte ratio (MLR), as well as the platelet count and lymphocyte-to-platelet ratio (LPR), which are usually available or can be calculated after a differential blood count has been performed, could be used to expand or complement the AIFELL score. The importance of detailed analyses of these simple blood parameters has already been highlighted previously . FurtherBiomarkers, such as interleukin-(IL)-1, IL-6, cardiac troponin I, d-dimers, ferritin or procalcitonin, were originally not included as components of the AIFELL score since they are not measured routinely, are usually more expensive and are generally not easily available.Our study has several limitations: During the second wave, hospitalized patients tended to be more severely ill compared to the first wave. Therefore, less severe COVID-19 stages (I and IIa) are underrepresented in our cohort. It was unknown which strains of SARS-CoV-2 were causing the infection at the time of the study, since sequencing was not routinely performed during the second wave. Other limitations of the study are its retrospective nature and the single-center setting. Due to the retrospective nature of the study, not all data sets were complete. Information about altered smell or taste was missing in many cases, since it was not routinely asked when medical history was obtained. Missing information was rated as negative for the respective component of the score. This may have led to lower average AIFELL scores and explains the low number of patients with the highest AIFELL score of 6 points. Obviously, further research is necessary to validate the AIFELL score as a prediction tool, especially the prospective use of the score in a multicenter setting with a larger cohort of patients. The strengths of the current study are the external application of the score outside of the original hospital setting  and a mixed cohort of consecutive hospitalized patients during a predefined time frame.COVID-19 still dominates the medical, social and political discourse, resulting in efforts to develop mechanisms for stopping future waves of the pandemic . During Currently available or upcoming IgG1-based monoclonal antibody treatments of COVID-19, such as casirivimab/imdevimab (RegN-Cov2) , sotroviIn future, after the end of the current COVID-19 pandemic, SARS-CoV-2 may become a relevant respiratory virus of seasonal nature, similar to influenza, with a potential to cause severe complications. Therefore, clinical instruments, such as the AIFELL score, that facilitate triage and enable the prediction of severe stages may remain useful, even after the pandemic situation has largely subsided.The AIFELL score is a simple clinical instrument to predict disease severity or disease progression in patients hospitalized due to COVID-19. This finding supplements its capability to be used as a triage tool in the emergency room setting.


PC-AKI occurred in 22 (5.4%) patients who received CTA only and 18 (3.1%) patients who received CTA and additional CTP . In 31/40 (77.5%) patients who suffered PC-AKI, a non-CM-related cause of renal function impairment was identified. Stroke etiology (hemorrhagic vs. ischemic) and indicators of prior kidney disease were independent predictors of PC-AKI. Conclusions: Additional administration of CM for perfusion imaging in acute stroke did not show a relevant influence on the occurrence of PC-AKI. Patients with intracranial hemorrhage and/or prior kidney disease are at particular risk of developing AKI.Background: Our aim was to investigate the relationship between additional iodinated contrast medium (CM) application for acute stroke imaging and Post-Contrast Acute Kidney Injury (PC-AKI). Methods: We performed a retrospective analysis of consecutive patients with acute stroke who received a CT angiogram (CTA) with or without additional CT perfusion (CTP) at admission between 2017 and 2020. The primary endpoint was the incidence of PC-AKI. Potential causes of renal function impairment were recorded and logistic regression was performed to determine predictors of PC-AKI. Results: Of 3134 cases screened,  Neuroradiological imaging is a key component to provide optimal clinical care for patients with acute stroke who are at high risk of disability or death. In the context of acute stroke assessment, a comprehensive CT protocol includes a combination of a non-contrast CT (NCCT) scan of the brain and a CT angiogram (CTA) of the extracranial and intracranial neck vessels to detect large vessel occlusion. Moreover, CT perfusion (CTP) is widely used in many tertiary neurovascular centers to identify patients suitable for reperfusion therapies in an extended time window, including endovascular treatment ,2 as welWe therefore aimed to examine the incidence, potential causes, and predictors of PC-AKI after acute stroke imaging in a large cohort of consecutive stroke patients in a tertiary care center in Germany.This was a retrospective study, including data of all patients with acute stroke admitted to the comprehensive stroke center of the University Hospital Essen between January 2017 and March 2020 meeting the following inclusion criteria: acute ischemic or hemorrhagic stroke, age above 18 years, direct admission to our hospital , CTA and/or CTP study on the day of admission, and available creatinine tests at admission as well as during the following three days. Patients with prior chronic kidney disease requiring dialysis were excluded. All patients with acute ischemic stroke (AIS) and intracranial hemorrhage (ICH) admitted to our stroke center are prospectively collected in a local stroke registry database. The core dataset is entered by the attending physician on admission, completed during hospitalization, and validated by the attending senior stroke neurologist. Structured data of instrument-based examinations are entered by study personnel, and laboratory examinations are exported from the local archiving system. Finally, all data are validated by a senior stroke neurologist (BF). For patients who fulfilled the criteria of PC-AKI, potential causes of AKI other than CM administration were determined by reviewing hospital records.This study was approved by the local ethics committee, and consent requirement was waived.A standardized Stroke-CT protocol for suspected stroke was used, including initial NCCT, CTA from aortic arch to vertex, and optional CTP of the brain performed as implemented by the vendor. All scans were performed at one of three commercially available, modern multi-slice CT scanners: one single-source 128-slice SOMATOM Definition AS+, one dual-source 128-slice SOMATOM Definition Flash, and one dual-source 192-slice SOMATOM Force . For CTA, 70 mL of non-ionic iodinated contrast agent  was injected, and for CTP, an additional 50 mL of non-ionic iodinated contrast agent  was injected. For mechanical thrombectomies, 100 mL of non-ionic iodinated contrast agent  was initially available, followed by a refill of 50 mL if necessary.At our institution, stroke patients receive a total of 1000 mL intravenous volume supplementation with normal isotonic (0.9%) saline at high infusion rate (300 mL/h) at admission, followed by additional 1000 mL at 40 mL/h.The primary endpoint was Post-Contrast Acute Kidney Injury (PC-AKI), defined as an increase in serum creatinine of ≥0.3 mg/dL, or of ≥1.5–1.9 times baseline (KDIGO definition of AKI) in the 48–72 h following CM administration. We hereby used the definition of the updated ESUR Contrast Medium Safety Committee guidelines .We performed unadjusted and adjusted logistic regression to compare the occurrence of endpoints in stroke patients who received CTA on admission (CTA-only group) with those who received CTA and additional CTP (CTA-plus group). Additionally, in our primary analysis, the CTA-plus group included patients who received CM due to multiple CTA, CTP, or DSA for endovascular therapy (EVT). Results are presented as odds ratio (OR) and 95% confidence interval (95% CI), both for the effect of the CTA-group and the effect of different covariates.p < 0.05 was considered to be the minimal level of statistical significance. The data that support the findings of this study are either shown in the manuscript or are available in the Based on the nature and distribution of the data, descriptive statistics are presented as count and percent, median with interquartile range, or mean with standard deviation. Chi-square tests were used for categorical variables and, based on the distribution of data, t-tests, or Mann–Whitney–U tests were used for continuous variables. Statistical analyses were performed with SPSS  and SAS , and a 2-sided A total of 3114 patients with acute ischemic or hemorrhagic stroke were registered between January 2017 and March 2020. A total of 989 patients met the predefined inclusion and exclusion criteria of the presented analysis , while 4An increase in creatinine corresponding to the criteria of PC-AKI occurred in 22 (5.4%) patients in the CTA-only group and 18 (3.1%) in the CTA-plus group . CIN occurred in 27 (6.7%) patients in the CTA-only group and 22 (3.8%) in the CTA-plus group . As no relevant differences between incidences of PC-AKI and CIN could be observed in further analysis, results on the latter are only reported in the n = 5, CTA-plus group n = 4). None of them required hemodialysis.In 31 of 40 cases that fulfilled the criteria of PC-AKI, common causes of AKI other than the administration of CM on admission were found to be a probable reason for creatinine increase: eight had severe infection or sepsis, another eight patients required intensive care with high amounts of catecholamine, six suffered from rhabdomyolysis and exsiccosis after lying on the floor prior to admission, three suffered from cardiogenic shock, two had cardiac decompensation with severe hypotonia, one received intravenous acyclovir despite chronic kidney failure, and another patient had post-renal AKI due to an obstructing tumor mass. The remaining nine stroke patients with PC-AKI had no other obvious reason for an increase in creatinine . In patients with AIS, significant effects were observed for chronic kidney disease, elevated creatinine levels, reduced glomerular filtration rate, high NIHSS, and altered level of consciousness at admission when added as single covariates to the analysis of the CTA-group effect . SimilarWe investigated PC-AKI in a large cohort of acute stroke patients, and our study reveals the following major findings: first, we did not observe any relationship between imaging modality at admission  and incidence of PC-AKI. Second, non-CM-related causes of AKI were present in more than three-quarters of patients who fulfilled the criteria of PC-AKI following acute stroke imaging. Third, stroke etiology and indicators of prior kidney disease were major risk factors for the development of PC-AKI.The overall incidence of PC-AKI was 4%, and cases were evenly distributed between imaging modality groups. The amount of CM administered was not a predicting variable for the incidence of PC-AKI, and none of the patients required hemodialysis. These findings are in line with previous studies that did not find an association between renal function impairment and CM-administration in acute stroke patients ,12,13,14We found that up to 77.5% of patients with PC-AKI had a non-CM-related primary cause of renal function impairment in our study. This finding supports the hypothesis that the association of CM administration and AKI is coincident rather than causal and has probably been overestimated in the past .Stroke etiology, however, was a major risk factor for PC-AKI in our cohort of acute stroke patients. While the rate of PC-AKI in patients with AIS was 3.0%, it was up to 10.9% in patients with ICH. These findings are in line with a meta-analysis that reported a general prevalence of AKI in 12.9% (95% CI 10.3–15.5) of patients with AIS, and 19.0% (95% CI 8.3–29.7) in patients with ICH ,17.After excluding patients with ICH from the analysis in order to eliminate the masking effect and further investigate predictors of PC-AKI, the influence of other variables became apparent: indicators of prior kidney injury, as well as severity of the stroke index, were associated with the occurrence of PC-AKI after acute stroke imaging. We conducted a nonrandomized, retrospective analysis, and our study has limitations. Some potential factors not documented in our local stroke registry database, including prior or concomitant medication that may have contributed to PC-AKI, could have been missed. The amount of CM used for DSA was only documented in 50 mL steps. Despite our investigating a large cohort of acute stroke patients, the overall number of cases with a diagnosis of PC-AKI was relatively small. Furthermore, although a primary cause of renal impairment other than CM administration seemed evident from a clinical point of view in most patients with PC-AKI, our study cannot assess whether and to what extent CM administration may have been a precipitating factor for AKI in these patients.Nonetheless, our analysis provides real-world data on PC-AKI after acute stroke imaging from a tertiary-care center with standardized stroke imaging and hydration protocols. In the absence of prospectively conducted randomized controlled trials, these findings give further insight into the controversial relationship between CM administration and AKI in acute stroke patients. In conclusion, the influence of additional CM administration for acute stroke imaging on the development of PC-AKI is minimal and most likely an innocent bystander. Stroke patients in general, and those with hemorrhagic stroke or prior kidney damage in particular, are at relevant risk of developing AKI. Hydration protocols and a high vigilance for the occurrence of AKI are of great importance in acute stroke patients.


Was the 2017 “Muslim ban” executive order associated with changes in health care utilization by people born in Muslim-majority countries living in Minneapolis-St. Paul, Minnesota?This cohort study of 252 594 patients found that after the executive order was issued, there was an increase in missed primary care appointments and increased emergency department visits among people from Muslim-majority countries living in Minneapolis-St. Paul.Changes in health care utilization among people from Muslim-majority countries after the Muslim ban may reflect changes in population health influenced by federal immigration policy. This cohort study investigates the association between health care utilization and the so-called Muslim ban executive order for people born in Muslim-majority countries and living in Minneapolis-St. Paul, Minnesota. The health effects of restrictive immigration and refugee policies targeting individuals from Muslim-majority countries are largely unknown.To analyze whether President Trump’s 2017 executive order 13769, “Protecting the Nation from Foreign Terrorist Entry into the United States” (known as the “Muslim ban” executive order) was associated with changes in health care utilization by people born in targeted nations living in the US.This retrospective cohort study included adult patients treated at Minneapolis-St. Paul HealthPartners primary care clinics or emergency departments (EDs) between January 1, 2016, and December 31, 2017. Patients were categorized as (1) born in Muslim ban–targeted nations, (2) born in Muslim-majority nations not listed in the executive order, or (3) non–Latinx and born in the US. Data were analyzed from October 1, 2019, to May 12, 2021.Executive order 13769, “Protecting the Nation from Foreign Terrorist Entry into the United States.”Primary outcomes included the number of (1) primary care clinic visits, (2) missed primary care appointments, (3) primary care stress-responsive diagnoses, (4) ED visits, and (5) ED stress-responsive diagnoses. Visit trends were evaluated before and after the Muslim ban issuance using linear regression, and differences between the study groups after the executive order issuance were evaluated using difference-in-difference analyses.P = .02) and approximately 232 additional ED visits by individuals from Muslim ban–targeted nations .A total of 252 594 patients were included in the analysis: 5667 in group 1 , 1254 in group 2 , and 245 673 in group 3 . Group 1 was predominantly born in Somalia (5231 of 5667 [92.3%]) and insured by Medicare or Medicaid (4428 [78.1%]). Before the Muslim ban, primary care visits and stress-responsive diagnoses were increasing for individuals from Muslim-majority nations (groups 1 and 2). In the year after the ban, there were approximately 101 additional missed primary care appointments among people from Muslim-majority countries not named in the ban (point estimate [SE], 6.73 [2.90]; Results of this cohort study suggest that after issuance of the Muslim ban executive order, missed primary care appointments and ED visits increased among people from Muslim-majority countries living in Minneapolis-St. Paul. The ban underwent multiple legal challenges but was upheld by the US Supreme Court in 2018.3 On January 20, 2021, President Biden repealed the Muslim ban by executive order.4The 2016 US presidential election was marked by anti-Muslim and anti-immigrant rhetoric, and the subsequent Trump administration introduced multiple restrictive immigration policies targeting individuals from Muslim-majority and Latin American countries.5 Over the past 2 decades, there has been an increase in hate crimes6 and social hostility8 directed toward US Muslims—experiences that negatively affect health. Following the September 11 attacks, Arab Americans, including Muslim Arab Americans, experienced increased rates of anxiety, depression, and low birth weights.12 Islamophobia and restrictive entry policies have been associated with worse health outcomes among migrants, placing Muslim American immigrants and refugees at increased vulnerability as they are additionally targeted by immigration and refugee policy changes, above and beyond discrimination faced by US Muslims more broadly.15 Recent research has shown increased incidence of preterm birth among people from Muslim-majority countries after the Muslim ban was issued.16 However, it is largely unknown and can be difficult to measure how health and health care utilization in Muslim American immigrant and refugee communities changes in response to shifting sociopolitical climates due to several methodological challenges. National health and health care surveys, as well as administrative data sets, do not routinely capture religious affiliation, and naming and country-of-origin algorithms are not precise enough to distinguish people who are Muslim.18Policies like the Muslim ban exacerbate heightened levels of discrimination, hostility, and “othering” that US Muslims experience.19To describe changes in health care utilization of people from Muslim-majority countries after enactment of the Muslim ban, we examined changes in primary care and emergency department (ED) utilization by people from Muslim ban–targeted nations living in the Minneapolis-St. Paul, Minnesota, metropolitan area, which is home to the largest Somali Muslim community in the US.STROBE) reporting guideline.20We conducted a retrospective, cohort study documenting trends in primary care and ED utilization, missed scheduled appointments, and stress-responsive conditions among individuals from Muslim ban–targeted nations living in Minneapolis-St. Paul 1 year before  to 1 year after  issuance of the ban. For outcomes with similar visit and diagnosis trends before the ban was issued, we used a difference-in-difference analysis to compare differences in utilization trends between people born in Muslim ban–targeted nations and non–Latinx US-born citizens. Supplementary analyses compared trends among people born in a Muslim-majority nation not listed in the Muslim ban to non–Latinx US-born citizens. Patient demographic, visit, and diagnosis data were extracted from the HealthPartners electronic health record (EHR) by a HealthPartners data analyst. HealthPartners and the institutional review board of Yale University approved this study and waived the need for patient informed consent. All records were deidentified and assigned a unique study identification number before secure filing from HealthPartners to the Yale analytic team. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology  were born in Somalia.21The Minneapolis-St. Paul metropolitan area has 3.63 million residents and the largest Somali Muslim population in the US. In 2017, approximately 252 000 area residents (59.3%) were White non-Latinx individuals, 81 900 (19.3%) were Black non-Latinx individuals, 26 800 (6.3%) were Asian non-Latinx individuals, and 40 900 (9.6%) were Latinx individuals.23We analyzed EHR data from HealthPartners, one of Minneapolis-St. Paul’s largest health care and insurance organizations, serving over 1.2 million patients at 55 primary care centers, 22 acute care centers, and 8 hospitals. Although religion is not recorded in the EHR, the HealthPartners EHR is unique in that it includes nation of origin information. This allowed us to characterize patients receiving care from January 1, 2016, to December 31, 2017, into 3 groups: (1) adults born in a country listed in the executive order (group 1) ; (2) aduWe hypothesized that after issuance of the Muslim ban, we would observe increased health care visits for stress-responsive diagnoses, increased primary care missed appointments, and increased ED visits. Primary outcomes included the number of (1) primary care visits, (2) missed primary care appointments, (3) primary care stress-responsive diagnoses, (4) ED visits, and (5) ED stress-responsive diagnoses.30 Diagnoses considered to be stress responsive were agreed upon by consensus , codes in 6 categories: mental health, sleep disorders, gastrointestinal symptoms, neurologic symptoms, food-related disorders, and pain syndromes.Primary care visits, missed appointments, and stress-responsive diagnoses were analyzed as counts per 1000 people. Kept and missed visit trends were examined overall, regardless of diagnosis, and stress-responsive diagnoses were analyzed separately. We identified stress-responsive diagnoses through literature review and opinion by study team members with expertise in Muslim, immigrant, and refugee health and who provide primary care to people from Muslim-majority countries living in Minneapolis-St.Paul.ICD-10 codes for acute coronary syndrome, assault, suicide attempt, and syncope  in linear regression models. For outcomes that followed approximately parallel trends before the ban was issued, we conducted difference-in-difference analyses.The primary difference-in-difference analyses estimated the change in outcomes between pre- and post-ban periods among individuals from Muslim ban–targeted nations above and beyond the change observed among non–Latinx US-born individuals. For the difference-in-difference analyses, we fit the linear regression model described in equation 1:Y is a count per person per 30-day time period. The designations Muslim ban targeted and Muslim ban targeted × post-Muslim ban represent being from a Muslim ban–targeted nation and being from a Muslim ban–targeted nation after the ban was issued, respectively. For each model, we estimated the average difference in differences (β2) over increasing time intervals centered on the ban issuance date, beginning with 30 days before and after the ban was issued and increasing by 30-day increments to 360 days before and after issuance.We compared the 360 days before and after the ban was issued, divided into 24 distinct, 30-day periods. Each outcome 34 Second, we used a generalized synthetic control method to reweight members of group 3 to produce a reference group with demographic characteristics and pre–Muslim ban outcomes more similar to those observed in group 1. Models were fit using the R package gsynth with parametric bootstrap standard errors.35 These robustness checks are reported in eTables 8-10 and in the eFigure in the P values were 2-sided, and P < .05 was considered significant. Data were analyzed from October 1, 2019, to May 12, 2021.Demographic details were not included in the primary analysis because they do not vary over time. However, it is important to account for demographic details, because there are important differences between study groups that could influence health care utilization. In order to account for different characteristics between study groups and assess robustness, we compared changes in health care utilization by individuals from Muslim ban–targeted nations to 2 subgroups of non–Latinx US-born individuals demographically similar to individuals in group 1 (Muslim ban-targeted): a matched comparison group and a synthetic control. First, we matched on age, sex, race/ethnicity, and insurance type  to reweight members of group 3  and identify a subset of group 3 with similar characteristics as group 1 (Muslim ban–targeted). We used the R package MatchIt, version 3.6.1, (R Core Team) to identify this reference group, then fit a weighted version of the model described in equation 1  in group 1, 1254 (0.5%) in group 2, and 245 673 (97.3%) in group 3 . People Similar trends between study groups were observed before Muslim ban issuance for missed primary care appointments, ED visits, and ED stress-responsive diagnoses. Mean rates of daily clinic visits and stress-responsive diagnoses were similar across all 3 groups in early 2016 . AlthougUS-born non-Latinx individuals had higher baseline ED utilization and ED stress-responsive diagnoses; however, trends before the Muslim ban were fairly similar for all 3 groups . For groFor the 3 outcomes with similar trends before the Muslim ban across study groups , we used difference-in-difference analyses to estimate the magnitude of changes after the ban was issued within groups 1 and 2 above and beyond the change seen in group 3  . SimilarP = .02), suggesting that this group missed approximately 101 additional primary care appointments beyond what they would have been expected to miss if following the trend of non-Latinx US-born individuals.We found a larger and statistically significant estimated additional increase in missed appointments for group 2 after the ban was issued (eTable 11 in the P = .03) suggests that approximately 232 additional ED visits were made by individuals from Muslim ban–targeted nations in the 360 days after the Muslim ban was issued beyond what would have been estimated if ED utilization had followed a trend similar to that seen in group 3. This outcome was especially pronounced in the first 30 to 60 days after the ban was issued  C.13Executive order 13769 was a major policy change restricting travel of immigrants and refugees from 7 Muslim-majority nations to the US. This cohort study found that after the Muslim ban was issued, there was an immediate and significant increase in ED visits among people from nations targeted by the ban. Primary care utilization and primary care visits for stress-responsive diagnoses were already increasing before the ban was issued, most notably after the 2016 presidential election. These findings may reflect elevated cumulative stress due to multiple restrictive policies and an increasingly hostile climate toward Muslim immigrants and refugees in the US and are consistent with other literature examining the health effects of restrictive entry policies in high-income countries.29 Factors not recorded in EHRs that may increase susceptibility to stress include prior negative interactions with the US immigration and/or refugee administration, changes in employment status, changes in comorbidities, and other immigration-related issues, such as awaiting family reunification and, for people who were refugees, prior time spent in a refugee camp (if any).37Estimated differences in ED utilization may seem small; however, they are average differences per 1000 people per 30-day period, and small per-person averages can result in substantial population health changes. All estimates may mask heterogeneity in health care utilization in a diverse population after the Muslim ban was issued, although the majority of people in group 1 were born in Somalia. Use of EHR data limits our ability to identify subgroups of people who may respond differently to stress. Individual-level factors, such as prior trauma, religion, acculturation, and a sense of belonging with one’s ethnic group, can influence coping.40 Ethnic enclaves may protect immigrants from discrimination and related negative health effects. One study conducted after the 2016 presidential election found that residence in a Mexican enclave attenuated the risk of low birth weight for mothers of Mexican origin.30 A similar effect was demonstrated in a replication of a study by Lauderdale et al.12 The initial study demonstrated lower birth weights among Arab Americans after September 11, 2001, but this effect was not observed in Detroit, which has a large Arab American community.12Potential changes in utilization after the Muslim ban may have been attenuated by factors specific to Minneapolis-St. Paul and to the Somali American community there, which may not be present in other cities and states. Social capital and ethnic enclaves are important protective factors that may mitigate the harms from federal policy changes such as the Muslim ban. Social capital, or the ability to secure benefits through social networks and social structures, is strong among Somali Americans in Minneapolis-St. Paul compared with other US cities, including having elected representatives to city council and the US House of Representatives; such representation may attenuate the negative mental health effects of discrimination.31 Although there are many differences in health care utilization by nation and regions of origin, overall, immigrants tend to have lower health care utilization compared with people born in the US, which may be influenced by multiple factors, including service accessibility, age, interpreter availability, acculturation, health-related beliefs, and comfort getting care within institutional medical establishments.32Research that aims to understand how immigration and refugee policy influences the health of Muslim immigrants and refugees is limited by the lack of available population-level data. Although increases in health care utilization may reflect increased community stress, this study did not directly measure stress or its association with health care utilization. It is important to consider that health care utilization may not be the most sensitive population-level outcome to use as a proxy measure for increased stress in immigrant communities.30 Furthermore, we were studying a small population; as a result, only relatively large changes were easily measured. This may have resulted in an inability to detect small but important changes and precluded nuanced analyses of utilization trends.There are several limitations to this study. First, evaluating population-level health changes among Muslim American immigrants and refugees after the Muslim ban was issued was challenging, as most EHR and health care data sources do not capture religious affiliation. As such, we used country of origin to estimate Muslim American immigrant and refugee health care utilization.Second, although we described trends in clinic utilization and stress-responsive diagnoses, we did not compare changes after the Muslim ban in these outcomes across groups, because trends diverged in the year before the ban was issued. Although we were examining changes in utilization around a distinct event, the Muslim ban was issued 7 days after President Trump’s inauguration, following a campaign characterized by anti-immigrant and anti-Muslim rhetoric, and it did not fully go into effect until June 2018. Therefore, estimated changes may have captured the cumulative effect of multiple events occurring around the time of the ban’s issuance and may not have reflected its full influence over time. Dedicated studies are needed to more fully evaluate the health effects of the 2016 election and the immigration and refugee policies of the Trump administration.Third, Muslim American immigrants and refugees living in and around Minneapolis-St. Paul are not a homogenous group, and health care access and utilization may vary by countries of origin, ethnicities, and socioeconomic factors. Somali Americans, who are often racialized as Black, experience both anti-Black racism and anti-Muslim sentiment differently from Arab Muslim American immigrants and refugees racialized as White people. The compounded effects of Islamophobia, xenophobia, and anti-Black racism experienced by Somali Americans may have influenced changes in health and the health care utilization trends observed in this study. These circumstances are particularly relevant in Minneapolis-St. Paul, where in the wake of the murder of George Floyd, both the State of Minnesota and the Department of Justice have launched investigations into racist, discriminatory behavior in the Minneapolis police force. Further work is needed to better understand how anti-Black racism in Minneapolis-St. Paul, among police and in general, may influence health and decisions to utilize health care among Muslim American immigrants and refugees overall and Somali Americans in particular.41; our study did not capture any spillover effects on nonimmigrant US Muslim populations who may still be subject to discrimination following anti-Muslim rhetoric and policy changes. This study also did not examine spillover effects onto immigrant groups not from Muslim-majority countries, such as Latinx immigrants. Future studies are needed to fully evaluate these effects.This study also did not account for second-generation immigrants or children of refugees who may experience social and familial stress due to the Muslim ban but would be included in the US-born comparison group, thereby reducing detected differences between study groups. Approximately one-quarter of US Muslims are born in the US with US-born parents Because the majority of our study sample was composed of people from Somalia and the analysis was focused in 1 metropolitan area, the study group and location were not representative of all groups targeted by the Muslim ban, thereby limiting the generalizability of our findings. In other locations with smaller refugee and/or immigrant communities and fewer social supports, there may be larger negative outcomes associated with restrictive immigration and refugee policies.The results of this cohort study suggest that after issuance of the Muslim ban, ED visits increased among people from targeted Muslim-majority nations. Further investigations are needed to elucidate the health effects of restrictive immigration policies and cumulative social stress on Muslim American immigrants and refugees and identify protective factors that convey individual and population-level resilience to political social stressors.


Moreover, non-significant (p >0.05) differences in shooting scores were apparent between chronotype groups in the morning  and afternoon (moderate-large effects). Shooting accuracy appears to remain consistent across morning and afternoon performances irrespective of player chronotype in a professional basketball team, suggesting coaches may not need to schedule training sessions involving shooting tasks at specific times of the day to optimise shooting accuracy in players.Athlete chronotype has been documented to underpin diurnal variations in skill execution across various team sports. However, no research has explored the effects of athlete chronotype on basketball-specific skills at different times of the day. Therefore, the aim of this study was to explore diurnal variations in basketball shooting accuracy according to chronotype. Professional, male basketball players (n = 13) completed a Morningness- Eveningness Questionnaire and were categorised into chronotypes using a tertile split technique . Players completed separate trials of a shooting accuracy test in the morning (08:00-09:30h) and afternoon (15:00-16:30h) with each trial consisting of 20 shots attempted from four court locations at either two- or three-point distances and one-shot location from the free-throw line . Each shot attempt was scored using a 0-3-point scale with higher scores awarded to more accurate shots. Non-significant ( Sleeping behaviour and diurnal activities  underpin the circadian rhythm of an individual resulting in a delayed or advanced acrophase (peak) in psychobiological variables (e.g. body temperature) across the day5. Variations in circadian rhythms and subsequent psychobiological acrophases across individuals led to the identification of three broad categories of chronotypes: morning types (M-types), evening types (E-types), and neither types . The natural light/ dark cycle suits M-types who display a preference for earlier bed and wake times compared to E-types who tend to display a preference for later bed and wake times with the preferences of N-types falling between M-types and E-types8.Chronotype is a behavioural phenotype expressing the circadian rhythmicity of an individual to indicate their inclination towards a preference for ‘morningness’ or ‘eveningness’10. Indeed, sports performance has been shown to vary between chronotypes according to the time of day, with most research measuring physical performance (e.g. race time) in aerobic sports involving gross motor skills13. For instance, M-type rowers12, swimmers11, and runners13 have been shown to perform significantly (p <0.05) better in the morning compared to the evening. In contrast, N-type and E-type athletes have demonstrated diurnal variation in swimming performance with significantly (p <0.05) faster race times in the evening11 compared to morning. However, no differences in performance times were observed in N-type and E-type rowers12 between morning and afternoon performances. The varied findings observed among N-type and E-type endurance athletes may have been due to the time-of-day at which performance was assessed in previous studies. Specifically, N-type and E-type swimmers were assessed at 18:30-19:00h compared to morning . Conversely, N-type and E-type rowers12, demonstrated consistent performances when assessed between 05:00-07:00h and 16:30- 18:00h. Consequently, greater discrepancies in aerobic performance involving gross motor skills may occur when morning assessments are compared to assessments conducted later in the day, which coincide with proposed peaks in body temperature .The circadian rhythm in psychobiological variables specific to each chronotype has been proposed to underpin diurnal variations in sports performance across athletes17. For example, tennis first serve velocity , badminton serve accuracy , and soccer volleying, chipping , and dribbling9 performance have been shown to be superior later in the day compared to morning (i.e. 07:00-09:00h). However, the applicability of current data regarding diurnal variations in sport-specific skills according to chronotype are limited due to research examining skill-based performances exclusively in one chronotype group rather than across multiple chronotype groups17. Further research is therefore warranted to determine the effect of athlete chronotype on sport-specific skill performance across different times of the day.Unlike gross motor skills, performance of fine motor skills reliant on cognitive and sensory-motor components has been shown to peak in both the afternoon and evening compared to the morning across a range of sporting tasks17, no studies have specifically examined basketball skills. Basketball is a global team sport that involves execution of various skills during game-play18. Among the varied skills performed in basketball, shooting accuracy is a strong determinant  of team ranking in professional basketball players19. In this regard, examining diurnal variations in basketball shooting accuracy according to chronotype is important given players typically train and compete at different times of the day21. Identifying diurnal variations in shooting accuracy according to chronotype can inform coaching staff on the prescription of training schedules to individually optimise shooting performance across players. Therefore, the aims of this study were to: 1) examine diurnal variations in shooting accuracy according to player chronotype and 2) compare shooting accuracy between chronotypes at different times of the day.While data are available demonstrating the effect of diurnal variation on the performance of sports- specific skills across different times of the dayBasketball players (n = 13) were recruited from the same basketball team registered in the National Basketball League (NBL), which is highest professional basketball competition in Australia. All playing positions were represented among the sample including guards (n = 7), forwards (n = 4), and centres (n = 2). Descriptive statistics of the players according to chronotype group are presented in p <0.01) and retest reliability  in assessing basketball shooting accuracy in semi-professional, male and female basketball players24. Non-perimeter shooting players completed shots from five locations, including four locations around the key and one location at the centre of the free-throw line  on the same indoor, hardwood basketball court in a randomised manner  between 12:00-13:00h to determine their self-reported chronotyped manner . Testingd manner . During 23. Scores ranged from 0-3, with attempts scored retrospectively by the same assessor using captured video. Overall test performance was measured as the total summed score from all shots taken across all five positions. This scoring system has previously demonstrated strong intrarater reliability when applied in the original version of the BJSAT .During the test protocol, shooting positions were marked on the court as 60-cm x 60-cm squares with tape. A video camera  was positioned on the halfway line, focused on the backboard to score shot accuracy as described in 25. Players then shot four attempts from each position in a sequential manner before undergoing 2 min of passive rest prior to trial commencement .Players undertook a standardised 5-min warm-up consisting of jogging, countermovement jumps, and dynamic stretching before commencing the familiarisation trial . This waencement . Using aencement  and atte27. Responses are assigned a value with the sum of scores ranging between 16-8627. The sum of scores is used to determine chronotype where M-types reflect scores ranging between 59-86, N-types between 42-58, and E-types between 16- 4127. In this study, three players were categorised as M-types, nine players as N-types, and one player as an E-type. To equilibrate sample sizes, the data were split into tertiles for analysis11, where five players were assigned to the first tertile representing the M-types (MEQ >54), four players were assigned to the second tertile representing the N-types (MEQ 47-53), and four players were assigned to the third tertile representing the E-types (MEQ <46).The MEQ is a 19-item questionnaire used to establish when the respondent feels most inclined to complete certain behaviours during a typical dayp <0.05). Therefore, differences in shooting accuracy between morning and afternoon trials for each chronotype  were assessed with separate Wilcoxon signed-rank tests. Differences in shooting accuracy between chronotypes were assessed with Kruskal-Wallis tests separately for the morning and afternoon trials with post hoc analyses conducted using Mann-Whitney U tests. Statistical significance for all analyses was set at p <0.05; however, a Bonferonni-adjusted a-level (p <0.017) was used in post hoc tests. Cohen’s R effect sizes28 were calculated to quantify the magnitude of pairwise differences in shooting accuracy between morning and afternoon trials for each chronotype group as well as between chronotypes in both trials, with effects interpreted as small (<0.30), moderate (0.30-0.49), or large . Given data were not normally distributed, descriptive results are expressed as the median and IQR.Data were analysed using SPSS statistics . The Shapiro-Wilk test demonstrated the data were not normally distributed (p >0.05) differences in shooting scores between morning and afternoon trials for each chronotype group. Effect size analyses demonstrated all chronotype groups performed better in the morning compared to afternoon . Likewise, there were nonsignificant differences (p >0.05) in shooting scores between chronotype groups in the morning and afternoon trials. Effect size analyses showed M-types scored higher (moderate effects) than E-types in morning and afternoon trials, while N-types scored higher than E-types and M-types in the morning  and afternoon   .The main aims of this pilot study were to 1) explore diurnal variations in shooting accuracy according to player chronotype and 2) compare shooting accuracy between chronotype groups at different times of the day in professional basketball players. The key findings were shooting accuracy was consistent across morning and afternoon trials irrespective of player chronotype, and player chronotype exerted nonsignificant effects on shooting accuracy in the morning and afternoon.16), badminton serving accuracy , and soccer chipping, volleying , and dribbling execution9 being significantly (p <0.05) better in the afternoon and evening compared to the morning (07:00-09:00h). The contrast between the current findings and those provided in previous research may be attributed to the participants recruited in each study. In this regard, the current study examined professional basketball players compared to previous studies that examined amateur17 and semi-professional athletes16. Indeed, professional athletes likely possess a higher degree skill mastery specific to their chosen sports compared to lower-level athletes, which may make them more resistant to diurnal variations in skill execution. However, the notion that professional athletes may be more resistant to diurnal variation compared to lower-level athletes can only be speculated as research on this topic examining professional team sport athletes is only in its infancy.This is the first study to examine the effect of player chronotype on sports-specific skills in professional basketball players. There were non-significant differences in shooting accuracy between morning and afternoon trials across chronotype groups despite effect size analyses showing favourable performance in the morning for each chronotype . Furthermore, non-significant differences in shooting accuracy were evident between chronotype groups at each timepoint, with similar effect size magnitudes evident across morning and afternoon trials for each pairwise comparison  These results contrast the hypothesis that diurnal variations in shooting scores would emerge according to chronotype (i.e. higher scores would be achieved at the timepoint suited to the chronotype group being tested) and in comparisons between chronotypes (i.e. effect magnitudes would fluctuate in favour of chronotypes suited to the time-of-day being tested). Furthermore, our findings contrast previous data in other sports indicating diurnal variations exist in skill performance predominantly examining N-type athletes with tennis first serve accuracy  in the morning, consistently exposing them to early wake times. In contrast, only games were consistently completed in the afternoon or evening (1-2 games per week), meaning performances at later times in the day were habitually completed less often across the season. Exposure of players to early wake times in completing morning training sessions may phase advance the circadian rhythms of N-types and E-types to that indicative of M-types30. It has been established that chronotype can be modulated to phase advance or delay acrophases based on the effect of exogenous  and endogenous  factors14. Accordingly, it would be reasonable to presume if the same training time for all players resulted in a phase advanced circadian rhythm of N-types and E-types to emulate that of M-types, a null effect of chronotype on shooting accuracy would result. In this way, the time of day at which an athlete habitually trains has only recently begun to be investigated as a mechanism to shift the acrophase of peak performance to that when training takes place with Rae, Stephenson2 reporting ~70% of swimmers (n = 26) performed significantly (p <0.05) better in the trial that aligned to the time at which they habitually trained (morning vs. afternoon). While this phenomenon was not directly investigated in the current study, the effects observed lend support for the habitual training time of players contributing to the reported findings. However, further research is needed investigating the effect of habitual training times on sport-specific skills in basketball and other team sports to validate this supposition.While non-significant differences in shooting scores were evident between timepoints for each chronotype group, the 32. Furthermore, the small sample size did not allow for players to be matched on key aspects across groups, such as playing position, experience, and shooting ability, which made between- group comparisons difficult to interpret at each timepoint. The small sample size also reduced the statistical power in comparisons made. A second limitation of the current study is that players were only assessed at two time points across the day. Provided the busy training schedules of professional basketball players, the scheduled afternoon trial (15:00-16:30h) was the most practical time to consistently assess players later in the day while the morning trial time (8:00- 9:30hr) overlapped with the typical training time for the recruited players. It would be beneficial to repeat this study by assessing larger samples of professional basketball players across more time points including the typical time that games scheduled at night are played (>19:00h). Finally, it is acknowledged that due to player availability, some players could only complete the afternoon trial on the same day that morning training sessions were completed (>4 hr following training), while other players completed the afternoon trial on days without any scheduled training. While the residual fatigue from training in the morning may have impacted the performance of players in the afternoon trial, there was a similar number of players completing afternoon trials following morning training across groups to nullify this effect .It is acknowledged that the key limitation of this study is the sample size distribution across chronotype groups (i.e. <5 players). However, only a single professional basketball team was able to be recruited given the additional travel, costs, and labour, and difficulties associated with recruiting multiple teams from the same league. The single-team recruitment therefore limited the number of definite M-type and E-type players included, with a tertile split approach taken to ensure an even distribution of players across groups in the current study. This approach has been adopted in other chronotype studies examining athletes given the limited M-types and E-types prevalentIt appears player chronotype does not affect shooting accuracy in professional, male basketball players with shooting performance remaining consistent across morning and afternoon trials in each chronotype group. Alternatively, habitual training time may exert a greater effect on basketball shooting accuracy. From a practical perspective, basketball coaching staff may be permitted to schedule training sessions involving shooting tasks at preferred times across the day with confidence that shooting performance will not exhibit diurnal variations between morning or afternoon sessions. However, coaches may endeavour to match habitual training times with that of games to ensure greatest specificity and align player circadian rhythms to competition.small-large effects in shooting accuracy were evident favouring the morning trial across chronotype groups, suggesting habitual training times may exert a prominent effect on shooting accuracy in a team setting. Further research examining wider basketball player samples is encouraged to investigate the interrelating effects of chronotype and habitual training time on skill performance in basketball players.There were non-significant differences in shooting accuracy between morning and afternoon trials irrespective of player chronotype nor between chronotype groups at each timepoint. However,


Adolescent girls and young women (AGYW) disproportionately experience gender-based violence (GBV), which can increase during emergencies like the COVID-19 pandemic.A cohort of youth ages 15–24 in Nairobi, Kenya was surveyed at three time points over an 18-month period prior to and during the COVID-19 pandemic: June–August 2019 (prepandemic), August–October 2020 (12-month follow-up) and May 2021 (18-month follow-up). We characterise (1) prevalence, relative timing and help-seeking for leading forms of GBV, (2) GBV trajectories over 18 months and (3) associations of individual, dyad and COVID-related factors on GBV trajectories among AGYW (n=612) in Nairobi, Kenya. Virtual focus group discussions (n=12) and interviews (n=40) contextualise quantitative results.Intimate partner violence (IPV) prevalence hovered at 17% across time points ; past 6 months at 18-month follow-up (2021)); non-partner sexual violence (SV) was 3% at 12-month and 18-month follow-up. Overall, 27.6% of AGYW experienced IPV during the pandemic. IPV during the pandemic was associated with work as the primary pre-COVID activity, low social support and partner age difference >4 years. Among AGYW partnered at all three time points, 66.2% stayed IPV-free (no IPV), 9.2% saw IPV resolve by 18-month follow-up, while 11.1% had IPV start and 13.6% experienced intermittent IPV. Help-seeking for IPV and SV in 2020  increased to 21.7% and 15.1%, respectively, by 2021. Qualitative results speak to impacts of curfews, and pandemic-related financial stress in prompting conflict and threatening traditional gender roles, and underlying conditions that enable IPV.The persistence of IPV against AGYW in Nairobi prior to and during the COVID-19 pandemic reflects endemic conditions and pandemic-specific stressors. Youth, including unmarried youth, remain a priority population for GBV prevention and survivor-centred response. Global evidence suggests increases in gender-based violence (GBV) among adult women following COVID-19 mitigation efforts implemented beginning in early 2020.Women in Nairobi, Kenya are highly affected by GBV; increases in household tension and conflict among adult women have been reported since the start of the COVID-19 pandemic, as have modest increases in household violence and increases in violence outside the home.Adolescent girls and young women, including unmarried youth, are disproportionately affected by GBV, yet remain poorly understood with regard to COVID-19-related impacts; longitudinal data on risk trajectories are lacking.In this cohort of urban adolescent girls and young women (AGYW), prevalence of intimate partner violence (IPV) was consistent at approximately 17% across three time points prior to and during the COVID-19 pandemic (ever at prepandemic (2019); past 12 months at 12-month follow-up (2020); past 6 months at 18-month month follow-up (2021)); non-partner sexual violence was less pervasive (3%) at 2020 and 2021 time points.IPV trajectories illustrate movement both in and out of relative relationship safety.Heightened risk for IPV during COVID-19 was identified among AGYW engaged in the work force pre-COVID, those with low social support, and those in relationship with older partners (>4 years).Qualitative data contextualise results and articulate the impact of curfews and financial stressors, as well as underlying conditions that enable abuse independent of the pandemic.This first study to characterise COVID-relative IPV trajectories among AGYW in Nairobi confirms persistent risk both prior to and during the COVID-19 pandemic.Both primary prevention and survivor-centred support services are needed to meet the unique needs of AGYW and overcome the stigma of accessing care.The disruption of the pandemic may afford a window to reset harmful underlying social norms and gendered social systems that perpetuate violence against young women with impunity.Gender-based violence (GBV) threatens health and human rights, with most severe consequences including injury and death.9Adolescent girls and young women (AGYW) are an under-recognised yet critical population for GBV prevention and response. Interpersonal violence is the fifth-leading source of disability-adjusted life-years lost among youth ages 10–24 globally.In Kenya, as in many settings, GBV was prevalent prior to the COVID-19 pandemic, with an estimated 27.2% of AGYW ages 15–24 reporting past 12-month physical or sexual IPV.22In their 2021 Research Round Up,Against this backdrop, this study examines (1) prevalence and help-seeking related to leading forms of GBV, (2) trajectories from prepandemic to 18-month follow-up and (3) associations of individual, dyad and COVID-related factors on GBV trajectories among a cohort of AGYW in Nairobi, Kenya, first recruited in 2019. Results provide timely evidence to guide safety planning and supports for youth during the remainder of the pandemic, recovery investments that respond to the safety needs of AGYW, and insight into the needs of this population in future emergencies.This study is embedded in the Nairobi Youth Respondent Driven Sampling Survey (YRDSS), an ongoing cohort study of adolescents and young adults, and draws on data from three time points over an 18-month period prior to and during the COVID-19 pandemic. The YRDSS began recruitment in June–August 2019 using respondent-driven sampling (RDS), a chain-based recruitment method that begins with purposefully selected seeds, followed by monitored peer-to-peer coupon distribution.33–35Trained resident enumerators (REs) conducted data collection in either English or Swahili using OpenDataKit on tablets or smart phones. Data collection at pre-pandemic baseline occurred via self-administered survey at community-based sites; due to the COVID-19 restrictions, 2020 and 2021 rounds were conducted by phone with added privacy protections. REs are trained in sensitive data collection concerning family planning and sexual activity, and received specialised training specific to GBV protections. They are ages 28–41 years to minimise intergenerational conversations on sensitive topics. All data were collected in accordance with best practices for GBV research,Focus group discussions (FGDs) and individual in-depth interviews (IDIs) were conducted by trained interviewers among male and female youth participants and community stakeholders via the Zoom videoconferencing platform. FGDs were conducted in August 2020 with youth  and stakeholders working at youth-service community-based organisations . IDIs were conducted with cohort members following their survey completion at 12-month follow-up (n=20) and 18-month follow-up (n=20); purposive sampling sought representation across age, gender, pre-COVID activity (school or work), family planning and violence experience. All qualitative activities followed a semistructured interview guide that included, but was not limited to, risks for IPV and SV. The IDI design maximised breadth in perspective and point-in-time inputs; sampling at each wave was independent and did not prioritise longitudinal trajectories.IPV was assessed with a shortened Revised Conflict Tactics Scale-2.IPV  outcomes were constructed based on these assessments: (1) IPV experience at each time point, (2) IPV during the pandemic, indicated by presence of IPV since COVID-19 restrictions at 2020 and/or at 2021 survey and (3) IPV trajectory, a mutually exclusive categorical variable reflecting: sustained safety (no IPV at any time point), IPV cessation , IPV initiation  and intermittent IPV (IPV pre-COVID and 2021 but not 2020 or 2020 but not 2019 or 2021).Partnership was assessed with a single item; at pre-pandemic participants were asked if they were currently ‘involved with someone in a sexual or romantic relationship’. At subsequent rounds, participants were asked if they had a ‘sexual or dating partner’ in the last 12 months and 6 months, respectively .Non-partner SV was assessed at 12-month follow-up (past 12 months) and 18-month follow-up (past 6 months) via single item: ‘Have you had sex when you did not want to with anyone else (not a partner) due to threats, pressure, or force?’).Among AGYW who reported experiencing IPV or non-partner SV, a single item assessed help-seeking ‘Did you seek help for any experiences of harm or unwanted sex?’) at 12-month follow-up (2020) and 18-month follow-up (2021).Standard demographic assessments included age, education, subjective household socioeconomic status (SES),40Dyad-level assessments for partnered AGYW included 2020 partner cohabitation status, age difference with current partner (dichotomised as ≤4 years difference and >4 years difference), transactional relationship , and relationship fear .Assessments of changes since COVID-19 restrictions at 2020 survey included changes in the amount of time at home and with their partners, respectively , changes in personal control to leave the house , changes in economic reliance on others (more reliant vs not more reliant).40Individual, dyad, and COVID-related assessments aligned with best practices; existing measures were used when possible, including from Evidence-based Measures of Empowerment for Research on Gender Equality.Non-partner SV analyses were conducted among all female participants at 12-month follow-up (2020) (n=612) and 18-month follow-up (2021) (n=591). IPV analyses were limited to partnered AGYW . Cross-sectional analyses were further restricted to those with complete IPV data (<3% missing) per time point. Analyses specific to IPV during the pandemic were limited to AGYW with 12-month follow-up (2020) or 18-month follow-up (2021) data (n=363) and IPV trajectory analysis was conducted with the subset who were currently partnered and who had complete IPV data at all three rounds (n=246). Attrition and partner status were non-differential to IPV reported at the last survey round.Prevalence of IPV, non-partner SV and related help-seeking, respectively, were calculated for each survey round for which data were available. At 12-month follow-up (2020), we characterise timing of violence, and changes in violence severity, relative to COVID-19 restrictions.Prevalence of IPV at any point during the COVID-19 pandemic was calculated for the overall sample, and by individual, dyad and COVID-19-related factors assessed at 2020 survey; bivariate differences were evaluated by design-based F-statistics. Finally, a multivariable logistic regression model was constructed using generalised linear model (GLM) link log and family binomial. Factors associated with IPV bivariately at p<0.10 were included in the initial model. Due to collinearity among key variables, a theoretically informed manual stepwise model building process was used to fit a parsimonious model that accommodated key demographic variables.IPV trajectories prior to and through the pandemic were first visualised using a Sankey diagram for AGYW who were partnered at all three time points (n=246). Categorical IPV trajectories  were described and compared by individual, dyad, and COVID-related factors, with differences assessed via design-based F-statistics.All analyses were conducted using Stata V.17.0. To mitigate potential biases introduced by the recruitment strategy, sampling weights accommodate the RDS study design using RDS-II (Volz-Heckathorn) weights, postestimation adjustment based on 2014 KHDS population data  and lost to follow-up adjustment where relevant. Analysis adjusts for clustering by RDS seed chain at baseline.FGDs and IDIs were audiorecorded, transcribed verbatim and translated to English language (if needed). Resulting transcripts were coded using both inductive and deductive techniques; guided by gender theoryThis community-engaged study sought public and end-user input at all phases. During the formative research stage prior to the 2019 cohort recruitment, input from community-based, youth-serving organisations informed the study recruitment strategy for feasibility, survey measures and constructs to ensure relevance, and study logistics to maximise participant comfort and confidentiality. The qualitative data collection in 2020 generated refinements to survey content for 2021 round. All recruitment and procedures were conducted by trained REs selected from underlying communities, and who provided inputs on measures for clarity and aided in results interpretation. Findings were disseminated in November 2020 and again in September 2021 with stakeholders spanning policy sector, government representatives, elders/faith leaders, community-based organisations, and youth leaders from the study communities.IPV prevalence was 17.5% (lifetime) with current/recent partner at pre-pandemic (2019), 17.3% past-year at 12-month follow-up (2020), and 17.5% past-6 months at 18-month follow-up (2021) . Among AAmong partnered AGYW, 27.6% experienced IPV during the pandemic  . IPV durIn adjusted analyses, IPV experience during COVID-19 remained associated with low social support ; partner age difference >4 years  and pre-COVID main activity of paid work .Among AGYW who were partnered at all three time points (n=246), 66.2% had sustained safety (no IPV) across all three time points, 9.2% transitioned from pre-COVID IPV to safety (cessation), 11.1% experienced new IPV during the pandemic (initiated) and 13.6% indicated intermittent IPV . TrajectQualitative themes included impact of the COVID-19 mandated restrictions, pandemic-related psychological and financial stress, and endemic conditions that give rise to violence.The COVID-19 restrictions on IPV risks were often shaped by marital/cohabitation status. Among those residing at home, being out past curfew enabled vulnerability to IPV.According to me, conflict in relationships like now there are curfew hours so maybe you went out and time really went, time went by without you noticing and you remained there and your parents didn’t know where you went. And now we come back to the relationship, you are with this guy and now he has the chance to do with you anything he likes because he knows you can’t go anywhere. It is past curfew, you can’t leave the house. So, he might do anything to you .42For other AGYW, particularly those still living with parents, restrictions allowed them to avoid or end violent relationships.According to me, it has affected positively. Because before, the violence was there so much but nowadays it is hard to meet, and when you meet it is hard. So, if there is any violence which comes up, you break up .42Among cohabitating couples, increased time at home with a partner due to COVID-19 restrictions increased potential for conflict and in turn, IPV.Eehe (meaning yes) [violence] has increased let’s say in this family the guy is the type who likes to get annoyed so fast, you know before he could leave to work so there would be no much quarrels. You know right now they are in the house together all through so right now it is fights every time .Further, inability to leave the home due to curfew hindered ability to escape to safety when experiencing violence.So, you find any quarrel, the female becomes subjected to violence but can’t go out because it’s curfew time, she can’t go anywhere so she is patient with the beatings, so you find violence is there, it affects more the female than the male partner .Pandemic-related restrictions reduced IPV risk in indirect ways; specifically, curfews were described as reducing partners’ alcohol consumption, thus lowering the risk of violence.For relationship[violence] it has reduced, why? Like let’s say like the way men used to like to stay out in bars, they stay there for long. So, right now where will they go? Coz (because) let’s say curfew is nine, they will be at home at nine…Whereby there will be no conflicts, conflict there up and down .Okay people’s husbands because we have at this age, our age 15–24, we have people who have already have married. So, their husbands are getting home earlier and if they are getting home earlier, there are things like beer, people are not drinking since that, drinking time is at night if you tell a person that you are drinking at one during the day that is not lunch. So, it is happening like many people are now sober. And you know when people are sober, violence reduces .Psychological and financial stress related to pandemic-induced job loss was further described as an indirect risk for increased IPV in the home.We can see now gender-based violence coming up especially for the young couples […] There is a lot of emotional and psychological stress that has come with Covid so their relationships have been affected so much and with loss of employment. Their financial status is not very stable. So that also has affected their relationship (Stakeholder).So in the family you find that let’s say the boy or father was the bread winner in the house. So due to lack of money, we know not all have money, poverty brings a lot of things like conflicts, so you find there are disagreement in the house so you find there will be fights .The economic strife of COVID-19 also increased IPV risk by threatening traditional gender norms that position men as primary earners within the household/family.Conflicts have also increased in a way because there are no finances in the house. You know men really like being on the upper limit because I am giving out the money you know and now he doesn’t have money so the slightest thing in the house makes him angry so this has really led to conflict in the house .Like, like right now, like men are the ones who mainly provide and since corona came, many people lost their jobs a lot. So, such a man was the one providing. Like job is over, right now he is not providing, so, it brings conflict at home .Endemic risk factors for relationship violence were also described, including alcohol and financial conflict unrelated to the pandemic.[relationship violence] is fueled by drunkenness . At the household, financial issues, lack of money contributes to it. Because maybe you want food but the partner does not have money. So he ends up getting upset, you do not understand him, you start quarreling .I am the one who started because he was not supporting the child. He thought I have another man. So, I started avoiding him. I told him that it is not a must for him to support the child. He got angry. When he got angry, he thought that I have my own things and that I want to play him. He called me and I refused to go. He called me for two weeks and I refused to go. The third week he came to quarrel here at our home…So, he came and quarreled and I went out. Now, I do not know whether he had drunk or what was wrong. So, it look like a fight. He told me to give him his child and I refused. So, it was like a fight… It was a fight because he hit me .Notably, young women did not discuss personal experiences seeking help for violence. In reflecting on GBV services, stakeholders and key informants described a cascade of barriers including gaps in knowledge, COVID-19 related closures and stigma.Yeah, so I think the…the places are there, there are facilities that offer such kind of ah…assistance. However, there is still room to improve in terms of information dissemination and knowledge of ah…the youth in terms of where they can access the services. (Stakeholder)I think with this CovidCOVID-19 and you know some of the safe houses have been closed it is difficult for them to access the services or the safety spaces that were opened before the pandemic. (Key Informant)Social stigma related to violence reporting also limited AGYWs’ ability to seek help.Remember also not everyone who will be assaulted will go to the facility. There is also some element of stigma with it. So, whether there is COVID or not those who would have gone to the facility to seek health services after having experienced domestic violence, they will still go. And those who will not go because of issues of stigma still they won’t. When it comes to the services, the services have been there and they are still there (Key Informant).Additional perceived barriers to help-seeking included the climate of impunity for violence against women, as well as fear of retribution.Yes, I think young people are aware where to report [GBV] but I think we need to strengthen something eh. Because when these persons report and nothing happens, then this persons gets fear because if he was reporting that he was being… there is an incest, there is a rape and nothing happens and then the person who had done that is still loitering within the community, the society, then there is that fear of insecurity to this persons who has reported. (Stakeholder)Findings from this first prospective study of GBV trajectories among AGYW in Nairobi, Kenya demonstrate striking consistency in IPV prevalence (~17%) prior to and during the COVID-19 pandemic. Over one in four (27.6%) AGYW were affected by IPV at some point during the pandemic by the 2021 data collection round. By contrast, non-partner SV was far less prevalent, limiting trajectory analysis. Current results from this sample of primarily non-cohabitating urban AGYW expand a global dialogue primarily focused on IPV-related pandemic impacts among cohabitating couples, and affirm risk of violence to AGYW during public health emergencies. The relative consistency in IPV prevalence from pre-COVID through the pandemic argues for recognition of endemic violence against AGYW. IPV prevention and response investments must be prioritised during COVID-19, and sustained beyond this global health emergency. Recovery efforts must address pandemic-related needs for safety, while working to overcome the normative, social and economic systems that give rise to IPV and other forms of violence against AGYW.Against the dearth of prospective data on IPV trajectories for AGYW in Nairobi and elsewhere, the IPV-related transitions in and out of safety among AGYW across the three time points  are highThe socioeconomic patterning of IPV for AGYW was complex. Economically vulnerable AGYW, that is, those with low household SES and having less than secondary education, had higher IPV risk. A cross-sectional study among women in three counties in Kenya found a similar relationship between domestic violence and SES.The increases in help-seeking for both IPV and non-partner SV observed from 12-month follow-up (2020) to 18-month follow-up (2021) are promising, particularly as violence-related help-seeking is low in Kenya and globally, particularly for formal supports.The relatively low prevalence of non-partner SV (~3%) may be considered surprising, given local reports of increased violence during the pandemic. However, national data from Kenya illustrate that intimate partners are the primary perpetrators of SV against young women,The current levels of violence against AGYW are alarming yet highly actionable, even in pandemic conditions. The COVID-19 pandemic galvanised recognition of GBV globally and in Kenya, creating the necessary policy window for meaningful prevention and response. In May, 2021, Kenya president Uhuru Kenyatta pledged an unprecedented investment of US$23 million by 2022 and a further US$50 million by 2026 address GBV.Results should be understood in light of several limitations. Two key measurement issues limit precision. First, the referent time periods for the IPV assessments vary, and only two of the three represent true, time-bound period prevalences, limiting comparability. Second, the IPV and SV measures were single assessments to limit survey length and minimise risk to participants; single items and short-form assessment are less sensitive,The COVID-19 pandemic has far-reaching implications for global health and safety. AGYW in Nairobi, Kenya remain at risk for IPV during COVID-19, reflecting a combination of pandemic-specific influences together with underlying social and economic disparities rooted in gender inequality. Simultaneously, AGYW stand to gain the most from effective prevention and response, as disrupting violence early may interrupt longer-term cycles of harm. Sustained, comprehensive investments in GBV prevention and response are needed to ensure progress toward the Sustainable Development Goal of elimination of violence against women, and its positive cascade impact on social, economic and well-being of women and families for generations to come. Effective prevention and response programming must be sustained during emergencies and scaled, with monitoring and impact evaluation to ensure goals are met. Essential steps include ending stigma and silence on GBV for AGYW and facilitating a culture of help-seeking and survivor-centre support, particularly for youth.