Identifying Cases of Shoulder Injury Related to Vaccine Administration (SIRVA) in the United States: Development and Validation of a Natural Language Processing Method.

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.

Risk for Shoulder Conditions After Vaccination: A Population-Based Study Using Real-World Data.

BACKGROUND: Although shoulder conditions have been reported as an adverse event after intramuscular vaccination in the deltoid muscle, epidemiologic data on shoulder conditions after vaccination are limited. OBJECTIVE: To estimate the risk for shoulder conditions after vaccination and assess possible risk factors. DESIGN: Retrospective cohort study. SETTING: Kaiser Permanente Southern California, a large integrated health care organization. PARTICIPANTS: Kaiser Permanente Southern California members aged 3 years or older who had an intramuscular vaccination administered in the deltoid muscle between 1 April 2016 and 31 December 2017. MEASUREMENTS: A natural language processing (NLP) algorithm was used to identify potential shoulder conditions among vaccinated persons with shoulder disorder diagnosis codes. All NLP-identified cases were manually chart confirmed on the basis of our case definition. The characteristics of vaccinated persons with and without shoulder conditions were compared. RESULTS: Among 3 758 764 administered vaccinations, 371 cases of shoulder condition were identified, with an estimated incidence of 0.99 (95% CI, 0.89 to 1.09) per 10 000 vaccinations. The incidence was 1.22 (CI, 1.10 to 1.35) for the adult (aged ≥18 years) and 0.05 (CI, 0.02 to 0.14) for the pediatric (aged 3 to 17 years) vaccinated populations. In the adult vaccinated population, advanced age, female sex, an increased number of outpatient visits in the 6 months before vaccination, lower Charlson Comorbidity Index, and pneumococcal conjugate vaccine were associated with a higher risk for shoulder conditions. Among influenza vaccines, quadrivalent vaccines were associated with an increased risk for shoulder conditions. Simultaneous administration of vaccines was associated with a higher risk for shoulder conditions among elderly persons. LIMITATION: Generalizability to other health care settings, use of administrative data, and residual confounding. CONCLUSION: These population-based data suggest a small absolute risk for shoulder conditions after vaccination. Given the high burden of shoulder conditions, clinicians should pay attention to any factors that may further increase risks. PRIMARY FUNDING SOURCE: Centers for Disease Control and Prevention.

Outcomes post fragility fracture among members of an integrated healthcare organization.

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. INTRODUCTION: The objective of this study was to characterize osteoporosis-related fracture incidence and identify predictors of subsequent fractures and mortality. METHODS: 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. RESULTS: 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 < 0.0001). CONCLUSION: Morbidity is high in the year following a fragility fracture and men, patients with a spine or hip fracture, and those aged ≥ 65 have a greater disease burden.

COVID-19 Vaccination and Non-COVID-19 Mortality Risk - Seven Integrated Health Care Organizations, United States, December 14, 2020-July 31, 2021.

By September 21, 2021, an estimated 182 million persons in the United States were fully vaccinated against COVID-19.* Clinical trials indicate that Pfizer-BioNTech (BNT162b2), Moderna (mRNA-1273), and Janssen (Johnson & Johnson; Ad.26.COV2.S) vaccines are effective and generally well tolerated (1-3). However, daily vaccination rates have declined approximately 78% since April 13, 2021†; vaccine safety concerns have contributed to vaccine hesitancy (4). A cohort study of 19,625 nursing home residents found that those who received an mRNA vaccine (Pfizer-BioNTech or Moderna) had lower all-cause mortality than did unvaccinated residents (5), but no studies comparing mortality rates within the general population of vaccinated and unvaccinated persons have been conducted. To assess mortality not associated with COVID-19 (non-COVID-19 mortality) after COVID-19 vaccination in a general population setting, a cohort study was conducted during December 2020-July 2021 among approximately 11 million persons enrolled in seven Vaccine Safety Datalink (VSD) sites.§ After standardizing mortality rates by age and sex, this study found that COVID-19 vaccine recipients had lower non-COVID-19 mortality than did unvaccinated persons. After adjusting for demographic characteristics and VSD site, this study found that adjusted relative risk (aRR) of non-COVID-19 mortality for the Pfizer-BioNTech vaccine was 0.41 (95% confidence interval [CI] = 0.38-0.44) after dose 1 and 0.34 (95% CI = 0.33-0.36) after dose 2. The aRRs of non-COVID-19 mortality for the Moderna vaccine were 0.34 (95% CI = 0.32-0.37) after dose 1 and 0.31 (95% CI = 0.30-0.33) after dose 2. The aRR after receipt of the Janssen vaccine was 0.54 (95% CI = 0.49-0.59). There is no increased risk for mortality among COVID-19 vaccine recipients. This finding reinforces the safety profile of currently approved COVID-19 vaccines in the United States.

Disparities in Outpatient and Telehealth Visits During the COVID-19 Pandemic in a Large Integrated Health Care Organization: Retrospective Cohort Study.

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

Correction: Impact of the COVID-19 Pandemic on Health Care Utilization in a Large Integrated Health Care System: Retrospective Cohort Study.

[This corrects the article DOI: 10.2196/26558.].

Impact of the COVID-19 Pandemic on Health Care Utilization in a Large Integrated Health Care System: Retrospective Cohort Study.

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<.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). 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.