Predictors of Nursing Home Residents' Time to Hospitalization

Objectives: To model the predictors of the time to first acute hospitalization for nursing home residents, and accounting for previous hospitalizations, model the predictors of time between subsequent hospitalizations. Data sources: Merged file from New York State for the period 1998–2004 consisting of nursing home information from the minimum dataset and hospitalization information from the Statewide Planning and Research Cooperative System. Study design: Accelerated failure time models were used to estimate the model parameters and predict survival times. The models were fit to observations from 50 percent of the nursing homes and validated on the remaining observations. Principal findings: Pressure ulcers and facility-level deficiencies were associated with a decreased time to first hospitalization, while the presence of advance directives and facility staffing was associated with an increased time. These predictors of the time to first hospitalization model had effects of similar magnitude in predicting the time between subsequent hospitalizations. Conclusions: This study provides novel evidence suggesting modifiable patient and nursing home characteristics are associated with the time to first hospitalization and time to subsequent hospitalizations for nursing home residents

Estimating cost-offsets of new medications: Use of new antipsychotics and mental health costs for schizophrenia

Estimation of the effect of one treatment compared to another in the absence of randomization is a common problem in biostatistics. An increasingly popular approach involves instrumental variables—variables that are predictive of who received a treatment yet not directly predictive of the outcome. When treatment is binary, many estimators have been proposed: method-of-moments estimators using a two-stage least-squares procedure, generalized-method-of-moments estimators using two-stage predictor substitution or two-stage residual inclusion procedures, and likelihood-based latent variable approaches. The critical assumptions to the consistency of two-stage procedures and of the likelihood-based procedures differ. Because neither set of assumptions can be completely tested from the observed data alone, comparing the results from the different approaches is an important sensitivity analysis. We provide a general statistical framework for estimation of the casual effect of a binary treatment on a continuous outcome using simultaneous equations to specify models. A comparison of health care costs for adults with schizophrenia treated with newer atypical antipsychotics and those treated with conventional antipsychotic medications illustrates our methods. Surprisingly large differences in the results among the methods are investigated using a simulation study. Several new findings concerning the performance in terms of precision and robustness of each approach in different situations are obtained. We illustrate that in general supplemental information is needed to determine which analysis, if any, is trustworthy and reaffirm that comparing results from different approaches is a valuable sensitivity analysis

Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice

Para-infectious brain injury in COVID-19 persists at follow-up despite attenuated cytokine and autoantibody responses

To understand neurological complications of COVID-19 better both acutely and for recovery, we measured markers of brain injury, inflammatory mediators, and autoantibodies in 203 hospitalised participants; 111 with acute sera (1–11 days post-admission) and 92 convalescent sera (56 with COVID-19-associated neurological diagnoses). Here we show that compared to 60 uninfected controls, tTau, GFAP, NfL, and UCH-L1 are increased with COVID-19 infection at acute timepoints and NfL and GFAP are significantly higher in participants with neurological complications. Inflammatory mediators (IL-6, IL-12p40, HGF, M-CSF, CCL2, and IL-1RA) are associated with both altered consciousness and markers of brain injury. Autoantibodies are more common in COVID-19 than controls and some (including against MYL7, UCH-L1, and GRIN3B) are more frequent with altered consciousness. Additionally, convalescent participants with neurological complications show elevated GFAP and NfL, unrelated to attenuated systemic inflammatory mediators and to autoantibody responses. Overall, neurological complications of COVID-19 are associated with evidence of neuroglial injury in both acute and late disease and these correlate with dysregulated innate and adaptive immune responses acutely

Some new considerations for the statistical analysis of an assay

Statistical analysis is a vital component of an assay. In an immunoassay, the diagnosis of an illness or the determination of a treatment may be at stake. In this and other assays, it is essential that the assay be analyzed with the greatest accuracy. Standard models for assays tend to have several complicating characteristics which have led to approximate rather than exact evaluation of inferences. The main focus of this thesis is the development of methods that do not compromise the accuracy of the statistical analysis of an assay. For the most part, a Bayesian view is taken. There are many philosophical arguments in favour of the Bayesian approach. However, the involvement of the Bayesian paradigm in this thesis is through necessity rather than philosophy. The frequentist paradigm provides no apparent means of evaluating many of the calculations involved in the analysis of an assay. On the contrary, there is a formal procedure for solving all inference and decision making problems under the Bayesian paradigm. Heterogeneity in the variance of the response is one of the complicating characteristics of assay data. Inferences for heteroscedastic regression models are strongly dependent on the fitted variance function. The estimation of the variance function for an assay is addressed in this thesis. Fitting the assay model and drawing inferences about the parameters is only one side of the statistical analysis of an assay. The other endeavour is the assessment of the quality of the assay as a measuring device. This is needed in order to maintain the quality of the assay over time and potentially for use as a criterion for the determination of an assay's optimal analytical and statistical (i.e. experimental) designs. The assessment of the quality of an assay has also been compromised by frequentist approximations. The development and analysis of a Bayesian model for an assay along with the assessment of the quality of an assay join variance function estimation as the focal points of this thesis

Telehealth Use Following COVID-19 Within Patient-Sharing Physician Networks at a Rural Comprehensive Cancer Center: Cross-sectional Analysis

BackgroundIn response to the COVID-19 pandemic, cancer centers rapidly adopted telehealth to deliver care remotely. Telehealth will likely remain a model of care for years to come and may not only affect the way oncologists deliver care to their own patients but also the physicians with whom they share patients. ObjectiveThis study aimed to examine oncologist characteristics associated with telehealth use and compare patient-sharing networks before and after the COVID-19 pandemic in a rural catchment area with a particular focus on the ties between physicians at the comprehensive cancer center and regional facilities. MethodsIn this retrospective observational study, we obtained deidentified electronic health record data for individuals diagnosed with breast, colorectal, or lung cancer at Dartmouth Health in New Hampshire from 2018-2020. Hierarchical logistic regression was used to identify physician factors associated with telehealth encounters post COVID-19. Patient-sharing networks for each cancer type before and post COVID-19 were characterized with global network measures. Exponential-family random graph models were performed to estimate homophily terms for the likelihood of ties existing between physicians colocated at the hub comprehensive cancer center. ResultsOf the 12,559 encounters between patients and oncologists post COVID-19, 1228 (9.8%) were via telehealth. Patient encounters with breast oncologists who practiced at the hub hospital were over twice as likely to occur via telehealth compared to encounters with oncologists who practiced in regional facilities (odds ratio 2.2, 95% CI 1.17-4.15; P=.01). Patient encounters with oncologists who practiced in multiple locations were less likely to occur via telehealth, and this association was statistically significant for lung cancer care (odds ratio 0.26, 95% CI 0.09-0.76; P=.01). We observed an increase in ties between oncologists at the hub hospital and oncologists at regional facilities in the lung cancer network post COVID-19 compared to before COVID-19 (93/318, 29.3%, vs 79/370, 21.6%, respectively), which was also reflected in the lower homophily coefficients post COVID-19 compared to before COVID-19 for physicians being colocated at the hub hospital (estimate: 1.92, 95% CI 1.46-2.51, vs 2.45, 95% CI 1.98-3.02). There were no significant differences observed in breast cancer or colorectal cancer networks. ConclusionsTelehealth use and associated changes to patient-sharing patterns associated with telehealth varied by cancer type, suggesting disparate approaches for integrating telehealth across clinical groups within this health system. The limited changes to the patient-sharing patterns between oncologists at the hub hospital and regional facilities suggest that telehealth was less likely to create new referral patterns between these types of facilities and rather replace care that would otherwise have been delivered in person. However, this study was limited to the 2 years immediately following the initial outbreak of COVID-19, and longer-term follow-up may uncover delayed effects that were not observed in this study period

Impact of COVID-19 on Diagnostic Cardiac Procedural Volume in Oceania: The IAEA Non-Invasive Cardiology Protocol Survey on COVID-19 (INCAPS COVID)

Objectives: The INCAPS COVID Oceania study aimed to assess the impact caused by the COVID-19 pandemic on cardiac procedure volume provided in the Oceania region. Methods: A retrospective survey was performed comparing procedure volumes within March 2019 (pre-COVID-19) with April 2020 (during first wave of COVID-19 pandemic). Sixty-three (63) health care facilities within Oceania that perform cardiac diagnostic procedures were surveyed, including a mixture of metropolitan and regional, hospital and outpatient, public and private sites, and 846 facilities outside of Oceania. The percentage change in procedure volume was measured between March 2019 and April 2020, compared by test type and by facility. Results: In Oceania, the total cardiac diagnostic procedure volume was reduced by 52.2% from March 2019 to April 2020, compared to a reduction of 75.9% seen in the rest of the world (p<0.001). Within Oceania sites, this reduction varied significantly between procedure types, but not between types of health care facility. All procedure types (other than stress cardiac magnetic resonance [CMR] and positron emission tomography [PET]) saw significant reductions in volume over this time period (p<0.001). In Oceania, transthoracic echocardiography (TTE) decreased by 51.6%, transoesophageal echocardiography (TOE) by 74.0%, and stress tests by 65% overall, which was more pronounced for stress electrocardiograph (ECG) (81.8%) and stress echocardiography (76.7%) compared to stress single-photon emission computerised tomography (SPECT) (44.3%). Invasive coronary angiography decreased by 36.7% in Oceania. Conclusion: A significant reduction in cardiac diagnostic procedure volume was seen across all facility types in Oceania and was likely a function of recommendations from cardiac societies and directives from government to minimise spread of COVID-19 amongst patients and staff. Longer term evaluation is important to assess for negative patient outcomes which may relate to deferral of usual models of care within cardiology