Repeat emergency department visits by nursing home residents: a cohort study using health administrative data

BACKGROUND

Nursing home (NH) residents are frequent users of emergency departments (ED) and while prior research suggests that repeat visits are common, there is little data describing this phenomenon. Our objectives were to describe repeat ED visits over one year, identify risk factors for repeat use, and characterize "frequent" ED visitors.

METHODS

Using provincial administrative data from Ontario, Canada, we identified all NH residents 65 years or older who visited an ED at least once between January 1 and March 31, 2010 and then followed them for one year to capture all additional ED visits. Frequent ED visitors were defined as those who had 3 or more repeat ED visits. We used logistic regression to estimate risk factors for any repeat ED visit and for being a frequent visitor and Andersen-Gill regression to estimate risk factors for the rate of repeat ED visits.

RESULTS

In a cohort of 25,653 residents (mean age 84.5 (SD = 7.5) years, 68.2% female), 48.8% had at least one repeat ED visit. Residents who experienced a repeat ED visit were generally similar to others but they tended to be slightly younger, have a higher proportion male, and a higher proportion with minimal cognitive or physical impairment. Risk factors for a repeat ED visit included: being male (adjusted odds ratio 1.27, (95% confidence interval 1.19-1.36)), diagnoses such as diabetes (AOR 1.28 (1.19-1.37)) and congestive heart failure (1.26 (1.16-1.37)), while severe cognitive impairment (AOR 0.92 (0.84-0.99)) and 5 or more chronic conditions (AOR 0.82 (0.71-0.95)) appeared protective. Eleven percent of residents were identified as frequent ED visitors, and they were more often younger then 75 years, male, and less likely to have Alzheimer's disease or other dementias than non-frequent visitors.

CONCLUSIONS

Repeat ED visits were common among NH residents but a relatively small group accounted for the largest number of visits. Although there were few clear defining characteristics, our findings suggest that medically complex residents and younger residents without cognitive impairments are at risk for such outcomes.

Estimating and testing direct genetic effects in directed acyclic graphs using estimating equations

In genetic association studies, it is important to distinguish direct and indirect genetic effects in order to build truly functional models. For this purpose, we consider a directed acyclic graph setting with genetic variants, primary and intermediate phenotypes, and confounding factors. In order to make valid statistical inference on direct genetic effects on the primary phenotype, it is necessary to consider all potential effects in the graph, and we propose to use the estimating equations method with robust Huber-White sandwich standard errors. We evaluate the proposed causal inference based on estimating equations (CIEE) method and compare it with traditional multiple regression methods, the structural equation modeling method, and sequential G-estimation methods through a simulation study for the analysis of (completely observed) quantitative traits and time-to-event traits subject to censoring as primary phenotypes. The results show that CIEE provides valid estimators and inference by successfully removing the effect of intermediate phenotypes from the primary phenotype and is robust against measured and unmeasured confounding of the indirect effect through observed factors. All other methods except the sequential G-estimation method for quantitative traits fail in some scenarios where their test statistics yield inflated type I errors. In the analysis of the Genetic Analysis Workshop 19 dataset, we estimate and test genetic effects on blood pressure accounting for intermediate gene expression phenotypes. The results show that CIEE can identify genetic variants that would be missed by traditional regression analyses. CIEE is computationally fast, widely applicable to different fields, and available as an R package.

Breast cancer detection among young survivors of pediatric Hodgkin lymphoma with screening magnetic resonance imaging

BACKGROUND

Female survivors of pediatric Hodgkin lymphoma (HL) who have received chest radiotherapy are at increased risk of breast cancer. Guidelines for early breast cancer screening among these survivors are based on little data regarding clinical outcomes. This study reports outcomes of breast cancer screening with MRI and mammography (MMG) after childhood HL.

METHODS

We evaluated the results of breast MRI and MMG screening among 96 female survivors of childhood HL treated with chest radiotherapy. Outcomes measured included imaging sensitivity and specificity, breast cancer characteristics, and incidence of additional imaging and breast biopsy.

RESULTS

Median age at first screening was 30 years, and the median number of MRI screening rounds was 3. Ten breast cancers were detected in 9 women at a median age of 39 years (range, 24-43 years). Half were invasive and half were preinvasive. The median size of invasive tumors was 8 mm (range, 3-15 mm), and none had lymph node involvement. Sensitivity and specificity of the screening modalities were as follows: for MRI alone, 80% and 93.5%, respectively; MMG alone, 70% and 95%, respectively; both modalities combined, 100% and 88.6%, respectively. All invasive tumors were detected by MRI. Additional investigations were required in 52 patients, (54%), and 26 patients (27%) required breast biopsy, with 10 patients requiring more than 1 biopsy.

CONCLUSIONS

Screening including breast MRI with MMG has high sensitivity and specificity in pediatric HL survivors, with breast cancers detected at an early stage, although it is associated with a substantial rate of additional investigations. Cancer 2014;120:2507–2513. © 2014 The Authors. Cancer published by Wiley Periodicals, Inc. on behalf of American Cancer Society.

Screening female survivors of pediatric Hodgkin Lymphoma for breast cancer with MRI and mammography detected tumors at an earlier stage than prior studies of mammography alone, although a substantial proportion of women required additional tests for benign imaging findings. The 5-year cumulative incidence of invasive or preinvasive tumors after initiating screening was 10.8%.

Comparing the cohort design and the nested case-control design in the presence of both time-invariant and time-dependent treatment and competing risks: bias and precision

Purpose

Observational studies using electronic administrative healthcare databases are often used to estimate the effects of treatments and exposures. Traditionally, a cohort design has been used to estimate these effects, but increasingly, studies are using a nested case–control (NCC) design. The relative statistical efficiency of these two designs has not been examined in detail.

Methods

We used Monte Carlo simulations to compare these two designs in terms of the bias and precision of effect estimates. We examined three different settings: (A) treatment occurred at baseline, and there was a single outcome of interest; (B) treatment was time varying, and there was a single outcome; and C treatment occurred at baseline, and there was a secondary event that competed with the primary event of interest. Comparisons were made of percentage bias, length of 95% confidence interval, and mean squared error (MSE) as a combined measure of bias and precision.

Results

In Setting A, bias was similar between designs, but the cohort design was more precise and had a lower MSE in all scenarios. In Settings B and C, the cohort design was more precise and had a lower MSE in all scenarios. In both Settings B and C, the NCC design tended to result in estimates with greater bias compared with the cohort design.

Conclusions

We conclude that in a range of settings and scenarios, the cohort design is superior in terms of precision and MSE. Copyright © 2012 John Wiley & Sons, Ltd.