Search

The COVID-19 pandemic caused an abrupt drop in in-person health care (inpatient, Emergency Department, outpatient) and an increase in telehealth care, which poses challenges in vaccine safety studies that identify outcomes from in-person encounters. We examined the changes in incidence rates of selected encounter-based outcomes during the COVID-19 pandemic.We assembled a cohort of members from 8 Vaccine Safety Datalink sites from January 1, 2017 through December 31, 2020. Using ICD-10 diagnosis codes or laboratory criteria, we identified 21 incident outcomes in traditional in-person settings and all settings. We defined 4 periods in 2020: January-February (pre-pandemic), April-June (early pandemic), July-September (middle pandemic), and October-December (late pandemic). We defined four corresponding periods in each year during 2017-2019. We calculated incidence rates, conducted difference in difference (DiD) analyses, and reported ratios of incidence rate ratios (RRR) to examine changes in rates from pre-pandemic to early, middle, and late pandemic in 2020, after adjusting for changes across similar periods in 2017-2019.Among 10 million members, regardless of setting and after adjusting for changes during 2017-2019, we found that incidence rates of acute disseminated encephalomyelitis, encephalitis/myelitis/encephalomyelitis/meningoencephalitis, and thrombotic thrombocytopenic purpura did not significantly change from the pre-pandemic to early, middle or late pandemic periods (p-values ≥ 0.05). Incidence rates decreased from the pre-pandemic to early pandemic period during 2020 for acute myocardial infarction, anaphylaxis, appendicitis, Bell's palsy, convulsions/seizures, Guillain-Barré syndrome, immune thrombocytopenia (ITP), narcolepsy/cataplexy, hemorrhagic stroke, ischemic stroke, and venous thromboembolism (p-values 0.05). Incidence rates of Bell's palsy, ITP, and narcolepsy/cataplexy were higher in all settings than in traditional in-person settings during the three pandemic periods (p-values 0.05).Rates of some clinical outcomes during the pandemic changed and should not be used as historical background rates in vaccine safety studies. Inclusion of telehealth visits should be considered for vaccine studies involving Bell's palsy, ITP, and narcolepsy/cataplexy.

This study highlights an unmet need in osteoporosis management, suggesting that beyond bone mineral density and fracture history, gender, fracture type, and age should be considered for fracture risk assessment. Following fragility fracture, men, patients with a spine or hip fracture, and those aged ≥ 65 have a higher disease burden. The objective of this study was to characterize osteoporosis-related fracture incidence and identify predictors of subsequent fractures and mortality. This retrospective cohort study, conducted within Kaiser Permanente Southern California, included patients aged ≥ 50 years with qualifying fractures from 1/1/2007 to 12/31/2016, identified from diagnosis/procedure codes. Rates for fracture incidence, mortality, and resource utilization in the year post-fracture are reported. Associations between index fracture types and demographic/clinical characteristics, and mortality, subsequent fracture, and rehospitalization outcomes were estimated. Of 63,755 eligible patients, 66.7% were ≥ 65 years and 69.1% female. Index fractures included nonhip/nonspine (64.4%), hip (25.3%), and spine (10.3%). Age-adjusted subsequent fracture rate/100 person-years was higher for those with an index spine (14.5) versus hip fracture (6.3). Hospitalization rate/100 person-years was highest for patients ≥ 65 (31.8) and for spine fractures (43.5). Men (vs women) had higher age-adjusted rates of hospitalization (19.4; 17.7), emergency room visits (73.8; 66.3), and use of rehabilitation services (31.7; 27.2). The 30-day age-adjusted mortality rate/100 person-years was 46.7, 32.4, and 15.5 for spine, hip, and nonspine/nonhip fractures. The 1-year age-adjusted mortality rate/100 person-years was 14.7 for spine and 15.6 for hip fractures. In multivariable analyses, spine and hip fractures (vs nonhip/nonspine fractures) were significant predictors of 1-year mortality, all-cause and osteoporosis-related hospitalization, and nursing home use (all P-values

The safety of COVID-19 vaccines plays an important role in addressing vaccine hesitancy. We conducted a large cohort study to evaluate the risk of non-COVID-19 mortality after COVID-19 vaccination while adjusting for confounders including individual-level demographics, clinical risk factors, health care utilization, and community-level socioeconomic risk factors.The retrospective cohort study consisted of members from seven Vaccine Safety Datalink sites from December 14, 2020 through August 31, 2021. We conducted three separate analyses for each of the three COVID-19 vaccines used in the US. Crude non-COVID-19 mortality rates were reported by vaccine type, age, sex, and race/ethnicity. The counting process model for survival analyses was used to analyze non-COVID-19 mortality where a new observation period began when the vaccination status changed upon receipt of the first dose and the second dose. We used calendar time as the basic time scale in survival analyses to implicitly adjust for season and other temporal trend factors. A propensity score approach was used to adjust for the potential imbalance in confounders between the vaccinated and comparison groups.For each vaccine type and across age, sex, and race/ethnicity groups, crude non-COVID-19 mortality rates among COVID-19 vaccinees were lower than those among comparators. After adjusting for confounders with the propensity score approach, the adjusted hazard ratios (aHRs) were 0.46 (95% confidence interval [CI], 0.44-0.49) after dose 1 and 0.48 (95% CI, 0.46-0.50) after dose 2 of the BNT162b2 vaccine, 0.41 (95% CI, 0.39-0.44) after dose 1 and 0.38 (95% CI, 0.37-0.40) after dose 2 of the mRNA-1273 vaccine, and 0.55 (95% CI, 0.51-0.59) after receipt of Ad26.COV2.S.While residual confounding bias remained after adjusting for several individual-level and community-level risk factors, no increased risk was found for non-COVID-19 mortality among recipients of three COVID-19 vaccines used in the US.

Pregnant women might be at increased risk for severe coronavirus disease 2019 (COVID-19), possibly related to changes in their immune system and respiratory physiology* (1) Further, adverse birth outcomes, such as preterm delivery and stillbirth, might be more common among pregnant women infected with SARS-CoV-2, the virus that causes COVID-19 (2,3) Information about SARS-CoV-2 infection during pregnancy is rapidly growing;however, data on reasons for hospital admission, pregnancy-specific characteristics, and birth outcomes among pregnant women hospitalized with SARS-CoV-2 infections are limited During March 1-May 30, 2020, as part of Vaccine Safety Datalink (VSD)(†) surveillance of COVID-19 hospitalizations, 105 hospitalized pregnant women with SARS-CoV-2 infection were identified, including 62 (59%) hospitalized for obstetric reasons (i e , labor and delivery or another pregnancy-related indication) and 43 (41%) hospitalized for COVID-19 illness without an obstetric reason Overall, 50 (81%) of 62 pregnant women with SARS-CoV-2 infection who were admitted for obstetric reasons were asymptomatic Among 43 pregnant women hospitalized for COVID-19, 13 (30%) required intensive care unit (ICU) admission, six (14%) required mechanical ventilation, and one died from COVID-19 Prepregnancy obesity was more common (44%) among pregnant women hospitalized for COVID-19 than that among asymptomatic pregnant women hospitalized for obstetric reasons (31%) Likewise, the rate of gestational diabetes (26%) among pregnant women hospitalized for COVID-19 was higher than it was among women hospitalized for obstetric reasons (8%) Preterm delivery occurred in 15% of pregnancies among 93 women who delivered, and stillbirths (fetal death at ≥20 weeks' gestation) occurred in 3% Antenatal counseling emphasizing preventive measures (e g , use of masks, frequent hand washing, and social distancing) might help prevent COVID-19 among pregnant women,(§) especially those with prepregnancy obesity and gestational diabetes, which might reduce adverse pregnancy outcomes

Item does not contain fulltext Individuals are readily able to extract and encode statistical information from their environment (or statistical learning). However, the bulk of the literature has primarily focused on conditional statistical learning (i.e. the ability to learn joint and conditional relationships between stimuli), and has largely neglected distributional statistical learning (i.e. the ability to learn the frequency and variability of distributions). In this paper, we investigate how and how well distributional learning can be measured by exploring the relationship between and psychometric properties of two measures: discrimination judgements and frequency estimates. Reliable performance was observed in both measures across two different distributional learning tasks (natural and artificial). Discrimination judgements and frequency estimates also significantly correlated with one another in both tasks, and performance on all tasks accounted for the majority of variance across tasks (55%). These results suggest that distributional learning can be measured reliably, and may tap into both the ability to discriminate between relative frequencies and to explicitly estimate them. CogSci 2020: 42nd Annual Conference of the Cognitive Science Society (29 July - 1 August 2020)

Background Shoulder injury related to vaccine administration (SIRVA) accounts for more than half of all claims received by the National Vaccine Injury Compensation Program. However, due to the difficulty of finding SIRVA cases in large health care databases, population-based studies are scarce. Objective The goal of the research was to develop a natural language processing (NLP) method to identify SIRVA cases from clinical notes. Methods We conducted the study among members of a large integrated health care organization who were vaccinated between April 1, 2016, and December 31, 2017, and had subsequent diagnosis codes indicative of shoulder injury. Based on a training data set with a chart review reference standard of 164 cases, we developed an NLP algorithm to extract shoulder disorder information, including prior vaccination, anatomic location, temporality and causality. The algorithm identified 3 groups of positive SIRVA cases (definite, probable, and possible) based on the strength of evidence. We compared NLP results to a chart review reference standard of 100 vaccinated cases. We then applied the final automated NLP algorithm to a broader cohort of vaccinated persons with a shoulder injury diagnosis code and performed manual chart confirmation on a random sample of NLP-identified definite cases and all NLP-identified probable and possible cases. Results In the validation sample, the NLP algorithm had 100% accuracy for identifying 4 SIRVA cases and 96 cases without SIRVA. In the broader cohort of 53,585 vaccinations, the NLP algorithm identified 291 definite, 124 probable, and 52 possible SIRVA cases. The chart-confirmation rates for these groups were 95.5% (278/291), 67.7% (84/124), and 17.3% (9/52), respectively. Conclusions The algorithm performed with high sensitivity and reasonable specificity in identifying positive SIRVA cases. The NLP algorithm can potentially be used in future population-based studies to identify this rare adverse event, avoiding labor-intensive chart review validation.

BackgroundShoulder injury related to vaccine administration (SIRVA) accounts for more than half of all claims received by the National Vaccine Injury Compensation Program. However, there is a lack of population-based studies due to the challenge of identifying SIRVA cases in large health care databases.ObjectiveTo develop a natural language processing (NLP) method to identify SIRVA cases from clinical notes.MethodsWe conducted the study among members of a large integrated health care organization who were vaccinated between 04/1/2016 and 12/31/2017 and had subsequent diagnosis codes indicative of shoulder injury. Based on a training dataset with a chart review reference standard of 164 individuals, we developed an NLP algorithm to extract shoulder disorder information, including prior vaccination, anatomic location, temporality and causality. The algorithm identified three groups of positive SIRVA cases (definite, probable and possible) based on the strength of evidence. We compared NLP results to a chart review reference standard of 100 vaccinated individuals. We then applied the final automated NLP algorithm to a broader cohort of vaccinated individuals with a shoulder injury diagnosis code and performed manual chart confirmation on a random sample of NLP-identified definite cases and all NLP-identified probable and possible cases.ResultsIn the validation sample, the NLP algorithm had 100% accuracy for identifying 4 SIRVA cases and 96 individuals without SIRVA. In the broader cohort of 53,585 individuals, the NLP algorithm identified 291 definite, 124 probable, and 52 possible SIRVA cases. The chart-confirmation rates for these groups were 95.3%, 67.7% and 18.9%, respectively.ConclusionsThe algorithm performed with high sensitivity and reasonable specificity in identifying positive SIRVA cases. The NLP algorithm can potentially be used in future population-based studies to identify this rare adverse event, avoiding labor-intensive chart review validation.

The COVID-19 pandemic has caused an abrupt reduction in the use of in-person health care, accompanied by a corresponding surge in the use of telehealth services. However, the extent and nature of changes in health care utilization during the pandemic may differ by care setting. Knowledge of the impact of the pandemic on health care utilization is important to health care organizations and policy makers.The aims of this study are (1) to evaluate changes in in-person health care utilization and telehealth visits during the COVID-19 pandemic and (2) to assess the difference in changes in health care utilization between the pandemic year 2020 and the prepandemic year 2019.We retrospectively assembled a cohort consisting of members of a large integrated health care organization, who were enrolled between January 6 and November 2, 2019 (prepandemic year), and between January 5 and October 31, 2020 (pandemic year). The rates of visits were calculated weekly for four settings: inpatient, emergency department (ED), outpatient, and telehealth. Using Poisson models, we assessed the impact of the pandemic on health care utilization during the early days of the pandemic and conducted difference-in-deference (DID) analyses to measure the changes in health care utilization, adjusting for the trend of health care utilization in the prepandemic year.In the early days of the pandemic, we observed significant reductions in inpatient, ED, and outpatient utilization (by 30.2%, 37.0%, and 80.9%, respectively). By contrast, there was a 4-fold increase in telehealth visits between weeks 8 (February 23) and 12 (March 22) in 2020. DID analyses revealed that after adjusting for prepandemic secular trends, the reductions in inpatient, ED, and outpatient visit rates in the early days of the pandemic were 1.6, 8.9, and 367.2 visits per 100 person-years (P.001), respectively, while the increase in telehealth visits was 272.9 visits per 100 person-years (P.001). Further analyses suggested that the increase in telehealth visits offset the reduction in outpatient visits by week 26 (June 28, 2020).In-person health care utilization decreased drastically during the early period of the pandemic, but there was a corresponding increase in telehealth visits during the same period. By end-June 2020, the combined outpatient and telehealth visits had recovered to prepandemic levels.

UNSTRUCTURED REMOVE

BACKGROUND Dramatic decreases in outpatient visits and sudden increases in telehealth visits were observed during the COVID-19 pandemic, but it was unclear whether these changes differed by patient demographics and socioeconomic status. OBJECTIVE This study aimed to assess the impact of the pandemic on in-person outpatient and telehealth visits (telephone and video) by demographic characteristics and household income in a diverse population. METHODS We calculated weekly rates of outpatient and telehealth visits by age, sex, race/ethnicity, and neighborhood-level median household income among members of Kaiser Permanente Southern California (KPSC) from January 5, 2020, to October 31, 2020, and the corresponding period in 2019. We estimated the percentage change in visit rates during the early pandemic period (March 22 to April 25, 2020) and the late pandemic period (October 4 to October 31, 2020) from the prepandemic period (January 5 to March 7, 2020) in Poisson regression models for each subgroup while adjusting for seasonality using 2019 data. We examined if the changes in visit rates differed by subgroups statistically by comparing their 95% CIs. RESULTS Among 4.56 million KPSC members enrolled in January 2020, 15.0% (n=682,947) were ≥65 years old, 51.5% (n=2,345,020) were female, 39.4% (n=1,795,994) were Hispanic, and 7.7% (n=350,721) lived in an area of median household income CONCLUSIONS The Hispanic group and low-income group had the largest percentage increase in telehealth utilization in response to the COVID-19 pandemic. The use of virtual care potentially mitigated the impact of the pandemic on health care utilization in these vulnerable populations.

Background The COVID-19 pandemic has caused an abrupt reduction in the use of in-person health care, accompanied by a corresponding surge in the use of telehealth services. However, the extent and nature of changes in health care utilization during the pandemic may differ by care setting. Knowledge of the impact of the pandemic on health care utilization is important to health care organizations and policy makers. Objective The aims of this study are (1) to evaluate changes in in-person health care utilization and telehealth visits during the COVID-19 pandemic and (2) to assess the difference in changes in health care utilization between the pandemic year 2020 and the prepandemic year 2019. Methods We retrospectively assembled a cohort consisting of members of a large integrated health care organization, who were enrolled between January 6 and November 2, 2019 (prepandemic year), and between January 5 and October 31, 2020 (pandemic year). The rates of visits were calculated weekly for four settings: inpatient, emergency department (ED), outpatient, and telehealth. Using Poisson models, we assessed the impact of the pandemic on health care utilization during the early days of the pandemic and conducted difference-in-deference (DID) analyses to measure the changes in health care utilization, adjusting for the trend of health care utilization in the prepandemic year. Results In the early days of the pandemic, we observed significant reductions in inpatient, ED, and outpatient utilization (by 30.2%, 37.0%, and 80.9%, respectively). By contrast, there was a 4-fold increase in telehealth visits between weeks 8 (February 23) and 12 (March 22) in 2020. DID analyses revealed that after adjusting for prepandemic secular trends, the reductions in inpatient, ED, and outpatient visit rates in the early days of the pandemic were 1.6, 8.9, and 367.2 visits per 100 person-years (P Conclusions In-person health care utilization decreased drastically during the early period of the pandemic, but there was a corresponding increase in telehealth visits during the same period. By end-June 2020, the combined outpatient and telehealth visits had recovered to prepandemic levels.

BACKGROUND The COVID-19 pandemic has caused an abrupt reduction in the use of in-person health care, accompanied by a corresponding surge in the use of telehealth services. However, the extent and nature of changes in health care utilization during the pandemic may differ by care setting. Knowledge of the impact of the pandemic on health care utilization is important to health care organizations and policy makers. OBJECTIVE The aims of this study are (1) to evaluate changes in in-person health care utilization and telehealth visits during the COVID-19 pandemic and (2) to assess the difference in changes in health care utilization between the pandemic year 2020 and the prepandemic year 2019. METHODS We retrospectively assembled a cohort consisting of members of a large integrated health care organization, who were enrolled between January 6 and November 2, 2019 (prepandemic year), and between January 5 and October 31, 2020 (pandemic year). The rates of visits were calculated weekly for four settings: inpatient, emergency department (ED), outpatient, and telehealth. Using Poisson models, we assessed the impact of the pandemic on health care utilization during the early days of the pandemic and conducted difference-in-deference (DID) analyses to measure the changes in health care utilization, adjusting for the trend of health care utilization in the prepandemic year. RESULTS In the early days of the pandemic, we observed significant reductions in inpatient, ED, and outpatient utilization (by 30.2%, 37.0%, and 80.9%, respectively). By contrast, there was a 4-fold increase in telehealth visits between weeks 8 (February 23) and 12 (March 22) in 2020. DID analyses revealed that after adjusting for prepandemic secular trends, the reductions in inpatient, ED, and outpatient visit rates in the early days of the pandemic were 1.6, 8.9, and 367.2 visits per 100 person-years (PP CONCLUSIONS In-person health care utilization decreased drastically during the early period of the pandemic, but there was a corresponding increase in telehealth visits during the same period. By end-June 2020, the combined outpatient and telehealth visits had recovered to prepandemic levels.

BACKGROUND: Bisphosphonates are effective in reducing hip and osteoporotic fractures. However, concerns about atypical femur fractures have contributed to substantially decreased bisphosphonate use, and the incidence of hip fractures may be increasing. Important uncertainties remain regarding the association between atypical femur fractures and bisphosphonates and other risk factors. METHODS: We studied women 50 years of age or older who were receiving bisphosphonates and who were enrolled in the Kaiser Permanente Southern California health care system; women were followed from January 1, 2007, to November 30, 2017. The primary outcome was atypical femur fracture. Data on risk factors, including bisphosphonate use, were obtained from electronic health records. Fractures were radiographically adjudicated. Multivariable Cox models were used. The risk–benefit profile was modeled for 1 to 10 years of bisphosphonate use to compare associated atypical fractures with other fractures prevented. RESULTS: Among 196,129 women, 277 atypical femur fractures occurred. After multivariable adjustment, the risk of atypical fracture increased with longer duration of bisphosphonate use: the hazard ratio as compared with less than 3 months increased from 8.86 (95% confidence interval [CI], 2.79 to 28.20) for 3 years to less than 5 years to 43.51 (95% CI, 13.70 to 138.15) for 8 years or more. Other risk factors included race (hazard ratio for Asians vs. Whites, 4.84; 95% CI, 3.57 to 6.56), height, weight, and glucocorticoid use. Bisphosphonate discontinuation was associated with a rapid decrease in the risk of atypical fracture. Decreases in the risk of osteoporotic and hip fractures during 1 to 10 years of bisphosphonate use far outweighed the increased risk of atypical fracture among Whites but less so among Asians. After 3 years, 149 hip fractures were prevented and 2 bisphosphonate-associated atypical fractures occurred in Whites, as compared with 91 and 8, respectively, in Asians. CONCLUSIONS: The risk of atypical femur fracture increased with longer duration of bisphosphonate use and rapidly decreased after bisphosphonate discontinuation. Asians had a higher risk than Whites. The absolute risk of atypical femur fracture remained very low as compared with reductions in the risk of hip and other fractures with bisphosphonate treatment. (Funded by Kaiser Permanente and others.)