Has the COVID-19 pandemic changed existing patterns of non-COVID-19 health care utilization? A retrospective analysis of six regions in Europe.

BACKGROUND: Resilience of national health systems in Europe remains a major concern in times of multiple crises and as more evidence is emerging relating to the indirect effects of the COVID-19 pandemic on health care utilization (HCU), resulting from de-prioritization of regular, non-pandemic healthcare services. Most extant studies focus on regional, disease specific or early pandemic HCU creating difficulties in comparing across multiple countries. We provide a comparatively broad definition of HCU across multiple countries, with potential to expand across regions and timeframes. METHODS: Using a cross-country federated research infrastructure (FRI), we examined HCU for acute cardiovascular events, elective surgeries and serious trauma. Aggregated data were used in forecast modelling to identify changes from predicted European age-standardized counts via fitted regressions (2017-19), compared against post-pandemic data. RESULTS: We found that elective surgeries were most affected, universally falling below predicted levels in 2020. For cardiovascular HCU, we found lower-than-expected cases in every region for heart attacks and displayed large sex differences. Serious trauma was the least impacted by the COVID-19 pandemic. CONCLUSION: The strength of this study comes from the use of the European Population Health Information Research Infrastructure's (PHIRI) FRI, allowing for rapid analysis of regional differences to assess indirect impacts of events such as pandemics. There are marked differences in the capacity of services to return to normal in terms of elective surgery; additionally, we found considerable differences between men and women which requires further research on potential sex or gender patterns of HCU during crises.

Individual participant data from digital sources informed and improved precision in the evaluation of predictive biomarkers in Bayesian network meta-analysis.

OBJECTIVES: We aimed to develop a network meta-analytic model for the evaluation of treatment effectiveness within predictive biomarker subgroups, by combining evidence from individual participant data (IPD) from digital sources (in the absence of randomized controlled trials) and aggregate data (AD). STUDY DESIGN AND SETTING: A Bayesian framework was developed for modeling time-to-event data to evaluate predictive biomarkers. IPD were sourced from electronic health records, using a target trial emulation approach, or digitized Kaplan-Meier curves. The model is illustrated using two examples: breast cancer with a hormone receptor biomarker, and metastatic colorectal cancer with the Kirsten Rat Sarcoma (KRAS) biomarker. RESULTS: The model allowed for the estimation of treatment effects in two subgroups of patients defined by their biomarker status. Effectiveness of taxanes did not differ in hormone receptor positive and negative breast cancer patients. Epidermal growth factor receptor inhibitors were more effective than chemotherapy in KRAS wild type colorectal cancer patients but not in patients with KRAS mutant status. Use of IPD reduced uncertainty of the subgroup-specific treatment effect estimates by up to 49%. CONCLUSION: Utilization of IPD allowed for more detailed evaluation of predictive biomarkers and cancer therapies and improved precision of the estimates compared to use of AD alone.

Clustering long-term health conditions among 67728 people with multimorbidity using electronic health records in Scotland.

There is still limited understanding of how chronic conditions co-occur in patients with multimorbidity and what are the consequences for patients and the health care system. Most reported clusters of conditions have not considered the demographic characteristics of these patients during the clustering process. The study used data for all registered patients that were resident in Fife or Tayside, Scotland and aged 25 years or more on 1st January 2000 and who were followed up until 31st December 2018. We used linked demographic information, and secondary care electronic health records from 1st January 2000. Individuals with at least two of the 31 Elixhauser Comorbidity Index conditions were identified as having multimorbidity. Market basket analysis was used to cluster the conditions for the whole population and then repeatedly stratified by age, sex and deprivation. 318,235 individuals were included in the analysis, with 67,728 (21·3%) having multimorbidity. We identified five distinct clusters of conditions in the population with multimorbidity: alcohol misuse, cancer, obesity, renal failure, and heart failure. Clusters of long-term conditions differed by age, sex and socioeconomic deprivation, with some clusters not present for specific strata and others including additional conditions. These findings highlight the importance of considering demographic factors during both clustering analysis and intervention planning for individuals with multiple long-term conditions. By taking these factors into account, the healthcare system may be better equipped to develop tailored interventions that address the needs of complex patients.

Patient factors associated with onabotulinum toxin A treatment outcome in women with detrusor overactivity.

OBJECTIVE: To evaluate potential predictors of non-response to treatment with 200U onabotulinum toxin A (onaBoNTA) in women with refractory detrusor overactivity (DO). SUBJECTS AND METHODS: A secondary analysis of a randomized trial of 200U onaBoNTA versus placebo in women with refractory DO analyzed baseline and 6 week follow-up data. Univariate and multivariate logistic regression were used to assess demographic factors and baseline clinical parameters on non-response to treatment defined as 20% or less improvement in urinary urgency and leakage episodes, 10% or less in voiding frequency, not achieving continence, and "no change" or worse on PGI-I score at 6 weeks. RESULTS: One Hundred and twenty-two women were included. Twenty-nine (23.8%), 24 (19.7%), and 19 (15.6%) were non-responders to treatment for urgency, voiding, and leakage episodes, respectively. Fifty-nine (48.4%) failed to achieve continence, and 28 (23%) were non-responders on the PGI-I scale. Smoking status (OR: 2.89 95%CI 1.08, 7.73, P = 0.034) predicted non-response in urgency episodes, and higher baseline leakage episodes (OR: 1.17 95%CI 1.04, 1.31, P = 0.007) predicted non-response in achieving continence. Increasing age (OR 1.04, 95%CI 1.0, 1.09, P = 0.063) and body mass index (BMI) (OR 1.07, 95%CI 1.0, 1.16, P = 0.065) showed marginal associations with non-response on the PGI-I scale. CONCLUSION: onaBoNTA is an effective treatment for refractory DO, but some fail to respond. For identification of women at risk, our data indicate smokers should be advised of a lesser chance of successful treatment. Older women, those with high BMI and with more severe leakage also have a higher risk of failure. Neurourol. Urodynam. 36:426-431, 2017. © 2016 Wiley Periodicals, Inc.