Comparison of Low-Value Care Among Commercial and Medicaid Enrollees.

BACKGROUND: Low-value healthcare is costly and inefficient and may adversely affect patient outcomes. Despite increases in low-value service use, little is known about how the receipt of low-value care differs across payers. OBJECTIVE: To evaluate differences in the use of low-value care between patients with commercial versus Medicaid coverage. DESIGN: Retrospective observational analysis of the 2017 Rhode Island All-payer Claims Database, estimating the probability of receiving each of 14 low-value services between commercial and Medicaid enrollees, adjusting for patient sociodemographic and clinical characteristics. Ensemble machine learning minimized the possibility of model misspecification. PARTICIPANTS: Medicaid and commercial enrollees aged 18-64 with continuous coverage and an encounter at which they were at risk of receiving a low-value service. INTERVENTION: Enrollment in Medicaid or Commercial insurance. MAIN MEASURES: Use of one of 14 validated measures of low-value care. KEY RESULTS: Among 110,609 patients, Medicaid enrollees were younger, had more comorbidities, and were more likely to be female than commercial enrollees. Medicaid enrollees had higher rates of use for 7 low-value care measures, and those with commercial coverage had higher rates for 5 measures. Across all measures of low-value care, commercial enrollees received more (risk difference [RD] 6.8 percentage points; CI: 6.6 to 7.0) low-value services than their counterparts with Medicaid. Commercial enrollees were also more likely to receive low-value services typically performed in the emergency room (RD 11.4 percentage points; CI: 10.7 to 12.2) and services that were less expensive (RD 15.3 percentage points; CI 14.6 to 16.0). CONCLUSION: Differences in the provision of low-value care varied across measures, though average use was slightly higher among commercial than Medicaid enrollees. This difference was more pronounced for less expensive services indicating that financial incentives may not be the sole driver of low-value care.

US Black-White Differences in Mortality Risk Among Transgender and Cisgender People in Private Insurance, 2011-2019.

Objectives. To compare survival by gender and race among transgender and cisgender people enrolled in private insurance in the United States between 2011 and 2019. Methods. We examined Optum's Clinformatics Data Mart Database. We identified transgender enrollees using claims related to gender-affirming care. Our analytic sample included those we identified as transgender and a 10% random sample of cisgender enrollees. We limited our sample to those 18 years or older who were non-Hispanic Black or White. We identified 18 033 transgender and more than 4 million cisgender enrollees. We fit Kaplan-Meier survival curves and calculated standardized mortality ratios while adjusting for census region. Results. Black transfeminine and nonbinary people assigned male sex at birth were 2.73 times more likely to die than other Black transgender people and 2.38 and 3.34 times more likely than Black cisgender men and women, respectively; similar results were found when White transfeminine and nonbinary people assigned male sex at birth were compared with White cisgender cohorts. Conclusions. Our findings highlight glaring inequities in mortality risks among Black transfeminine and nonbinary people assigned male sex at birth and underscore the need to monitor mortality risks in transgender populations and address the social conditions that increase these risks. (Am J Public Health. 2022;112(10):1507-1514. https://doi.org/10.2105/AJPH.2022.306963).

Differences in All-Cause Mortality Among Transgender and Non-Transgender People Enrolled in Private Insurance.

Few studies have analyzed mortality rates among transgender (trans) populations in the United States and compared them to the rates of non-trans populations. Using private insurance data from 2011 to 2019, we estimated age-specific all-cause mortality rates among a subset of trans people enrolled in private insurance and compared them to a 10% randomly selected non-trans cohort. Overall, we found that trans people were nearly twice as likely to die over the period as their non-trans counterparts. When stratifying by gender, we found key disparities within trans populations, with people on the trans feminine to nonbinary spectrum being at the greatest risk of mortality compared to non-trans males and females. While we found that people on the trans masculine to nonbinary spectrum were at a similar risk of overall mortality compared to non-trans females, their overall mortality rate was statistically smaller than that of non-trans males. These findings provide evidence that some trans and non-trans populations experience substantially different mortality conditions across the life course and necessitate further study.

Clinical impact of COVID-19 on patients with cancer (CCC19): a cohort study.

BACKGROUND: Data on patients with COVID-19 who have cancer are lacking. Here we characterise the outcomes of a cohort of patients with cancer and COVID-19 and identify potential prognostic factors for mortality and severe illness. METHODS: In this cohort study, we collected de-identified data on patients with active or previous malignancy, aged 18 years and older, with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection from the USA, Canada, and Spain from the COVID-19 and Cancer Consortium (CCC19) database for whom baseline data were added between March 17 and April 16, 2020. We collected data on baseline clinical conditions, medications, cancer diagnosis and treatment, and COVID-19 disease course. The primary endpoint was all-cause mortality within 30 days of diagnosis of COVID-19. We assessed the association between the outcome and potential prognostic variables using logistic regression analyses, partially adjusted for age, sex, smoking status, and obesity. This study is registered with ClinicalTrials.gov, NCT04354701, and is ongoing. FINDINGS: Of 1035 records entered into the CCC19 database during the study period, 928 patients met inclusion criteria for our analysis. Median age was 66 years (IQR 57-76), 279 (30%) were aged 75 years or older, and 468 (50%) patients were male. The most prevalent malignancies were breast (191 [21%]) and prostate (152 [16%]). 366 (39%) patients were on active anticancer treatment, and 396 (43%) had active (measurable) cancer. At analysis (May 7, 2020), 121 (13%) patients had died. In logistic regression analysis, independent factors associated with increased 30-day mortality, after partial adjustment, were: increased age (per 10 years; partially adjusted odds ratio 1·84, 95% CI 1·53-2·21), male sex (1·63, 1·07-2·48), smoking status (former smoker vs never smoked: 1·60, 1·03-2·47), number of comorbidities (two vs none: 4·50, 1·33-15·28), Eastern Cooperative Oncology Group performance status of 2 or higher (status of 2 vs 0 or 1: 3·89, 2·11-7·18), active cancer (progressing vs remission: 5·20, 2·77-9·77), and receipt of azithromycin plus hydroxychloroquine (vs treatment with neither: 2·93, 1·79-4·79; confounding by indication cannot be excluded). Compared with residence in the US-Northeast, residence in Canada (0·24, 0·07-0·84) or the US-Midwest (0·50, 0·28-0·90) were associated with decreased 30-day all-cause mortality. Race and ethnicity, obesity status, cancer type, type of anticancer therapy, and recent surgery were not associated with mortality. INTERPRETATION: Among patients with cancer and COVID-19, 30-day all-cause mortality was high and associated with general risk factors and risk factors unique to patients with cancer. Longer follow-up is needed to better understand the effect of COVID-19 on outcomes in patients with cancer, including the ability to continue specific cancer treatments. FUNDING: American Cancer Society, National Institutes of Health, and Hope Foundation for Cancer Research.

Elimination of Cost Sharing for Screening Mammography in Medicare Advantage Plans.

BACKGROUND: The Affordable Care Act (ACA) required most insurers and the Medicare program to eliminate cost sharing for screening mammography. METHODS: We conducted a difference-in-differences study of biennial screening mammography among 15,085 women 65 to 74 years of age in 24 Medicare Advantage plans that eliminated cost sharing to provide full coverage for screening mammography, as compared with 52,035 women in 48 matched control plans that had and maintained full coverage. RESULTS: In plans that eliminated cost sharing, adjusted rates of biennial screening mammography increased from 59.9% (95% confidence interval [CI], 54.9 to 65.0) in the 2-year period before cost-sharing elimination to 65.4% (95% CI, 61.8 to 69.0) in the 2-year period thereafter. In control plans, the rates of biennial mammography were 73.1% (95% CI, 69.2 to 77.0) and 72.8% (95% CI, 69.7 to 76.0) during the same periods, yielding a difference in differences of 5.7 percentage points (95% CI, 3.0 to 8.4). The difference in differences was 9.8 percentage points (95% CI, 4.5 to 15.2) among women living in the areas with the highest quartile of educational attainment versus 4.3 percentage points (95% CI, 0.2 to 8.4) among women in the lowest quartile. As indicated by the difference-in-differences estimates, after the elimination of cost sharing, the rate of biennial mammography increased by 6.5 percentage points (95% CI, 3.7 to 9.4) for white women and 8.4 percentage points (95% CI, 2.5 to 14.4) for black women but was almost unchanged for Hispanic women (0.4 percentage points; 95% CI, -7.3 to 8.1). CONCLUSIONS: The elimination of cost sharing for screening mammography under the ACA was associated with an increase in rates of use of this service among older women for whom screening is recommended. The effect was attenuated among women living in areas with lower educational attainment and was negligible among Hispanic women. (Funded by the National Institute on Aging.).

Making Narrative Statements to Describe Treatment Effects.

Accurately describing treatment effects using plain language and narrative statements is a critical step in communicating research findings to end users. However, the process of developing these narratives has not been historically guided by a specific framework. The Agency for Healthcare Research and Quality Evidence-based Practice Center Program developed guidance for narrative summaries of treatment effects that identifies five constructs. We explicitly identify these constructs to facilitate developing narrative statements: (1) direction of effect, (2) size of effect, (3) clinical importance, (4) statistical significance, and (5) strength or certainty of evidence. These constructs clearly overlap. It may not always be feasible to address all five constructs. Based on context and intended audience, investigators can determine which constructs will be most important to address in narrative statements.

Pharmacotherapy for smoking cessation: effects by subgroup defined by genetically informed biomarkers.

This review has been withdrawn because it has been found to be in breach of the Cochrane Commercial Sponsorship policy clause 2:  'Individuals who are currently employed or where employed any time in the last three years by a company that has a real or potential financial interest in the outcome of the review (including but not limited to drug companies or medical device manufacturers); or who hold or have applied for a patent related to the review are prohibited from being Cochrane Review authors. In most cases, current or previous employment would be characterized by the affiliation statement made by the author at the title registration, protocol, or review stage of the review'.

Hospital Readmission Rates in Medicare Advantage and Traditional Medicare: A Retrospective Population-Based Analysis.

Background: Medicare's Hospital Readmissions Reduction Program reports risk-standardized readmission rates for traditional Medicare but not Medicare Advantage beneficiaries. Objective: To compare readmission rates between Medicare Advantage and traditional Medicare. Design: Retrospective cohort study linking the Medicare Provider Analysis and Review (MedPAR) file with the Healthcare Effectiveness Data and Information Set (HEDIS). Setting: 4748 U.S. acute care hospitals. Patients: Patients aged 65 years or older hospitalized for acute myocardial infarction (AMI) (n = 841 613), congestive heart failure (CHF) (n = 1 458 652), or pneumonia (n = 2 020 365) between 2011 and 2014. Measurements: 30-day readmissions. Results: Among admissions for AMI, CHF, and pneumonia identified in MedPAR, 29.2%, 38.0%, and 37.2%, respectively, did not have a corresponding record in HEDIS. Of these, 18.9% for AMI, 23.7% for CHF, and 18.3% for pneumonia resulted in a readmission that was identified in MedPAR. However, among index admissions appearing in HEDIS, 14.4% for AMI, 18.4% for CHF, and 13.9% for pneumonia resulted in a readmission. Patients in Medicare Advantage had lower unadjusted readmission rates than those in traditional Medicare for all 3 conditions (16.6% vs. 17.1% for AMI, 21.4% vs. 21.7% for CHF, and 16.3% vs. 16.4% for pneumonia). However, after standardization, patients in Medicare Advantage had higher readmission rates than patients in traditional Medicare for AMI (17.2% vs. 16.9%; difference, 0.3 percentage point [95% CI, 0.1 to 0.5 percentage point]), CHF (21.7% vs. 21.4%; difference, 0.3 percentage point [CI, 0.2 to 0.5 percentage point]), and pneumonia (16.5% vs. 16.0%; difference, 0.5 percentage point [95% CI, 0.4 to 0.6 percentage point]). Rate differences increased between 2011 and 2014. Limitation: Potential unobserved differences between populations. Conclusion: The HEDIS data underreported hospital admissions for 3 common medical conditions, and readmission rates were higher among patients with underreported admissions. Medicare Advantage beneficiaries had higher risk-adjusted 30-day readmission rates than traditional Medicare beneficiaries. Primary Funding Source: National Institute on Aging.

Association of Diagnosis Coding With Differences in Risk-Adjusted Short-term Mortality Between Critical Access and Non-Critical Access Hospitals.

Importance: Critical access hospitals (CAHs) provide care to rural communities. Increasing mortality rates have been reported for CAHs relative to non-CAHs. Because Medicare reimburses CAHs at cost, CAHs may report fewer diagnoses than non-CAHs, which may affect risk-adjusted comparisons of outcomes. Objective: To assess serial differences in risk-adjusted mortality rates between CAHs and non-CAHs after accounting for differences in diagnosis coding. Design, Setting, and Participants: Serial cross-sectional study of rural Medicare Fee-for-Service beneficiaries admitted to US CAHs and non-CAHs for pneumonia, heart failure, chronic obstructive pulmonary disease, arrhythmia, urinary tract infection, septicemia, and stroke from 2007 to 2017. The final date of follow-up was December 31, 2017. Exposure: Admission to a CAH vs non-CAH. Main Outcomes and Measures: Discharge diagnosis count including trends from 2010 to 2011 when Medicare expanded the allowable number of billing codes for hospitalizations, and combined in-hospital and 30-day postdischarge mortality adjusted for demographics, primary diagnosis, preexisting conditions, and with vs without further adjustment for Hierarchical Condition Category (HCC) score to understand the contribution of in-hospital secondary diagnoses. Results: There were 4 094 720 hospitalizations (17% CAH) for 2 850 194 unique Medicare beneficiaries (mean [SD] age, 76.3 [11.7] years; 55.5% women). Patients in CAHs were older (median age, 80.1 vs 76.8 years) and more likely to be female (58% vs 55%). In 2010, the adjusted mean discharge diagnosis count was 7.52 for CAHs vs 8.53 for non-CAHs (difference, -0.99 [95% CI, -1.08 to -0.90]; P < .001). In 2011, the CAH vs non-CAH difference in diagnoses coded increased (P < .001 for interaction between CAH and year) to 9.27 vs 12.23 (difference, -2.96 [95% CI, -3.19 to -2.73]; P < .001). Adjusted mortality rates from the model with HCC were 13.52% for CAHs vs 11.44% for non-CAHs (percentage point difference, 2.08 [95% CI, 1.74 to 2.42]; P < .001) in 2007 and increased to 15.97% vs 12.46% (difference, 3.52 [95% CI, 3.09 to 3.94]; P < .001) in 2017 (P < .001 for interaction). Adjusted mortality rates from the model without HCC were not significantly different between CAHs and non-CAHs in all years except 2007 (12.19% vs 11.74%; difference, 0.45 [95% CI, 0.12 to 0.79]; P = .008) and 2010 (12.71% vs 12.28%; difference, 0.42 [95% CI, 0.07 to 0.77]; P = .02). Conclusions and Relevance: For rural Medicare beneficiaries hospitalized from 2007 to 2017, CAHs submitted significantly fewer hospital diagnosis codes than non-CAHs, and short-term mortality rates adjusted for preexisting conditions but not in-hospital comorbidity measures were not significantly different by hospital type in most years. The findings suggest that short-term mortality outcomes at CAHs may not differ from those of non-CAHs after accounting for different coding practices for in-hospital comorbidities.

Association of the extent of therapy with prostate cancer in those receiving testosterone therapy in a US commercial insurance claims database.

BACKGROUND: Conflicting evidence remains in the association of testosterone therapy (TTh) with prostate cancer (PCa). This inconsistency maybe due, in part, to the small sample sizes from previous studies and an incomplete assessment of comorbidities, particularly diabetes. OBJECTIVE: We investigated the association of PCa with TTh (injection or gel) and different TTh doses and determined whether this association varies by the presence of diabetes at baseline in a large, nationally representative, commercially insured cohort. DESIGN: We conducted a retrospective cohort study of 189 491 men aged 40-60 years old in the IBM MarketScan® Commercial Database, which included 1424 PCa cases diagnosed from 2011 to 2014. TTh was defined using CPT codes from inpatient and outpatient, and NDC codes from pharmacy claims. Multivariable adjusted Cox proportional hazards models were used to compute hazard ratios for patients with incident PCa. RESULTS: We found a 33% reduced association of PCa after comparing the highest category (>12) of TTh injections with the lowest (1-2 injections) category (HR = 0.67, 95% CI: 0.54-0.82). Similar statistical significant inverse association for PCa was observed for men who received TTh topical gels (>330 vs 1- to 60-days supply). Among nondiabetics, we found significant inverse association between TTh (injection and gel) and PCa, but a weak interaction between TTh injections and diabetes (P = .05). CONCLUSION: Overall, increased use of TTh is inversely associated with PCa and this remained significant only among nondiabetics. These findings warrant further investigation in large randomized placebo-controlled trials to infer any health benefit by TTh.

Smoking Cessation Pharmacotherapy Based on Genetically-Informed Biomarkers: What is the Evidence?

INTRODUCTION: Pharmacogenomic studies have used genetic variants to identify smokers likely to respond to pharmacological treatments for smoking cessation. METHODS: We performed a systematic review and meta-analysis of primary and secondary analyses of trials of smoking cessation pharmacotherapies. Eligible were trials with data on a priori selected single nucleotide polymorphisms, replicated non-single nucleotide polymorphisms, and/or the nicotine metabolite ratio. We estimated the genotype × treatment interaction as the ratio of risk ratios (RRR) for treatment effects across genotype groups. RESULTS: We identified 18 trials (N = 9017 participants), including 40 active (bupropion, nicotine replacement therapy [NRT], varenicline, or combination therapies) versus placebo comparisons and 16 active versus active comparisons. There was statistical evidence of heterogeneity across rs16969968 genotypes in CHRNA5 with regard to both 6-month abstinence and end-of-treatment abstinence in non-Hispanic black smokers and end-of-treatment abstinence in non-Hispanic white smokers. There was also heterogeneity across rs1051730 genotypes in CHRNA3 with regard to end-of-treatment abstinence in non-Hispanic white smokers. There was no clear statistical evidence for other genotype-by-treatment combinations. Compared with placebo, NRT was more effective among non-Hispanic black smokers with rs16969968-GG with regard to both 6-month abstinence (RRR for GG vs. GA or AA, 3.51; 95% confidence interval [CI] = 1.19 to 10.30) and end-of-treatment abstinence (RRR for GG vs. GA or AA, 5.84; 95% CI = 1.89 to 18.10). Among non-Hispanic white smokers, NRT effectiveness relative to placebo was comparable across rs1051730 and rs169969960 genotypes. CONCLUSIONS: We did not identify widespread differential effects of smoking cessation pharmacotherapies based on genotype. The quality of the evidence is generally moderate. IMPLICATIONS: Although we identified some evidence of genotype × treatment interactions, the vast majority of analyses did not provide evidence of differential treatment response by genotype. Where we find some evidence, these results should be considered preliminary and interpreted with caution because of the small number of contributing trials per genotype comparison, the wide confidence intervals, and the moderate quality of evidence. Prospective trials and individual-patient data meta-analyses accounting for heterogeneity of treatment effects through modeling are needed to assess the clinical utility of genetically informed biomarkers to guide pharmacotherapy choice for smoking cessation.

Genome-wide association study identifies the GLDC/IL33 locus associated with survival of osteosarcoma patients.

Survival rates for osteosarcoma, the most common primary bone cancer, have changed little over the past three decades and are particularly low for patients with metastatic disease. We conducted a multi-institutional genome-wide association study (GWAS) to identify germline genetic variants associated with overall survival in 632 patients with osteosarcoma, including 523 patients of European ancestry and 109 from Brazil. We conducted a time-to-event analysis and estimated hazard ratios (HR) and 95% confidence intervals (CI) using Cox proportional hazards models, with and without adjustment for metastatic disease. The results were combined across the European and Brazilian case sets using a random-effects meta-analysis. The strongest association after meta-analysis was for rs3765555 at 9p24.1, which was inversely associated with overall survival (HR = 1.76; 95% CI 1.41-2.18, p = 4.84 × 10-7 ). After imputation across this region, the combined analysis identified two SNPs that reached genome-wide significance. The strongest single association was with rs55933544 (HR = 1.9; 95% CI 1.5-2.4; p = 1.3 × 10-8 ), which localizes to the GLDC gene, adjacent to the IL33 gene and was consistent across both the European and Brazilian case sets. Using publicly available data, the risk allele was associated with lower expression of IL33 and low expression of IL33 was associated with poor survival in an independent set of patients with osteosarcoma. In conclusion, we have identified the GLDC/IL33 locus on chromosome 9p24.1 as associated with overall survival in patients with osteosarcoma. Further studies are needed to confirm this association and shed light on the biological underpinnings of this susceptibility locus.

Pharmacotherapy for smoking cessation: effects by subgroup defined by genetically informed biomarkers.

BACKGROUND: Smoking cessation therapies are not effective for all smokers, and researchers are interested in identifying those subgroups of individuals (e.g. based on genotype) who respond best to specific treatments. OBJECTIVES: To assess whether quit rates vary by genetically informed biomarkers within pharmacotherapy treatment arms and as compared with placebo. To assess the effects of pharmacotherapies for smoking cessation in subgroups of smokers defined by genotype for identified genome-wide significant polymorphisms. SEARCH METHODS: We searched the Cochrane Tobacco Addiction Group specialised register, clinical trial registries, and genetics databases for trials of pharmacotherapies for smoking cessation from inception until 16 August 2016. SELECTION CRITERIA: We included randomised controlled trials (RCTs) that recruited adult smokers and reported pharmacogenomic analyses from trials of smoking cessation pharmacotherapies versus controls. Eligible trials included those with data on a priori genome-wide significant (P < 5 × 10-8) single-nucleotide polymorphisms (SNPs), replicated non-SNPs, and/or the nicotine metabolite ratio (NMR), hereafter collectively described as biomarkers. DATA COLLECTION AND ANALYSIS: We used standard methodological procedures expected by Cochrane. The primary outcome was smoking abstinence at six months after treatment. The secondary outcome was abstinence at end of treatment (EOT). We conducted two types of meta-analyses- one in which we assessed smoking cessation of active treatment versus placebo within genotype groups, and another in which we compared smoking cessation across genotype groups within treatment arms. We carried out analyses separately in non-Hispanic whites (NHWs) and non-Hispanic blacks (NHBs). We assessed heterogeneity between genotype groups using T², I², and Cochrane Q statistics. MAIN RESULTS: Analyses included 18 trials including 9017 participants, of whom 6924 were NHW and 2093 NHB participants. Data were available for the following biomarkers: nine SNPs (rs1051730 (CHRNA3); rs16969968, rs588765, and rs2036527 (CHRNA5); rs3733829 and rs7937 (in EGLN2, near CYP2A6); rs1329650 and rs1028936 (LOC100188947); and rs215605 (PDE1C)), two variable number tandem repeats (VNTRs; DRD4 and SLC6A4), and the NMR. Included data produced a total of 40 active versus placebo comparisons, 16 active versus active comparisons, and 64 between-genotype comparisons within treatment arms.For those meta-analyses showing statistically significant heterogeneity between genotype groups, we found the quality of evidence (GRADE) to be generally moderate. We downgraded quality most often because of imprecision or risk of bias due to potential selection bias in genotyping trial participants. Comparisons of relative treatment effects by genotypeFor six-month abstinence, we found statistically significant heterogeneity between genotypes (rs16969968) for nicotine replacement therapy (NRT) versus placebo at six months for NHB participants (P = 0.03; n = 2 trials), but not for other biomarkers or treatment comparisons. Six-month abstinence was increased in the active NRT group as compared to placebo among participants with a GG genotype (risk ratio (RR) 1.47, 95% confidence interval (CI) 1.07 to 2.03), but not in the combined group of participants with a GA or AA genotype (RR 0.43, 95% CI 0.15 to 1.26; ratio of risk ratios (RRR) GG vs GA or AA of 3.51, 95% CI 1.19 to 10.3). Comparisons of treatment effects between genotype groups within pharmacotherapy randomisation armsFor those receiving active NRT, treatment was more effective in achieving six-month abstinence among individuals with a slow NMR than among those with a normal NMR among NHW and NHB combined participants (normal NMR vs slow NMR: RR 0.54, 95% CI 0.37 to 0.78; n = 2 trials). We found no such differences in treatment effects between genotypes at six months for any of the other biomarkers among individuals who received pharmacotherapy or placebo. AUTHORS' CONCLUSIONS: We did not identify widespread differential treatment effects of pharmacotherapy based on genotype. Some genotype groups within certain ethnic groups may benefit more from NRT or may benefit less from the combination of bupropion with NRT. The reader should interpret these results with caution because none of the statistically significant meta-analyses included more than two trials per genotype comparison, many confidence intervals were wide, and the quality of this evidence (GRADE) was generally moderate. Although we found evidence of superior NRT efficacy for NMR slow versus normal metabolisers, because of the lack of heterogeneity between NMR groups, we cannot conclude that NRT is more effective for slow metabolisers. Access to additional data from multiple trials is needed, particularly for comparisons of different pharmacotherapies.

Cross-protection of the Bivalent Human Papillomavirus (HPV) Vaccine Against Variants of Genetically Related High-Risk HPV Infections.

BACKGROUND: Results from the Costa Rica Vaccine Trial (CVT) demonstrated partial cross-protection by the bivalent human papillomavirus (HPV) vaccine, which targets HPV-16 and HPV-18, against HPV-31, -33, and -45 infection and an increased incidence of HPV-51 infection. METHODS: A study nested within the CVT intention-to-treat cohort was designed to assess high-risk HPV variant lineage-specific vaccine efficacy (VE). The 2 main end points were (1) long-term incident infections persisting for ≥2 years and/or progression to high-grade squamous intraepithelial lesions (ie, cervical intraepithelial neoplasia grade 2/3 [CIN 2/3]) and (2) incident transient infections lasting for <2 years. For efficiency, incident infections due to HPV-16, -18, -31, -33, -35, -45, and -51 resulting in persistent infection and/or CIN 2/3 were matched (ratio, 1:2) to the more-frequent transient viral infections, by HPV type. Variant lineages were determined by sequencing the upstream regulatory region and/or E6 region. RESULTS: VEs against persistent or transient infections with HPV-16, -18, -33, -35, -45, and -51 did not differ significantly by variant lineage. As the possible exception, VEs against persistent infection and/or CIN 2/3 due to HPV-31 A/B and HPV-31C variants were -7.1% (95% confidence interval [CI], -33.9% to 0%) and 86.4% (95% CI, 65.1%-97.1%), respectively (P = .02 for test of equal VE). No difference in VE was observed by variant among transient HPV-31 infections (P = .68). CONCLUSIONS: Overall, sequence variation at the variant level does not appear to explain partial cross-protection by the bivalent HPV vaccine.

The power of meta-analysis in genome-wide association studies.

Meta-analysis of multiple genome-wide association (GWA) studies has become common practice over the past few years. The main advantage of this technique is the maximization of power to detect subtle genetic effects for common traits. Moreover, one can use meta-analysis to probe and identify heterogeneity in the effect sizes across the combined studies. In this review, we systematically appraise and evaluate the characteristics of GWA meta-analyses with 10,000 or more subjects published up to June 2012. We provide an overview of the current landscape of variants discovered by GWA meta-analyses, and we discuss and assess with extrapolations from empirical data the value of larger meta-analyses for the discovery of additional genetic associations and new biology in the future. Finally, we discuss some emerging logistical and practical issues related to the conduct of meta-analysis of GWA studies.

Evaluation of excess significance bias in animal studies of neurological diseases.

Animal studies generate valuable hypotheses that lead to the conduct of preventive or therapeutic clinical trials. We assessed whether there is evidence for excess statistical significance in results of animal studies on neurological disorders, suggesting biases. We used data from meta-analyses of interventions deposited in Collaborative Approach to Meta-Analysis and Review of Animal Data in Experimental Studies (CAMARADES). The number of observed studies with statistically significant results (O) was compared with the expected number (E), based on the statistical power of each study under different assumptions for the plausible effect size. We assessed 4,445 datasets synthesized in 160 meta-analyses on Alzheimer disease (n = 2), experimental autoimmune encephalomyelitis (n = 34), focal ischemia (n = 16), intracerebral hemorrhage (n = 61), Parkinson disease (n = 45), and spinal cord injury (n = 2). 112 meta-analyses (70%) found nominally (p≤0.05) statistically significant summary fixed effects. Assuming the effect size in the most precise study to be a plausible effect, 919 out of 4,445 nominally significant results were expected versus 1,719 observed (p<10⁻9). Excess significance was present across all neurological disorders, in all subgroups defined by methodological characteristics, and also according to alternative plausible effects. Asymmetry tests also showed evidence of small-study effects in 74 (46%) meta-analyses. Significantly effective interventions with more than 500 animals, and no hints of bias were seen in eight (5%) meta-analyses. Overall, there are too many animal studies with statistically significant results in the literature of neurological disorders. This observation suggests strong biases, with selective analysis and outcome reporting biases being plausible explanations, and provides novel evidence on how these biases might influence the whole research domain of neurological animal literature.

Multivariate piecewise exponential survival modeling.

In this article, we develop a piecewise Poisson regression method to analyze survival data from complex sample surveys involving cluster-correlated, differential selection probabilities, and longitudinal responses, to conveniently draw inference on absolute risks in time intervals that are prespecified by investigators. Extensive simulations evaluate the developed methods with extensions to multiple covariates under various complex sample designs, including stratified sampling, sampling with selection probability proportional to a measure of size (PPS), and a multi-stage cluster sampling. We applied our methods to a study of mortality in men diagnosed with prostate cancer in the Prostate, Lung, Colorectal, and Ovarian (PLCO) cancer screening trial to investigate whether a biomarker available from biospecimens collected near time of diagnosis stratifies subsequent risk of death. Poisson regression coefficients and absolute risks of mortality (and the corresponding 95% confidence intervals) for prespecified age intervals by biomarker levels are estimated. We conclude with a brief discussion of the motivation, methods, and findings of the study.

A systematic appraisal of field synopses in genetic epidemiology: a HuGE review.

Evidence from genetic association studies is accumulating rapidly. Field synopses have recently arisen as an unbiased way of systematically synthesizing this evidence. We performed a systematic review and appraisal of published field synopses in genetic epidemiology and assessed their main findings and methodological characteristics. We identified 61 eligible field synopses, published between January 1, 2007, and October 31, 2013, on 52 outcomes reporting 734 significant associations at the P < 0.05 level. The median odds ratio for these associations was 1.25 (interquartile range, 1.15-1.43). Egger's test was the most common method (n = 30 synopses) of assessing publication bias. Only 12 synopses (20%) used the Venice criteria to evaluate the epidemiologic credibility of their findings (n = 449 variants). Eleven synopses (18%) were accompanied by an online database that has been regularly updated. These synopses received more citations (P = 0.01) and needed a larger research team (P = 0.02) than synopses without an online database. Overall, field synopses are becoming a valuable tool for the identification of common genetic variants, especially when researchers follow relevant methodological guidelines. Our work provides a summary of the current status of the field synopses published to date and may help interested readers efficiently identify the online resources containing the relevant genetic evidence.

Invited commentary: How big is that interaction (in my community)--and in which direction?

In an accompanying article, Turner et al. (Am J Epidemiol. 2014;180(12):1145-1149) compare the joint effects of smoking and air pollution to make inferences about the reduction in lung cancer mortality achieved when reducing each exposure separately and when reducing both together. In this commentary, we use first principles to quantify the difference between the risk or mortality reduction obtained from reducing each of 2 exposures together and the sum of the risk differences obtained from reducing the 2 exposures separately. Metrics of the impact of joint effects or comparisons of joint effects presented in units of absolute risk, such as Rothman's I, can provide more meaningful quantitative measures of public health impact than unitless metrics (e.g., ratios) and standardized metrics (e.g., the population attributable fraction) of potential interventions for reducing smoking and air pollution exposure. In particular, the venerable attributable community risk metric can provide an estimate of the community impact of such interventions in units of absolute risk. A spreadsheet we provide demonstrates the calculation of the various metrics for hypothetical data similar to those reported by Turner et al. Using algebra, graphics, and examples, we show that positive interaction, or synergy, on the additive scale implies that the impact on risk reduction from a program that applies both interventions will be lesser than the sum of the impacts of the separate interventions.