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BackgroundContinued automobile driving is important for the wellbeing and independence of older adults. Frailty has been associated with a variety of negative health outcomes, but studies are lacking on the potential association between frailty and driving status. The present study uses data from The National Health and Aging Trends Study (NHATS) to assess if the presence of frailty is associated with being a current non-driver.MethodsNHATS is a nationally representative cohort study of Medicare beneficiaries (aged ≥65) that have been followed since 2011. We examined frailty status at baseline (Fried’s frailty phenotype) and driving status over 4 years (from 2011 to 2014) excluding never drivers at baseline. Multivariable Poisson regression was used to obtain incidence rate ratios, adjusting for covariates and clustering. To account for the repeated measures in the data collection, generalized estimating equations (GEE) were employed.ResultsA significant association between baseline frailty and driving status was observed at all four time points. At T4, frail participants at baseline had an incidence rate for becoming a current non-driver 1.80 times (or an 80% increase) that of non-frail participants at baseline (adjusted 95% confidence interval (CI) 1.56–2.07).ConclusionsFrailty was associated with an increased rate of being a current non-driver. Based on this association, we posit that screening for and intervening on frailty may help certain older adults who are at risk for becoming a current non-driver to remain on the road longer.

Background Driving a motor vehicle is an important aspect of mobility for older adults. Limited lower extremity functioning performance, as measured by the Short Physical Performance Battery (SPPB), has been associated with various negative health outcomes, but little is known about the association of SPPB scores with driving status. Objective The purpose of this study was to evaluate whether lower (poorer) SPPB scores are associated with an increased rate for being a current nondriver among a nationally representative sample of community-dwelling older adults. Design The National Health and Aging Trends Study is a longitudinal cohort study. Methods A population of 5935 participants, surveyed annually from 2011 to 2014 for the National Health and Aging Trends Study, was used to examine the relationship between SPPB and driving status. Using weighted data, multivariable Poisson regression with generalized estimating equations was used to calculate the rate ratios, adjusting for covariates and clustering due to the complex survey design. Results Participants with a low (poor) SPPB score (0–5) had a rate for being a current nondriver 2.01 times the rate (or 101% increase) of those with a high (good) SPPB score (10–12) (adjusted 95% confidence interval = 1.78–2.26). Limitations Current nondrivers were not asked whether they planned to resume driving if they had not driven in the previous year. Conclusions Unlike other factors, such as cognitive decline, lower SPPB scores (poorer lower extremity functioning) are significantly associated with an increased rate of being a current nondriver and are a modifiable risk factor. Further research is needed to examine whether optimum exercises and other physical therapist interventions focused on improving lower extremity strength and balance ultimately improve driving outcomes.

BackgroundPublication bias and other reporting bias have been well documented for journal articles, but no study has evaluated the nature of results posted at ClinicalTrials.gov. We aimed to assess how many randomized controlled trials (RCTs) with results posted at ClinicalTrials.gov report statistically significant results and whether the proportion of trials with significant results differs when no treatment effect estimate or p-value is posted.MethodsWe searched ClinicalTrials.gov in June 2015 for all studies with results posted. We included completed RCTs with a superiority hypothesis and considered results for the first primary outcome with results posted. For each trial, we assessed whether a treatment effect estimate and/or p-value was reported at ClinicalTrials.gov and if yes, whether results were statistically significant. If no treatment effect estimate or p-value was reported, we calculated the treatment effect and corresponding p-value using results per arm posted at ClinicalTrials.gov when sufficient data were reported.ResultsFrom the 17,536 studies with results posted at ClinicalTrials.gov, we identified 2823 completed phase 3 or 4 randomized trials with a superiority hypothesis. Of these, 1400 (50%) reported a treatment effect estimate and/or p-value. Results were statistically significant for 844 trials (60%), with a median p-value of 0.01 (Q1-Q3: 0.001–0.26). For the 1423 trials with no treatment effect estimate or p-value posted, we could calculate the treatment effect and corresponding p-value using results reported per arm for 929 (65%). For 494 trials (35%), p-values could not be calculated mainly because of insufficient reporting, censored data, or repeated measurements over time. For the 929 trials we could calculate p-values, we found statistically significant results for 342 (37%), with a median p-value of 0.19 (Q1-Q3: 0.005–0.59).ConclusionsHalf of the trials with results posted at ClinicalTrials.gov reported a treatment effect estimate and/or p-value, with significant results for 60% of these. p-values could be calculated from results reported per arm at ClinicalTrials.gov for only 65% of the other trials. The proportion of significant results was much lower for these trials, which suggests a selective posting of treatment effect estimates and/or p-values when results are statistically significant.

Chronic obstructive pulmonary disease (COPD) poses a significant but heterogeneous burden to individuals and healthcare systems. Policymakers develop targeted policies to minimize this burden but need personalized tools to evaluate novel interventions and target them to subpopulations most likely to benefit. We developed a platform to identify subgroups that are at increased risk of emergency department visits, hospitalizations and mortality and to provide stratified patient input in economic evaluations of COPD interventions. We relied on administrative and survey data from Ontario, Canada and applied a combination of microsimulation and multi-state modeling methods. We illustrated the functionality of the platform by quantifying outcomes across smoking status (current, former, never smokers) and by estimating the effect of smoking cessation on resource use and survival, by comparing outcomes of hypothetical cohorts of smokers who quit at diagnosis and smokers that continued to smoke post diagnosis. The cumulative incidence of all-cause mortality was 37.9% (95% CI: 34.9, 41.4) for never smokers, 34.7% (95% CI: 32.1, 36.9) for current smokers, and 46.4% (95% CI: 43.6, 49.0) for former smokers, at 14 years. Over 14 years, smokers who did not quit at diagnosis had 16.3% (95% CI: 9.6, 38.4%) more COPD-related emergency department visits than smokers who quit at diagnosis. In summary, we combined methods from clinical and economic modeling to create a novel tool that policymakers and health economists can use to inform future COPD policy decisions and quantify the effect of modifying COPD risk factors on resource utilization and morality.