Multilevel Quality Indicators: Methodology and Monte Carlo Evidence.

BACKGROUND: Quality indicators are frequently used to assess the performance of health care providers, in particular hospitals. Established approaches to the design of such indicators are subject to distortions due to indirect standardization and high variance of estimators. Indicators for geographical regions are rarely considered. OBJECTIVES: To develop and evaluate a methodology of multilevel quality indicators (MQIs) for both health care providers and geographical regions. RESEARCH DESIGN: We formally derived MQIs from a statistical multilevel model, which may include characteristics of patients, providers, and regions. We used Monte Carlo simulation to assess the performance of MQIs relative to established approaches based on the standardized mortality/morbidity ratio (SMR) and the risk-standardized mortality rate (RSMR). MEASURES: Rank correlation between true provider/region effects and quality indicator estimates; shares of the 10% best and 10% worst providers identified by the quality indicators. RESULTS: The proposed MQIs are (1) standardized hospital outcome rate (SHOR), (2) regional SHOR, and (3) regional standardized patient outcome rate. Monte Carlo simulations indicated that the SHOR provides substantially better estimates of provider performance than the SMR and risk-standardized mortality rate in almost all scenarios. The regional standardized patient outcome rate was slightly more stable than the regional SMR. We also found that modeling of regional characteristics generally improves the adequacy of provider-level estimates. CONCLUSIONS: MQIs methodology facilitates adequate and efficient estimation of quality indicators for both health care providers and geographical regions.

Development of a risk score to identify patients at high risk for a severe course of COVID-19.

Aim: We aimed to develop a risk score to calculate a person's individual risk for a severe COVID-19 course (POINTED score) to support prioritization of especially vulnerable patients for a (booster) vaccination. Subject and methods: This cohort study was based on German claims data and included 623,363 individuals with a COVID-19 diagnosis in 2020. The outcome was COVID-19 related treatment in an intensive care unit, mechanical ventilation, or death after a COVID-19 infection. Data were split into a training and a test sample. Poisson regression models with robust standard errors including 35 predefined risk factors were calculated. Coefficients were rescaled with a min-max normalization to derive numeric score values between 0 and 20 for each risk factor. The scores' discriminatory ability was evaluated by calculating the area under the curve (AUC). Results: Besides age, down syndrome and hematologic cancer with therapy, immunosuppressive therapy, and other neurological conditions were the risk factors with the highest risk for a severe COVID-19 course. The AUC of the POINTED score was 0.889, indicating very good predictive validity. Conclusion: The POINTED score is a valid tool to calculate a person's risk for a severe COVID-19 course. Supplementary Information: The online version contains supplementary material available at 10.1007/s10389-023-01884-7.

The Long-Term Sequelae of Traumatic Brain Injury Over 10 Years of Follow-Up­A Matched Cohort Study Based on Routine Data of a Statutory Health Insurance Carrier

BACKGROUND: The acute effects of traumatic brain injury (TBI) are well documented, but there is no systematic quantification of its long-term sequelae in Germanlanguage literature. The purpose of this article is to compare the frequency of conditions linked to prior TBI with their frequency in the non-brain-injured population. METHODS: A matched cohort study was carried out on the basis of routine data from the BARMER statutory health insurance carrier. The exposure group consisted of patients treated over the period 2006-2009 for TBI at a variety of treatment intensities, including persons with multiple organ trauma. The control group consisted of BARMER insurees without prior TBI who were matched with the patients in the exposure group for age, sex, and pre-existing diseases. Late sequelae were sought in the routine data for a period of ten years after the injury. The outcome rates of the exposure and control groups were compared with Kaplan-Meier estimators and Poisson regression. RESULTS: 114 296 persons with TBI in the period 2006-2009 were included in the study. The mortality within ten years of TBI was 305 per 1000 individuals. The relative mortality in the exposure group was higher than that in control individuals of the same age and sex, with an incidence rate ratio (IRR) of 1.67 (95% confidence interval, [1.60; 1.74]). Immobility, dementia, epilepsy, endocrine disorders, functional disorders, depression, anxiety, cognitive deficits, headache, and sleep disorders were also more common in the exposure group. Persons with TBI requiring highintensity treatment displayed the highest relative incidence rates of the conditions studied over 10 years of follow-up. Persons who had been admitted to the hospital because of TBI had higher relative incidence rates for epilepsy and dementia than those who had been cared for on an outpatient basis. CONCLUSION: Adverse sequelae of TBI can still be seen ten years after the exposure. These patients die earlier than persons without TBI and suffer earlier and more frequently from associated conditions.

Post-COVID-19-associated morbidity in children, adolescents, and adults: A matched cohort study including more than 157,000 individuals with COVID-19 in Germany.

BACKGROUND: Long-term health sequelae of the Coronavirus Disease 2019 (COVID-19) are a major public health concern. However, evidence on post-acute COVID-19 syndrome (post-COVID-19) is still limited, particularly for children and adolescents. Utilizing comprehensive healthcare data on approximately 46% of the German population, we investigated post-COVID-19-associated morbidity in children/adolescents and adults. METHODS AND FINDINGS: We used routine data from German statutory health insurance organizations covering the period between January 1, 2019 and December 31, 2020. The base population included all individuals insured for at least 1 day in 2020. Based on documented diagnoses, we identified individuals with polymerase chain reaction (PCR)-confirmed COVID-19 through June 30, 2020. A control cohort was assigned using 1:5 exact matching on age and sex, and propensity score matching on preexisting medical conditions. The date of COVID-19 diagnosis was used as index date for both cohorts, which were followed for incident morbidity outcomes documented in the second quarter after index date or later.Overall, 96 prespecified outcomes were aggregated into 13 diagnosis/symptom complexes and 3 domains (physical health, mental health, and physical/mental overlap domain). We used Poisson regression to estimate incidence rate ratios (IRRs) with 95% confidence intervals (95% CIs). The study population included 11,950 children/adolescents (48.1% female, 67.2% aged between 0 and 11 years) and 145,184 adults (60.2% female, 51.1% aged between 18 and 49 years). The mean follow-up time was 236 days (standard deviation (SD) = 44 days, range = 121 to 339 days) in children/adolescents and 254 days (SD = 36 days, range = 93 to 340 days) in adults. COVID-19 and control cohort were well balanced regarding covariates. The specific outcomes with the highest IRR and an incidence rate (IR) of at least 1/100 person-years in the COVID-19 cohort in children and adolescents were malaise/fatigue/exhaustion (IRR: 2.28, 95% CI: 1.71 to 3.06, p < 0.01, IR COVID-19: 12.58, IR Control: 5.51), cough (IRR: 1.74, 95% CI: 1.48 to 2.04, p < 0.01, IR COVID-19: 36.56, IR Control: 21.06), and throat/chest pain (IRR: 1.72, 95% CI: 1.39 to 2.12, p < 0.01, IR COVID-19: 20.01, IR Control: 11.66). In adults, these included disturbances of smell and taste (IRR: 6.69, 95% CI: 5.88 to 7.60, p < 0.01, IR COVID-19: 12.42, IR Control: 1.86), fever (IRR: 3.33, 95% CI: 3.01 to 3.68, p < 0.01, IR COVID-19: 11.53, IR Control: 3.46), and dyspnea (IRR: 2.88, 95% CI: 2.74 to 3.02, p < 0.01, IR COVID-19: 43.91, IR Control: 15.27). For all health outcomes combined, IRs per 1,000 person-years in the COVID-19 cohort were significantly higher than those in the control cohort in both children/adolescents (IRR: 1.30, 95% CI: 1.25 to 1.35, p < 0.01, IR COVID-19: 436.91, IR Control: 335.98) and adults (IRR: 1.33, 95% CI: 1.31 to 1.34, p < 0.01, IR COVID-19: 615.82, IR Control: 464.15). The relative magnitude of increased documented morbidity was similar for the physical, mental, and physical/mental overlap domain. In the COVID-19 cohort, IRs were significantly higher in all 13 diagnosis/symptom complexes in adults and in 10 diagnosis/symptom complexes in children/adolescents. IRR estimates were similar for age groups 0 to 11 and 12 to 17. IRs in children/adolescents were consistently lower than those in adults. Limitations of our study include potentially unmeasured confounding and detection bias. CONCLUSIONS: In this retrospective matched cohort study, we observed significant new onset morbidity in children, adolescents, and adults across 13 prespecified diagnosis/symptom complexes, following COVID-19 infection. These findings expand the existing available evidence on post-COVID-19 conditions in younger age groups and confirm previous findings in adults. TRIAL REGISTRATION: ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT05074953.

Optimising the impact of COVID-19 vaccination on mortality and hospitalisations using an individual additive risk measuring approach based on a risk adjustment scheme.

In this population-based cohort study, billing data from German statutory health insurance (BARMER, 10% of population) are used to develop a prioritisation model for COVID-19 vaccinations based on cumulative underlying conditions. Using a morbidity-based classification system, prevalence and risks for COVID-19-related hospitalisations, ventilations and deaths are estimated. Trisomies, behavioural and developmental disorders (relative risk: 2.09), dementia and organic psychoorganic syndromes (POS) (2.23) and (metastasised) malignant neoplasms (1.99) were identified as the most important conditions for escalations of COVID-19 infection. Moreover, optimal vaccination priority schedules for participants are established on the basis of individual cumulative escalation risk and are compared to the prioritisation scheme chosen by the German Government. We estimate how many people would have already received a vaccination prior to escalation. Vaccination schedules based on individual cumulative risk are shown to be 85% faster than random schedules in preventing deaths, and as much as 57% faster than the German approach, which was based primarily on age and specific diseases. In terms of hospitalisation avoidance, the individual cumulative risk approach was 51% and 28% faster. On this basis, it is concluded that using individual cumulative risk-based vaccination schedules, healthcare systems can be relieved and escalations more optimally avoided.