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Objective To examine the association between the presence of individual principal investigators’ financial ties to the manufacturer of the study drug and the trial’s outcomes after accounting for source of research funding.Design Cross sectional study of randomized controlled trials (RCTs).Setting Studies published in “core clinical” journals, as identified by Medline, between 1 January 2013 and 31 December 2013.Participants Random sample of RCTs focused on drug efficacy.Main outcome measure Association between financial ties of principal investigators and study outcome.Results A total of 190 papers describing 195 studies met inclusion criteria. Financial ties between principal investigators and the pharmaceutical industry were present in 132 (67.7%) studies. Of 397 principal investigators, 231 (58%) had financial ties and 166 (42%) did not. Of all principal investigators, 156 (39%) reported advisor/consultancy payments, 81 (20%) reported speakers’ fees, 81 (20%) reported unspecified financial ties, 52 (13%) reported honorariums, 52 (13%) reported employee relationships, 52 (13%) reported travel fees, 41 (10%) reported stock ownership, and 20 (5%) reported having a patent related to the study drug. The prevalence of financial ties of principal investigators was 76% (103/136) among positive studies and 49% (29/59) among negative studies. In unadjusted analyses, the presence of a financial tie was associated with a positive study outcome (odds ratio 3.23, 95% confidence interval 1.7 to 6.1). In the primary multivariate analysis, a financial tie was significantly associated with positive RCT outcome after adjustment for the study funding source (odds ratio 3.57 (1.7 to 7.7). The secondary analysis controlled for additional RCT characteristics such as study phase, sample size, country of first authors, specialty, trial registration, study design, type of analysis, comparator, and outcome measure. These characteristics did not appreciably affect the relation between financial ties and study outcomes (odds ratio 3.37, 1.4 to 7.9).Conclusions Financial ties of principal investigators were independently associated with positive clinical trial results. These findings may be suggestive of bias in the evidence base.

Routine preoperative testing is not recommended before cataract surgery. In this analysis of Medicare claims data, preoperative testing was common and varied widely among ophthalmologists; the ophthalmologist performing the surgery was the strongest predictor of testing. Cataract surgery is the most common elective surgery among Medicare beneficiaries, with 1.7 million procedures performed annually. 1 It is also very safe, with less than a 1% risk of major adverse cardiac events or death. 2 The mean duration of cataract surgery is 18 minutes, 3 and virtually all surgical procedures are performed in an ambulatory setting with topical anesthesia. 4 However, because patients are typically elderly with multiple coexisting conditions, 5 , 6 physicians frequently order routine preoperative tests because of concerns about patient safety, worries about medicolegal risks, and the perception that other physicians expect preoperative testing. 6 , 7 Despite these common justifications, previous . . .

Social strain was determined using a previously validated 12-item scale asking how often one’s spouse, children, family, or friends a) “criticize you?,” b) “make too many demands on you?,” c) “let you down,” or d) “get on your nerves?,” (responses: not at all, a little, some, a lot) with an answer of “a lot” to any question classified as strain from that specific relationship.2 Individuals were categorized as “any” social strain if they reported strain in any of the relationships (“a lot” on ≥ 1 of the 12 above items).2 Operative stress score was included as a covariate and describes the degree of physiologic stress from the surgery (Range: 1–5, 1 “very low” to 5 “very high” physiological stress).4 We report adjusted odds ratios and adjusted probabilities derived from multivariable logistic regression models adjusted for age, gender, race/ethnicity, education, and operative stress score and utilizing nationally-representative sample weights.4 All analyses were performed using STATA 17.0 and SAS 9.4. Health and Retirement Study Sample Characteristics Characteristics Total (N = 3817) Age at interview before surgery < 65* 181 7% 65–74 1850 51% 75–84 1346 30% 85 +  440 13% Gender Men 1687 46% Women 2130 54% Race/Ethnicity White, non-hispanic 3185 86% Black, non-hispanic 398 8% Hispanic 176 5% Other 58 2% Marital status Married or partnered 1426 39% Single, divorced or windowed 2390 61% Highest education level High School or higher 3098 82% Less than high school 719 19% Overall household wealth < 6000 408 12% 6000–81000 618 16% 81000–239000 856 21%  >  = 239,000 1935 51% Diagnosis of chronic conditions Cancer 860 22% Lung Disease 534 14% Stroke 459 12% Diabetes 988 26% Heart disease 1375 36% Depression 807 23% Difficulty with Activities of Daily Living Walking across the room 357 11% Getting dressed 473 14% Eating 151 5% Bathing 314 10% Using the toilet 279 8% Getting in and out of bed 239 8% Any of the 6 ADLs 876 25% Operative Stress Score† 1 1802 47% 2 1369 36% 3–5 646 17% Strain in each relationship‡ Spouse 315 / 2426 14% Children 251 / 3447 8% Family 222 / 3469 7% Friends 73 / 3484 3% Any relationship 663/2220 32% *Participants who were < 65 years old prior to surgery were 65 or older after surgery † Operative stress score indicates the degree of physiologic stress from surgeries, with 1 as “very low” physiologic stress to 5 as “very high” physiologic stress3 ‡ Social strain was determined by asking how often one’s spouse, children, family, or friends a) “criticize you?,” b) “make too many demands on you?,” c) “let you down,” or d) “get on your nerves?,” (responses: not at all, a little, some, a lot) with an answer of “a lot” to any question classified as strain from that specific relationship. [...]identifying who will be the primary supportive relationship with patients prior to surgery and setting expectations about how they want them to be involved may be important.1, 6 This study has limitations. Sponsor's Role: This article was supported by a grant from the National Institute on Aging K76AG059931.The National Institute on Aging had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Background Our goal was to compare hospital scores from the most widely used commercial website in the USA to hospital scores from more systematic measures of patient experience and outcomes, and to assess what drives variation in the commercial website scores. Methods For a national sample of US hospitals, we compared scores on Yelp.com, which aggregates website visitor ratings (1–5 stars), with traditional measures of hospital quality. We calculated correlations between hospital Yelp scores and the following: hospital percent high ratings (9 or 10, scale 0–10) on the ‘Overall’ item on the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey; hospital individual HCAHPS domain scores (eg, nurse communication, pain control); hospital 30-day mortality; and hospital 30-day readmission rates. Results Of hospitals reporting HCAHPS (n=3796), 962 (25%) had scores on Yelp. Among hospitals with >5 Yelp ratings, the correlation of percent high ratings between Yelp and HCAHPS was 0.49 (p<0.001). The percent high ratings within each HCAHPS domain increased monotonically with increasing Yelp scores (p≤0.001 for all domains). Percent high ratings in Yelp and HCAHPS were statistically significantly correlated with lower mortality for myocardial infarction (MI; −0.19 for Yelp and −0.13 for HCAHPS) and pneumonia (−0.14 and −0.18), and fewer readmissions for MI (−0.17 and −0.39), heart failure (−0.31 and −0.39), and pneumonia (−0.18 and −0.27). Conclusions These data suggest that rater experiences for Yelp and HCAHPS may be similar, and that consumers posting ratings on Yelp may observe aspects of care related to important patient outcomes.

Guidelines recommend incorporating life expectancy (LE) into clinical decision-making for preventive interventions such as cancer screening. Previous research focused on mortality risk (e.g. 28% at 4 years) which is more difficult to interpret than LE (e.g. 7.3 years) for both patients and clinicians. Our objective was to utilize the Gompertz Law of Human Mortality which states that mortality risk doubles in a fixed time interval to transform the Lee mortality index into a LE calculator. We examined community-dwelling older adults age 50 and over enrolled in the nationally representative 1998 wave of the Health and Retirement Study or HRS (response rate 81%), dividing study respondents into development (n = 11701) and validation (n = 8009) cohorts. In the development cohort, we fit proportional hazards Gompertz survival functions for each of the risk groups defined by the Lee mortality index. We validated our LE estimates by comparing our predicted LE with observed survival in the HRS validation cohort and an external validation cohort from the 2004 wave of the English Longitudinal Study on Ageing or ELSA (n = 7042). The ELSA cohort had a lower 8-year mortality risk (14%) compared to our HRS development (23%) and validation cohorts (25%). Our model had good discrimination in the validation cohorts (Harrell's c 0.78 in HRS and 0.80 in the ELSA). Our predicted LE's were similar to observed survival in the HRS validation cohort without evidence of miscalibration (Hosmer-Lemeshow, p = 0.2 at 8 years). However, our predicted LE's were longer than observed survival in the ELSA cohort with evidence of miscalibration (Hosmer-Lemeshow, p<0.001 at 8 years) reflecting the lower mortality rate in ELSA. We transformed a previously validated mortality index into a LE calculator that incorporated patient-level risk factors. Our LE calculator may help clinicians determine which preventive interventions are most appropriate for older US adults.

Mortality from all causes is higher for persons with less education and for black persons. This study examined cause-specific mortality to estimate the relative contribution of major health problems to these educational and racial disparities in life expectancy. The diseases that contributed most to the educational disparity were smoking-related diseases. Ischemic heart disease accounted for 12 percent of the disparity, lung cancer 8 percent, stroke 6 percent, congestive heart failure 5 percent, and lung disease 5 percent. The racial disparity was not driven by mortality from smoking-related illness but by hypertension, human immunodeficiency virus infection, diabetes, and trauma. Mortality rates in the United States have declined dramatically over the past century. Yet persons with fewer years of education and black persons still live approximately six fewer years than better-educated persons and whites, respectively. 1 , 2 Consequently, the Healthy People 2010 initiative 3 has made the elimination of disparities in health its primary goal. The task of eliminating health disparities seems overwhelming, since minorities and the less educated have higher mortality rates for a wide range of diseases, including stroke, 4 , 5 diabetes, 6 , 7 cancer, 8 – 11 heart disease, 12 – 15 the acquired immunodeficiency syndrome (AIDS), 16 , 17 and lung disease. 11 , 18 However, we might . . .

To determine whether retinal axonal loss is detectable in patients with a clinically isolated syndrome (CIS), a first clinical demyelinating attack suggestive of multiple sclerosis (MS), and examine patterns of retinal axonal loss across MS disease subtypes. Spectral-domain Optical Coherence Tomography was performed in 541 patients with MS, including 45 with high-risk CIS, 403 with relapsing-remitting (RR)MS, 60 with secondary-progressive (SP)MS and 33 with primary-progressive (PP)MS, and 53 unaffected controls. Differences in retinal nerve fiber layer (RNFL) thickness and macular volume were analyzed using multiple linear regression and associations with age and disease duration were examined in a cross-sectional analysis. In eyes without a clinical history of optic neuritis (designated as \"eyes without optic neuritis\"), the total and temporal peripapillary RNFL was thinner in CIS patients compared to controls (temporal RNFL by -5.4 µm [95% CI -0.9 to--9.9 µm, p = 0.02] adjusting for age and sex). The total (p = 0.01) and temporal (p = 0.03) RNFL was also thinner in CIS patients with clinical disease for less than 1 year compared to controls. In eyes without optic neuritis, total and temporal RNFL thickness was nearly identical between primary and secondary progressive MS, but total macular volume was slightly lower in the primary progressive group (p<0.05). Retinal axonal loss is increasingly prominent in more advanced stages of disease--progressive MS>RRMS>CIS--with proportionally greater thinning in eyes previously affected by clinically evident optic neuritis. Retinal axonal loss begins early in the course of MS. In the absence of clinically evident optic neuritis, RNFL thinning is nearly identical between progressive MS subtypes.

Polypharmacy is common in older patients but relationships between polypharmacy and common co-morbid conditions have not been elucidated. Our goal was to determine relationships between daily oral medication use and common co-morbid disease dyads and triads using comprehensive medication and diagnostic data from a national sample of nursing homes (NH). Retrospective, cross-sectional study. Nationally representative sample of U.S. Nursing Homes. Nationally representative sample of long-term stay residents (n = 11734, 75% women) aged 65 years or older. Diagnosis and medication data were analyzed. Proportion of daily oral medication intake attributed to treatment of common two-(dyads) and three-disease (triad) combinations and \"health maintenance\" agents (vitamins, dietary supplements, stool softeners without related diagnoses) was determined. Older NH residents received slightly >8 oral medications/day with the number related to number of medical diagnoses (p < .0001). One third of chronic oral medication intake/day (excluding health maintenance agents) could be attributed to dyad combinations and about half to triad combinations despite an average of 5 other diagnoses. Triads were comprised of hypertension +/- arthritis +/- vascular disease, +/-depression, +/- osteoporosis +/- gastroesophageal reflux disease and +/- diabetes. Health maintenance agents accounted for 15-17% of daily oral medication intake (1.4 medications) such that almost two-thirds of daily oral medications were attributable to disease triads plus health maintenance. Fewer medications were prescribed for NH residents over age 85 (decreased ACE inhibitor and HMG CoA reductase inhibitor USE (p < .001)) while use of Alzheimer medications was higher (p < .01). A large fraction of daily oral medications were attributed to management of common co-morbid disease dyads and triads. Efforts to reduce polypharmacy and unwanted medication interactions could focus on regimens for common co-morbid dyads and triads in varying populations.

ABSTRACT BACKGROUND AND OBJECTIVE Quality prescribing for older adults involves multiple considerations. We evaluated multiple aspects of prescribing quality in older veterans to develop an integrated view of prescribing problems and to understand how the prevalence of these problems varies across clinically important subgroups of older adults. DESIGN AND PARTICIPANTS Cross-sectional observational study of veterans age 65 years and older who received medications from Department of Veterans Affairs (VA) pharmacies in 2007. MAIN MEASURES Using VA pharmacy data linked with encounter, laboratory and other data, we assessed five types of prescribing problems. KEY RESULTS Among 462,405 patients age 65 and older, mean age was 75 years, 98 % were male, and patients were prescribed a median of five medications. Half of patients (50 %) had one or more prescribing problems, including 12 % taking one or more medications at an inappropriately high dose, 30 % with drug–drug interactions, 3 % with drug–disease interactions, and 26 % taking one or more Beers criteria drugs. In addition, 16 % were taking a high-risk drug (warfarin, insulin, and/or digoxin). On multivariable analysis, age was not strongly associated with four of the five types of prescribing issues assessed (relative risk < 1.3 across age groups), and comorbid burden conferred substantially increased risk only for drug–disease interactions and use of high-risk drugs. In contrast, the number of drugs used was consistently the strongest predictor of prescribing problems. Patients in the highest quartile of medication use had 6.6-fold to12.5-fold greater risk of each type of prescribing problem compared to patients in the lowest quartile ( P  < 0.001 for each). CONCLUSIONS The number of medications used is by far the strongest risk factor for each of five types of prescribing problems. Efforts to improve prescribing should especially target patients taking multiple medications.